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27Apr

NEXT STEP – Junior Data Analyst Service Excellence –

Rentokil Initial è il maggior fornitore al mondo di servizi per le aziende.

Da oltre 90 anni l’azienda si occupa di Pest Control e Hygiene Services. Attiva in oltre 90 Paesi nel mondo – in Europa, Asia, Oceania, America e Africa – conta più di 57.000 dipendenti e un fatturato annuo di £ 3.5 miliardi.

Rentokil Initial Italia offre i punti di forza e l’esperienza di una organizzazione multi-nazionale, pur mantenendo l’agilità e le caratteristiche di una società locale.

L’attività è focalizzata sulla fornitura di un ottimo servizio alla sua vasta gamma di clienti. La filiale italiana opera oggi con due divisioni: Initial Hygiene, specializzata in servizi per l’igiene, che si è ampliata grazie all’acquisizione di CWS-boco Italia, e Rentokil Pest Control, che nel 2021 si è ampliata grazie all’acquisizione di Gico Systems, dedicata ai servizi per la disinfestazione e monitoraggio degli infestanti.

Lo staff è composto ora da circa 650 dipendenti, che assicurano la copertura del servizio su tutto il territorio nazionale agli oltre 28.000 clienti.

Rentokil Initial Italia è alla ricerca di un/una brillante Data Analyst a riporto diretto del nostro Service Excellence Manager della Business Unit Hygiene, e che supporterà nelle analisi e all’interpretazione dei dati finalizzate al miglioramento delle performance e della qualità del servizio Rentokil Initial Italia.

I principali compiti del Junior Data Analyst sono:

Estrazione, elaborazione e creazione di database mediante la creazione di codici ad hoc
Supporto, creazione e gestione di reporting periodici e di tools digitali collegati ai database creati ed autoaggiornanti
Analisi dei contratti mirate alla revisione e ottimizzazione delle frequenze di servizio
Analisi di produttività atte al miglioramento delle performances del Servizio, al contenimento dei costi e alla massima soddisfazione e qualità del servizio erogato ai nostriClienti
Collaborazione nell’ implementazione di progetti Corporate

COSA TI ASPETTA:

Un rimborso spese interessante di €800 lordi al mese;
Un tutor a te dedicato che ti supporterà nell’inserirti velocemente nella nostra realtà e nelle dinamiche del tuo dipartimento per poterti muovere con sempre più autonomia;
Accesso 24/7 alla nostra piattaforma di e-learning “U+”;
Possibilità di partecipare alle iniziative del programma Wellbeing 2024 offerte da Rentokil Initial Italia ai suoi dipendenti;
Possibilità di adesione alle convenzioni con scontistiche della piattaforma We+ aperta ai dipendenti Rentokil Initial Italia;
Un network di circa 20+ professionisti che hanno partecipato alle edizioni precedenti di NEXT STEP in qualità di interns o tutor

La ricerca si intende rivolta ai candidati senza alcuna discriminazione di genere, età o provenienza.

Ci teniamo molto alla privacy dei nostri candidati.
Pertanto, prima di candidarsi è necessario prendere visione della nostra informativa al link di seguito indicato:
https://www.rentokil-initial.com/site-services/cookie-and-privacy-policy/privacy-policy/italian_privacy_notice.aspx?__hstc=49426105.dd62cc660ea46de775918a7402af7235.1702330099304.1702330099304.1702330099304.1&__hssc=49426105.2.1702330099305&__hsfp=2860141656

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27Apr

FlashSpeech: A Novel Speech Generation System that Significantly Reduces Computational Costs while Maintaining High-Quality Speech Output

In recent years, speech synthesis has undergone a profound transformation thanks to the emergence of large-scale generative models. This evolution has led to significant strides in zero-shot speech synthesis systems, including text-to-speech (TTS), voice conversion (VC), and editing. These systems aim to generate speech by incorporating unseen speaker characteristics from a reference audio segment during inference without requiring additional training data.

The latest advancements in this domain leverage language and diffusion-style models for in-context speech generation on large-scale datasets. However, due to the intrinsic mechanisms of language and diffusion models, the generation process of these methods often entails extensive computational time and cost.

To tackle the challenge of slow generation speed while upholding high-quality speech synthesis, a team of researchers has introduced FlashSpeech as a groundbreaking stride towards efficient zero-shot speech synthesis. This novel approach builds upon recent advancements in generative models, particularly the latent consistency model (LCM), which paves a promising path for accelerating inference speed.

FlashSpeech leverages the LCM and adopts the encoder of a neural audio codec to convert speech waveforms into latent vectors as the training target. To train the model efficiently, the researchers introduce adversarial consistency training, a novel technique that combines consistency and adversarial training using pre-trained speech-language models as discriminators.

One of FlashSpeech’s key components is the prosody generator module, which enhances the diversity of prosody while maintaining stability. By conditioning the LCM on prior vectors obtained from a phoneme encoder, a prompt encoder, and the prosody generator, FlashSpeech achieves more diverse expressions and prosody in the generated speech.

When it comes to performance, FlashSpeech not only surpasses strong baselines in audio quality but also matches them in speaker similarity. What’s truly remarkable is that it achieves this at a speed approximately 20 times faster than comparable systems, marking an unprecedented level of efficiency in zero-shot speech synthesis.

The introduction of FlashSpeech signifies a significant leap forward in the field of zero-shot speech synthesis. By addressing the core limitations of existing approaches and harnessing recent innovations in generative modeling, FlashSpeech presents a compelling solution for real-world applications that demand rapid and high-quality speech synthesis.

With its efficient generation speed and superior performance, FlashSpeech holds immense promise for a variety of applications, including virtual assistants, audio content creation, and accessibility tools. As the field continues to evolve, FlashSpeech sets a new standard for efficient and effective zero-shot speech synthesis systems.

Check out the Paper and Project. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter. Join our Telegram Channel, Discord Channel, and LinkedIn Group.

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Arshad is an intern at MarktechPost. He is currently pursuing his Int. MSc Physics from the Indian Institute of Technology Kharagpur. Understanding things to the fundamental level leads to new discoveries which lead to advancement in technology. He is passionate about understanding the nature fundamentally with the help of tools like mathematical models, ML models and AI.

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27Apr

Data Analyst – Cernusco sul Naviglio – 875 at Rentokil

Rentokil Initial è il maggior fornitore al mondo di servizi per le aziende.

Rentokil Initial Italia offre i punti di forza e l’esperienza di una organizzazione multi-nazionale, pur mantenendo l’agilità e le caratteristiche di una società locale.

Lo staff è composto ora da circa 650 dipendenti, che assicurano la copertura del servizio su tutto il territorio nazionale agli oltre 28.000 clienti.

Rentokil Initial Italia è alla ricerca di un brillante Junior Data Analyst per supportare il team Service Excellence in tutte le attività volte a migliorare le nostre capacità decisionali basate sui dati per clienti, prospect e assistenza tecnica.

La tua missione quotidiana sarà facilitare il processo decisionale e supportare i team interni in analisi complesse per migliorare il nostro ciclo di vita del servizio nelle attività di disinfestazione

Riportando al Service Excellence Manager:

Supporterai l’impostazione, il miglioramento continuo e l’automazione di un nuovo sistema di reportistica per aiutare i team di assistenza e vendita ad avere le informazioni giuste e prendere decisioni basate sui dati su uno specifico servizio di disinfestazione
Ti prenderai cura dei dati: dall’estrazione e raccolta da diverse fonti alla visualizzazione dei dati
Produrrai report settimanali/mensili e analisi ad hoc in base ai KPI aziendali
Ti consulterai quotidianamente con il tuo team, il resto dei team di assistenza tecnica e aziendale per comprendere le dinamiche aziendali

Il pacchetto retributivo sarà commisurato all’esperienza professionale nel settore.

Completano il pacchetto retributivo:

Dotazione di cellulare aziendale
Partecipazione al piano incentivante
Ticket Restaurant da 7,5 €

La ricerca si intende rivolta ai candidati senza alcuna discriminazione di genere, età o provenienza.

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26Apr

SenseTime from China Launched SenseNova 5.0: Unleashing High-Speed, Low-Cost Large-Scale Modeling, Challenging GPT-4 Turbo’s Performance

Artificial intelligence continues evolving, pushing data processing and computational efficiency boundaries. A standout development in this space is the emergence of large-scale AI models that are not just expansive but also uniquely capable of handling complex datasets and multi-faceted tasks with greater precision and speed. These models advance various technologies, from automated reasoning to complex problem-solving across multiple domains.

One persistent challenge in AI has been optimizing the balance between computational power and efficiency. Traditional AI systems rely heavily on cloud-based infrastructures, which, while powerful, often suffer from significant latency issues. This lag can be detrimental in scenarios where real-time data processing is crucial, such as autonomous driving systems or medical diagnostics.

The current generation of AI models has seen significant enhancements in response to these limitations. These models are increasingly hosted on centralized servers and capable of running on local devices at the edge of networks. This shift significantly reduces latency by processing data where it is collected, but these setups often require more refined and capable handling of data to maintain efficiency.

SenseTime from China has launched the RiRiXin SenseNova 5.0. This model represents a leap in AI capabilities, employing a hybrid expert architecture that leverages both the depth of cloud computing and the responsiveness of edge computing technologies. The model trained on over 10TB tokens, encompassing extensive synthetic data. It’s equipped to handle 200K context windows during reasoning. Its focus lies on boosting proficiency in knowledge, mathematics, reasoning, and coding, achieving or surpassing 10% in mainstream objective evaluations, surpassing the performance of GPT-4 Turbo.

The SenseNova 5.0 model notably excels in its operational metrics. Compared to its predecessors, it has achieved a performance improvement of over 10% in mainstream objective evaluations. Specifically, it has shown prowess in enhancing knowledge-based tasks and multi-modal functions, including image and language processing. It supports an inference speed of up to 109.5 words per second, over five times faster than the human eye can read.

SenseTime has equipped the model to operate seamlessly across various devices, like mobile phones and tablets, integrating edge computing solutions that significantly reduce cloud server dependency. This integration has substantially reduced inference costs by up to 80% compared to similar models in the industry. The deployment of these models in specialized sectors like finance, medicine, and government operations has demonstrated both high efficiency and cost-effectiveness, offering scalable solutions that adapt quickly to user demands.

In conclusion, SenseTime’s development of the RiRiXin SenseNova 5.0 model marks a transformative step in artificial intelligence. By harmonizing high-level data processing with swift, localized computation, this model sets a new standard in the efficiency and application of AI technology. The significant reductions in latency and operational costs, the model’s adaptability across various platforms, and its superior performance in multi-modal evaluations underscore its potential to enhance a wide range of AI-driven services and applications, making advanced AI more accessible and practical for everyday use.

Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.

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26Apr

Neural Flow Diffusion Models (NFDM): A Novel Machine Learning Framework that Enhances Diffusion Models by Supporting a Broader Range of Forward Processes Beyond the Fixed Linear Gaussian

The probabilistic machine learning class, generative models, has many uses in different domains, including the visual and performing arts, the medical industry, and even physics. To generate new samples that are similar to the original data, generative models are very good at building probability distributions that appropriately describe datasets. These features are perfect for generating synthetic datasets to supplement training data (data augmentation) and discovering latent structures and patterns in an unsupervised learning environment.

The two main steps in building diffusion models, which are a type of generative model, are the forward and reverse processes. Over time, the data distribution becomes corrupted by the forward process, going from its original condition to a noisy one. The reverse process can restore data distribution by learning to invert corruptions introduced by the forward process. In this approach, it can train itself to produce data out of thin air. Diffusion models have shown impressive performance in several fields. The majority of current diffusion models, however, assume a fixed forward process that is Gaussian in nature, rendering them incapable of task adaptation or target simplification during the reverse process.

New research by the University of Amsterdam and Constructor University, Bremen, introduces Neural Flow Diffusion Models (NFDM). This framework enables the forward process to specify and learn latent variable distributions. Suppose any continuous (and learnable) distribution can be represented as an invertible mapping applied to noise. In that case, NFDM may accommodate it, unlike traditional diffusion models that depend on a conditional Gaussian forward process. Additionally, the researchers minimize a variational upper bound on the negative log-likelihood (NLL) using an end-to-end optimization technique that does not include simulation. In addition, they suggest a parameterization for the forward process that is based on efficient neural networks. This will allow it to learn the data distribution more easily and adapt to the reverse process while training.

Using NFDM’s adaptability, the researchers delve deeper into training with limits on the inverse process to acquire generative dynamics with targeted attributes. A curvature penalty on the deterministic generating trajectories is considered a case study. The empirical results show better computing efficiency than baselines on synthetic datasets, MNIST, CIFAR-10, and downsampled ImageNet.

Presenting their experimental findings on CIFAR-10, ImageNet 32 and 64, the team showcased the vast potential of NFDM with a learnable forward process. The state-of-the-art NLL results they achieved are crucial for a myriad of applications, including data compression, anomaly detection, and out-of-distribution detection. They also demonstrated NFDM’s application in learning generative processes with specific attributes, such as dynamics with straight-line trajectories. In these cases, NFDM led to significantly faster sampling rates, improved generation quality, and required fewer sampling steps, underscoring its practical value.

The researchers are candid about the considerations that must be made when adopting NFDM. They acknowledge that compared to traditional diffusion models, the computational costs increase when a neural network is used to parameterize the forward process. Their results indicate that NFDM optimization iterations take around 2.2 times longer than traditional diffusion models. However, they believe that NFDM’s potential in various fields and practical applications is driven by its flexibility in learning generative processes. They also propose potential avenues for improvement, such as incorporating orthogonal methods like distillation, changing the target, and exploring different parameterizations.

Check out the Paper. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter. Join our Telegram Channel, Discord Channel, and LinkedIn Group.

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Dhanshree Shenwai is a Computer Science Engineer and has a good experience in FinTech companies covering Financial, Cards & Payments and Banking domain with keen interest in applications of AI. She is enthusiastic about exploring new technologies and advancements in today’s evolving world making everyone’s life easy.

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25Apr

Researchers at MIT Propose ‘MAIA’: An Artificial Intelligence System that Uses Neural Network Models to Automate Neural Model Understanding Tasks

MIT CSAIL researchers introduced MAIA (Multimodal Automated Interpretability Agent) to address the challenge of understanding neural models, especially in computer vision, where interpreting the behavior of complex models is essential for improving accuracy and robustness and identifying biases. Current methods rely on manual effort, like exploratory data analysis, hypothesis formulation, and controlled experimentation, making the process slow and expensive. MAIA (Multimodal Automated Interpretability Agent) uses neural models to automate interpretability tasks, such as feature interpretation and failure mode discovery.

Existing approaches to model interpretability are often unscalable and inaccurate, limiting their utility to hypothesis generation rather than providing actionable insights. MAIA, on the other hand, automates interpretability tasks through a modular framework. It utilizes a pre-trained vision-language model as its backbone and provides a set of tools that enable the system to conduct experiments on neural models iteratively. These tools include synthesizing and editing inputs, computing exemplars from real-world datasets, and summarizing experimental results.

MAIA’s ability to generate descriptions of neural model behavior is compared to both baseline methods and human expert labels, demonstrating its effectiveness in understanding model behavior.

MAIA’s framework is designed to freely conduct experiments on neural systems by composing interpretability tasks into Python programs. Leveraging a pre-trained multimodal model, MAIA can process images directly and design experiments to answer user queries about model behavior. The System class within MAIA’s API instruments the system to be interpreted, making subcomponents individually callable for experimentation. Meanwhile, the Tools class comprises a suite of functions enabling MAIA to write modular programs that test hypotheses about system behavior.

The evaluation of MAIA on the black-box neuron description task demonstrates its ability to produce predictive explanations of vision system components, identify spurious features, and automatically detect biases in classifiers. It is effective in generating descriptions of both real and synthetic neurons, outperforms baseline methods, and approaches human expert labels.

In conclusion, MAIA presents a promising solution to the challenge of understanding neural models by automating interpretability tasks. MAIA streamlines the process of understanding model behavior by combining a pre-trained vision-language model with a set of interpretability tools. While human supervision is still necessary to avoid common pitfalls and maximize effectiveness, MAIA’s framework demonstrates high potential utility in the interpretability workflow, offering a flexible and adaptable approach to understanding complex neural systems. Overall, MAIA significantly helps in bridging the gap between human interpretability and automated techniques in model understanding and analysis.

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Pragati Jhunjhunwala is a consulting intern at MarktechPost. She is currently pursuing her B.Tech from the Indian Institute of Technology(IIT), Kharagpur. She is a tech enthusiast and has a keen interest in the scope of software and data science applications. She is always reading about the developments in different field of AI and ML.

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24Apr

Meet CopilotKit: An Open-Source Copilot Platform for Seamless AI Integration in Any Application

What is CopilotKit?

CopilotKit is an open-source framework designed to facilitate the integration of AI into applications. With 4.4k+💫Git Stars, it has received great appreciation within the open-source community. It helps to create custom AI copilots, including in-app AI chatbots and agents capable of interacting dynamically with the application’s environment. The framework is built to streamline integrating AI by handling complex aspects like app context awareness and interaction.

Please star CopilotKit to support their work:

https://github.com/CopilotKit/CopilotKit

Challenges Resolved Through CopilotKit

Here are the four challenges of many that CopilotKit helps with:

Components of CopilotKit
The CopilotKit offers many components that you can use for your applications. It has native support for LangChain, LangGraph, and LangServe and also provides built-in native UI/UX components that you can use as part of your applications:

CopilotChat: This tool enables the building of app-aware AI chatbots that can interact with the app’s frontend and backend, as well as third-party services.
CopilotTextarea: It acts as a drop-in replacement for any ‘<textarea/>’ and offers AI-assisted text generation and editing.
In-App Agents: CopilotKit allows real-time context access to applications and lets agents take action within applications.
Co-Agents: It will soon be released and can enable end-users to intervene and restart agent operations if needed.
Purpose-specific LLM chains: It customizes the language model chains for specific applications.
Built-in UI Components: It also Includes components like ‘CopilotSidebar’ and ‘CopilotPopup’ for UI customization.

How does CopilotKit work?

Let’s look at key points about how CopilotKit works:

Framework-first: a framework for connecting every component of your application to the copilot engine.
The copilot engine: Receives the user request, pulls in the relevant application context, formats it for the LLM, then initiates in-app action on the user’s behalf. Integrates deeply with the front and backend.
AI Components: customizable & headless UI components for native AI features: chatbots, AI agents & AI-powered textareas.
Generative UI: custom interactive user interfaces rendered inside the chat, rendered alongside AI-initiated actions.
In-app agents: bring LangChain agents as interactive components of the application. They can see realtime application context, and initiate action inside the application.
Copilot Cloud: turnkey cloud services for scaling and productionizing copilots: copilot memory & chat histories, guardrails, self-learning (the copilot gets smarter with use)
Simplicity in Integration: CopilotKit integration into existing app infrastructures is facilitated through simple entry points, making applications with advanced AI functionalities easy to use.

Use Case: CoPilotKit Presentation Creator

Let’s build something cool using CopilotKit, a text-to-powerpoint creator application.

We have to fulfill some prerequisites before proceeding further:

Now, Let’s follow the essential steps to get the desired app for slide creation through the following steps:

git clone https://github.com/CopilotKit/presentation-demo

Navigate to the cloned repo and install the packages:

npm install

Create a “.env.local” file in the root directory of the project and mention the two API keys obtained in the prerequisite part:

OPENAI_API_KEY = "...."
TAVILY_API_KEY = "........"

npm run dev

Open http://localhost:3000 in your browser to see the app:

A CopilotSidebar will be here. Let’s enter this prompt: “Create a slide on the benefits of AI in healthcare.” You will get the desired slide:

Here’s what CopiloKit did on the backend:

It takes the prompt and sends it to TAVILY to research the topic.
The response can then be forwarded to OpenAI for creating the slide content.
CopiloKit then places the output from OpenAI LLM in the desired places, using its update functionalities.

Trending Examples of CoipilotKit Application

Chat with Your Resume: AI-powered resume builder application using Nextjs, CopilotKit & OpenAI.
Text-to-Powerpoint Application: This AI-powered PowerPoint application can search the web to make a presentation about any topic automatically. It integrates AI into your app using Next.js, OpenAI, LangChain & Tavily, and CopilotKit.
AI-Powered Blogging Platform: AI-powered blogging platform that can search the web and research any topic for a blog article using Next.js, OpenAI, LangChain & Tavily, CopilotKit, and Supabase.

Conclusion
The introduction of CopilotKit reveals a robust and promising framework for smoothly integrating AI capabilities into your applications. By incorporating CopilotKit, developers gain access to a suite of tools that provides a simplified method for creating interactive AI features with user enhancement through intuitive interfaces like CopilotChat, CopilotSidebar, and CopilotTextarea. The up-front installation process, comprehensive documentation, and illustrative code examples ensure that even a person who is not tech-savvy and new to AI can smoothly embark on this journey confidently. Whether you’re trying to build AI-driven chatbots, enrich text areas with smart completions, or create fully customized AI interactions within your apps, CopilotKit can help you.

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24Apr

Virtual Chief Information Security Officer at

Convergence Networks is one of North America’s leading managed service and managed security providers. We are a service company focused on helping clients leverage technology as a strategic tool and proactively protecting their business. We are fueled by providing outstanding service and sharing our passion for innovative technology as part of our integrated solutions.

POSITION SUMMARY

vCISOs are seen as the information security expert by our clients and have the opportunity to help design and lead their overall security posture into the future. vCISOs work with a myriad of different businesses that operate in varying industries, each of which ultimately rely on having a secure network as the foundation of their operations. vCISOs are responsible for assisting new and existing clients with establishing and maintaining their information security management programs to help ensure the confidentiality, integrity, and availability of their information technology systems, networks, and data. Through established Convergence Networks consulting processes and procedures, the vCISO assists with the direction and management of a client’s strategic, operational, and budgeting efforts. With the purpose of protecting their organization’s information assets, you will gain an understanding of the client’s business, the industry they operate in, and leverage that understanding to provide consulting and guidance to business leaders.

WHAT WILL YOU DO AS A VCISO?

Assist client senior leadership in making informed technical and risk management decisions by providing subject matter expertise on a broad range of technologies, information security standards, risk management and compliance requirements.
Establish strong relationships with clients as a foundation of trust and mutual respect between their organization and Convergence Networks.
Visit client sites, establishing a regular onsite meeting rhythm with your client base.
Provide confident consultation and solution proposals based upon a deep understanding of the customer’s business needs, existing IT system posture, adversity to risk, perceived pain-points, cultural influences, and financial/regulatory constraints.
Assist customers with integration of information security into their business strategy, processes, and culture.
Collaborate with clients to identify opportunities to improve risk posture, developing solutions for remediating or mitigating risks based upon the business objectives, financial constraints, regulatory requirements, and adversity to risk.
Create, maintain, and grow strong professional relationships with the key stakeholders and decision makers within your customer base, allowing you to have otherwise difficult financial conversations regarding invoices, recurring contracts, conflicts with support staff, etc.
Stay abreast of trends, advances, and solutions within the broader IT and IT security industries.
Ongoing education and research on how new governmental legislation and compliance regulations affect client security policies, practices, and procedures.
Provide recommendations on IT best practices and security awareness training for clients.
Serve as a liaison to auditors, assessors and examiners.
Strive to continuously increase the value proposition of our recurring monthly service agreements with existing customers.

WHAT SKILLS DO I NEED TO BE A SUCCESSFUL VCISO? 

Excellent communications skills both verbally written.
IT management and support history, including a mix of strategic consultation and network administration.

Working knowledge of networking technologies such as firewalls, routers, switches, firewall access controls, VPNs, perimeter security, network access controls, network monitoring software, end-point protection, data loss prevention, security information and event management.
Strong working knowledge of security threats, vulnerabilities, and exploits, as well as safeguards to address them.
IT project management experience.
Outstanding knowledge and understanding of the three fundamental safeguard types – technical, administrative, and physical.
Experience in cyber risk management and incident response planning.
Knowledge of risk assessment procedures, role-based authorization methodologies, authentication technologies and security attack pathologies.
Experience developing and authoring information security policies, standards, processes, procedures, and guidelines.
Sound knowledge of identity & access management, vulnerability assessment tools (Nessus etc.), and data encryption technologies.
Strategic leader who can drive a vision for cybersecurity while simultaneously striving for tactical results.
Experience communicating security related concepts to a broad range of technical and non-technical audiences.
Experience relating business requirements and risks to technology proposals for security-related issues.
Proponent of continuous improvement processes and the ability to challenge the status quo.
High level of personal integrity, the ability to professionally handle confidential matters, and demonstrate appropriate levels of judgement and maturity when advising client senior leadership.

Patience, empathy, confidence, and customer service skills. You will be working with a myriad of businesses, technologies, applications, and personalities.
Ability to work effectively within a team as well as independently.
Ability to stay focused and effective in a fast-paced environment.
Talented interpersonal abilities to build rapport with clients and teammates alike.
Self-starter with a positive attitude.
Strong sense of initiative and ownership over work.
Exceptional follow-through skills.
Ability to work effectively and complete assigned tasks with minimal supervision.

WHAT ARE THE QUALIFICATIONS I NEED TO HAVE?

High school diploma or equivalent.
Certified Information Systems Security Professional (CISSP) or equivalent, or must be willing to obtain in the first year of employment.
5+ years of relevant information security experience.
Possess or be willing to earn one or more of the following credentials within the CMMC ecosystem:
- Registered Practitioner (RP)
- Certified Professional (CP)
- Certified Assessor (CA)

WHAT QUALIFICATIONS WOULD REALLY HELP SET ME APART FROM OTHER APPLICANTS? 

Associate degree or higher in Information Technology, IT Assurance, or Information Security (Cybersecurity).
Active security certifications such as CISA, CRISC, CISM, GSEC, Security+.
Experience in relevant industries such as Healthcare, Defense, Payment Processing, etc.
Knowledge and experience working with Microsoft 365, Microsoft Azure, and other Cloud service offerings.
Familiarity with the MSP environment including associated tools, such as ConnectWise, Kaseya, M365, Azure, etc.

WHAT IS THE WORK ENVIRONMENT LIKE?

Normal office working conditions. Work requires regular sitting/standing at a desk,
working with a computer. This position requires standing, walking, sitting, using
hands, seeing, reaching, talking, writing, and hearing; it may require occasionally
carrying or lifting equipment if working on-site.
Position may require hours that exceed normal working hours per day during peak
periods; on-call or travel work may include nights or weekends
Position requires regular contact with others – in meetings, by phone or by email.
Interactions focus on data collection, problem solving, needs analysis and technical
training development. Interactions are initiated in person or electronically. Position
may require some travel to Convergence or client sites.

WHY SHOULD YOU WORK HERE? 

Culture of unity, transparency, and trust. Our leadership team wants you to be successful at Convergence, and we will do anything we can to support your personal
and professional growth.
Group benefits plans (including medical, dental, vision in US and health savings and
dental in Canada, including retirement plans (401k and RRSP).
Education and certification reimbursement is also available so we can help you grow.
We believe feedback makes us better. You can expect regular meetings with your
manager and quarterly conversations about your performance and growth.
Outstanding teammates. We’re very selective to make sure we have the best staff
available for you to work alongside!
Many teambuilding and company events throughout the year so you can get to know
your teammates on a more personal level, as well as kick back and have some
fun (families are oftentimes included as well).

PERFECT FIT…

If this sounds like your type of place and you can wow us with your spectacular skill set, then we would love to hear from you!

We are an equal opportunity employer and invite diversity in our applicants; our differences make us stronger! We welcome and encourage applications from qualified candidates of all races, sexes, colors, religions, sexual orientations, disabilities, ages, and gender identities. Accommodation is available upon request for candidates taking part in all stages of the selection process. Please contact

**@co*****************.com

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24Apr

Chief Information Security Officer at HC1 Hamilton

Location:

1 Hamilton County Square – Noblesville, Indiana, 46060

Hamilton County is one of Indiana’s fastest growing, highest educated and wealthiest counties in the state and the Midwest. Two-thirds of the workforce lives and works in the county — a percent that has remained constant since 2010. Hamilton County is continually recognized in rankings ranging from “Best Cities to Relocate To” and “Best City to Raise a Family” to “Healthiest County in Indiana” and “Happiest Suburbs in the Nation”. Come be a part of all Hamilton County has to offer as we work together to serve the citizens of Hamilton County.

Job Description:

POSITION: Chief Information Security Officer

DEPARTMENT: Information System Services

WORK SCHEDULE: 8:00 a.m. – 4:30 p.m., M-F

STATUS: Full-time

FLSA STATUS: Exempt

Hourly Rate: $52.3544

DUTIES:

Oversees, establishes, and executes, security strategies, policies, architecture, standards, processes, and assessments ensuring information assets critical processes are protected.

Provides insight into risks associated with all manner of service delivery, including processing, data storage, network security, and associated technology.

Oversees safeguarding the computer networks and systems by identifying risks, planning, and implementing security measures, and monitoring security systems to protect sensitive data, systems from infiltration, and cyber-attacks.

Provides leadership, direction, and prioritization using risk-based approach, in assessing and evaluating information security risks with high levels of integrity and discretion, advising, and consulting with executives to identify risks and ensure execution of agreed upon mitigation and remediation steps.

Establishes and maintains policy, procedure, and compliance documentation, for proper operation and maintenance.

Identifies and conducts periodic assessments to mitigate risks.

Implements security measures and technologies to mitigate risks, support business, and technology solutions, including designing, developing, and introducing security enhancement projects.

Develops and coordinates plans for incident response to ensure that critical services are maintained.

Manages and promotes understanding of regulatory requirements to appropriate leaders to ensure execution of required testing and auditing activities through internal and external parties leading to successful certification and compliance.

Monitors emerging threats and recommends appropriate action to ISS Director.

Oversees business continuity and disaster recovery policy including training, testing, and coordination with departments and staff for disaster planning and preparation.

Coordinates with ISS security procurement agreements, contracts, statements of work, enforcement of security standards, and vendor relationships.

Serves as Security Advisor to ISS Director on all technology matters.

Serves as technology security expert for security tools, applications, and processes.

Interacts directly with infrastructure team to align and execute infrastructure changes to support security practices.

Attends conferences, meetings, and training to maintain knowledge on industry trends, security practices, standards, and technology updates.

Performs related duties as assigned.

I. JOB REQUIREMENTS:

Minimum job requirements include: (6) six years of relevant experience as senior security staff member, (5) five years of experience developing and implementing security plans, (3) three years of experience performing risk analysis and/or equivalent combination of education and experience.

Preferred job requirements include: Baccalaureate Degree in computer science, information technology, systems engineering, or related technical field of study, (10) ten years of experience with Microsoft Windows server, (8) eight years of experience in senior level information security role for a local government, (2) two years of experience with Microsoft SQL Server and Oracle database; and (2) two years of experience with multi-factor authentication.

Possession of and/or ability to obtain and maintain security management certification (CISM or CCISO) within one year of employment, and Certified Information Systems Security Professional certification and Certified Ethical Hacker or similar penetration testing certification preferred.

Practical knowledge of and ability to make practical application of standard principles of information security and disaster recovery, including policies, protocol, procedures, and resources necessary to make assessments and perform critical processes.

Practical knowledge of standard office policies and procedures with

Working knowledge of server, desktop, and laptop architecture and ability to read and interpret various technical manuals, cyber security frameworks and the latest security principles, techniques, and protocols.

Knowledge of and ability to properly operate MS Windows, Windows Server, Linux, VMWare data center virtualization, Active Directory, group policy, DNS, encryption, software lifecycle management, endpoint protection, system configuration management, remote access, technology, and multi-factor authentication.

Knowledge of and ability to properly operate LAN, WAN, VPN, routers and switches, firewall technology, IDS/IPS, SIEM and DLP.

Ability to properly operate various standard office equipment, including computer, printer, and telephone systems.

Ability to effectively communicate orally and in writing with co-workers both technical and non-technical staff, other County departments, vendors, the public, including being sensitive to professional ethics, gender, cultural diversities, and disabilities.

Ability to understand and follow written and oral instructions and directions, and appropriately respond to constructive criticism.

Shall comply with all employer and department personnel policies and work rules, including, but not limited to, attendance, safety, drug-free workplace, and personal conduct.

Ability to provide public access to or maintain the highest standards of confidentiality of department information and records according to state requirements.

Ability to work alone and with others in a team environment, often under time pressure, and maintain appropriate, respectful interrelationships with co-workers.

Ability to plan and lay out assigned work projects, work on several tasks at the same time, and complete assignments effectively amidst frequent distractions and interruptions.

Ability to perform attention to detail with analytical capabilities and problem-solving skills and organizational skills.

Ability to understand, memorize, retain, and carry out written and oral instructions, present findings in oral or written form, and appropriately respond to constructive criticism.

Ability to prepare and deliver presentations.

Ability to plan and layout work assignments and knowledge of people and locations.

Ability to occasionally work extended, evening and/or weekend hours, and occasionally travel out of town for training and seminars, sometimes overnight.

Possession of a valid driver’s license and demonstrated safe driving record.

II. DIFFICULTY OF WORK:

Incumbent performs a broad array of duties which are of substantial intricacy with interrelationships among them not always self-evident. Incumbent performs according to technical manuals and department policies and procedures and uses analysis and independent judgment in identifying and solving security, risks, threats, and other information security issues.

III. RESPONSIBILITY:

Incumbent contributes to overall departmental operations by exercising independent judgment in applying departmental objectives to specific cases and circumstances. Incumbent discusses with supervisor any interpretations of departmental objectives and work is periodically reviewed for soundness of judgment and overall conformity with departmental standards.

IV. PERSONAL WORK RELATIONSHIPS:

Incumbent maintains frequent contact with co-workers, other County departments, vendors, and the public for purposes of exchanging information, rendering service and providing security for data and technology compliance.

Incumbent reports directly to ISS Director.

V. PHYSICAL EFFORT AND WORK ENVIRONMENT:

Incumbent performs duties in a standard office environment involving sitting and walking at will, sitting for long periods, keyboarding, driving, close vision, color perception, hearing sounds/communication, handling/grasping/fingering objects, lifting/carrying objects weighing under 50 pounds, crouching/kneeling, bending, reaching, and speaking clearly.

Incumbent is occasionally required to work extended, evening and/or weekend hours, and occasionally travels out of town for training and seminars, sometimes overnight.

Proposed Hourly Rate:

Hamilton County is an Equal Opportunity Employer. We participate in E-Verify.

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23Apr

Nota AI Researchers Introduce LD-Pruner: A Novel Performance-Preserving Structured Pruning Method for Compressing Latent Diffusion Models LDMs

Generative models have emerged as transformative tools across various domains, including computer vision and natural language processing, by learning data distributions and generating samples from them. Among these models, Diffusion Models (DMs) have garnered attention for their ability to produce high-quality images. Latent Diffusion Models (LDMs) stand out for their rapid generation capabilities and reduced computational cost. However, deploying LDMs on resource-limited devices remains challenging due to significant compute requirements, particularly from the Unet component.

Researchers have explored various compression techniques for LDMs to address this challenge, aiming to reduce computational overhead while maintaining performance. These strategies include quantization, low-rank filter decomposition, token merging, and pruning. Pruning, traditionally used for compressing convolutional networks, has been adapted to DMs through methods like Diff-Pruning, which identifies non-contributory diffusion steps and important weights to reduce computational complexity.

While pruning offers promise for LDM compression, its adaptability and effectiveness across various tasks still need to be improved. Moreover, evaluating pruning’s impact on generative models presents challenges due to the complexity and resource-intensive nature of performance metrics like Frechet Inception Distance (FID). In response, the researchers from Nota AI propose a novel task-agnostic metric for measuring the importance of individual operators in LDMs, leveraging the latent space during the pruning process.

Their proposed approach ensures independence from output types and enhances computational efficiency by operating in the latent space, where data is compact. This allows for seamless adaptation to different tasks without requiring task-specific adjustments. The method effectively identifies and removes components with minimal contribution to the output, resulting in compressed models with faster inference speeds and fewer parameters.

Their study introduces a comprehensive metric for comparing LDM latent and formulates a task-agnostic algorithm for compressing LDMs through architectural pruning. Experimental results across various tasks demonstrate the versatility and effectiveness of the proposed approach, promising wider applicability of LDMs in resource-constrained environments.

Furthermore, their proposed approach offers a nuanced understanding of the latent representations of LDMs through the novel metric, which is grounded in rigorous experimental evaluations and logical reasoning. By thoroughly assessing each element of the metric’s design, the researchers ensure its effectiveness in accurately and sensitively comparing LDM latent. This level of granularity enhances the interpretability of the pruning process and enables precise identification of components for removal while preserving output quality.

In addition to its technical contributions, their study showcases the proposed method’s practical applicability across three distinct tasks: text-to-image (T2I) generation, Unconditional Image Generation (UIG), and Unconditional Audio Generation (UAG). The successful execution of these experiments underscores the approach’s versatility and potential impact in diverse real-world scenarios. Their research validates the proposed method by demonstrating its effectiveness across multiple tasks. It opens avenues for its adoption in various applications, further advancing the field of generative modeling and compression techniques.

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