When new virus and its respective disease cause more infections, it is very important to decide the strategies to control the spread and determine its impact. Considering the recent example of Coronavirus initially identified in Wuhan China has now targeted Italy badly, It is very important to study different forecasting models to control this pandemic. In the view of this, this study present the comparative analysis of various forecasting models, their classification and the techniques used. The detail analysis of forecasting models with respect to the parameters like data source, techniques, algorithms, mathematical parameters is also presented in this study. In the sequel, this study also presents useful recommendations to help government and healthcare community in designing better strategies and in taking productive decisions to control this outbreak.

Sudoku is a very popular pass-time game that had gained a lot of popularity when it was published by The Times in London. We have used Java Server Faces (JSF) as a tool for Full-Stack development of the Sudoku as a web browser based game. By building this project we had gained a better understanding of the framework. In this work we present an algorithm for solving the Sudoku problem and a safe way of validating the results keeping scalability in mind.

This work aims to review the state-of-the-art deep learning architectures in Cyber Security applications by highlighting the contributions and challenges from various recent research papers.

This paper presents a way to generate the correct SPICE sub- circuit using only parameters from the manufacturer's diode datasheet. Characterization tool software was created and tested and is now freely available to download and use.

The ‘brain-mind-intelligence’ structure may be considered analogous to a ‘computer-operating system (OS)-application’ construct. Software development would be far more difficult without the benefit of abstractions like file and memory handles, and graphical user interfaces, implemented in operating systems. The easy access to application-level semantics, provided by the infrastructure of the OS, simplifies application development.  Abstractions can play a similar role in producing intelligent systems. The power of abstractions may be determined by their ability to unify the treatment of several types of higher concepts. Unifying various concepts in the domain of intelligence, such as problems, solutions, objects, and emotions, through abstractions will be of immense value. This paper shows how ‘higher-level perceptions’ could be used to serve this objective and construct an OS-like infrastructure. With the infrastructure in place, ordinary software objects developed and deployed using standard methods, become accessible using the semantics provided by the infrastructure. Now, after brief training exercises, the objects would be available for intelligent use. The focus, therefore, is on the design of the infrastructure that could play a mind-like role in humans. The resulting model is then evaluated in terms of features against criteria set forth independently, by Newell, Sun, Vernon, and others. In addition, a new measure of intelligence introduced in this paper is used to compare the proposed modeling method with some of the major methods developed over the years.

Optical artificial neural networks (ONNs) — analog computing hardware tailored for machine learning — have significant potential for ultra-high computing speed and energy efficiency. We propose a new approach to architectures for ONNs based on integrated Kerr micro-comb sources that is programmable, highly scalable and capable of reaching ultra-high speeds. We experimentally demonstrate the building block of the ONN — a single neuron perceptron — by mapping synapses onto 49 wavelengths of a micro-comb to achieve a high single-unit throughput of 11.9 Giga-FLOPS at 8 bits per FLOP, corresponding to 95.2 Gbps. We test the perceptron on simple standard benchmark datasets — handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. This performance is a direct result of the record small wavelength spacing (49GHz) for a coherent integrated microcomb source, which results in an unprecedented number of wavelengths for neuromorphic optics. Finally, we propose an approach to scaling the perceptron to a deep learning network using the same single micro-comb device and standard off-the-shelf telecommunications technology, for high-throughput operation involving full matrix multiplication for applications such as real-time massive data processing for unmanned vehicle and aircraft tracking.

At present, the Internet is facing numerous attacks of different kinds that put its data at risk. The safety of information within the network is, therefore, a significant concern. To prevent the loss of incredibly valuable information, the Intrusion Detection System (IDS) was developed to recognize the outbreak of a stream of attacks and notify the network system administrator providing network security. IDS is a predictive model which detect network traffic as routine or deviated. Software-Defined Networks (SDN) is a revolutionary paradigm that isolates the control plane from the data plane, transforming the concept of a software-driven network. Through this data and control plane separation, SDN provides us the opportunity to create a manageable and programmable network, allowing applications in the top plane to access physical devices via the controller. The controller residing in the control plane, executes the network modules, and establishes flow rules to forward packets in the switches residing in the data plane. Often adversaries target the SDN controller to subdue the control plane, which is considered the brain of the SDN, which provides a plethora of functionalities such as regulating flow control to switches or routers in the data plane below via southbound Application Programming Interfaces (APIs) and business and application logic in the application plane above via northbound APIs to implement sophisticated networks. However, the control plane becomes a tempting prospect for security attacks from adversaries because of its centralization feature. This paper includes an in-depth overview of the notable published articles from 2015 to 2021 that used traditional Machine Learning (ML) and Deep Learning (DL) techniques to construct an IDS solution to provide security for SDN. We also present two detailed taxonomic studies regarding IDS, and ML-DL techniques based on their learning categories, exploring various IDS solutions to secure the SDN paradigm. We have also conducted brief research on a few benchmark datasets used to construct IDS in the SDN paradigm. To conclude the survey, we provide a discussion that sheds light on continuous challenges and IDS issues for SDN security.

Real world signal acquisition through sensors, is at the heart of modern digital revolution. However, almost every signal acquisition systems are contaminated with noise and outliers. Precise detection, and curation of data is an essential step to reveal the true-nature of the uncorrupted observations. With the exploding volumes of digital data sources, there is a critical need for a robust but easy-to-operate, low-latency, generic yet highly customizable, outlier detection and curation tool, easily accessible, adaptable to diverse types of data sources. Existing methods often boil down to data smoothing that inherently cause valuable information loss. We have developed a C++ based, software tool to decontaminate time- series and matrix like data sources, with the goal of recovering the ground-truth. The SOCKS tool would be made available as an open-source software for broader adoption in the scientific community. Our work calls for a philosophical shift in the design pipelines of real- world data processing. We propose, raw data should be decontaminated first, through conditional flagging of outliers, curation of flagged points, followed by iterative, parametrically tuned, asymptotic converge to the ground-truth as accurately as possible, before performing traditional data processing tasks.

The sudden development of the COVID-19 pandemic exposed the limitations in modern healthcare systems to handle public health emergencies. It is evident that adopting innovative technologies such as blockchain can help in effective planning operations and resource deployments. Blockchain technology can play an important role in the healthcare sector such as improved clinical trial data management by reducing delays in regulatory approvals, streamline the communication between diverse stakeholders of the supply chain etc. Moreover, the spread of misinformation has intensely increased during the outbreak and existing platforms lack the ability to validate the authenticity of data, causing people to panic and act irrationally. Thus, developing a blockchain-based tracking system is important to ensure that the information received by the public and government agencies are reliable and trustworthy. In this paper, we focus on blockchain abilities to track the COVID-19 data collected from various sources including news, healthcare professionals, researchers etc, verify and append them in a secure and trusted distributed ledger. Thus, we propose a generic framework using Ethereum smart contracts and oracles to track real-time data related to the number of new cases, deaths and recovered cases obtained from trusted sources. We present detailed algorithms that capture the interactions between stakeholders in the network. The smart contract code was developed and tested in Remix environment. We present the cost and security analysis incurred by the stakeholders and highlight the challenges and future directions of our work. Our work demonstrates that the proposed solution is economically feasible and ensures data integrity, security, transparency, data traceability among stakeholders.

Recent results in language processing show that language models are capable of performing several natural language tasks without the need of supervised learning. A challenging task for pre-trained language models is dialogue summarization. One way of generating summaries is engineering prompt templates for few-shot training. However, a static approach of creating prompts leads to unreliable outcomes between different classes of dialogues. Focusing on the dialogues structure properties, we propose a scoring system to improve the few-shot training performances by building tuned prompts composed by the highest scored dialogue samples. Our evaluation based on ROUGE scores and human evaluation shows that there is improvement for the experiments where we made use of the score system. The positive results are validated by all the three large-scale datasets we used in testing. All experiments were performed within the framework of the GPT-3 API.

On-wafer evaluation of white thermal and shot noise in nanoscale MOSFETs is demonstrated by directly sensing the drain current under zero- and nonzsero-drain-bias (Vd) conditions for the first time, without recourse to a hot noise source, commonly needed in noise figure measurement. The dependence of white noise intensity on the drain bias clearly shows thermal noise at Vd=0 V and shot noise at Vd>0 V with its gate-bias-dependent suppression. An empirical expression for the Fano factor (shot-noise suppression factor) that is well-behaved even at Vd=0V exactly and suitable for measurement-based evaluation is proposed. The direct measurement approach could allow more accurate and predictive noise modeling of RF MOSFETs than has conventionally been possible.

According to the World Health Organization (WHO), hundreds of million people have some type of visual disability. Vision impairment has a personal impact with lifelong consequences because more than 80% of our perception, cognition, learning, and daily activities are mediated through vision. Moreover, in the era of rapid advancements in artificial intelligence (AI), visually impaired and blind people face challenges at work and in education because of inaccessibility to AI technologies. In this regard, we present an assistive mobile application with an intuitive user interface (UI) for visually impaired and blind people to interact with ChatGPT via natural conversation. The app employs automatic speech recognition (ASR), text-to-speech (TTS), keyword spotting (KWS), voice activity detection (VAD), and a convenient UI to interact with ChatGPT effortlessly. We have made the source code, pre-trained models, and UI publicly available at https://github.com/IS2AI/talk-llm to stimulate the development of assistive mobile applications.

Healthcare data are inherently multimodal. Almost all data generated and acquired during a patient's life can be hypothesized to contain information relevant to providing optimal personalized healthcare. Data sources such as ECGs, doctor's notes, histopathological and radiological images all contribute to inform a physician's treatment decision. However, most machine learning methods in healthcare focus on single-modality data. This becomes particularly apparent within the field of radiology, which, due to its information density, accessibility, and computational interpretability, constitutes a central pillar in the healthcare data landscape and traditionally has been one of the key target areas of medically-focused machine learning. Computer-assisted diagnostic systems of the future should be capable of simultaneously processing multimodal data, thereby mimicking physicians, who also consider a multitude of resources when treating patients. Before this background, this review offers a comprehensive assessment of multimodal machine learning methods that combine data from radiology and other medical disciplines. It establishes a modality-based taxonomy, discusses common architectures and design principles, evaluation approaches, challenges, and future directions. This work will enable researchers and clinicians to understand the topography of the domain, describe the state-of-the-art, and detect research gaps for future research in multimodal medical machine learning.

The year 2020 has witnessed the emergence of coronavirus (COVID-19) that has rapidly spread and adversely affected the global economy, health, and human lives. The COVID-19 pandemic has exposed the limitations of existing healthcare systems regarding their inadequacy to timely and efficiently handle public health emergencies. A large portion of today’s healthcare systems are centralized and fall short in providing necessary information security and privacy, data immutability, transparency, and traceability features to detect fraud related to COVID-19 vaccination certification, and anti-body testing. Blockchain technology can assist in combating the COVID-19 pandemic by ensuring safe and reliable medical supplies, accurate identification of virus hot spots, and establishing data provenance to verify the genuineness of personal protective equipment. This paper discusses the potential blockchain applications for the COVID-19 pandemic. It presents the high-level design of three blockchain-based systems to enable governments and medical professionals to efficiently handle health emergencies caused by COVID-19. It discusses the important ongoing blockchain-based research projects, use cases, and case studies to demonstrate the adoption of blockchain technology for COVID-19. Finally, it identifies and discusses future research challenges, along with their key causes and guidelines.

Powered prostheses can enable individuals with above-knee amputations to ascend stairs step-over-step. To accomplish this task, available stair ascent controllers impose a pre-defined joint impedance behavior or follow a pre-programmed position trajectory. These control approaches have proved successful in the laboratory. However, they are not robust to changes in stair height or cadence, which is essential for real-world ambulation. Here we present an adaptive stair ascent controller that enables individuals with above-knee amputations to climb stairs of varying stair heights at their preferred cadence and with their preferred gait pattern. We found that modulating the prosthesis knee and ankle position as a function of the user's thigh in swing provides toe clearance for varying stair heights. In stance, modulating the torque-angle relationship as a function of the prosthesis knee position at foot contact provides sufficient torque assistance for climbing stairs of different heights. Furthermore, the proposed controller enables individuals to climb stairs at their preferred cadence and gait pattern, such as step-by-step, step-over-step, and two-steps. The proposed adaptive stair controller may improve the robustness of powered prostheses to environmental and human variance, enabling powered prostheses to more easily move from the lab to the real-world.

This paper comprehensively addresses homomorphic encryption from both theoretical and practical perspectives. The paper delves into the mathematical foundations required to understand fully homomorphic encryption FHE. It consequently covers design fundamentals and security properties of FHE, and describes the main FHE schemes based on various mathematical problems. On a more practical level, the paper presents a view on privacy-preserving Machine Learning using homomorphic encryption, then surveys FHE at length from an engineering angle, covering the potential application of FHE in fog computing, and cloud computing services. It also provides a comprehensive analysis of existing state-of-the-art FHE libraries and tools, implemented in software and hardware, and the performance thereof.

A zero-knowledge (ZK) proof guarantees that the result of a computation is correct while keeping part of the computation details private. Some ZK proofs are tiny and can be verified in short time, which makes them one of the most promising technologies for solving two key aspects: the challenge of enabling privacy to public and transparent distributed ledgers and, enhancing the scalability limitations of distributed ledgers. Most practical ZK systems require the computation to be expressed as an arithmetic circuit that is encoded as a set of equations called rank-1 constraint system (R1CS). In this paper, we present \circom, a programming language and a compiler for designing arithmetic circuits that are compiled to R1CS. More precisely, with \circom, programmers can design arithmetic circuits, and the compiler outputs (i) a file with the R1CS description, (ii) \wasm and \cpp programs to efficiently compute all values of the circuit. We also provide an open-source library called \circomlib, with multiple circuit templates. Moreover, \circom can be complemented with \snarkjs, a tool for generating and validating ZK proofs from R1CS. Altogether, our software tools abstract the complexity of the proving mechanisms and provide a friendly interface to model low-level descriptions of arithmetic circuits.

Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted. Researchers have also worked on technical analysis of stocks with a goal of identifying patterns in the stock price movements using advanced data mining techniques. In this work, we propose an approach of hybrid modeling for stock price prediction building different machine learning and deep learning-based models. For the purpose of our study, we have used NIFTY 50 index values of the National Stock Exchange (NSE) of India, during the period December 29, 2014 till July 31, 2020. We have built eight regression models using the training data that consisted of NIFTY 50 index records from December 29, 2014 till December 28, 2018. Using these regression models, we predicted the open values of NIFTY 50 for the period December 31, 2018 till July 31, 2020. We, then, augment the predictive power of our forecasting framework by building four deep learning-based regression models using long-and short-term memory (LSTM) networks with a novel approach of walk-forward validation. Using the grid-searching technique, the hyperparameters of the LSTM models are optimized so that it is ensured that validation losses stabilize with the increasing number of epochs, and the convergence of the validation accuracy is achieved. We exploit the power of LSTM regression models in forecasting the future NIFTY 50 open values using four different models that differ in their architecture and in the structure of their input data. Extensive results are presented on various metrics for all the regression models. The results clearly indicate that the LSTM-based univariate model that uses one-week prior data as input for predicting the next week’s open value of the NIFTY 50 time series is the most accurate model.