Reviewerly/exHarmony huggingface dataset:
Behavior-based user authentication on mobile devices in various usage contexts

Abstract Reliable and non-intrusive user identification and authentication on mobile devices, such as smartphones, are topical tasks today. The majority of state-of-the-art solutions in this domain are based on “device unlock” scenario—checking of information (authentication factors) provided by the user for unlocking a smartphone. As such factors, we may use either single strong authentication factor, for example, password or PIN, or several “weaker” factors, such as tokens, biometrics, or geolocation data. However, these solutions require additional actions from a user, for example, password typing or taking a fingerprint, that may be inappropriate for on-the-fly authentication. In addition, biometric-based user authentication systems tend to be prone to presentation attack (spoofing) and typically perform well in fixed positions only, such as still standing or sitting. We propose BehaviorID solution that is passwordless (transparent) user-adaptive context-dependent authentication method. The feature of BehaviorID is usage of new “device lock” scenario—smartphone is stayed unlocked and can be fast locked if non-owner’s actions are detected. This is achieved by tracking of user’s behavior with embedded sensors after triggering events, such as actions in banking apps, e-mails, and social services. The advanced adaptive recurrent neural network (A-RNN) is used for accurate estimation and adaptation of behavioral patterns to a new usage context. Thus, proposed BehaviorID solution allows reliable user authentication in various usage contexts by preserving low battery consumption. Performance evaluation of both state-of-the-art and proposed solutions in various usage contexts proved the effectiveness of BehaviorID in real situations. Proposed solution allows reducing error levels up to three times in comparison with modern Abuhamad’s solutions (Abuhamad et al., IEEE Internet Things J 7(6):5008–5020, 2020) (about $$0.3\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>0.3</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> false acceptance rate (FAR) and $$1.3\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>1.3</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> false rejection rate (FRR)) by preserving high robustness to spoofing attack ( $$2.5\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>2.5</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> spoof acceptance rate (SAR)). In addition, BehaviorID showed low drift of error level in case of long-term usage in contrast to modern solutions. This makes the proposed BehaviorID solution an attractive candidate for next-generation behavior-based user authentication systems on mobile devices.
SDCBench: A Benchmark Suite for Workload Colocation and Evaluation in Datacenters

Colocating workloads are commonly used in datacenters to improve server utilization. However, the unpredictable application performance degradation caused by the contention for shared resources makes the problem difficult and limits the efficiency of this approach. This problem has sparked research in hardware and software techniques that focus on enhancing the datacenters’ isolation abilities. There is still lack of a comprehensive benchmark suite to evaluate such techniques. To address this problem, we present SDCBench, a new benchmark suite that is specifically designed for workload colocation and characterization in datacenters. SDCBench includes 16 applications that span a wide range of cloud scenarios, which are carefully selected from the existing benchmarks using the clustering analysis method. SDCBench implements a robust statistical methodology to support workload colocation and proposes a concept of latency entropy for measuring the isolation ability of cloud systems. It enables cloud tenants to understand the performance isolation ability in datacenters and choose their best-fitted cloud services. For cloud providers, it also helps them to improve the quality of service to increase their revenues. Experimental results show that SDCBench can simulate different workload colocation scenarios by generating pressures on multidimensional resources with simple configurations. We also use SDCBench to compare the latency entropies in public cloud platforms such as Huawei Cloud and AWS Cloud and a local prototype system FlameCluster-II; the evaluation results show FlameCluster-II has the best performance isolation ability over these three cloud systems, with 0.99 of experience availability and 0.29 of latency entropy.
Smart-Contract-Based Automation for OF-RAN Processes: A Federated Learning Use-Case

The opportunistic fog radio access network (OF-RAN) expands its offloading computation capacity on-demand by establishing virtual fog access points (v-FAPs), comprising user devices with idle resources recruited opportunistically to execute the offloaded tasks in a distributed manner. OF-RAN is attractive for providing computation offloading services to resource-limited Internet-of-Things (IoT) devices from vertical industrial applications such as smart transportation, tourism, mobile healthcare, and public safety. However, the current OF-RAN design is lacking a trusted and distributed mechanism for automating its processes such as v-FAP formation and service execution. Motivated by the recent emergence of blockchain, with smart contracts as an enabler of trusted and distributed systems, we propose an automated mechanism for OF-RAN processes using smart contracts. To demonstrate how our smart-contract-based automation for OF-RAN could apply in real life, a federated deep learning (DL) use-case where a resource-limited client offloads the resource-intensive training of its DL model to a v-FAP is implemented and evaluated. The results validate the DL and blockchain performances of the proposed smart-contract-enabled OF-RAN. The appropriate setting of process parameters to meet the often competing requirements is also demonstrated.
Algebraic tools for default modal systems

Abstract Default Logics are a family of non-monotonic formalisms having so-called defaults and extensions as their common foundation. Traditionally, default logics have been defined and dealt with via syntactic notions of consequence in propositional or first-order logic. Here, we build default logics on modal logics. First, we present these default logics syntactically. Then, we elaborate on an algebraic counterpart. More precisely, we extend the notion of a modal algebra to accommodate for defaults and extensions. Our algebraic view of default logics concludes with an algebraic completeness result and a way of comparing default logics borrowing ideas from the concept of bisimulation in modal logic. To our knowledge, this take on default logics approach is novel. Interestingly, it also lays the groundwork for studying default logics from a dynamic logic perspective.
Deep learning model for person identification and verification on low illumination images

Performances of the deep learning models for person identification and verification are degraded on low illumination images due to the generated facial embeddings being unable to be matched with the trained good illumination facial embeddings. Person verification on low illumination images is a challenging task. The existing techniques have adopted an approach of enhancing the low illumination images and performing the person verification in the enhanced images. But these techniques have not achieved satisfactory results, because the gamma value is kept constant in the power law intensity transformation function to enhance the images. To obtain better performance, we propose a deep learning-based framework that consists of a contrast enhancement module, called as contrast enhancement network (CENet); person identification; and person verification modules. The CENet is built based on the residual network, which predicts the gamma value based on the illumination of the input image. The predicted value is used to perform gamma correction on the image to improve the brightness difference between the faces and their background, whereas the existing techniques are keeping the gamma value as constant for image enhancement. After performing the image enhancement, the enhanced image is given as input to the person identification module. Then the detected faces are verified by the person verification module. Experimental results show that the proposed framework has achieved an improvement of 3.4% to 13% in person identification and verification accuracy on the extended yale face dataset to the existing methods.
(Retracted) Face recognition technology in classroom environment based on ResNet neural network

With the rapid development of electronic computers and information technology, face recognition is widely used in fields such as enterprises, entertainment, information security, and daily life. However, the current face recognition technology is still relatively poor in distinguishing facial features, resulting in a low accuracy of face recognition, which cannot meet increasing application requirements. For this reason, it is necessary to develop more accurate face recognition technology. Residual neural network (ResNet) neural network is a deep residual learning convolutional neural network, which can be used to solve the degeneration problem (i.e., after adding more layers to the neural network, the performance drops rapidly) of deep-learning neural networks. We aim to study the specific principles, calculation methods, and characteristics of the ResNet neural network, analyze the complexity of the classroom environment, propose the use of ResNet network for face feature extraction, and use additive angular margin loss for deep face recognition (ArcFace) as the loss function of the ResNet network to improve the ResNet network. Because the ArcFace function has the advantages of high performance, easy programming, low complexity and high training efficiency. We use this scheme to conduct field tests of face recognition on many students in the classroom. The test results show that this scheme enhances the discrimination of facial features and improves the accuracy of face recognition. In the case of different numbers of people, facial defects, and strong light exposure, the system can still detect and recognize faces stably. In the case of facial defects, the recognition accuracy rate is still 63.3%, and the recognition accuracy rate is still more than 60% under the illumination of strong light, and the recognition accuracy rate under correct conditions is more than 70%.
Man-hours Prediction Model for Estimating the Development Cost of AI-Based Software

The artificial intelligence software market is expected to grow sixfold from 2020 to 2025. However, the software development process is not standardized and there is no standard for calculating the cost. Accordingly, each AI software development company calculates the input man-hours according to their respective development procedures and presents this as the basis for the development cost. In this study, the development stage of “artificial intelligence-based software” that learns with a large amount of data and derives and applies an algorithm was defined, and the required labor was collected by conducting a survey on the number of man-hours required for each development stage targeting developers. Correlation analysis and regression analysis were performed between the collected man-hours for each development stage, and a model for predicting the man-hours for each development stage was derived. As a result of testing the model, it showed an accuracy of 92% compared to the collected airborne effort. The man-hour prediction model proposed in this study is expected to be a tool that can be used simply for estimating man-hours and costs.
Expressive Interaction Design Using Facial Muscles as Controllers

Here we describe a proof-of-concept case study focusing on the design and development of a novel computer interface that uses facial muscles to control interactivity within a virtual environment. We have developed a system comprised of skin-mounted electrodes that detect underlying muscle activity through electromyography. The signals from the electrodes are filtered and smoothed, then used as input data to an application that displays a virtual environment with a 3D animated avatar. The user’s expressions control the facial movements of the avatar, thus conveying user emotions through real-time animation of a representative face in a virtual scenario. To achieve this, we collaborated with our Public and Patient Involvement focus group to discuss concepts and design appropriate interactions, while simultaneously developing a prototype system. Programmers and 3D artists worked together to create a system whereby individual user facial muscles are connected to 3D animated models of the same muscle features represented in an avatar, providing the user with an option to receive visual and numerical feedback on the extent of their muscle control. Using the prototype system, people can communicate facial expressions virtually with each other, without the need for a camera. This research is part of an on-going project to develop a facial muscle rehabilitation system that can be optimized to help patients with conditions such as hypomimia.
Twitter Sentiment Analysis on Oxygen Supply During Covid 19 Outbreak

The COVID-19 emerged as a pandemic and affected many nations. World Health Organization [WHO] declared it as a worldwide pandemic alert on March 11, 2020, people had to stay indoors with lockdowns imposed, turning all daily activities to a halt. The lockdown was lifted in phase by manner from June 2020 as the cases were in control. A rise in pandemic was observed in many countries again in 2021, it was termed as the second wave of COVID-19. In India daily cases reached the mark of 4 lakhs in April 2021. This resulted in increased demand for oxygen supplies and other medical equipment's to tackle the situation. With this, social media or the microblogging platforms like Twitter became a popular means of expressing emotions, making request for help and a daily information channel. The present study analyses the Twitter data extracted using Twitter API. It analyses and classifies people's sentiments related to the supply of oxygen during the second wave of the pandemic in India. The paper analyses the sentiment of the tweets for Indian users from June 20th, 2021, to June 26th, 2021, using Natural language processing (NLP) and Machine Learning (ML) techniques.
Analysis for Cost Optimized EO Design of a Reversible Boolean Function Using MZIs

The concept of making any logical (Boolean) function reversible is gaining much attention in the recent past for its applications in nanotechnology, quantum dot cellular automata and quantum computing etc. The use of reversible Boolean function not only simplifies back tracing, but it also allows efficient computation because of the non erasure of stored data, which lowers heat dissipation. This paper selects a reversible Boolean expression and explores its realization using electro-optic Mach-Zehnder Interferometer. Various design combinations are explored to implement the cost optimized reversible configurations. The designs are carried out in OptiBPM platform and the truth table is verified using power equations simulated in MATLAB.
Monitoring Pedestrian Social Distance System for COVID-19

COVID-19 is a transmissible infection triggered by a coronavirus that has newly detected effect. Most patients infected by COVID-19 are undergoing minor to severe breathing disease and could able to recover without any therapy. Old persons, and others with underlying medical conditions such as cardiovascular, diabetes and so on are further prone to become disabled. The best measure to avert and slow down the transmission is to be informed about the COVID-19 virus, disease it causes and how it spreads. Protecting yourself and others from the infectious disease by washing hands or using an alcohol based sanitizer frequently and avoid touching face and most importantly, following social distancing in Public Places. The proposed detection tool was intended to warn users that a video feed will keep a shielded distance from each other. The camera’s video framework was used as an input and YOLOv3 algorithm for object detection is pre-trained. The distance between persons can be measured and a red frame shows any incompatible pair of persons. The result reveals that the method proposed can calculate metrics for social distance in videos among several persons.
News Text Summarization Based on Multi-Feature and Fuzzy Logic

Text Summarization as a phenomenon has always been present and rather an evolving one with the advent of new technologies both in terms of data collection as well for the processing of this data. One reason of using text summarization is the huge amount of data floating over the internet in the form of text files, comments which is though potent enough to be used to extract useful information. but since the amount of text present in these sources is too huge, so the need of text summarization becomes justified by every argument. Some of the areas where text summarization is vastly used is applications involved in providing capsule information such as compact news applications, or websites providing academic notes for various examinations This paper presents an auto text summarizer application which takes the URL of a web page as input, performs summarization on the selected elements and then presents this summarized text content on the front end of a web application. At the backend, the process of scraping of web page content (if an http URL is provided as input) using beautiful soup library or reading of text provided takes place. news in short forms, or micro blogging websites. The scraped content after being preprocessed properly is summarized using a suitable library which in our case is one among NLTK, Spacy, Genism and Sumy. The summarized content is presented at the frontend using flask framework of Python. The results produced using different libraries are compared in the end in terms of reading time of the summarized content. The application uses extractive text summarization technique in order to achieve its result which is a compact summary of the textual data prepared from the keywords already present in the document.
A Machine Learning Study of 534,023 Medicare Beneficiaries with COVID-19: Implications for Personalized Risk Prediction for People Over 65

Background:The global outbreak of COVID-19 has resulted in over 378 million infections and worldwide casualties have surpassed 5.6 million.Identifying individuals at highest risk of COVID-19 death may help risk evaluation and health service provision.Personalized risk prediction that uses a broad range of comorbidities requires a cohort size larger than that reported in prior studies.Methods: Medicare claims data was used to identify patients age 65 years or older with diagnosis of COVID-19 between April 1, 2020 and August 31, 2020.Demographic characteristics, chronic medical conditions, and other patient risk factors that existed before the advent of COVID-19 were identified.A random forest model was used to empirically explore factors associated with COVID-19 death.The independent impact of factors identified were quantified using multivariate logistic regression with random effects. Results:We identified 534,023 COVID-19 patients of whom 38,066 had an inpatient death.Demographic characteristics associated with COVID-19 death included advanced age (85 years or older: aOR: 2.07; 95% CI, 1.99-2.16),male sex (aOR, 1.88; 95% CI, 1.82-1.94),and non-white race (Hispanic: aOR, 1.74; 95% CI, 1.66-1.83).Leading comorbidities associated with COVID-19 mortality included sickle cell disease (aOR, 1.73; 95% CI, 1.21-2.47),chronic kidney disease (aOR, 1.32; 95% CI, leukemias and lymphomas (aOR, 1.22; 95% CI, heart failure (aOR, 1.19; 95% CI, and diabetes (aOR, 1.18; 95% CI,. Conclusions:We created a personalized risk prediction calculator to identify candidates for early vaccine and therapeutics allocation (www.predictcovidrisk.com).These findings may be used to protect those at greatest risk of death from COVID-19.
A Taxonomy and Capacity Planning Technique for Sustainable Cloud Computing – An Extensive Overview

This overview of study intends to provide a thorough taxonomy of sustainable cloud computing capacity planning strategies. Several academic and industrial organizations have suggested several approaches to sustainability, and this taxonomy is used to analyze them. These modern methods have been analyzed and grouped together according to their shared traits and characteristics. This study takes a holistic look at sustainable Cloud Data Centers (CDCs), surveying the supporting methods and technologies along the way. It provides examples of successful capacity planning in sustainable CDCs based on research and practice from academia and industry. Moreover, the paper presents the most recent findings on what it takes to make CDCs viable. In addition, the difficulties of integration and the unanswered questions of sustainable CDC research have been discussed.
Lemmaless Induction in Trace Logic

We present a novel approach to automate the verification of first-order inductive program properties capturing the partial correctness of imperative program loops with branching, integers and arrays. We rely on trace logic, an instance of first-order logic with theories, to express first-order program semantics by quantifying over program execution timepoints. Program verification in trace logic is translated into a first-order theorem proving problem where, to date, effective reasoning has required the introduction of so-called trace lemmas to establish inductive properties. In this work, we extend trace logic with generic induction schemata over timepoints and loop counters, reducing reliance on trace lemmas. Inferring and proving loop invariants becomes an inductive inference step within superposition-based first-order theorem proving. We implemented our approach in the Rapid framework, using the first-order theorem prover Vampire. Our extensive experimental analysis shows that automating inductive verification in trace logic is an improvement compared to existing approaches.
Isabelle/HOL/GST: A Formal Proof Environment for Generalized Set Theories

A generalized set theory (GST) is like a standard set theory but also can have non-set structured objects that can contain other structured objects including sets. This paper presents Isabelle/HOL support for GSTs, which are treated as type classes that combine features that specify kinds of mathematical objects, e.g., sets, ordinal numbers, functions, etc. GSTs can have an exception feature that eases representing partial functions and undefinedness. When assembling a GST, extra axioms are generated following a user-modifiable policy to fill specification gaps. Specialized type-like predicates called soft types are used extensively. Although a GST can be used without a model, for confidence in its consistency we build a model for each GST from components that specify each feature’s contribution to each tier of a von-Neumann-style cumulative hierarchy defined via ordinal recursion, and we then connect the model to a separate type which the GST occupies.
Re-imagining the Isabelle Archive of Formal Proofs

Since its inception in 2004 the Archive of Formal Proofs has grown in size but its interface and functionality have only been minimally improved. To transform the AFP into a more user-friendly and effective resource, we redesigned the website to meet modern web standards and practices. We ensure that our work is community-driven by basing the redesign on results from a survey of the Isabelle community. The site generation uses Hugo and is implemented as a proper Isabelle component, which also allows us to adapt the AFP metadata model to avoid inconsistencies in the future. Notable improvements include a responsive design, new theory browsing interface, integrated search, and enhanced navigation.