Reviewerly/exHarmony huggingface dataset:
Self-Care Assessment for Daily Living Using Machine Learning Mechanism

Nowadays, activities of daily living (ADL) recognition system has been considered an important field of computer vision. Wearable and optical sensors are widely used to assess the daily living activities in healthy people and people with certain disorders. Although conventional ADL utilizes RGB optical sensors but an RGB-D camera with features of identifying depth (distance information) and visual cues has greatly enhanced the performance of activity recognition. In this paper, an RGB-D-based ADL recognition system has been presented. Initially, human silhouette has been extracted from the noisy background of RGB and depth images to track human movement in a scene. Based on these silhouettes, full body features and point based features have been extracted which are further optimized with probability based incremental learning (PBIL) algorithm. Finally, random forest classifier has been used to classify activities into different categories. The n-fold cross-validation scheme has been used to measure the viability of the proposed model on the RGBD-AC benchmark dataset and has achieved an accuracy of 92.71% over other state-of-the-art methodologies.
Non-parametric Learning of Embeddings for Relational Data Using Gaifman Locality Theorem

We consider the problem of full model learning from relational data. To this effect, we construct embeddings using symbolic trees learned in a non-parametric manner. The trees are treated as a decision-list of first order rules that are then partially grounded and counted over local neighborhoods of a Gaifman graph to obtain the feature representations. We propose the first method for learning these relational features using a Gaifman graph by using relational tree distances. Our empirical evaluation on real data sets demonstrates the superiority of our approach over handcrafted rules, classical rule-learning approaches, the state-of-the-art relational learning methods and embedding methods.
Blockchain-Centered E-voting System

Voting is the process by which people with several different opinions come together to collectively agree on a single opinion. Traditionally, voting was done using pen and paper, but as the technology evolves and we stepped into the digital era, also voting has kept its pace via new techniques and integrating itself with blockchain, i.e., a distributed technology. The digital era is marked with a new technological advancement integrating various sub-mechanisms into a robust, more secure, and immutable mechanism known as distributed ledger technology. Blockchain being a similar technology offers a vast range of applications benefiting from shared economies. This paper proposes a solution using blockchain as a base technology, trying to eliminate the disadvantages of the conventional voting system. At the same time, showcasing the advantages and benefits offered by blockchain technology like immutability, security, distributed ledger, faster settlements, and consensus algorithms. This paper also details the implementation of the solution using various other technologies like solidity, react.js, and a few testing frameworks like mocha and chai; which not only improves the security and transparency for the user but on the other hand, helps in decreasing the cost of elections.
An Economic and Non-cooperative Load-balancing Framework among Federated Cloudlets

Edge computing servers like cloudlets from different service providers compensate scarce computational, memory, and energy resources of mobile devices, are distributed across access networks. However, depending on the mobility pattern and dynamically varying computational requirements of associated mobile devices, cloudlets at different parts of the network become either overloaded or under-loaded. Hence, load balancing among neighboring cloudlets appears to be an essential research problem. Nonetheless, the existing load balancing frameworks are unsuitable for low-latency applications. Thus, in this paper, we propose an economic and non-cooperative load balancing game for low-latency applications among federated neighboring cloudlets from the same as well as different service providers and heterogeneous classes of job requests. Firstly, we propose a centralized incentive mechanism to compute the pure strategy Nash equilibrium load balancing strategies of the cloudlets under the supervision of a neutral mediator. With this mechanism, we ensure that the truthful revelation of private information to the mediator is a weakly-dominant strategy for all the federated cloudlets. Secondly, we propose a continuous-action reinforcement learning automata-based algorithm, which allows each cloudlet to independently compute the Nash equilibrium in a completely distributed network setting. We critically study the convergence properties of the designed learning algorithm, scaffolding our understanding of the underlying load balancing game for faster convergence. Furthermore, through extensive simulations, we study the impacts of exploration and exploitation on learning accuracy. This is the first study to show the effectiveness of reinforcement learning algorithms for load balancing games among neighboring cloudlets.
Historical Arabic Images Classification and Retrieval Using Siamese Deep Learning Model

Classifying the visual features in images to retrieve a specific image is a significant problem within the computer vision field especially when dealing with historical faded colored images. Thus, there were lots of efforts trying to automate the classification operation and retrieve similar images accurately. To reach this goal, we developed a VGG19 deep convolutional neural network to extract the visual features from the images automatically. Then, the distances among the extracted features vectors are measured and a similarity score is generated using a Siamese deep neural network. The Siamese model built and trained at first from scratch but, it didn't generated high evaluation metrices. Thus, we re-built it from VGG19 pre-trained deep learning model to generate higher evaluation metrices. Afterward, three different distance metrics combined with the Sigmoid activation function are experimented looking for the most accurate method for measuring the similarities among the retrieved images. Reaching that the highest evaluation parameters generated using the Cosine distance metric. Moreover, the Graphics Processing Unit (GPU) utilized to run the code instead of running it on the Central Processing Unit (CPU). This step optimized the execution further since it expedited both the training and the retrieval time efficiently. After extensive experimentation, we reached satisfactory solution recording 0.98 and 0.99 F-score for the classification and for the retrieval, respectively.
COVID-19 and privacy: a European dilemma

Purpose This paper aims at discussing the options available to governments when it comes to the use of technology to contain the spread of the COVID-19 virus. Design/methodology/approach This is an opinion piece, based on very recent developments (COVID-19), and based on a well-known trade-off between privacy and state surveillance, especially in times of crisis that threaten the survival of a nation. Findings The main finding is that technology alone will not help, and there are several reasons to doubt that the recently proposed European system to track the contagion in a privacy-preserving way (pan-European privacy preserving proximity tracing [PEPP-PT]) would be a fully effective solution. Research limitations/implications This is a short paper, which is very dependent on current developments. It was written in a very short time, so the level of depth in the references to the literature and the caselaw is limited. The main implication is that this paper is very far from the final word in the analysis of the interplay between technology and society, especially in democratic countries. Practical implications There is a need to ensure that the temporary measures that will be adopted during the pandemic do not extend to the post-COVID-19 period. Originality/value To the best of the author’s knowledge, this is a very fresh debate; the paper is thus original and proposes one of the first structured comments to the PEPP-PT and DP-3T conceptual designs.
DIGITALIZATION OF EDUCATION: PEDAGOGCIAL PRIORITIES

The paper analyses the impact of globalization and technological development of society on scientific activity, education system, organization and technologies of the educational process; pedagogical priorities of digitalization of education are identified: joining the efforts of scientists and practitioners for a comprehensive analysis and interdisciplinary solutions of modern problems of digital educational environment, pedagogical principles of digitalization of education; formulation of conceptual provisions of digital pedagogy and definition of the basic pedagogical concepts of digital education, development of digital didactics, revision of forms, methods, means and technologies of teaching, education and development of the learner in digital space; identification of features and directions of education of children and youth in the digital environment, new forms and approaches to organizing the educational impact on children and youth in the digital environment, outlining the risks of digitalization of education and means of overcoming them; it is shown that the mission of modern education is to provide society and every citizen with a transition to a digital age focusing on different tools, different living environment and training not only young people but also middle-aged and older people for new types and kinds of work.
Geometric Analysis and YOLO Algorithm for Automatic Face Detection System in a Security Setting

Abstract Face detection is a computer technology that determines the location and size of the human face in an arbitrary digital image employed for authentication. Face recognition technology plays a vital role in network security, video compression, content indexing, and retrieval since "humans" are the focal point of many videos. Face recognition-based network access control makes it almost difficult for hackers to obtain a user’s "password" and improves user-friendliness in human-computer interaction. In this paper, an automatic face detection system that accurately detects human faces and ignores any other object that is not a human face using geometric analysis and the you-only-look-once (YOLO) algorithm is introduced. The system is able to predict the ages and genders of the faces detected. It also detects the facial landmarks of the faces and indicates the emotions of the faces detected. A sample of four faces is considered for testing the system; thus, accurately detecting gender and emotion but not age correctly. In all, the evaluation shows about 80% accuracy. With the results got, the system can support security and analytics. It can be used to get analytics in an event with a lot of attendees and can also be used to get a facial mapping of someone involved in a crime scene for security purposes.
Progress in Natural Language Processing and Language Understanding

Computers have been successful in deciphering structured data, i.e., those that have a standardized format such as numbers and spreadsheets. Despite the success, a surge in the availability of unstructured data over the last decade has motivated the exploration of new approaches to process and analyze unstructured data. Natural language processing (NLP) concerns how computers can address the problem of analyzing natural language, a ubiquitous form of unstructured data. With ground-breaking technological advances in computational capabilities and the ever-expanding realm of data, research in natural language processing has grown exponentially in the last two decades. With a strong focus on natural language understanding, this chapter presents a discussion on the metamorphosis of the field leading up to the prominent, state-of-the-art learning techniques in language understanding, toward the overall goal of enabling human-like comprehension in machines. Human language is complex and ambiguous, thus making language processing challenging. Complexity of human language allows for infinite ways of expression; however, this diversity hinders the creation of a consistent metric to evaluate performance. The chapter additionally presents recent developments in NLP benchmarks that have been instrumental in assessing the language understanding ability of machines. With these standardized benchmarks, NLP models have witnessed drastic improvements and have made their way into commercial applications. Despite the ongoing progress, addressing questions about fairness, ethical consequences, and the essence of “understanding” language become crucial as these attributes play a critical role in the development of machines that are fully capable of processing human language.
A Simulated Annealing Meta-heuristic for Concept Learning in Description Logics

Ontologies – providing an explicit schema for underlying data – often serve as background knowledge for machine learning approaches. Similar to ILP methods, concept learning utilizes such ontologies to learn concept expressions from examples in a supervised manner. This learning process is usually cast as a search process through the space of ontologically valid concept expressions, guided by heuristics. Such heuristics usually try to balance explorative and exploitative behaviors of the learning algorithms. While exploration ensures a good coverage of the search space, exploitation focuses on those parts of the search space likely to contain accurate concept expressions. However, at their extreme ends, both paradigms are impractical: A totally random explorative approach will only find good solutions by chance, whereas a greedy but myopic, exploitative attempt might easily get trapped in local optima. To combine the advantages of both paradigms, different meta-heuristics have been proposed. In this paper, we examine the Simulated Annealing meta-heuristic and how it can be used to balance the exploration-exploitation trade-off in concept learning. In different experimental settings, we analyse how and where existing concept learning algorithms can benefit from the Simulated Annealing meta-heuristic.
Feature Learning by Least Generalization

This paper provides an empirical study for feature learning based on induction. We encode image data into first-order expressions and compute their least generalization. An interesting question is whether the least generalization can extract a common pattern of input data. We introduce three different methods for feature extraction based on symbolic manipulation. We perform experiments using the MNIST datasets and show that the proposed methods successfully capture features from training data and classify test data in around 90% accuracies. The results of this paper show potentials of induction and symbolic reasoning to feature learning or pattern recognition from raw data.
A Framework for e-Voting System Based on Blockchain and Distributed Ledger Technologies

Election allows the voter of a country to select the most suitable group of candidates to run the government. Election in Pakistan is simply paper-based method but some certain political and socio-economic issues turn that simple process in complicated and disputes once. Solutions of such problems are consisting of many methods including the e-voting system. The e-voting system facilitates the voters to cast their votes by electronic means with very easy and convenient way. This also allows maintaining the security and secrecy of the voter along with election process. Electronic voting reduces the human-involvement throughout the process from start to the end. Such system is not established yet in Pakistan. Hence, every election is disputed always. In this paper we proposed the framework of an e-voting system and simulation with the use of blockchain ledger technology. The proposed framework owns the capacity to deal with ballots in Pakistan. The novelty of the framework is that the e-voting system is linked to National Database and Registration Authority (NADRA) database which assures voter's validation. The fundamental requirements were kept in consideration during the performance evaluation of the system. The results are processed and depicted specially with reference to the number of voters and voting stations, real time of vote casting, network bandwidth and controlling principles. The results support the decision makers and guide them in the foundation and customization of an e-voting system in Pakistan.
FHP: Facial and Hair Feature Processor for Hairstyle Recommendation

Hairstyles contribute majorly to an individual’s physical appearance. It is commonly observed that there is a huge gap between customer’s expectations and the barber’s understanding leading to post-haircut dissatisfaction. A major cause of this problem is that users lack the understanding of utilizing feature information while selecting a suitable hairstyle. A model that extracts essential features for decision-making can improve customer satisfaction. In this paper, we propose the FHP architecture that extracts features from user-submitted images that are crucial to hairstyle recommendations. The architecture consists of two major pipelines for extracting facial and hair features. The process starts with image segmentation and parsing. The segmented facial and hair regions are given as input to the two pipelines for extracting face shape, skin tone, hair texture, and hair color. These four features can then be used in the development of an expert system for hairstyle recommendations.
Constructing Collective Signature Schemes Using Problem of Finding Roots Modulo

Digital signature schemes are often built based on the difficulty of the discrete logarithm problems, of the problem of factor analysis, of the problem of finding the roots modulo of large primes or a combination of the difficult problems mentioned above. In this paper, we use the new difficult problem, which is to find the root in the finite ground field to build representative collective signature schemes, but the chosen modulo p has a special structure distinct , where is an even number and are prime numbers of equal magnitude, about . The characteristics of the proposed scheme are: i) The private key of each signer consists of 2 components (), randomly selected, but the public key has only one component () calculated by the formula and ; and ii) The generated signature consists of a set of 3 components (2). We use the technique of hiding the signer's public key Y, which is the coefficient λ generated by the group nanager, in the process of forming the group signature and representative collective signature to enhance the privacy of all members of the signing collective.
Programmatic Policy Extraction by Iterative Local Search

Reinforcement learning policies are often represented by neural networks, but programmatic policies are preferred in some cases because they are more interpretable, amenable to formal verification, or generalize better. While efficient algorithms for learning neural policies exist, learning programmatic policies is challenging. Combining imitation-projection and dataset aggregation with a local search heuristic, we present a simple and direct approach to extracting a programmatic policy from a pretrained neural policy. After examining our local search heuristic on a programming by example problem, we demonstrate our programmatic policy extraction method on a pendulum swing-up problem. Both when trained using a hand crafted expert policy and a learned neural policy, our method discovers simple and interpretable policies that perform almost as well as the original.
Implication of Optimizing NPU Dataflows on Neural Architecture Search for Mobile Devices

Recent advances in deep learning have made it possible to implement artificial intelligence in mobile devices. Many studies have put a lot of effort into developing lightweight deep learning models optimized for mobile devices. To overcome the performance limitations of manually designed deep learning models, an automated search algorithm, called neural architecture search ( NAS ), has been proposed. However, studies on the effect of hardware architecture of the mobile device on the performance of NAS have been less explored. In this article, we show the importance of optimizing a hardware architecture, namely, NPU dataflow, when searching for a more accurate yet fast deep learning model. To do so, we first implement an optimization framework, named FlowOptimizer, for generating a best possible NPU dataflow for a given deep learning operator. Then, we utilize this framework during the latency-aware NAS to find the model with the highest accuracy satisfying the latency constraint. As a result, we show that the searched model with FlowOptimizer outperforms the performance by 87.1% and 92.3% on average compared to the searched model with NVDLA and Eyeriss, respectively, with better accuracy on a proxy dataset. We also show that the searched model can be transferred to a larger model to classify a more complex image dataset, i.e., ImageNet, achieving 0.2%/5.4% higher Top-1/Top-5 accuracy compared to MobileNetV2-1.0 with 3.6 \( \times \) lower latency.
Non-Parametric Uncertainty Bias and Variance Estimation via Nested Bootstrapping and Influence Functions

In using limited datasets, modeling the uncertainty via non-parametric methods arguably provides more robust estimators of the unknown value of interest. We propose a novel nested bootstrap method that accounts for the uncertainty from various sources (input data, model, and estimation) more robustly. The nested bootstrap is particularly apt to the more nuanced conditional settings in constructing prediction rules but is easily generalizable. We utilize influence functions to estimate the bias due to input uncertainty and devise a procedure to correct the estimators&#x0027; bias in a simulation optimization routine. Implementations in the context of feature selection via simulation optimization on two simulated datasets prove a significant improvement in robustness and accuracy.
Intelligent Data Transfer To Ensure Data Privacy In Large Enterprises

Enterprises exchange huge volumes of consumer and employee data across several business functions and diversified system landscapes in their day-to-day business. Data is the driving force to make more meaningful and informed decisions and it is the backbone for all kinds of innovations maximizing the profitability of the organizations. With rapid digitization, there is exponential growth in the amount of data collected and exchanged. Hence the paradigm shift had intensified the perplexities of issues around data privacy leading to growing concerns about exchanging data securely. In this paper, a novel model is proposed to handle secure data exchanges intelligently using partial homomorphic encryption with machine learning algorithms such as Linear regression and Bayesian ridge by ensuring the integrity and confidentiality of data fulfilling privacy principles. Later a use case is considered to evaluate the proposed methodology and performance evaluation is carried out between linear regression and Bayes theorem after applying partial homomorphic encryption.
Implementation of Financial Audited Robot Question and Answer Technology of Feature Processing and Improved Bi-LSTM

In order to improve the accuracy of financial robot audit question answering, we propose, on the premise of processing corpus features, combining Bi-LSTM network and CRF to identify the domain entities, so as to solve the problem of low recognition rate of financial knowledge domain entities, and introducing the mechanism based on attention and CNN network to construct the multigranularity feature question-answering matching model. Finally, the above methods are verified by experiments. The results show that the AUC, MAP, and MRR increase by 0.74%, 0.85%, and 0.81%, respectively, indicating the feasibility of the improved method.