Reviewerly/exHarmony huggingface dataset:
Web-Based Modernized Architecture Over Cloud Computing for Facial Extraction and Recognition

Web content is diverse and is regarded as the primary source of accessible information that can be accessed through reference links. Web facial images are one type of web content that relates to important web pages and is considered important information for individuals. This chapter proposes face recognition as a service architecture that is based on real-world images from the web. The proposed service is implemented as a service for other third parties via cloud computing; additionally, its architecture is built via cloud using virtual machines that can be expanded based on resource demands. Web crawlers crawl web pages and retrieve images for elastic cloud storage. The collected images are then used to remove human faces and prepare the face images for identification and identifying the matched face of the set through successive phases. This chapter used PCA for features extraction and KNN for identification. Experiments show that increasing the number of crawler instances improves crawling speed and improves face recognition accuracy by preferring Euclidean over other metrics.
To Predict or Not to Predict: The Case of the Emergency Department

Coupling healthcare datasets with advanced statistical methods has the potential to improve the efficiency and quality of healthcare dramatically. However, data used for predictive decision making in healthcare delivery has significant variable costs. We offer a novel and generalizable framework that helps reduce the costs associated with the use of data for healthcare analytics while maintaining high predictive accuracy. We utilize this approach in the emergency department (ED) context and specifically in predicting whether a patient will be admitted to an interior hospital unit or discharged from the ED. Data used to generate this prediction are available at different times, introducing a time‐cost associated with different data used in the prediction. We focus on minimizing the time‐cost of prediction without sacrificing accuracy. Using our approach, we are able to reduce the time‐cost of prediction by more than two‐thirds and significantly reduce the need to use privacy‐sensitive features. Yet, we still maintain high accuracy of prediction that is comparable to standard approaches which do not reduce data costs (area under the ROC curve of 0.86). Our work has significant potential value to healthcare entities and contributes to a growing stream of work on how to realize the value of healthcare analytics efforts.
Four Uncertain Sampling Methods are Superior to Random Sampling Method in Classification

Active learning has been widely used because it can automatically select the unlabeled samples with the largest amount of information for manual labeling. Therefore, it could solve the problem of knowledge bottleneck. Selective sampling belongs to the active learning approach, reducing labeling costs to supplement training data by requiring labels to provide only the most informative, unlabeled examples. This additional information is added to an original, stochastically selected training set in the expectation of improving the performance of generalization of the learning machine[12]. Uncertainty sampling belonging to selective sampling is one of the key techniques of active learning, which uses a classifier to identify the least reliable unlabeled samples[1]. In addition, active learning is a method applied to classifier. In this paper, four methods under uncertainty sampling are used: Least Confidence Sampling, Margin of Confidence Sampling, Ratio of Confidence Sampling, and Entropy-based Sampling. According to the results, the four methods of uncertain sampling have higher accuracy than the random sampling method.
Machine Learning Model for Recommending Suitable Courses of Study to Candidates in Nigerian Universities

The diversity of courses and complications of admission requirements are complex tasks particularly in Nigerian Universities where a number of parameters are used during the admission process. These courses may be wrongly assigned to applicants who have not met the minimum requirements. In a previous related work, a model was developed to address this issue. However, the model considered only seven subjects out of the mandatory nine subjects required of every senior secondary school student to register (O’Level). Such a decision may be to the detriment to the candidates because credits may be required from those subjects that were not considered. This paper tends to enhance the existing model to address all these issues. Grade of nine Secondary school subjects, the aggregate score of Unified Tertiary Matriculation Examination (UTME) and post-UTME, and catchment area are used as parameters in this study. The results were obtained when various reference classifiers were trained and tested using the processed dataset of the O’Level and JAMB results of candidates seeking admission into the university. Individual classifiers namely, Logistic Regression, Naive Bayes, Decision Tree, K-Nearest Neighbor, and Random Forest were trained and evaluated using reference performance metrics namely precision, recall, and f1-score. The resulting best classifier, the Random Forest, has shown to be correct 94.94% of the time and is capable of detecting correctly 94.17% of the classes. Since the precision and recall are similar in value, the f1-score tends to favor this classifier also with a value of 93.19%.
Improved Filtering Techniques for Single- and Multi-Trace Side-Channel Analysis

Side-channel analysis (SCA) attacks constantly improve and evolve. Implementations are therefore designed to withstand strong SCA adversaries. Different side channels exhibit varying statistical characteristics of the sensed or exfiltrated leakage, as well as the embedding of different countermeasures. This makes it crucial to improve and adapt pre-processing and denoising techniques, and abilities to evaluate the adversarial best-case scenario. We address two popular SCA scenarios: (1) a single-trace context, modeling an adversary that captures only one leakage trace, and (2) a multi-trace (or statistical) scenario, that models the classical SCA context. Given that horizontal attacks, localized electromagnetic attacks and remote-SCA attacks are becoming evermore powerful, both scenarios are of interest and importance. In the single-trace context, we improve on existing Singular Spectral Analysis (SSA) based techniques by utilizing spectral property variations over time that stem from the cryptographic implementation. By adapting overlapped-SSA and optimizing over the method parameters, we achieve a significantly shorter computation time, which is the main challenge of the SSA-based technique, and a higher information gain (in terms of the Signal-to-Noise Ratio (SNR)). In the multi-trace context, a profiling strategy is proposed to optimize a Band-Pass Filter (BPF) based on a low-computational cost criterion, which is shown to be efficient for unprotected and low protection level countermeasures. In addition, a slightly more computationally intensive optimized ‘shaped’ filter is presented that utilizes a frequency-domain SNR-based coefficient thresholding. Our experimental results exhibit significant improvements over a set of various implementations embedded with countermeasures in hardware and software platforms, corresponding to varying baseline SNR levels and statistical leakage characteristics.
STree: A Single Multi-class Oblique Decision Tree Based on Support Vector Machines

We propose a new oblique decision tree algorithm based on support vector machines. Our algorithm produces a single model for a multi-class target variable. On the contrary to previous works that manage the multi-class problem by using clustering at each split, we test all the one-vs-rest labels at each split, choosing the one which minimizes an impurity measure. The experimental evaluation carried out over 49 datasets shows that our algorithm is ranked before those used for comparison, and significantly outperforms all of them when the SVM hyperparameters are carefully tuned.
Markovian Learning Methods in Decision-Making Systems

The optimal decision-making task based on the Markovian learning methods is investigated. The stochastic and deterministic learning methods are described. The decision-making problem is formulated. The problem of Markovian learning of an agent making optimal decisions in a deterministic environment was solved on the example of finding the shortest path in the cell space. The mathematical formulation of the decision - making problem with deterministic and stochastic strategies based on recurrent estimation of criterion functions of utility of states and efficiency of options of actions of the agent was provided. The evaluation of criterion functions values takes place in real time on the basis of reinforced Q-learning and does not require a model of the environment, which is important for practical applications of decision-making in conditions of uncertainty. The algorithmic and software tools for the decision making modelling in uncertainty conditions are developed. The computer simulation results of decision-making process in cellular space are discussed and presented.
A Transformer-Based Math Language Model for Handwritten Math Expression Recognition

Handwritten mathematical expressions (HMEs) contain ambiguities in their interpretations, even for humans sometimes. Several math symbols are very similar in the writing style, such as dot and comma or “0”, “O”, and “o”, which is a challenge for HME recognition systems to handle without using contextual information. To address this problem, this paper presents a Transformer-based Math Language Model (TMLM). Based on the self-attention mechanism, the high-level representation of an input token in a sequence of tokens is computed by how it is related to the previous tokens. Thus, TMLM can capture long dependencies and correlations among symbols and relations in a mathematical expression (ME). We trained the proposed language model using a corpus of approximately 70,000 LaTeX sequences provided in CROHME 2016. TMLM achieved the perplexity of 4.42, which outperformed the previous math language models, i.e., the N-gram and recurrent neural network-based language models. In addition, we combine TMLM into a stochastic context-free grammar-based HME recognition system using a weighting parameter to re-rank the top-10 best candidates. The expression rates on the testing sets of CROHME 2016 and CROHME 2019 were improved by 2.97 and 0.83 percentage points, respectively.
ResNet CNN with LSTM Based Tamil Text Detection from Video Frames

Text content in videos includes applications such as library video retrievals, live-streaming advertisements, opinion mining, and video synthesis. The key components of such systems include video text detection and acknowledgments. This paper provides a framework to detect and accept text video frames, aiming specifically at the cursive script of Tamil text. The model consists of a text detector, script identifier, and text recognizer. The identification in video frames of textual regions is performed using deep neural networks as object detectors. Textual script content is associated with convolutional neural networks (CNNs) and recognized by combining ResNet CNNs with long short-term memory (LSTM) networks. A residual mapping underlies the network. In ResNet, skipping condenses the network into few layers to help it learn more quickly. Bidirectional LSTM enables networks to always have information about the sequence, backward as well as forward. For fast pattern learning processes, this combination uses the residual learning process. In comparison to the existing approaches, the proposed approach’s results show improved accuracy, precision and F-measures. The combination of ResNet CNNs and bidirectional LSTMs has high recognition rates for detecting video texts in Tamil cursive script.
Decomposition-Based Classifier Chains for Multi-Dimensional Classification

In multi-dimensional classification, the semantics of objects are characterized by multiple class variables from different dimensions. To model the dependencies among class variables, one natural strategy is to build a number of multiclass classifiers in a chaining structure, one per dimension, where the subsequent classifiers on the chain augment the feature space with all labeling information used by the preceding classifiers. However, it is shown that this strategy cannot compete with existing state-of-the-art approaches via comparative studies. One possible reason is that inaccurate predictions of preceding classifiers would degenerate the performance of subsequent ones. Besides, it is more difficult to learn a multiclass classifier than a binary one with the same accuracy, and better performance can be expected if the multi-dimensional classification problem can be solved by building multiple binary classifiers in a chaining structure. Based on these conjectures, this article proposes an approach, which builds a chain of binary classifiers to solve the multi-dimensional classification problem with the help of one-versus-one decomposition. To address the issue that different one-versus-one decomposed problems involve different training examples, the feature space is augmented with the binary predictions of preceding classifiers on the chain to train the subsequent ones. To alleviate the effect of the specified chaining order, the ensemble version of the proposed approach is further investigated. Comparative studies over 20 benchmark datasets clearly show the superiority of the proposed approach against the state-of-the-art multi-dimensional classification baselines.
VisLan: A Tool for Visualizing Landmark files in Source Code

A large body of research is available on software clustering, code navigation, and many tools have been developed to comprehend large software systems. However, little attention has been paid to the area of visual clustering. In this paper, we leverage static dependencies and visual clustering to identity landmarks which are important legibility features that developers use as reference points during code navigation. This paper describes VisLan, a tool for visualizing landmark files in source code. We discuss the key features of VisLan and improvements for future work.
Compressing Deep Neural Network: A Black-Box System Identification Approach

This work proposes a new approach to deep neural network (DNN) compression. We employ black-box function approximation techniques from signal processing to compress. DNN, in general, can approximate non-smooth and piecewise smooth functions. With only this assumption, we model the function that the DNN has learnt as a piecewise linear function. This is a standard function approximation approach. We compared our approach with two state-of-the-art techniques - spatial singular value decomposition and channel pruning with weight reconstruction; and one of state-of-practice tool - OpenVINO. Two well known 1D DNN models for time series classification - ResNet and InceptionTime were compressed. Results show that our model yields better compression at comparable losses in accuracy on majority of the datasets.
OA-GAN: Overfitting Avoidance Method of GAN oversampling based on xAI

The most representative method of deep learning is data-driven learning. These methods are often data-dependent, and lack of data leads to poor learning. There is a GAN method that creates a likely image as a way to solve a problem that lacks data. The GAN determines that the discriminator is fake/real with respect to the image created so that the generator learns. However, overfitting problems when the discriminator becomes overly dependent on the learning data. In this paper, we explain overfitting problem when the discriminator decides to fake/real using xAI. Depending on the area of the described image, it is possible to limit the learning of the discriminator to avoid overfitting. By doing so, the generator can produce similar but more diverse images.
What does the User Want? Information Gain for Hierarchical Dialogue Policy Optimisation

The dialogue management component of a task-oriented dialogue system is typically optimised via reinforcement learning (RL). Optimisation via RL is highly susceptible to sample inefficiency and instability. The hierarchical approach called Feudal Dialogue Management takes a step towards more efficient learning by decomposing the action space. However, it still suffers from instability due to the reward only being provided at the end of the dialogue. We propose the usage of an intrinsic reward based on information gain to address this issue. Our proposed reward favours actions that resolve uncertainty or query the user whenever necessary. It enables the policy to learn how to retrieve the users' needs efficiently, which is an integral aspect in every task-oriented conversation. Our algorithm, which we call FeudalGain, achieves state-of-the-art results in most environments of the PyDial framework, outperforming much more complex approaches. We confirm the sample efficiency and stability of our algorithm through experiments in simulation and a human trial.
Dynamic, Discreet, Robotic Compression Garment for Real-Time Stress Assessment and Intervention

This paper presents the design and development of a wearable robotic compression garment and integrated controller to automatically detect stressful situations in real-time and provide compression as an intervention for stress reduction. The developed garment leverages traditional apparel assembly techniques for more feasible manufacture. The robotic/active garment material that provides compression uses dynamically activated materials, known as shape memory alloys (SMAs), as a way to provide personalized stress intervention. This dual mode system functions either within an integrated home intervention system with a central hub and voice-activated conversational agent or as a separate system controlled using a touch-based or voice-activated mobile application. The system presents garment improvements by addressing wearability, thermal insulation, and pressure consistency, as well as control portability and scalability.
Utilizing IPFS and Private Blockchain to Secure Forensic Information

Forensic Science includes scientific methods to find out the actual cause of a crime and to bring justice to the victims. Forensic reports incorporate information regarding different crimes. These details are considered as extremely valuable and confidential as it helps the law enforcement agencies and prosecutors to ensure punishment to the blameworthy persons. These reports require security only to restrict access to the authorized persons. Blockchain stores every transaction occurring in the system and these transactions cannot be removed or modified because of their immutability. In this work, Inter-Planetary File System (IPFS) and Hyperledger based private blockchain are assembled to implement a secure forensic information storing system. Our system enables the tracing of any illegitimate en-trance or data tempering by the intruders. Our proposed hybrid approach surpasses the classical public blockchain systems i.e. Bitcoin and Ethereum in terms of transaction processing time achieving an average of 11.99 seconds per transaction. This system also facilitates the storing of heavyweight features which is not possible inside the existing blockchain frameworks.
Augmented Reality Using ViXAssist and HoloLens 2 for Automotive Service and Maintenance

The cost and complexity of service and maintenance are increasing as vehicles designs become more sophisticated. Holovis developed an AR maintenance system for a luxury vehicle OEM using the HoloLens2 and VixAssist to support service engineers at dealerships worldwide. The system is hands-free, allowing engineers to carry out service and maintenance tasks. Annotations can be added to selected Points of Interests (POI) via a virtual keyboard. Annotations are automatically uploaded to a remote server, which can be accessed by a separate third-party PLM or project management system to facilitate data integration. Users can select a POI and access relevant technical information, which is displayed as a floating window within the environment. This simplifies information access and eliminates the need for printed manuals. Holovis demonstrated that the technology has reached a level of maturity for early adopters. However, there remains a number of technical and ergonomics challenges that need to be resolve for wide-spread field operations.
Evaluation of Optimal Occasional Tilt on Photovoltaic Power Plant Energy Efficiency and Land Use Requirements, Iran

The paper studies the optimum panel horizontal orientation angle toward the Sun and the optimum time interval of the panel’s movement. The optimum time intervals or panel movement can change the rate of input energy to the panel surface in Iran. For this purpose, a neural network has been trained to estimate the intensity of solar radiation in Iran. After model validation, the intensity of solar radiation has been estimated by selecting adequate geographical regions. Based on the intensity of sunlight, Iran has been divided into ten regions. In these regions, 40 cities have been randomly selected to study the effect of the panel’s angle variations within appropriate time intervals, as well as equal time intervals. The results show that the choice of the mounting system with the possibility of five angles’ implementation can increase the amount of solar energy between 3.9% and 7.4%. Compared to this number of angles at the equal time intervals, the amount of incoming solar energy has increased by 3% to 7%. In the first and second cases, the area of the power plant increases by about 12% to 24% compared to the yearly optimum tilt angle. Moreover, the amount of radiation incoming to the panel with the optimum operating angle is in alignment with the results of PVsyst software.
Neural Multigranular 2-tuple Average Operator in Neural-Symbolic Decision Support Systems

Construction of integrated neural-symbolic systems is an actual and challenging task. Such hybrid systems combine advantages of connectionist and symbolic approaches. In particular, neural-symbolic systems are characterized by robust learning and distributed neural computations. At the same time, they can be interpreted, described and analyzed in logical terms. Especially high interpretability is important for Decision Support Systems that operate with symbolic structures describing the problem situation, stakeholders, assessment criteria and where the reasoning process should be transparent for the Decision Maker. Such requirements bring the task of designing integrated neural-symbolic Decision Support Systems to the higher level of complexity. This paper examines underlying algorithms of a specific part of Decision Support Systems that is aggregation of experts’ assessments to make the choice among alternative solutions of the given problem. Such fuzzy and uncertain assessments are often represented as 2-tuple model or its derivatives and algorithms that aggregate them are often called operator. We demonstrate that the simple aggregation operator can be expressed in a fully connectionist form with the help of Neural Turing Machine architecture. This result sheds new light on the way principles of symbolic computation can be implemented by connectionist mechanisms.