Reviewerly/exHarmony huggingface dataset:
Hyperparameter Tuning of Deep learning Models in Keras

Hyperparameter tuning or optimization is one of the fundamental way to improve the performance of the machine learning models. Hyper parameter is a parameter passed during the learning process of the model to make corrections or adjustments to the learning process. To generalise diverse data patterns, the same machine learning model may require different constraints, weights, or learning rates. Hyperparameters are the term for these kind of measurements. These parameters have been trial-and-error tested to ensure that the model can solve the machine learning task optimally. This paper focus on the science of hyperparameter tuning using some tools with experimental values and results of each experiments. We have also documented 4 metrics to analyze the hyperparameter tuning results and benchmark the outcome. The experimental results of two tools used commonly for deep learning models namely Keras tuner and AiSara tuner are captured in the article. All relevant experimental code is also available for readers in authors github repository. The metrics used to benchmark the results are accuracy, search time, cost and complexity and expalinability. The results indicate the overall performance of AiSara tuner in search time, cost and complexity and expalinability matrices are superior to keras tuners.
SIRA: Scale illumination rotation affine invariant mask R-CNN for pedestrian detection

In this paper, we resolve the challenging obstacle of detecting pedestrians with the ubiquity of irregularities in scale, rotation, and the illumination of the natural scene images natively. Pedestrian instances with such obstacles exhibit significantly unique characteristics. Thus, it strongly influences the performance of pedestrian detection techniques. We propose the new robust Scale Illumination Rotation and Affine invariant Mask R-CNN (SIRA M-RCNN) framework for overcoming the predecessor's difficulties. The first phase of the proposed system deals with illumination variation by histogram analysis. Further, we use the contourlet transformation, and the directional filter bank for the generation of the rotational invariant features. Finally, we use Affine Scale Invariant Feature Transform (ASIFT) to find points that are translation and scale-invariant. Extensive evaluation of the benchmark database will prove the effectiveness of SIRA M-RCNN. The experimental results achieve state-of-the-art performance and show a significant performance improvement in pedestrian detection.
Assessment of consumer VR-headsets’ objective and subjective field of view (FoV) and its feasibility for visual field testing

Abstract Virtual reality as a research environment has seen a boost in its popularity during the last decades. Not only the usage fields for this technology have broadened, but also a research niche has appeared as the hardware improved and became more affordable. Experiments in vision research are constructed upon the basis of accurately displaying stimuli with a specific position and size. For classical screen setups, viewing distance and pixel position on the screen define the perceived position for subjects in a relatively precise fashion. However, projection fidelity in HMDs strongly depends on eye and face physiological parameters. This study introduces an inexpensive method to measure the perceived field of view and its dependence upon the eye position and the interpupillary distance, using a super wide angle camera. Measurements of multiple consumer VR headsets show that manufacturers’ claims regarding field of view of their HMDs are mostly unrealistic. Additionally, we performed a “Goldmann” perimetry test in VR to obtain subjective results as a validation of the objective camera measurements. Based on this novel data, the applicability of these devices to test humans’ field of view was evaluated.
Experimentation for Chatbot Usability Evaluation: A Secondary Study

Interest in chatbot development is on the rise. As a usability evaluation is an essential step in chatbot development, the number of experimental studies on chatbot usability has grown as well. As a result, we think a systematic mapping study is opportune. We analyzed more than 700 sources and retrieved 28 primary studies. By aggregating the research questions and examining the characteristics and metrics used to evaluate the usability of chatbots in experiments, it is possible to identify the state of the art in chatbot usability experimentation. We conducted a systematic mapping study to identify the research questions, characteristics, and metrics used to evaluate the usability of chatbots in experiments. Most experiments adopted a within-subjects design. On the other hand, few experiments provided raw data, and only one of the identified papers was part of a family of experiments. Effectiveness, efficiency, and satisfaction are usability characteristics used to identify how well users can learn and use chatbots to achieve their goals and how satisfied users are during the interaction. Generally, the experimental results revealed that chatbots have several advantages (e.g., they provide a real-time response and they improve ease of use) and some shortcomings (e.g., natural language processing, which is rated as the weakness most in need of improvement). This research offers an overview of chatbot usability experimentation. The increasing interest in this area is very recent, as works did not start to be published until 2018. Chatbot usability experiments should be more replicable to improve the reliability and transparency of the experimental results.
Performance evaluation of point-of-use UVC-LED water disinfection photoreactors using CFD and response surface methodology

Chlorination has been the traditional method of water disinfection. With the advent of new technologies and significant issues encountered in chlorination, physical methods gradually prevailed. Ultraviolet (UV) radiation technology is a physical disinfection method that has been improved rapidly in recent years. This technology, mainly based on UV mercury lamps, has excellent potential to inactivate microorganisms in water. However, UV lamps are fragile and may cause the leakage of mercury into the environment. As the extension of visible light emitting diodes (LED) progress in solid-state physics, the nature-friendly technology of UV-LEDs has emerged. This paper analyzes the performance of cylindrical water disinfection UV-LED photoreactors made of UV reflective materials using computational fluid dynamics (CFD). The response surface methodology (RSM) is used for shape optimization of the photoreactors working with one UV-LED at the flow rate of liter per minute (LPM). Typical configurations including the U-, L-, and S-shaped are examined. The analyses provide the optimal radius and length of the photoreactors for different configurations. The performance of photoreactors can be enhanced by more than 1-Log of E. Coli by choosing the appropriate length and radius for each configuration. Also, the choice of configuration and LED location may affect the performance up to 2-Log of E. Coli. Our results show that the U- and S-shaped photoreactors can outperform the L-shaped for photoreactors made of UV reflective materials. The right choice of length, radius, and LED location can lead to 4-Log of E. Coli for a S-shaped photoreactor with a single 25 mW UVC-LED.
Data fusion fault tolerant strategy for a quadrotor UAV under sensors and software faults

This article presents the design and implementation of a fault tolerant architecture for sensor fusion that tolerates faults on a quadrotor unmanned aerial vehicle (UAV). It aims to tolerate both hardware sensors faults (GPS jamming, IMU lock or freezing, magnetometer sensitivity to high power magnetic fields...) and software faults (faults in the Kalman filter, bad parameters initialization....). The proposed architecture uses data fusion with Kalman filters in order to estimate the states (position and orientation) of the UAV. It includes an analytical redundancy using the dynamic model of the system. The estimations of the defined Kalman filters and the dynamic model feed a weighted average voter, which increases the accuracy of the outputs and the error detection process. The proposed architecture allows multiple recovery solutions to a faulty system and thus increasing its flexibility. The architecture is validated using numerical simulations and experimental flights in real outdoor environment using a quadrotor.
Analysing app reviews for software engineering: a systematic literature review

Abstract App reviews found in app stores can provide critically valuable information to help software engineers understand user requirements and to design, debug, and evolve software products. Over the last ten years, a vast amount of research has been produced to study what useful information might be found in app reviews, and how to mine and organise such information as efficiently as possible. This paper presents a comprehensive survey of this research, covering 182 papers published between 2012 and 2020. This survey classifies app review analysis not only in terms of mined information and applied data mining techniques but also, and most importantly, in terms of supported software engineering activities. The survey also reports on the quality and results of empirical evaluation of existing techniques and identifies important avenues for further research. This survey can be of interest to researchers and commercial organisations developing app review analysis techniques and to software engineers considering to use app review analysis.
Quantum Fourier analysis for multivariate functions and applications to a class of Schrödinger-type partial differential equations

In this work we develop a highly efficient representation of functions and differential operators based on Fourier analysis. Using this representation, we create a variational hybrid quantum algorithm to solve static, Schr\"odinger-type, Hamiltonian partial differential equations (PDEs), using space-efficient variational circuits, including the symmetries of the problem, and global and gradient-based optimizers. We use this algorithm to benchmark the performance of the representation techniques by means of the computation of the ground state in three PDEs, i.e., the one-dimensional quantum harmonic oscillator and the transmon and flux qubits, studying how they would perform in ideal and near-term quantum computers. With the Fourier methods developed here, we obtain low infidelities of order ${10}^{\ensuremath{-}4}--{10}^{\ensuremath{-}5}$ using only three to four qubits, demonstrating the high compression of information in a quantum computer. Practical fidelities are limited by the noise and the errors of the evaluation of the cost function in real computers, but they can also be improved through error mitigation techniques.
Improving Arabic Cognitive Distortion Classification in Twitter using BERTopic

Social media platforms allow users to share thoughts, experiences, and beliefs.These platforms represent a rich resource for natural language processing techniques to make inferences in the context of cognitive psychology.Some inaccurate and biased thinking patterns are defined as cognitive distortions.Detecting these distortions helps users restructure how to perceive thoughts in a healthier way.This paper proposed a machine learning-based approach to improve cognitive distortions' classification of the Arabic content over Twitter.One of the challenges that face this task is the text shortness, which results in a sparsity of co-occurrence patterns and a lack of context information (semantic features).The proposed approach enriches text representation by defining the latent topics within tweets.Although classification is a supervised learning concept, the enrichment step uses unsupervised learning.The proposed algorithm utilizes a transformer-based topic modeling (BERTopic).It employs two types of document representations and performs averaging and concatenation to produce contextual topic embeddings.A comparative analysis of F1-score, precision, recall, and accuracy is presented.The experimental results demonstrate that our enriched representation outperformed the baseline models by different rates.These encouraging results suggest that using latent topic distribution, obtained from the BERTopic technique, can improve the classifier's ability to distinguish between different CD categories.
An Evaluation of Hardware-Efficient Quantum Neural Networks for Image Data Classification

Quantum computing is expected to fundamentally change computer systems in the future. Recently, a new research topic of quantum computing is the hybrid quantum–classical approach for machine learning, in which a parameterized quantum circuit, also called quantum neural network (QNN), is optimized by a classical computer. This hybrid approach can have the benefits of both quantum computing and classical machine learning methods. In this early stage, it is of crucial importance to understand the new characteristics of quantum neural networks for different machine learning tasks. In this paper, we will study quantum neural networks for the task of classifying images, which are high-dimensional spatial data. In contrast to previous evaluations of low-dimensional or scalar data, we will investigate the impacts of practical encoding types, circuit depth, bias term, and readout on classification performance on the popular MNIST image dataset. Various interesting findings on learning behaviors of different QNNs are obtained through experimental results. To the best of our knowledge, this is the first work that considers various QNN aspects for image data.
Teachers' training for the use of digital tools of the formative assessment in the implementation of the concept of the New Ukrainian School

The article deals with teachers’ training to the use of digital tools of formative assessment. The authors analyze a number of legal documents in the field of education, which emphasize the introduction of formative assessment in the practice of assessment of primary and basic school pupils. Formative assessment is defined as the assessment to identify learning needs and traces each student’s progress, it is carried out on a constant basis and aims to support, stimulate, motivate to learn. The important features of this type of evaluation are criterion, interactivity, consistency. The components of formative assessment are peer assessment, self-assessment, observation, development of logic goals and criteria for their achievement, feedback.The programme of advanced training for teachers of different profiles, deputy heads of educational work on the topic ``Theory and Practice of Application of Formative Assessment at a Primary School'' was presented. The experience of such programme in advanced training courses at Municipal Higher Educational Establishment “Kherson Academy of Continuing Education of Kherson Regional Council” is presented.The authors describe a pedagogical experiment conducted on the basis of Municipal Higher Educational Establishment “Kherson Academy of Continuing Education of Kherson Regional Council” on the use of digital tools during formative assessment. The experiment covered 162 primary school teachers who were trained under that program. During the practical classes, teachers worked with such digital tools as MentiMeter, Plickers, Kahoot, Poll Everywhere. The results of the survey showed the relevance of the course and the need for its implementation in postgraduate pedagogical education for teachers who will teach at the New Ukrainian School.
A novel AR remote collaborative platform for sharing 2.5D gestures and gaze

Although sharing gestures and gaze can improve AR remote collaboration, most current systems only enable collaborators to share 2D or 3D gestures, and the unimodal HCI interface remains dominant. To address this problem, we describe a novel remote collaborative platform based on 2.5D gestures and gaze (2.5DGG), which supports an expert who collaborates with a worker (e.g., during assembly or training tasks). We investigate the impact of sharing the remote site's 2.5DGG using spatial AR (SAR) remote collaboration in manufacturing. Compared to other systems, there is a key advantage that it can provide more natural and intuitive multimodal interaction based on 2.5DGG. We track the remote experts' gestures and eye gaze using Leap Motion and aGlass, respectively, in a VR space displaying the live video stream of the local physical workspace and visualize them onto the local work scenario by a projector. The results of an exploratory user study demonstrate that 2.5DGG has a clear difference in performance time and collaborative experience, and it is better than the traditional one.
A Task Allocation Strategy of the UAV Swarm Based on Multi-Discrete Wolf Pack Algorithm

With the continuous development of artificial intelligence, swarm control and other technologies, the application of Unmanned Aerial Vehicles (UAVs) in the battlefield is more and more extensive, and the UAV swarm is increasingly playing a prominent role in the future of warfare. How tasks are assigned in the dynamic and complex battlefield environment is very important. This paper proposes a task assignment model and its objective function based on dynamic information convergence. In order to resolve this multidimensional function, the Wolf Pack Algorithm (WPA) is selected as the alternative optimization algorithm. This is because its functional optimization of high-dimensional complex problems is better than other intelligent algorithms, and the fact that it is more suitable for UAV swarm task allocation scenarios. Based on the traditional WPA algorithm, this paper proposes a Multi-discrete Wolf Pack Algorithm (MDWPA) to solve the UAV task assignment problem in a complex environment through the discretization of wandering, calling, sieging behavior, and new individual supplement. Through Orthogonal Experiment Design (OED) and analysis of variance, the results show that MDWPA performs with better accuracy, robustness, and convergence rate and can effectively solve the task assignment problem of UAVs in a complex dynamic environment.
Patient privacy in smart cities by blockchain technology and feature selection with Harris Hawks Optimization (HHO) algorithm and machine learning

A medical center in the smart cities of the future needs data security and confidentiality to treat patients accurately. One mechanism for sending medical data is to send information to other medical centers without preserving confidentiality. This method is not impressive because in treating people, the privacy of medical information is a principle. In the proposed framework, the opinion of experts from other medical centers for the treatment of patients is received and consider the best therapy. The proposed method has two layers. In the first layer, data transmission uses blockchain. In the second layer, blocks related to patients’ records analyze by machine learning methods. Patient records place in a block of the blockchain. Block of patient sends to other medical centers. Each treatment center can recommend the proposed type of treatment and blockchain attachment and send it to all nodes and treatment centers. Each medical center receiving data of the patients, then predicts the treatment using data mining methods. Sending medical data between medical centers with blockchain and maintaining confidentiality is one of the innovations of this article. The proposed method is a binary version of the HHO algorithm for feature selection. Another innovation of this research is the use of majority voting learning in diagnosing the type of disease in medical centers. Implementation of the proposed system shows that the blockchain preserves data confidentiality of about 100%. The reliability and reliability of the proposed framework are much higher than the centralized method. The result shows that the accuracy, sensitivity, and precision of the proposed method for diagnosing heart disease are 92.75%, 92.15%, and 95.69%, respectively. The proposed method has a lower error in diagnosing heart disease from ANN, SVM, DT, RF, AdaBoost, and BN.
Channel response-aware photonic neural network accelerators for high-speed inference through bandwidth-limited optics

Photonic neural network accelerators (PNNAs) have been lately brought into the spotlight as a new class of custom hardware that can leverage the maturity of photonic integration towards addressing the low-energy and computational power requirements of deep learning (DL) workloads. Transferring, however, the high-speed credentials of photonic circuitry into analogue neuromorphic computing necessitates a new set of DL training methods aligned along certain analogue photonic hardware characteristics. Herein, we present a novel channel response-aware (CRA) DL architecture that can address the implementation challenges of high-speed compute rates on bandwidth-limited photonic devices by incorporating their frequency response into the training procedure. The proposed architecture was validated both through software and experimentally by implementing the output layer of a neural network (NN) that classifies images of the MNIST dataset on an integrated SiPho coherent linear neuron (COLN) with a 3dB channel bandwidth of 7 GHz. A comparative analysis between the baseline and CRA model at 20, 25 and 32GMAC/sec/axon revealed respective experimental accuracies of 98.5%, 97.3% and 92.1% for the CRA model, outperforming the baseline model by 7.9%, 12.3% and 15.6%, respectively.
Patient-Controlled Mechanism Using Pseudonymization Technique for Ensuring the Security and Privacy of Electronic Health Records

An Internet-based Electronic Health Record (EHR) system allows patients to access their medical history whenever they need it. Access to patient records and transactions related to diagnosis is helpful to patients and the health care department and executives. But this practice may lead to major privacy concerns of patients' private data. For EHR adaptation, the major elements are laws and regulations, monetary inducement and hurdles, technology state, and corporation effect. In this paper, I have proposed a Patient Controlled mechanism using the pseudonymization technique for ensuring the security and privacy of Electronic Health records. It is found that most of the potential approaches have used pseudonymization techniques to deal with the issues involved in a healthcare information system. This proposed solution is simple and efficiently ensures the privacy of patient data. Comparative analysis with other existing approaches has been undertaken.
Critical Success Factors of Blockchain adoption in Green Supply Chain Management: Contribution through an Interpretive Structural Model

The main purpose of this paper is to study the critical success factors (CSFs) of the blockchain technology (BCT) adoption in green supply chain management (GSCM) which might be literally afirst attempt and also propose aconceptual framework of aGSCM model adopting the BCT, which will be promoting the combination of these two areas in the future. Acritical literature review of the BCT, GSCM, and BCT-based GSCM was conducted to identify the most relevant factors of BCT adoption, followed by the model formulation with the help of interpretive structural modelling (ISM) consisting of CSFs and relationships between those based on experts’ views. The overall results emphasized that ‘recording and trading’-related factors may contribute to the BCT adoption, while others like smart contract must be enhanced. This study supports previous conceptual work on BCT and GSCM and could serve as astarting point to assist in decision-making.