Reviewerly/exHarmony huggingface dataset:
A Computational Method of Optimizing Chess Compositions to Enhance Aesthetic Appeal

Chess compositions are typically positions set up with a stipulation, e.g., ‘White to Play and Mate in 3’. They are also usually challenging, educational or possess some aesthetic appeal. In this article, a computational method of optimizing compositions is presented that can help improve them, primarily in terms of aesthetics as described by the conventions of problem composition. This is useful in automatic chess composition, which is fairly new in artificial intelligence (AI) and still a largely open area of research. The method involves seven aspects, namely 1) removing pieces, 2) swapping white pieces for weaker ones, 3) swapping black pieces for stronger ones, 4) moving the white king to avoid check, 5) looking for a shorter mate, 6) moving the white king closer to the other pieces, and 7) preventing major black pieces from being en prise (capturable). All of these are applied in order and together whilst preserving the original solution as much as possible. The effectiveness of the method is demonstrated using examples (before and after optimization) illustrating the positive changes to the positions. Human expert assessment is also shown to be comparable in outcome. This method can therefore be used to automatically improve, in general, the aesthetic appeal of compositions to a significant or noticeable degree.
Emergent Insight of the Cyber Security Management for Saudi Arabian Universities: A Content Analysis

While cyber security has become a prominent concept of emerging information governance, the Kingdom of Saudi Arabia has been dealing with severe threats to individual and organizational IT systems for a long time. These risks have recently permeated into educational institutions, thereby undermining the confidentiality of information as well as the delivery of education. Recent research has identified various causes and possible solutions to the problem. However, most scholars have considered a reductionist approach, in which the ability of computer configurations to prevent unwanted intrusions is evaluated by breaking them down to their constituent parts. This method is inadequate at studying complex adaptive systems. Therefore, the proposed project is designed to utilize a holistic stance to assess the cybersecurity management and policies in Saudi Arabian universities. Qualitative research, entailing a thorough critical review of ten public universities, will be utilized to investigate the subject matter. The subsequent recommendations can be adopted to enhance the security of IT systems, not only in institutional settings but also in any other environment in which such structures are used.
On Differential Drive Robot Learning Convex Policy with Application to Path-Tracking

This paper presents an experimental validation of a learning convex policy for path-tracking on a differential drive robot. An online implementation of the convex control policy (COCP) is provided in the ROS environment using the CVXGEN package that runs on the on-board computer in a real-time application. The control policies are trained in an off-board computer considering a stochastic kinematic description of the robot and using an approximate gradient method for a given cost-to-go metric function. The policy is validated through simulation and experimental evaluation. In addition, to certify the training efficacy, the experiment is also evaluated using the untuned policy. A discussion regarding trajectory errors is presented as well as final considerations for the solver and real-time concerns.
Performance and Fault Tolerance Trade-offs in Sharded Permissioned Blockchains

Blockchain has become a promising technology in distributed systems in recent years, but scalability remains a major limitation. The traditional approach to scalability, namely sharding, does not solve the problem easily because the process of interleaving blocks stored in different shards to create a unified master ledger is difficult to parallelize effectively. This paper examines two techniques for interleaving the shards of permissioned blockchains, which we refer to as strong temporal coupling and weak temporal coupling. We describe the algorithmic foundations of both techniques, and evaluate their latency-throughput tradeoff experimentally. Strong coupling loses performance when different shards grow unevenly, but outperforms weak coupling in a wide-area environment due to its inherent efficiency. Weak coupling, in contrast, deals naturally with load imbalance across shards and in fact tolerates shard failures without any additional effort, but loses performance when running on a high-latency network due to the additional coordination performed.
Foreground Feature Selection and Alignment for Adaptive Object Detection

Recently, remarkable progress has been witnessed in adaptive object detection, which aims to mitigate the distributional shifts between source domain and target domain. Domain-adversarial learning methods align the features of different levels to minimize the domain discrepancy, which have been proven effective for adapting object detectors. Most domain adaptation methods align whole-image features. Therefore, foreground alignment may be interfered by the backgrounds. In this work, we propose Foreground Feature Alignment Framework (FFAF) that strengthens the foreground alignment. One of our key contributions is the Foreground Selection Module (FSM), which captures the foreground features that are crucial for object detection and helpful for subsequent feature alignment. Additionally, we align the foreground features by integrating multi-level domain classifiers. Multi-level Domain adaptation (MDA) can simultaneously bridge the domain gap at various representation levels. We evaluate our method with multiple experiments, whose results demonstrate that our method achieves significant improvements in different cross-domain object detection tasks.
Dynamical Self-energy Mapping (DSEM) for Creation of Sparse Hamiltonians Suitable for Quantum Computing

We present a two-step procedure called the dynamical self-energy mapping (DSEM) that allows us to find a sparse Hamiltonian representation for molecular problems. In the first part of this procedure, the approximate self-energy of a molecular system is evaluated using a low-level method and subsequently a sparse Hamiltonian is found that best recovers this low-level dynamic self-energy. In the second step, such a sparse Hamiltonian is used by a high-level method that delivers a highly accurate dynamical part of the self-energy that is employed in later calculations. The tests conducted on small molecular problems show that the sparse Hamiltonian parameterizations lead to very good total energies. DSEM has the potential to be used as a classical-quantum hybrid algorithm for quantum computing where the sparse Hamiltonian containing only O(n2) terms on a Gaussian orbital basis, where n is the number of orbitals in the system, could reduce the depth of the quantum circuit by at least an order of magnitude when compared with simulations involving a full Hamiltonian.
Assessment of computer vision methods for motion tracking of planar mechanisms

One of the main challenges on the use of planar mechanisms is to verify and monitor that the trajectories described by the mechanism correspond to those originally required. However, very few research studies have focused on tracking and monitoring the motion of target points located on the mechanisms during operation conditions. In this paper, a comparative study to evaluate the performance of several computer vision methods (CVMs) when used in motion tracking of planar mechanisms is presented. The aim is to compare and identify the best CVM, in terms of precision, speed, low cost, and computational performance, to track the movement of planar mechanisms. For this purpose, a case study corresponding to a planar four-bar mechanism is selected and analysed. The results show that the vision methods based on the homogeneous and non-homogeneous solution of the camera calibration matrix are a technological alternative for monitoring motion trajectories of planar mechanisms.
Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution

Although deep convolutional neural networks (CNNs) have obtained outstanding performance in image super-resolution (SR), their computational cost increases geometrically as CNN models get deeper and wider. Meanwhile, the features of intermediate layers are treated equally across the channel, thus hindering the representational capability of CNNs. In this paper, we propose an attention-aware linear depthwise network for single image SR, named ALDNet. Specifically, linear depthwise convolution allows CNN-based SR models to preserve useful information for reconstructing a super-resolved image while reducing computational burden. Furthermore, we design an attention-aware branch to enhance the representation ability of depthwise convolution layers by making full use of depthwise filter interdependencies. Experiments on publicly available benchmark datasets show that ALDNet achieves superior performance to traditional depthwise separable convolutions in terms of quantitative measurements and visual quality.
Development and Verification of a System to Support Cooperative Learning Based on Peer Review by Visual Video Annotation on Portable Mobile Device

This study developed a system that supports video annotation on mobile devices for non-face-to-face learning. We considered three functions supporting video annotation in the “VOVAM” system based on two surveys. It was found that learners were able to determine the evaluation contents to annotate on mobile devices. We also found that the D&D annotation function was easy to operate, and it was easy to set the target position to the time corresponding to the content that the learners wanted to annotate on the mobile device. We found that the marker overlap playback function could improve visual comprehension during reflection activities even if the markers from other evaluators were displayed on the mobile devices.
Event Graph Neural Network for Opinion Target Classification of Microblog Comments

Opinion target classification of microblog comments is one of the most important tasks for public opinion analysis about an event. Due to the high cost of manual labeling, opinion target classification is generally considered as a weak-supervised task. This article attempts to address the opinion target classification of microblog comments through an event graph convolution network (EventGCN) in a weak-supervised manner. Specifically, we take microblog contents and comments as document nodes, and construct an event graph with three typical relationships of event microblogs, including the co-occurrence relationship of event keywords extracted from microblogs, the reply relationship of comments, and the document similarity. Finally, under the supervision of a small number of labels, both word features and comment features can be represented well to complete the classification. The experimental results on two event microblog datasets show that EventGCN can significantly improve the classification performance compared with other baseline models.
Dynamic Fizeau interferometer for vibration optical measurement

It is difficult for Fizeau interferometer to take its common optical path characteristics and anti-vibration measurement requirements into account. In this paper, a dynamic Fizeau interferometer (DFI) is proposed for anti-vibration optical measurement, and the advantages of common optical path and high accuracy of Fizeau interferometer are retained. DFI is divided into conventional Fizeau interferometer and vibration measurement part. An assistant mirror is added to the vibration measurement part to separate the information of the reference wavefront and the test wavefront with the shortcoherence laser. The vibration information of the Fizeau cavity is obtained by measuring the vibration of each surface separately. The vibration coefficients of the reference wavefront and the test wavefront are extracted by the algorithm, and then combined with the interferograms of the Fizeau interference part. The phase to be measured is finally recovered. DFI provides a solution for anti-vibration measurement of Fizeau interferometry, which has high application value in optical measurement.
Robust Image Watermarking Using LWT Transform and Stacking Ensemble Classifier

To maintain the relationship between robustness and imperceptibility is very challenging for conventional digital watermarking methods. The proposed digital image watermarking algorithm efficiently manages the value of robustness and imperceptibility. The proposed algorithm has applied the lifting wavelet transform to extract the features from the source image and symbol image. The LWT transform overcomes the limitation of the wavelet transform. The proposed algorithm focusses on pattern-based embedding process involved in the watermark. For the generation and formation of pattern, stack-based ensemble classifier has been applied. The stack-based ensemble classifier is derived in two-stage, and it uses three classifiers, such as SVM, KNN and DT. The glowworm swarm optimization is applied to the symbol image in order to select an optimal coefficient for the embedding process. The proposed algorithm is then simulated in MATLAB software, and further the parameters such as PSNR, NC, SSIM and BER are measured. The proposed algorithm is compared with DWT-SVD and SVM. From the results, it is observed that the proposed algorithm is better than the existing digital image watermarking algorithms.
Proposal of a New Scalar Gauge in Relativity Field Equation

In this paper we propose a new gauge term in addition to the conventional gauge to acquire complete solution for the linear approximated gravitational equation.The calculation to make general form for the linear gravitational equation uses the well-known Nöether's theorem saying that gauge symmetry is equal to conservation law.The unsolved coefficients in the equation require another condition which is leading to new gauge term.This proposed new gauge is a tensor product by a scalar quantity with a metric tensor having the trace value of 2. The scalar component in the 5th row and column of Kaluza-Klein's metric tensor can be found as 2 diagonal components in our proposed 4×4 metric tensor.We also show that only a constant scalar gauge can be allowed in the curved space-time although arbitrary gauge can exist in the linear space-time.
Prediction of Bedridden Duration of Hospitalized Patients by Machine Learning Based on EMRs at Admission

Being bedridden is a frequent comorbid condition that leads to a series of complications in clinical practice. The present study aimed to predict bedridden duration of hospitalized patients based on EMR at admission by machine learning. The medical data of 4345 hospitalized patients who were bedridden for at least 24 hours after admission were retrospectively collected. After preprocessing of the data, features for modeling were selected by support vector machine recursive feature elimination. Thereafter, logistic regression, support vector machine, and extreme gradient boosting algorithms were adopted to predict the bedridden duration. The feasibility and efficacy of above models were evaluated by performance indicators. Our results demonstrated that the most important features related to bedridden duration were Charlson Comorbidity Index, age, bedridden duration before admission, mobility capability, and perceptual ability. The extreme gradient boosting algorithm showed the best performance (accuracy, 0.797; area under the curve, 0.841) when compared with support vector machine (accuracy, 0.771; area under the curve, 0.803) and logistic regression (accuracy, 0.765; area under the curve, 0.809) algorithms. Meanwhile, the extreme gradient boosting algorithm had a higher sensitivity (0.856), specificity (0.650), and F1 score (0.858) than that of support vector machine algorithm (0.843, 0.589, and 0.841) and logistic regression (0.852, 0.545, and 0.839), respectively. These findings indicate that machine learning based on EMRs at admission is a feasible avenue to predict the bedridden duration. The extreme gradient boosting algorithm shows great potential for further clinical application.
Parallel Genetic Algorithm for Sink Nodes Placement to Maximize Network Reliability

A parallel genetic algorithm for optimizing the location of sink nodes of a wireless sensor network is proposed. As an optimization criterion we consider a reliability of a wireless sensor network under the assumption that nodes of a wireless sensor network are subject of random independent failures due to scuffing, intrusions, or other reasons. As a result, some sensors can become disconnected from sink nodes that collect data from all the sensors. A random graph with unreliable nodes and absolutely reliable edges is used as a model of such wireless sensor networks. By wireless sensor network reliability we mean the mathematical expectation of the number of sensors connected to any sink node (MENC). For reliability calculation a well-known factoring method is used. Various optimization algorithms are considered: a canonical genetic algorithm, a module genetic algorithm, an island genetic algorithm, and an island genetic algorithm with migration. The results of the numerical experiments are given.
Use of Machine Learning for the Unknown Values in Database Transformation Processes

Relational databases have fulfilled the role of a primary data repository for decades. With the development of modern technologies, the type of databases has come to the forefront which - unlike relational databases - have an unconventional structure which allows greater flexibility in data storage. Since the design and implementation of non-relational databases, which dates to the end of the 20th century, various types of non-relational databases have been proposed - the most popular non-relational databases include document, graphic, column and key-value databases. Although the flexibility of non-relational databases is an excellent feature in many respects, the freedom of the data structure and the freedom of defining data type cause considerable problems in number of database aspects. One of the situations in which the freedom of the data structure causes problems is the transformation of non-relational databases (the most common document database MongoDB or DynamoDB) to relational or other type of database. Undefined values often cause a termination of a running process and/or higher error rate in the transformation process. For these reasons, it is necessary to solve problems with undefined values either before or during the process of transformation and thus avoid application errors. To this end, we have created a module for solution of the problem of undefined values in the transformation of a non-relational database MongoDB to a relational database Oracle. The main purpose of this article is not to eliminate errors when starting the process, but to design and implement a module that can apply a dynamic solution based on input data and the selection of the target database when unknown values occur in the non-relational database MongoDB.
Enhanced Multi-view Matrix Factorization with Shared Representation

Multi-view data is widely used in the real world, and traditional machine learning methods are not specifically designed for multi-view data. The goal of multi-view learning is to learn practical patterns from the divergent data sources. However, most previous researches focused on fitting feature embedding in target tasks, so researchers put forward with the algorithm which aims to learn appropriate patterns in data with associative properties. In this paper, a multi-view deep matrix factorization model is proposed for feature representation. First, the model constructs a multiple input neural network with shared hidden layers for finding a low-dimensional representation of all views. Second, the quality of representation matrix is evaluated using discriminators to improve the feature extraction capability of matrix factorization. Finally, the effectiveness of the proposed method is verified through comparative experiments on six real-world datasets.
3D Multi-object Detection and Tracking with Sparse Stationary LiDAR

The advent of low-cost LiDAR in recent years makes it feasible for LiDAR to be used in visual surveillance applications such as detection and tracking of players in a football game. However, the extreme sparsity of point cloud acquired by such LiDAR is a challenge for object detection and tracking in large-scale scenes. To alleviate this problem, we propose a method of multi-object detection and tracking from sparse point clouds comprising a short-term tracklet regression stage and a 3D D-IoU data association stage. In the former stage, temporal information is aggregated by the proposed temporal fusion module to predict short-term tracklets formed by three bounding boxes. In the latter stage, the Distance-IoU scores of current tracklets and historical trajectories are computed to associate the data using Hungarian matching algorithm. To reduce the cost of manual annotations, we build a simulated point cloud dataset using Google Research Football for training. A real test dataset of football game is acquired by Livox Mid-100 LiDAR. Our experimental results on both datasets show that fusing multi-frames conduces to improving detection and tracking performance from sparse point clouds. Our 3D D-IoU tracking method also gets a promising performance on the nuScenes autonomous driving dataset.
Modelling Human Activity using Smartphone Data

Background: Over time, multichannel time series data were utilized for the purpose of modeling human activity. Instruments such as an accelerometer and gyroscope which had sensors embedded in them, recorded sensor data which were then utilized to record 6-axes, single dimensional convolution for the purpose of formulating a deep CNN. The resultant network achieved 94.79% activity recognition accuracy on raw sensor data, and 95.57% accuracy when Fast Fourier Transform (FFT) knowledge was added to the sensor data. Objective: This study helps to achieve an orderly report of daily Human activities for the overall balanced lifestyle of a healthy human being. Methods: Interfacing is done using Arduino Uno, Raspberry-Pi 3, heart rate sensor and accelerometer ADXL345 to generate real time values of day-to-day human activities such as walking, sleeping, climbing upstairs/downstairs and so on. Initially, the heart pulse of our four tested individuals is recorded and tabulated to depict and draw conclusions all the way from “Low BP” to “Heavy Exercise”. The convolution neural network is initially trained with an online human activity dataset and tested using our real time generated values which are sent to the MAC OS using a Bluetooth interface. Results: We obtain graphical representations of the amount of each activity performed by the test set of individuals, and in turn conclusions which suggest increase or decrease in the consistency of certain activities to the users, depicted through our developed iOS application, “Fitnesse”. Conclusion: The result of this works is used to improve the daily health routines and the overall lifestyle of distressed patients.