Reviewerly/exHarmony huggingface dataset:
Image Processing for Facial Expression using Machine Learning Algorithm

Visual detections of face expression have become much difficult job and yields in reduced preciseness against the automated face expression identification making use of image processing for emotion identification and take lesser time and less efforts with increased accurate outcomes. Effective identification and classification of face-based expressions are found to be beneficial in people's behavioral monitoring system. This paper presents detailed texture bound analyzes architecture framework and codebook generation for face expression detection and classification method. The proposed system acquires Local threshold-texture Patterns (LT - TP) from input face images of unique classes. The features from LT - TP were effective and the method was able to acquire discriminant data and indicate every class containing less dimensions in it. Then, unique codebooks are generated for each class of images. Finally, Classification is done by making use of multiclass typed SVM-Support Vector Machines. Experimentations are performed on Matlab on face dataset that contains minimum two unique classes namely normal and happy faces.
A Multi-objective Evolutionary Approach for Efficient Kernel Size and Shape for CNN

In order to improve the classification accuracy, state-of-the-art CNNs usually involve large and complex convolutional layers. However, for certain applications, e.g. Internet of Things (IoT), where such CNNs are to be implemented on resource-constrained platforms, the CNN architectures have to be small and efficient. To deal with this problem, reducing the resource consumption in convolutional layers has become one of the most significant efforts when designing CNNs. In this work, a multi-objective optimisation approach is proposed to trade-off between the amount of computation and network accuracy by using Multi-Objective Evolutionary Algorithms (MOEAs). The size and number of convolution kernels used are proportional to computational resource consumption of CNNs. Therefore, this paper considers reducing the computational resource consumption by reducing the size of kernels in convolutional layers. In addition to kernel size unconventional kernel shapes are investigated and results show these clearly improve performance over using traditionally square convolution kernels alone. The main contributions of this paper are therefore a methodology to significantly reduce computational cost of CNNs, based on unconventional kernel shapes, and provide different trade-offs for specific use cases. The experimental results further demonstrate that the proposed method achieves large improvements in resource consumption with no significant reduction in network performance. Compared with the benchmark CNN, the best trade-off architecture shows a reduction in multiplications of up to 4.06X and with slight increase in classification accuracy on CIFAR-10 dataset.
A Study of Moba Game APP Design Methods Based on Design Psychology

With the progress of society and the popularity of smartphones, more and more people are becoming mobile game players. Most of them are willing to invest time and money in mobile games. Game design as an emerging discipline is gradually emerging. It is more cross-cutting than other traditional design disciplines, with psychology being an important part of it. The goal of designing a game product is to create a better gaming experience that people can have fun with. Therefore, studying the psychology of the player is central to our mastery of the gaming experience. This paper will use design psychology as a basis to explore how game apps can meet the psychological needs of players. Take the current popular MOBA mobile game product “Honor of Kings” as an example. The impact of its excellent game product design elements on players' psychology is studied. The paper not only examines and summarizes the principles and methods of game product design from a theoretical level but also focuses on the exploration of the technical aspects of the game. The conclusions help to understand the behavior of game users and their patterns, and provide some reference value for the development of future game products.
A 161.6 TOPS/W Mixed-mode Computing-in-Memory Processor for Energy-Efficient Mixed-Precision Deep Neural Networks

A Mixed-mode Computing-in memory (CIM) processor for the mixed-precision Deep Neural Network (DNN) processing is proposed. Due to the bit-serial processing for the multi-bit data, the previous CIM processors could not exploit the energy-efficient computation of mixed-precision DNNs. This paper proposes an energy-efficient mixed-mode CIM processor with two key features: 1) Mixed-Mode Mixed-precision CIM (M3-CIM) which achieves 55.46% energy efficiency improvement. 2) Digital-CIM for In-memory MAC for the increased throughput of M3-CIM. The proposed CIM processor was simulated in 28nm CMOS technology and occupies 1.96 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . It achieves a state-of-the-art energy efficiency of 161.6 TOPS/W with 72.8% accuracy at ImageNet (ResNet50).
Sun Tracking and Control Design for PV Solar energy system

In this modern era of the rapid increase in population, a high rise in technology, and a large number of machinery installed, fuel demand has increased significantly. Non-renewable energies contribute a lot to producing energy worldwide, and that's why they are decreasing at an alarming rate. As an alternative, renewable energies have a high potential to solve this upcoming issue. In this paper, sunlight is utilized for the location of Islamabad, and an active solar tracker is designed. The objective is to develop a cost-effective system with low maintenance requirements. The tracking mechanism is modeled by two sensors, LDR and PV sensor. LDR sensor generates high resistance when light is incident on them, thus reducing the voltage production. PV sensors produce a voltage when sunlight is incident on them, and a voltage drop occurs if a shadow occurs. A thin plate between two LDR sensors or two PV sensors will cast a shadow according to the sun's position. It will create a voltage difference between the two sides, thus causing the system to track the sun. For smooth movement, a servomotor is an effective choice. The system is integrated with a microcontroller for a feedback system of output; Arduino Uno will regulate the uniform and accurate movement of the system. The research on azimuth and elevation angles for the location of an installment is also included in this paper. Different tests are performed for comparative study for both sensors to have performance analysis.
Concept Maps in Augmented Reality to Improve the Learning Process and the Retrieval of Information

Abstract Memorization techniques are of primary importance in education. Two relevant and extensively used techniques are the Concept maps and the method of loci. Both methods are based on the visualization of information, which helps memorization and retrieval. For these reasons, they are also considered inclusive learning tools for people with Specific Learning Disability. Augmented Reality is a technology that has gained popularity in many sectors, from industry to the medical one, for its effectiveness in visualizing graphical items on top of real contexts. The paper demonstrates that Augmented Reality can also be beneficial for representing and interacting with Concept maps in a 3D virtual space that is linked to the real world. Specifically, the authors developed an Augmented Reality application in which the key features of both the Concept maps (such as visual-spatial logic and concepts organization) and of the method of loci are integrated with those of Augmented Reality technologies to improve the learning process and the retrieval of information.
The Effectiveness of Administrative Performance and Its Impact on the Use of Decision Support Systems in Universities: Al-Azhar University in Gaza as a Model

The aim of the research is to identify the effectiveness of administrative performance and its impact on the use of decision support systems in universities: Al-Azhar University in Gaza as a model. The research was applied to 61 academics in administrative positions at Al-Azhar University in Gaza. The descriptive analytical approach was used and secondary data was obtained through a survey list that was distributed to the research community using the comprehensive inventory method, where the questionnaire was distributed to all members of the study community and it was retrieved 40 questionnaires with a recovery rate of 65.6%. The research concluded with a set of results, the most important of which is the existence of a direct statistically significant relationship between the effectiveness of administrative performance and the use of decision support systems at Al-Azhar University in Gaza.And there is a statistically significant effect of the effectiveness of administrative performance in using decision support systems at Al-Azhar University in Gaza. And that there are no statistically significant differences between the averages of the respondents' answers due to (Age Group, Academic Degree, Number of Years of Service, and Job Title). The research reached a set of recommendations, the most important of which is the importance of universities reformulating the procedures, the nature of work, and the organizational structure in those universities in a way that reduces centralization, and avoids duplication, routine, and repeated control, which contributes to improving performance and achieving goals. He urged universities to involve their employees when developing their plan, by communicating with all faculties and their departments and holding brainstorming sessions, workshops and periodic meetings with all departments. And the need for the higher management of universities to pay attention to decision support systems, and to make intensive and continuous efforts to support and enhance efforts aimed at enriching and strengthening the role of decision support systems in universities.
HRL2E: Hierarchical Reinforcement Learning with Low-level Ensemble

Goal-conditioned hierarchical reinforcement learning (HRL) is a promising approach to solve challenging tasks with sparse rewards and long horizons. However, it suffers from the non-stationary problem due to the updating and unstable low level. To stabilize the low level more quickly and accelerate the non-stationary stage, we propose a novel HRL method: Hierarchical Reinforcement Learning with Low-level Ensemble (HRL2E). In HRL2E, the high level generates goals as high-level actions based on current states. Then the low level made up of several homogeneous policies attempts to complete these goals within a specific timestep budget. The improvement of our approach to the general goal-conditioned HRL algorithms can be summarized in two aspects. First, we estimate the target value function with the ensemble, stabilizing the training process. Second, we propose the Gates module composed of several scoring machines to score each low-level policy and judge which one has the most success potential to execute a specific goal. We adopt Twin Delayed Deep Deterministic Policy Gradient (TD3) in each level. Experimental comparison between our method and state-of-the-art goal-conditioned HRL methods on challenging continuous control tasks in MuJoCo domains shows our method can significantly accelerate training.
Trial for Collecting, Sharing and Preserving Digital Research Assets in Asia

Collecting, sharing, and preserving digital research assets are essential for improving knowledge production’s efficiency and reliability. However, efforts have been insufficient, and many digital assets have disappeared. We have established a research project RsDA with libraries, archives, and museums of major universities and research institutes in the Asian region. This paper will introduce the RsDA Inventory, an inventory of online databases related to the Asian region, as a core outcome of the project. We migrated the inventory data to Wikidata throughout the project, and behind that, the guidelines and data models played essential roles. RsDA activities and outcomes will contribute to realizing an open academic information environment and long-term preservation of such information in the Asian region.
Efficient Word Size Modular Multiplication over Signed Integers

As an efficient multiplication method for polynomial rings, Number Theoretic Transform (NTT) is a fundamental algorithm that is both practically useful and theoretically established. Chung et al. proposed a method to perform NTT-based polynomial multiplication for NTT-unfriendly rings that do not have suitable primitive roots. They applied their proposal to lattice-based cryptography using NTT-unfriendly rings and speeded up several schemes. At ARITH 2021, Plantard proposed a modular multiplication algorithm that improves the speed of NTT if moduli are not large (a few dozen of bits), which is the case for typical lattice-based cryptography. It is natural to expect that Plantard's method improves Chung et al.‘s NTT when applied to them, however, this is not possible as Chung et al. requires the use of signed integers while Plantard's method assumes unsigned integers. A simple fix would cause a slowdown and a non-constant-time operation. To overcome this problem, we propose an efficient method for calculating the modular multiplication for signed integers based on Plantard's method. Our proposal generally incurs no overhead from the original and works in a constant-time fashion. To show the effectiveness of our proposal, we provide experimental implementation results on a lattice-based cryptographic scheme Saber. Currently, NIST is selecting candidates for standardization of post-quantum cryp-tography in preparation for the compromise of current public key cryptography by quantum computers, and has completed the selection of the final candidates. Saber is one of the finalists for the NIST standardization project,
Unknown materials about Usein Abdulrefioglu Bodaninsky (1877–1938)

The materials discovered recently in the Russian State Library in Moscow are unknown to modern researchers of the activities of the founder of BIKAMZ A. Bodaninsky. In particular, information about the large funds that were issued to Bodaninsky by the Bakhchisaray vaqf “Tole” after graduating from the Tatar teacher`s school in Simferopol, which played a major role in his trip and training in Moscow at the Stroganov School of Technical Drawing. New in the article are also materials related to the appeals of U. Bodaninsky in 1916 to Taurida Governor and also “Society for Protection of Historical and Cultural Monuments” in Petrograd, related to the intentions of opening an ethnographic museum in Bakhchisaray. Also little known are the frequent trips of U. Bodaninsky in 1915–1916, when he created the “Society of Lovers of Art and Ethnography” from local youths which played an important role, including material, at the official opening the Bakhchisaray museum in1917, because all members of a large society annually contributed a certain fee to the activities of the museum.
The Best of Both Worlds: Distributed PCA That is Both Exact and Communication Efficient

The effectiveness of machine learning algorithms largely depends on the goodness of the representation of data. While the massiveness in dimension and amount of modern day data requires dimension reduction and feature extraction for efficient use of available computational resources, the use of un-correlated features is known to enhance the performance of such machine learning algorithms. Thus, an efficient representation learning approach should focus on dimension reduction as well as uncorrelated feature extraction. Even though Principal Component Analysis (PCA) and linear autoencoders are fundamental data processing tools largely used for dimension reduction, they can also be used to extract uncorrelated features when engineered properly. At the same time, factors like ever-increasing volume of data or inherently distributed data generation impede the use of existing centralized solutions for representation learning that require availability of data at a single location. This paper proposes two variants of an algorithm called FAST-PCA (Fast and exAct diSTributed PCA) based on a feedforward neural network-based system that learn data representations in a distributed setting such that they are reduced in dimension as well as have uncorrelated features. The proposed variants are meant to curb the communication overheads prevalent in the existing solutions and are shown to converge to the exact solutions at a linear rate. These claims are further supported by extensive numerical experiments.
Sistem Pembelajaran Online Sebagai Mediator Kreativitas SDM, Ketidakpastian Lingkungan, dan Teknologi Informasi pada Kinerja PTS

The purpose of the study was to examine and find empirical evidence regarding the effect of human resource creativity, environmental uncertainty, and the use of information technology on online learning systems, and the effect of online learning systems on the performance of private universities. The research was conducted at private universities throughout Lombok Island, West Nusa Tenggara Province. The total population in this study is 26 private universities, where each university will make 2 people as samples. So that the sample in this study amounted to 52 respondents. The data analysis method used is the PLS-SEM method. The results of the study stated that the creativity of human resources and the use of information technology had a positive effect on the online learning system. Meanwhile, environmental uncertainty has no effect on the online learning system. Furthermore, the online learning system has a positive effect on the performance of private universities.&#x0D; Keywords: Private Higher Education Performance, Online Learning System, Human Resource Creativity, Environmental Uncertainty, Information Technology
An Overview of Multi-View Methods for Text Clustering

Text clustering has become an important challenge in text mining and machine learning, which partitions a specific documents’ collection into groups according to certain similarity/dissimilarity criterion. With advances in information acquisition technologies, textual data can frequently be represented using different techniques, generating multi-view data. We propose in this chapter an overview of the existing clustering methods with a special emphasis on multi-view text clustering methods. We design a new categorizing model based on the main properties pointed out in the multi-view textual clustering method. To evaluate their performance, we perform extensive experiments on several real-world textual data sets. Based on the experimental results, we provide some insights for researchers who want to decide the best method to use when a task of clustering multi-view textual data is required.
Niching-Assisted Genetic Programming for Finding Multiple High-Quality Classifiers

Explainable artificial intelligence (XAI) is a recent research focus, aiming to gain trust in machine learning models with clear insights into how the models make certain predictions. Due to its ability to evolve potentially interpretable classifiers, genetic programming (GP) is generally well-suited to XAI. However, many learning algorithms including GP usually learn a single best model. In practice, the best model in terms of training classification accuracy/error rate may not be the most appropriate one from the perspective of a domain expert due to overfitting and limited data. Multiple explicit and high-quality classifiers with the same training performance are therefore needed to increase the chances that the generated models will be considered more reasonable to experts. Therefore, this study designs a niching-assisted GP approach for classification. The results show that the proposed method can significantly increase the classification accuracy on most of the tested datasets. Further analysis shows that the designed algorithm can find different GP programs with the same classification performance, providing good interpretability for classification tasks.
Investigation of Two Complexity Sources for Statistical Graphs

Statistical graphs are becoming common in many fields. Modern authoring tools offer easy ways to create complex graphs. Despite multiple sources of advice, graph designers have little objective evidence on which to base design decisions to alleviate complexity. We aimed to fill a gap in the evidence of the difficulty created by two design features: adding one data series to a simple graph or adding dependent axes to a graph. We found main effects of adding a data series, of a greater number of data points, and an additional dependent axis, but primarily on the time readers studied the graph. Via deeper analysis, we explained the difficulty graph readers experienced when answering questions. That explanation focuses on the selection of points from a source graph to be part of the query. Based on our results, we recommend avenues for research on graph complexity and graph comprehension measurement.
Coupled Learning for Kernel Representation and Graph Tensor in Multi-view Subspace Clustering

An increasing amount of attention have been attracted in multi-view subspace clustering, whose impressive performance can be achieved by means of the self-expressive property, under the assumption of linear relations between multi-view data samples. However, most of them fail to recover the nonlinear relations between multi-view data for deeper study, and additionally they are incapable of discovering the comprehensiveness and higher-order correlations among multiple views. To deal with these challenges, we propose a novel model termed Coupled Learning for Kernel Representation and Graph Tensor (CLKT) in Multi-view Subspace Clustering, where both nonlinear relations and higher-order correlations among multiple affinity graphs are jointly learned in a unified framework. Optimal solutions of the proposed method can be obtained by an alternative minimizing optimization strategy. Extensive experiments on six real-world datasets indicate the superiority of CLKT compared with the state-of-the-art methods.
Efficient Neural Network Pruning Using Model-Based Reinforcement Learning

Model compression plays a vital role in the deployment of neural networks (NNs) in resource-constrained devices. Rule-based conventional NN pruning is sub-optimal due to the enormous design space that cannot be examined entirely by hand. To overcome this issue, automated NN pruning leverages a reinforcement learning agent to automatically find the best combination of parameters to be removed from a given model. We propose a novel RL-based automated pruning algorithm that, unlike existing RL-based methods, determines the environmental variables using a State Predictor Network as a simulated environment instead of validating the pruned model in run time. Testing our method on the YOLOv4 detector, a model with 49 % sparsity was produced with 7.2 % higher mAP. This result outperforms our handcrafted pruning methods for YOLOv4 by 2.3 % mAP and 17.1 % sparsity. Regarding total development time, our method is 146.2 times faster than the state-of-the-art PuRL method using NVIDIA Titan X GPU. The implementation of the proposed solution is available at: https://github.com/bencsikb/Efficient_RLPruning