Reviewerly/exHarmony huggingface dataset:
Exploring the first-move balance point of Go-Moku based on reinforcement learning and Monte Carlo tree search

In most chess games without additional rule restrictions, the side that makes the first move (i.e., the first-move side) has an absolute advantage, which affects the game's balance to a certain extent. Artificial intelligence (AI) training in some chess games can be bottlenecked by the imbalance of opening moves, making it challenging to improve chess strength. The first-move balance problem can be explored to achieve a balanced win rate in chess games. This study uses Go-Moku as an example to explore the first-move balance point problem for different sizes of Go-Moku boards. We design a self-playing Go-Moku intelligence algorithm using deep reinforcement learning and Monte Carlo tree search (MCTS), which can considerably save arithmetic power without affecting the strength of the AI. To address the characteristics of Go-Moku and its complexity, we propose an algorithm using dynamic MCTS simulation counts, which only employs a reasonable amount of hyperparameters to achieve better performance with the cost of a relatively small number of simulations. By symmetrically expanding the data and optimizing the exploration and selection allocation, the training efficiency of the Go-Moku AI is improved through Multiple Process Interface (MPI) multi-processes. Building the test model of first-move balance points for a universal Go-Moku board, we obtain a set of first-move balance points for different board sizes. The first-move balance point of Go-Moku that makes the game even is found by simulating the game win rate for all first-move drop points. The experimental results demonstrate that the proposed algorithm can achieve world-leading chess strength in Gomocup by engine play tests and can find the first-move balance point of Go-Moku on boards of various sizes. The results of this study will help optimize the rule setting of Go-Moku and improve the training efficiency of AI in the field of Go-Moku, which can be extended to the exploration of balance in other chess games.
Text Similarity Calculation Method Based on Optimized Cosine Distance

In the text similarity calculation algorithm, the distance algorithm is widely used to measure the distance difference between two text vectors, and the cosine distance algorithm is fully verified. However, the measurement Angle of cosine distance focuses on the included Angle after text vectorization, that is, the direction of vector, without considering the speed of change of each vector. The improved algorithm proposed in this paper not only considers the direction dimension of the vector, but also studies the influence of the change of each dimension on the text similarity of cosine distance measure. Experiments show that compared with traditional cosine similarity, the improved algorithm in this paper has obvious improvement in accuracy and efficiency.
Perceptible sentiment analysis of students' WhatsApp group chats in valence, arousal, and dominance space

Sentiment analysis is a vastly established domain for social media monitoring, feedback insights, and commercial or political campaigns. It allows us to gain an overview of the wider public opinion on certain topics. Nowadays, different social media platforms play a crucial role in web-based sentiment analysis and emotion detection from distinct perspectives. Likewise, WhatsApp is probably the most popular messaging app, allowing users to send messages, images, audio, or videos. However, it is still highly under-explored for any type of linguistic synthesis and analysis. Like many other groups of people, students use WhatsApp for various purposes, even more since the last two years of the pandemic phase, for instance, class communication, study group communication, etc. In this paper, we present a novel approach to analyze the sentiments and emotions of students in valence, arousal, and dominance space by classifying the messages from their WhatsApp group chat. The emotional dimensions of valence, arousal and dominance (VAD) can derive a person’s interest (attraction), level of activation, and perceived level of control for a particular situation from textual communication. We propose a vanilla SVM model fused with a language classifier to calculate each message's sentiment ratings. Finally, using the SVM classifier, we classify the sentiment ratings concerning the degree of the VAD scale. The data were analyzed using a qualitative content analysis method. The results of the study in the form of cumulative sentiment scale and sentiment clustering in VAD space reveal that the students' WhatsApp groups were mostly used for sharing information, exchanging ideas, and discussing issues, with mostly neutral to positive sentiment viewpoints for the provided topics of discussions.
Transfer of arbitrary quantum states between separated superconducting cavities via an ensemble of nitrogen-vacancy centers

Transfer of quantum states between distant nodes is one of the most important preconditions for realizing large-scale quantum information processing. We here propose two ways to transfer an arbitrary quantum state between two separated superconducting cavities coupled to an ensemble of nitrogen-vacancy centers (NV ensemble). By using the double-swap scheme and theory of the dispersive regime, quantum states can be deterministically transferred between two superconducting cavities. With current hybrid circuit QED technology, the numerical simulations are performed to demonstrate that the single-photon state and cat state can be high-fidelity transferred between two superconducting cavities.
Face Mask Detection and Social Distancing Using Machine Learning with Haar Cascade Algorithm

COVID-19 is indeed newsworthy as it has reached a global emergency with an impending spread. Wearing a face covering and maintaining physical distance is a smart move recommended by the WHO. People infected with COVID-19 have difficulty breathing with shortness of breath. Environmental elements of those involved can be contaminated by infectious droplets. It is mandatory to wear a cover and follow a physical withdrawal, but many residents ignore the rules. In such situations, regular checks for facial coverings openly places and it are normal to force fines. As article recognition has unfurled to be a receptive biometric process, it has been broadly applied in observation, security, independent driving, and so on. With the fast improvement of profound learning models, object locators are exceptionally reasonable to foster social removing and facial covering indicators to direct the group by means of CCTV and observation cameras. The paper studies different profound learning organizations to foster such locators. In this review, the current article discovery models utilized for reconnaissance and individuals location are examined. The one-stage and two-stage identifiers alongside their applications and execution are framed by exhaustive way.
A Survey of Extended Reality (XR) Standards

This paper surveys completed and ongoing work of Standards Developing Organizations (SDOs), fora and consortia dealing with technical aspects of extended Reality (XR). In line with the XR definition introduced by [1], specifications considered here address environments containing real or virtual components or a combination thereof, i.e. standards dealing with, inter alia, Augmented Reality (AR), Virtual Reality (VR), and metaverse. While aiming to be comprehensive, this survey can only provide a snapshot of the standards landscape taken at the time of submission of the manuscript.
Xscope: Hunting for Cross-Chain Bridge Attacks

Cross-Chain bridges have become the most popular solution to support asset interoperability between heterogeneous blockchains. However, while providing efficient and flexible cross-chain asset transfer, the complex workflow involving both on-chain smart contracts and off-chain programs causes emerging security issues. In the past year, there have been more than ten severe attacks against cross-chain bridges, causing billions of loss. With few studies focusing on the security of cross-chain bridges, the community still lacks the knowledge and tools to mitigate this significant threat. To bridge the gap, we conduct the first study on the security of cross-chain bridges. We document three new classes of security bugs and propose a set of security properties and patterns to characterize them. Based on those patterns, we design Xscope, an automatic tool to find security violations in cross-chain bridges and detect real-world attacks. We evaluate Xscope on four popular cross-chain bridges. It successfully detects all known attacks and finds suspicious attacks unreported before. A video of Xscope is available at https://youtu.be/vMRO_qOqtXY.
A multitask cascading convolutional neural network for high-accuracy pointer meter automatic recognition in outdoor environments

Abstract Pointer meter automatic recognition (PMAR) in outdoor environments is a challenging task. Due to variable weather and uneven lighting factors, hand-crafted features or shallow learning techniques have low accuracy in meter recognition. In this paper, a multitask cascading convolutional neural network (MC-CNN) is proposed to improve the accuracy of meter recognition in outdoor environments. The proposed MC-CNN uses cascaded CNN, including three stages of meter detection, meter cropping and meter reading. Firstly, the YOLOV4 Network is used for meter detection to quickly determine the meter location from captured images. In order to accurately cluster pointer meter prior boxes in the YOLOV4 Network, an improved K-means algorithm is presented to further enhance the detection accuracy. Then, the detected meter images are cropped out of the captured images to remove redundant backgrounds. Finally, a meter-reading network based on an adaptive attention residual module (AARM) is proposed for reading meters from cropped images. The proposed AARM not only contains an attention mechanism to focus on essential information and efficiently diminish useless information, but also extracts information features from meter images adaptively. The experimental results show that the proposed MC-CNN can effectively achieve outdoor meter recognition, with high recognition accuracy and low relative error. The recognition accuracy can reach 92.6%. The average relative error is 2.5655%, which is about 3% less than the error in other methods. What is more, the proposed approach can obtain rich information about the type, limits, units and readings of the pointer meter and can be used when multiple pointer meters exist in one captured image simultaneously. Additionally, the proposed approach can significantly improve the accuracy of the recognized readings, and is also robust to natural environments.
Fast measurement of surface defects on large components with dynamic phase-shifting digital holographic microscopy

The microscopic characterization of the surface defects on large opto-electronics components is essential to ensure the performance of major equipment. However, existing technology cannot achieve efficient and reliable fast cross-scale measurement due to the requirement on vertical scanning or polarization modulation for multi-frame capturing at each position. An effective approach is proposed to solve this problem, which needs to record only one interferometric hologram at each location and achieves full-area measurement by traversing over the surface. The overlapped area between adjacent regions are specified by wavefront registration and the topography in this area is obtained by two-step phase shifting and holographic reconstruction. Experimental results demonstrate that the proposed method can achieve fast and fast measurement of large specular components with high accuracy and high efficiency.
Dijkstra's algorithm to find the nearest vaccine location

Since the start of Covid-19 pandemic has made many people look for vaccine locations. In general, Dijkstra algorithm is used to find the shortest path. The shortest path problem concentrates on finding the path with the minimum distance. The Dijkstra algorithm was chosen because it has several advantages other than advantageous in terms of running time to find the shortest path. This research focuses on calculation and implementation of Dijkstra algorithm that will result the shortest path with visual by Google Maps API. The project is tested with manual calculation first by using graph explanation. The graph is obtained based on route data obtained with the help of google maps then it is tested by implementing the Dijkstra algorithm using HTML, JavaScript and also Google Maps API to visualize the nearest vaccine location. From the research founded that there were shortcomings problem where program had to enter each road turn as a vertex and an edge, If not including the entire path that was formed, it would pass through buildings or structures that should not be passed. The results that will be displayed on the website based on the purpose of the research.
Optimization of an Intelligent Wheelchair: LiDAR and Camera Vision for Obstacle Avoidance

According to the Wheelchair Foundation, over 131 million individuals depend on a wheelchair [1]. Wheelchairs drastically improve the quality of life by providing partially disabled persons with mobility. Moreover, wheelchairs have become a central component of many hospitals, necessary to transport patients. However, manual wheelchairs limit users’ or helpers’ freedom by requiring direct actuation, either by turning the wheels or pushing the wheelchair. Electric wheelchairs allow users to navigate a wheelchair through a single control mechanism to improve flexibility. Autonomous wheelchairs are the next step in improving the quality of life and further enhancing user freedom [2]. This paper presents the optimization of an automated wheelchair implementing unique and novel path planning and localization indoors. The results illustrate the implementation of camera vision with LiDAR for obstacle avoidance and advancements in a modular infrastructure.
DimSum: A Decentralized Approach to Multi-language Semantics and Verification

Prior work on multi-language program verification has achieved impressive results, including the compositional verification of complex compilers. But the existing approaches to this problem impose a variety of restrictions on the overall structure of multi-language programs (e.g. fixing the source language, fixing the set of involved languages, fixing the memory model, or fixing the semantics of interoperation). In this paper, we explore the problem of how to avoid such global restrictions. Concretely, we present DimSum : a new, decentralized approach to multi-language semantics and verification, which we have implemented in the Coq proof assistant. Decentralization means that we can define and reason about languages independently from each other (as independent modules communicating via events), but also combine and translate between them when necessary (via a library of combinators). We apply DimSum to a high-level imperative language Rec (with an abstract memory model and function calls), a low-level assembly language Asm (with a concrete memory model, arbitrary jumps, and syscalls), and a mathematical specification language Spec. We evaluate DimSum on two case studies: an Asm library extending Rec with support for pointer comparison, and a coroutine library for Rec written in Asm. In both cases, we show how DimSum allows the Asm libraries to be abstracted to Rec-level specifications, despite the behavior of the Asm libraries not being syntactically expressible in Rec itself. We also verify an optimizing multi-pass compiler from Rec to Asm, showing that it is compatible with these Asm libraries.
Event-Based Optical Flow Estimation with Spatio-Temporal Backpropagation Trained Spiking Neural Network

The advantages of an event camera, such as low power consumption, large dynamic range, and low data redundancy, enable it to shine in extreme environments where traditional image sensors are not competent, especially in high-speed moving target capture and extreme lighting conditions. Optical flow reflects the target's movement information, and the target's detailed movement can be obtained using the event camera's optical flow information. However, the existing neural network methods for optical flow prediction of event cameras has the problems of extensive computation and high energy consumption in hardware implementation. The spike neural network has spatiotemporal coding characteristics, so it can be compatible with the spatiotemporal data of an event camera. Moreover, the sparse coding characteristic of the spike neural network makes it run with ultra-low power consumption on neuromorphic hardware. However, because of the algorithmic and training complexity, the spike neural network has not been applied in the prediction of the optical flow for the event camera. For this case, this paper proposes an end-to-end spike neural network to predict the optical flow of the discrete spatiotemporal data stream for the event camera. The network is trained with the spatio-temporal backpropagation method in a self-supervised way, which fully combines the spatiotemporal characteristics of the event camera while improving the network performance. Compared with the existing methods on the public dataset, the experimental results show that the method proposed in this paper is equivalent to the best existing methods in terms of optical flow prediction accuracy, and it can save 99% more power consumption than the existing algorithm, which is greatly beneficial to the hardware implementation of the event camera optical flow prediction., laying the groundwork for future low-power hardware implementation of optical flow prediction for event cameras.
A Multi-population-Based Algorithm with Different Ways of Subpopulations Cooperation

Metaheuristic methods are designed to solve continuous and discrete problems. Such methods include population based algorithms (PBAs). They are distinguished by the flexibility of defining the fitness function, therefore they are a good alternative to gradient methods. However, creating new variants of PBAs that work similarly and differ in detail might be problematic. Therefore, it is interesting to combine existing PBAs in order to increase their effectiveness. One of the hybrid methods is the Multi-population Nature-Inspired Algorithm (MNIA), which uses search operators from different PBAs. The formula of MNIA’s operation is based on the appropriate cooperation of its subpopulations. That is why in this paper we focus on expanding MNIAs with various schemes of such cooperation. In particular, we analyze various combinations of migration models, intervals, and topologies. The proposed solutions were tested and compared using generally known benchmark functions. The obtained results showed an advantage of certain patterns of cooperation of the - subpopulations, which confirmed the validity of the adopted assumptions.
The relationship between perceived corruption and FDI: a longitudinal study in the context of Egypt

This paper investigates the dynamic relationship between perceived corruption and foreign direct investment (FDI) in Egypt during the period 1970–2019. Using a novel back-casting methodology, it extrapolates perceived corruption time series between 1970 and 1980. The results of the Johansen cointegration technique and the multivariate vector error correction model show a positive relationship between perceived corruption and FDI, supporting the “greasing the wheels” effect of corruption. This positive association can be explained by several factors, such as the cross-interdependence of rent-generating assets with perceived corruption and FDI, and the use of FDI data based on the balance of payments that has growing financial-flows and phantom-FDI components. The findings of this paper have important policy implications. Improving the fundamental governance structure in Egypt should be accompanied by a comprehensive investment facilitation strategy to compensate for the removal of “grease” from the “wheels”.
Advantages and opportunities of the IOTA tangle for health data management

The merging of Distributed Ledger Technology (DLT) and the Internet of Things (IoT) has opened new opportunities for innovation in health data management. Issues such as security breaches, privacy violations and fragmented data are just some of the problems that DLT might solve. This research investigates how the IOTA Tangle can provide reliable and secure health data management.
Internet of Things Impact on Web Technology and Enterprise Systems

It's been clearer over the last two decades that computer science plays a crucial role in the growth of every company. With each passing cycle, the IT industry blossoms a brand-new subfield. The Internet of Things (IoT) refers to the idea of connecting any device to the Internet in order to interact with the consumer and convert it into an intelligent device; this is a relatively new concept in the realm of information technology (IT) and the Internet, but it is gaining popularity and importance in people's daily lives. The Internet of Things is predicted to play a pivotal role in the development of business IT infrastructure. With the help of IoT, we can link billions of smart gadgets that can talk to one another in an automated fashion. One of the most rapidly expanding fields in computer history, with an expected 50 billion devices by the end of 2020. Security techniques, such as encryption, authentication, access control, network security, and application security, are rendered useless when applied to Internet of Things (IoT) devices because of these inherent weaknesses. The Internet of Things (IoT) has attracted the attention of many academics and industry leaders, who have published studies and made statements on the significance of IoT architectural layers for corporate systems (ESs). This literature study included a wide variety of studies that implemented and developed IoT technology in several human domains, including healthcare, smart homes and cities, assisted charities, and monitoring systems. The article also detailed the computers and programs that facilitated each study. Plus, the reliable algorithm used in the research articles' tests and analyses of data. Lastly, drawing parallels between these publications helps to provide insight on the difficulties and possibilities facing potential IoT impact researchers in the industrial sector.&#x0D;
Ultra-Large-Scale Deterministic Entanglement Containing <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mn>2</mml:mn><mml:mo>×</mml:mo><mml:mn>20</mml:mn><mml:mtext> </mml:mtext><mml:mn>400</mml:mn></mml:mrow></mml:math> Optical Modes Based on Time-Delayed Quantum Interferometer

Quantum entanglement is an indispensable resource for implementing quantum information processing. The scale of quantum entanglement directly determines its quantum information processing capability. Therefore, it is of great importance to generate ultra-large-scale (ULS) quantum entanglement for the development of quantum information science and technology. Many efforts have been made to increase the scale of quantum entanglement. Recently, time-domain multiplexing has been introduced into continuous-variable (CV) quantum systems to greatly enlarge the scale of quantum entanglement. In this Letter, based on a time-delayed quantum interferometer, we theoretically propose and experimentally demonstrate a scheme for generating an ULS CV deterministic entanglement containing 2×20 400 optical modes. In addition, such ULS entanglement contains 81 596 squeezed modes. Our results provide a new platform for implementing ULS CV quantum information processing.Received 24 March 2022Revised 24 October 2022Accepted 18 January 2023DOI:https://doi.org/10.1103/PhysRevLett.130.060801© 2023 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasEntanglement productionOptical quantum information processingQuantum entanglementQuantum opticsQuantum Information