Reviewerly/exHarmony huggingface dataset:
Metaverse for Social Good

In recent years, the metaverse has attracted enormous attention from around the world with the development of related technologies. The expected metaverse should be a realistic society with more direct and physical interactions, while the concepts of race, gender, and even physical disability would be weakened, which would be highly beneficial for society. However, the development of metaverse is still in its infancy, with great potential for improvement. Regarding metaverse's huge potential, industry has already come forward with advance preparation, accompanied by feverish investment, but there are few discussions about metaverse in academia to scientifically guide its development. In this paper, we highlight the representative applications for social good. Then we propose a three-layer metaverse architecture from a macro perspective, containing infrastructure, interaction, and ecosystem. Moreover, we journey toward both a historical and novel metaverse with a detailed timeline and table of specific attributes. Lastly, we illustrate our implemented blockchain-driven metaverse prototype of a university campus and discuss the prototype design and insights.
A New Novel Hybrid Dynamic Color Segmentation Model for Road Signs in Noisy Conditions

Image segmentation is the most important process in road sign detection and classification systems. In road sign systems, the spatial information of road signs are very important for safety issues. Road sign segmentation is a complex segmentation task because of the different road sign colors and shapes that make it difficult to use specific threshold. Most road sign segmentation studies do good in ideal situations, but many problems need to be solved when the road signs are in poor lighting and noisy conditions. This paper proposes a hybrid dynamic threshold color segmentation technique for road sign images. In a pre-processing step, the authors use the histogram analysis, noise reduction with a Gaussian filter, adaptive histogram equalization, and conversion from RGB space to YCbCr or HSV color spaces. Next, a segmentation threshold is selected dynamically and used to segment the pre-processed image. The method was tested on outdoor images under noisy conditions and was able to accurately segment road signs with different colors (red, blue, and yellow) and shapes.
New advances in automatic shearer cutting technology for thin seams in Chinese underground coal mines

Automatic mining technology is the ideal path and the inevitable way to improve production efficiency, reduce labour intensity and ensure safety for thin coal seam. Recently, while automatic mining technology is increasingly applied in China for thin seams, the corresponding automatic cutting technology has made new advances. Among them, mnemonic cutting technology has been fruitful industrial tested with suitable conditions. Simultaneously, another one called cutting trace pre-set technology of the shearer has been put forward. Using this method, the cutting trace in the area with coal thickness changed and geological structures can be preset. What's more, self-adaptive regulation strategy of cutting trace based on coal-rock recognition by monitoring current of cutting motor was discussed. Then, the main problems and development trends of automatic mining in China was also proposed.
Deep Learning-Based Human and Vehicle Detection in Drone Videos

Nowadays, the detection and tracking of stationary or moving objects have begun to be of great importance for military applications as well as for civilian applications. In this case, it is necessary to use deep learning methodologies in order to effectively meet the emerging needs. This study, it is aimed to detect the people and vehicles in the videos recorded by drones in an environment suitable for field conditions. For this purpose, DarkNet-53 architecture in YOLOv3 was used to detect the presence of people and vehicles in motion in videos with 25 (Frame Per Second) images transferred to the screen in one second. The convolutional neural network has been developed by supporting it with various hyperparameter optimizations and an accuracy rate of 78 percent has been achieved.
Comparative Study on the Effect of Shape Complexity on the Efficiency of Different Overlay Analysis Algorithms

Polygon shape is an important factor affecting the efficiency of vector polygon overlay analysis. Aiming at the problems that polygon shape is difficult to accurately measure and different algorithms have different sensitivities to the same polygon in vector polygon overlay analysis, 27 shape variables are selected from literature to establish the shape complexity models for Vatti, Greiner-Hormann and Martinez algorithms by multiple linear regression analysis in this paper. These three algorithms are suitable for arbitrary polygon clipping and include intersection, union, difference and symmetric difference operators. The experimental results show that the calculated performances of the intersection, union, difference and symmetric difference operators belonging to the same algorithm are similar to their sensitivity to the same polygon shape. The fitting correlation coefficient R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of the shape complexity model based on the Greiner-Hormann algorithm is the highest among the three algorithms, which can explain 99% of the performance of this algorithm.
Intelligent System for Forecasting Failure of Agile Projects

Revealing the failure of agile software projects is a great challenge faced by software companies. This paper focuses on the using of intelligent techniques such as fuzzy logic, multiple linear regressions, support vector machine, neural network to address this challenge. This paper also presents a review of some works related to this area of interest. In this paper, the researchers propose an approach for revealing the failure of agile software projects based on two intelligent techniques: fuzzy logic and multiple linear regressions (MLR). MLR is used to determine crucial failure factors of agile software projects. Fuzzy logic is used for revealing failure of agile software projects.
Security Magnification in Supply Chain Management Using Blockchain Technology

In supply chain management the necessity for data transparency is very essential as it is key to create trust between retailers and customers. But data managed by centralized controllers face several vulnerabilities and security threats like data breaches, data confidentiality, and many more. Blockchain is a digital and distributed ledger has acquired great popularity in recent years, due to its security, immutability, and transparency in data. It solves many challenges like keeping the data secure by using cryptographic algorithms. It is a decentralized ledger for recording, managing, storing, and transmitting data in a peer-to-peer network. This paper aims to provide a brief survey on the magnification of security in supply chain operations using blockchain, further indicating the challenges encountered during the integration. The work presents a descriptive study of past literature on blockchain for intensifying security in supply chain operations by examining the features provided by blockchain technology. Further, the paper provides an insight into how blockchain is transforming the business by providing safe and automated solutions. Additionally, this paper highlights the motivation behind using blockchain technology in supply chain management. Further, the work investigates how leveraging blockchain can help in overcoming vulnerabilities and avoiding fraudulent activities in the traditional supply chain. Finally, the paper highlights the uses of the blockchain-based business and enumerates the related future research directions.
Code design and latency analysis of distributed matrix multiplication with straggling servers in fading channels

This paper exploits coding to speed up computation offloading in a multi-server mobile edge computing (MEC) network with straggling servers and channel fading. The specific task we consider is to compute the product between a user-generated input data matrix and a large-scale model matrix that is stored distributively across the multiple edge nodes. The key idea of coding is to introduce computation redundancy to improve robustness against straggling servers and to create communication redundancy to improve reliability against channel fading. We utilize the hybrid design of maximum distance separable (MDS) coding and repetition coding. Based on the hybrid coding scheme, we conduct theoretical analysis on the average task uploading time, average edge computing time, and average output downloading time, respectively and then obtain the end-to-end task execution time. Numerical results demonstrate that when the task uploading phase or the edge computing phase is the performance bottleneck, the hybrid coding reduces to MDS coding; when the downlink transmission is the bottleneck, the hybrid coding reduces to repetition coding. The hybrid coding also outperforms the entangled polynomial coding that causes higher uplink and downlink communication loads.
Data Trading for Blockchain-Based Data Market in Cyber-Physical-Social Smart Systems

With the increasing popularity of smart services and applications, massive amounts of data generated in the cyber-physical-social smart system (CPS3) have become a valuable commodity. To make data tradable in CPS3 and guarantee the data security, this paper proposes a blockchain-based data market model to enable data trading between the CPS3 operator and CPS3 users. The data trading mechanisms are formulated as two kinds of non-cooperative games, namely, independent optimization problem and competition-enhanced optimization problem. We analyze the game equilibrium of the two problems and validate the existence and uniqueness of the Nash equilibrium. Numerical results demonstrate the performance advantage of the proposed scheme.
On Robustness of Mutual Funds Categorization and Distance Metric Learning

Identifying similar mutual funds among a given universe of funds has many applications, including competitor analysis, marketing and sales, tax loss harvesting, and so on. For a contemporary analyst, the most popular approach to finding similar funds is to look up a categorization system such as Morningstar categorization. Morningstar categorization has been heavily investigated by academic researchers from various angles, including using unsupervised clustering techniques in which clusters were found to be inconsistent with categorization. Recently, however, categorization has been studied using supervised classification techniques, with the categories being the target labels. Categorization was indeed learnable with very high accuracy using a purely data-driven approach, causing a paradox: Clustering was inconsistent with respect to categorization, whereas supervised classification was able to reproduce (near) complete categorization. Here, the authors resolve this apparent paradox by pointing out incorrect uses and interpretations of machine learning techniques in the previous academic literature. The authors demonstrate that by using an appropriate list of variables and metrics to identify the optimal number of clusters and preprocessing the data using distance metric learning, one can indeed reproduce the Morningstar categorization using a data-driven approach. The present work puts an end to the debate on this issue and establishes that the Morningstar categorization is indeed intrinsically rigorous, consistent, rule-based, and reproducible using data-driven approaches, if machine learning techniques are correctly implemented. <b>Key Findings</b> ▪ Academic literature has time and again questioned the consistency and robustness of mutual fund’s categorization systems, such as Morningstar categorization, by contrasting them with unsupervised clustering of funds. ▪ Here, the authors settle the debate in favor of Morningstar categorization by pointing out the use of incorrect lists of variables and interpretation of machine learning algorithms in the previous literature, while emphasizing that the main missing piece from the machine learning side in previous research was the appropriate distance metric. ▪ The authors employ a machine learning technique called distance metric learning and reproduce the Morningstar categorization completely using a data-driven approach.
On Wasted Contributions: Understanding the Dynamics of Contributor-Abandoned Pull Requests–A Mixed-Methods Study of 10 Large Open-Source Projects

Pull-based development has enabled numerous volunteers to contribute to open-source projects with fewer barriers. Nevertheless, a considerable amount of pull requests (PRs) with valid contributions are abandoned by their contributors , wasting the effort and time put in by both the contributors and maintainers. To better understand the underlying dynamics of contributor-abandoned PRs, we conduct a mixed-methods study using both quantitative and qualitative methods. We curate a dataset consisting of 265,325 PRs including 4,450 abandoned ones from ten popular and mature GitHub projects and measure 16 features characterizing PRs, contributors, review processes, and projects. Using statistical and machine learning techniques, we find that complex PRs, novice contributors, and lengthy reviews have a higher probability of abandonment and the rate of PR abandonment fluctuates alongside the projects’ maturity or workload. To identify why contributors abandon their PRs, we also manually examine a random sample of 354 abandoned PRs. We observe that the most frequent abandonment reasons are related to the obstacles faced by contributors, followed by the hurdles imposed by maintainers during the review process. Finally, we survey the top core maintainers of the studied projects to understand their perspectives on dealing with PR abandonment and on our findings.
Efficient SMC Protocol Based on Multi-Bit Fully Homomorphic Encryption

Aiming at the problems of large ciphertext size and low efficiency in the current secure multi-party computation (SMC) protocol based on fully homomorphic encryption (FHE), the paper proves that the fully homomorphic encryption scheme that supports multi-bit encryption proposed by Chen Li et al. satisfies the key homomorphism. Based on this scheme and threshold decryption, a three-round, interactive, leveled, secure multi-party computation protocol under the Common Random String (CRS) model is designed. The protocol is proved to be safe under the semi-honest model and the semi-malicious model. From the non-interactive zero-knowledge proof, it can be concluded that the protocol is also safe under the malicious model. Its security can be attributed to the Decisional Learning With Errors (DLWE) and a variant of this problem (some-are-errorless LWE). Compared with the existing secure multi-party computation protocol based on fully homomorphic encryption under the CRS model, the ciphertext size of this protocol is smaller, the efficiency is higher, the storage overhead is smaller, and the overall performance is better than the existing protocol.
Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions

Self-driving cars, i.e., fully automated cars, will spread in the upcoming two decades, according to the representatives of automotive industries; owing to technological breakthroughs in the fourth industrial revolution, as the introduction of deep learning has completely changed the concept of automation. There is considerable research being conducted regarding object detection systems, for instance, lane, pedestrian, or signal detection. This paper specifically focuses on pedestrian detection while the car is moving on the road, where speed and environmental conditions affect visibility. To explore the environmental conditions, a pedestrian custom dataset based on Common Object in Context (COCO) is used. The images are manipulated with the inverse gamma correction method, in which pixel values are changed to make a sequence of bright and dark images. The gamma correction method is directly related to luminance intensity. This paper presents a flexible, simple detection system called Mask R-CNN, which works on top of the Faster R-CNN (Region Based Convolutional Neural Network) model. Mask R-CNN uses one extra feature instance segmentation in addition to two available features in the Faster R-CNN, called object recognition. The performance of the Mask R-CNN models is checked by using different Convolutional Neural Network (CNN) models as a backbone. This approach might help future work, especially when dealing with different lighting conditions.
Wireless Federated Learning over MIMO Networks: Joint Device Scheduling and Beamforming Design

Federated learning (FL) is recognized as a key enabling technology to support distributed artificial intelligence (AI) services in future 6G. By supporting decentralized data training and collaborative model training among devices, FL inherently tames privacy leakage and reduces transmission costs. Whereas, the performance of the wireless FL is typically restricted by the communication latency. Multiple-input multiple-output (MIMO) technique is one promising solution to build up a communication-efficient edge FL system with limited radio resources. In this paper, we propose a novel joint device scheduling and receive beamforming design approach to reduce the FL convergence gap over shared wireless MIMO networks. Specifically, we theoretically establish the convergence analysis of the FL process, and then apply the proposed device scheduling policy to maximize the number of weighted devices under the FL system latency and sum power constraints. Numerical results verify the theoretical analysis of the FL convergence and exhibit the appealing learning performance of the proposed approach.
GamutNet: Restoring Wide-Gamut Colors for Camera-Captured Images

Most cameras still encode images in the small-gamut sRGB color space. The reliance on sRGB is disappointing as modern display hardware and image-editing software are capable of using wider-gamut color spaces. Converting a small-gamut image to a wider-gamut is a challenging problem. Many devices and software use colorimetric strategies that map colors from the small gamut to their equivalent colors in the wider gamut. This colorimetric approach avoids visual changes in the image but leaves much of the target wide-gamut space unused. Noncolorimetric approaches stretch or expand the small-gamut colors to enhance image colors while risking color distortions. We take a unique approach to gamut expansion by treating it as a restoration problem. A key insight used in our approach is that cameras internally encode images in a wide-gamut color space (i.e., ProPhoto) before compressing and clipping the colors to sRGB's smaller gamut. Based on this insight, we use a softwarebased camera ISP to generate a dataset of 5,000 image pairs of images encoded in both sRGB and ProPhoto. This dataset enables us to train a neural network to perform wide-gamut color restoration. Our deep-learning strategy achieves significant improvements over existing solutions and produces color-rich images with few to no visual artifacts.
Responsive Visualization Design for Mobile Devices

The term responsive describes aspects of a visualization that automatically adapt to changes in, for example, device characteristics, environment, usage context, or data. The chapter discusses aspects of responsive mobile data visualization, and summarizes the types of change that a visualization must respond to and how they can be sensed on mobile devices. Ten responsive visualization design strategies are reviewed, including adaptations of scale, layout, and visual encoding, as well as attentional cues and specific interactions. The chapter concludes with future research directions pertaining to responsive visualization design for mobile devices and beyond.
ForumSum: A Multi-Speaker Conversation Summarization Dataset

Abstractive summarization quality had large improvements since recent language pretraining techniques.However, currently there is a lack of datasets for the growing needs of conversation summarization applications.Thus we collected ForumSum 1 , a diverse and highquality conversation summarization dataset with human written summaries.The conversations in ForumSum dataset are collected from a wide variety of internet forums.To make the dataset easily expandable, we also release the process of dataset creation.Our experiments show that models trained on ForumSum have better zero-shot and few-shot transferability to other datasets than the existing large chat summarization dataset SAMSum.We also show that using a conversational corpus for pre-training improves the quality of the chat summarization model.
Composable Partial Multiparty Session Types

We introduce partial sessions and partial (multiparty) session types, in order to deal with open systems, i.e., systems with missing components. Partial sessions can be composed, and the type of the resulting system is derived from those of its components without knowing any suitable global type nor the types of missing parts. Incompatible types, due to e.g. miscommunications or deadlocks, are detected at the merging phase. We apply these types to a process calculus, for which we prove subject reduction and progress, so that well-typed systems never violate the prescribed constraints. Therefore, partial session types support the development of systems by incremental assembling of components.
Describing the Experiences of Students with ADHD Learning Science Content with Emerging Technologies

Emerging technologies, such as virtual reality, haptics, and 3-dimensionality, provide novel opportunities to allow students to investigate scientific phenomena by fostering perceptions of virtual presence, the feeling of being sensorially immersed and authentically interacting within a computer-generated virtual learning environment (VLE). Neurotypical learners are largely represented in VLE research on science learning, with fewer with neurodivergent learners, such as students with ADHD. This descriptive case study sought to address the dearth in the literature on neurodivergent students’ experiences, with emerging technologies, for learning science. Specifically, the case describes the extent to which neurodivergent learners experience the affordances of VLEs for science learning, as compared to their neurotypical peers, in: zooming, spatially orienting and rotating objects, viewing multiple representations and abstract processes in real-time, as well engaging in risk through multiple trials. Five middle grades students (diagnosed with ADHD) were assessed and observed using a tool (zSpace) that combines emerging technologies to learn cardiac anatomy and physiology. Students’ utterances of virtual presence and technological affordances were coded, and frequency counts and percentages were calculated, both individually and collectively. The results found that students most described sensory (41%), control (30%), and realism (26%) constructs with fewer reports of holding their attention (3%). Analyses of cardiac assessments found gains in scores for spatial rotation and viewing abstract processes, no change in score in viewing multiple representations, and a decrease in scores for spatial orientation. This case study provides unique insight into the needs of neurodivergent learners when using emerging technologies for science learning.