Reviewerly/exHarmony huggingface dataset:
The Rise of the ‘Immersive Virtual Online Avatar Society’: Does an Online Community Established in the Virtual Space Constitute a ‘Real’ Society?

Abstract This video article series investigates the emergence of a ‘digital haven’, that hosts a new type of society. These people are converging in digitally constructed realities for multiple reasons: some seek refuge from the harsh realities of the contemporary social order, others investigate new ways to socialise, or seek somewhere where the limitations of the real world don’t apply. Both science-fiction media and academics predicted that once virtual reality technologies ( vr ) reach the general consumer, society would change (Caddy, 2019; Gibson, 1984; Heim, 1993; Virtual Reality Society, 2017). In recent years the number of households with vr devices has increased (Bol, 2018; Petrock, 2021). This article suggests that vr technology has given birth to an ‘Immersive Virtual Online Avatar Society’. This society harbours many occasional visitors, but also some permanent virtual residents. Important questions arise; starting with: “Does an online community established in the virtual space constitute a ‘real’ society?” This will be investigated with sources from virtual worlds developed with the social multi-user vr software VRChat, drawn from academic research, from video recordings of interactions in VRChat and from philosophical inquiry into the author’s personal experiences and the experiences of other users.
A novel multi-domain machine reading comprehension model with domain interference mitigation

Machine reading comprehension (MRC), as an important task in natural language processing (NLP), is to automatically answer the question after reading a passage. In this aspect, dominant studies mainly focus on domain-specific models. However, domain-specific models trained only on single domain data often cannot achieve satisfactory performance. Although using data of other domains can bring improvement to some extent, building MRC models specific to each domain also makes deployment more difficult in practice. In this paper, we propose a multi-domain MRC model based on knowledge distillation (KD) with domain interference mitigation. Specifically, we employ KD to train a joint model by simultaneously using the multi-domain data and the output distributions of all domain-specific models. In this way, our joint model can better exploit multi-domain data while enabling simpler deployment at the same time. Moreover, to deal with the gradient conflict caused by using data of different domains, we resort to measuring domain-level gradient similarity, based on which an improved PCGrad (short for projecting conflicting gradients) algorithm with adaptive learning rate is proposed. The algorithm mitigates domain interference to improve our joint model across domains. Experimental results and in-depth analysis demonstrate the effectiveness of our joint model and mitigating domain interference further improves the overall performance of our model on a set of benchmark datasets.
LANTERN: Boredom-conscious Natural Language Description Generation of Query Execution Plans for Database Education

The database systems course in an undergraduate computer science degree program is gaining increasing importance due to the continuous supply of database-related jobs as well as the rise of Data Science. A key learning goal of learners taking such a course is to understand how SQL queries are executed in an RDBMS in practice. An RDBMS typically exposes a query execution plan (QEP) in a visual or textual format, which describes the execution steps for a given query. However, it is often daunting for a learner to comprehend these QEPs containing vendor-specific implementation details. In this demonstration, we present a novel, generic, and portable system called LANTERN that generates a natural language (NL)-based description of the execution strategy chosen by the underlying RDBMS to process a query. It provides a declarative framework called POOL for subject matter experts (SME) to efficiently create and manipulate the NL descriptions of physical operators of any RDBMS. It then exploits POOL to generate the NL descriptions of QEPs by integrating a rule-based and a deep learning-based techniques to infuse language variability in the descriptions. Such an NL generation strategy mitigates the impact of boredom on learners caused by repeated exposure of similar text generated by a rule-based system.
Monitoring the Abnormal Human Behaviors in Substations based on Probabilistic Behaviours Prediction and YOLO-V5

To ensure the safety of substation personnel, unsafe behaviors including not wearing safety helmet, entering dangerous area or smoking are prohibited by the security regulations. Recently, with the large-scale installation of substation video monitoring equipment, it is promising to find the anomalies through automatic image object detection methods. Therefore, this paper presents a security monitoring system for the detection of abnormal behaviors in substations. The system inputs the video monitoring and builds upon a regression deep convolutional neural network for predicting the probability of abnormal behaviors. Based on YOLO-V5 algorithms, the deep neural network is trained by minimizing the multi-part loss function considering locating, sizing, classifying and the probability of abnormal behaviors. The experiment shows that the proposed method can confidently and automatically monitor the abnormal behaviors of people in the substations, locating each person and wearing of helmets, which validates the application potentials of the proposed method under complex environments in substations.
Server Security in Cloud Computing Using Blockchain

In modern times, sharing data is the one of the very few things which is done by anyone and everyone. Much of this sharing is done digitally, i.e., over the internet, which makes it the most recurrent way of doing the sharing globally. Enablement of the sharing is aided using copious Cloud Service Providers, allowing the end user, not only the ability of sharing the data but also, storing it. But with the amenities, comes the risk of intentional and unintentional manipulation of the tons of data that is stored and shared in every minute. Breaches like Data Piracy, Hack Attacks etc. are the most common threats that tempers with security of the cloud in these times. It is the need of the hour to make the sensitive data stored by the user safe from intentional/unintentional misuse/manipulation. Thereby, it is necessary to make this system more secure to ensure and maintain the confidentiality of the user data. In this paper, we have ventured the introduction of a system that anchorage Blockchain for securing the data over the cloud. To ensure safety of the user's data, blockchain enables a prominent Controlled Access Mechanism. This mechanism accredits the user to share personalized hyperlinks deliberated to a single user. This approach logs details of all the actions and operations that are being done on or with the data and are at owner's disposal at all points. Actual Proprietary and privacy are few of the many benefits which are provided by this solution ensuring a more secure cloud space for the data.
Blockchain and Cryptocurrency Development Without Regulation

Blockchain is a technology based on a chain of interconnected blocks containing transaction history and user data. Blockchain permitted the creation of crypto-currency. Among its advantages are decentralization, transparency, and integrity. This technology has increased with COVID-19 with the accentuation of the wave of digitalization. Classic finance systems felt overwhelmed by events and tried to catch up with this new wave by creating their cryptocurrency and embarking on this new world of digital finance where regulation and control are nonexistent. Many central banks see the introduction of central bank digital currencies. But the expansion of these cryptocurrencies could present risks in terms of transmission of monetary policy, monetary creation, and financial stability. In this work, the authors present the evolution of cryptocurrency and the reaction of classic finance systems to this wave of digitalization of transactions and especially to an absence of regulation.
State estimation of nonlinear time-varying complex networks with time-varying sensor delay for unknown noise distributions

The state estimation for nonlinear time-varying complex networks is a common problem, especially for the systems with time-varying sensor delay. This paper presents an artificial immune strategy based on particle filtering algorithm to estimate the states. Firstly, considering the nonlinear time-varying complex networks with time-varying sensor delay, this paper gives a theoretical proof that particle filter can be used for the state estimation. On this basis, this paper adopts the artificial immune algorithm to solve the problem which is particle impoverishment in particle algorithm. This paper gives the convergence of the proposed algorithm theoretically. Furthermore, for nonlinear time-varying complex networks with unknown noise distributions, this paper gives an improved algorithm which uses an improved kernel density estimation algorithm which has window width varying with probability density to estimate noise distributions. Simulation results demonstrate the effectiveness of the scheme, especially when nonlinear time-varying complex networks have time-varying sensor delay and noise distributions are unknown.
Sem-TED: Semantic Twitter Event Detection and Adapting with News Stories

Public acceptance of social networks has made the analysis of these networks essential. Event detection in these networks including Twitter is one of the most momentous subjects in the field of natural language processing and text mining. In this paper, we investigated how to link popular social media topics and news stories using transformer models and neural networks. Accordingly, this study consists of two parts: First, detecting popular topics and, second, linking them to the news. Event detection techniques have been applied to detect popular topics, while an event detection method comprises text preprocessing, text embedding using Sentence Transformer, dimension reduction using the UMAP algorithm, and grouping them using the HDBSCAN algorithm. To examine relevance or non-relevance between the news and topics, a single-layer perceptron neural network is applied, in which the output of the model indicates relevance or nonrelevance. We have implemented the mentioned parts and have investigated them on a small sampling of two known datasets. The evaluation outcomes reveal that the first part leads to an average improvement of 8% compared to the entity-based methods. Moreover, the results of the second part demonstrate that the used neural network in this study has a better performance comparing several other methods.
Feasibility of a Device for Gaze Interaction by Visually-Evoked Brain Signals

A dry-electrode head-mounted sensor for visually-evoked electroencephalogram (EEG) signals has been introduced to the gamer market, and provides wireless, low-cost tracking of a user’s gaze fixation on target areas in real-time. Unlike traditional EEG sensors, this new device is easy to set up for non-professionals. We conducted a Fitts’ law study (N = 6) and found the mean throughput (TP) to be 0.82 bits/s. The sensor yielded robust performance with error rates below 1%. The overall median activation time (AT) was 2.35 s with a minuscule difference between one or nine concurrent targets. We discuss whether the method might supplement camera-based gaze interaction, for example, in gaze typing or wheelchair control, and note some limitations, such as a slow AT, the difficulty of calibration with thick hair, and the limit of 10 concurrent targets.
Academic Librarians and Research Data Services: Preparation and Attitudes Revisited

Carol Tenopir et al. published a landmark study of research data services librarians in 2013. The object of this study is to determine if and how librarians’ attitudes about RDS and their perceptions of their own preparedness to provide RDS have changed as the apparent need for and importance of RDS has continued to grow since the publication of Tenopir’s research. This research project was completed by surveying academic librarians in ARL libraries. The survey was distributed to the deans & directors of ARL libraries, to be disseminated to the librarians there most directly involved in RDS. After analyzing the data, I was able to determine that there has been a measurable increase in the perceived importance of RDS among librarians working with data. There has also been a significant increase in the proportion of librarians working with RDS who feel that they have the skills and support needed to do their work. It is my conclusion that RDS seems to be gaining traction within academic libraries, but it is unclear if this is due to libraries placing additional importance on these services.
Dataset of stopwords extracted from Uzbek texts

Filtering stop words is an important task when processing text queries to search for information in large data sets. It enables a reduction of the search space without losing the semantic meaning. The stop words, which have only grammatical roles and not contributing to information content still add up to the complexity of the query. Existing mathematical models that are used to tackle this problem are not suitable for all families of natural languages [1]. For example, they do not cover families of languages to which Uzbek can be included. In the present work, the collocation method of this problem is o ered for families of languages that include the Uzbek language as well. This method concerns the so-called agglutinative languages, in which the task of recognizing stop words is much more difficult, since the stop words are "masked" in the text. In this work the unigram, the bigram and the collocation methods are applied to the "School corpus" that corresponds to the type of languages being studied.
Hybrid control for combining model-based and model-free reinforcement learning

We develop an approach to improve the learning capabilities of robotic systems by combining learned predictive models with experience-based state-action policy mappings. Predictive models provide an understanding of the task and the dynamics, while experience-based (model-free) policy mappings encode favorable actions that override planned actions. We refer to our approach of systematically combining model-based and model-free learning methods as hybrid learning. Our approach efficiently learns motor skills and improves the performance of predictive models and experience-based policies. Moreover, our approach enables policies (both model-based and model-free) to be updated using any off-policy reinforcement learning method. We derive a deterministic method of hybrid learning by optimally switching between learning modalities. We adapt our method to a stochastic variation that relaxes some of the key assumptions in the original derivation. Our deterministic and stochastic variations are tested on a variety of robot control benchmark tasks in simulation as well as a hardware manipulation task. We extend our approach for use with imitation learning methods, where experience is provided through demonstrations, and we test the expanded capability with a real-world pick-and-place task. The results show that our method is capable of improving the performance and sample efficiency of learning motor skills in a variety of experimental domains.
QEGCN: An FPGA-based accelerator for quantized GCNs with edge-level parallelism

Graph convolutional networks(GCNs) have been successfully applied to many fields such as social networks, knowledge graphs, and recommend systems. The purpose of this research is to design and implement an accelerator for quantized GCNs with edge-level parallelism. We explore the viability of training quantized GCNs, enabling the usage of low precision integer arithmetic during inference. GCNs trained with QEGCN for INT8 quantization perform as well as FP32 models in three most used datasets. Data quantization can significantly reduce the use of logical resources and energy consumption without reducing accuracy. Based on the SAGA-NN model, we improved the original algorithm to achieve parallelism at the edge level. Unlike previous work, we implement the pipeline structure for apply and gather operations at the edge level to achieve fine-grained parallelism. Using our framework, we generate accelerators for GCN models on a state-of-the-art FPGA platform and evaluate our designs. Compared to HyGCN, EnGN, and AWB-GCN, our work achieves 42.1x, 19.3x, 8.31x speedup and 11.6x, 8.1x, 41.68x energy savings on average, respectively. • Quantize the graph convolutional network to reduce computational effort without accuracy loss. • Exploit feature and edge level parallelism to achieve excellent performance and energy efficiency. • Implement a fine-grained pipeline structure to accelerate GCNs.
Point diffraction interferometer based on a silicon nitride waveguide spherical wave source

A point diffraction interferometer based on silicon nitride waveguide (WG-PDI), adopting a silicon nitride waveguide spherical wave source (WG-SWS) with Si substrate and SiO2 cladding, is proposed for spherical surface testing. The WG-SWS is used to overcome the drawbacks of the existing spherical wave sources, which can generate high accuracy and high numerical aperture spherical reference wave. In this paper, the theory of the WG-PDI is described, and the possible errors introduced by the device are analyzed. In addition, the lateral deviation between the curvature center of the test wave and the curvature center of the reference wave cannot be eliminated in the reflected configuration of the pinhole diffraction interferometer. After analyzing the influence of the systematic error introduced by the lateral deviation, the semi-reflective film was coated on the output facet of the waveguide spherical wave source to realize point diffraction interference without lateral deviation. Finally, the surface error of a spherical surface was measured by WG-PDI. The experimental results agree well with those measured by the ZYGO interferometer.
Online Big Data Physical Education Classroom Based on Monitoring Network and Artificial Intelligence

The new ecology of monitoring network and artificial intelligence (AI) has injected new vitality into traditional education and brought new opportunities for the development of education. We implemented the online big data (BD) physical education classroom based on monitoring network and AI, as well as the integrated teaching practice of health data inside and outside the physical education classroom, built a practice model integrating data collection, data analysis, and data application, and explored its practical effectiveness using the literature method and mathematical analysis method. The results show that the practice can achieve data management of physical education courses, promote the improvement of students’ physical health, achieve the purpose of real-time monitoring of students’ independent exercise status, and achieve the goal of getting students out of the dormitory and into the playground.
Comparative Analysis of Rotten Tomatoes Movie Reviews using Sentiment Analysis

For many people, deciding on a movie night begins with a search of online review sites. Consumer information has been redefined by social media platforms. Regarding movies, one such platform is Rotten Tomatoes, which is focused on in this project. The goal is to test various machine learning methods for predicting the sentiment of unseen reviews using an upgraded corpus with more information about the opinions for various sub- categories. The Amazon Mechanical Turk-annotated corpus of review of movies mentioned on Rotten Tomatoes is used, which is considerably optimised and annotated with a high emotion attribute. This work tests if tighter sentiment annotations for each root and span for a set of characters that is parsed into a training set can aid in determining general emotion or sentiment or opinion of unnoticed phrases.
Path Planning and Guidance Laws of a Formula Student Driverless Car

Autonomous driving has been a topic of great interest in several areas, of which motor racing is no exception. The aim of this work is the autonomous control of the future Formula Student Lisboa vehicle, by implementing different strategies for control and path planning, with the purpose of minimising race lap times. These strategies are tested in simulation, using a realistic model of the prototype. The approach followed involves the decoupling of the lateral and longitudinal subsystems and obtaining the reference path using artificial potential fields, combined with a two-pass algorithm developed to generate a speed profile. In this way, a sub-optimal solution is reached that adequately portrays the expected behaviour of a human driver while respecting traction conditions. The process of generating the speed reference requires prior knowledge of the track layout. This assumption is then eased for obstacle avoidance, i.e., for a scenario where, in addition to the track limits, unknown static obstacles are present. A decoupled control approach is followed controlling each of the two subsystems individually.
LA-ICP-MS Mapping of Barren Sandstone from the Proterozoic Athabasca Basin (Canada)—Footprint of U- and REE-Rich Basinal Fluids

The Proterozoic Athabasca Basin hosts a large number of high-grade, large-tonnage unconformity-related uranium (U) deposits, many of which are also enriched in rare earth elements (REE). The basin also contains hydrothermal REE mineralization unassociated with U. Previous studies postulated that U and REE were derived from either the basin or the basement; however, the exact source of the metals remains ambiguous. This study provides evidence of U- and REE-rich fluids throughout the Athabasca Basin through laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) mapping of barren sandstone distal to mineralized areas. The results indicate that elevated U and REE concentrations mainly occur in the matrix; there are strong positive correlations between U and REE, Th, P and Sr, and moderate positive correlations between U and Zr, Ba, Fe, Al, K and Ca, but the few spots with the highest U are unrelated to these elements. Quantitative evaluation of the element correlations, together with scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS) analysis, suggests that most of the elevated U and REE are hosted in aluminum phosphate sulfate (APS) minerals rather than apatite and monazite. As the APS minerals are of diagenetic-hydrothermal origin, the results testify to the presence of U- and REE-rich fluids within the Athabasca Basin. The elevated Th/U ratio (~10) and REE pattern (strong heavy rare earth element (HREE) depletion) are consistent with a model in which large amounts of U and REE (especially HREE) were leached from the sandstone within the Athabasca Basin and contributed to U and REE mineralization near the unconformity between the sedimentary rocks in the basin and underlying basement rocks. This study demonstrates that LA-ICP-MS mapping can be effectively used to evaluate microscale distribution of elements and their mobility in sedimentary rocks to address mineralization related problems.