Reviewerly/exHarmony huggingface dataset:
Robust Anomaly Detection for Multivariate Data of Spacecraft Through Recurrent Neural Networks and Extreme Value Theory

Spacecraft anomaly detection which could find anomalies in the telemetry or test data in advance and avoid the occurrence of catastrophic failures after taking corresponding measures has elicited the attention of researchers both in academia and aerospace industry. Current spacecraft anomaly detection systems require costly knowledge and human expertise to identify a true anomaly. Moreover, some new problems and challenges such as large volume of test data, imbalanced data distribution and the scarcity of faulty labeled samples have emerged. In this work, we propose an unsupervised anomaly detection algorithm combining Gated Recurrent Unit (GRU) based Recurrent Neural Network (RNN) and Extreme Value Theory (EVT). First, we develop a two-layer ensemble learning based predictor framework which stacks three GRU-based networks with different architectures to learn and capture the normal behavior of multiple channels of data. Then, the prediction errors are calculated and smoothed using Exponentially Weighted Moving Average (EWMA) algorithm. Next, we propose a detection rule setting anomaly threshold automatically through EVT which does not assume any parent distribution on the prediction errors. To the best of our knowledge, it is the first attempt that stacked GRU-based predictors with EVT has been employed into the spacecraft anomaly detection. Through extensive experiments conducted on public datasets as well as real data sampled from a launch vehicle, we show that the proposed detection algorithm is superior to other state-of-the-art anomaly detection approaches in terms of model performance and robustness.
A Survey of Opponent Modeling in Adversarial Domains

Opponent modeling is the ability to use prior knowledge and observations in order to predict the behavior of an opponent. This survey presents a comprehensive overview of existing opponent modeling techniques for adversarial domains, many of which must address stochastic, continuous, or concurrent actions, and sparse, partially observable payoff structures. We discuss all the components of opponent modeling systems, including feature extraction, learning algorithms, and strategy abstractions. These discussions lead us to propose a new form of analysis for describing and predicting the evolution of game states over time. We then introduce a new framework that facilitates method comparison, analyze a representative selection of techniques using the proposed framework, and highlight common trends among recently proposed methods. Finally, we list several open problems and discuss future research directions inspired by AI research on opponent modeling and related research in other disciplines.
COVID-19, Distance Learning and the Digital Divide: A Case of Higher Education in the United States and Pakistan

This multiple case study describes the experiences of students, faculty and administrators with distance learning during this pandemic in the context of a Midwestern University in United States and an elitist University in Lahore, Pakistan. The participants were invited to talk about their experiences through a Zoom interview. Data were analyzed thematically and the findings revealed that the issue of the digital divide was as much as problem in higher education as in K-12. Digital divide in Pakistan is far greater due to lack of investment in educational technology. The paper ends with acknowledging the potential limitations and making recommendations for leadership and teaching practice.
Short-Term Solar Irradiance Prediction Based on Multichannel LSTM Neural Networks Using Edge-Based IoT System

Most photovoltaic power generation methods use global level irradiance (GHI) as the main input and output. However, randomness, instability, and intermittency are the main factors that seriously degrade the solar irradiance prediction results. Traditional data-driven prediction models are difficult for accurate predictions. In this study, a multichannel deep learning model named multichannel, wavelet transform combining convolutional neural network and bidirectional long short-term memory (MC-WT-CBiLSTM) framework-based edge computing and IoT system is proposed to improve the GHI prediction accuracy. The solar irradiance data is decomposed by wavelet transform to reduce data complexity. Each decomposed component is inputted into the multichannel MC-CBiLSTM deep learning framework for forecasting and combined to produce the final results. The comparison with existing solar irradiance forecasting methods shows that the proposed MC-WT-CBiLSTM deep learning framework has obvious advantages in the prediction of various time horizons.
Digital platform interactivity and Jordanian social commerce purchase intention

Social commerce is a first-generation platform that uses a variety of social attributes to attract the attention of internet users. The impact of platform interactivity components; Content Usefulness, Engaging, Real-time communication, and E-wom on customers' purchase intentions was investigated in this study. A Quantitative research method was applied in this study using an online self-administrative questionnaire that was published using Google Docs to collect data, and 521 consumers participated in an online survey. The research used SPSS and AMOS to validate and test the hypotheses. All platform interactivity components were significantly affecting purchase intention. This study provides insights to marketing managers of how consumers perceive social commerce and what affecting them along with practical and theoretical implications.
Improving PIR Sensor Network-Based Activity Recognition with PCA and KNN

With the use of low-cost passive infrared (PIR) sensors in detecting movement, forming a wireless sensor network (WSN) combined with activity recognition (AR), activities or movements that exist in each room can be detected and can be used for health, home automation, and security purposes. Other studies have proven that the hierarchical hidden Markov model (HHMM) method, an a posteriori method is more accurate than unsupervised classification methods such as Naïve Bayes but in another study, unsupervised methods such as k-nearest neighbors (KNN) can show high performance because previously, the datasets go through pre-processing steps. The purpose of this study is to improve the performance of PIR sensor network-based AR using PCA as a pre-processing method and compare the performance with AR in previous studies. In addition, KNN is used as the classification method for AR. To do that, a PIR sensor network needs to be built. 4 PIR sensor nodes are used throughout a test environment house. There are 37150 data that has been collected from all PIR sensors stored in a span of 21 days to build the KNN model. The accuracy results obtained from the KNN model for AR classification is 0.94. The PCA-KNN proposed in this research proves to have higher performance than other studies that also implement AR with PIR sensor network. The proposed method is also a low-cost solution compared to other studies that also implement AR but with more complex sensor combinations.
Spatial nonlinear optics for securing information

The high degrees of freedom of light, various optical structures and optical materials can be explored and applied to develop optical encryption for securing information. An exciting optical image encryption approach has been proposed based on spatial nonlinear optics.
Understanding Artificial Intelligence in Research Libraries – Extensive Literature Review

Artificial intelligence (AI) now forms a part of various activities in the academic world. AI will also affect how research libraries perform and carry out their services and how the various kinds of data they hold in their repositories will be used in the future. For the moment, the landscape is complex and unclear, and library personnel and leaders are uncertain about where they should lay the path ahead. This extensive literature review provides an overview of how research libraries understand, react to, and work with AI. This paper examines the roles conceived for libraries and librarians, their users, and AI. Finally, design thinking is presented as an approach to solving emerging issues with AI and opening up opportunities for this technology at a more strategic level.
3D CNN Architectures and Attention Mechanisms for Deepfake Detection

Abstract Manipulated images and videos have become increasingly realistic due to the tremendous progress of deep convolutional neural networks (CNNs). While technically intriguing, such progress raises a number of social concerns related to the advent and spread of fake information and fake news. Such concerns necessitate the introduction of robust and reliable methods for fake image and video detection. Toward this in this work, we study the ability of state-of-the-art video CNNs including 3D ResNet, 3D ResNeXt, and I3D in detecting manipulated videos. In addition, and toward a more robust detection, we investigate the effectiveness of attention mechanisms in this context. Such mechanisms are introduced in CNN architectures in order to ensure that robust features are being learnt. We test two attention mechanisms, namely SE-block and Non-local networks. We present related experimental results on videos tampered by four manipulation techniques, as included in the FaceForensics++ dataset. We investigate three scenarios, where the networks are trained to detect (a) all manipulated videos, (b) each manipulation technique individually, as well as (c) the veracity of videos pertaining to manipulation techniques not included in the train set.
Deciphering multiple ore-forming processes of the Shuangqishan orogenic gold deposit, Southeast China by in situ analysis of pyrite

Gold deposits are characteristically formed via multiple mineralization and remobilization events. Inferences on the complex formation history of such deposits from ore textures can be ambiguous in the lack of chemical and isotopic evidence as exemplified by the Shuangqishan orogenic gold deposit in Southeast China. We, therefore, conducted LA-ICP-MS spot analysis coupled with LA-MC-ICP-MS sulfur and lead isotope analysis on texturally complex gold-bearing pyrite from the Shuangqishan deposit. Gold distribution in different syn- to late-ore pyrite generations indicate initial evenly deposition of nano- to micro-inclusions of Au-Ag-Pb-Bi tellurides within pyrite grains and subsequent Au remobilization following brittle deformation and fluid-mediated coupled dissolution-reprecipitation (CDR) reactions. Gold dissolved from the pyrite during the reworking event became re-concentrated and re-precipitated as visible gold either as inclusions in pyrite or larger free gold grains within microfractures of pyrite. The distribution of Bi-Te-Pb-Ag assemblage is comparable to that of Au, illustrating that these trace metals became redistributed in a similar fashion. Early, undeformed pyrite has the least radiogenic Pb isotopic compositions. Late, porous and inclusion-rich pyrite, brecciated pyrite, and recrystallized Te-Bi minerals have variable but in general more radiogenic Pb isotope ratios (higher 206Pb/204Pb and 207Pb/204Pb ratios), which are attributed to modification and precipitation from a more evolved crustal fluid during the re-precipitation of native gold. The Pb isotopic signature of the younger pyrite generations was likely inherited from Pb released during modification of earlier proto-pyrite and a second, crustal lead source that was involved in the hydrothermal system during later remobilization. The δ34S values for the early proto-pyrite and the reworked pyrite generations within the individual vein stage are broadly similar. The variations in sulfur isotope compositions reflect the change in physicochemical conditions (temperature and oxygen fugacity) during proto-ore formation. Our study highlights the importance of metal recycling from early proto-auriferous pyrite during later reworking and gold remobilization, which led to visible gold and high-grade ore shoot formation. The extensive variations of trace element concentrations and S-Pb isotope compositions of proto and reworked pyrites record the complicated Au mobilization and deposition processes and help to understand how gold ores evolved.
Blockchain Adoption Factors, Enablers, and Barriers in Fisheries Supply Chain: Preliminary Findings from a Systematic Literature Review

Technological adoption has become a key goal of digital transformation within firms, affecting many facets of an organisation, such as attaining competitive advantage, increased revenue, reduction of operational costs, and improving operational efficiency.Blockchain as a decentralised peer-to-peer technology appears uniquely suited to being deployed within complex food supply chains such as the fisheries industry, which is the focus of this study.In this context, blockchain technology can be used for a variety of purposes, such as providence authentication, handling and storage, transparency, counterfeit prevention, food forensics, and enhanced supply chain resilience.The purpose of this study is to synthesise existing research on the interrelationship between blockchain technology and the factors that determine adoption, as well as identifying the enablers and barriers.This preliminary work identified key themes emerging from the blockchain literature, suggesting that adoption factors are wide ranging, encompassing aspects including organisational readiness, security, complexity, partnerships, competition, governmental influence, and transparency.The thematic analysis of enablers and barriers to blockchain adoption identified the broad theme of resources as the key enabler and integration as the key barrier.These preliminary findings add to the growing body of research, including increased understanding of the current state of academic research in the areas of blockchain adoption factors, blockchain adoption in supply chains, blockchain adoption in the fisheries industry, and the enablers and barriers to adoption.This study is the initial step in a large-scale study with the next phase comprising case studies of specific fishery supply chain stakeholders.
MT-teql

Natural Language Interface to Database (NLIDB) translates human utterances into SQL queries and enables database interactions for non-expert users. Recently, neural network models have become a major approach to implementing NLIDB. However, neural NLIDB faces challenges due to variations in natural language and database schema design. For instance, one user intent or database conceptual model can be expressed in various forms. However, existing benchmarks, using hold-out datasets, cannot provide thorough understanding of how good neural NLIDBs really are in real-world situations and its robustness against such variations. A key difficulty is to annotate SQL queries for inputs under real-world variations, requiring considerable manual effort and expert knowledge. To systematically assess the robustness of neural NLIDBs without extensive manual effort, we propose MT-Teql, a unified framework to benchmark NLIDBs against real-world language and schema variations. Inspired by recent advances in DBMS metamorphic testing, MT-Teql implements semantics-preserving transformations on utterances and database schemas to generate their variants. NLIDBs can thus be examined for robustness utilizing utterances/schemas and their variants without requiring manual intervention. We benchmarked nine neural NLIDBs using 62,430 inputs and identified 15,433 defects. We analyzed potential root causes of defects and conducted a user study to show how MT-Teql can assist developers to systematically assess NLIDBs. We further show that the transformed (error-triggering) inputs can be used to augment popular NLIDBs and eliminate 46.5%(±5.0%) errors made by them without compromising their accuracy on standard benchmarks. We summarize lessons from this study that can provide insights to select and design NLIDBs that fit particular usage scenarios.
Small Traffic Sign Detection in Big Images: Searching Needle in a Hay

Traffic sign detection is an essential module of self-driving cars and driver assistance system. The major challenge being, traffic sign appear relatively smaller in road view images. It covers only 1%-2% of the total image area. Hence, its challenging to detect very small traffic sign in a larger image covering huge background of similar shape objects. Thus, we propose YOLOv3 network layers pruning and patch wise training strategy for small sized traffic sign detection. This aids in improving recall percentage and mean Average Precision. We also propose anchor box selection algorithm that uses bounding box dimension density to obtain optimal anchor set for the dataset. This reduces false positives and log-average miss rate. The proposed approach is evaluated on German traffic sign detection benchmark and Swedish traffic sign dataset and proves that it achieved a good balance between mAP and inference time.
S-Walk

Session-based recommendation (SR) predicts the next items from a sequence of previous items consumed by an anonymous user. Most existing SR models focus only on modeling intra-session characteristics but pay less attention to inter-session relationships of items, which has the potential to improve accuracy. Another critical aspect of recommender systems is computational efficiency and scalability, considering practical feasibility in commercial applications. To account for both accuracy and scalability, we propose a novel session-based recommendation with a random walk, namely S-Walk. Precisely, S-Walk effectively captures intra- and inter-session correlations by handling high-order relationships among items using random walks with restart (RWR). By adopting linear models with closed-form solutions for transition and teleportation matrices that constitute RWR, S-Walk is highly efficient and scalable. Extensive experiments demonstrate that S-Walk achieves comparable or state-of-the-art performance in various metrics on four benchmark datasets. Moreover, the model learned by S-Walk can be highly compressed without sacrificing accuracy, conducting two or more orders of magnitude faster inference than existing DNN-based models, making it suitable for large-scale commercial systems.
CNN-LSTM Hybrid Real-Time IoT-Based Cognitive Approaches for ISLR with WebRTC: Auditory Impaired Assistive Technology

In the era of modern technology, people may readily communicate through facial expressions, body language, and other means. As the use of the Internet evolves, it may be a boon to the medical fields. Recently, the Internet of Medical Things (IoMT) has provided a broader platform to handle difficulties linked to healthcare, including people's listening and hearing impairment. Although there are many translators that exist to help people of various linguistic backgrounds communicate more effectively. Using kinesics linguistics, one may assess or comprehend the communications of auditory and hearing-impaired persons who are standing next to each other. When looking at the present COVID-19 scenario, individuals are still linked in some way via online platforms; however, persons with disabilities have communication challenges with online platforms. The work provided in this research serves as a communication bridge inside the challenged community and the rest of the globe. The proposed work for Indian Sign Linguistic Recognition (ISLR) uses three-dimensional convolutional neural networks (3D-CNNs) and long short-term memory (LSTM) technique for analysis. A conventional hand gesture recognition system involves identifying the hand and its location or orientation, extracting certain essential features and applying an appropriate machine learning algorithm to recognise the completed action. In the calling interface of the web application, WebRTC has been implemented. A teleprompting technology is also used in the web app, which transforms sign language into audible sound. The proposed web app's average recognition rate is 97.21%.
Coordinated Multi-Robotic Vehicles Navigation and Control in Shop Floor Automation

In this paper, we propose a global navigation function applied to model predictive control (MPC) for autonomous mobile robots, with application to warehouse automation. The approach considers static and dynamic obstacles and generates smooth, collision-free trajectories. The navigation function is based on a potential field derived from an E* graph search algorithm on a discrete occupancy grid and by bicubic interpolation. It has convergent behavior from anywhere to the target and is computed in advance to increase computational efficiency. The novel optimization strategy used in MPC combines a discrete set of velocity candidates with randomly perturbed candidates from particle swarm optimization. Adaptive horizon length is used to improve performance. The efficiency of the proposed approaches is validated using simulations and experimental results.
Cryptoeconomics

Cryptoeconomics describes an interdisciplinary, emergent and experimental field that draws on ideas and concepts from economics, game theory and related disciplines in the design of peer-to-peer cryptographic systems.Cryptoeconomic systems try to guarantee certain kinds of information security properties using incentives and/or penalties to regulate the distribution of efforts, goods and services in new digital economies.Cryptoeconomics is an embryonic field at present and can be taken to include several areas of focus: information security engineering, mechanism design, token engineering and market design.This portmanteau of cryptography and economics raises questions regarding the epistemic novelty of cryptoeconomics, as distinct from its constituent components.
GDSRec: Graph-Based DecentralizedCollaborative Filtering for SocialRecommendation

Generating recommendations based on user-item interactions and user-user social relations is a common use case in web-based systems. These connections can be naturally represented as graph-structured data and thus utilizing graph neural networks (GNNs) for social recommendation has become a promising research direction. However, existing graph-based methods fails to consider the bias offsets of users (items). For example, a low rating from a fastidious user may not imply a negative attitude toward this item because the user tends to assign low ratings in common cases. Such statistics should be considered into the graph modeling procedure. While some past work considers the biases, we argue that these proposed methods only treat them as scalars and can not capture the complete bias information hidden in data. Besides, social connections between users should also be differentiable so that users with similar item preference would have more influence on each other. To this end, we propose Graph-Based Decentralized Collaborative Filtering for Social Recommendation (GDSRec). GDSRec treats the biases as vectors and fuses them into the process of learning user and item representations. The statistical bias offsets are captured by decentralized neighborhood aggregation while the social connection strength is defined according to the preference similarity and then incorporated into the model design. We conduct extensive experiments on two benchmark datasets to verify the effectiveness of the proposed model. Experimental results show that the proposed GDSRec achieves superior performance compared with state-of-the-art related baselines. Our implementations are available in \url{https://github.com/MEICRS/GDSRec}.