Reviewerly/exHarmony huggingface dataset:
An Efficient Mining for Recommendation System for Academics

At present time huge numbers of research articles are available on World Wide Web in any domain. The research scholar explores a research papers to get the appropriate information and it takes time and effort of the researcher. In this scenario, there is the need for a researcher to search a research based on its research article. In the present paper a method of Knowledge ablation from a collection of research articles, is presented to evolve a system research paper recommendation system (RPRS), which would generate the recommendations for research article based on researcher choice. The RPRS accumulate the knowledge ablated from the pertinent research articles in the form of semantic tree. It accumulates all the literal sub parts with their reckoning in nodes. These parts are arranged based on their types in such a way that the leaf nodes stores the words with its prospect, the higher layer gives details about dictum with its reckoning, next to it an abstract. A Bayesian network is applied to construct a verisimilitude model which would quotation the pertinent tidings from the knowledge tree to construct the recommendation and word would be scored through TF-IDF value.
Factors influencing Bitcoin investment intention: the case of Oman

The current study examines the factors that might increase the investment in Bitcoin among Muslim communities. Oman is selected as a setting for this study due to the rapid progress in the Islamic finance and investment areas that the country has witnessed though ventured in this field relatively late compared to other countries. The study used a survey questionnaire to collect data for a sample of 200 respondents. Subsequently, the collected data was analysed using structural equation modelling (SEM) as well as basic descriptive statistics and one sample t-test. The findings revealed that the respondents perceive themselves to have sufficient awareness and knowledge of the Bitcoin concept and benefits, as well as the techniques used to manage a Bitcoin account. In addition, the findings revealed that factors such as perceived ease of use, compatibility, awareness and facilitating conditions have a significant impact on Omani communities' intention to invest in Bitcoin.
Structured Access

Abstract Structured access is an emerging paradigm for the safe deployment of artificial intelligence (AI). Instead of openly disseminating AI systems, developers facilitate controlled, arm’s length interactions with their AI systems. The aim is to prevent dangerous AI capabilities from being widely accessible, whilst preserving access to AI capabilities that can be used safely. The developer must both restrict how the AI system can be used and prevent the user from circumventing these restrictions through modification or reverse engineering of the AI system. Structured access is most effective when implemented through cloud-based AI services, rather than disseminating AI software that runs locally on users’ hardware. Cloud-based interfaces provide the AI developer greater scope for controlling how the AI system is used, and for protecting against unauthorized modifications to the system’s design. This chapter expands the discussion of “publication norms” in the AI community, which to date has focused on the question of how the informational content of AI research projects should be disseminated (e.g., code and models). Although this is an important question, there are limits to what can be achieved through the control of information flows. Structured access views AI software not only as information that can be shared but also as a tool with which users can have arm’s length interactions. There are early examples of structured access being practiced by AI developers, but there is much room for further development, both in the functionality of cloud-based interfaces and in the wider institutional framework.
A Machine Learning Approach for Sentiment Analysis of Book Reviews in Bangla Language

With the advent of technology, Sentiment polarity detection has recently piqued the interest of NLP researchers. Sentiment analysis determines the profound meaning of an article. Due to COVID-19 pandemic, online shopping is the safest way of shopping. Moreover, there are product quality and service issues. Our target is to analyze the book reviews which provide positive and negative reviews in Bangla language. For this, a total of 5500 user generated Bengali reviews are collected from various book review pages of social media. In order to get the best possible result, sentiment analysis is used. Thereafter, five different algorithms are applied to predict with almost high accuracy. Among them, the Random Forest provides us the maximum accuracy which is 98.39%.
Unsupervised Key Event Detection from Massive Text Corpora

Automated event detection from news corpora is a crucial task towards mining fast-evolving structured knowledge. As real-world events have different granularities, from the top-level themes to key events and then to event mentions corresponding to concrete actions, there are generally two lines of research: (1) theme detection identifies from a news corpus major themes (e.g., "2019 Hong Kong Protests" vs. "2020 U.S. Presidential Election") that have very distinct semantics; and (2) action extraction extracts from one document mention-level actions (e.g., "the police hit the left arm of the protester") that are too fine-grained for comprehending the event. In this paper, we propose a new task, key event detection at the intermediate level, aiming to detect from a news corpus key events (e.g., "HK Airport Protest on Aug. 12-14"), each happening at a particular time/location and focusing on the same topic. This task can bridge event understanding and structuring and is inherently challenging because of the thematic and temporal closeness of key events and the scarcity of labeled data due to the fast-evolving nature of news articles. To address these challenges, we develop an unsupervised key event detection framework, EvMine, that (1) extracts temporally frequent peak phrases using a novel ttf-itf score, (2) merges peak phrases into event-indicative feature sets by detecting communities from our designed peak phrase graph that captures document co-occurrences, semantic similarities, and temporal closeness signals, and (3) iteratively retrieves documents related to each key event by training a classifier with automatically generated pseudo labels from the event-indicative feature sets and refining the detected key events using the retrieved documents. Extensive experiments and case studies show EvMine outperforms all the baseline methods and its ablations on two real-world news corpora.
UAVSwarm Dataset: An Unmanned Aerial Vehicle Swarm Dataset for Multiple Object Tracking

In recent years, with the rapid development of unmanned aerial vehicles (UAV) technology and swarm intelligence technology, hundreds of small-scale and low-cost UAV constitute swarms carry out complex combat tasks in the form of ad hoc networks, which brings great threats and challenges to low-altitude airspace defense. Security requirements for low-altitude airspace defense, using visual detection technology to detect and track incoming UAV swarms, is the premise of anti-UAV strategy. Therefore, this study first collected many UAV swarm videos and manually annotated a dataset named UAVSwarm dataset for UAV swarm detection and tracking; thirteen different scenes and more than nineteen types of UAV were recorded, including 12,598 annotated images—the number of UAV in each sequence is 3 to 23. Then, two advanced depth detection models are used as strong benchmarks, namely Faster R-CNN and YOLOX. Finally, two state-of-the-art multi-object tracking (MOT) models, GNMOT and ByteTrack, are used to conduct comprehensive tests and performance verification on the dataset and evaluation metrics. The experimental results show that the dataset has good availability, consistency, and universality. The UAVSwarm dataset can be widely used in training and testing of various UAV detection tasks and UAV swarm MOT tasks.
Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection

Federated learning (FL) enables multiple clients to collaboratively train an accurate global model while protecting clients' data privacy. However, FL is susceptible to Byzantine attacks from malicious participants. Although the problem has gained significant attention, existing defenses have several flaws: the server irrationally chooses malicious clients for aggregation even after they have been detected in previous rounds; the defenses perform ineffectively against sybil attacks or in the heterogeneous data setting. To overcome these issues, we propose MAB-RFL, a new method for robust aggregation in FL. By modelling the client selection as an extended multi-armed bandit (MAB) problem, we propose an adaptive client selection strategy to choose honest clients that are more likely to contribute high-quality updates. We then propose two approaches to identify malicious updates from sybil and non-sybil attacks, based on which rewards for each client selection decision can be accurately evaluated to discourage malicious behaviors. MAB-RFL achieves a satisfying balance between exploration and exploitation on the potential benign clients. Extensive experimental results show that MAB-RFL outperforms existing defenses in three attack scenarios under different percentages of attackers.
Global state estimation under sequential measurement fusion for clustered sensor networks with cross-correlated measurement noises

In practical multi-sensor fusion estimation, both the expensive communication burden of long-distance connection sensor networks and cross-correlated noises are usually inevitable, and need to be taken into account. To adequately address these difficulties, a global fusion estimation problem is studied for clustered sensor networks with cross-correlated measurement noises. To begin, we derive direct and indirect sequential measurement fusion (SMF) schemes as fusion rules, relying on whether a weighted least square method is used directly for the arriving measurements. We discover that the direct SMF has lower computational complexity than the indirect SMF, but the estimation accuracy of the latter is higher when the number of sensors exceeds three. Then, a globally indirect sequential measurement fusion estimation (GISMF) algorithm is proposed by using the indirect SMF, which is more computationally efficient and suitable for asynchronous fusion than that of batch fusion estimation. Accuracy relations among the GISMF, the batch state fusion estimation, and the centralized Kalman fuser (CKF) are proved. Moreover, the equality of the estimation accuracy between the GISMF and the CKF is also performed if all the measurement noises are mutually independent or if the measurement noises are only cross-correlated within the same cluster. Finally, several simulation results are implemented to show the effectiveness of the presented algorithm.
OTIF: Efficient Tracker Pre-processing over Large Video Datasets

Performing analytics tasks over large-scale video datasets is increasingly common in a wide range of applications, from traffic planning to sports analytics. These tasks generally involve object detection and tracking operations that require pre-processing the video through expensive machine learning models. To address this cost, several video query optimizers have recently been proposed. Broadly, these methods trade large reductions in pre-processing cost for increases in query execution cost: during query execution, they apply query-specific machine learning operations over portions of the video dataset. Although video query optimizers reduce the overall cost of executing a single query over large video datasets compared to naive object tracking methods, executing several queries over the same video remains cost-prohibitive; moreover, the high per-query latency makes these systems unsuitable for exploratory analytics where fast response times are crucial.
The role of inflation in financial development–economic growth link in sub-Saharan Africa

The impact of financial development (FD) on economic growth (EG) is well documented. However, studies on how inflation mediates the impact of FD on EG produce inconclusive findings. Meanwhile, the tripartite relationship among FD, inflation and EG is particularly crucial for sub-Saharan African countries given that these countries are largely inflationary on the back of under-developed financial sectors and low EG. The inconclusive evidence presented by the existing studies limits policy making. This study therefore re-examines whether inflation mediates the FD–EG nexus by utilizing a panel data obtained from 36 countries in sub-Saharan Africa. The study uses the sample splitting threshold approach to investigate this relationship. The findings identify inflation thresholds of 7.65% and 6.76% at which the impact of FD on EG changes sign. Specifically, irrespective of the indicator of FD, higher FD significantly increases EG at low inflation rates. However, beyond the estimated thresholds, the impact of FD on EG is insignificant, revealing that higher inflation does not support the growth-enhancing effect of FD. The research recommends that inflation should be kept below the identified thresholds for FD to spur EG.
FairNeuron

With Deep Neural Network (DNN) being integrated into a growing number of critical systems with far-reaching impacts on society, there are increasing concerns on their ethical performance, such as fairness. Unfortunately, model fairness and accuracy in many cases are contradictory goals to optimize during model training. To solve this issue, there has been a number of works trying to improve model fairness by formalizing an adversarial game in the model level. This approach introduces an adversary that evaluates the fairness of a model besides its prediction accuracy on the main task, and performs joint-optimization to achieve a balanced result. In this paper, we noticed that when performing backward propagation based training, such contradictory phenomenon are also observable on individual neuron level. Based on this observation, we propose FairNeuron, a DNN model automatic repairing tool, to mitigate fairness concerns and balance the accuracy-fairness trade-off without introducing another model. It works on detecting neurons with contradictory optimization directions from accuracy and fairness training goals, and achieving a trade-off by selective dropout. Comparing with state-of-the-art methods, our approach is lightweight, scaling to large models and more efficient. Our evaluation on three datasets shows that FairNeuron can effectively improve all models' fairness while maintaining a stable utility.
MOTFR: Multiple Object Tracking Based on Feature Recoding

The stable continuation of trajectories among different targets has always been the key to the tracking performance of multi-object tracking (MOT) tasks. If features of the target are aggregated and classified simply, the discriminant features of the target will be ignored. This will affect the robustness of the trajectory generated by the model. Meanwhile, many popular models are keen to execute detection and feature extraction tasks in parallel. But these two tasks will conflict with each other when optimized respectively. Therefore, we propose our tracker MOTFR to solve the above problems. In this paper, we propose a Locally Shared Information Decoupling Module (LSIDM) to reduce task optimization conflicts while ensuring the necessary information sharing. Meanwhile, a feature recoding module for deep extraction of identity discriminative features is proposed, which is called the Feature Purification Module (FPM). By combining LSIDM and FPM modules, the model utilizes the discriminative appearance features to guide the optimization of detection and further improves the performance of our model. To solve the problem of targets disappearing due to various abnormal occlusion, a Short-term Trajectory Online Complement Strategy (STOCS) is proposed to realize the trajectories continuation of these targets in the tracking stage. Through sufficient experiments, we demonstrate the superior performance of our MOTFR, which guarantees high-quality detection while achieving the stability of the target trajectory.
Dynamic DV-QKD Networking in Trusted-Node-Free Software-Defined Optical Networks

We demonstrate for the first time a four-node trusted-node-free metro network configuration with dynamic discrete-variable quantum key distribution DV-QKD networking capabilities across four optical network nodes. The network allows the dynamic deployment of any QKD link between two nodes of the network, while a QKD-aware centralised software-defined networking (SDN) controller is utilised to provide dynamicity in switching and rerouting. The feasibility of coexisting a quantum channel with carrier-grade classical optical channels where both the quantum and classical channels are in the C-band over field-deployed metropolitan networks and laboratory-based fibres (<10 km) is experimentally explored in terms of achievable quantum bit error rate, secret key rate as well as classical signal bit error rate. Moreover, coexistence analysis over multi-hops configuration using different switching scenarios is also presented. The secret key rate dropped 43% when coexisting one classical channel with 150 GHz spacing from the quantum channel for multiple links. This is due to the noise leakage from the Raman scattering into the 100 GHz bandwidth of the internal filter of the Bob DV-QKD unit. When coexisting four classical channels with 150 GHz spacing between quantum and the nearest classical channel, the quantum channel deteriorates faster due to the combination of Raman noise, other nonlinearities and high aggregated launch power causing the QBER value to exceed the threshold of 6% leading the SKR to reach a value of zero bps at a launch power of 7 dB per channel. Furthermore, the coexistence of a quantum channel and six classical channels through a field-deployed fibre test network is examined.
The Proposal for the Artificial Intelligence Act: Considerations around Some Key Concepts

The proposal for the Artificial Intelligence (“AI”) Act has broken new ground in many respects. Most visibly, the proposal introduces the first comprehensive draft regulatory framework for AI in the EU and, for the time being, on a global level. In addition, the proposal contains several innovative approaches linked to the specificities of its subject matter and to the fact that it has to interact as smoothly as possible with a very wide range of existing legal frameworks in the EU.A number of important choices were therefore made to ensure that the AI Act could meet quite unprecedented challenges. The paper aims to briefly outline some of those choices with the hope to help facilitating the understanding of the overall logic of the proposal and it is structured as follows.After some introductory statements, section II explains the classification of AI systems as products. Section III delves into the essential features of the so-called New Legislative Framework (NLF), a well-known and experimented type of EU legislation that constitutes the fundamental regulatory model of the AI Act. This section also highlights certain adaptations made to the NLF tools in order to take into account certain specificities of AI systems. Having clarified the philosophy behind and the core architecture of the AI Act, section IV discusses briefly how that architecture has been shaped by a number of important points of contact (at times real “interlocks”) between the AI Act and other existing or proposed EU legal acts beyond the realm of NLF product legislation.
The use of social networks, usefulness and ease of use, enjoyment through positive emotions and their influence on school satisfaction mediated by school achievement

The purpose of this study was to establish the relationship between the use of social networks, usefulness and ease of use, enjoyment through positive emotions and their influence on school satisfaction mediated by school achievement. The investigation was carried out in a state of social isolation due to the presence of Covid-19. The sample for the study consisted of 108 students from the 3rd to the 5th year of studies at the secondary education level of a public educational institution in the Arequipa region in Peru. The methodology that was used had two well-defined phases, firstly, an Exploratory Factor Analysis (EFA) was carried out, this analysis had the objective of condensing the information in original variables to smaller series, secondly, an Analysis Confirmatory Factorial (AFC), through the modelling of structural equations based on variances, called PLS-SEM. It is concluded that the proposed structural model, which uses school achievement as a mediation variable, is explained from its variance in 81.6%, and the factorial load it exerts on the dependent variable such as school satisfaction is significant and decisive.
A new approach for physical human activity recognition from sensor signals based on motif patterns and long-short term memory

Numerous studies have been carried out in recent years on the recognition, tracking, and discrimination of human activities. Automatic recognition of physical activities is often referred to as human activity recognition (HAR). There are generally vision-based and sensor-based approaches for activity recognition. The computer vision-based approach generally works well in laboratory conditions, but it can fail in real-world problems due to clutter, variable light intensity, and contrast. Sensor-based HAR systems are realized by continuously monitoring and analyzing physiological signals measured from heterogeneous sensors connected to the person's body. In this study, the Motif Patterns (MP) approach, which extracts features from sensor signals, is proposed for HAR. The success of the HAR systems depends on the effectiveness of the features extracted from the signals. The LSTM network is a special kind of recurrent neural network that has been used to make very successful predictions on time series data where long-term dependencies are. The LSTM network type offers a successful solution approach to solving long-term dependencies problems such as human activity recognition. The classification process was carried out with Long-Short Term Memory (LSTM) using MP features extracted from accelerometer, gyroscope, and magnetometer sensor signals. A large dataset of 9120 signals was used to test the proposed approach. A high success rate of 98.42 % was achieved with the proposed MP + LSTM method. As a result, it has been seen that the proposed approach has been obtained with a high success rate for HAR using sensor signals.