Reviewerly/exHarmony huggingface dataset:
Effects of Boredom on the Academic Engagement of Students during Online Class

This research study focuses on determining the boredom and work engagement of the students in the online class setting.Boredom is a prevalent emotion of becoming uninterested in any activity.It is a common emotion for everyone and significantly affects the work engagement of the students in an online class.The researchers gathered data from 200 students from different universities.Student boredom was measured using the Utrecht Boredom Scale, which contains eight items capturing boredom's affective, cognitive, and behavioral manifestations.On the other hand, work engagement is measured using a 3-item version of the Utrecht Work Engagement Scale, which contains items measuring vigor, dedication, and absorption.The association between boredom and variables of work engagement is analyzed using the Pearson correlation test.Results revealed that students who felt bored during online learning displayed a low level of vigor and absorption.In contrast, students who showed a high level of vigor said a higher level of absorption and dedication during online learning.The result opens an opportunity to make the learning system in online classes better because it is more susceptible to boredom that lessens the work engagement of the students.
Orientation-based Feature Aggregation for Multi-Target Multi-Camera Tracking

Multi-target multi-camera tracking (MTMCT) is a crucial component in traffic flow analysis for smart transportation systems. MTMCT generates vehicle trajectories from the surveillance videos across the cameras at different intersections. Variable vehicle orientations during the driving process degrade the MTMCT precision. Besides, time-varying traffic flow between cameras could lead to trajectory mismatching under constant travel time constraint algorithms. In this paper, an orientation-based MTMCT method considering time-varying traffic flow is proposed. First, stacked re-identification backbones (SRB) are employed to merge the features extracted by different backbones for generating a discriminative appearance feature. Second, to re-duce the information loss, orientation-based feature aggregation (OFA) is proposed to represent a trajectory consisting of vehicles with different orientations comprehensively. Third, considering the time-varying traffic flow, dynamic spatio-temporal strategy (DSS) is designed to segment the whole videos into time windows and construct fine-grained travel time probability functions to narrow the gap between the model and realistic situation. Experiments validate the effectiveness of the proposed MTMCT method. Our method outperforms other works on the IDP metric.
A Novel Matrix for Analyzing Cloud Services in Top MNCs

Multinational companies are taking advantage of the services provided through a cloud service provider (CS P). It is generally observed that, the companies provide customized services to the user as an added benefit rather than using the initial services. The motive of the proposed study is to build a trusted relationship between the user and service provider. This study analyzes several parameters to scale up an approach by adopting advanced technologies. In this study, a matrix has been prepared by including the utility value for the fruit factors used by the user. Compatibility connection between multiple customers are also measured by obtaining services from the particular company. The proposed matrix can identify the actual use of the significant cloud computing features.
HFGCN: High-speed and Fully-optimized GCN Accelerator

graph convolutional network (GCN) is a type of neural network that inference new nodes based on the connectivity of the graphs. GCN requires high-calculation volume for processing, similar to other neural networks requiring significant calculation. In this paper, we propose a new hardware architecture for GCN that tackles the problem of wasted cycles during processing. We propose a new scheduler module that reduces memory access through aggregation and an optimized systolic array with improved delay. We compare our study with the state-of-the-art GCN accelerator and show outperforming results.
Underwater Target Motion Analysis with Dynamic Sensor Selection in Multi-Sensor Environment

The objective of our work is to track an underwater target moving in 3D space using a multi-sensor network of passive sonobuoys spread over the sea surface. All the buoys are equipped with passive SONAR that measures the bearing and elevation angles of the target. The tracker is located remotely from the sensors, and the measurements from the sonobuoys are sent to a common central tracker for further processing. But due to some physical constraints, it is not always possible to send the data from all the sensors together. To select a set of sensors, we have designed a cost function by utilizing the Fisher information matrix (FIM) of the estimated target states. The optimum solution for this cost function gives the desired sensor subset. The measurements obtained from these selected sensors are used to perform the target state estimation using various non-linear estimators. The performances of these estimators are compared in terms of root mean square (RMSE) error of target states and percentage track divergence.
E-Governence Services using Artificial Intelligence Techniques

In a wide variety of fields, AI technology (AI) has lately evolved to state-of-the-art levels. It still has a lot of obstacles to overcome before it can be ready for e-government applications, including setting up the systems and interacting with residents online. In this research, we tackle the problems with e-government systems & suggest a framework that makes use of AI technology to improve and streamline e-government operations. We first design a model for the administration of e-government information resources in particular. Second, we frequently create a variety of deep-learning algorithms with the intention of transforming numerous e-government services. Third, we tend to suggest an intelligent e-government platform architecture that facilitates the emergence and execution of AI e-government applications. Our overriding objective aims to advance this level of e-government services by using reliable AI approaches in order to shorten processing times, reduce costs, and raise public satisfaction.
Personalized Secure E-Identity Card Using Block Chain

Blockchain technology is a new technology in digital platforms for making transactions in a decentralized manner. Blockchain is used for last some year for bitcoin. The preceding block’s cryptographic hash, a timestamp, and a transaction are all included in every block in the blockchain. As a result, it is noticeable for security on an identity card. Implementation of blockchain technology for digital identity card maintains a secure network for the user. Identity card has personal information and some important information included. Individual person identity cards are stored in file with encryption method which will hacked by hackers. So a system using blockchain technology for identity card is proposed. In distribute file system, data security for information exchange and storage is provided using blockchain and IPFS (Interplanetary File System) for storing and communicating data securely.
Mobile Charging System Based on Coin Detection

The demand for mobile phones is increasing day by day. Modern civilization would not function without cell phones, which are both the main source of entertainment and a lifeline for those in need. However, serious battery problems in mobile phones continue to be a serious problem for manufacturers. The given document describes an effective, affordable and ready-to-use solution for individuals to deal with this problem. The purpose of this paper is to install these vending machine-like facilities so that mobile phones can be charged very cheaply. This solution can be used in busy areas such as airports, markets, conference rooms and rest stops.
予測パターンマイニングモデルの最適化アルゴリズム

In various data analysis tasks, modeling patterns such as sets, series, and graphs as features is useful not only for improving prediction performance, but also for knowledge extraction. However, considering all possible combination of patterns exponentially increases the number of model parameters, making it difficult to train models using ordinary optimization algorithms. In this study, we introduce an optimization algorithm that uses convex optimization and pattern mining approaches to efficiently train predictive pattern mining models.
Application of Fuzzy Comprehensive Evaluation Based on GA in Psychological Measurement

The is the process of evaluating and quantifying the degree of consistency between two or more sets by using fuzzy set theory. It is a way to evaluate the quality of any data. For example, it can be used as a tool to quantify the degree of similarity, difference, overlap, etc. of two or more sets. Method to evaluate the reliability and effectiveness of psychological measurement. FCE uses fuzzy logic to evaluate the measurement process, which means that it can be used in all types of applications, such as research, clinical practice and education. The main idea behind FCE is to use fuzzy sets instead of traditional statistical methods to evaluate reliability and effectiveness, because they are more suitable for complex situations involving many factors. In other words, fuzzy sets can handle more variables than ordinary measures without losing their explicit characteristics.
Leveraging Eye Tracking Technology for a Situation-Aware Writing Assistant

Intelligent writing assistants use artificial intelligence to support the partial automation of the writing process. Existing research has investigated the interaction between humans and automated systems and has identified the maintenance of situation awareness (SA) as a key challenge for humans. Especially in the context of intelligent writing assistants, humans have to maintain SA as they are held responsible for the written text. Eye tracking is the key technology that enables the non-invasive detection of situation awareness based on eye movements. Building on existing research on human-robot/AI collaboration and their interplay with SA theory, we propose the augmentation of human interaction with intelligent writing assistants through the use of eye tracking technology. On this basis, writing assistants can be adapted to users’ cognitive states such as SA. We argue that for the successful implementation of intelligent writing assistants in the real world, eye-based analysis of SA and augmentation are key.
Study on Text Irregular Image Algorithm Based on Convolutional Neural Network

In view of character defects due to irregular interference of text images, this paper proposed a restoration algorithm model based on convolutional neural network and key point detection, implements restoration training on the defective character areas.By studying and analyzing the characteristics of ancient text images of different styles, a digital text image database was established.Based on the Chinese character regional positioning technology and key point detection technology, the restoration effect was evaluated and the training test was conducted after restoration of defective strokes.
Epistemic planning for multi-robot systems in communication-restricted environments

Many real-world robotic applications such as search and rescue, disaster relief, and inspection operations are often set in unstructured environments with a restricted or unreliable communication infrastructure. In such environments, a multi-robot system must either be deployed to i) remain constantly connected, hence sacrificing operational efficiency or ii) allow disconnections considering when and how to regroup. In communication-restricted environments, we insist that the latter approach is desired to achieve a robust and predictable method for cooperative planning. One of the main challenges in achieving this goal is that optimal planning in partially unknown environments without communication requires an intractable sequence of possibilities. To solve this problem, we propose a novel epistemic planning approach for propagating beliefs about the system's states during communication loss to ensure cooperative operations. Typically used for discrete multi-player games or natural language processing, epistemic planning is a powerful representation of reasoning through events, actions, and belief revisions, given new information. Most robot applications use traditional planning to interact with their immediate environment and only consider knowledge of their own state. By including an epistemic notion in planning, a robot may enact depth-of-reasoning about the system's state, analyzing its beliefs about each robot in the system. In this method, a set of possible beliefs about other robots in the system are propagated using a Frontier-based planner to accomplish the coverage objective. As disconnections occur, each robot tracks beliefs about the system state and reasons about multiple objectives: i) coverage of the environment, ii) dissemination of new observations, and iii) possible information sharing from other robots. A task allocation optimization algorithm with gossip protocol is used in conjunction with the epistemic planning mechanism to locally optimize all three objectives, considering that in a partially unknown environment, the belief propagation may not be safe or possible to follow and that another robot may be attempting an information relay using the belief state. Results indicate that our framework performs better than the standard solution for communication restrictions and even shows similar performance to simulations with no communication limitations. Extensive experiments provide evidence of the framework's performance in real-world scenarios.
Knowledge Management as a Tool for Improving Work Performance in Selected Law Firms in Nairobi County, Kenya: An Assessment

Knowledge management is recognized as an important tool for sustaining competitive merit and improving performance of work. It is a means through which law firms can clout their knowledge base and intensify work performance. The assessment of knowledge management performance has become increasingly invaluable since it provides the indicator for guiding organizations to improve their work execution and competitiveness. Many law firms have no documented and planned approach to tap the organization’s knowledge and make it accessible to the staff for enhancement of service delivery. &#x0D; The study was informed by the learning organization theory. The research design for this study was a descriptive and positivism case study that was analysed largely through quantitative methods with a small qualitative component. Purposive sampling design was employed to select 222 law firms, qualitative data was collected using focus group discussions from 12 key informants supported by document review. The results revealed that most of the respondents 86.5% were aware of knowledge management whereas 64.9% indicated that knowledge management contributed to their law firm’s legal practice. From the inferential statistics analysis, there was a significant relationship and influence across different attributes examined that knowledge management is a tool for improving work performance which is, p&lt;0.05. This was also reflected in focus group discussion results.&#x0D; Awareness of knowledge management and contribution of knowledge management to legal practise were significant tools for improving work performance. The study recommended awareness forums for knowledge management.
An extended view on measuring tor AS-level adversaries

Tor provides anonymity to millions of users around the globe which has made it a valuable target for malicious actors. As a low-latency anonymity system, it is vulnerable to traffic correlation attacks from strong passive adversaries such as large autonomous systems (ASes). In preliminary work Mayer et al.(2020), we have developed a measurement approach utilizing the RIPE Atlas framework – a network of more than 11,000 probes worldwide – to infer the risk of deanonymization for IPv4 clients in Germany and the US. In this paper, we apply our methodology to additional scenarios providing a broader picture of the potential for deanonymization in the Tor network. In particular, we (a) repeat our earlier (2020) measurements in 2022 to observe changes over time, (b) adopt our approach for IPv6 to analyze the risk of deanonymization when using this next-generation Internet protocol, and (c) investigate the current situation in Russia, where censorship has been intensified after the beginning of Russia’s full-scale invasion of Ukraine. According to our results, Tor provides user anonymity at consistent quality: While individual numbers vary in dependence of client and destination, we were able to identify ASes with the potential to conduct deanonymization attacks. For clients in Germany and the US, the overall picture, however, has not changed since 2020. In addition, the protocols (IPv4 vs. IPv6) do not significantly impact the risk of deanonymization. Russian users are able to securely evade censorship using Tor. Their general risk of deanonymization is, in fact, lower than in the other investigated countries. Beyond, the few ASes with the potential to successfully perform deanonymization are operated by Western companies, further reducing the risk for Russian users.
Piecewise Rapidly-Exploring Random Tree Star

In this paper, we propose the piecewise technique of Rapidly-exploring Random Tree-Star (P-RRT*) algorithm used in low or medium specification agent(s) (rovers) in the two- dimensional (2-D) workspace. The traditional RRT, RRT*, and other path planning algorithms however efficient they have become; all treat a given environment as a whole and attempt to find a feasible path. This may result in higher memory utilization and a significant increase in processing time.We utilize the RRT* algorithm as the base and integrate it with the piecewise approach. Through P-RRT* technique, given an environment with no obstacles, we attempt to minimize the three vital elements used in the RRT* path planning algorithm (memory, power consumption, and time).A 2D simulation is utilized for demonstration purposes. Given a large workspace, we simulate over subregional workspaces where the number of nodes and step size are adjusted properly to minimize the cost. The simulation results show that dividing the entire simulation workspace into subregions and treating each subregion as a new workspace not only reduces memory utilization and processing time but also the power consumption as a result.The simulation results are shown versus the traditional RRT* algorithm; similar constraints are set for both the piecewise RRT* technique and the traditional RRT* algorithm; meaning that the number of nodes and step size is the same for both methods.
Extraction of Unstructured Electronic Healthcare Records using Natural Language Processing

Artificial Intelligence in the healthcare sector is becoming increasingly essential to extract huge texts for decision-making. Extraction of clinical data is a fundamental task in Medical Natural language processing. This process is still challenging through deep learning due to critical medical data, lack of interpretability, and limited availability. Text extraction from Electronic Healthcare records is crucial for improving patient care and understanding clinical decision-making. It also supports analysing the patients’ feedback and physician notes to identify areas for improvement in patients’ satisfaction and care quality. This helps in drug discovery and development through clinical data patterns. The proposed research focuses on implementing Natural language processing methods for data processing like classification and prediction, Word Sense Disambiguation, Segmentation, and word Embedding. These methods can process vast amounts of medical text data for decision support, research, and drug discovery. It can increase the possibility of identifying the patients who may at risk for certain conditions and diseases related to cancer and comparing it with their medical history. The chief aim is to provide improvised data analyses that could further improve their treatment.
Visualization System Suite for Probabilistic Weather Forecasting

Today, forecasts from most media sources are communicated in a deterministic manner that supplies explicit weather prediction information for timeframes and specific events. For the most part, these basic forecasts provide enough information to allow a consumer to confidently make plans. However, there are situations in which forecasts uncertainty matters, and a deterministic weather forecast does not provide enough information for a consumer to plan confidently. By reporting probabilistic information such as forecast confidence and by quantifying uncertainty, users would be able to trust the forecast and proceed with making their plans, or know to check back frequently for updates if the forecast is uncertain and likely to change. In this paper, we present a series of visualization concepts that are used to present weather uncertainty, along with corresponding in person and online user studies. The goals of our research are to use visualization to instill appropriate confidence in weather predictions, drive people’s decisions, and propel the world by helping people to find good times for outside activities while protecting health, safety, and business interests. We hope that the outcome will enable consumers to perform at their best aided by more informative weather information.Deterministic weather forecasts are easily consumed and are found everywhere from apps on mobile phones, to social media sites, radio, and television. Our research provides a roadmap for augmenting these with forecast uncertainty and probabilities. One example of communicating forecast uncertainty may occur with winter storm tracks several days prior to an event. An on-camera meteorologist might describe to their viewers that if the winter storm goes north of the region one type of winter precipitation will fall but if it takes a southerly track a different outcome is expected. As the event gets closer, the meteorologist can hone the forecast and also provide a level of confidence to the viewer. In this example a human meteorologist describes the possible weather outcomes and to plan appropriately.
Multiple object tracking with appearance feature prediction and similarity fusion

Object tracking is a crucial research area within the field of intelligent transportation, providing a vital foundation for anomalous behavior analysis and traffic statistics. Although pedestrian detectors have shown impressive results, leading to the advancement of detection-based tracking methods, target association in complex scenarios remains a difficult and less efficient task due to the lack of feature robustness in the presence of partial occlusions. In the proposed tracking method, we extract convolutional features on each entire object and its local blocks, segmented by the superpixel algorithm. Aiming to emphasize the global and local information respectively, the global features for each entire object are extracted from the last layer of the backbone network, while local features are derived from a specific intermediate layer of the backbone network. The association between tracked targets and detected pedestrian candidates relies on fused similarity degrees. Furthermore, we use the transformer’s self-attention mechanism to predict features for the current frame based on the information within past frames, aiming to eliminate the effects of target appearance variations. Additionally, we remove redundant background pixels in the detected rectangles of pedestrian candidates by using a background modeling algorithm. Experimental results demonstrate that the tracker proposed in this paper outperforms other trackers across five publicly available datasets, indicating its effectiveness and potential for further development.