Reviewerly/exHarmony huggingface dataset:
The Architecture of College Psychological Teaching Management System Based on Data Mining Technology

In order to meet the requirements of the development of educational informatization, combined with data mining technology, this article studies the architecture of the college psychological teaching management system. To experience the benefits brought by the Internet age, in order to further improve performance and meet the needs of the Internet information age, many colleges and universities have begun to strengthen the development of teaching management. Based on this background, this article proposes a standardized mode of mental health management in colleges and universities based on information technology. With the help of data warehouse and data mining technology, the ID3 algorithm, organizational rules, and other algorithms are used to develop a psychology subject evaluation system. Based on the improvement of grades and the behavioral characteristics of college students, this article formulates cross-platform operation rules, determines the B/S type as the normal state, and solves the best problem through comparison and selection. Then, a more specific system architecture design is carried out. The architecture of the software system selects the classic three-tier structure, which makes the client independent of the use platform and meets the requirements of user diversity and system portability. The design of functional modules such as the system fully meets the requirements of the ideological education management system for college students in the above analysis, with reasonable structure and clear logic. Finally, the advantages of the designed system architecture are summarized, including clarifying the grading idea of ideological education and training. It expands the transplantation ability of the system in a cross-platform way.
Spam Detection for Social Media Networks Using Machine Learning

People frequently examine internet product reviews before purchasing a product. More merchants aim to deceive users in order to earn a profit. Because customers are misled in this way, it&#x0027;s critical to be aware of and delete fraudulent reviews. This study examines machine learning-based spam detection approaches and discusses their general perspectives and outcomes. Knowing how important customer reviews are to a product&#x0027;s success, marketers frequently try to fool customers by publishing phoney remarks. Merchants have the option of posting updates themselves or hiring others to do so for them. Comment or review spam is the practice of sending out false updates. Spam senders might be recruited to leave favorable or negative reviews that harm competitor business. By 2020, the Canadian Competition Bureau gave a warning to its citizens officially, stating that they should be careful of fraudulent reviews and estimating that one off three of online reviews is fake. Poll fiction taken from more than twenty-five thousand participants by 2020 claims that more than seventy percent of consumers trust online reviews. As a result, spam reviews are a major source of concern nowadays. Based on the goal of the proposed techniques, the majority of the published articles dealing with this subject can be segregated into three. Tactics can be used to get spam reviews, individual spam senders, or spams sent by groups. Because the spam sent in group methods haven&#x0027;t been thoroughly investigated; they aren&#x0027;t discussed in this work. Spam detection is a machine learning issue that requires supervision. This means you&#x0027;ll need to give your machine learning model a set of spam and ham message examples and tell it to look for the relevant patterns that distinguish the two groups. Most email service providers have large databases of labelled emails.
A Probabilistic Approach for Detecting Human Motion in Video Sequence using Gaussian Mixture Model

The detection and recognition of the human body in motion are two essential and demanding problems in visual surveillance systems. The detection process is slow and chal-lenging due to the dynamic nature of the background and the occlusion of moving objects. This paper presents a probabilistic approach to model the dynamic behaviour of the background and accordingly detect multiple moving human bodies in a video sequence. The Gaussian Mixture Model (GMM) is used for adaptive background modelling. Blobs are drawn on the most significant and moving clusters of pixels in a binary image obtained after background subtraction to detect and track the moving humans. This simplified approach results in forming a pattern of various structural blobs. Finally, we demonstrate the effectiveness of our proposed approach with many tests on the AVG- TownCentre and PETS09-S2L1 datasets. The detection speed of the proposed algorithm is 30 fps and 32 fps for AVG-TownCentre and PETS09-S2L1 datasets, respectively, have been recorded. Also, we have reported a less computation time of 3.8ms for the proposed model.
Sharing Confidential Images with Abbreviated Shares using Steganography and AES Algorithm

‘Steganography’ derives from the Greek steganos (=“covered”) and graphy (=“writing”) so its literal meaning is “covered writing.” It is concerned with communication that is ‘invisible’. There are many steganographic approaches for hiding vital information in various file formats, some of which are more difficult than others, and each has its own set of strengths and weaknesses. In the Least Significant Bit (LSB) embedding approach, data is embedded in the cover picture's least significant bits in a manner that renders the unaided eye incapable of discerning the image in the cover file. This approach could work in both 24-bit and 8-bit settings. In BPCS steganography, the image is the vessel data, secret info is embedded in the vessel's bit-planes. Without degradation of image quality, we replace with secret data, every “noise-like” region within the bit-planes of the vessel image. This steganography may be called “BPCS-Steganography,” or Bit-Plane Complexity Segmentation Steganography. In this paper we describe an experimental approach to hide the secret image by using LSB and BPCS steganography and AES algorithm.
Darknet on OpenCL: A multiplatform tool for object detection and classification

Abstract The goal of this article is to overview the challenges and problems on the way from the state‐of‐the‐art CUDA accelerated neural network code to multi‐GPU code. For this purpose, the authors describe the journey of porting that existing in GitHub, a fully featured CUDA‐accelerated Darknet engine, to OpenCL. This article presents the lessons learned and the techniques that were put in place for this porting. There are few other implementations on GitHub that leverage the OpenCL standard, and a few have tried to port Darknet as well. Darknet is a well‐known convolutional neural network (CNN) framework. The authors of this article investigated all aspects of porting and achieved a fully featured Darknet engine on OpenCL. The effort was focused not only on classification using YOLO1, YOLO2, YOLO3, and YOLO4 CNN models. Other aspects were also covered, such as training neural networks and benchmarks to identify weak points in the implementation. Compared with the standard CPU version, the GPU computing code substantially improves the Darknet computing time by using underutilized hardware in existing systems. If the system is OpenCL‐based, it is practically hardware‐independent. The authors also improved the CUDA version as Darknet‐vNext.
Tokyo, Denver, Helsinki, Lisbon or the Professor? A Framework for Understanding Cybercriminal Roles in Darknet Markets

There is comparatively little information about the roles and the separation of these roles within financially-motivated cybercrime online. As Darknet Markets (DNMs) are online fora, roles can often be conflated with membership or user types within such fora, e.g., administrator, new user, etc. The insights presented in this paper are grounded in a Conversation Analysis of underground forum threads in combination with Social Network Analysis of the relationships between actors in these fora and an automated analysis of the thematic scope of their communications using NLP techniques. This results in a more nuanced understanding of roles, and the power relationships between roles, as they emerge through and are defined by linguistic interactions. Based on this mixed methods approach, we developed a dynamic typology of three key roles within DNMs that goes beyond a basic supply-demand logic: entrepreneurs, influencers and gatekeepers. A closer analysis of these roles can contribute to a better understanding of emerging trends in a forum and allow for the identification and prioritisation of high-risk targets.
Spatiotemporal redundancy removal in immersive video coding

In this paper, the authors describe two methods designed for reducing the spatiotemporal redundancy of the video within the MPEG Immersive video (MIV) encoder: patch occupation modification and cluster splitting. These methods allow optimizing two important parameters of the immersive video: bitrate and pixelrate. The patch occupation modification method significantly decreases the number of active pixels within texture and depth video produced by the MIV encoder. Cluster splitting decreases the total area needed for storing the texture and depth information from multiple input views, decreasing the pixelrate. Both methods proposed by the authors of this paper were appreciated by the experts of the ISO/IEC JTC1/SC29/WG11 MPEG and are included in the Test Model for MPEG Immersive video (TMIV), which is the reference software implementation of the MIV standard.
<i>FM fact label</i>

Recognizing specific characteristics of feature models (FM) can be challenging due to the different nature and domains of the models. There are several metrics to characterize FMs. However, there is no standard way to visualize and identify the properties that make an FM unique and distinguishable. We propose FM Fact Label as a tool to visualize an FM characterization based on its metadata, structural measures, and analytical metrics. Although existing tools can provide a visualization of the FM and report some metrics, the feature diagram of large-scale FMs becomes ineffective to take an overall shape of the FM properties. Moreover, the reported metrics are often embedded in the tool user interface, preventing further analysis. FM Fact Label is a standalone web-based tool that provides a configurable and interactive visualization of FM characterizations that can be exported to several formats. Our contribution becomes important because the Universal Variability Language (UVL) is starting to gain attraction in the software product line community as a unified textual language to specify FMs and share knowledge. With this contribution, we help to advance the UVL ecosystem one step forward while providing a common representation for the results of existing analysis tools.
Artificial Intelligence in Education

Artificial intelligence-deployed applications for learning are in use and made way into the field of education for quite some time. Therefore, its requirement forces teachers, learners, and stakeholders more than ever in the past. The aim of the current study is to provide focus on the matter that implementation of AI is not a choice but a need. As education technology evolves as a new standard, all the stakeholders involved in education must deploy AI to obtain the basic education goals, i.e., it must be individualized, effective, transformative, output based, integrative and long lasting. The current research is to portray the transformation in methods of education and put forward the current directions in incorporating artificial intelligence. The investigators contemplate on this issue and attempt to address how advancement of AI is contemporaneous with advancement in education. The pursuit to examine this circumstantial believes to add productive surface for a fruitful consideration on probing the power of artificial intelligence-based e-learning applications.
Uncertainty visualization: Fundamentals and recent developments

Abstract This paper provides a brief overview of uncertainty visualization along with some fundamental considerations on uncertainty propagation and modeling. Starting from the visualization pipeline, we discuss how the different stages along this pipeline can be affected by uncertainty and how they can deal with this and propagate uncertainty information to subsequent processing steps. We illustrate recent advances in the field with a number of examples from a wide range of applications: uncertainty visualization of hierarchical data, multivariate time series, stochastic partial differential equations, and data from linguistic annotation.
The Research of Adaptive Data Desensitization Method Based on Middle Platform

With the popularization of Middle Platform characterized by data aggregating and governance, the security protection of data is paying more and more attention, and the data desensitization technology is widely used. In order to solve the high threshold for use, high customization, and lack of stability caused by conventional data desensitization methods, a desensitization strategy configuration system based on desensitization intensity and desensitization algorithm weight was established. The methods of reidentification risk assessment and information security attribute assessment were used to classify and quantify configuration items, and then, an adaptive desensitization strategy configuration method was proposed which not only simplifies the configuration process but also provides reliable desensitization data flexibly and stably for application requirements. It is beneficial to the development of an intelligent automatic data desensitization system.
Later credits: resourceful reasoning for the later modality

In the past two decades, step-indexed logical relations and separation logics have both come to play a major role in semantics and verification research. More recently, they have been married together in the form of step-indexed separation logics like VST, iCAP, and Iris, which provide powerful tools for (among other things) building semantic models of richly typed languages like Rust. In these logics, propositions are given semantics using a step-indexed model, and step-indexed reasoning is reflected into the logic through the so-called “later” modality. On the one hand, this modality provides an elegant, high-level account of step-indexed reasoning; on the other hand, when used in sufficiently sophisticated ways, it can become a nuisance, turning perfectly natural proof strategies into dead ends. In this work, we introduce later credits , a new technique for escaping later-modality quagmires. By leveraging the second ancestor of these logics—separation logic—later credits turn “the right to eliminate a later” into an ownable resource, which is subject to all the traditional modular reasoning principles of separation logic. We develop the theory of later credits in the context of Iris, and present several challenging examples of proofs and proof patterns which were previously not possible in Iris but are now possible due to later credits.
Satellite Fog Detection at Dawn and Dusk Based on the Deep Learning Algorithm under Terrain-Restriction

Fog generally forms at dawn and dusk, which exerts serious impacts on public traffic and human health. Terrain strongly affects fog formation, which provides a useful clue for fog detection from satellite observation. With the aid of the advanced Himawari-8 imager data (H8/AHI), this study develops a deep learning algorithm for fog detection at dawn and dusk under terrain-restriction and enhanced channel domain attention mechanism (DDF-Net). The DDF-Net is based on the traditional U-Net model, with the digital elevation model (DEM) data acting as the auxiliary information to separate fog from the low stratus. Furthermore, the squeeze-and-excitation networks (SE-Net) is integrated to optimize the information extraction for eliminating the influence of solar zenith angles (SZA) on the spectral characteristics over a large region. Results show acceptable accuracy of the DDF-Net. The overall probability of detection (POD) is 84.0% at dawn and 83.7% at dusk. In addition, the terrain-restriction strategy improves the results at the edges of foggy regions and reduces the false alarm rate (FAR) for low stratus. The accuracy is expected to be improved when training at a season or month scale, rather than at a longer temporal scale. Results of our study help to improve the accuracy of fog detection, which could further support the relevant traffic planning or healthy travel.
Analysis of Students' Difficulties in Reading Comprehension Narrative Text Faced by Ninth Grade Junior High School Students

This study aimed to find out the difficulties in reading comprehension of narrative text faced by students ninth-grade at SMP Muhammadiyah 3 Waru. This study used a qualitative descriptive method where data collection techniques is interview. The sample of this study were students of class 9-A SMP Muhammadiyah 3 Waru which consisted of 29 students. However, in this study only 15 students were willing to participate in the interview. The results of this study has shown that students had three difficulties in reading comprehension of narrtive text, namely, students’ difficult to understand the word, difficult to understand long sentences and difficult to understand the main idea.
Leadership behavior and innovation performance in the UAE's telecom and ICT industries

Purpose This study aimed to determine the indirect effects of transformational leadership and transactional leadership on innovation performance through the serial mediation of climate for innovation and individual creativity. Design/methodology/approach Data was collected from 139 employees working in various ICT and telecommunication industries in the United Arab Emirates (UAE). Findings The analysis results revealed positive and significant relationships between transactional leadership and the creation of a climate for innovation for individuals' creativity and innovation performance. The results also indicated employees' perceptions of a supportive climate for innovation mediation, the transformational and transactional leadership and individual’ creativity relationships. Practical implications This research suggests that investing in organizational climate improvement provides a dynamic platform for creativity and innovation in the workplace. Originality/value This study is one of the first to investigate the relationships in ICT and telecommunication organizations in a developing country, the UAE. The study includes associations between transformational and transactional leaderships and employees' sense of creativity and innovation performance and the impact of employees' perceptions of a supportive climate for innovation.
Recommendation of Knowledge Graph Convolutional Networks Based on Multilayer BiLSTM and Self-Attention

To solve the problems of cold start, sparse data, and poor recommendation performance in collaborative filtering recommendation, an end-to-end framework algorithm based on BiLSTM and BAGCN was proposed. In order to discover the higher-order structural information in the knowledge graph, stacked BiLSTM is used to extract the features of embedded entities and relationships, respectively, and the depth dependence features of user-item interaction matrix are mined. The neighborhood representation of each entity is then calculated by sampling adjacent entities of a fixed size. Then, the self-attention mechanism is used to learn the semantic association between entities and neighboring entities to obtain the final neighborhood information. Aggregators are used to combine neighborhood information and bias information when computing node representations. By extending the sampling of adjacent entities to multihop simulation of higher-order adjacent information, users’ potential long-distance interests can be captured. Compared with the baseline model, the superiority of this method is verified.
Binsec/Rel: Symbolic Binary Analyzer for Security with Applications to Constant-Time and Secret-Erasure

This article tackles the problem of designing efficient binary-level verification for a subset of information flow properties encompassing constant-time and secret-erasure . These properties are crucial for cryptographic implementations but are generally not preserved by compilers. Our proposal builds on relational symbolic execution enhanced with new optimizations dedicated to information flow and binary-level analysis, yielding a dramatic improvement over prior work based on symbolic execution. We implement a prototype, Binsec/Rel , for bug-finding and bounded-verification of constant-time and secret-erasure and perform extensive experiments on a set of 338 cryptographic implementations, demonstrating the benefits of our approach. Using Binsec/Rel , we also automate two prior manual studies on preservation of constant-time and secret-erasure by compilers for a total of 4,148 and 1,156 binaries, respectively. Interestingly, our analysis highlights incorrect usages of volatile data pointer for secret-erasure and shows that scrubbing mechanisms based on volatile function pointers can introduce additional register spilling that might break secret-erasure. We also discovered that gcc -O0 and backend passes of clang introduce violations of constant-time in implementations that were previously deemed secure by a state-of-the-art constant-time verification tool operating at LLVM level, showing the importance of reasoning at binary level.
Hybrid Quantum Genetic Algorithm for the 0-1 Knapsack Problem in the IBM Qiskit Simulator

In this work, a novel Hybrid Quantum Genetic Algorithm (HQGA) for the 0-1 Knapsack Problem (KP) is presented. It is based on quantum computing principles, such as qubits, superposition, and entanglement of states. The HQGA was simulated in the Qiskit simulator. Qiskit simulator is a platform developed by IBM that allows working with quantum computers at the level of circuits, pulses, and algorithms. The performance of HQGA is evaluated in three strongly correlated KP data sets, and computational results are compared with a Quantum-Inspired Evolutionary Algorithm (QIEA), a modified version of a QIEA (QIEA-Q), and a modified version of the HQGA (HQGA-Q). Experimental results demonstrate that the proposed HQGA can obtain the best solutions in all the KP data sets, and performs well on robustness.
Fault Diagnosis of Check Valve Based on KPLS Optimal Feature Selection and Kernel Extreme Learning Machine

The check valve is the core part of high-pressure diaphragm pumps. It has complex operation conditions and has difficulty characterizing fault states completely with its single feature. Therefore, a fault signal diagnosis model based on the kernel extreme learning machine (KELM) was constructed to diagnose the check valve. The model adopts a multi-feature extraction method and reduces dimensionality through kernel partial least squares (KPLS). Firstly, we divided the check valve vibration signal into several non-overlapping samples. Then, we extracted 16 time-domain features, 13 frequency-domain features, 16 wavelet packet energy features, and energy entropy features from each sample to construct a multi-feature set characterizing the operation state of the check valve. Next, we used the KPLS method to optimize the 45 dimension multi-feature data and employed the processed feature set to establish a KELM fault diagnosis model. Experiments showed that the method based on KPLS optimal feature selection could fully characterize the operating state of the equipment with an accuracy rate of 96.88%. This result indicates the high accuracy and effectiveness of the multi-feature set constructed with the KELM fault diagnosis model.