Reviewerly/exHarmony huggingface dataset:
BeAwareOfYourAct: A Framework for Behavioural Action Detection in Workplace through Deep Learning Analysis and Augmented Action Pattern Recognition

The purpose of this study is to propose a system framework that addresses the problem in monitoring malicious behavior of an individual towards someone in the same vicinity through multi-person detection video processing. The real-time video rendering analysis and behavioral action detection is an efficient method in line with pervasive or any event related atrocious behavior towards an individual. In the development of the system several algorithms will be utilized such as object detection, action recognition algorithms and techniques (e.g. Human Activity Recognition, Supervised Learning Data Representation, etc.) which will heavily rely on Regions with Convolutional Neural Network (RCNN). The capability of Artificial Intelligence (A.I.) to analyze real-time video rendering through the training data to be supplied as a point of reference in deep learning analysis, any act that might cause discomfort to the victim can be detected to be reported immediately to the Officer-in-charge preventing the maltreatment to further escalate. In order to accomplish the said study that will serve as a pillar for the system development. It is concluded that this system framework of real-time monitoring is a great approach in preventing certain abuse from starting and will make the conduct of investigation more precise and efficient should a harassment come into place. Through this framework it can make the vicinity more conducive to healthy communication and prevent intimidating words to be casually mentioned.
Research on Adaptive Recommendation of English Learning Resources Based on Collaborative Filtering

In addition to continuing to expand the original teaching resource pool, various local educational institutions and schools have begun to vigorously build the teaching resource cloud under the current turbulent cloud computing technology. There are some common problems in the past resource push mode, such as emphasizing resources over process, emphasizing transmission over sharing, emphasizing resource aggregation over maintenance and construction, etc. The research of intelligent adaptive recommendation technology for teaching resources plays an important role in efficient presentation and accurate acquisition of teaching resources. Collaborative filtering technology is to find similar neighbors of users or resources based on explicit and implicit preferences expressed by users, and to generate recommendation results accordingly. This paper expounds the importance of the utilization and construction of learning resources in the process of autonomous learning, and puts forward an adaptive recommendation strategy of English learning resources based on collaborative filtering algorithm to meet learners' personalized learning needs. When the technology is implemented, the data representation is simple, and the algorithm is easy to implement, so it is easy to popularize.
Deep Residual Neural Networks with Attention Mechanism for Spatial Image Steganalysis

Image steganalysis has been explored for decades to detect whether an image has hidden secret data. Many recent works have shown that CNNs (Convolutional Neural Networks) trained with rich features perform better than traditional two-step machine learning approaches. Some CNNs reach high precision in the classification task of steganalysis. However, such precision is insufficient for actual security requirements, especially when the image steganography payload is small. To further improve the precision for steganalysis, we propose a residual based CNN model with channel attention mechanism and an innovative pre-processing activation strategy that outperforms previous works in classification precision. Our CNN adopts SRM (Spatial Rich Models) to get rich features and utilizes depth wise separable convolution and Squeeze-and-Excitation attention module to build a CNN with residual structure. In addition, there are two linear fully-connected layers at the end as classifiers. We test our model with WOW and S-UNIWARD steganography at 0.2bbp and 0.4bbp payloads on BOSSbase 1.01 dataset. Our model out-performs previous works, including GBRAS-Net, YEDROUDJ-Net, ZHU-Net, and five others, in terms of classification precision with these four configurations, showing an even more significant precision advantage in the small payload. With the challenging configuration of S-UNIWARD steganography at 0.2bbp payload, the precision of our model is 5.7% higher than the previous best work.
Parallel-Friendly and Work-Efficient Single Source Shortest Path Algorithm on Single-Node System

The Single-Source Shortest Path (SSSP) algorithm is one of the most important kernels used by a variety of high-level graph processing applications. Although having been extensively studied for single-node scenarios, SSSP algorithm still faces difficulties when being deployed on scale-free graph, which brings about redundant edge traversals, expensive atomic operations and random memory access. In this paper, three parallel-friendly and work-efficient opti-mizations are introduced to improve the performance of parallel SSSP algorithm with shared memory on the single-node system. First, a label-aware pruning technique is proposed to reduce the redundant edge traversals. Then, atomic operations caused by race conditions are eliminated by using destination-driven sub graph partitioning. Finally, we propose a double-ordered bucket strategy to further improve the efficiency of SSSP. The experiment demonstrates that the average speedup reaches to 27.58x compared with original implement for R-MAT graphs. Meanwhile, our method achieves up to 2.12x for R-MAT graphs and 4.45 x for real-world graphs on average compared to previous state-of-the-art parallel implementation.
Deep Learning Approaches in Sentiment Analysis

With the development of social media, such as Twitter and MicroBlog, sentiment analysis becomes a quite useful tool for mining the subjective emotion information from the text, which may have important applications in product information feedback, commodity recommendation algorithms, public opinion monitoring, hot event tracking and so on. Though there are many relevant types of research about sentiment analysis, the relevant surveys are not perfect or do not cover the current new methods. Therefore, this survey provides an overview of sentiment analysis to summarize and review the recent methods and resources.
Research on Computer Intelligent Image Recognition Technology Based on Machine Learning

The computer's intelligent image recognition technology is immature, and there are still many areas that need to be improved. Therefore, the research on computer intelligent image recognition technology is long-term and requires attention. The development of the scientific and technological revolution has promoted the arrival of the Internet era, and computer intelligent image recognition technology has become more and more mature. The development of computer intelligent image recognition technology is related to the healthy and orderly development of my country's national economy, especially the development and application of computer intelligent technology is becoming more and more extensive. In today's increasingly fierce international competition, computer image recognition technology can be said to be related to the orderly development of our country's economy, and its development is also related to the orderly progress of other fields in our country. If this technology is widely promoted, it can help people quickly obtain the information they need, and with the help of powerful data analysis capabilities and processing capabilities to help corporate decision-makers make important decisions. This article has carried on the relevant elaboration from the aspect of computer image recognition technology, hoping to be helpful to the development of computer engineering in our country.
Dramatic Increase in Fear-Related Discussion on Twitter during COVID-19: Analysis, Topic Modeling and Tweets Classification

COVID-19 is one the most lethal virus, causing millions of death to date. It was initially detected in Wuhan, China. It then spread rapidly around the globe, which resultantly created major setbacks in the public health sector. The reason of millions of deaths is not only due to the virus itself but it is also linked to peoples’ mental state, and sentiments triggered by the fear of the virus. These sentiments are predominantly available on posts/tweets on social media. This paper presents a novel approach for exploratory data analysis of twitter to understand the emotions of general public; country wise, and user wise. Firstly K-Means clustering is employed for topic modeling to categorize the emotions in each tweet. Further supervised machine learning techniques are used to categorize the multi-label tweets. This research concluded that Fear was the most common emotion in twitter discussion. Furthermore, we classified the dataset by performing decision tree (DT), logistic regression (LR), and support vector machine (SVM), finally this paper concluded the results of classification, which shows that SVM can attain better classification accuracy (99%) for COVID-19 text classification.
MyGuide: A Multi-Platform Guidance and Counseling Student Services System

This study focused on the development of MyGuide: A Multi-Platform Guidance and Counseling Student Services System for Don Mariano Marcos Memorial State University - Mid La Union Campus College of Information Technology (DMMMSU-MLUC CIT). It sought to achieve two specific objectives: (1) to develop the Multi-platform Guidance and Counseling Student Services System using with Extreme Programming (XP) software development process; and (2) to determine the software quality of the developed system in terms of functionality, usability, reliability, performance, and supportability. The researchers used descriptive and developmental research designs to develop and evaluate the system. A total of 55 respondents composed of 50 CIT students 4 IT experts and 1 Guidance Counselor participated in the evaluation of the software quality using FURPS questionnaire.
A Study on the Usability of Text Analysis on Web Artifacts for Digital Forensic Investigation

Digital forensic investigation is not new in the field of forensic science which faced several challenges over the past decades. Due to the complexity of performing investigative inquiry on a large amount of data on digital evidence, the choice of appropriate forensic tools, skills, and expertise poses issues and challenges in digital forensic investigation. In this study, an effective approach to performing investigative inquiry based on web browser artifacts was pursued by utilizing text analysis to identify the user's characteristics. Information extraction was performed from a publicly available dataset by extracting, analyzing web browser artifacts, and performing in-house data labeling. Data labeling of the processed web artifacts was conducted and evaluated by an expert volunteer for investigative purposes to achieve the goal of persona characteristic identification of a computer user. Thus, helping investigators interpret textual-based evidence fast even though with a notable amount of data being analyzed versus when human-based analysis is manually performed in the pursuit of solving crimes committed using the internet.
A Secure and Trusted Data Sharing Scheme Based on Blockchain for Government Data

There are many problems existing in government data sharing, such as poor security, privacy disclosure and low tamper-resistant modification. To address this issue, we propose a secure and trusted data-sharing model for government data sharing. Given the features of transparency and decentralization, blockchain makes data visible to everyone on the network and is inappropriate for certain situations. This paper transfers the original data to the InterPlanetary File System (IPFS) stor-age. To ensure the security of data sharing, achieve a multi- keyword searchable mechanism and speed up computations of encryption, we propose a cryptographic primitive developed by extending ciphertext policy attribute-based encryption (CP-ABE) and searchable encryption (SE). The experiment result shows that the data-sharing model can prevent malicious attacks and improve the speed of encryption and decryption.
Personalized Recommendation Algorithm Based on Implicit Semantic Model

Collaborative filtering-based recommendation algorithm is widely used in recommendation systems, but it has the problem of data sparsity. This research designed a collaborative filtering algorithm based on implicit semantic model to solve the above problems. The algorithm trains the prediction model by learning the characteristics of user scoring data, and fills it into the original scoring matrix. Then, a collaborative filtering algorithm based on implicit semantic model is used to obtain the prediction score. Through experimental verification, the proposed recommendation algorithm can provide accurate and fast personalized recommendation services.
STEAM: SVDD-based Anomaly Detection on Attributed Networks via Multi-Autoencoders

Anomaly detection on attributed networks is intended to find instances that dramatically different from other instances in terms of attributes or structure. However, most existing methods ignore the adverse effects of abnormal nodes on normal nodes and are unable to capture high-dimensional cross-modal information from the structure and attributes. To tackle this drawback, we propose an SVDD-based anomaly detection framework, named STEAM, which detects abnormal nodes by fusing attributes and structure. First, STEAM uses node structure and its attributes information to learn two independent representations of nodes, respectively. Secondly, to alleviate the interference of abnormal nodes, we introduce Support Vector Data Description (SVDD), which helps us to learn the hypersphere from the structure representations of normal nodes. Thirdly, the two representations of the nodes are fused to reconstruct the attributed networks. Finally, we use a combination of the distance of every node to the hypersphere center and the reconstruction error of the attributed network to determine whether the node is anomalous or not. The performance of STEAM is assessed by 6 public datasets. Numerous experimental results have illustrated that STEAM is effective in detecting anomalous nodes in attributed networks.
Application of VR Technology Based on Media Convergence

The rise of VR is a signal that a new mode of information carrying is gradually accepted by the public. As a result, understanding the characteristics of this mode will give us great inspiration for how to properly use this type of technology. Therefore, this article hopes to show the historical context of the development of VR technology first, so as to help us understand the process of VR technology iteration until now. From the perspective of media technology, the paper takes the application of modern VR technology as the main observation object, and to understand both advantages and characteristics of this technology compared with traditional media. Then, this article will further analyze the attempts of VR technology in convergence with practical case Bury Me Here. In the case, it will mainly discuss two aspects: plot and scene. At the end of the paper, the summary of VR in the field of interactive media and the prospect of the future are made.
A Lightweight Authentication Protocol for NFC-enabled Drug Anti-Counterfeiting System

With the increase in sales of counterfeit drug, and its anti-counterfeiting has always been a concern issue. The commonly used anti-counterfeiting method is to paste an image label recording the drug data on the drug package, and the consumers can judge the authenticity of the drug by reading the data recorded on the label. However, the label is easily to be cloned and difficult to play an effective anti-counterfeiting role. Implementing verification of Near Field Communication (NFC) tag by means of authentication protocol provides a new solution for drug anti-counterfeiting. This paper proposes a lightweight authentication protocol for drug anti-counterfeiting system that supports the updating of NFC tags. The proposed protocol uses NFC tag as drug anti-counterfeiting label. If the tag is authenticated by the anti-counterfeiting server, the user gets the real data of the drug. Besides, the identity of NFC tag also be updated to prevent adversaries from obtaining, and using them for counterfeit. The security of proposed protocol is emulated by using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. In addition, the protocol's ability to defend against known attacks is analyzed. Finally, the energy consumption of the protocol is evaluated.
CH-Go: Online Go System Based on Chunk Data Storage

The training and running of an online Go system require the support of effective data management systems to deal with vast data, such as the initial Go game records, the feature data set obtained by representation learning, the experience data set of self-play, the randomly sampled Monte Carlo tree, and so on. Previous work has rarely mentioned this problem, but the ability and efficiency of data management systems determine the accuracy and speed of the Go system. To tackle this issue, we propose an online Go game system based on the chunk data storage method (CH-Go), which processes the format of 160k Go game data released by Kiseido Go Server (KGS) and designs a Go encoder with 11 planes, a parallel processor and generator for better memory performance. Specifically, we store the data in chunks, take the chunk size of 1024 as a batch, and save the features and labels of each chunk as binary files. Then a small set of data is randomly sampled each time for the neural network training, which is accessed by batch through yield method. The training part of the prototype includes three modules: supervised learning module, reinforcement learning module, and an online module. Firstly, we apply Zobrist-guided hash coding to speed up the Go board construction. Then we train a supervised learning policy network to initialize the self-play for generation of experience data with 160k Go game data released by KGS. Finally, we conduct reinforcement learning based on REINFORCE algorithm. Experiments show that the training accuracy of CH-Go in the sampled 150 games is 99.14%, and the accuracy in the test set is as high as 98.82 %. Under the condition of limited local computing power and time, we have achieved a better level of intelligence. Given the current situation that classical systems such as GOLAXY are not free and open, CH-Go has realized and maintained complete Internet openness.
MBTI BERT: A Transformer-Based Machine Learning Approach Using MBTI Model For Textual Inputs

Social media platforms like Reddit provide a rich text source of information about the behavior, opinions and interests of online users. While the posts may vary in scope from users, the writing style and the use of phrases stays relatively similar. Just like a personality test, which predicts the personality out of the answers a person gives to a set of questions, there might be a potential to automate and connect the writing style to a specific MBTI personality class. In this paper, we use a huge amount of data from Reddit users for which we have the results of a Myers-Briggs type indicator test, to predict their type out of their textual input. For this task, we used different machine learning models to implement various techniques to create the final prediction. The methodologies involve evaluating different feature sets to examine if the word group size of the single features and the overall size of the feature sets has a significant influence on the outcomes. Eventually, different models were trained and evaluated on a unified training and test set to assess which model performs better with the proposed computational linguistic problem. The training showed that a huge imbalance in the training data was a major issue for the models. In our final examinations, the BERT model achieved the best results by a small margin against the XGBoost and SVM models.
BTDFS: B+ Tree-Based Distributed File Storing Mechanism for Improving Small File Storage

Hadoop is currently the most popular big data processing architecture, which provides a processing framework for managing and analyzing massive data. Hadoop Distributed File System (HDFS) is the core component for storing data in the Hadoop systems. It performs well when storing and managing large-sized files. However, it shows extremely bad performance when dealing with a large number of small files, which is reflected in the consumption of a large amount of memory space of the NameNode node and the low efficiency of accessing small files. Archive file is proposed to solve this problem. It combines small files into larger merged files for storage, and uses index files to record the relevant information of small files in the merged files. However, archive files (e.g., Hadoop Archive, MapFile) all require additional processing and multiple I/O operations to obtain index information before actually accessing the file content, which reduces the efficiency of file access. This paper proposes a novel distributed file storing mechanism called B+ Tree-Based Distributed File Storing mechanism (BTDFS), which combines a large number of small files into a merged file, records the index information of the small files in the merged file, and organizes the index information into a B+ tree for storage. Using BTDFS, while reducing the metadata consumption of the NameNode node, improves the access performance of a single small file with additional abilities, which provides convenience for analyzing a large number of small files. Finally, this paper designs corresponding experiments for the proposed BTDFS, and tests and analyzes the memory usage in N ameN ode and the access performance of small files. The experimental results show that the proposed BTDFS can effectively reduce the memory usage of the NameNode, and the efficiency of accessing small files is improved compared with state-of-the-art methods.
Secure Single-Server Fuzzy Deduplication without Interactive Proof-of-Ownership in Cloud

The redundant of multimedia data made an unnecessary waste in encrypted cloud storage, unlike text with completely consistent content, multimedia data allows a certain degree of similarity in deduplication, In this work, we focus on the multimedia data which takes a seriously proportion of storage in scenarios such as data outsourcing to propose secure fuzzy deduplication without the additional servers based on Convergent Encryption(CE), say the Single-server Fuzzy Deduplication (SSFD). Compared to the related fuzzy deduplication, SSFD is strong at resisting brute-force attacks caused by server-server collusion, moreover, we also put server-client collusion attacks into security solutions. Additionally, to enhance the security of data, the proposed scheme provides both protection against replay attacks and verification of label consistency and adds no extra communication such as Proof of Ownership(PoW)in interaction. We separately presented a formal security analysis and performed performance at last to prove security solutions and evaluate the experimental results, it shows SSFD provides both a reliable fuzzy images secure deduplication protocol and a computationally feasible solution.