Reviewerly/exHarmony huggingface dataset:
Review on Information Privacy Protection based on Blockchain Principle

As a distributed general ledger technology, a basic platform of smart contract, and a new distributed computing paradigm, blockchain is bound to have an impact on the development of all walks of life, and drive a new round of technology and application changes. To have an impact on the development of all walks of life and drive a new round of technology and application changes, blockchain technology improves efficiency, reduces costs, and enhances data security. However, it also faces serious information security problems. However, while block technology improves efficiency, reducing costs and improving data security, block technology also faces serious information privacy leakage problems. promote the research, development and application of blockchain in information privacy protection. This paper uses the literature analysis method to analyze the advantages and disadvantages of the blockchain in the information privacy protection from the aspects of the blockchain privacy protection This paper uses the literature analysis method to analyze the advantages and disadvantages of the blockchain in the information privacy protection from the aspects of the blockchain privacy protection , analyze the privacy leakage threat of the blockchain in the network layer, transaction layer and application layer, and summarizes the main technologies of the blockchain in the information privacy protection of data users.
English Vocabulary Teaching Strategies for Middle School Students — Based on the English Curriculum Standard for Compulsory Education (2022 Edition) in China

The revision of the English Curriculum Standards for Compulsory Education (2022 Edition) (hereinafter referred to as the “New Curriculum Standard”) aims to strengthen the cultivation of students’ core competencies, highlight student-centered learning, and arouse teacher’s attention to student’s development and learning needs. Guided by the New Curriculum Standard, this article, based on the writer’s teaching experience, explores efficient vocabulary teaching methods that point to students’ core competencies and lays a foundation for students’ English learning.
Towards Maximising Hardware Resources and Design Efficiency via High-Speed Implementation of HMAC based on SHA-256 Design

Some applications, such as Message Authentication Code (MAC), rely on different hashing operations. There are various hash functions, including Message-Digest 5 (MD5), RACE Integrity Primitives Evaluation Message Digest 160 (RIPEMD-160), Secure Hash Algorithm 1 (SHA-1), and Secure Hash Algorithm 256 (SHA-256), among others. The network layer is the third of seven layers of the Open Systems Interconnection (OSI) concept, also known as the Internet. It handles network addressing and physical data routing. Nowadays, enhanced internet security is necessary to safeguard networks from illegal surveillance. As a result, Internet Protocol Security (IPsec) introduces secure communication across the Internet by encrypting and/or authenticating network traffic at the IP level. IPsec is an internet-based security protocol. Encapsulating Security Payload (ESP) and Authentication Header (AH) protocols are separated into two protocols. The MAC value is stored in the authentication data files of the Authentication Header and Encapsulating Security Payload. This article analyses a fast implementation of the Hash-based Message Authentication Code (HMAC), which uses its algorithm to ensure the validity and integrity of data to optimise hardware efficiency and design efficacy using the SHA-256 algorithm. During data transfer, HMAC is critical for message authentication. It was successfully developed using Verilog Hardware Description Language (HDL) code with the implementation of a Field Programmable Gate Array (FPGA) device using the Altera Quartus II Computer-Aided Design (CAD) tool to enhance the maximum frequency of the design. The accuracy of the HMAC design, which is based on the SHA-256 design, was examined and confirmed using ModelSim. The results indicate that the maximum frequency of the HMAC-SHA-256 design is approximately 195.16 MHz.
Attention-based Spatialized Word Embedding Bi-LSTM Model for Sentiment Analysis

Movie reviews provide a medium of communication for the movie fans community. Movie reviews not only help viewers and potential viewers to obtain a general opinion about a movie but also allow the fans to construct an opinion of the movie. In this work, an analysis of over 60,000 movie reviews has been implemented to find meaningful text representation via text embedding. We improved the text embedding by proposing an attention-based Bidirectional Long-Short Term Memory (Bi-LSTM) network by using over 60,000 movie review text data as the training set and over 20,000 movie review text data as the testing set. Based on the data features, we performed a probabilistic analysis of the information related to words and phrases, combined the analysis results with text embedding, spatialized the text embedding, and compared the performance of the proposed attention-based spatialized word embedding Bi-LSTM model with several traditional machine learning models. The attention-based spatialized word embedding Bi-LSTM model proposed in this paper achieves an F1 score of 0.91 on the movie review sentiment classification dataset, with a prediction accuracy of 91%, outperforming the results of the current state-of-the-art research. The model can effectively identify the sentimental tendencies of movie reviews and use the analyzed sentimental tendencies to guide consumers in their consumption and obtain feedback on movie content.
IEEE ITSC 2022 [Conference Activities]

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Modified particle filter based on feature extraction coupling for track-before-detect algorithm

For traditional particle filter track-before-detect(TBD) algorithms, when tracking targets, these algorithms require inter frame accumulation of multiple frames to accurately achieve target tracking. In this paper, we modify the traditional particle filter TBD by adding the feature extraction, in which the feature extract is used to classify the target scope and clutter scope before target tracking to narrow down the scope of particle generation. The simulation results show that the modified particle filter algorithm has significantly modified tracking performance compared to traditional particle filter algorithms, and has engineering application value.
Effects of Surface Curvature on SAR Image Formation

Synthetic-aperture radar (SAR) imaging techniques are used for producing microwave and millimeter-wave 3D high-resolution images of multi-layered non-conductive composite structures. This paper presents the utility of SAR algorithms when imaging non-planar geometries. Generally, curved and non-planar geometries result in distortion and clutter in the produced SAR images due to variations in refractions and distances to the surface of the sample. These variations result in distortion and clutter in the resultant SAR images. Furthermore, multi-layered SAR algorithms are not applicable to such geometries, which limits the efficacy of the SAR algorithm. In order to reduce image distortion and allow for using multi-layered SAR algorithm, a method is described and used to compensate for variations in the distance of the antenna to the sample surface. After this compensation, multi-layered SAR algorithms are used to produce focused and clutter-free images.
Analisis Sentimen Opini Publik Terhadap Chatgpt di Twitter Menggunakan Metode Naive Bayes

The presence of ChatGPT, which is based on Artificial Intelligence (AI), has attracted public attention and become a polemic. This application is very helpful for users in finding information, translating languages, and generating text similar to human speech. OpenAI's decision to launch the ChatGPT Plus service is related to its monetization strategy and maintaining user interest. In this regard, a study was conducted using the Naïve Bayes method to classify the sentiment of the Twitter community towards ChatGPT in the period 30/11/2022 to 31/12/2022. This study took 5001 tweet data from the available dataset, and used TF-IDF feature extraction and BOW feature extraction, with data division into 70% training data and 30% test data. This research gets the highest accuracy value of 59% using BOW feature extraction.
Enhancing Night-to-Day Image Translation with Semantic Prior and Reference Image Guidance

Current unpaired image-to-image translation models deal with the datasets on unpaired domains effectively but face the challenge of mapping images from domains with scarce information to domains with abundant information due to the degradation of visibility and the loss of semantic information. To improve the quality of night-to-day translation further, we propose a novel image translation method named “RefN2D-Guide GAN” that utilizes reference images to improve the adaptability of the encoder within the generator through the feature matching loss. Moreover, we introduce a segmentation module to assist in preserving the semantic details of the generated images without the need for ground true annotations. The incorporation of the embedding consistency loss differentiates the roles of the encoder and decoder and facilitates the transfer of learned representation to both translation directions. Our experimental results show that our proposed method can effectively enhance the quality of night-to-day image translation on the night training set of the ACDC dataset and achieve higher mIoU on the translated images.
SMST: A Saliency Map to Scanpath Transformer

Regarding the virtual reality (VR) environment, scanpath prediction is critical for saving rendering resources and guiding content design due to its omnidirectional characteristic. However, only a few scanpath prediction models have been proposed, compared with the blossoming of saliency map prediction models. Focusing on scanpath prediction in the omnidirectional image area, this paper introduces a novel model that transforms the predicted saliency map into a potential scanpath, named saliency map to scanpath transformer (SMST). The model comprises three parts, filtering, clustering, and routing. Given a predicted saliency map in the VR environment, we first filter out mediocre areas and obtain the filtered saliency map. We then acquire a fixation set by clustering the filtered saliency map based on saliency distribution and taking the centers of the resulting clusters as potential fixation points. The fixation points are then connected, with the weights of the connections defined by the cylindrical distance and the primary visual feature of the original image. Based on the network composed of fully connected fixation points, the scanpath prediction model is converted to a routing problem, which aims to find the optimal route that reaches all the fixation points once and only once. We then propose a scanpath prediction model using an ant colony optimization algorithm. We evaluate the proposed model on multiple kinds of predicted saliency maps, and the prediction performance is promising.
Data Protection: Privacy-preserving Data Collection with Validation

The ubiquitous data collection has raised potential risks of leaking physical and private attribute information associated with individuals in a collected dataset. A data collector who wants to collect data for provisioning its machine learning (ML)-based services requires establishing a privacy-preserving data collection protocol for data owners. In this work, we design, implement, and evaluate a novel privacy-preserving data collection protocol. Specifically, we validate the functionality of the data collection protocol on behalf of data owners. First, the ML-based services are not always predefined, it is challenging for a data collector to combat inference of private attributes and user identity from the collected data while maintaining the utility of data. To address the challenge, we reconstruct the data by designing a data transformation model based on the autoencoder and clustering. Second, it is necessary to ensure that the reconstructed data satisfy certain privacy-preserving properties as untrusted data collectors can provide the data transformation models. Therefore, we utilize detection models and design an efficient enclave-based mechanism to validate that the reconstructed data's private attribute estimation probability is bounded by the predefined thresholds. Extensive experiments demonstrate our protocol's effectiveness, such as significantly reducing the accuracy of private attribute detection
Preface: IV 2023

Do aspects of our lives depend on and are driven by data, information, knowledge, user experience, and cultural influences in the current information era? Does the infrastructure of any information-dependent society rely on the quality of data, information, and analysis of such entities from past and present and projected future activities and, most importantly, how it is intended to be applied? Information Visualization, Analytics, Machine Learning, Artificial Intelligence, and Application domains are state-of-the-art developments that effectively enhance understanding of these well-established drivers. Several key interdependent variables are emerging that are becoming the focus of scientific activities, such as Information and Data Science. Aspects tightly tie raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition. Its dependencies on the application domain and its evolution steer the next generation of research activities. From raw data to knowledge, processing the relationship between these phases has added new impetus to understanding and communicating these. The tradition of use and communication by visualization is deep-rooted. It helps us investigate new meanings for the humanities, history of art, design, human factors, and user experience, leading to knowledge discoveries and hypothesis analysis. Modern-day computer-aided analytics and visualization have added momentum in developing tools that exploit metaphor-driven techniques within many applied domains to simply storytelling through data. The methods are developed beyond visualization to simplify the complexities, reveal ambiguity, and work with incompleteness. The next phase of this evolving field is to understand uncertainty, risk analysis, and tapping into unknowns; this uncertainty is built into all stages of the processes, from raw data to the knowledge acquisition stage. But there is a new twist: fast-developing generative AI with ever-increasing access to data outsmarting humans in decision-making. A new evolutionary step in the human journey, no doubt.
Multi-Hypothesis Marginal Multi-Target Bayes Filter for a Heavy-Tailed Observation Noise

A multi-hypothesis marginal multi-target Bayes filter for heavy-tailed observation noise is proposed to track multiple targets in the presence of clutter, missed detection, and target appearing and disappearing. The proposed filter propagates the existence probabilities and probability density functions (PDFs) of targets in the filter recursion. It uses the Student’s t distribution to model the heavy-tailed non-Gaussian observation noise, and employs the variational Bayes technique to acquire the approximate distributions of individual targets. K-best hypotheses, obtained by minimizing the negative log-generalized-likelihood ratio, are used to establish the existence probabilities and PDFs of targets in the filter recursion. Experimental results indicate that the proposed filter achieves better tracking performance than other filters.
Improving Students’ Speaking Ability through Morning English Program Extracurricular Activities in Indonesia

This study aim was to describe the activities of the Morning English Program (MEP) and their impact on improving students’ speaking skills. This study used a qualitative descriptive research method. To collect the data, the researchers of the administered questionnaire were divided into two groups: an English teacher in Indonesia at SMK Bhakti Mulia Pare and 44 of 1the 11th grade students who became members of the MEP. Then, the researcher interviewed five students and one teacher to expand the information received. Data analysis carried out was collecting data, displaying data, and drawing conclusions. The reliability of the study data was checked through data triangulation. The results of1this study identified the MEP activities consisted of speaking, reading aloud, telling stories and group discussions. Following the implementation of these activities, students' speaking skills which include grammatical accuracy, pronunciation skills, vocabulary mastery, fluency, and the ability to organize concepts while speaking. In addition, students experienced lower anxiety and increased interest in learning English. This positive impact enhances the teacher's role in using appropriate teaching methods and creating a positive learning environment. Conclusion. Henceforth, this study illustrates the importance of planning before implementing an English program and adopting similar activities to improve students' speaking skills.
CT scan pancreatic cancer segmentation and classification using deep learning and the tunicate swarm algorithm

Pancreatic cancer (PC) is a very lethal disease with a low survival rate, making timely and accurate diagnoses critical for successful treatment. PC classification in computed tomography (CT) scans is a vital task that aims to accurately discriminate between tumorous and non-tumorous pancreatic tissues. CT images provide detailed cross-sectional images of the pancreas, which allows oncologists and radiologists to analyse the characteristics and morphology of the tissue. Machine learning (ML) approaches, together with deep learning (DL) algorithms, are commonly explored to improve and automate the performance of PC classification in CT scans. DL algorithms, particularly convolutional neural networks (CNNs), are broadly utilized for medical image analysis tasks, involving segmentation and classification. This study explores the design of a tunicate swarm algorithm with deep learning-based pancreatic cancer segmentation and classification (TSADL-PCSC) technique on CT scans. The purpose of the TSADL-PCSC technique is to design an effectual and accurate model to improve the diagnostic performance of PC. To accomplish this, the TSADL-PCSC technique employs a W-Net segmentation approach to define the affected region on the CT scans. In addition, the TSADL-PCSC technique utilizes the GhostNet feature extractor to create a group of feature vectors. For PC classification, the deep echo state network (DESN) model is applied in this study. Finally, the hyperparameter tuning of the DESN approach occurs utilizing the TSA which assists in attaining improved classification performance. The experimental outcome of the TSADL-PCSC method was tested on a benchmark CT scan database. The obtained outcomes highlighted the significance of the TSADL-PCSC technique over other approaches to PC classification.
Lightweight Graph Convolutional Collaborative Filtering Recommendation Approach Incorporating Social Relationships

Graph convolutional network (GCN) has rapidly developed in various fields due to its powerful modeling capability. However, most of the researches directly inherit the complex design of GCN, such as feature transformation and nonlinear activation, which lacks thorough ablation analysis on GCN. In addition, implicit feedback is not fully utilized and data sparsity is not well resolved, which are also shortcomings of current recommendation algorithms. To solve the above problems, this paper proposes a lightweight graph convolutional collaborative filtering (F-LightGCCF) recommendation approach incorporating social relationships. Firstly, it abandons the design of feature transformation and nonlinear activation in graph convolutional models and simplifies model training. Additionally, a series of intermediate feedback from users’ implicit negative feedback is generated by taking advantage of social networks, which improves the utilization of implicit negative feedback. Secondly, it can model the long-range dependencies between users and items by using the dual attention mechanism, aggregating the contribution values of neighboring nodes and the importance of the learning vectors in each layer of the graph convolution layer respectively. Lastly, the inner product operation is used to obtain the association score between users and items. Extensive experiment results on two real-world datasets show that F-LightGCCF outperforms existing state-of-the-art recommendation methods. Further ablation studies and analyses validate the efficiency and effectiveness of the F-LightGCCF model.
A seven-parameter BRDF model for retro-reflection

A new seven-parameter BRDF model inspired by five-parameter BRDF model and fourteen-parameter BRDF model was developed. A retro-reflection part due to the back-reflected was introduced in the seven-parameter BRDF model. To assess the performance of the seven-parameter BRDF model, we measured BRDF of three kinds of samples and simulated the undetermined parameters of the five-parameter BRDF model and the seven-parameter BRDF model using the hybrid artificial bee colony algorithm, respectively. The experimental results demonstrate that the seven-parameter BRDF model has a good agreement with the retro-reflection peak and outperforms the five-parameter BRDF model in accuracy.
Indexing Identifier Services

Where do you look for identifiers? If you’re looking for a URI, the IANA has a registry of schemes, like https, mailto, and tel. These days, to resolve an identifier, you generally use the https scheme, which has an authority component in its URI format.
INDUSTRIAL INTERNET OF THINGS (IIOT) - SECURITY WEAKNESSES AND MOST COMMON TYPES OF ATTACKS – A SYSTEMATIC LITERATURE REVIEW

In recent years there has been a growing interest in the Industrial Internet of Things (IIoT) coming from business and scientific communities alike. One of the elementary concepts of the proposed Industry 4.0 is the IIoT which proposes the implementation of the regular Internet of Things (IoT) concept on a much larger scale within the industrial facilities, thus interconnecting devices in industrial settings. While the main focus of the scientific community is on the cost/benefit analysis and practical application of the mentioned concept, one often overlooked aspect is its security. The following paper presents a comprehensive systematic literature review for the Industrial Internet of Things security. It contains a review of the most common types of attacks committed within the Industrial Internet of Things and a consequential analysis of the weaknesses those attacks exposed.
Norman Henry Anderson (1925–2022).

This article memorializes Norman Henry Anderson (1925-2022), best known for his information integration theory (IIT). Norman Anderson's work was influential in its time, and his legacy endures. He was the recipient of the 1972 American Association for the Advancement of Science Prize for Behavioral Science Research, and, as a tribute to his work, scholars in the field established a conference that continues to this day: the International Information Integration Theory/Functional Measurement Conference. Highlights of Anderson's career and professional contributions are noted. (PsycInfo Database Record (c) 2023 APA, all rights reserved).