Reviewerly/exHarmony huggingface dataset:
cXR+ Voxel-Based Semantic Compression for Networked Immersion

Extended Reality (XR) applications, which may encompass Augmented Reality (AR) and Virtual Reality (VR), commonly involve immersive virtual environments that are based on real physical environments. Transmitting the extensive color and depth image data for representing a physical environment over a communication network to create a corresponding immersive environment at a remote location is very challenging due to the enormous data volumes and the time constraints of real-time immersion. We explore semantic compression, which conveys the semantic meaning of the data through color-codes (CCs) to reduce the transmitted data volume. The creation of an immersive environment conventionally involves five steps (with corresponding output): calibration (single-frame point cloud), registration (registered point cloud), volume reconstruction (voxel frame), marching cubes (meshes), and rendering (3D environment). We develop the novel cXR+ semantic compression that splits the volume reconstruction into a client-side virtual network function (VNF) that represents the registered point cloud as CCs for network transmission to a server-side VNF that decodes the CCs to obtain the voxel frames so as to complete the volume reconstruction. We consider an analogous splitting of the calibration into two VNFs with Graph Coloring (GC) compression of the color and depth image data as a comparison benchmark. Additionally, we consider the transmission of the raw and JPG compressed point cloud data as well as the volume reconstruction at the client-side and subsequent transmission of the voxel frames as benchmarks. We conduct measurements of the compression (computing) time, transmission time, compression ratio, and reconstruction accuracy for real-world data sets so as to elucidate the trade-offs in employing voxel-based semantic compression for transmitting immersive environments over communication networks.
Blockchain data secure sharing protocol based on threshold Paillier algorithm

With the development of Internet technology, secure storage and secure sharing of data have become increasingly important. Traditional data sharing schemes exist a series of problems including lack of security and low efficiency. In this paper, we construct a secure and efficient data sharing scheme based on threshold Paillier algorithm and blockchain technology, which achieves secure data storage and sharing without a third-party institution. Firstly, we propose a (t,l) threshold Paillier blockchain data sharing scheme, which effectively prevents decryption failures caused by the loss of a single node's private key. Secondly, we propose a combined on-chain and off-chain data storage scheme, we store the ciphertext on the cloud server and the ciphertext hash value on the blockchain, which not only ensures the integrity of the data but also solves the storage limitation problem on the blockchain. Finally, we use the simulation paradigm to prove the security of the scheme in the semi-honest model. The discussion results of the comparison and the analysis of performance show that the blockchain data security sharing scheme proposed in this paper has lower computational overhead and higher security than other similar schemes.
Mitigating Cross-Site Request Forgery Threats in the Web

This study focuses on the development of a web browser extension designed to detect and prevent phishing attacks and Cross-Site Request Forgery (CSRF) vulnerabilities. The extension is built using HTML, CSS, and JavaScript and incorporates a machine learning model that was trained using the random forest algorithm. This algorithm was selected due to its high accuracy in comparison to other models tested. The primary function of the extension is to scan a website link for potential vulnerabilities and provide users with real-time protection against phishing attacks. This is achieved by using the trained machine learning model to analyze various characteristics of the website and determine if it poses a risk to the user's security. In addition to providing phishing protection, the extension also offers defense against Cross-Site Request Forgery attacks by preventing unauthorized actions from being executed on a user's behalf. This is achieved by verifying the authenticity of incoming requests and ensuring that only trusted sources are able to execute actions. Overall, this study intends to provide a comprehensive solution for protecting users against phishing attacks and cross-site request forgery vulnerabilities while browsing the web. The web extension is user-friendly and easy to install, making it accessible to a wide range of users.
Sistem Cerdas Penilaian Ujian Essay Menggunakan Metode Cosine Similarity

Technological developments have grown rapidly without exception in the world of education. The development of technology in the field of education can be seen by the number of information systems used for the learning process. The online learning process has many obstacles, especially when the exam is being conducted. Not all educators can conduct online exams by utilizing information technology. One of the most difficult exams is the essay exam. The essay exam that was conducted experienced many obstacles in terms of correcting and assigning grades to students. By utilizing intelligent information technology, this can be solved because everything can be done by the system automatically. This study aims to build an intelligent system for assessing essay exams using the cosine similarity method. In the system built, all the exam processes can be done automatically. The system can correct the answers written by students then the system will also automatically give a value according to the answers entered. The results of the study indicate that the system built can assist teachers in conducting online essay exams effectively and efficiently. This is because the system can correct students' answers by automatically matching the answers from the teacher.
A Robot-Assisted Microscopy System for Digital Image Correlation in Fatigue Crack Growth Testing

Abstract Background Digital image correlation (DIC) with microscopes has become an important experimental tool in fracture mechanics to study local effects such as the plastic zone, crack closure, crack deflection or crack branching. High-resolution light microscopes provide 2D images but the field of view is limited to a small area and very sensitive to its alignment. A flexible positioning system is therefore needed to collect such DIC data during the entire fatigue crack growth process. Objective We present in our paper a new experimental setup for local high-resolution 2D DIC measurements at any location and at any time during fatigue crack growth experiments with a non-fixed DIC microscopy system. Methods We use a robot to move the 2D DIC microscope to any location on the surface of the specimen. Optical and tactile methods automatically adjust the system and ensure highest image quality as well as accurate alignment. In addition, an advanced repositioning method reduces out-of-plane motion effects. Results The robot is able to achieve a repositioning accuracy of less than 0.06 mm in vector space, resulting in very low Von Mises strain scattering of 0.07 to 0.09% in the DIC evaluation. The system minimizes systematic errors caused by translation and rotational deviations. Effects such as crack deflection, crack branching or the plastic zone of a fatigue crack can be investigated with a field of view of 10.2 x 6.4 mm 2 . Conclusions The robot supported DIC system generates up to 8000 high-quality DIC images in an experiment that enables the application of digital evaluation algorithms. Redundant information create confidence in the results as all revealed effects are comprehensible. This increases the information content of a single fatigue crack growth test and accelerates knowledge generation.
Rubbing salt in the wound? A large-scale investigation into the effects of refactoring on security

Software refactoring is a behavior-preserving activity to improve the source code quality without changing its external behavior. Unfortunately, it is often a manual and error-prone task that may induce regressions in the source code. Researchers have provided initial compelling evidence of the relation between refactoring and defects, yet little is known about how much it may impact software security. This paper bridges this knowledge gap by presenting a large-scale empirical investigation into the effects of refactoring on the security profile of applications. We conduct a three-level mining software repository study to establish the impact of 14 refactoring types on (i) security-related metrics, (ii) security technical debt, and (iii) the introduction of known vulnerabilities. The study covers 39 projects and a total amount of 7,708 refactoring commits. The key results show that refactoring has a limited connection to security. However, Inline Method and Extract Interface statistically contribute to improving some security aspects connected to encapsulating security-critical code components. Extract Superclass and Pull Up Attribute refactoring are commonly found in commits violating specific security best practices for writing secure code. Finally, Extract Superclass and Extract & Move Method refactoring tend to occur more often in commits contributing to the introduction of vulnerabilities. We conclude by distilling lessons learned and recommendations for researchers and practitioners.
Forensic linguistics: A scientometric review

Forensic linguistics is a distinct field of study in the science of language that places significant emphasis on the observation of language usage in our daily lives, including spoken and written language, listening, and reading. This focus on language usage provides a legal perspective for the analysis of language. Initially, forensic linguistics was confined to the identification of spoken and written documents in legal settings, police language, and prison language; however, the field has expanded to include speech detection, text detection, plagiarism detection, social media verbal violence detection, social security detection, and discrimination detection. In this study, we examined the development of forensic linguistics through the use of knowledge maps. We conducted a scientometric analysis of 6,490 triangulated documents from three major knowledge databases (Scopus, WOS, and Lens) that were published between 1936 and 2022. The development of forensic linguistics was measured using eight bibliometric indicators and eight scientometric indicators, and we used CiteSpace 5.8.R3 and VOSviewer 1.6.18 software packages to create knowledge maps and tabulations. Our major findings include the identification of commonly used keywords in forensic linguistics, such as human, linguistics, legal translation, language, speech recognition, legal language, authorship attribution, and natural language processing system. Additionally, the following terms were observed to be synonymous with forensic linguistics: linguistic law, language policy, language and law, official language, legal translation, linguistic rights, and legal linguistics. Our scientometric analysis allowed us to group the 6,460 documents in forensic linguistics into various clusters based on research patterns in the field, such as the role of forensic linguists in legal contexts, legal translation, legal composition, forensic voice comparison, authorship attribution, and human language technologies. Other clusters included the use of forensic linguistics in police interview settings, public service and courtroom settings, linguistic rights, as well as online debate. The study has implications for researchers, writers, public speakers, YouTubers, and all social media users, TV reporters, news reporters, and media professionals. In contemporary society, there is a rapid proliferation of ignorance concerning copyrights and the rights of others. Therefore, it is crucial to raise awareness among the general public about these rights.
Transferable adversarial masked self-distillation for unsupervised domain adaptation

Abstract Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to a related unlabeled target domain. Most existing works focus on minimizing the domain discrepancy to learn global domain-invariant representation using CNN-based architecture while ignoring both transferable and discriminative local representation, e.g, pixel-level and patch-level representation. In this paper, we propose the Transferable Adversarial Masked Self-distillation based on Vision Transformer architecture to enhance the transferability of UDA, named TAMS. Specifically, TAMS jointly optimizes three objectives to learn both task-specific class-level global representation and domain-specific local representation. First, we introduce adversarial masked self-distillation objective to distill representation from a full image to the representation predicted from a masked image, which aims to learn task-specific global class-level representation. Second, we introduce masked image modeling objectives to learn local pixel-level representation. Third, we introduce an adversarial weighted cross-domain adaptation objective to capture discriminative potentials of patch tokens, which aims to learn both transferable and discriminative domain-specific patch-level representation. Extensive studies on four benchmarks and the experimental results show that our proposed method can achieve remarkable improvements compared to previous state-of-the-art UDA methods.
Enhancing Phase Measurement by a Factor of Two in the Stokes Correlation

Phase loss is a typical problem in the optical domain, and optical detectors only measure the amplitude distribution of a signal without its phase. However, an optimal phase is desired in a variety of practical applications, such as optical metrology, nondestructive testing, and quantitative microscopy. Several methods have been proposed to quantitatively measure phase, among which interferometry is one of the most commonly used. An intensity interferometer has also been used to recover phase and enhance the phase difference measurement via the intensity correlation. In this paper, we present and examine another technique based on the Stokes correlation for enhancing phase measurement by a factor of two. The enhancement in phase measurement is accomplished through an evaluation of the correlation between two points of Stokes fluctuations of randomly scattered light and by recovering the enhanced phase of the object by using three-step phase shifting along with the Stokes correlations. This technique is expected to be useful for imaging and the experimental measurement of the phase of a weak signal.
Using EfficientNet-B7 (CNN), Variational Auto Encoder (VAE) and Siamese Twins’ Networks to Evaluate Human Exercises as Super Objects in a TSSCI Images

In this article, we introduce a new approach to human movement by defining the movement as a static super object represented by a single two-dimensional image. The described method is applicable in remote healthcare applications, such as physiotherapeutic exercises. It allows researchers to label and describe the entire exercise as a standalone object, isolated from the reference video. This approach allows us to perform various tasks, including detecting similar movements in a video, measuring and comparing movements, generating new similar movements, and defining choreography by controlling specific parameters in the human body skeleton. As a result of the presented approach, we can eliminate the need to label images manually, disregard the problem of finding the start and the end of an exercise, overcome synchronization issues between movements, and perform any deep learning network-based operation that processes super objects in images in general. As part of this article, we will demonstrate two application use cases: one illustrates how to verify and score a fitness exercise. In contrast, the other illustrates how to generate similar movements in the human skeleton space by addressing the challenge of supplying sufficient training data for deep learning applications (DL). A variational auto encoder (VAE) simulator and an EfficientNet-B7 classifier architecture embedded within a Siamese twin neural network are presented in this paper in order to demonstrate the two use cases. These use cases demonstrate the versatility of our innovative concept in measuring, categorizing, inferring human behavior, and generating gestures for other researchers.
Complex Traffic Scene Image Classification Based on Sparse Optimization Boundary Semantics Deep Learning

With the rapid development of intelligent traffic information monitoring technology, accurate identification of vehicles, pedestrians and other objects on the road has become particularly important. Therefore, in order to improve the recognition and classification accuracy of image objects in complex traffic scenes, this paper proposes a segmentation method of semantic redefine segmentation using image boundary region. First, we use the SegNet semantic segmentation model to obtain the rough classification features of the vehicle road object, then use the simple linear iterative clustering (SLIC) algorithm to obtain the over segmented area of the image, which can determine the classification of each pixel in each super pixel area, and then optimize the target segmentation of the boundary and small areas in the vehicle road image. Finally, the edge recovery ability of condition random field (CRF) is used to refine the image boundary. The experimental results show that compared with FCN-8s and SegNet, the pixel accuracy of the proposed algorithm in this paper improves by 2.33% and 0.57%, respectively. And compared with Unet, the algorithm in this paper performs better when dealing with multi-target segmentation.
A Novel Traffic Prediction Method Using Machine Learning for Energy Efficiency in Service Provider Networks

This paper presents a systematic approach for solving complex prediction problems with a focus on energy efficiency. The approach involves using neural networks, specifically recurrent and sequential networks, as the main tool for prediction. In order to test the methodology, a case study was conducted in the telecommunications industry to address the problem of energy efficiency in data centers. The case study involved comparing four recurrent and sequential neural networks, including recurrent neural networks (RNNs), long short-term memory (LSTM), gated recurrent units (GRUs), and online sequential extreme learning machine (OS-ELM), to determine the best network in terms of prediction accuracy and computational time. The results show that OS-ELM outperformed the other networks in both accuracy and computational efficiency. The simulation was applied to real traffic data and showed potential energy savings of up to 12.2% in a single day. This highlights the importance of energy efficiency and the potential for the methodology to be applied to other industries. The methodology can be further developed as technology and data continue to advance, making it a promising solution for a wide range of prediction problems.
Image Edge Detection Based on Fractional-Order Ant Colony Algorithm

Edge detection is a highly researched topic in the field of image processing, with numerous methods proposed by previous scholars. Among these, ant colony algorithms have emerged as a promising approach for detecting image edges. These algorithms have demonstrated high efficacy in accurately identifying edges within images. For this paper, due to the long-term memory, nonlocality, and weak singularity of fractional calculus, fractional-order ant colony algorithm combined with fractional differential mask and coefficient of variation (FACAFCV) for image edge detection is proposed. If we set the order of the fractional-order ant colony algorithm and fractional differential mask to v=0, the edge detection method we propose becomes an integer-order edge detection method. We conduct experiments on images that are corrupted by multiplicative noise, as well as on an edge detection dataset. Our experimental results demonstrate that our method is able to detect image edges, while also mitigating the impact of multiplicative noise. These results indicate that our method has the potential to be a valuable tool for edge detection in practical applications.
Scene‐Aware 3D Multi‐Human Motion Capture from a Single Camera

Abstract In this work, we consider the problem of estimating the 3D position of multiple humans in a scene as well as their body shape and articulation from a single RGB video recorded with a static camera. In contrast to expensive marker‐based or multi‐view systems, our lightweight setup is ideal for private users as it enables an affordable 3D motion capture that is easy to install and does not require expert knowledge. To deal with this challenging setting, we leverage recent advances in computer vision using large‐scale pre‐trained models for a variety of modalities, including 2D body joints, joint angles, normalized disparity maps, and human segmentation masks. Thus, we introduce the first non‐linear optimization‐based approach that jointly solves for the 3D position of each human, their articulated pose, their individual shapes as well as the scale of the scene. In particular, we estimate the scene depth and person scale from normalized disparity predictions using the 2D body joints and joint angles. Given the per‐frame scene depth, we reconstruct a point‐cloud of the static scene in 3D space. Finally, given the per‐frame 3D estimates of the humans and scene point‐cloud, we perform a space‐time coherent optimization over the video to ensure temporal, spatial and physical plausibility. We evaluate our method on established multi‐person 3D human pose benchmarks where we consistently outperform previous methods and we qualitatively demonstrate that our method is robust to in‐the‐wild conditions including challenging scenes with people of different sizes. Code: https://github.com/dluvizon/scene‐aware‐3d‐multi‐human
Artificial Intelligence and Information Processing: A Systematic Literature Review

This study aims to understand the development trends and research structure of articles on artificial intelligence (AI) and information processing in the past 10 years. In particular, this study analyzed 13,294 papers published from 2012 to 2021 in the Web of Science, used the bibliometric analysis method to visualize the data of the papers, and drew a scientific knowledge map. By exploring the development of mainstream journals, author and country rankings, keyword evolution, and research field rankings in the past 10 years, this study uncovered key trends affecting AI progress and information processing that provide insights and serve as an important reference for future AI research and information processing. The results revealed a gradual increase in publications over the past decade, with explosive growth after 2020. The most prolific researchers in this field were Xu, Z.S.; Pedrycz, W.; Herrera-Viedma, E.; the major contributing countries were China, the USA, and Spain. In the AI and information processing research, keywords including “Deep learning”, “Machine learning”, and “Feature extraction” are components that play a crucial role. Additionally, the most representative research areas were “Engineering”, “Operations Research and Management Science”, and “Automation Control Systems”. Overall, this study used bibliometric analysis to provide an overview of the latest trends in artificial intelligence and information processing. Although AI and information processing have been applied to various research areas, many other sub-topics can be further applied. Based on the findings, this study presented research insights and proposed suggestions for future research directions on AI and information processing.