Reviewerly/exHarmony huggingface dataset:
Decision Support System for Determining the Best Customer Using the Simple Multi-Attribute Rating Technique (SMART)

&#x0D; Jaya Bila Makmur is a nine-ingredient grocery store with products sold including cooking oil, instant noodles, rice, margarine, sugar, salt, and so on. With the continuous increase in the number of similar grocery stores on the market, business owners must have a strategy so that customers remain loyal and don't move elsewhere. One of the strategies is to give rewards or gifts to loyal customers for their cooperation so far, but the decisions taken by business owners are still not quite right. Then a decision support system (DSS) is needed that is able to provide alternative solutions. This system was built using PHP and MySQL, modeled using UML, and tested with Blackbox Testing. The method used in DSS uses the Simple Multi-Attribute Rating Technique (SMART) for multi-criteria decision making. The findings of this study are intended to assist in identifying the best customers who shop frequently and will become repeat customers. By implementing this decision-support system, business owners can improve their competitiveness and competence in the business world. From the results of the application built, it is hoped that it will make it easier for users to choose and determine the best customers according to the specified criteria.&#x0D; &#x0D;
Nonlinear Encryption for Multiple Images Based on a Joint Transform Correlator and the Gyrator Transform

A novel nonlinear encryption–decryption system based on a joint transform correlator (JTC) and the Gyrator transform (GT) for the simultaneous encryption and decryption of multiple images in grayscale is proposed. This security system features a high level of security for the single real-valued encrypted image and a high image quality for the multiple decrypted images. The multispectral or color images are considered as a special case, taking each color component as a grayscale image. All multiple grayscale images (original images) to encrypt are encoded in phase and placed in the input plane of the JTC at the same time without overlapping. We introduce two random-phase masks (RPMs) keys for each image to encrypt at the input plane of the JTC-based encryption system. The total number of the RPM keys is given by the double of the total number of the grayscale images to be encrypted. The use of several RPMs as keys improves the security of the encrypted image. The joint Gyrator power distribution (JGPD) is the intensity of the GT of the input plane of the JTC. We obtain only a single real-valued encrypted image with a high level of security for all the multiple grayscale images to encrypt by introducing two new suitable nonlinear modifications on the JGPD. The security keys are given by the RPMs and the rotation angle of the GT. The decryption system is implemented by two successive GTs applied to the encrypted image and the security keys given by the RPMs and considering the rotation angle of the GT. We can simultaneously retrieve the various information of the original images at the output plane of the decryption system when all the security keys are correct. Another result due to the appropriate definition of the two nonlinear operations applied on the JGPD is the retrieval of the multiple decrypted images with a high image quality. The numerical simulations are computed with the purpose of demonstrating the validity and performance of the novel encryption–decryption system.
MMPTRACK: Large-scale Densely Annotated Multi-camera Multiple People Tracking Benchmark

Multi-camera tracking systems are gaining popularity in applications that demand high-quality tracking results, such as frictionless checkout. In cluttered and crowded environments, monocular multi-object tracking (MOT) systems often fail due to occlusions. Multiple highly overlapped cameras are capable of recovering partial 3D information. When used properly, 3D data can significantly alleviate the occlusion issue. However, training a multi-camera tracker demands a large-scale multi-camera tracking dataset with diverse camera settings and backgrounds. These requirements make the collection of multi-camera tracking dataset challenging and expensive. The cost of creating such a dataset has limited the availability and scale of datasets in this domain. Instead, we appeal to an auto-annotation system to reduce the cost, which uses overlapped and calibrated depth and RGB cameras to build a 3D tracker and automatically generates the 3D tracking results. The results are manually checked and corrected to ensure the label quality, which is much cheaper than solely manual annotation. Next, the 3D tracking results are projected to each calibrated RGB camera view to create 2D tracking results. In this way, we collect and annotate a large-scale densely labeled multi-camera tracking dataset from five different environments. We have conducted extensive experiments using two real-time multi-camera trackers and a person re-identification (ReID) model under different settings. This dataset provides a reliable benchmark for multi-camera, multi-object tracking systems in cluttered and crowded environments. We expect this benchmark to encourage more research attempts in this domain. Our dataset will be publicly released upon the acceptance of this work.
Table Tennis Track Detection Based on Temporal Feature Multiplexing Network

Recording the trajectory of table tennis balls in real-time enables the analysis of the opponent’s attacking characteristics and weaknesses. The current analysis of the ball paths mainly relied on human viewing, which lacked certain theoretical data support. In order to solve the problem of the lack of objective data analysis in the research of table tennis competition, a target detection algorithm-based table tennis trajectory extraction network was proposed to record the trajectory of the table tennis movement in video. The network improved the feature reuse rate in order to achieve a lightweight network and enhance the detection accuracy. The core of the network was the “feature store &amp; return” module, which could store the output of the current network layer and pass the features to the input of the network layer at the next moment to achieve efficient reuse of the features. In this module, the Transformer model was used to secondarily process the features, build the global association information, and enhance the feature richness of the feature map. According to the designed experiments, the detection accuracy of the network was 96.8% for table tennis and 89.1% for target localization. Moreover, the parameter size of the model was only 7.68 MB, and the detection frame rate could reach 634.19 FPS using the hardware for the tests. In summary, the network designed in this paper has the characteristics of both lightweight and high precision in table tennis detection, and the performance of the proposed model significantly outperforms that of the existing models.
On the accuracy of code complexity metrics: A neuroscience-based guideline for improvement

Complexity is the key element of software quality. This article investigates the problem of measuring code complexity and discusses the results of a controlled experiment to compare different views and methods to measure code complexity. Participants (27 programmers) were asked to read and (try to) understand a set of programs, while the complexity of such programs is assessed through different methods and perspectives: (a) classic code complexity metrics such as McCabe and Halstead metrics, (b) cognitive complexity metrics based on scored code constructs, (c) cognitive complexity metrics from state-of-the-art tools such as SonarQube, (d) human-centered metrics relying on the direct assessment of programmers' behavioral features (e.g., reading time, and revisits) using eye tracking, and (e) cognitive load/mental effort assessed using electroencephalography (EEG). The human-centered perspective was complemented by the subjective evaluation of participants on the mental effort required to understand the programs using the NASA Task Load Index (TLX). Additionally, the evaluation of the code complexity is measured at both the program level and, whenever possible, at the very low level of code constructs/code regions, to identify the actual code elements and the code context that may trigger a complexity surge in the programmers' perception of code comprehension difficulty. The programmers' cognitive load measured using EEG was used as a reference to evaluate how the different metrics can express the (human) difficulty in comprehending the code. Extensive experimental results show that popular metrics such as V(g) and the complexity metric from SonarSource tools deviate considerably from the programmers' perception of code complexity and often do not show the expected monotonic behavior. The article summarizes the findings in a set of guidelines to improve existing code complexity metrics, particularly state-of-the-art metrics such as cognitive complexity from SonarSource tools.
Annual Reports Readability From Linguistic and Communication Perspectives: Systematic Literature Review

This research presents a new theoretical framework through assessing readability research based on the linguistics and communication perspectives to determine the obfuscation probabilities and how to mitigate them. Therefore, this systematic literature review analyzed 219 papers using the SCOPUS and Web of Science databases. Findings show that in every language approach, there is an obfuscation level for annual reports, depending on the weakness of a particular component of the text communication process, starting from the use of a complex writing style and ending with the imposition of specific methods of presentation, while suggesting ways to mitigate the obfuscation.
NNLQP: A Multi-Platform Neural Network Latency Query and Prediction System with An Evolving Database

Deep neural networks (DNNs) are widely used in various applications. The accurate and latency feedback is essential for model design and deployment. In this work, we attempt to alleviate the cost of model latency acquisition from two aspects: latency query and latency prediction. To ease the difficulty of acquiring model latency on multi-platform, our latency query system can automatically convert DNN model into the corresponding executable format, and measure latency on the target hardware. Powered by this, latency queries can be fulfilled with a simple interface calling. For the efficient utilization of previous latency knowledge, we employ a MySQL database to store numerous models and the corresponding latencies. In our system, the efficiency of latency query can be boosted by 1.8 ×. For latency prediction, we first represent neural networks with the unified GNN-based graph embedding. With the help of the evolving database, our model-based latency predictor achieves better performance, which realizes 12.31% accuracy improvement compared with existing methods. Our codes are open-sourced at https://github.com/ModelTC/NNLQP.
MULTIFACETED INVESTIGATION OF PORPHYRY CU-AU-MO DEPOSIT IN HYDROTHERMAL ALTERATION ZONES WITHIN THE GOLD FIELD OF ILESHA SCHIST BELT

This study was carried out to map the distribution of porphyry systems within Ilesha Schist belt Southwestern Nigeria for easy accessibility of commercial gold as well as copper and molybdenum deposits to boost exploitation activities within the area. Airborne magnetic and radiometric data of the area were utilized. The acquired total magnetic intensity map of the field was enhanced with reduction to equator, vertical derivative, analytic signal and spectral analysis. Enhanced total magnetic intensity map of the study area revealed magnetic anomalies related to concealed intrusive rock associated with Cu-Au-Mo deposit at depth ranges from 306 – 421 m. Vertical derivative map revealed shallow magnetic bodies in the southern and western parts, which are probable zones for Cu-Au-Mo deposit. Analytical signal map, with magnetic zones of high intensity value between 0.1 nT/M and 1.7 nT/M highlighted faulted basement blocks impregnated with porphyry the Cu-Au-Mo deposit. Airborne radiometry image shows radioelement distributions for different lithologies. High concentration of potassium signatures observed over some parts of the area revealed hydrothermal alteration zones favourable to concentration of Cu-Au-Mo deposits. Computed Th/k ratio and composite potassium maps enhanced the signature of potassium enrichment associated with hydrothermal alteration zones. Validation of airborne results using geochemical analyses confirms the presence of copper-gold-molybdenum elements within the mapped hydrothermal alteration zones. This research has identified other valuable ore deposit, such as copper and molybdenum, to enhance exploitation activities within the area.
SQL Injection Attack: Quick View

SQL injection is a type of security vulnerability that occurs in database-driven web applications where an attacker injects malicious code into the application to gain unauthorized access to sensitive information. This paper aims to provide a comprehensive and systematic review of the existing methods for preventing and detecting SQL injection attacks. The review covers a range of techniques, including input validation, parameterized queries, and intrusion detection systems, as well as the advantages and disadvantages of each method. The most common prevention techniques include input validation, parameterized queries, and stored procedures, while the most common detection techniques include intrusion detection systems (IDS), honeypots, and signature-based detection. The choice of method will depend on the specific requirements of the organization and the level of security required. Still, a combination of prevention and detection methods is likely to be the most effective way to secure web applications against SQL injection attacks. The paper concludes that SQL injection attacks continue to be a significant security threat to web applications, and it is essential for organizations to implement effective prevention and detection methods to secure their web applications against SQL injection attacks.
Accurate light field depth estimation under occlusion

Epipolar plane images (EPIs) have advantages in light field depth estimation, but the current EPI-based methods only use one aspect of the information related to the line or its surroundings in EPIs. Moreover, most current methods merely extract the depth map of the target view, ignoring the depth information from other views. To fully utilize the available information, we first introduce a novel data cost by comprehensively utilizing the characteristics of pixel consistency on the line and region difference around the line in EPIs to improve the robustness against occlusion and noise. Then, we put forward a multi-view depth integration strategy that copes with weak texture and occlusion areas. Finally, an edging preserving filter is applied to further refine the depth map. Experiments on synthetic and real light field datasets show that the proposed method outperforms the state-of-the-art light field depth estimation algorithms, especially in the presence of occluded pixels.
Label-affinity Self-adaptive Central Similarity Hashing for Image Retrieval

Due to the usage of global similarity, the hashing methods based on predefined hash centers have achieved more accurate retrieval results than the pairwise/triplet-based methods. Nevertheless, the fixed hash centers lack the perception of data distribution and are limited by the pre-determined Hadamard matrix, which consider neither the label semantic information nor the object scale size, resulting in sub-optimal retrieval performance and weak generalization ability. In this paper, we (1) adopt the label semantic information to generate self-adaptive hash centers and (2) propose the label-affinity coefficient ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lac</i> ) that considers the scale size of each label/object appearing in the given image to calculate the real hash centroid for this image. Based on this, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Label-affinity Self-adaptive Central Similarity Hashing (LSCSH)</i> for image retrieval. LSCSH consists of a hash code generator module and a hash center adapter module. First, we obtain the label word vector ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the word vector representation of each class label) via the Word2Vector technique to generate and update the hash centers that adapt to the distribution of both label word vectors and generated hash codes. Second, we learn <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lac</i> to indicate the dominance of different labels corresponding to objects in each given image, which considers the unequal scales of each object (corresponding to a label) to calculate a more accurate hash centroid for each image. Last but not least, we design an asynchronous learning mechanism to enable each hash code and its corresponding hash centroid to adapt to each other dynamically. We conduct extensive experiments on 5 image datasets including CIFAR-10, ImageNet, VOC2012, MS-COCO and NUS-WIDE. The experimental results demonstrate that LSCSH can achieve the state-of-the-art visual retrieval performance on both single-label and multi-label image datasets. The code of this work is released at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/lzHZWZ/LSCSH_sourcecode.git</uri> .
Access control mechanism for the Internet of Things based on blockchain and inner product encryption

The dynamic, massive, and lightweight properties of the Internet of Things device nodes lead to complicated application environments, and traditional access control mechanisms cannot match the current security requirements of the Internet of Things. Although attribute encryption schemes support more fine-grained access control, their overt access policies can endanger users' privacy. To address the above problems, this paper proposes an Internet of Things access control mechanism based on blockchain and inner product encryption. First, the mechanism employs blockchain technology to provide distributed and decentralized access control management in the Internet of Things, allowing for dynamic management of the Internet of Things data while avoiding the problem of a single point of failure. And utilizing the tamper-evident feature of blockchain, this system stores the hash value of the ciphertext in the third-party storage on the chain to ensure the integrity of the ciphertext so that the third-party storage can be trusted. Furthermore, malicious user access is prevented by deploying smart contracts on the blockchain, which provide automatic and traceable access control to massive amounts of Internet of Things data. Then, in order to guarantee data security and user privacy for lightweight Internet of Things devices, fine-grained access control and complete hiding of access policies are achieved using the property of vector representation of attributes in inner product encryption. Finally, the experimental findings reveal that the mechanism has high efficiency while providing security and can meet the particular access control requirements of the Internet of Things.
ProMask: Probability mask representation for skeleton detection

Detecting object skeletons in natural images presents challenges due to varied object scales and complex backgrounds. The skeleton is a highly compressing shape representation, which can bring some essential advantages but cause difficulties in detection. This skeleton line occupies a small part of the image and is overly sensitive to spatial position. Inspired by these issues, we propose the ProMask, which is a novel skeleton detection model. The ProMask includes the probability mask representation and vector router. This skeleton probability mask describes the gradual formation process of skeleton points, which can achieve high detection performance and robustness. Moreover, the vector router module possesses two sets of orthogonal basis vectors in a two-dimensional space, which can dynamically adjust the predicted skeleton position. Experiments show that our approach realizes better performance, efficiency, and robustness than state-of-the-art methods. We consider that our proposed skeleton probability representation will serve as a standard configuration for future skeleton detection, since it is reasonable, simple, and very effective.
Variational quantum approximate support vector machine with inference transfer

A kernel-based quantum classifier is the most practical and influential quantum machine learning technique for the hyper-linear classification of complex data. We propose a Variational Quantum Approximate Support Vector Machine (VQASVM) algorithm that demonstrates empirical sub-quadratic run-time complexity with quantum operations feasible even in NISQ computers. We experimented our algorithm with toy example dataset on cloud-based NISQ machines as a proof of concept. We also numerically investigated its performance on the standard Iris flower and MNIST datasets to confirm the practicality and scalability.
Z-number-valued rule-based classification system

The fuzzy rule-based classification system (FRBCS) is a popular tool for classification problems due to its interpretability and comprehensibility. As an extension of fuzzy numbers, the concept of Z-number is a more appropriate formal structure to describe uncertain and partially reliable information. A Z-number is an ordered pair of fuzzy numbers, where the second fuzzy number describes the reliability of the first one. As a result of its representation capability, it can receive better classification results. However, there is still a gap in the application of Z-numbers to classification problems due to their high computation complexity. To take advantage of the Z-number, we design a simple way to make Z-numbers apply to classification problems. Use the second fuzzy number to adjust the first fuzzy number to fit the training data. Then we create a kind of Z-number-valued if–then rule by extending the fuzzy if–then rule. In addition, a Z-number-valued rule-based classification system (ZRBCS) is developed, including two main processes: rule generation and new pattern classification. The developed system can cover more information than the classic fuzzy rule-based system, which can improve classification effects. The proposed ZRBCS is compared with classical FRBCS with/without certain degrees and three classical classification algorithms. According to statistical tests, ZRBCS is superior to FRBCS and two other algorithms.