Reviewerly/exHarmony huggingface dataset:
IDENTIFICATION OF AREAS OF HIGH POTENTIAL-BEARING MINERALIZATIONS, EASTERN DESERT OF EGYPT: INSIGHTS FROM AIRBORNE GAMMA-RAY SPECTROMETRIC DATA

A major number of mineral deposits are related to hydrothermal processes. Therefore, the mapping of the hydrothermally alteration areas, connected with mineralization, is crucial in the search for metal deposits. To achieve this purpose, areas of hydrothermal alterations are targeted by processing the airborne gamma-ray spectrometric data to detect areas enriched in potassium that may indicate the existence of potassic alteration zones, which are often associated with hydrothermal alterations accompanied by mineral deposits. Data processing and analysis were performed using the K/eTh ratio, deviation of ideal potassium (Kd) and F-parameter. Besides, determining the environmental radiation risk due to the fact that the study area contains many excavation sites for various mining materials and quarries. Hydrothermal alteration indicator maps show five known mineralizations of gold, copper, molybdenum, fluorite and wolframite, which are associated with hydrothermal processes that took place in the study area. It is possible to note the intimate correlation of mineral deposits present in areas with high potassium content. Positive correlation between the computed hydrothermal alteration indicators (K/eTh, Kd and F-parameter), shown on the ternary image map, revealed favorable high and intermediate targets for the detection of various mineral deposits in the study area. On the other hand, the estimated mean values of absorbed dose rate for all rock units were within the permissible range (28-120 nGy h-1), and the mean annual effective dose rate was below the permissible limit of 1.0 mSv y-1 for these rocks.
Asymmetric Secure Multi-party Signing Protocol for the Identity-Based Signature Scheme in the IEEE P1363 Standard for Public Key Cryptography

The identity-based signature (IBS) is an essential cryptographic primitive for secure communication in electronic commerce and IoT network. Since there are various kinds of devices corresponding to diversified security requirements, it could be a worthwhile trade-off to preserve the secret key by signing in a multi-party setting. However, most multi-party setting solutions start with resetting their one-time secret subkey, which could be a potential leakage of sensitive information. To tackle this question, we proposed an asymmetric secure multi-party signing protocol for the identity-based signature in the IEEE P1363 standard. Specially, our multi-party signature scheme is proved to be secure against a static malicious adversary corrupting all-but-two parties under the Paillier encryption scheme’s security assumptions. What’s more, the performance will be presented in a theoretical analysis way to show our scheme holds reasonable communication traffic and computation cost.
Thinking Outside of the Box: Engaging Auditors’ Innovation Mindset to Improve Auditors’ Fraud Judgments and Actions in a Data-Analytic Environment

Data analytics has the potential to help auditors effectively respond to audit risk and improve detection of misstatements and fraud. However, harnessing these benefits may require auditors to adopt an innovation mindset. Firms and regulators have urged auditors to adopt an innovation mindset, however, the impact of this mindset on audit quality has yet to be studied. Innovation is defined as creativity in action. Therefore, building on psychology theory on creativity, we develop an innovation mindset intervention designed to enhance auditors’ cognitive flexibility and improve their ability to generate effective fraud audit procedures when interpreting data analytic output. Further, with the advent of data analytics auditors are often encouraged to provide value-added insights to the client during the audit, in addition to their primary goal of obtaining high audit quality. While regulators have expressed concern about potential unintended consequences of this secondary goal, we draw from psychology research and predict that it may in fact strengthen the innovation mindset effect. We experimentally demonstrate as in practice, that auditors without an innovation mindset struggle to effectively identify fraud. However, the innovation mindset intervention significantly improves auditors’ development of effective fraud-related audit procedures. Moreover, this effect is amplified when auditors are asked to generate client insights, as this intervening goal boosts creativity, thus further enhancing auditors’ decision-making quality. Thus, our results are informative to research, practice, and standard setting.
Image Semantic Segmentation for Autonomous Driving Based on Improved U-Net

Image semantic segmentation has become an essential part of autonomous driving.To further improve the generalization ability and the robustness of semantic segmentation algorithms, a lightweight algorithm network based on Squeeze-and-Excitation Attention Mechanism (SE) and Depthwise Separable Convolution (DSC) is designed.Meanwhile, Adam-GC, an Adam optimization algorithm based on Gradient Compression (GC), is proposed to improve the training speed, segmentation accuracy, generalization ability and stability of the algorithm network.To verify and compare the effectiveness of the algorithm network proposed in this paper, the trained network model is used for experimental verification and comparative test on the Cityscapes semantic segmentation dataset.The validation and comparison results show that the overall segmentation results of the algorithm network can achieve 78.02% MIoU on Cityscapes validation set, which is better than the basic algorithm network and the other latest semantic segmentation algorithms network.Besides meeting the stability and accuracy requirements, it has a particular significance for the development of image semantic segmentation.
HTFuzz: Heap Operation Sequence Sensitive Fuzzing

Heap-based temporal vulnerabilities (i.e., use-after-free, double-free and null pointer dereference) are highly sensitive to heap operation (e.g., memory allocation, deallocation and access) sequences. To efficiently find such vulnerabilities, traditional code coverage-guided fuzzing solutions could be promoted by integrating heap operation sequence feedback. But current sequence sensitive solutions have limitations in practice.
Which Exception Shall We Throw?

Although the exception handling mechanism is critical for resolving runtime errors, bugs inside this process can have far-reaching impacts. Therefore, researchers have proposed various approaches to assist catching and handling such thrown exceptions and to detect corresponding bugs. If the thrown exceptions themselves are incorrect, their errors will never be correctly caught and handled. Like bugs in catching and handling exceptions, wrong thrown exceptions have caused real critical bugs. However, to the best of our knowledge, no approach has been proposed to recommend which exceptions shall be thrown. Exceptions are widely adopted in programs, often poorly documented, and sometimes ambiguous, making the rules of throwing correct exceptions rather complicated. A project team can leverage exceptions in a way totally different from other teams. As a result, even experienced programmers can have difficulties in determining which exception shall be thrown, although they have the skills to implement its surrounding code. In this paper, we propose the first approach, ThEx, to predict which exception(s) shall be thrown under a given programming context. The basic idea is to learn a classification model from existing thrown exceptions in source files. Here, the learning features are extracted from various code information surrounding the thrown exceptions, such as the thrown locations and related variable names. Then, given a new context, ThEx can predict its best exception(s).
IGraph/M: graph theory and network analysis for Mathematica

IGraph/M 1 is an efficient general purpose graph theory and network analysis package for Mathematica (Wolfram Research, Inc., 2022).IGraph/M serves as the Wolfram Language interfaces to the igraph C library (Csárdi et al., 2022;Csárdi & Nepusz, 2006), and also provides several unique pieces of functionality not yet present in igraph, but made possible by combining its capabilities with Mathematica's.The package is designed to support both graph theoretical research as well as the analysis of large-scale empirical networks.
The Dimensionality of Perceived Augmented Reality Technological Attributes (ARTA) in Using the Virtual Fitting System: Testing Hierarchical Factor Structural Models

The purpose of this study was to identify the underlying dimensions of AR technological attributes (ARTA), and estimate the hierarchical factor models for using a virtual fitting system in fashion retailing. The sample in this study consisted of consumers in their 20s and 30s. A total of 173 usable responses were obtained from volunteers who agreed to participate in the lab experiments and a survey with a questionnaire. The first-order confirmatory factor analysis confirmed that perceived ARTA consisted of five factors: augmentation, vividness, visual comfort, responsiveness, and organized system in using the virtual fitting system. Also, the estimated second-order factor model showed that the five ARTA factors were predicted by higher-order two constructs: the first
Combination of Deep Learner Network and Transformer for 3D Human Pose Estimation

Deep neural networks (DNNs) have attained the maximum performance today not just for human pose estimation but also for other machine vision applications (e.g., semantic segmentation, object detection, image classification). Besides, the Transformer shows its good performance for extracting the information in temporal information for video challenges. As a result, the combination of deep learner and transformer gains a better performance than only the utility one, especially for 3D human pose estimation. At the start point, input the 2D key point into the deep learner layer and transformer and then use the additional function to combine the extracted information. Finally, the network collects more data in terms of using the fully connected layer to generate the 3D human pose which makes the result increased precision efficiency. Our research would also reveal the relationship between the use of the deep learner and transformer. When compared to the baseline-DNNs, the suggested architecture outperforms the baseline-DNNs average error under Protocol 1 and Protocol 2 in the Human3.6M dataset, which is now available as a popular dataset for 3D human pose estimation.
An End-to-End Underwater-Image-Enhancement Framework Based on Fractional Integral Retinex and Unsupervised Autoencoder

As an essential low-level computer vision task for remotely operated underwater robots and unmanned underwater vehicles to detect and understand the underwater environment, underwater image enhancement is facing challenges of light scattering, absorption, and distortion. Instead of using a specific underwater imaging model to mitigate the degradation of underwater images, we propose an end-to-end underwater-image-enhancement framework that combines fractional integral-based Retinex and an encoder–decoder network. The proposed variant of Retinex aims to alleviate haze and color distortion in the input image while preserving edges to a large extent by utilizing a modified fractional integral filter. The encoder–decoder network with channel-wise attention modules trained in an unsupervised manner to overcome the lack of paired underwater image datasets is designed to refine the output of the Retinex. Our framework was evaluated under qualitative and quantitative metrics on several public underwater image datasets and yielded satisfactory enhancement results on the evaluation set.
Application of Gray Scale Matrix Technique for Identification of Lombok Songket Patterns Based on Backpropagation Learning

Songket is a woven fabric created by prying the threads and adding more weft to create an embossed decorative pattern on a cotton or silk thread woven background. While songket from many places share similar motifs, when examined closely, the motifs of songket from various regions differ, one of which is in the Province of West Nusa Tenggara, namely Lombok Island. To assist the public in recognizing the many varieties of Lombok songket motifs, the researchers used digital image processing technology, including pattern recognition, to distinguish the distinctive patterns of Lombok songket. The Gray Level Co-occurrence Matrix (GLCM) technique and Backpropagation Neural Networks are used to build a pattern identification system to analyze the Lombok songket theme. Before beginning the feature extraction process, the RGB color image has converted to grayscale (grayscale), which is resized. Simultaneously, a Backpropagation Neural Network is employed to classify Lombok songket theme variations. This study used songket motif photos consisting of a sample of 15 songket motifs with the same color theme that was captured eight times, four of which were used as training data and kept in the database. Four additional photos were utilized as test data or data from sources other than the database. When the systemâ€™s ability to recognize the pattern of Lombok songket motifs is tested, the maximum average recognition percentage at a 0Â° angle is 88.33 percent. In comparison, the lowest average recognition percentage at a 90Â° angle is 68.33 percent.
Image Detection for Common Human Skin Diseases in Indonesia Using CNN and Ensemble Learning Method

Skin disease is a common health problem throughout the world which is one of the main causes of global disease. Skin and subcutaneous diseases managed to contribute 1.79% of global diseases and also became the fourth leading cause of the burden of non-fatal diseases and disability in 2013. Indonesia was ranked 29th out of 195 countries in Asia which indirectly contributed to in contributing to the transmission of skin diseases due to several causes such as lack of access to health care services, poor hygiene conditions, and also population density. Based on the information revealed in the book entitled illustrated guide on various skin diseases commonly found in Indonesia, it is stated that skin diseases ranging from herpes, ringworm, chickenpox, scabies, to psoriasis are often found in Indonesia. With current technological advances, it is possible for humans to be able to recognize various skin diseases with the help of the Convolutional Neural Network (CNN) Method. A total of 1203 images containing types of skin diseases such as herpes simplex, pityriasis, psoriasis, tinea corporis, scabies, and also vitiligo will be a class in the classification process, but because most images are still unbalanced and do not have strong object elements, it is necessary to do this. data preparation and data balancing is also needed so that the architectural model will not be difficult to learn. By using k-fold cross validation and carrying out the ensemble method, the results of the model evaluation will be in the form of an accuracy matrix where the results of each model will be compared and it will be determined which model is the best based on the results obtained. The test results that produce Cross Validation show that the RGB image is superior where the accuracy value obtained is 49% and the Grayscale image has an accuracy of 47%. however, when compared with the ensemble results, Grayscale images have superior accuracy results, namely the accuracy results are 93% and RGB images produce only 86.
An Analysis of the Prevention and Detection of Cross Site Scripting Attack

As technology progresses, web based applications are becoming more vulnerable and threats to it’s security is increasing day by day. One of the ways by which an attacker can attack any web application is cross site scripting attack. The loop holes in a web based application can be exploited by a hacker in ways like, session-hijacking, cookie-stealing, malicious redirection etc. In this survey paper we focuses on the current XSS attack detection techniques and their limitations.
LASUA: A Lightweight Authentication Scheme with User Anonymity for IoT-Enabled Mobile Cloud

Mobile Cloud Computing (MCC) also known as on-demand computing uses cloud computing to deliver applications to mobile devices. This new computational paradigm model which plays a big part in the Internet of Things (IoT), has increased its popularity even more during Covid-19 pandemic and became a necessity when schools, businesses and hospitals must work remotely. We can access and process remote data which are stored over the cloud server in real-time by connecting to a wireless network. For accessing any cloud server, a mutual authentication and key agreement between a mobile user and a cloud server provider is required. However, existing authentication schemes for MCC fail to provide user anonymity, server anonymity and user untraceability. Therefore, we propose a Lightweight Authentication Scheme with User Anonymity (LASUA) which artfully employs Elliptic Curve Cryptography (ECC), random number, time stamps, one-way hash functions, concatenation, XOR operations and fuzzy extractor for biometric to enable various security features including anonymity and resistance against various attacks. LASUA utilises the hardness of ECC to provide top-notch security with low computation and communication cost, a perfect solution for resource constrained devices.
A Two-layer Game-based Incentive Mechanism for Decentralized Crowdsourcing

Decentralized crowdsourcing removes the dependence on a trusted centralized platform based on blockchain and ensures system stability through the consensus of miners. The lack of centralized supervision requires all kinds of system nodes to voluntarily participate while their behaviors are profit-driven and unpredictable, thus introducing challenges to system performance. Moreover, the nodes in a decentralized crowdsourcing system inherently observe little information about the system status; therefore, it is difficult for them to discover and adopt theoretically optimal strategies. Current literature still lacks an effective mechanism to motivate the participation of all types of nodes. To this end, this paper employs a two-layer game model to simulate the interactions in the decentralized crowdsourcing system for investigating the participation willingness of different system nodes. Specifically, we apply a Stackelberg game to model the interactions of a crowdsourcing requester and other nodes, where the requester decides its reward policy and the others respond by selecting their roles to play. In addition, the interaction of the bounded rational other nodes is further represented as an evolutionary game. After analyzing the game model, we further design an incentive mechanism to maximize the requester utility while motivating other nodes to actively participate in the crowdsourcing. Through experimental simulations, we verify the effectiveness of the proposed incentive mechanism.
Imaging as a (pre)clinical tool in parasitology

Imaging of parasites is central to diagnosis of many parasitic diseases and has thus far played an important role in the development of antiparasitic strategies. The development of novel imaging technologies has revolutionized medicine in fields other than parasitology and has also opened up new avenues for the visualization of parasites. Here we review the role imaging technology has played so far in parasitology and how it may spur further advancement. We point out possibilities to improve current microscopy-based diagnostic methods and how to extend them with radiological imaging modalities. We also highlight in vivo tracking of parasites as a readout for efficacy of new antiparasitic strategies and as a source of fundamental insights for rational design.
A Comprehensive Study: Image Forensic Analysis Traditional to Cognitive Image Processing

Image and video forensics is the biggest challenge in current digital era due to rapid change or modification in digital content by using lots of available free software and editing tools. Authenticity of image and video is explained from image acquisition phase to storage phase through various forensics methods. The aim of this survey is to investigate image and video forensics methods using the intrinsic footprints of digital image during the entire image processing life cycle in the traditional image processing paradigm to cognitive image processing paradigm.
Privacy Pro: Spam Calls Detection Using Voice Signature Analysis and Behavior-Based Filtering

Voice spam has become a critical problem that has caused many hassles in people's lives in past years. With the emergence of cold business calls, Voice Spam Calls Detection has gained considerable attention from the research community. Previous approaches to solving this problem are overly complicated, lack precise results, and are challenging to implement in real time. This work aims to provide a simplified framework based on caller behavior patterns to create an anti-spam approach. The strategy assumes that fraudsters with a profit motive act differently than genuine callers and have a distinctive voice pattern. Such unique patterns can be generalized and combined with simple mathematical approaches to aid in filtering spam calls. The suggested approach is appropriate for identifying spam calls in many contexts and is more effective than current spam call defense strategies.
Deploy on extranet based on CAWS Anywhere Server Volunteer meteorological observation station

The CAWSAnyWhere Server2013 unified data collection platform for regional automatic stations can be compatible with various types of automatic weather station equipment from various manufacturers, and is suitable for the central station operation management software of voluntary meteorological observation stations.CAWSAnyWhere Server2013 is used to deploy voluntary meteorological observation stations on the Internet to realize unified data receiving and processing of automatic weather stations.Users of all industries can obtain and query real-time and historical observation data more easily by installing and applying the application software of the central station.The installation, setting and commissioning of the central station and the application software of CAWSAnyWhere Server2013 require the close cooperation of technical personnel of meteorological equipment support, network management and manufacturers.Through the analysis of examples, it is proposed that the technical key points of CAWSAnyWhere Server2013 deployment of voluntary meteorological observation stations on the Internet include: DTU debugging of collector, software configuration and debugging of central station, database environment configuration, network environment configuration, application terminal software development and application, etc.
Research on a New Mode of Integration of Production and Education for Applied Undergraduates

The integration of production and education is one of the important measures to deepen the reform of my country's talent training model.In the current practice of applying the integration of production and education in the application-oriented undergraduate course, the teaching application value of the integration of production and education can also be found, which can make the difference between schools and enterprises.Link up and jointly cultivate applied talents.Change the traditional educational ideology and educational system, strengthen the integration, and break the existing educational predicament.From the perspective of the path and new model of the integration of production and education in application-oriented undergraduate colleges, this paper discusses the new model of application-oriented undergraduate talent training under the background of the integration of production and education.