Reviewerly/exHarmony huggingface dataset:
Effective Web Application Vulnerability Testing System Using Proposed XSS_SQL_Scanning_Algorithm

Nowadays, many people use the internet for more than one purposes. Among these purposes, they mostly apply the web application which is one of the internet usage technologies. A web application is composed of a web server and web browser in other terms client-side and server-side. Web applications are typically developed with a limitation of time and usually, application developers make mistakes in the code which can cause application vulnerabilities. If the vulnerability appears, some of the irresponsible people who are attackers will exploit web applications through a vulnerability to obtain some privileges in the system. Due to the widespread use of web applications, it is essential to discover vulnerabilities to avoid the exploitation of web applications. Various well-known scanners are available for detecting vulnerabilities. In this paper, our proposed algorithm can also find out vulnerability as these scanners. The proposed algorithm presented in this paper can find the two types of vulnerability, Structured Query Language (SQL) injection and Cross-site Scripting (XSS) attacks that are a huge risk for victim businesses and they are mostly occur in the web application. Besides, our proposed algorithm applies the Naive pattern matching algorithm even though other several methods completed in the string searching process, because they are still having complexities in constructing the preprocessing phase. Moreover, the response message returned by the proposed algorithm is too short enough to match by this pattern matching algorithm approach. The proposed system does not take too much system memory so that it saves the memory consumption. Finally, we test our proposed system using the well-known scanner and evaluate how accurate the results based on having false negative and false positive rate.
Vehicle Detection using Upper Local Ternary Features with SVM Classification

Local Ternary Pattern (LTP) is an extension of Local Binary Pattern (LBP), which is known as a standard textural descriptor for various recognition systems. There are a lot of usages of LBP and LTP in the variety of pattern recognition in different fields. It is also efficient features for vehicle detection, recognition and monitoring. This paper proposed an algorithm that applied the operator to upper LTP (upper-LTP) matrix as feature vector to calculate instead of pattern stream generation. This paper analyzes the accuracy rate between the Complemented- Uniform Local Binary Pattern (Complemented-ULBP) which is our previous work, Uniform Local Binary Pattern (ULBP), simple Local Binary Pattern (LBP) and the proposed system, Upper-LTP, with SVM classification.
Statistical Models and Machine Learning to Advance Code Completion: Are We There Yet?

Code completion, commonly used by software developers, is the third most important feature in an IDE as code completion helps speed up the process of coding by filling in the desired code and reducing common mistakes. The early, traditional code completion approaches rely on program analysis to produce a long, alphabetically sorted list of potential suggested code elements. More advanced code completion approaches have leveraged statistical models as well as machine learning (including deep learning) in suggesting the code to be completed. This article will systematically review the advances of various code completion approaches and point out the limitations and future direction to further improve the state-of-the-art techniques in this area.
Efficacy of virtual reality-based exposure therapy for the treatment of fear of flying: a systematic review

Abstract Previous research has shown that virtual reality (VR)-based exposure therapy is effective in the treatment of anxiety disorders. However, more information regarding the specific features of this intervention for the treatment of fear of flying (FoF) is needed. The primary aim of this systematic review was to update and analyse the existing data on the efficacy of VR exposure treatment (VRET) in FoF, providing information on the optimal methodological conditions for its administration. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed to select the articles. PsycInfo, Medline and Web of Science were chosen as databases with a wide range of publications related to health and psychology. Thirty-three eligible articles were included in this review. Results showed that participants’ anxiety decreased after being systematically exposed to flight-related VR environments. VRET is at least as effective as other evidence-based treatments, such as cognitive behavioural therapy (CBT) or in vivo exposure therapy (IVET), and therefore is a highly recommended alternative when IVET is difficult to administer, and an excellent complement to enhance CBT efficacy. Regarding sensory cues, the addition of motion feedback synchronised with visual and auditory cues during the exposure to VR environments might improve the efficacy of VRET for FoF, but more research supporting this statement is still needed.
Healthcare and Cybersecurity: Taking a Zero Trust Approach

This article looks at Healthcare and the issues that exist with current cybersecurity measures. As hacks including ransomware attacks become more commonplace it is important to provide safeguards to protect the data of the patients and the healthcare organization. Examples of breaches are looked at with insights on how they happened and what could have been done to prevent them. A newer approach known as Zero-Trust which addresses 7 key areas to protect is reviewed and shown how its applicability to healthcare can make a difference in protecting individuals and organizations.
Autonomous Maneuver Decision-Making Through Curriculum Learning and Reinforcement Learning With Sparse Rewards

Reinforcement learning is an effective approach for solving decision-making problems. However, when using reinforcement learning to solve maneuver decision-making with sparse rewards, it costs too much time for training, and the final performance may not be satisfactory. In order to overcome the shortcomings, the method for maneuver decision-making based on curriculum learning and reinforcement learning is proposed. First, three curricula are designed to address the maneuver decision-making problem: angle curriculum, distance curriculum and hybrid curriculum. They are proposed according to the intuition that closer destinations are easier to arrive at. Then, they are used to train agents and compared with the original method without any curriculum. The training results show that angle curriculum can increase the speed and stability of training, and improve the performance of maneuver decision-making; distance curriculum can increase the speed and stability of agent training; hybrid curriculum is not better than the other curricula, because it makes the agent get stuck at the local optimum. The simulation results show that after training, the agent can handle the situations where targets come from different directions, and the maneuver decision-makings are rational, effective, and interpretable, whereas the method without curriculum is invalid.
A BP Method for Track-Before-Detect

Tracking an unknown number of low-observable objects is notoriously challenging. This letter proposes a sequential Bayesian estimation method based on the track-before-detect (TBD) approach. In TBD, raw sensor measurements are directly used by the tracking algorithm without any preprocessing. Our proposed method is based on a new statistical model that introduces a new object hypothesis for each data cell of the raw sensor measurements. It allows objects to interact and contribute to more than one data cell. Based on the factor graph representing our statistical model, we derive the message passing equations of the proposed belief propagation (BP) method for TBD. Approximations are applied to certain BP messages to reduce computational complexity and improve scalability. In a simulation experiment, our proposed BP-based TBD method outperforms two other state-of-the-art TBD methods.
Impact of Integration of Technology on Teaching and Learning in the Primary Schools Classroom on Economic Growth

This study sought to investigate the impact of technology integration on teaching and learning in the primary school classroom on economic growth. Two hundred teachers from ten public primary schools in Kozani, Greece, were randomly chosen to complete the survey questionnaire for this quantitative research. The data show that most instructors are average users and that many teachers choose to use different technology tools in their office for work rather than in their classroom for improved teaching and learning. Furthermore, the findings reveal that instructors should always be prepared and well-equipped in terms of technical competencies and a positive attitude to give students technology-based learning opportunities to increase their learning quality. Other elements of technology integration, particularly from a management perspective, such as strategic planning and policymaking, should be considered in future research. The study confirmed that an empowered group of pupils with technology skills helps in economic transformation. They can engage in different technology-based innovations that help create jobs, solve business problems, and solve communities’ problems. All this contributes to economic development. Therefore, it is imperative to promote ICT tools in teaching and learning to contribute significantly to sustainable economic growth and development.
Enhanced MQTT Method with IoT Data Priority Controls for Scalability and Realiability on Early Landslide Warning System

Recent catastrophic landslide disasters in Japan have highlighted the need for a quick and reliable warning system. The Early Landslide Warning System, which utilizes many IoT sensors, is considered effective in observing landslides. However, the number of IoT connections and the accuracy of the sensors still need to be improved, especially when compared to conventional survey methods. Therefore, this paper proposes the Enhanced MQTT method for the Early Landslide Warning System, and the proposed method improves connectivity, reduces latency, and minimizes transmitting errors by assigning priority values to IoT data. In detail, the proposed method first assigns the priority value to the user property field in the MQTT header according to the abnormality of the observed IoT data. Secondly, the proposed priority value stores the IoT data in the broker node’s priority queues. Finally, the IoT data is asynchronously transmitted to the publisher from the high-priority queue. Then, this paper reports the experiments of the Enhanced MQTT methods by the prototype system, and the effectivity and the future studies are discussed in the paper.
Geometry-Aware Coverage Path Planning for Depowdering on Complex 3D Surfaces

This letter presents a new approach to obtaining nearly complete coverage paths (CP) with low overlapping on 3D general surfaces using mesh models. The CP is obtained by segmenting the mesh model into a given number of clusters using constrained centroidal Voronoi tessellation (CCVT) and finding the shortest path from cluster centroids using the geodesic metric efficiently. We introduce a new cost function to harmoniously achieve uniform areas of the obtained clusters and a restriction on the variation of triangle normals during the construction of CCVTs. The obtained clusters can be used to construct high-quality viewpoints (VP) for visual coverage tasks. Here, we utilize the planned VPs as cleaning configurations to perform residual powder removal in additive manufacturing using manipulator robots. The self-occlusion of VPs and ensuring collision-free robot configurations are addressed by integrating a proposed optimization-based strategy to find a set of candidate rays for each VP into the motion planning phase. CP planning benchmarks and physical experiments are conducted to demonstrate the effectiveness of the proposed approach. We show that our approach can compute the CPs and VPs of various mesh models with a massive number of triangles within a reasonable time.
Cascade RDN: Towards Accurate Localization in Industrial Visual Anomaly Detection With Structural Anomaly Generation

Unsupervised visual anomaly detection uses only anomaly-free images to detect anomalous patterns, whose recent methods mainly focus on the anomaly classification sub-task but neglect to localize anomalies accurately. Existing reconstruction-based and representation-based methods yield anomaly score maps that often predict erroneous responses. The method jointly trained with the discriminative network is not suitable for anomalies other than texture types. This letter propose a novel cascade reconstruction-discriminant network (Cascade RDN), which adopts a cascade structure to obtain representative discriminative reconstruction embedding. The bi-direction channel attention module is designed to enable the two discriminative sub-networks to boost each other. Moreover, a general structural anomaly generation is presented to complement the existing textured anomaly generation to cover all types of surface anomalies. The proposed method outperforms previous anomaly localization methods by 7%-10% in AP on two challenging benchmarks MVTec AD and BTAD. Meanwhile, it achieves state-of-the-art anomaly classification scores on BTAD.
Boosting Semantic Segmentation of Aerial Images via Decoupled and Multilevel Compaction and Dispersion

Semantic segmentation is a valuable task in practical applications for aerial images. Nevertheless, the segmentation performance is unsatisfactory due to aerial images’ huge intra-class variance and inter-class similarity. To solve this problem, we propose an approach to increase the distinction between classes and compact the features of the same class. Specifically, since a single aerial image contains only a small number of categories, which is fatal for previous contrastive learning, we discard InfoNCE loss in contrastive learning and use the simple Mean Square Error (MSE) loss that does not require negative samples to decouple the dispersion and compaction operations. Besides, we set up more representative prototypes for classes and extend the prototypes to the whole dataset level, which we call image- and dataset-level prototypes. Based on the calculated prototypes, we propose Multi-level intra-class Feature Compaction (MFC) and Multi-level inter-class Feature Dispersion (MFD) to compact the features of the same class and disperse the features of different classes in the latent feature space. More importantly, some measures are proposed to ensure the two do not conflict. MFC and MFD can be applied to any existing segmentation network to improve performance significantly without increasing computational complexity during inference. Moreover, we feed the calculated multi-level prototypes directly into the classifier, thus keeping the feature extraction and classifier consistent. Results on four challenging datasets, Deepglobe, iSAID, Potsdam, and Vaihingen, demonstrate the significant effect of our method, and sufficient ablation studies verify the role of each module.
Development of Synthetic Data Benchmarks for Evaluating Feature Selection Algorithms

The primary objective of this paper is to present a set of synthetically generated datasets as a benchmark for evaluating feature selection algorithms (FSAs). The use of synthetic datasets is encouraged because of their utility in controlling data parameters, including the exact number of relevant, redundant, and irrelevant features. This paper proposes four numeric datasets with several sources of inspiration, namely based on geometric objects, trigonometric equations and multi-cut linear combinations. These synthetically generated datasets come with a fixed number of relevant, redundant and irrelevant features, which are then evaluated using feature selection algorithms currently popular within industry and academia. This highlights the function of these datasets as benchmarks for future researchers in the field of feature selection. Accordingly, the datasets will also be made available through GitHub for use as evaluation metrics, whilst the code is made available to be modified according to the application for the researcher. This may include research into the performance of FSAs, the development of new synthetic data, and beyond.
Field Programmable Gate Arrays-Based Design of Electrical Power Measurement using Goertzel Algorithm

Using field programmable gate arrays (FPGAs) as a hardware platform still plays a significant role in digital signal processing. It is because the FPGA has a relatively strong influence on the processing speed and task distribution of its components in parallel. This paper aims to design a hardware-based power meter by applying the Goertzel algorithm, in this case, using an FPGA. The FPGA reconfiguration language used in this design is very high-speed integrated circuit hardware description language (VHDL). The design results that have been synthesized are three top-level modules consisting of an analog capture circuit controller, a DSP module that implements the Goertzel algorithm, and a DAC. The analog capture circuit controller module has three input types and nine output types. While DSP modules have four input types and four output types, and DAC modules have four input types and six output types. The design of this power meter produced an average power factor of 0.96.
Docker and Kubernetes Pipeline for DevOps Software Defect Prediction with MLOps Approach

Software defects are common when it comes to software development. However, in reality, this is very detrimental for companies and organizations that are developing software. Prediction of software defects in the early stages of development can be a solution to this problem. Of course, the method used needs to be considered when developing a model for predicting software defects. The software continues to experience development, so the prediction model must always be updated so that it can adapt to existing conditions. This study proposes the MLOps approach, which combines development and operation processes to develop a software defect prediction model. We will create a prediction model and then create a Docker and Kubernetes pipeline to automate the entire software defect prediction process so that it can speed up the development process and have good performance. We are comparing the performance evaluation results of the proposed method with the traditional method, which is run manually by Docker. The results showed that the entire source dataset had a fairly good accuracy rate of 76%-83% and a good recall rate of 79%-94%. The precision and recall values were also very good. Apart from that, it also produces a good Fl-score value of 84%-90%. And the development time until the model’s release is shorter: the average time is 7:02 minutes. Performance monitoring on the built-in web server is also easy to do and shows very good results. The web server can receive up to 156. $6/$sec requests in all models based on the dataset used, with the highest error rate at 45.03%. The use of the Docker and Kubernetes pipelines with the MLOps approach has been proven to have good performance, and the development of software defect models can be sped up.
The Effectiveness of Code Reviews on Improving Software Quality: An Empirical Study

This study intends to investigate how well code reviews contribute to higher software quality. A group of developers working on the study examine source code to find flaws, improve readability, and guarantee compliance with coding standards. The research on the effects of code review on defect discovery, defect prevention, and code maintainability is given together with a thorough overview of the literature on code review and software quality. This study has shown that code review is a highly efficient way to raise the caliber of software. According to the study of several studies and trials, code review significantly reduces flaws and improves code maintainability. Code review helps to increase client satisfaction by making sure the product satisfies their needs. The goal of this study is to highlight the value of code review as a quality assurance technique in software development workflows. The study's findings provide useful information for software development teams by emphasizing the advantages of code review for raising software quality and customer satisfaction. The results of this study can assist software development teams in incorporating code review into their workflows as a normal procedure, which would improve software quality and cut down on mistakes. In conclusion, this study shows that code review is a highly efficient way to raise the caliber of software. In terms of fault detection and prevention, code maintainability, and customer satisfaction, the study underlines the benefits of code review. This study can influence software development teams to make code review a common practice by highlighting its advantages, which would increase product quality and client satisfaction.
Can gaze control steering?

When steering a trajectory, we direct our gaze to locations (1-3 s ahead) that we want to steer through. How and why are these active gaze patterns conducive to successful steering? While various sources of visual information have been identified that could support steering control, the role of stereotypical gaze patterns during steering remains unclear. Here, experimental and computational approaches are combined to investigate a possible direct connection between gaze and steering: Is there enough information in gaze direction that it could be used in isolation to steer through a series of waypoints? For this, we test steering models using waypoints supplied from human gaze data, as well as waypoints specified by optical features of the environment. Steering-by-gaze was modeled using a "pure-pursuit" controller (computing a circular trajectory toward a steering point), or a simple "proportional" controller (yaw-rate set proportional to the visual angle of the steering point). Both controllers produced successful steering when using human gaze data as the input. The models generalized using the same parameters across two scenarios: (a) steering through a slalom of three visible waypoints located within lane boundaries and (b) steering a series of connected S bends comprising visible waypoints without a visible road. While the trajectories on average broadly matched those generated by humans, the differences in individual trajectories were not captured by the models. We suggest that "looking where we are going" provides useful information and that this can often be adequate to guide steering. Capturing variation in human steering responses, however, likely requires more sophisticated models or additional sensory information.