Reviewerly/exHarmony huggingface dataset:
Analysis of SQL Injection Attack Detection and Prevention on MySQL Database Using Input Categorization and Input Verifier

Data leakage affects confidentiality and integrity, which can harm various parties. According to OWASP (Open Web Application Security Project) research, SQL injection attacks rank first in the top web application vulnerabilities. Moreover, the website is directly connected. SQL injection attacks are common on MySQL databases because they are generally more popular than other database systems. One of the efforts to detect and prevent SQL injection attacks is to use input categorization techniques and input verifiers based on input. Application development using SDLC Waterfall. The analysis is obtained from the test results using sqlmap and manually. This paper provides an overview of detection and prevention efforts with input categorization approaches and input verifiers based on the type of SQL injection attack. All applications without prevention and detection can be attacked, while applications with prevention and detection cannot be attacked. This paper designs and develops a web application with and without SQL injection attack detection and prevention using input categorization and input verifier. The results obtained, input categorization, and input verification techniques can detect and prevent SQL injection attacks based on their type, including union-based SQL injection, error-based SQL injection, and blind SQL injection. Input categorization and input verifier can be used in addition to the use of an encrypted database.
Meter Recognition of Converter Station Based on the Improved YOLOX

The inspection of converter station is a scene of daily inspection of power system. How to accurately detect various meters through camera is one of the key technologies of converter station inspection. In order to reduce the operation cost of converter station and make the inspection work more standard and reliable, we propose a method of meter recognition in converter station based on improved YOLOX. In the proposed method, three schemes are used to make the model more sensitive to small and incomplete meters and has a better performance under adverse illumination conditions. Firstly, we cascade the PANet as the neck of YOLOX to further enhance the process of feature fusion. Secondly, the CBAM attention mechanism is used in the cascaded neck to improve the ability of extracting effective features from all kinds of meters. Thirdly, the imbalance of samples is better solved by using Focal Loss as the confidence loss. Finally, the experimental results show that the improved model can effectively improve the detection accuracy of YOLOX, while maintaining a high inference speed, a small number of parameters and a low cost of computation, which proves the feasibility and effectiveness of the proposed method.
Poor Population Classification System Using Convolutional Neural Network (CNN)

This research aims to obtain data on population poverty in Indonesia. Accurate poverty data is an important instrument for the government to focus on improving the living standards of the poor. One way to distinguish a family, including poor or not poor, is from the condition of the house in which they live. Where one indicator of a house is declared poor, namely if the type of wall of the house is made of bamboo/grass/low-quality wood/walls without plaster. This study proposes a Convolutional Neural Network (CNN) method to identify and classify types of residential houses. From the experimental results, the accuracy of the test results is 94 percent.
Blind Quantum Computation Using Single Qubit Gates

Blind Quantum Computation (BQC) allows a user with limited quantum capabilities to collaborate with a powerful quantum server to complete the users' computational duties while keeping private data hidden. Two sorts of BQC protocols that deserve to be recognized from the user's perspective exist. In the single server mode, from the perspective of users' quantum ability, one type is the quantum state preparation based models only rquire the user to prepare single quantum state. Another type is quantum measurement based model, such as Morimae's BQC model where the user just requires single quantum state measurement capability. In this paper, we provide a novel BQC model, and based on this model, we create a BQC protocol which only requires the user to construct two single quantum bit gates to get desired result. The results indicate that the model we propose is valid.
Energy Consumption Analysis of Various Dynamic Virtual Machine Consolidation Techniques in Cloud Data Center

Data centers in the cloud computing industry need tremendous energy to keep up with the rising demand for cloud resources. The two primary subsystems that make up a data center's energy usage are computing and cooling. The approach known as Dynamic Virtual Machine Consolidation (DVMC) is frequently used to lower the energy use of computer systems. Dynamic consolidation of virtual machines manages the data center in terms of energy consumption and quality of services. Energy consumption, active and overloaded Physical Machines (PMs), and VM migration counts are the key parameters to assess the performance of various VM consolidation algorithms. In this article, we have conducted simulation-based experiments and collected the energy consumption, active and overloaded hosts, and VM migration counts in each time interval of simulation duration. We plotted the results, and a comparative study was conducted to analyse the outcomes of different consolidation algorithms in terms of energy consumption and service quality. Our main goal is to figure out how the performance of the algorithm can be enhanced.
Network Public Opinion Sentiment Analysis based on Bert Model

To solve the high complexity of traditional methods, this paper proposes a Bert-based network public opinion sentiment analysis method to improve the analysis efficiency of sentiment tendency. This method maps the input text sequence to the three spaces of Query, Key and Value to obtain the query vector, key vector and value vector. For each query vector, use the Softmax on the inner product of the query vector and the key vector to obtain the encoded vector. Then input the encoded vector into the trained classifier to obtain the recognition result. This method can overcome the shortcomings of ignoring context by RNN, and simplify the algorithm complexity to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(n)$</tex> from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(n^{2})$</tex> of RNN and Text-CNN. The experiment verifies the performance of the proposed method on the social network data set. Its public opinion classification accuracy rate is 98.72% and f1_score is 98.5%, which prove that this method can achieve a good performance on the network public opinion sentiment analysis.
Spectral Features based Robust Speech Spoofing Detection System

Speech Replay can be used, with the recorded speech of any individual, for spoofing or gaining fraudulent access through an automatic speaker verification system. In this research work, Speech spoofing detection has been developed in MATLAB platform that detects the spoofing by identifying between two classes a speech may belong to, namely the Original (genuine) and the Recorded then Replayed (R-R) Speech. This work develops and implements the detection of the speech spoofing, carried out in English language with the use of spectral features (Mel Frequency Cepstral Coefficients-MFCC's) of the speech samples recorded, in two classes, as mentioned above (O, R-R). Classification of speech samples is performed by Gaussian Mixture Models (GMM). In the proposed work, the original (noiseless) speech samples and their R-R versions were used to train the GMM's for 2 classes and the testing work has been performed using a large corpus of (i) original (almost noiseless), (ii) the concatenated versions of originals of both classes with various noises such as crowd-talking, fan, traffic, light, medium and heavy rain, construction site etc. and also (iii) the random samples collected at various public places, to test the robustness of the system. Optimum detection is observed by comparing the performance of the GMM with variation in (i) the number of MFCC, (ii) Gausses (components), (iii) the elapsed time and (iv) number of iterations. The average spoofing detection performance is resulted to be 95.93 percent.
Usability Evaluation of Academic Information System Using the WEBUSE Method: A Study on University of Trunojoyo Madura Web Portal

A website is a platform to convey information, one of them is the academic information system on higher education (SIAKAD). It is used by lecturers and students. However, the users often need help to understand the function of the features or commands available in it. Consequently, the users require a longer time to access information and provide feedback. Therefore, this study was conducted to evaluate and determine the level of usability of the system and give some recommendations for improvement. The result of the questionnaire from users reflects user satisfaction. The level of usability was determined based on the respondents’ answers. A case study was conducted on the website siakad.trunojoyo.ac.id. According to the findings of this study, ten attributes have a moderate level of usability, whereas the other fourteen attributes have an excellent level. The testing on each attribute has the highest and lowest values. For example, the attribute with the lowest value is the user interface (UI) design on the web (0.508), and the system which does not contain too much advertising is the highest value (0.737). All in all, the highest level of usability is the website does not have too many advertisements. On the other hand, the lowest level is the attribute of the user interface (UI) design. Finally, the primary issue is content updates are frequently delayed.
Camera Calibration Algorithm for Industrial Robot

This research paper aims to present an algorithm for camera calibration for an industrial robot using a target pattern from a 2D image utilizing camera vision and MATLAB. The camera sensor will fetch data and send it to the application for image processing. Camera calibration using a planar checkboard grid to get camera parameters for industrial robotics was performed. The study attempts to identify the commands to which the robot articulates its movements. Performing both tasks and integrating them into a system requires the development of a control algorithm that initializes a trajectory of robot’s joint angles to place the end to the target point captured in the 2D image. The method of using camera calibration was done to identify the camera’s intrinsic parameters. They are applying the parameters to the perspective camera algorithm. The robot model is also presented, converting the CAD model to controllable block diagrams. Joint space trajectory was discussed for movement simulation.
Sentiment Analysis of Government Policy Management on the Handling of Covid-19 Using Naive Bayes with Feature Selection

The government’s policy in dealing with the Covid-19 pandemic that has entered Indonesia has generated a response from the public, including Twitter social media users. Responses or comments from the community are also very religious, ranging from positive responses in supporting government policies in dealing with this Covid-19 to negative reactions in the form of criticism of the government, which is considered to have underestimated this virus. We use sentiment analysis to determine whether an opinion or comment containing a statement is positive or negative. The comments are rated by Indonesian language experts. In this study, the sentiment analysis process uses the TF-IDF method for word weighting, the information gain method for feature selection, and the Naive Bayes method for classifying public opinion. The results of this study indicate that the Naive Bayes algorithm is quite good in the text data classification model, with the highest accuracy, precision, and recall levels of 87.0%, 89.0%, and 98.0%, respectively. The average accuracy, precision, and recall levels are 86.2%, 87.6%, and 97%, respectively. This research can provide accurate and valuable information for the community regarding the response of Twitter users to the policy of handling Covid-19 in Indonesia.
Analysis of RSS Patterns to Detect Rogue Access Points

The possibility for attacks that target the security and protection of data within the network has increased due to the increasing use of wireless LANs. Access points play a critical role in the growth of wireless local networks by providing users with benefits in interacting with the network and detecting attackers. This feature enables users who are not permitted to access this network's information and data to locate unauthorized (rogue) access points, which can be used for illegal reasons. This paper proposes an algorithm based on a set of features collected by legitimate access points using a Wi-Fi analysis app installed on the mobile phone, depending on the behavior of legitimate access points and using machine learning mechanisms, specifically the KNN model. The characteristics of each access point and their predicted patterns of behavior influence the proposed algorithm. The machine learning algorithm's total accuracy is 94.9%.
Lifelong Learning for Human-Object Interaction Detection

Human-Object Interaction (HOI) Detection is a critical task in scene understanding, which aims to detect the triplet<human, object, interaction> in images or videos. Existing methods solve this problem under a strong assumption that all triplets that are to be detected would be available during training stage. However, in real scene, new HOIs may be introduced continuously, which requires the trained model to have the ability to identify new classes without forgetting old ones. Due to the limitations of storage, computing resources and the privacy of data, it is impractical to train the model from scratch using old and new data every time. In this paper, we propose a new HOI detection task scenario called Lifelong Learning Human-Object Interaction Detection (LL-HOI) which is more natural than the existing closed-world one and solve this problem in an incremental and contrastive learning manner (Fig. 1). Our method is composed of two stages according to under incremental setting or not: 1) identify humans, objects and actions in HOIs using backbone detector and contrastive learning and 2) incrementally learn new HOI classes without forgetting previously learned ones. Besides, to address the catastrophic forgetting problem, we propose a Feature Replay Network (FRN) based on contrastive learning to adaptively process the images conditioned on the incremental process. Extensive experiments on HICO-DET and HOI-W datasets demonstrate the effectiveness and superiority of our method on lifelong human-object interaction detection.
Exploiting Emotion for Diverse Response Generation in Multi-turn Dialogues

Generating diversified and affected context-consistent responses is key to intelligent dialogue systems. Previous works about emotional response generation mainly refer to incorporate the emotional category into the decoding process, ignoring the historical emotion in the conversation context. In addition, existing methods mainly fail to consider the diversity of responses and emotional categories simultaneously. However, the response diversity is crucial to improve the quality of human-machine conversations. In this paper, we propose an Emo-CV Aemodel that is able to capture the response variations and consider the emotional signal in the context to generate diverse responses. In detail, we design an encoder-decoder framework with conditional variational autoencoder to incorporate a latent variable for exploiting emotion in the process of producing diverse responses. To the best of our knowledge, we are the first to consider the response diversity and emotional information of response generation in open-domain multi-turn dialogues. Our experimental results on a daily Chinese conversation dataset show that Emo- CVAE outperforms the state-of-the-art baselines in terms of Perplexity, Distinct-1, Distinct-2 and human evaluation.
Visual Scene Induced Three-stream Network for Efficient Action Recognition

According to the mechanism of human behavior reasoning, human eyes not only pay attention to the spatiotemporal information of videos, but also pay attention to the details of action joints. We present a general and flexible framework for action recognition. This framework, called three-stream network, aims to capture the scene information of videos, the behavioral information of skeletons and corresponding action attribute relationships. Analyzing the correlation between visual scene and action information attributes can effectively assist the reasoning of action categories. The decision fusion network fuses the multi-modality complementary information. Thus, the performance of model is improved. Furtherly, two wide datasets named UCF101 and HMDB51 are used to verify the model. By comparing the experimental results with the state-of-the-art algorithms, the effectiveness and superiority of the proposed model are illustrated.
SRP-Blockchain: A Framework for Scientific Research Profile on the Blockchain

The development of a number of cryptocurrencies, such as Bitcoin [1], was the impetus for the initial adoption of blockchain technology. At this point in time, blockchain is regarded as a versatile technology that possesses immense potential. Applications that are constructed using blockchain technology will, by default, come equipped with properties such as authenticity, immutability, and consensus. In addition, any information that is stored on a public blockchain can be accessed at any time and from any location. The administration and upkeep of Scientific data present significant opportunities for applying blockchain technology. This paper provides a description of the SRP-Blockchain, also known as Blockchain-based Scientific Research Profile. This record is designed to organize and disseminate the achievements of researchers in the academic and research communities. The strategy that utilizes the blockchain makes it possible for parties to securely transfer, share, and distribute research information in a manner that is both straightforward and uncomplicated. Articles published in journals, book chapters, conference proceedings, abstracts, and other types of publications may fall under this category of records.
A Novel Image Encryption using 3D Logistic Map and Improved Chirikov Map

Images are one of the essential data being generated daily. Many images are taken in our day-to-day life. It is vital to keep those images secure. This paper proposes a new image encryption algorithm utilizing a 3D Logistic Map and an improved Chirikov Map. There have been several suggested image encryption techniques using chaotic maps. Some of these are time expensive and sophisticated, while others have limited key space. A nonlinear 3D Logistic Map performs better in terms of randomization attributes and security measures. With the modification in the Chirikov Map from the standard map adds more robustness to the algorithm by producing a cipher image which is a more secure and random image compared with the plain-text image. This paper performed well-known cryptographic tests to assess the security and performance analysis of the proposed scheme. The proposed scheme is resistant to brute force attacks and differential cryptanalysis. The algorithm is susceptible, even to a slight change in the secret key.
ASMOD: Adaptive Saliency Map on Object Detection

Although deep neural networks (DNNs) have achieved remarkable results in many fields, their interpretability is poor, and the decision-making process is not clear, which is often considered as a black-box model. In this paper, we propose a method Adaptive Saliency Map on Object Detection (ASMOD), multi-scale mask and suppression threshold are added to D-RISE black-box method for object detection task, which effectively improves the problem of fuzzy decision region and poor interpretability when generating saliency map (estimating the prominence of each pixel model prediction). Meanwhile, we applied the improved method to the four target detectors with different structures for explanation, analysis and comparison, and gave suggestions for improvement. We use two metrics: Automatic Deletion / Insertion metric and Pointing game metric based on manual annotation to validate our improved performance, the results show that our method can better adapt to multi-scale targets and reduce irrelevant parts, thus enhancing the interpretability of the model and the credibility of the saliency map.
Performance Comparison of Word2Vec Models for Detecting Arabic Hate Speech on Social Networks

Detecting hate speech has become increasingly important for online communities. Despite emerging research to address the problem, more efforts are still needed to improve the performance of detection methods for the Arabic language. In this paper, we compare the performance of two Word2Vec models; the continuous bag of words (CBoW) and skip-gram using a dataset collected from multiple social media platforms; Facebook, Twitter, YouTube, and Instagram. We compared seven machine learning (ML) algorithms and measured their performance using different evaluation metrics. The CBoW model outperformed the skip-gram model in terms of accuracy. In addition, using a higher embedding dimensional value resulted in a better performance.
MODELING THE STRUCTURE OF PROFESSIONAL-IMAGE POTENTIAL OF A MODERN PRIMARY EDUCATION TEACHER IN THE PREPARATION AND DEVELOPMENT OF EMPIRICAL RESEARCH

The article presents the results of organizing and conducting an expert assessment of generalized individual psychological characteristics characterizing the professional image potential identified in the framework of the study, which made it possible to prepare a structural model consisting of four components, including twelve relevant structural components, as well as their classification and ranking in terms of significance.
A Quadratic Surface Minimax Probability Machine for Imbalanced Classification

In this paper, a kernel-free minimax probability machine model for imbalanced classification is proposed. In this model, a quadratic surface is adopted directly for separating the data points into two classes. By using two symmetry constraints to define the two worst-case classification accuracy rates, the model of maximizing both the F1 value of the minority class and the classification accuracy rate of all the data points is proposed. The proposed model corresponds to a fractional programming problem. Since the two worst-case classification accuracy rates are the symmetry, the proposed model can be further simplified. After this, the alternating descent algorithm is adopted for efficiently solving. The proposed method reduces the computational costs by both using the kernel-free technique and adopting the efficient algorithm. Some numerical tests on benchmark datasets are conducted to investigate the classification performance of the proposed method. The numerical results demonstrate that the proposed method performs better when compared with the other state-of-the-art methods, especially for classifying the imbalanced datasets. The better performance for the imbalanced classification is also demonstrated on a Wholesale customers dataset. This method can provide methodological support for the research in areas such as customer segmentation.