Reviewerly/exHarmony huggingface dataset:
Performance Analysis of Identifying SQL Injection Vulnerability in the Context of Bangladeshi Websites

SQL injection is one of the most dangerous vulnerabilities for Web applications, and it is becoming a more common source of assaults as more and more systems migrate to the Web. Hackers modify the SQL query submitted by the user and insert malicious code into it. As a result, they get access to the database and change the data. Like developed countries, developing countries provide services to their citizens through various online portals, web applications, and websites to keep up with the global pace of digitalization. When it comes to websites, cybersecurity is one of the most frequently discussed topics, and ensuring the confidentiality and integrity of data have become increasingly important to businesses. Unfortunately, many web-based services are vulnerable to serious security threats due to a failure to consider vulnerability issues during the development phase. Vulnerability statistics are required for these developing countries to gain insight into the current security status of the web services provided. SQL injection is one of the most widely used techniques by hackers to take advantage of a security flaw in a website. The primary focus of this work is to compare performances of detecting SQLi vulnerabilities by presenting step by demonstration of two different third-party tools namely Havij and SQLmap. For the purpose of performance measure, we have tested 150 Bangladeshi websites that include government, educational, financial, and NGOs throughout this process to check their SQLi vulnerabilities. As a result of the country's recent focus on digitizing government services, the country already offers a wide range of online services to its citizens, and we wanted to learn more about them. The testing results revealed that the vast majority of the 150 websites tested from various categories are primarily vulnerable to boolean-based blind SQL Injection.
Data Confirmation Scheme based on Auditable CP-ABE

Ensuring data rights, openness and transaction flow is important in today’s digital economy. Few scholars have studied in the area of data confirmation, it is only with the development of blockchain that it has started to be taken seriously. However, blockchain has open and transparent natures, so there exists a certain probability of exposing the privacy of data owners. Therefore, in this paper we propose a new measure of data confirmation based on Ciphertext-Policy Attribute-Base Encryption(CP-ABE). The information with unique identification of the data owner is embedded in the ciphertext of CP-ABE by paillier homomorphic encryption, and the data can have multiple sharers. No one has access to the plaintext during the whole confirmation process, which reduces the risk of source data leakage.
An improved attribute based encryption model with linked hashing technique for cloud data security

Two of the characteristics of the Attribute-Based Encryption (ABE) system that is effective for delivering data security are one-to-many encryption and fine-grained access control. By utilising cloud technology, businesses may concentrate on their core competencies rather than dealing with labor-intensive data administration duties. Despite all of the advantages of cloud computing, security and privacy issues still exist. This study presents an introduction to ABE concepts, research challenges, and applications to resource discovery and data security in cloud computing systems. Flexible access control to encrypted data may be effectively controlled with attribute-based encryption (ABE). Modern apps increasingly employ ABE as a method of data security. However, privacy considerations are not taken into account by the current ABE systems when creating keys. This study examines how ABE may be used to protect sensitive user data, especially in cloud computing settings. The purpose of this study is to improve the suitability of Attribute Based Encryption with Linked Hashing Model (ABE-LHM) for cloud storage security. The research focuses on giving encryption complete control over access rights and making it feasible to manage keys even in the case of many independent authorities when it comes to user protection. This study suggests that the conventional method for maintaining data’s privacy is to encrypt it before uploading. The proposed model is compared with the traditional model and the results show that the proposed model is better in providing security.
Obstacle detection of indoor mobile robot based on binocular vision

Aiming at the uncertainty of obstacles in indoor mobile robot operation environment, this paper designs a binocular vision obstacle detection system that does not depend on the a priori knowledge of obstacles and background. Specifically, we build a binocular vision system, calibrate the camera according to the binocular vision theory, and obtain the camera calibration parameters. The binocular image is corrected by calibration parameters and epipolar constraints to solve the non coplanar problem of image distortion. SGM (semi global matching) algorithm is used to obtain disparity map to ensure the rapidity and robustness of disparity acquisition. Finally, an obstacle detection algorithm is proposed. The ground detection model was established to constrain detection distance, introduce area threshold, filter interference, extract obstacles, and measure obstacles. Experimental results show that the system can effectively detect and measure obstacles ahead, and the relative error within 4m is 2.56%, which meets the requirements of indoor mobile robot obstacle detection.
Two-Dimensional Attentive Fusion for Multi-Modal Learning of Neuroimaging and Genomics Data

Human exposure to reality is multi-modal, and the brain processes it through multi-sensory stimulation. As such, using multi-source intelligence can potentially improve results motivated by human learning. The key challenge in multi-modal learning is to integrate the modalities through a sensible fusion. We propose mBAM - a novel fusion technique inspired by the bottleneck attention module (BAM) to leverage the knowledge from diverse data modes. We combine this module with a deep multi-modal framework for classifying mental disorders. The joint architecture extracts relevant features from diverse inputs - from brain imagery to genomic variables to classify schizophrenia. The model's prediction accuracy is 95.6% (P < 0.0001), outperforming state-of-the-art unimodal and multi-modal models for the task. Moreover, the scheme provides inherent interpretability that helps identify concepts significant for the neural network's decision and explains the underlying factors of the diseases.
Rethinking the Feature Iteration Process of Graph Convolution Networks

Node classification is a fundamental research problem in graph neural networks(GNNs), which uses node's feature and label to capture node embedding in a low dimension. The existing graph node classification approaches mainly focus on GNNs from global and local perspectives. The relevant research is relatively insufficient for the micro perspective, which refers to the feature itself. In this paper, we prove that deeper GCNs' features will be updated with the same coefficient in the same dimension, limiting deeper GCNs' expression. To overcome the limits of the deeper GCN model, we propose a zero feature (k-ZF) method to train GCNs. Specifically, k-ZF randomly sets the initial k feature value to zero, acting as a data rectifier and augmenter, and is also a skill equipped with GCNs models and other GCNs skills. Extensive experiments based on three public datasets show that k-ZF significantly improves GCNs in the feature aspect and achieves competitive accuracy.
FA-LLLing for RSA: Lattice-based Fault Attacks against RSA Encryption and Signature

At CT-RSA 2022, it was shown that combining the power of lattice reduction algorithms with that of fault injection allows not only to carve new attack paths, as previously known, but also to pave existing ones, so to speak. Indeed, using faulty results to build an instance of the Hidden Number Problem, and eventually solving it, can allow an attacker to consider less restrictive fault models than before. In this article, we introduce two new fault attacks on both RSA encryption and signature using this approach. Our lattice-based attack can require as few as 2 faulty ciphertexts and signatures respectively to reveal the hidden secrets with a 32-bit random fault model. At the other end of the fault model spectrum, our attack is still successful considering a very permissive fault model where the attacker can randomly alter up to 98% of the targeted value.
Lipschitz Bound Analysis of Neural Networks

Lipschitz Bound Estimation is an effective method of regularizing deep neural networks to make them robust against adversarial attacks. This is useful in a variety of applications ranging from reinforcement learning to autonomous systems. In this paper, we highlight the significant gap in obtaining a non-trivial Lipschitz bound certificate for Convolutional Neural Networks (CNNs) and empirically support it with extensive graphical analysis. We also show that unrolling Convolutional layers or Toeplitz matrices can be employed to convert Convolutional Neural Networks (CNNs) to a Fully Connected Network. Further, we propose a simple algorithm to show the existing 20x-50x gap in a particular data distribution between the actual lipschitz constant and the obtained tight bound. We also ran sets of thorough experiments on various network architectures and benchmark them on datasets like MNIST and CIFAR-10. All these proposals are supported by extensive testing, graphs, histograms and comparative analysis.
Blockchain, IoT and AI revolution within transport and logistics

The digital transformation of the supply chain is the most important turning point of this sector and remains a crucial issue for many transport actors. In a competitive market, an efficient and flexible supply chain is a real springboard. The companies are therefore looking for tools that allow them to optimize their processes as well as their operational efficiency. It is in this ambitious challenge that the new technologies, and in particular Blockchain and Internet of things (IoT), playa key role. To this end, this article aims to provide a global analysis of the theoretical and practical foundations of Blockchain and IoT, besides to highlight their contribution to the performance of logistics and transportation.
Quality-Aware Real-Time Augmented Reality Visualization under Delay Constraints

Augmented reality (AR) is one of emerging applications in modern multimedia systems research. Due to intensive time-consuming computations for AR visualization in mobile devices, quality-aware real-time computing under delay constraints is essentially required. Inspired by Lyapunov optimization framework, this paper proposes a time-average quality maximization method for the AR visualization under delay considerations.
Panoramic image style transfer technology based on multi-attention fusion

research-article Share on Panoramic image style transfer technology based on multi-attention fusion Authors: Xin Xiang School of Information Engineering, Guangdong University of Technology, China School of Information Engineering, Guangdong University of Technology, China 0000-0002-6927-6844View Profile , Wujian Ye School of Information Engineering, Guangdong University of Technology, China School of Information Engineering, Guangdong University of Technology, China 0000-0002-8163-5133View Profile , Yijun Liu School of Information Engineering, Guangdong University of Technology, China School of Information Engineering, Guangdong University of Technology, China 0000-0002-4044-4680View Profile Authors Info & Claims CSSE '22: Proceedings of the 5th International Conference on Computer Science and Software EngineeringOctober 2022Pages 293–299https://doi.org/10.1145/3569966.3570056Published:20 December 2022Publication History 0citation21DownloadsMetricsTotal Citations0Total Downloads21Last 12 Months21Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Collaborative Community Knowledge Building with Personalized Question Recommendations

Inquiry-based learning focuses on asking questions and finding answers through investigation while engaging students in active learning. Decentralized knowledge building in the community can benefit from applying the approach to modern web technologies and collaboration. Distributed Noracle is a decentralized, question-based tool for building community knowledge in collaboration among participants that helps learners understand and reflect on individual learning and community processes. However, learners may encounter vast and complex space graphs, which leads to difficulties pursuing their goal of reflection and understanding the knowledge of the community. Community knowledge building is essential as it allows community members to share their knowledge and expertise. We extended the Distributed Noracle application with a recommender system that can suggest relevant questions and spaces to community members to address this problem. The recommendations can be accessed within the Distributed Noracle application or through our developed chatbot for community chat environments like Rocket.Chat or Slack. Our evaluation shows that our recommender system achieves the intended purpose and helps learners to find exciting questions in vast question spaces in a short amount of time.
Determination of Land Suitability for Herbal Plants Using FMADM With Weighted Product

Herbal plants have been used for generations by the community as alternative medicine because herbal plants have properties to treat various diseases. The development of traditional medicine that is increasingly globalized makes the number of herbal plant productions increase. The availability of herbal plants can be done by cultivating herbal plants in the correct location. However, in the cultivation of herbal plants, harvest failures are often found due to climatic factors and soil conditions that are not following the requirements for growing these herbal plants. Therefore, recommendations about the right location for growing herbal plants are needed. This study uses Fuzzy Multiple Attribute Decision Making (FMADM) with the Weighted Product (WP) method, which is applied to a decision support system to find suitable herbal plants planted in a field. The criteria used are based on plant growth consisting of rainfall, annual average temperature, altitude, and soil acidity. The result of this study is a decision support system that can provide recommendations for herbal plants that are suitable for planting in the three locations in East Kalimantan Province based on the highest preference value.
FOV-based Model Predictive Object Tracking for Quadcopters

Modern developments in electronics, electric motors, and automatic control have contributed to making quadcopters cheap, easily accessible, and with a wide range of use. In this work, a model predictive controller for a quadcopter is developed for tracking a moving object within the field-of-view (FOV) of a camera mounted on the quadcopter. A mathematical representation of the quadcopter is presented with the help of quaternions, whereas the tracked object is modeled as a car. The FOV is defined with the help of a pinhole camera model and represents a nonlinear constraint within the MPC design. Not only is a desired point in space kept within the camera's FOV but also as close as possible to its principal axis. The method is tested in different scenarios of varying complexity. The results are evaluated in terms of flight quality, constraint compliance, and computation effort and demonstrate the potential of the concept to stabilize the quadcopter and track the desired object within the camera's FOV.
Object Detection in Remote Sensing Scenes Using Anchor-free Methods and Multi-vector Loss Functions

We propose a set of new loss functions applied to deep convolution neural network, which have a significant improvement on detecting remote-sensing objects. Object detection is one of the most fundamental tasks of computer vision. And object detection in remote sensing scenes is difficult because these scenes have lots of small, oriented, and densely arranged objects, which are hard to be detected accurately and quickly. In order to solve these difficulties, we use anchor-free method to design the detection algorithm, which can improve the detection speed. And our designed multi-vector bounding box and multi-vector loss functions are added to the baseline algorithms. These design can make detector regress the bounding box accurately. Experiments are carried out under two public datasets. The experimental results show that our design effectively improves the detection accuracy and speed of objects in remote sensing scene. Moreover, we make ablation study experiments on our multi-vector loss functions, which proves the rationality of our design.
Securing Sensor Data on Internet of Things (IoT) Devices

The amount of personal devices has been increasing at an unprecedented rate for the past few decades. Also, all these devices are becoming smarter (containing microprocessor, AI enabled), and all are having Internet connectivity. This has made the life much simpler and smarter, but it has also increased the privacy risks. One such sensitive data is health data. There has been multiple cases of breach into health data storage systems. Personal IoT devices such as fitness bands are very close to the user and keep track of many sensitive data such as heart rate, number of steps walked, and the places visited. Efficient methods to secure Internet of Things in medical services are a need of the hour. The innovation and research in the field of Internet of Things for health care promise to meet that demand. It has brought rapid developments in medical treatment, services, and health care, but the improvements in this field are facing challenges related to security and authentication. In this paper, we propose a novel method of securing sensor data on IoT devices. We make use of RSA encryption technique; the public key of the user is transferred to the IoT device at the time of connection, and this key is used to encrypt the sensor data. Only, this encrypted data are synchronized with the user’s account.
B-store, a General Block Storage and Retrieval System for Blockchain

The emergence of Hyperledger provides a variety of feasible and feature-rich solutions for smart contracts on the blockchain, promoting the popularity and spread of blockchain systems. Similar with Bitcoin and Ethereum, Hyperledger has accelerated the dramatic increasement in the scale of blockchain data. Applications based on blockchain systems need to quickly retrieve transaction data in blocks while processing and analyzing data. Currently, Hyperledger Fabric only provides limited retrieval functions, such as searching for blocks based on single fields like block height, block hash, and transaction hash. Such retrieval functions cannot meet the needs of current blockchain applications. In this paper, we propose a general blockchain data storage and retrieval system B-Store for the blockchain platform. B-Store divides the blockchain data into data segment and index segment. The index segment uses B+ tree as the underlying data structure, and provides two retrieval options: block retrieval and transaction retrieval. B-Store supports single-field or multi-field equivalent retrieval, range retrieval and top-k retrieval on block data and transaction records. We compare two blockchain storage solutions, Hyperledger Fabric and B-Store. The final experimental results show that B-Store achieves low additional storage and performance overhead when adding new blocks, but provides rich and efficient retrieval functions for blocks and transactions.
New Public Dataset for Classification of Inappropriate Comments in Slovak language

Discussion sections under online news articles enable the exchange of opinions and information between discussants and the provision of information to potential visitors and readers. Discussion moderators have the task of guarding the level of discussions and preventing the publication of harmful content (vulgarity, hate speech, abuse, etc.). The process of moderating discussions should ideally be predictable and based on clear, predetermined rules of conduct. Discussion participants should not have the feeling of either rigid censorship or passive toleration of inappropriate content. Manual moderation is time-consuming and not particularly popular among newspaper workers, especially when there is a large number of comments. For the above reasons, we decided to propose attributes that would help to detect inappropriate content automatically and perform machine learning for discussion comments, in order to guarantee both a certain degree of predictability and uni-formity in the classification of inappropriate comments and, secondly, to offer a list of potential machine learning models that are appropriate for the task. For evaluation purposes, we collected an entirely new dataset with 2,283 inappropri-ate comments (marked as such by human moderators) and 10,000 ordinary comments in the Slovak language from past discussions on a major Slovak online news portal. We offer this dataset freely to other researchers to develop and test their own algorithms. By way of illustrative example we have been able to classify inappropriate comments with a macro F-measure of 67.3% on the basis of various statistical attributes, which we consider an encouraging result given that Slovak is a highly inflected synthetic language.