Reviewerly/exHarmony huggingface dataset:
Vulnerability Analysis of Online Banking Sites to Cross-Site Scripting and Request Forgery Attacks: A Case Study in East Africa

Web applications are prone to several attacks. Two common threats are cross-site scripting attacks and cross-site request forgery. With internet banking becoming more popular in East Africa, the level of security that online banking services offer has become an increasing concern. This paper presents an analysis of the safety of these applications used by many unsuspecting customers seeking convenience and determines ways to detect and prevent these attacks from taking place. We assumed that if people with a technical background in IT and information security are vulnerable to CSRF and XSS attacks, the public would be even more vulnerable. Out of 96 users, 35 answered our survey, 53.1% of the respondents said they do not check the URLs of online banking websites they visit to ensure they are not on a phishing site. Secondly, only 36.4% of users considered the security implications of clicking on links in emails or even on banking websites all the time. Based on the interviews done, testing and analysis conducted, there is a clear indication that Internet banking users are vulnerable to XSS and CSRF. Notably, close to 50 % of the Internet banking users we interviewed reported that they do not receive ample tips from the banks regarding security issues to look out for when transacting online. The findings from this research help make recommendations to banks and users to ensure that future online banking transactions are done more securely.
Prediction-based UTXO Cache Optimization for Bitcoin Lightweight Full Nodes

Since version 0.11 of Bitcoin Core, a user can run a full node in pruning mode, i.e. a pruned node, in resource-limited devices. The pruned node is a lightweight full node as it can independently verify new transactions and blocks received from other peers in the Bitcoin network by only maintaining some recently verified blocks (not the complete blockchain) and the complete Unspent Transaction Output (UTXO) set. However, the rapid increase in the size of the UTXO set has caused the main part of the UTXO set to be stored in the low-speed disk, and thus slows down the verification speed of new blocks in lightweight full nodes. Existing verification schemes for pruned nodes do not take advantage of the fact that different UTXO-related transactions are included in a new block with different probabilities, resulting in poor verification performance. In this paper, we propose a prediction-based UTXO cache optimization mechanism to increase the verification speed of new blocks. In order to achieve higher prediction accuracy and reduce the memory requirements of UTXO set, we first design a method to synchronize unconfirmed transactions for lightweight full nodes to ensure that the local and miners&#x0027; unconfirmed transaction sets are highly consistent. Then, a lightweight full node predicts which unconfirmed transactions will have a greater probability of appearing in the new block by utilizing the fact that most miners will prioritize unconfirmed transactions to maximize the total transaction fee when mining a new block. Based on this mechanism, we can pre-load the UTXOs required for new block verification into the memory, thereby greatly improving the verification performance. Experimental results show that the proposed mechanism can accelerate the block verification of lightweight full nodes with small memory requirements.
HGENA: A Hyperbolic Graph Embedding Approach for Network Alignment

Cross-network alignment aims at identifying users who participate in different social networks, which benefits a variety of downstream social applications such as precise content delivery, fraud detection, and content/user recommender systems. Recent advances in network representations and graph neural networks have spurred various network structure-based methods for capturing underlying node similarities across social networks, thereby addressing the network alignment problem. However, most of the existing solutions rely on embedding methods that compute node similarity in Euclidean space, resulting in severe distortion or semantic loss when representing real-world social networks, which are usually scale free and with hierarchical structures. We address these issues by presenting a novel model: Hyperbolic Graph Embedding for Network Alignment (HGENA), which learns the structural semantics more efficiently by embedding nodes in hyperbolic space instead of Euclidean. HGENA overcomes the scalability issue since it requires far fewer dimensions in Riemannian manifolds and increases the capability of learning hierarchical structures, while enabling smaller distortion for tree-liked networks to facilitate node alignment. We also introduce alternative network mapping functions to compute node similarity across-network based on its distance on the Poincare ball. Experimental evaluations conducted on real world datasets demonstrate that HGENA achieves superior performance on social network alignment, especially for more tree-liked networks.
An Offline Writer-independent Signature Verification System using AutoEmbedder

Handwritten Signature is considered one of the most effective behavioral biometrics. It plays an important role in identifying and verifying persons for banking access control, criminal investigation, legal support, etc. Since the handwritten signature is used in such a high prominence, its misuse can be dangerous. Deep learning-based verification approaches are becoming extremely popular to reduce the risk of signatures misuse. Signature verification depends on pairwise constraints to verify if the person is genuine that he/she claims to be or forged. This paper proposes an Autoembedded system that uses Deep Neural Network (DNN) with the pairwise loss for signature verification. The model either generates embedding vectors closer to zero if the input pair is in the same class or generates a value greater or equal to $\alpha$ (a hyperparameter) that indicates a different class. The proposed approach uses a Siamese network that computes the pairwise distance in feature learning phase. The performance has been evaluated based on CEDAR dataset in a writer-independent (WI) context, and the experimental result shows clear distance between the genuine and forged signatures and verifies genuine ones.
Fault-Tolerant Control for Polynomial Fuzzy Systems based on Sum of Squares

This paper studies an active fault-tolerant control method for polynomial fuzzy systems (PFS) based on sum of square (SOS). The active fault-tolerant control method includes fault estimation and fault accommodation. First, an observer is designed to estimate the actuator fault and the state. Then, the fault accommodation is used to compensate the actuator fault online. The design conditions are derived from SOS formula. Finally, the feasibility and effectiveness are verified through the simulation of a numerical nonlinear model.
An Analysis of Ambiguity Detection Techniques for Software Requirement Specification

Software requirement specification document is the most important document in software program improvement process. All the following steps in software program development are influenced by this document. Requirements are the fundamental building blocks of every excellent product. A requirement analysis is one of the most significant phases in minimizing the complexity of product design. However, problems with requirements, such as ambiguity or inadequate requirement specifications, can lead to requirement misunderstanding, affecting testing operations and increasing the chance of project failure overruns in project time and cost. Several automated analysis techniques have been developed to improve the quality of requirements. There is presently no technique for minimising ambiguity caused by ambiguities in the Lexical, Syntactic, or Syntax. We will identify these uncertainties in this post and try to eliminate them in order to improve requirement quality.
A knowledge graph constructed for job-related crimes

Since the 18th National Congress, the Party Central Committee has greatly enhanced its efforts to "fight corruption and advocate honesty". While achieving excellent results, the rising number of crime cases has also brought great challenges to the work of the prosecution organs. At the same time, the traditional relational database has become more and more disadvantageous in dealing with large-scale, unstructured and complex dynamic data, and can no longer meet the actual needs. In order to further improve the efficiency and quality of prosecutors’ work, alleviate the problem of "too many cases and too few people", and promote the construction of "smart prosecution", this paper uses knowledge extraction and other methods to identify the key entities and relationships in the job-related crimes indictments of the Shanghai Pudong New District Procuratorate. Then using Neo4j graph database to store the extracted information and build a knowledge graph for job-related crime cases, which realizes the visualization and deep mining of job-related crime, providing a new way and tool for prosecution work as well as prosecution personnel to analyze and handle job-related crime cases.
Pelatihan UMKM Limbah Nanas Pewarna Alami Kain Jumputan Peluang Bisnis Kearifan Lokal Masyarakat Pinggiran Era Covid-19

The community is full of pressure due to the sudden emergence of the covid-19 outbreak which has an impact on MSME businesses, so the idea of ​​​​using pineapple waste is unique. Jumputan is a handicraft made by picking (picking up with your fingers) certain parts of the fabric, then producing a motif according to the pattern drawn from the dyeing process of natural dyes and textile dyes from tightly tied fabrics. Pineapple waste which is not useful for indigenous people into natural dyes to make fabric colors. Pineapple waste is often immediately thrown away, even though if it is used as an interesting work, it will be beneficial for the wider community. By turning Pineapple waste into a beautiful work of art, the price is high. Many people are interested in having it. Besides being able to be reused, the results of used Pineapple Waste craft products can be a business opportunity. Pineapple is usually made. Food jam to be eaten with bread. This time, pineapple waste is a light and easy to use material, even using and playing with Pineapple waste can produce interesting works that are expensive. Not a few people still think that Pineapple has a fragile character and easily rots, especially not durable, this time it was used for natural dyes for fabric jumputans
Optimizing the future of language teaching with technology in Japan.

Pandemic-era teaching has taken many forms: online, face-to-face, hybrid, and hyflex, among others. In this article, I make four predictions for the future of language teaching with technology in Japan and provide my recommendations for optimizing this future. First, I predict that online instruction will quickly fall back to pre-pandemic levels without interventions to prevent this. One area ripe for change is in virtual international experiences, such as online study abroad. Second, most institutions will adopt bring-your-own-device policies. Teachers will need more tech literacy to take full advantage of these devices. Third, institutions will recognize a growing variety of learning differences among students and rely on teachers to accommodate them. To support all learners, universal design will become standard. Fourth, although most instruction will return to the physical classroom, teachers will need to be constantly prepared to shift instruction online again. Emergency remote teaching will be inadequate. To prepare to meet these challenges and optimize the outcomes, teachers need quality professional development. Although current offerings from our institutions are inadequate, CALL experts can help to fill some of the unmet needs. I encourage CALL experts to push for the future of technology in education that they would like to see.
Computer Prediction Model of the Election Result through BP Neural Network and Principal Component Analysis

The change of political parties is often accompanied with the economic changes. We divided the impact on the economy into two parts, namely economic trend without policy intervention and economic trend under policy intervention. We established a regression model to predict the economic impact factors of China and the United States in the following 6 years. We used BP neural network to fit the data, thereby obtaining the quantitative relationship between economic indicators and various economic impact factors, so that we can obtain the trend without policy intervention.
A Multiple-Input Based Convolutional Neural Network in Breast Cancer Classification from Histopathological Images

Breast cancer is considered the second most common reason for death among women. The gold standard for detecting breast cancer is the visual interpretation of histopathological images, but it is a complicated process that takes years of experience and a lot of skills of the pathologists. Sometimes, the limitations of the visual interpretation and the lack of experience result in the failure of diagnosing breast cancer. So, the computer-aided diagnosis (CAD) system can be taken into consideration as a helping tool to reduce the error of diagnosis of breast cancer. In this paper, a novel approach based on convolutional neural networks is introduced to classify breast cancer from the histopathological images of the breast tissues. In the clinical process of breast cancer diagnosis, pathologists examine the histopathological images of the breast tissue at different magnification levels. In this research, a single convolutional neural networks model is used to take the input of the same image with four different magnification levels parallelly. Our proposed approach outperformed existing state-of-the-art approaches by a substantial margin.
Adaptive Fine-tuning for Deep Transfer Learning Based Traffic Signs Classification

In recent years, traffic signs recognition represents an important issue in intelligent transportation systems. Several systems use traffic signs recognition including, driving assistance systems, road safety, autonomous driving. The traffic signs recognition aims to read and interpret road signs to inform the driver if he could not see them or when the vehicle is in self-driving mode. Each category of traffic sign has a special shape and color. This includes regulatory, warning, and guide signs. This paper proposes a practical solution for traffic signs classification based on convolutional neural networks technique to classify input images into 43 classes. Also, this paper provides a comparison between the Support Vector Machine (SVM) and the Softmax classifier. We have analyzed the impact of fine-tuning the pre-trained CNN model in the transfer learning algorithm. As a result, the SVM classifier in CNN achieves an accuracy of 96.60%, whereas the Softmax classifier accuracy is 97.84%. Experimental results demonstrate that fine-tuning in transfer learning can lead to significant performances in terms of accuracy of classification.
Repeat, fast, and high-resolution mapping of fine-scale trace element distribution in pyrite and marcasite by LA-Q-ICP-MS with the Aerosol Rapid Introduction System (ARIS)

Abstract The minor and trace element composition of minerals provides critical insights into a variety of geological processes. Multi-element mapping by laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) is an important technique applied for this purpose and although the method is rapidly advancing, there remains a fundamental compromise between spatial resolution, detection limit, and experiment duration when using sequential mass analyzers. To address the limitation of limited analyte selection for high spatial resolution maps imposed by the sequential nature of typical quadrupole (Q)-ICP-MS, we tested the Aerosol Rapid Introduction System (ARIS) for repeat mapping of the same area. The ARIS is a high-speed transfer tubing system that reduces aerosol washout times, permitting resolution of individual pulses at 40–60 Hz. Here, the ARIS was tested not for pulse resolution but with novel operating conditions optimized to perform fast, high spatial resolution mapping of minor and trace element distribution in pyrite and marcasite. For this purpose, ablation was conducted with a 5 µm beam aperture, a repetition rate of 50 Hz, and a continuous stage scan speed of 40 µm s−1. For each LA-Q-ICP-MS map, data were acquired for six elements with an acquisition time of 20 ms per element. This deliberately reduced the individual pulse resolution of the ARIS but instead exploited the spatial resolution and sensitivity gains afforded by the high-laser repetition rate combined with efficient aerosol transfer. The new method successfully mapped trace elements at single to double-digit parts per million levels, and the maps reveal fine-scale zoning of trace elements with an effective x and y resolution of 5 µm, while white light interferometry showed that for each experiment, only ca. 1 µm of the sample was removed. Repeated mapping of the same area showed excellent correspondence not only between element concentrations in successive experiments but also in the shape, dimension, and location of regions of interest defined by concentration criteria. The very good repeatability of the elemental maps indicates that for studies requiring more analytes, successive mapping of additional elements is possible. By contrast with conventional very small spot (i.e., 5 µm) analysis, fast repetition rate and stage scan speed mapping avoids down-hole fractionation effects and minimizes accidental analysis of buried invisible inclusions. Compared to conventional LA-ICP-MS mapping, the method reduces the experiment time by 4–8 times.
State and Future Prospects of Artificial Intelligence (AI) in Ghana

This paper aims to give a broad scope of the current state of AI in Ghana. The paper highlights the existing institutions leveraging AI technologies, points out some current challenges with regards to AI adoption, and identifies some exciting prospects of AI given the current state of the country. Keywords: AI, Machine Learning, Data Science, Technology.
Distributed Computation of a Robust Estimator Based on Cauchy Noises

A real-time, recursive, multivariate estimation algorithm for time-invariant and time-varying linear systems with modelled Cauchy noises is developed. When previously compared to the Kalman Filter, the Multivariate Cauchy Estimator was shown to be robust against impulsive disturbances in the process or measurement functions, but proved computationally intractable for real-time estimation applications. Two significant insights allow for a reformulation of the Multivariate Cauchy Estimator to possess a streamlined recursive and computationally reduced characteristic function of the conditional probability density function of the system state-vector given the measurement sequence. This characteristic function is represented by a sum of terms, expanding with each measurement. First, we show that a cell-enumeration matrix can be computed for each hyperplane arrangement embedded within each term of the characteristic function of the Cauchy Estimator. We then show that functions used to formulate the terms of this characteristic function can be expressed as a vector of parameters operating on basis functions constructed from this enumeration matrix. This vector is obtained by solving an under-determined system of equations. We demonstrate that our reformulation allows all terms with equal hyperplane arrangements to be reduced into a unique set. Secondly, we take advantage of advances in parallel processing to exploit the inherent parallelism found in the characteristic function of the Cauchy Estimator. A three state time-invariant system example is used to illustrate the performance of the Cauchy Estimator against the Kalman Filter when subjected to Gaussian and Cauchy noises. We report computational savings of over 99% when compared to the previous formulation. Furthermore, we discuss the real-time architecture of the Cauchy Estimator and report the execution speeds for a three-state system implemented on a single NVIDIA GeForce GTX 1060 graphics processing unit (GPU).
Ocean Climate prediction using Adaptive Optimization Technique

The ocean parameters including Sea Surface Temperature (SST) and Density are indispensable in predicting the climate of a particular region which are useful in forecasting weather and planning maritime operations. Buoys used in capturing real-time data of ocean parameters will generally be in continuous movement due to dynamic effects in the sea and these buoys need constant monitoring, though they are anchored, they tend to break away during adverse situations. One way to determine the related ocean parameters, as well as the position of the buoys shortly, is by using a model which is data-driven, and it also creates a neural network to solve the prediction problem. This neural network is a time series regression method and is a branch of Recurrent Neural Network (RNN) called Long Short-Term Memory (LSTM). The main objective of our paper is to propose the use of the optimization technique Particle swarm Optimization (PSO) with LSTM for improving the accuracy of predicting the sea surface temperature and locate the position of sensor nodes as these neural network approaches converge and often tend to fall into the optimal local solution.
An Augmented Reality-Based System for Ultrasonically Measuring the Thickness of Thin-Walled Parts

Manual thickness measurement of thin-walled parts is largely demanded in industrial applications. Repetitive manual interventions and poor consistency of measurement data are two of the critical existing problems for manual thickness measurement, which reduce the measuring accuracy and efficiency. To address these problems, this article combines digital ultrasonic thickness with an augmented reality (AR)-based system to assist the manual thickness measurement of the thin-walled parts. A simple adaptive pattern projection method is proposed to replace the manual drawing of grids and thickness states on the part. A multifunctional tool is developed to automatically visualize and record the measured thickness data in real-time, which guarantees the consistency of measurement data. The communication within the proposed system is established to enhance the human–machine interaction experience, allowing the operator to focus on the thickness measurement itself. The accuracy of the projection position is analyzed and the maximum deviation is 1.5 mm. A case study is conducted to validate the proposed system. The proposed system largely improves digitization and decreases the workload of manual thickness measurement. The measurement time is shortened compared to the conventional thickness measurement method.
An Improved Expectation-Maximization Algorithm to Detect Liver Image Boundary in CT Scan Images

Liver segmentation is a prolific and important area of research that has been deeply studied for the last three decades. Its prominence is increasing in modern Computer-Aided disease Diagnosis (CAD) to deal with a huge amount of images. In this paper, the CT scan image is resized to 256 X 256, and noise is reduced by a median filter, and then local peaks are acquired. The optimal clusters (k) to be formed by Expectation-Maximization (EM) algorithm are obtained by setting the distance between local peaks and height greater than 5. Formulate k number of clusters using the EM algorithm. Crop random section of liver and obtain all the local peaks greater than average of local peaks. This provides the minimum and maximum threshold values using which a threshold-based segmentation is performed. The anticipated algorithm that is verified on 55 CT scan images offers promising results. The experimental outcomes are compared with the existing cluster-based liver segmentation algorithms.