Reviewerly/exHarmony huggingface dataset:
A hybrid model for the identification and classification of thyroid nodules in medical ultrasound images

Ultrasonography (USG) is one of the leading diagnostic methods for accurately distinguishing the early-stage of thyroid nodules. ANN-SVM hybrid model is proposed for the identification and classification of thyroid nodules in medical ultrasound images. After feature extraction using grey level co-occurrence matrix method, two experiments are performed. In the experiment-1, five different machine learning (ML) classifiers like random forest (RF), support vector machine (SVM), decision tree (DT), artificial neural network (ANN) and K-nearest neighbour (KNN) are used for classification. While in experiment-2, the two best classifiers based on the performance are hybrid together. The proposed hybrid model has achieved 84.12% accuracy, 85.14% sensitivity and 82.95% specificity on the public dataset having 295 USG images and 90% accuracy, 91.66% sensitivity and 87.5% specificity on the local dataset having 654 thyroid USG images. It has shown an improvement of 2% to 5% in the performance evaluation in comparison with the other state-of-the-art methods.
Local Motif Clustering via (Hyper)Graph Partitioning

Local clustering consists of finding a good cluster around a seed node in a graph. Recently local motif clustering has been proposed: it is a local clustering approach based on motifs rather than edges. Since this approach is recent, most algorithms to solve it are extensions of statistical and numerical methods previously used for local clustering, while combinatorial approaches are still few and simple. In this work, we build a (hyper)graph to represent the motif-distribution around the seed node. We solve this model using sophisticated (hyper)graph partitioners. On average, our algorithm computes clusters six times faster and three times better than the state-of-the-art for the triangle motif.
The Collapse of FTX: The End of Cryptocurrency’s Age of Innocence

The sudden collapse of the FTX exchange appears to have unearthed several fundamental ethical, regulatory, and policy-based flaws, inherently damaging the cryptocurrency industry at large. The following research briefly outlines the key events that led to the bankruptcy of FTX, while examining further industry-wide implications as a result of a complete risk management failure. Results indicate that the asset-price collapse of privately-issued cryptocurrencies, FTT and Serum, manifested directly in the collapse of FTX and the subsequent contagion effects across a range of financial market products. The collapse of FTX should serve as a significant warning to policymakers and regulators, specifically surrounding the valuation, possession, and corporate usage of permissionless, privately-issued cryptocurrencies.
Multi-neighborhood Guided Kendall Rank Correlation Coefficient for Feature Matching

Seeking feature correspondences among two or more images is an important problem in computer vision and image processing. The putative matches constructed by the similarity of feature descriptors are often contaminated by many false matches. Typically, the local neighborhood points of a true match point have a rank order, which will be maintained in the corresponding image, and we call it rank consistency. In this paper, we design a number of sorting plans to obtain the neighborhood rank lists by taking full advantage of the local neighborhood geometry structure. In order to measure the differences between rank lists, we adopt the statistically famous Kendall rank correlation coefficient and generalize its definition for matching problem. We design a neighborhood common element guidance strategy and a multi-neighborhood strategy to improve the universality and robustness of our method. Our method has linear complexity and it has superiority over state-of-the-art methods on several challenging data sets. It also performs well in image registration and loop-closure detection tasks. The source code of our method is publicly available. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sup>1</sup></xref> <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label><sup>1</sup></label> https://github.com/MnYangs/mGKRCC </fn>
AFast Multiple Image Encryption Algorithm Based on Hilbert Curve and Chaotic Map

In this paper, a fast multiple-image encryption technique is presented for gray-level images. The input images are scrambled using the Hilbert space-filling curve, column swapping, and row shifting. Following the scrambling stage, the images are subjected to a diffusion process. The 2D logistic map is used in the chaotic range to generate necessary parameters for both scrambling and diffusion phases. The suggested technique has a big keyspace and is sensitively dependent on the secret keys. The proposed technique can withstand cryptanalytic attacks such as exhaustive-search, differential, and statistical attacks, according to simulations of various performance criteria. The algorithm is also very fast in the sense that it can encrypt four 256 × 256 grayscale images in less than 0.17 seconds.
Estimation of Shade Losses in Unlabeled PV Data

We provide a methodology for estimating the losses due to shading for photovoltaic (PV) systems. We focus this work on estimating the losses from historical power production data that are unlabeled, i.e. power measurements with time stamps, but no other information such as site configuration or meteorological data. This approach is appropriate for analyzing field production data from fleets of distributed rooftop systems and is highly automatic, allowing for scaling to large fleets of heterogeneous PV systems.
TKDA: An Improved Method for K-degree Anonymity in Social Graphs

Data anonymization is one of the most important directions in privacy-preserving. However, research shows that simple anonymization of data does not protect privacy. To solve this problem, we present a novel and effective algorithm named tree-based K-degree anonymity (TKDA). We devise a new anonymity sequence generation method to reduce the information loss for social graphs. Then, the dynamic anonymization process is implemented by a depth-first search (DFS) traversal algorithm. Finally, the graph modification algorithm based on the anonymous sequence can keep the original graph structure stable. Average Path Length (APL), Average Clustering Coefficient (ACC), and Transitivity (T) are employed to evaluate the method. Experimental results on several datasets show that TKDA is closer to the values of the original graphs on the correlated three experimental metrics, which indicates that TKDA portrays the real data in more detail and improves the utility of the released data.
The effect of Compressor-Decompressor Networks with different image sizes on Mask Detection using Convolutional Neural Networks - VGG-16

Since a pandemic such as COVID-19 entered our lives, the importance of using face masks against such epidemics was experienced once again. Although the use of vaccines increasing the immunity against the COVID-19 virus prevents it from being as effective as in the early times, the use of face masks and their detection in crowded areas may still be important because another pandemic can occur any time. In this research, a preliminary work about a CNN-based compression-decompression model has been studied. In this two-staged model, first, the input image is compressed and its size is reduced to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$8\times 8$</tex> from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$32\times 32,\ 64\times 64$</tex> , or <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$128\times 128$</tex> . In the second stage, this <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$8\times 8$</tex> image is decompressed and reconstructed to its original size of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$32\times 32,\ 64\times 64$</tex> , or <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$128\times 128$</tex> depending on the input image size. Since the results are promising, such a system can be used to compress the image when it is acquired and then transfer it to another machine with better specifications to decompress and use more complex models for better classification accuracies. 90.9%, 91.7%, and 92.1% accuracies were obtained with the VGG-16 model by using <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$32\times 32,\ 64\times 64$</tex> , and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$128\times 128$</tex> images respectively. If a balance between network size and accuracy is considered, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$64\times 64$</tex> images are the optimal solution, but if the goal is better performance, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$128\times 128$</tex> images can be used to have higher accuracy by using higher-cost machines.
Modulation Recognition of Underwater Acoustic Communication Signals Based on Phase Diagram Entropy

The underwater acoustic (UWA) communication is an intense research topic due to the particularities that define it caused by the dispersion phenomenon. With the rapid development of UWA communication technology for numerous applications emerging in ocean exploration, environmental monitoring and military missions, the demand for UWA communication signal analysis has increased. The objective of this paper is to propose a way to characterize the modulations used in UWA communication based on the advantages offered by the phase diagram entropy. These characterizations can be the basis of the process of recognition and classification of the UWA modulations.
The Impact of Block Chain Technology in Activating the Skills of Accountants and Auditors:a Survey Study of the Opinions of a Sample of Academics in the Iraqi Universities

The research aims to test the extent of the impact of modern technologies represented by block chain technology in developing improving and changing the skill levels of accountants and auditors in the Iraqi environment.The research hypotheses are tested by means of a questionnaire form prepared for this purpose.It has been distributed to a group of academics represented by postgraduate students and professors in the field of accounting for a sample of Iraqi universities during the year 2021.As the number of valid forms reached 104 forms, a number of conclusions are reached namely: The technology of block chains positively affects the skills of accountants and auditors, in terms of the speed of completing financial transactions directly in a common and permanent record on the network at low cost and high quality.In addition, it is easy to execute the accounting work and submit proposals and financial analyzes to the administrative authorities to take appropriate decisions.The research also has reached a set of recommendations the most important of which are: The need of the companies to adopt block chain technology in practicing their financial activities to take advantage of the advantages provided by this technology and thus enhance the competitive advantage.In addition, the need for interest in developing the skills of accountants and auditors to know the block chain technology and how to deal with it through the establishment of training and scientific courses.
A DES, AES, DSS, and RSA-Based Security System for Protecting Sensitive Information During Communication and Providing Fast, Reliable File Identification

Web-based financial, insurance, and other corporate applications have made data security a top priority.Confidentiality, endpoint authentication, message integrity, and no repudiation are only some of the security criteria that must be met by such apps.Data security standards establish XML vocabularies and processing rules for encryption/decryption and signatures/validation of documents.In this research, we offer a safe architecture for exchanging sensitive documents, which involves the transfer of files between a sender (the server) and a receiver (the client) through a central gateway using web services applications.The only files that need to be sent over the internet are those that contain sensitive information, thus it's important to design a system with built-in security measures like digital signatures.Encryption/decryption, signature/validations, and web services have all seen their fair share of algorithmic innovation, implementation, and coding.
Deep Reinforcement Learning Based Adaptive 360-degree Video Streaming with Field of View Joint Prediction

With the development of 360-degree video and HTTP adaptive streaming (HAS), tile-based adaptive 360-degree video streaming has become a promising paradigm for reducing the bandwidth consumption of delivering the panoramic video content. However, there are two main challenges for the adaptive 360-degree video streaming, accurate long-term prediction of the future field of view (Fo V) and optimal adaptive bitrate (ABR) transmission strategy. In this paper, we propose an attention-based multi-user Fo V joint prediction approach to improve the accuracy, establishing a probability model of watching video tiles for users and applying Long Short-Term Memory (LSTM) network and DBSCAN clustering method. Furthermore, we present an adaptive 360-degree video streaming approach based on deep reinforcement learning (DRL), using A3C algorithm to optimize the QoE. The real-world trace-driven experiments demonstrate that our approach achieves about 8 % gains on user Fo V prediction precision and an increase at least 20 % on user QoE compared with the benchmarks.
Research on vision based outdoor blind guiding robot

This paper designs a blind guiding robot working in the outdoor environment. The robot improves the path planning of the artificial potential field method by acquiring the data from ultrasonic radar and inertial sensors. The vision system of the guide robot can obtain the category of obstacles ahead and their distance through binocular ranging and deep learning algorithm and inform the blind in the form of voice. The guide robot uses Mobilenet_SSD detection algorithm to detect six common obstacles, including person, bicycle, motorcycle, car, manhole cover, and stone ball roadblock. The vision system also recognizes the straight-line detection of string obstacles that pose a threat to the blind. The simulation and experimental results show that the blind guiding robot can successfully avoid obstacles and reach the target point by using the improved artificial potential field method. The vision system has realized the function of obtaining the types and distances of obstacles ahead, and the accuracy of binocular ranging in an outdoor environment can reach 93.3%.
Comparison of Deep Learning Models in Position Based Visual Servoing

In this paper a Position Based Visual Servoing (PBVS) algorithm using deep neural network is applied to a UR10 cobot. A pre-trained Convolutional Neural Network (CNN) will be re-purposed and fine-tuned in an offline stage. In order implement and validate the CNN for a visual servoing application, a dataset was created in a simulated environment by moving a simulated UR10 robot to various positions and capturing an image with the corresponding relative pose to the target object. The obtained dataset was validated with ground truth dataset collected using the real robot. To control the motion of the cobot (simulated/real) a meta-operating system and a vision based control law was designed in ROS. The visual servoing task is defined as a repositioning task whereby the performance of the visual servoing is evaluated by the convergence to one desired pose from various, arbitrarily selected starting poses. Three different network architectures were implemented and tested. The obtained results reveal that all network architecture can be successfully applied to visual servoing systems.
Convolution Neural Network for Predicting Alphabet Sign Language and Comparative Performance Analysis of CNN, KNN, and SVM Algorithms

The use of sign language is a crucial component of daily life for those who have trouble speaking or hearing. American Sign Language is challenging for computer vision to recognise due to its increasing complexity and high intraclass diversity. This research compares and evaluates the effectiveness of three machine learning algorithms in the task of sign language detection. By utilising the Convolution Neural Network, K-Nearest Neighbor, and Support Vector Machine algorithms, we hope to represent characteristics that will be learnt. After that, we must evaluate them in terms of their accuracy, recall, precision, F-Value, and mean absolute percentage error (MAPE). We need to determine which algorithm is most suited for classifying sign language images after taking into account all of these factors. Additionally, we are employing the CNN algorithm to recognise sign language in both text and audio.
MakeItSmile: Detail-Enhanced Smiling Face Reenactment

Given a target face and a driving face, face reenactment aims to transfer attributes from the driving face to the target face. In the last decade, a great number of methods have been proposed to generate realistic reenacted faces. However, when these methods are applied to generate a smiling face, most of them can only get a mouth with blurry teeth, making the reenacted face unrealistic. This problem is mainly caused by incomplete tooth structure in the target face image under the setting of one-shot reenactment. In order to obtain smiling reenacted faces with detailed tooth structure, our method uses the tooth information from the driving face rather than the target face. Furthermore, to better represent the tooth structure and expressions of the driving face, we extract the texture with a carefully designed geometry-aware encoder. By training the encoder with tooth segmentation task and non-identity classification task, we acquire refined tooth representations and meanwhile derive the non-identity part of the driving face. We also design a specific generator to fuse the tooth texture features into the target face. Moreover, we add a mouth loss function to further ensure the high definition of the smiling reenacted face. We compare our method to existing state-of-the-art approaches. The experiments show that our method gets comparable results on non-smiling face reenactment and has superior performance on smiling face reenactment.
A UI/UX Evaluation Framework for Blockchain-Based Applications

The decentralised nature and the traceability of multi-chain transactions increase the use of blockchain technology in various domains such as finance, supply chain, health, governance, and entertainment [1]. There remains a need for more studies about the usefulness of blockchain technology in different applications, particularly requiring an understanding of the effectiveness of blockchain adoption. There is also a need for providing standards for evaluating and measuring the User Interface (UI)/ User Experience (UX) functionalities. In fact, there are only a few studies that have evaluated the UI/UX aspects of the blockchain space. Appropriately evaluating the design of a UI will enable the enhancement of the UX and facilitates a deeper understanding of the usefulness of the technology. In this research, we propose a novel evaluation framework to measure the UI/UX aspects of blockchain-enabled applications based on the requirements, volume of data, and performance. We demonstrate the utility and usability evaluations of the framework through a case study of a visualisation tool for blockchain transactions. The outcome of the evaluation shows that due to the intrinsic nature of the blockchain the UI elements of its applications need special consideration in performance and responsiveness. These could enhance the potential use of this emerging technology and bring confidence among stakeholders to integrate that into their core business or daily life.
Groundwater Vulnerability and Potentially Toxic Elements Associated with the Iron Mining District of Ouixane (Northeast of Morocco)

This study aims to investigate the groundwater vulnerability concerning potentially toxic elements in the vicinity of the abandoned iron mine of Ouixane (Morocco). A modified DRASTIC method (DRSTI) is proposed with satisfactory results. High vulnerability zones represent 40% of the study area, while medium and low vulnerability represent, respectively, 42% and 18% of the study area. These results have been validated by groundwater geochemical analyses of potentially toxic elements carried out in the framework of previous studies in the same area. Thus, the superposition of the waste rock and tailings map with the vulnerability map showed that the latter is located in areas of high to medium vulnerability and therefore constitutes the main cause of the deterioration of the geochemical quality of groundwater. Otherwise, the vulnerability method showed that the main parameters that significantly affect the vulnerability are: the depth of water (D), net recharge (R), and the unsaturated zone (I), while the other parameters do not significantly affect the model used and do not have much influence on the results of the vulnerability assessment. The method used allowed us to locate the most vulnerable areas to potentially metallic toxic elements pollution resulting from the abandoned iron mine of Ouixane, and it constitutes a tool for decision support and for developing effective action plans to mitigate and monitor the effects of the transfer of potentially toxic elements pollution to groundwater.
B-SAHIC: A blockchain based secured and automated health insurance claim processing system

In recent years, blockchain technology has been successfully used in many distributed environments where different stakeholders, who do not have any trust between them, interact with each other through a secured and transparent platform. The use of blockchain in healthcare insurance industry has not been studied methodically. In this study, we develop a blockchain based Secured and Automated Health Insurance Claim (B-SAHIC) processing system. First, we design an Entity-Relationship (ER) diagram identifying all actors with their respective data and relations between them. We also develop the business model and algorithms for all necessary steps. We implement the system in Hyperledger Fabric and deploy a smart contract to implement these algorithms. We introduce CouchDB to store the OffChain data where we store World-State Database. B-SAHIC provides a web-based portal for all actors who interact with the blockchain. Privacy of clients’ claim-related data is ensured by encrypting treatment-related data with a new key that is derived uniquely for each new submission done of clients’ personal documents. We have also deployed Hyperledger Explorer, a user-friendly web application tool for monitoring the health state of each node participating on the blockchain network. We have studied the performance of B-SAHIC for two to six peer nodes. Moreover, our performance study shows that B-SAHIC is fast and scalable. In our study, the average query latency is decreased from 0.07 second (for two peer nodes) to 0.02 second (for six peer nodes) in case of 1300 queries per second while the average transaction latency remains unchanged (around 3.5 seconds) for 300 transactions per second. Moreover, B-SAHIC consumes minimum resources, around 350MB only for two peer nodes. We believe that the development process of this blockchain based platform can be applicable for the automation of other insurance industry too.