Reviewerly/exHarmony huggingface dataset:
The role of block chain technology and Internet of Things (IoT) to protect financial transactions in crypto currency market

The most well-known cryptocurrency, Bitcoin, is the one for which blockchain technology was created. A cryptocurrency is a digital means of exchange that uses encryption techniques to manage the creation of monetary units and verify the transfer of funds. A blockchain is a decentralised ledger that records all peer-to-peer transactions. Blockchain is also recognised as the protocol of trust. This security concept uses decentralisation to establish a global index of all financial transactions that occur in a distinct network and makes them absolute. Blockchain technology establishes trust and harmony in direct communication between two parties without the involvement of a third party. Participants can confirm transactions without the requirement for a central clearing authority using this technology. Third-party trusted intermediaries have a character for triggering transaction delays. There would be speedier reconciliation between transacting parties and participants if such intermediaries were not present. In addition, the Internet of Things is a new technology that allows virtual and physical items to link and communicate with one another, resulting in the creation of new digitised services. When the number of devices connected through an IoT network grows. However, this data is not encrypted; it does not provide privacy. In this regard, blockchain technology might give massive benefits because it can quickly secure the anonymity of the obtained data. Furthermore, Blockchain can assist in solving most of the security vulnerabilities and traceability concerns in financial transactions by keeping track of how devices interact. In this state, they are, and how they transact with other IoT devices. A blockchain is hack-resistant and has a low danger of being hacked to improve data security. This article aims to explain the structure and operation of Blockchain and IoT in the context of securing financial transactions in the cryptocurrency market.
GCN Inference Acceleration using High-Level Synthesis

GCN (Graph Convolutional Network) has become a promising solution for many applications, such as recommendation systems, social data mining, etc. Many of these applications requires low latency GCN inference.In this paper, we provide a case study of a GCN inference acceleration on FPGA. We explore high-level synthesis programming model to achieve low-latency inference. First, we propose a partition-centric mapping strategy to map the execution tasks of GCN onto FPGA to exploit data reuse, which reduces external memory access overhead. Second, we provide HLS-based kernel design with improved memory performance and achieve massive data parallelism. Third, we perform design space exploration to facilitate feasible pre-placement which avoids potential Place-and-Route (PnR) failures. We evaluate our design on a state-of-the-art FPGA platform using three commonly used datasets: Reddit, Yelp and Amazon-2M. We compare our design with two state-of-the-art libraries PyTorch-Geometric (PyG) and Deep Graph Library (DGL) running on high-end CPU and GPU by evaluating their latency and energy efficiency to perform full-batch GCN inference on a two-layer Vanilla-GCN model. Compared with PyG CPU version, our design reduces the latency by 59.95× and is 96.22× more energy efficient on the average. Compared with DGL, our design achieves 2.9 × –6.4× speedup and is 5.87× more energy efficient compared with the CPU version. Compared with the DGL GPU version, although the latency of our design is 1.67 × –2.5× that of DGL GPU, our design is 1.8× more energy efficient.
A Decision Support System for Face Sketch Synthesis Using Deep Learning and Artificial Intelligence

The recent development in the area of IoT technologies is likely to be implemented extensively in the next decade. There is a great increase in the crime rate, and the handling officers are responsible for dealing with a broad range of cyber and Internet issues during investigation. IoT technologies are helpful in the identification of suspects, and few technologies are available that use IoT and deep learning together for face sketch synthesis. Convolutional neural networks (CNNs) and other constructs of deep learning have become major tools in recent approaches. A new-found architecture of the neural network is anticipated in this work. It is called Spiral-Net, which is a modified version of U-Net fto perform face sketch synthesis (the phase is known as the compiler network C here). Spiral-Net performs in combination with a pre-trained Vgg-19 network called the feature extractor F. It first identifies the top n matches from viewed sketches to a given photo. F is again used to formulate a feature map based on the cosine distance of a candidate sketch formed by C from the top n matches. A customized CNN configuration (called the discriminator D) then computes loss functions based on differences between the candidate sketch and the feature. Values of these loss functions alternately update C and F. The ensemble of these nets is trained and tested on selected datasets, including CUFS, CUFSF, and a part of the IIT photo-sketch dataset. Results of this modified U-Net are acquired by the legacy NLDA (1998) scheme of face recognition and its newer version, OpenBR (2013), which demonstrate an improvement of 5% compared with the current state of the art in its relevant domain.
Novel Algorithm for Mobile Robot Path Planning in Constrained Environment

This paper presents a development of a novel path planning algorithm, called Generalized Laser simulator (GLS), for solving the mobile robot path planning problem in a two-dimensional map with the presence of constraints. This approach gives the possibility to find the path for a wheel mobile robot considering some constraints during the robot movement in both known and unknown environments. The feasible path is determined between the start and goal positions by generating wave of points in all direction towards the goal point with adhering to constraints. In simulation, the proposed method has been tested in several working environments with different degrees of complexity. The results demonstrated that the proposed method is able to generate efficiently an optimal collision-free path. Moreover, the performance of the proposed method was compared with the A-star and laser simulator (LS) algorithms in terms of path length, computational time and path smoothness. The results revealed that the proposed method has shortest path length, less computational time and the best smooth path. As an average, GLS is faster than A* and LS by 7.8 and 5.5 times, respectively and presents a path shorter than A* and LS by 1.2 and 1.5 times. In order to verify the performance of the developed method in dealing with constraints, an experimental study was carried out using a Wheeled Mobile Robot (WMR) platform in labs and roads. The experimental work investigates a complete autonomous WMR path planning in the lab and road environments using a live video streaming. Local maps were built using data from a live video streaming with real-time image processing to detect segments of the analogous-road in lab or real-road environments. The study shows that the proposed method is able to generate shortest path and best smooth trajectory from start to goal points in comparison with laser simulator.
Optical color image cryptosystem based on interference principle and deep learning

In this work, we propose a method for optical color image encryption based on interference principle and deep learning technique. After the interferograms being collected through three channels, a color image to be encoded can be captured optically and transformed into a color interferogram. An end-to-end DL model is trained to derive the mapping relationship between the interferograms and their original plaintexts. In this method, Arnold transform is used to scramble the recorded interferogram for achieving high security. The primary color image can be decrypted by using inverse Arnold transform and the trained learning model. The method provides the ability to decrypt a color image from only one color interferogram, which couldn’t be achieved by using conventional methods. In the decryption process, the optical keys used for traditional decryption are not required. The effectiveness and security of the cryptosystem are verified by computer simulations.
Image Encryption using Chaotic Techniques: A Survey Study

The sharing of information through unsecured networks like the internet has increased fast recently. This information could be text, audio, image, or video. The security of data transmission is an essential issue. Encryption is one of the most effective techniques to assure safe data transmission. Videos and Images size are very large and have a high correlation between neighboring pixels, so the classical encryption techniques such as RSA, DES, AES, etc., cannot be utilized for image encryption while chaotic image encryption schemes, which use a high level of unpredictability in key generation, can solve this problem. The chaos encryption main properties are pseudorandom number, sensitivity to initial conditions, and large key size. The goal of this study was to find the best chaotic map for picture encryption, this research looks into the use of chaos maps for image encryption, such as the Tent map, Logistic map, Sine map, and others. Arnold's Cat map, and also presents a comparison for their performance.
B-spline path planner for safe navigation of mobile robots

We propose a 2D path planning algorithm in a non-convex workspace defined as a sequence of connected convex polytopes. The reference path is parameterized as a B-spline curve, which is guaranteed to entirely remain within the workspace by exploiting the local convexity property and by formulating linear constraints on the control points of the B-spline. The novelties of the paper lie in the use of the equivalent Bézier representation of the B-spline curve, which significantly reduces the conservatism in the local convexity bound and in the integration of these constraints into a convex quadratic optimization problem, which minimizes the curve length. The algorithm is successfully validated in both simulations and experiments, by providing obstacle-free reference paths on real occupancy grid maps obtained from the laser scan data of a mobile robot platform.
Examining adults’ web navigation patterns in multi-layered hypertext environments

Search engine users, when presented with multiple web search results, must be able to flexibly navigate dependent on the type of search tasks. Users who demonstrate inappropriate navigation behavior would likely fail to obtain target information. However, very few studies have examined users' web navigation patterns and their effectiveness in completing different types of search tasks in multi-layered hypertext environments. To fill these research gaps, we used data composing a sample of 1408 adults in the United States and the United Kingdom who completed both a specific information-locating task and an amorphous information-evaluating task when participating in the Programme for the International Assessment of Adult Competencies (PIAAC) 2012. We extracted participants' clickstream data as well as their performance on the two tasks from recorded web navigation logs. Results from a series of latent class analyses and full-path sequence analyses showed five distinct behavioral patterns when participants performed the two tasks: of which, Flimsy, Breadth-first, Laborious, and Sampling patterns were revealed in both tasks, while the Satisficing pattern emerged exclusively in the specific information-locating task. Regarding pattern effectiveness, the Sampling pattern group outperformed other navigation pattern groups in the amorphous information-evaluating task, whereas the Satisficing pattern group performed best in the specific information-locating task.
HCDC-SRCF tracker: Learning an adaptively multi-feature fuse tracker in spatial regularized correlation filters framework

Integrating multi-feature based on multi-layer features from the convolutional network or based on multiple hand-crafted features has been proved to be an effective way for improving tracking performance. In this work, we investigate how to integrate multi-layer convolutional features with hand-crafted features. Specifically, an adaptive multi-feature fusion strategy is proposed based on convolutional features from ResNet-101 and hand-crafted features from HOG as well as Grayscale in spatial regularized correlation filter framework. We fully consider the complementary advantages of multi-layer convolutional features and hand-crafted features to construct a robust and reliable appearance representation of the target. Comprehensive experimental results on benchmark datasets demonstrate that our tracker has achieved significant performance improvements in various challenging environments. Compared to the trackers based only on multi-layer convolutional features or complete hand-crafted fusion features, the most important is that our proposed tracker obtains more competitive tracking performance. Our tracker is publicly available. You can find open-sourced code of our tracker at https://github.com/binger1225/HCDC-SRCF.
Reducing Communication Overhead of Federated Learning through Clustering Analysis

Training of machine learning models in a Datacen-ter, with data originated from edge nodes, incurs high communication overheads and violates a user's privacy. These challenges may be tackled by employing Federated Learning (FL) machine learning technique to train a model across multiple decentralized edge devices (workers) using local data. In this paper, we explore an approach that identifies the most representative updates made by workers and those are only uploaded to the central server for reducing network communication costs. Based on this idea, we propose a FL model that can mitigate communication overheads via clustering analysis of the worker local updates. The Cluster Analysis-based Federated Learning (CA-FL) model is studied and evaluated in human activity recognition (HAR) datasets. Our evaluation results show the robustness of CA - FL in comparison with traditional FL in terms of accuracy and communication costs on both IID and non-IID cases.
FANet: Feature aggregation network for RGBD saliency detection

The crucial issue in RGBD saliency detection is how to adequately mine and fuse the geometric information and the appearance information contained in depth maps and RGB images, respectively. In this paper, we propose a novel feature aggregation network FANet including a feature extraction module and an aggregation module for RGBD saliency detection. The premier characteristic of FANet is the feature aggregation module consisting of a designed region enhanced module (REM) and a series of deployed hierarchical fusion module (HFM). Specifically, on one hand, the REM provides the powerful capability in differentiating salient objects and background. On the other hand, the HFM is used to gradually integrate high-level semantic information and low-level spatial details, where the K-nearest neighbor graph neural networks (KGNNs) and the non-local module (NLM) are embedded into HFM to dig the geometric information and enhance high-level appearance features, respectively. Extensive experiments on five RGBD datasets show that our model achieves compelling performance against the current 11 state-of-the-art RGBD saliency models.
Co‐Producing Security: Platform Content Moderation and European Security Integration

The European Union (EU) seeks to play a leading role in steering the private work of online content moderation, as demonstrated by numerous policy and legislative initiatives in the domain. Two initiatives, in particular, are shaping terrorist content moderation: the creation of a EU Internet Referral Unit and the adoption of a Regulation on preventing the online dissemination of terrorist content (TERREG). This article analyses these initiatives and their practical effects. In particular, it unpacks the legal and technological mechanisms at the core of EU regulation in the realm of online terrorist content moderation, and how they co-produce security decisions across public and private spheres. Based on interviews, fieldwork observations and document analysis, we show how processes of referral and removal, and processes of flagging and filtering are key to EU-directed content moderation. In conclusion, we reflect on content moderation as a novel form of European security integration.
Machine learning and natural language processing to identify falls in electronic patient care records from ambulance attendances

We derived machine learning models utilizing features generated by natural language processing (NLP) of free-text data from an ambulance services provider to identify fall cases. The data comprised samples of electronic patient care records care records (ePCRs) from St John Western Australia (WA), the sole ambulance services provider in most of WA. We manually labeled fall cases by reviewing the free-text summary. The models used features including case characteristics (e.g., age) and text frequency-inverse document frequency (tf-idf) of each word of the free-text generated by NLP. Support vector machine (SVM) and random forest were used as classifiers. We compared the performance of the models against the manual identification of falls by recall, precision, and F-measure. A total of 9,447 cases (1%) were randomly sampled, of which 1,648 (17%) were labeled as fall. The best model was an SVM model using case characteristics and tf-idf's of the first 100 words of free-text, with recall of 0.84, precision of 0.86, and F-measure of 0.85. This performance was better than an SVM model with only case characteristics. Machine-learning models incorporated with features generated by NLP improved the performance of classifying fall cases compared with models without such features. Scope remains for further improvement.
Variability Debt: Characterization, Causes and Consequences

Variability is an inherent property of software systems to create families of products dealing with needs of different customers and environments. However, some practices to manage variability may incur technical debt. For example, the use of opportunistic reuse strategies, e.g., clone-and-own, harms maintenance and evolution activities; or deciding to abandon variability management and deriving a single product with all the features might threaten system usability. These examples are common problems found in practice but, to the best of or knowledge, not properly investigated from the perspective of technical debt. To expand the knowledge on the research and practice of technical debt in the perspective of variability management, we report results of this phenomenon, which we defined as variability debt. Our work is based on 52 industrial case studies that report problems observed in the use of opportunistic reuse. The results show that variability debt is caused by business, operational and technical aspects; leads to complex maintenance, creates difficulties to customize and create new products, misuse of human resources, usability problems; and impacts artifacts along the whole life-cycle. Although some of these issues are investigated in the field of systematic variability management, e.g., software product lines, our contribution is to present them from a technical debt perspective to enrich and create synergies between the two fields. As additional contribution, we present a catalog of variability debts in the light of technical debts found in the literature.
Integrated assessment and prospectivity mapping of geothermal resources for EGS in Brazil

Studies in Brazil related to geothermal resources have been carried out mainly by the National Observatory. Based on the results vast areas of Brazil have been delimited with a surface heat flow of over 60 mW/m2 and an excess temperature above 100 °C at depths between 3 and 6 km. The nature of the heat source and the mode of heat dissipation in these areas are largely unknown. Geothermal resources that might be associated with these areas can occur in hot sedimentary aquifers (HSA) and units of hot dry rocks (HDR) that are hosted, respectively, in intracratonic sedimentary basins and high heat producing granites (HHPG). The focus of our study is on finding resources of high enthalpy (above 150 °C) in HHPG, which regardless of their hydrological properties (porosity, permeability) could be considered as potential targets for enhanced geothermal systems (EGS). The structural provinces of Tocantins and Borborema were chosen as pilot regions based on both the indication of HDR and the wealth of open access georeferenced information. A comprehensive database was compiled and processed under GIS that includes geology, faults, soil typology, radiogenic heat, heat flow and geothermal gradient from wells, thermal spring locations, airborne gamma-ray spectrometry and magnetic surveys, terrestrial gravimetric surveys, and earthquake locations. In parallel, various critical factors were defined primarily to identify and characterize the most promising HHPG under insulating sedimentary rocks for EGS. These critical factors were grouped into three main categories labelled as: ‘thermal source’, ‘thermal insulation’ and ‘active pathways’. In the absence of exploratory data on existing EGS in Brazil these critical factors were used in this study along three separate work streams to develop a knowledge-driven model based on fuzzy logic for improving the knowledge on the prospectivity of geothermal resources. The developed model was further tested for its validation. The selected approach is expected to be applicable to mapping areas with EGS potential in other regions with similar geology and open access georeferenced databases.
Dynamic Educational Recommender System Based on Improved Recurrent Neural Networks Using Attention Technique

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Local path planning for autonomous mobile robots by integrating modified dynamic‐window approach and improved follow the gap method

Abstract Mobile robots need to automatically generate a safe, goal‐oriented, and fast collision‐free trajectory in real‐time during the movement in an indoor/outdoor environment. A planned trajectory must be adaptable and drivable with environmental changes where various static and moving obstacles may be present. The ultimate goal of a robot is to reach the destination without hitting any obstacles, therefore, a reactive local path planning algorithm is needed. In this paper, a novel local algorithm is proposed by integrating dynamic window approach (DWA) and improved follow the gap method (IFGM) to generate a collision‐free trajectory for a mobile robot which is capable to avoid any moving obstacles presenting in the surrounding environment. In this proposed method, first, a safety distance is maintained according to the relative position of obstacles and the robot. Moreover, find a feasible gap to direct the robot toward the desired goal. Besides, the heading angle is calculated to change the direction of the robot for avoiding collision with nearby obstacles. After that, calculate the appropriate velocity for the robot. Finally, a robust, safe, and goal‐directed trajectory is generated which does not suffer from global convergence and local minima problems. The performance and effectiveness of this proposed algorithm are evaluated by experimental results.