Reviewerly/exHarmony huggingface dataset:
Relation Transformation based on Graph Convolution Network for Entity Alignment

Entity alignment has lately received great attention for its key role in knowledge fusion, which has a goal that links entities with the same meaning from different knowledge graph. Most recently, many novel methods based on graph convolution network (GCN) have emerged as interesting models for entity alignment. Although these existing methods can further improve the quality of entity embedding, they ignored the influence of the embeddings of multiple relationships between entities on entity alignment. In addition, current models do not considerably learn the embeddings of relationship. To address these problems, we adopt a novel Relation Transformation based on Graph Convolution Network for entity alignment, named RT-GCN. Specifically, we point out a new GCN to aggregate the information of entities, which aims to strikingly obtain entities embeddings. Moreover, we develop a novel relational transformation method to generate relational embeddings. The crucial role of relational transformation is to strengthen the process of alignment and improve the robustness of the model. Experimental results on three datasets state that the proposed model RT-GCN performs surprisingly better than the state-of-the-art methods.
ADN: ATSS-based Deep Network for Pedestrian Detection on High-Resolution Images

This paper investigates the high-resolution pedestrian detection network AND(ATSS-based Deep Network for Pedestrian Detection on High-Resolution Images) to solve the problem that high-resolution images are difficult to be processed directly as well as pedestrian occlusion and pose variations. First, it obtains a deeper backbone network by stacking residual modules to extract multi-level features, improve the extraction ability of occluded target features and avoid network degradation. Subsequently, the deformable convolution is introduced to optimize the backbone network and expand the local receptive field, thus further optimizing the detection ability of deformable targets and occluded targets. On the PANDA dataset, the AP, AP50, and AP75 of ADN are 3.5, 2.7, and 4.2, which are higher than that for the baseline respectively. Compared with other state-of-the-art methods, experiments show that ADN effectively enhances the accuracy of pedestrian detection in high-resolution Imgaes, and it outperforms existing object detection algorithms.
Context-Aware Neural Machine Translation using Selected Context

Context-aware neural machine translation has attracted much attention recently by promising sophisticated contextual information integration into conventional neural machine translation. However, context-aware NMT is challenged with effective context aggregation and increased training time due integration of extra information. In this work, we study the effect of encoding selective contextual information using pre-trained models for effective contextual integration and performance optimization. We conduct experiments on different context selection methods and quantify that encoding selected context significantly reduces the training time while maintaining superiority over sentence-level NMT models. Specifically, we experimented on IWSLT English↔German translation task and show encoding selected keywords as context is sufficient and obtains best translation results.
A Pragmatic Decision Making Mechanism for Reusable Source Code Snippets Retrieval using Semantic Approach

Programmers frequently reuse exiting code available on internet sources like GitHub and Stack Overflow. Usually, programmers search for relevant/desired code by using natural language queries through search engines. However, the reusable code is very different from the normal textual searches. The main aim of code retrieval is to search for the most relevant snippet from a corpus of code snippets. The only way to efficiently retrieve the most relevant results is to eliminate the semantic gap between the code snippets and the user’s query (Natural language description) search in the repository. Bridging this gap is an immense challenge. The primary objective of the research is to retrieve code snippets that are semantically relevant to the user query and to provide programmers with the ability to locate source code that they can use when developing new applications. The proposed approach is implemented using a web platform through which users can search source code. In this research, a semantic model for code retrieval is used which generates meanings or synonyms of the words. The proposed model integrates Ontologies and Natural Language Processing. The proposed approach is evaluated on the dataset extracted from Stack Overflow. The performance measures and classification accuracy are computed using Mean Average Precision. The retrieved results are ranked using Levenshtein similarity, demonstrating that our approach outperforms a robust component matching significantly. Our evaluation shows that the use of semantic matching leads to improve retrieval effectiveness. This study marks a substantial advancement in the integration of programming expertise with code retrieval techniques. Moreover, this system enables users to know when and how it is used for successful semantic searching.
Using Autoencoders to Reduce Dimensionality of DICOM Metadata

Digital Imaging and Communication in Medicine (DICOM) is a standardized format for storing medical images enriched with descriptive data. It consists of a number of informative header tags. E.g., these tags contain information concerning imaging technique, patient weight, patient age and so on. In machine learning, these tags can, among other things, be used for categorization, classification, and general manipulation of DICOM data. However, because of their number and variety, using them for automation undoubtedly impacts computational performance. To tackle this problem, the possibility of using auto encoders is explored in this manuscript. It was hypothesized that clustering data compressed with autoencoders can achieve the same results as when working with raw - uncompressed data. To support this claim, clustering of compressed and uncom- pressed data was performed on a dataset of 25, 000 DICOM files from the Clinical Hospital Centre Rijeka PACS. The results show no significant difference between compressed and uncompressed data (p > 0.05), thus confirming that equally good clustering results can be accomplished using a smaller representation.
OKV: Optimized Key-Value Data Collection with Local Differential Privacy

Local differential privacy (LDP), where each user obfuscates their data locally before sending it to an untrustworthy data collector, provides a strict privacy guarantee for users' sensitive data. However, the existing key-value data collection mechanisms based on the LDP assume that all keys are equally sensitive, which leads to excessive protection and thus loss of utility. To address the reduced utility caused by overprotection, we introduce the notion of key-value data utility-optimized LDP (KV-ULDP), which only offers a basic LDP-equivalent privacy guarantee for sensitive keys and all values. Subsequently, we design a new framework, named optimized key-value data collection (OKV) with LDP, which satisfies the KV-ULDP with high utility while keeping secret for each user. We instantiate the OKV framework by using OKV-UE (based on Unary Encoding) and OKV-GRR (based on Generalized Randomized Response) mechanisms. The OKV-UE is effective with a large number of key types, and OKV-GRR works well under high privacy budgets. The theoretical analysis and the experiments on two real datasets show that our mechanisms outperform the existing key-value mechanisms with LDP in terms of utility.
Smart Grid Network Control Model Based on Blockchain

Traditionally, distribution network automation systems have a centralized architecture in which data from various field devices, such as measuring devices and remote termination devices, are centrally managed by a dispatch and data collection system. Unlike traditional methods, blockchain technology facilitates the transfer of digital assets between peer-to-peer nodes without any intermediaries. This article presents a decentralized system model built on the Cisco Packet Tracer program and a table with the time taken to exchange data between the virtual power plant, which can be used to calculate a profitable purchase or sale of energy.
Self Supervised Clustering of Traffic Scenes using Graph Representations

Examining graphs for similarity is a well-known challenge, but one that is mandatory for grouping graphs together. We present a data-driven method to cluster traffic scenes that is self-supervised, i.e. without manual labelling. We leverage the semantic scene graph model to create a generic graph embedding of the traffic scene, which is then mapped to a low-dimensional embedding space using a Siamese network, in which clustering is performed. In the training process of our novel approach, we augment existing traffic scenes in the Cartesian space to generate positive similarity samples. This allows us to overcome the challenge of reconstructing a graph and at the same time obtain a representation to describe the similarity of traffic scenes. We could show, that the resulting clusters possess common semantic characteristics. The approach was evaluated on the INTERACTION dataset.
YouTube Video Identification in Encrypted Network Traffic —A Case of Attacking Assumptions

Identifying the video in the network traffic is in fact a tough challenge for researchers. Recent studies on video identification in the network traffic use many different assumptions that increase the difficulty of practical deployment of the techniques. This study proposes an attack using a Convolutional Neural Network for video identification in the network traffic and also targets assumptions to understand their effects on the accuracy of the attack. Two basic assumptions are targeted in this work: (1) Time of starting the videos at the client and previous samples in the dataset used for training of models should be same and (2) all normalization techniques for normalizing features before feeding them to the classifier will work equally. The results show that even if the video starts playing from any random position, the convolutional neural network can identify the video with high accuracy of 95%. There is no restriction of the exact match between the start time of the video in the client and in the samples used for training. Moreover, max normalization technique provides better accuracy with the model than sigmoid function.
Detection of Human-Performance-Indicators with an intelligent seat post

In the recent past new technologies have made a major contribution to improve the performance of athletics. Nevertheless, conventional solutions for athlete training development and analysis mainly rely on physical performance metrics, such as the amount of covered tracks and durations. Little has been done to monitor the quality of the physical motion and actions. This limitation stems from lacking suitable qualitative metrics and measurement systems. This paper investigates a new approach towards human activity recognition for individual performance analysis, leveraging sensor data from Inertial Measurement Units (IMUs). We show how to integrate sensors into the seat post of a bicycle and that it is possible to derive human performance indicators. The results of this study encourage the further exploitation of our method to analyze how cyclists behave while riding,
Securing Nationwide Elections Through Blockchain Ledger, Utilising Encryption Hardware

Nowadays, the voting systems around the world have not been technologically updated much, despite the availability of modern and applicable technologies, that could easily overcome the main problems of elections. Blockchain ledger is the perfect candidate for implementing the whole election process. It will increase trust of all involved parties, create immutable records, secure the data used in the election process and make sure that “every vote counts”. The election process will be secured and verifiable with full accountability through data encryption, considering the effect of social conformity on collective voting behavior. Securing the election process and election results through technological advancements is a key element of sustaining the democratic processes around the world or develop a “decentralized democracy”. Every democratic society starts its long path of evolution exactly with the election process and thus it is of fundamental importance to secure this process, its results and provide maximum transparency to the population to avoid any misconceptions or even conspiracy theories. The blockchain ledger as a concept could potentially overcome most of the shortcomings of the current election processes used around the world, including e-voting and machine voting - intermediary processes that just replicate the ancient paper ballot voting mechanism, but in wrapped by electronic means. In this process-design proposal paper, the method for election voting, utilizing blockchain ledger overcomes many classical problems in the election process, such as preserving the secret of the vote, immutability, transparency, decentralization of the system, following the “every vote counts” concept and disallowing double voting. Considering various approaches to blockchain based voting, we have concluded that the best method is to implement a private governmental blockchain solution - both for reducing the costs and for allowing for various specific features to be implemented, such as partial public/private ledger. The consensus algorithm problem could easily be solved by implementing a PoS consensus on a large number of participating nodes, such as most citizens could participate as nodes, being part of a broader eID initiative. Such implementation would prove unrealistic to try and take over a network with millions of independent nodes. The present paper aims at providing only a model for blockchain elections and does not provide an off the shelf solution.
Dynamic Stability for UAV Path Planning

Modern Unmanned Aerial Vehicles (UAVs) are being widely used for civil as well as military missions such as package delivery, surveillance and photography. For all of these missions the UAVs are instructed to track a fixed or moving point or target. For that purposes many tracking control algorithm are developed. Those method should converge toward the target in the quickest possible time, they should be computationally efficient in order to reduce the computational burden on the UAV main processor and smooth in order to reduce the mechanical stress on the drone. The control problem is more pronounced in case it is applied for fixed wing drones. This paper propose a control tracking algorithm for HALE (High Altitude Long Endurance) drones. The method is nearly optimal, smooth and uses a computationally efficient algorithm. In order to evaluate the new control law, simulation results are presented and compared with older method. Finally, conclusions are drawn.
Blockchain Overcomes Corruption: Towards Smart Institutions and Governance

e-government services have evolved significantly over the past decade from paper Bureaucratic procedures for digital services. Transactions processed electronically require limited physical interaction with the public authorities, providing reduced time response, transparency, confidentiality, and integrity. Blockchain technology enhances many of the above characteristics because it facilitates the stability and transparency of recorded transactions and could help with building trust among the participants [1]. In this paper, a smart accounting system for public institutions (municipalities) with Blockchain technology prevents wasting public funds, and citizens become combative of corruption and waste of public funds when being informed of government projects implemented by municipalities through this system.
Communication Patterns for Evaluating Vehicular E/E Architectures

The continuous innovation and advancement in vehicle software functionality has driven the evolution of its deployment platforms through several generations of vehicular Electrical and Electronic (E/E) architectures. It is a daunting task to evaluate pros and cons of allocating the new as well as legacy functionality to these architectures. In this paper, we propose a novel approach that uses communication patterns as a metric to evaluate different vehicular E/E architectures and propose a suitable allocation for the functionality. First, we present the characteristics of these patterns in vehicular systems that are derived from the state-of-the-art review, standardized vehicular software architectures, well-known onboard vehicular, communication protocols, industrial requirements and use cases. We leverage the derived communication patterns and their characteristics to propose an evaluation approach for different architectural solutions for the functionality. We utilize a use case from the vehicle industry to demonstrate the applicability and usability of the proposed approach.
Systematic qualitative review of Cloud Computing Adoption Challenges of the SME retailers in UAE

Despite the numerous benefits of cloud computing and the Internet of Things (IOT), there remains a reluctance among UAE SMEs in the retail industry to use these technologies. This study is being performed to examine the function of IoT technology and cloud computing as a strategic instrument in the digitalization process of a retail SMEs. Despite independent research on these aspects of digital transformation in the past, no studies have been conducted to identify factors influencing SMEs motivation for incorporating these technologies into their strategy formulation and to gain a better understanding of the technology's possibilities in general as a strategic option for retailers in the UAE.The goal of this exploratory study is to sketch out the dynamics of a retailer's environment, which may influence decision-making concerning cloud and IoT deployment in the UAE's SME retail sector. Based on a thorough review of the literature, the ‘Unified Theory of Acceptance and Use of Technology’ (UTAUT) is implemented and extended, with the goal of conceptualizing a model that incorporates the discovered components. The findings of this study article may be used by academics for future research, as well as organizations to acquire fresh insights from their consumers in order to make informed and successful business decisions effectively.
An Approach to Design a Robot Platform with a Web-Based Virtual Reality Remote Control System

A robotic challenge research is to go beyond the traditional and collaborative robotic systems focusing on flexible mobile robots with simple and faster programming, flexible peripherals and grippers and full manual access for easy maintenance meeting the demands for fast layout change and reliable maintenance. Such designed robotic systems with up-todate achievements in intelligent navigation and cognitive sensing could ensure intelligent actions in unforeseen situations where is required human-machine interaction. The main goal of the paper is to present a concept of system architecture of near-autonomous robots with human remote-control takeover possibility is the main challenge of the paper where to allow the human to control a large number of service robots and the price is reasonable are main targets.
Implementation and Optimization of Neural Networks for Tiny Hardware Devices

Traditionally, neural network inferencing on tiny hardware devices took place in a centralized server-based manner. With more real-time applications coming into play, where security and latency are a concern, there has become a need to move inferencing to the edge. This paper describes a machine learning pipeline to carry neural networks from their initial forms to compressed forms deployable on tiny hardware devices, while maintaining acceptable accuracies of the optimized models. We will review the different software optimization techniques used to compress neural networks to their deployable forms. The prototype is a proof of concept showing that applying knowledge distillation from a highly accurate ResNet20 model to a simple CNN student model, followed by post-training quantization, achieves good multi-class accuracy on a constrained Arduino Nano 33 BLE Sense at low power consumption and with low inferencing latency.
Radiometric and Geometric Correction Methods for SWIR Cameras

Due to size of sample, camera’s field of view and the limitation of detection time, photoluminescence images of sample are captured by multiple cameras simultaneously and scans sample sequentially with image stitching. To ensure the smooth progress of image splicing, the composition cameras of each inspection machine need to perform radiometric correction and geometric correction. This paper presents the radiometric and geometric correction methods of these SWIR cameras.
Em-RIPE: Runtime Intrusion Prevention Evaluator for ARM Microcontroller Systems

Although they have been known for some time, the security implications of buffer overflows (BOF) continue to rouse great attention among software experts in the academic and commercial sectors. Recently, there has been particular interest in discussing how to mitigate risks deriving from BOF on embedded and loT devices, which have lower computational capabilities given their low-cost and low-power requirements. Although the literature is rich of solutions for these devices as well, authors often fail to quantitatively compare their techniques with related work from a security perspective, and mostly rely on qualitative analysis. Existing evaluator benchmarks (such as the famous RIPE, introduced in 2011) are designed to be used only on general-purpose systems, e.g., with a rich Linux OS and Intel architecture. This paper presents Em-RIPE, a prototype evaluation tool written for assessing protections applied to real-time embedded systems, such as microcontrollers equipped with ARM processors. This first version of the tool supports 105 different possible attack combinations, on which the resilience level of the platform under test can be measured. As experimental data, the obtained pro-tection coverage for major compiler-based firmware protections is reported.
The digital material formatting app prototype 3.0

There is the application prototype 3.0 developed for automatized digital material formatting. It is developed to solve the global problem as digital screen users complain and symptoms due to the huge digital near workload. That significantly increased during the Covid-19 pandemic when all everyday tasks moved to a digital environment, including work tasks and education for all age groups. It can bring severe vision impairments and even blindness in the long term as humans' visual system is not adapted well enough to the digital environment and long-term work via screens by using inappropriate text typographic parameters for digital reading purposes. The latest existing Microsoft technologies for development have been used to reach broader digital document types. As well as, there is the use of the Realm database in the app prototype more straightforward for future modification.
Sentiment Analysis of Bitcoin data by tweets through Naive Bayes

Bitcoin, as one of the most popular cryptocurrencies, is notorious for its concerning volatility on any given day, which leads to people having mixed feelings about it. This paper examines the opinions of people all over the world using data gathered from the social media platform Twitter, to decide if investing in bitcoin is a good idea for future. The opinions are analyzed and classified using the Bernoulli and Multinomial Naive Bayes model through sentiment analysis, the models implemented yielded 87 and 86 percent of accuracies respectively. Furthermore, the variation in sentiment over time is comparedto the variation in bitcoin price over the same period.