Reviewerly/exHarmony huggingface dataset:
Path planning for automatic parking based on improved Hybrid A* algorithm

In this paper, an automatic parking planning algorithm based on hybrid A* improvement is proposed for the unstructured environment of parking lots. In order to obtain an optimal trajectory under the conditions of safety and comfort, the algorithm makes the following adjustments: (1) Based on the kinematic and dynamical parameters of the experimental vehicle, the expansion search of the nodes is performed using clothoids to ensure the continuity of curvature. (2) The heuristic term of hybrid A* is improved by adding a penalty term of obstacle distance to the Reeds-shepp (RS) curve as the heuristic term, which improves the problem that the RS curve is too close to the obstacle. The effectiveness of the algorithm is evaluated based on simulation and real-world experimental results, and it is demonstrated that the algorithm can be widely used in automatic parking scenarios.
Embedding Energy Storage Systems into a Dynamic Knowledge Graph

This Article illustrates how a dynamic knowledge graph approach in the context of The World Avatar (TWA) project can support the decarbonization of energy systems by leveraging the existing energy storage system (ESS) selection framework to assist in the selection and optimal placement of the ESS. TWA is a dynamic knowledge graph based on the Semantic Web and its associated technologies, with intelligent agents operating on it. The agents act autonomously to update and extend TWA, and thus it evolves in time. TWA also provides the ability to consider different scenarios, referred to as parallel worlds, allowing for scenario analysis without mutual interference. A use case─the addition of a battery energy storage system to the Singapore Jurong Island electrical network─is introduced to demonstrate the application of this approach. The domain ontology, OntoPowSys, was extended to describe and instantiate the relevant ESSs considered in the use case. This extension is described in the Article using the description logic syntax. The Article also outlines the details of how the various agents involved in the use case are being integrated into TWA. The use case also highlights how the parallel world framework can facilitate scenario analysis by considering different scenarios without affecting the real-world representation.
Digital Video Manipulation Detection Technique Based on Compression Algorithms

Digital images and videos play a very important role in everyday life. Nowadays, people have access the affordable mobile devices equipped with advanced integrated cameras and powerful image processing applications. Technological development facilitates not only the generation of multimedia content, but also the intentional modification of it, either with recreational or malicious purposes. This is where forensic techniques to detect manipulation of images and videos become essential. This paper proposes a forensic technique by analysing compression algorithms used by the H.264 coding. The presence of recompression uses information of macroblocks, a characteristic of the H.264-MPEG4 standard, and motion vectors. A Vector Support Machine is used to create the model that allows to accurately detect if a video has been recompressed.
Clustering Document based Semantic Similarity System using TFIDF and K-Mean

The steady success of the Internet has led to an enormous rise in the volume of electronic text records. Sensitive tasks are increasingly being used to organize these materials in meaningful bundles. The standard clustering approach of documents was focused on statistical characteristics and clustering using the syntactic rather than semantic notion. This paper provides a new way to group documents based on textual similarities. Text synopses are found, identified, and stopped using the NLTK dictionary from Wikipedia and IMDB datasets. The next step is to build a vector space with TFIDF and cluster it using an algorithm K-mean. The results were obtained based on three proposed scenarios: 1) no treatment. 2) preprocessing without derivation, and 3) Derivative processing. The results showed that good similarity ratios were obtained for the internal evaluation when using (txt-sentoken data set) for all K values. In contrast, the best ratio was obtained with K = 20. In addition, as an external evaluation, purity measures were obtained and presented V measure of (txt). -sentoken) and the accuracy scale of (nltk-Reuter) gave the best results in three scenarios for K = 20 as subjective evaluation, the maximum time consumed with the first scenario (no preprocessing), and the minimum time recorded with the second scenario (excluding derivation).
Key lengths revisited: GPU-based brute force cryptanalysis of DES, 3DES, and PRESENT

Lightweight cryptographic algorithms that are tailored for resource-constrained devices sometimes use short keys which might be susceptible to brute force attacks. Such attacks using CPU or GPU might be costly because some lightweight algorithms are hardware-oriented and use bit-level operations. In this work we provide table-based CUDA optimizations for bit-oriented block ciphers DES/3DES and ISO/IEC standard PRESENT to provide software implementations without bit operations and show that cryptographic algorithms with short keys are susceptible to exhaustive search attacks on distributed multiple GPU settings. Our best optimizations provide 3.87 and 1.89 billion key searches per second for DES/3DES and PRESENT on an RTX 3070, respectively. These results outperform FPGA clusters like COPACOBANA in terms of price-performance ratio showing that exhaustive search on short keys might be practical without special devices. Therefore, an RTX 3070 can capture a DES key in 215 days and 20 million RTX 3070 GPUs can capture an 80-bit PRESENT key in a year. Thus, we recommend the removal of algorithms with 80-bit or shorter keys from the standards. Moreover, our optimizations provide 278.32 Gbps DES, 92.77 Gbps 3DES, and 115.73 Gbps PRESENT encryption throughput on an RTX 3070 when the ciphers are used in counter mode.
An Efficient Data Query Method of Blockchain Based on Index

In many blockchain applications, apart from storing the original data, it is also necessary store the data operation records in the blockchain for audit or security. Yet, the traditional blockchain, generally cannot meet the user's demand for data query. In order to solve this problem, we propose a novel block structure, the block body into an index layer and a data layer. On this basis, we further develop two in the index layer to aggregate the data and its operation records for efficient query. Specifically, we first build an index for the original data in blockchain by desigining the Abstract-Trie, which has efficient and stable query features. Secondly, we design another index called Operation-Record List to link the operation records to the corresponding original data, and store the first node of each list in the Abstract-Trie, so as to achieve the fast acquisition of the operation records of the original data. Simulation results based on Hyperledger Fabric verify the effectiveness of our optimized block structure in querying of the original data and its operation records in the blockchain.
AraScore: A deep learning-based system for Arabic short answer scoring

In the past years, Arabic NLP has been significantly lagging behind its English counterpart, but recent advancements in Natural Language Processing have made it possible for Arabic to catch up and show promising results for a multitude of tasks. Complex tasks such as short answer scoring, have been widely researched mainly for English, leveraging machine learning and state-of-the-art deep learning techniques. In this paper, we introduce the first deep learning-based system for Arabic short answer scoring, in efforts to provide a reliable system that can help teachers in the Arab world better utilize their time in other teaching activities that would elevate the quality of learning in the region. We empirically study different techniques and propose the best performing system based on our results, where we have achieved state-of-the-art performance, achieving a QWK score of 0.78 and showing how powerful and robust recent Arabic NLP tools have become.
Blockchain-based fog radio access networks: Architecture, key technologies, and challenges

Fog Radio Access Network (F-RAN) has been regarded as a promising solution to the alleviation of the ever-increasing traffic burden on current and future wireless networks, for it shifts the caching and computing resources from remote cloud to the network edge. However, it makes wireless networks more vulnerable to security attacks as well. To resolve this issue, in this article, we propose a secure yet trustless Blockchain-based F-RAN (BF-RAN), which allows a massive number of trustless devices to form a large-scale trusted cooperative network by leveraging the key features of blockchain, such as decentralization, tamper-proof, and traceability. The architecture of BF-RAN is first presented. Then, the key technologies, including access control, dynamic resource management, and network deployment are discussed. Finally, challenges and open problems in the BF-RAN are identified.
Blockchain, Smart Contracts, and Decentralized Applications: An Introduction

Blockchain, a most common synonym for distributed ledger (Tschorsch and Scheuermann in IEEE Commun Surv Tutor 18:2084–2123, 2016), is an emerging platform. Blockchain is used to support transactional services within a multi-party business network. The aim is to enable a significant reduction in cost and risk for all involved parties through the creation of novel and enhanced business models. Data maintained within the distributed ledger can only be accessed through the execution of a smart contract (Seijas, Scripting smart contracts for distributed ledger technology, International Association for Cryptologic Research, 1156, 2016) that governs a transaction with rules. The core driving idea for designing the architecture of a blockchain platform is to guarantee that no single business party can append, modify or delete any record residing within the ledger without going through the necessary consensus from other business parties within the network. This empowers the system and ensures the data is immutable. Examples of such data include but are not limited to legal and financial trails. Given the aforementioned important features, blockchain technology is a revolutionary trending technology with the ability to offer tremendous disrupting changes to different industries. Gartner's 2016 research report (Gartner, Gartner's 2016 hype cycle for emerging technologies special report, 2016) identified blockchain as one of the key platform-enabling technologies to track. While there is currently a lack of widely identified and accepted standards for blockchain technology, there is a rising agreement that blockchain is currently entering its height of extravagant expectations. The report anticipated that it would take 5 to 10 years for blockchain technology to be easily and widely adopted. Nowadays, almost five years later most blockchain efforts, especially the ones applied to business environments, are still in the early stages not reaching the widely adoption stage also not realizing a widely accepted standard so far. In this book chapter, an attempt will be made to cover the main concepts of blockchain, its underlying technologies, Smart Contracts, and Decentralized Applications (DApps).
A Survey on Capsule Networks: Evolution, Application, and Future Development

Capsule networks have drawn increasing attention since the concept is proposed in 2011. They hold advantages over convolutional neural networks (CNNs) on modeling part-to-whole relationships between entities and learning viewpoint invariant representations. A variety of improved designs and applications related to capsule networks have been explored within the past decade. This paper presents a relatively thorough survey of literatures corresponding to the evolution of the capsule networks and their applications. Different design of capsule networks from Hinton&#x2019;s group as well as other researchers are reviewed. Applications of capsule networks in various scenarios and tasks are elaborated, including hyperspectral image processing, medical image processing, facial image processing, text classification, object detection, image segmentation, few-shot learning, etc. The relation and comparison of capsule networks, CNNs, and transformers, and the trend of future development of capsule networks are discussed. We believe this review provides a good reference to researchers who are interested in capsule networks.
Robust Automatic Speaker Identification System Using Shuffled MFCC Features

Speaker Identification using deep learning is an ongoing active topic that has wide applications in voice authenticated and activated systems, forensic investigations, security, and remote transactions. Mel frequency cepstral coefficients (MFCC) feature-based speaker identification systems are proven to per- form very well with clean speech conditions. However, there is rapid degradation in the robustness with adverse conditions and short speech duration. To overcome this limitation, we propose a new speaker identification pipeline system grounded on novel MFCC based features called shuffled MFCC (SHMFCC) along with a new data augmentation approach. Moreover, the system uses a simple and efficient deep neural network model with variable-duration input speech signals and is proved to perform remarkably at different datasets with different environmental and noise conditions and for a high number of speakers.
Unpaired Screen-Shot Image Demoiréing With Cyclic Moiré Learning

We propose an end-to-end unpaired learning approach to screen-shot image demoiréing based on cyclic moiré learning. The proposed cyclic moiré learning algorithm consists of the moiréing network and the demoiréing network. The moiréing network generates moiré images to construct a pseudo-paired set of moiré and clean images. Then, the demoiréing network is trained in a supervised manner using the generated pseudo-paired dataset to remove moiré artifacts. In the moiréing network, the moiré generation is separately learned as global pixel intensity degradation and moiré pattern generation for more realistic moiré artifact generation. Furthermore, the moiréing network and the demoiréing network are integrated together to be trained in an end-to-end manner. Experimental results on different datasets demonstrate that the proposed algorithm significantly outperforms state-of-the-art unsupervised demoiréing algorithms as well as image restoration algorithms.
Appraisal of Multiple AES Modes Behavior using Traditional and Enhanced Substitution Boxes

The analysis of performance criteria for different cryptographic algorithms has increasingly been concerned in the last few years and that is because the majority of life applications need cryptographic algorithms to be involved in their structure to provide security for these applications such as banking services, e-government and online applications [1]. In this paper, the analytic study is executed on the most specific popular cryptographic algorithm which is “AES” technique [2][3] in order to research the impact of utilizing different models which are named “modes” on the behavior of “AES” technique and hence increases the cryptographic strength of “AES” technique. The criteria utilized to aid in judging on the influence of modes on the behavior of “AES” technique are enciphering time, throughput and strict avalanche criteria (SAC). Such influence has been clarified and presented through providing comparative analysis among modes of operation according to previously mentioned parameters. First the analytic study is proposed utilizing the traditional substitution box formula in constructing AES technique then an enhanced version of substitution box equation is included in AES technique to provide more complex and securing substitution box in constructing AES algorithm so as to measure the impact of changing the formula of substitution box on the behavior of AES technique and its cryptographic capabilities. Finally, the results of executing the modes on the behavior of AES technique in case of utilizing the traditional sbox and the enhanced version of s-box are listed in terms of the previously mentioned criteria enciphering time, throughput and (SAC) and consequently we come up with the conclusion that SAC parameter is the only criterion that shows the impact of utilizing different modes on the behavior of AES technique but the enciphering time and throughput has no role in showing the influence of utilizing distinctive modes on the behavior of AES technique regardless of changing the substitution box equation utilized in “AES” technique.
Quantum Circuit Transformation: A Monte Carlo Tree Search Framework

In the noisy intermediate-scale quantum era, quantum processing units suffer from, among others, highly limited connectivity between physical qubits. To make a quantum circuit effectively executable, a circuit transformation process is necessary to transform it, with overhead cost the smaller the better, into a functionally equivalent one so that the connectivity constraints imposed by the quantum processing unit are satisfied. Although several algorithms have been proposed for this goal, the overhead costs are often very high, which degenerates the fidelity of the obtained circuits sharply. One major reason for this lies in that, due to the high branching factor and vast search space, almost all of these algorithms only search very shallowly, and thus, very often, only (at most) locally optimal solutions can be reached. In this article, we propose a Monte Carlo Tree Search (MCTS) framework to tackle the circuit transformation problem, which enables the search process to go much deeper. The general framework supports implementations aiming to reduce either the size or depth of the output circuit through introducing SWAP or remote CNOT gates. The algorithms, called MCTS-Size and MCTS-Depth , are polynomial in all relevant parameters. Empirical results on extensive realistic circuits and IBM Q Tokyo show that the MCTS-based algorithms can reduce the size (respectively, depth) overhead by, on average, 66% (respectively, 84%) when compared with t \( \left| {\mathrm{ket}} \right\rangle \) , an industrial-level compiler.
THE XXI CENTURY COMPETENCIES FORMATION: THE METHODOLOGY OF FORESIGHT, “ADORNATION” AND DECONSTRUCTION

The methodology of foresight and “adornation” of competencies is analyzed. The concept of deconstruction of competencies is introduced. The article substantiates the necessity of foresight, adornment and deconstruction in the process of forming competencies of the future, the relationship of which can be represented both in the form of a cycle of successive technologies, and in the form of intertwining processes with one of the technologies dominating, depending on the specifics of the situation and the social context
Unsupervised Sounding Object Localization with Bottom-Up and Top-Down Attention

Learning to localize sounding objects in visual scenes without manual annotations has drawn increasing attention recently. In this paper, we propose an unsupervised sounding object localization algorithm by using bottom-up and top-down attention in visual scenes. The bottom-up attention module generates an objectness confidence map, while the top-down attention draws the similarity between sound and visual regions. Moreover, we propose a bottom-up attention loss function, which models the correlation relationship between bottom-up and top-down attention. Extensive experimental results demonstrate that our proposed unsupervised method significantly advances the state-of-the-art unsupervised methods. The source code is available at https://github.com/VISION-SJTU/USOL.
Techno-economic analysis of PV systems with manually adjustable tilt mechanisms

Solar radiation falling on photovoltaic (PV) panel surface can be maximized via solar tracking systems, however, in return for infeasible investments. On the other hand, manual and periodic tilt adjustment of PVs can increase energy yield significantly and cost-effectively. Therefore, this study presents a techno-economic analysis of 1 MW PV power plants with manually adjustable tilt mechanisms. Firstly, the optimal tilt angles for fixed and periodically adjusted (monthly, seasonal, and semi-annual) PV systems in locations with different solar characteristics in Turkey are estimated, and then an economic analysis is performed, including a sensitivity analysis. The results show that, manual tilt adjustment provides a remarkable net present value (NPV) increase in Turkey between 12.4 and 14.9% compared to fixed-tilt. The discounted payback period (DPBP) ranges between 9.20 and 12.36 years and can be shortened by 8–10 months. The internal rate of return (IRR) of 5.4–8.6% can be increased by 0.7–0.9%.
Assessment System of Presentation Slide Design Using Visual and Structural Features

Developing well-designed presentation slides is challenging for many people, especially novices. The ability to build high quality slideshows is becoming more important in society. In this study, a neural network was used to identify novice vs. well-designed presentation slides based on visual and structural features. For such a purpose, a dataset containing 1,080 slide pairs was newly constructed. One of each pair was created by a novice, and the other was the improved one by the same person according to the experts' advice. Ten checkpoints frequently pointed out by professional consultants were extracted and set as prediction targets. The intrinsic problem was that the label distribution was imbalanced, because only a part of the samples had corresponding design problems. Therefore, re-sampling methods for addressing class imbalance were applied to improve the accuracy of the proposed model. Furthermore, we combined the target task with an assistant task for transfer and multi-task learning, which helped the proposed model achieve better performance. After the optimal settings were used for each checkpoint, the average accuracy of the proposed model rose up to 81.79%. With the advice provided by our assessment system, the novices significantly improved their slide design.
FaaScinating Resilience for Serverless Function Choreographies in Federated Clouds

Cloud applications often benefit from deployment on serverless technology Function-as-a-Service (FaaS), which may instantly spawn numerous functions and charges users for the period when serverless functions are running. Maximum benefit is achieved when functions are orchestrated in a workflow or function choreographies (FCs). However, many provider limitations specific for FaaS, such as maximum concurrency or duration often increase the failure rate, which can severely hamper the execution of entire FCs. Current support for resilience is often limited to function retries or try-catch, which are applicable within the same cloud region only. To overcome these limitations, we introduce rAFCL, a middleware platform that maintains reliability of complex FCs in federated clouds. In order to support resilient FC execution under rAFCL, our model creates an alternative strategy for each function based on the required availability specified by the user. Alternative strategies are not restricted to the same cloud region, but may contain alternative functions across five providers, invoked concurrently in a single alternative plan or executed subsequently in multiple alternative plans. With this approach, rAFCL offers flexibility in terms of cost-performance trade-off. We evaluated rAFCL by running three real-life applications across three cloud providers. Experimental results demonstrated that rAFCL outperforms the resilience of AWS Step Functions, increasing the success rate of entire FC by 53.45%, while invoking only 3.94% more functions with zero wasted function invocations. rAFCL significantly improves availability of entire FCs to almost 1 and survives even after massive failures of alternative functions.