Reviewerly/exHarmony huggingface dataset:
PoEC: A Cross-Blockchain Consensus Mechanism for Governing Blockchain by Blockchain

The research on the governing blockchain by blockchain supervision system is an important development trend of blockchain technology. In this system there is a supervisory blockchain managing and governing the supervised blockchain based on blockchain technology, results in a uniquely cross-blockchain demand to consensus mechanism for solving the trust problem between supervisory blockchain and supervised blockchain. To solve this problem, this paper proposes a cross-blockchain consensus mechanism based on smart contract and a set of smart contracts endorse the cross-blockchain consensus. New consensus mechanism called Proof-of-Endorse-Contracts (PoEC) consensus, which firstly transfers the consensus reached in supervisory blockchain to supervised blockchain by supervisory nodes, then packages the supervisory block in supervisory blockchain and transmits it to the smart contract deployed in the supervised blockchain, finally miners in supervised blockchain will execute and package the new block according to the status of the smart contract. The core part of the consensus mechanism is Endorse Contracts which designed and implemented by us and verified the effectiveness through experiments. PoEC consensus mechanism and Endorse Contracts support the supervised blockchain to join the governing blockchain by blockchain system without changing the original consensus mechanism, which has the advantages of low cost, high scalability and being able to cross-blockchain. This paper proves that our method can provide a feasible cross-blockchain governance scheme for the field of blockchain governance.
Introduction—On the Need for Research on the Digital Literacy of Current and Future Teachers

The development of the information society forces a reflection on how the next generation of teachers are prepared for work in the school and non-formal education system. Current models for the education of pedagogical staff all over the world require the challenges posed by the convergence of analogue and digital media to be met, as well as for the continuous and intensive process of digitization to be taken into account, especially when that process is accelerated by circumstances beyond the control of the teachers themselves (e.g. pandemic situation, the accelerated development of e-services, the creation of new educational policies, and the informatization of the state, to name just a few).
EtherRider: A Decentralized Intercity Ride-Sharing Platform using Block-Chain Technology

Ridesharing entails the sharing of journeys in order to make optimal use of fuel by allowing people to go along the same route to share rides. It allows regular passengers to share trips with others, having the additive benefit of lowering travel costs and reducing traffic congestion. Most current ride choices rely on a centralized authority to enable the system, leaving it vulnerable to faults at a specific point in the system and raising concerns about privacy disclosure to attackers acting both within and outside. Furthermore, they are vulnerable to external threats and fraud, and the payment made by the current ride-sharing service provider is rather costly. As a result, we have proposed the system named EtherRider, based on the Ethereum blockchain technology. EtherRider enables drivers to provide transportation services without the need for a central system. Both the passenger and the driver will know about sharing ride details, secure their travel details, such as pick-up and drop-off locations, arrival/departure times, and secure payment through the ethereum blockchain.With a distributed ledger, drivers and riders could create a more user-driven,value-oriented marketplace.In the context of car-sharing systems, our work also indicates that the design of such an integrated platform is dependent on striking the correct balance between important design concepts (such as security and privacy, authenticity, traceability and reliability, scalability, and interoperability).
Interactive Robot Task Learning: Human Teaching Proficiency with Different Feedback Approaches

The deployment of versatile robot systems in diverse environments requires intuitive approaches for humans to flexibly teach them new skills. In our present work, we investigate different user feedback types to teach a real robot a new movement skill. We compare feedback as star ratings on an absolute scale for single roll-outs versus preference-based feedback for pairwise comparisons with respective optimization algorithms (i.e., a variation of co-variance matrix adaptation -evolution strategy (CMA-ES) and random optimization) to teach the robot the game of skill cup-and-ball. In an experimental investigation with users, we investigated the influence of the feedback type on the user experience of interacting with the different interfaces and the performance of the learning systems. While there is no significant difference for the subjective user experience between the conditions, there is a significant difference in learning performance. The preference-based system learned the task quicker, but this did not influence the users&#x2019; evaluation of it. In a follow-up study, we confirmed that the difference in learning performance indeed can be attributed to the human users&#x2019; performance.
An Efficient Axial-Attention Network for Video-Based Person Re-Identification

The Non-local self-attention mechanism can significantly improve the capability of feature representation with long-range dependencies at the cost of high computational complexity. To address the issue, the self-attention-based autoregressive axial transformer has been proposed to apply attention along a single axis of the feature maps instead of the whole ones with large receptive fields. It performs axial-attention twice along the height- and width-axis respectively in the spatial dimension of the feature maps for the image data. However, there is still room for improvement. We can convert the 2D spatial feature map into a 1D feature sequence and just perform axial-attention once along it to save more computing resources. Motivated by the insight, we propose an Efficient Axial-Attention Network (EAAN) for video-based person re-identification (Re-ID) to reduce computation and improve accuracy by serializing feature maps with multi-granularity and reducing the number of axial-attention runs. We also introduce a deserialization approach that can restore the shape of the feature maps. Moreover, we expand the CTN (Channel Transformer Network) to a wider range of uses. Additionally, we verify that the serialized feature sequence can enhance expressiveness in our EAAN with lower complexity. Experiments on MARS and DukeMTMC-VideoReID (DukeV) datasets show outstanding performance in computation efficiency and accuracy. It not only outperforms the state-of-the-art method on MARS by 0.3% in both Rank-1 and mAP, and surpasses that on DukeV by 0.1% in Rank-1 with equal mAP, but also reduces parameters and GFLOPS (Giga Floating-point Operations Per Second) by 16.9% and 6.6% respectively compared to another axial-attention-based method. The code will be available at https://github.com/hopstone/EAAN.
Comparative Analysis of Machine Learning Techniques for Software Effort Estimation

Software Effort Estimation (SEE) is paramount task in software development life cycle. It estimates the future effort which would be needed for the successful completion of project. The accuracy of estimation controls the project success. This paper conducts an empirical comparison among the most popular machine learning techniques for SEE to figure out the most accurate one. Three models namely ANN_SG, SVR_SG, and GLR_SG are developed using artificial neural network, support vector regression, and generalized linear regression techniques, respectively. Five datasets from PROMISE repository are used and accuracy measures for comparison are MAR (Mean Absolute Error) and MMRE (Mean Magnitude of Relative Error). It is found that ANN_SG outperforms the other models with better prediction accuracy, and it reduces the error rate for SVR_SG and GLR_SG by 58.4%, and 48.2% respectively. It can be concluded from the experimental results that the ANN_SG is best performer among the candidate models for SEE statistically.
Unsupervised Real-World Image Super-Resolution via Dual Synthetic-to-Realistic and Realistic-to-Synthetic Translations

Due to the challenges of collecting paired low-resolution (LR) and high-resolution (HR) images in real-world scenarios, most existing deep convolutional neural network (CNN)-based single image super-resolution (SR) models are trained with artificially synthesized LR-HR image pairs. However, the domain gap between the synthetic data for model training and the realistic data for testing degrades SR performance significantly, which discourages the application of SR models in practice. One possible solution is to learn from unpaired real-world LR and HR images for their accessibility. Predominant strategies are mainly based on unsupervised domain translation. Despite great advances, there are still noticeable domain gaps between the realistic-like/synthetic-like images generated by unpaired translation and the true realistic/synthetic ones. To address this problem, this letter proposes an effective unsupervised SR framework based on dual synthetic-to-realistic and realistic-to-synthetic translations, namely DTSR. Specifically, to bridge the domain gap between testing and training data, the SR model is optimized using HR images and their realistic-like LR counterparts produced by the synthetic-to-realistic translation. In turn, we propose to narrow the domain gap further via applying the realistic-to-synthetic translation to realistic LR images prior to super-resolving, which also makes the SR model super-resolve simpler examples in testing relative to model training. Moreover, focal frequency and bilateral filtering losses are particularly introduced into DTSR for better details restoration and artifacts suppression. Extensive experiments show that our DTSR outperforms several state-of-the-art models in terms of both quantitative and qualitative comparisons.
Stream Reasoning Playground

Stream Reasoning is a well established field not only in the Semantic Web, but is also adapted in the knowledge representation and reasoning and AI community in general. In the Semantic Web area, there have been valuable efforts in building data generators and benchmarks, however they are not well suited for evaluating more expressive stream reasoning approaches, since the focus is on a graph-based data model and more limited reasoning features, such as query answering. This paper aims at filling the gap, so the different communities can compare, discuss, and benchmark the various approaches for stream reasoning based on a common playground. We will present the stream reasoning playground that targets streaming reasoning as the first-class modelling and processing feature. Our playground includes an easy-to-extend platform for data stream generation with pluggable data formatters, whereby different data stream sources, and modelling problems for two interesting application scenarios, i.e., intelligent traffic management and vehicle stream data analytics, are provided. Furthermore, we present a more generic scenario for time-series data, where a workflow for streaming time-series data from various datasets is facilitated by using mapping functions. To illustrate a first application of the playground, we report on the usage experience of well-known stream reasoner developers in the "model and solve" Hackathon event of the annual Stream Reasoning workshop.
Classification of Authentic and Fake Online Reviews with Supervised Machine Learning Techniques

Online reviews are becoming more and more popular among consumers. Taking advantage of the Internet, consumers can read online reviews related to products or services, in order to make their purchase journey easier. In online review context, trust towards online reviews is an important determinant of customers’ purchase intentions. The authenticity of online reviews can be a critical element in building trust toward reviews. However, based on prior experiences of consumers, online reviews may not always provide authentic information. Occasionally, fake reviews which do not reflect authentic product experiences, arrive on websites. In this regard, the detection, and thus prevention of fake reviews, gains great importance. The purpose of this study is to propose a new way to predict fake reviews by implementing supervised machine learning models. To achieve this, a dataset consisting of 1,600 reviews was used, and features were extracted from this dataset. A variety of supervised learning classifiers were trained by using features from the dataset, and then tested. The performance of each prediction model was compared using certain metrics, and the best result was acquired using the Random Forest (RF) Classifier.
On the Properness of Incorporating Binary Classification Machine Learning Algorithms Into Safety-Critical Systems

Manufacturers are willing to incorporate Machine Learning (ML) algorithms into their systems, especially those considered as Safety-Critical Systems (SCS). ML algorithms that perform binary classification (i.e., Binary Classifiers (BCs)) find a wide applicability as error, intrusion or failure detectors, provided that their performance complies with SCS safety requirements. However, the performance analysis of BCs relies on metrics that were not developed with safety in mind and consequently may not provide meaningful evidence to decide whether to incorporate a BC into a SCS. In this paper, we empirically assess the properness of such incorporation by analyzing the distribution of misclassifications of BCs instead of simply counting misclassifications. This allows us to better assess the adequacy of a given BC by identifying areas of the classification space where the BC is likely to misclassify and therefore constitutes actionable information to deal with the SCS. Our assessment takes a deeper view of the classification performance concerning safety by using new metrics that consider the proportions of predictions that are/are not considered sufficiently safe to be used by incorporating SCS. The results of our experiment allow discussing the potential of such distribution analysis for deciding if a BC can be incorporated into a SCS.
Coverless Video Steganography Based on Frame Sequence Perceptual Distance Mapping

Most existing coverless video steganography algorithms use a particular video frame for information hiding. These methods do not reflect the unique sequential features of video carriers that are different from image and have poor robustness. We propose a coverless video steganography method based on frame sequence perceptual distance mapping. In this method, we introduce Learned Perceptual Image Patch Similarity (LPIPS) to quantify the similarity between consecutive video frames to obtain the sequential features of the video. Then we establish the relationship map between features and the hash sequence for information hiding. In addition, the MongoDB database is used to store the mapping relationship and speed up the index matching speed in the information hiding process. Experimental results show that the proposed method exhibits outstanding robustness under various noise attacks. Compared with the existing methods, the robustness to Gaussian noise and speckle noise is improved by more than 40%, and the algorithm has better practicability and feasibility.
Development of a platform for sensor systems support in sport

Biomechanical systems and applications in sport and rehabilitation regularly use different sensors. Use of sensors and smart equipment allows measurements of different physical quantities. As sensors can produce large amount of data, this has to be stored for post-processing and analysis. Every application has its own operation specific data flow and storage solution. Every application is different from another this is especially true in the relational database model. In this paper, we present a solution to this problem in a universal data model that can be used in any sensor-based application. We propose a cloud platform architecture that allows for the manipulation of sensor data and metadata. Signals and (meta) data that are associated with biomechanical systems are presented to underline the problem of universality; we present platform requirements and development of a cloud platform architecture. A shooting application usage with this platform is presented.
Nonsingular chattering-free barrier function finite time tracker for perturbed nth-order nonlinear systems and its application to chaotic color image scrambling

This study proposes a nonsingular barrier-function-based terminal sliding mode control technique for nth-order nonlinear dynamic systems. Its main objective is to guarantee the finite-time tracking performance in the presence of unmodeled dynamics, external disturbances and parameter variations. The proposed approach is synthesized using a novel barrier function-based terminal sliding surface to ensure the effective estimation of the system perturbations using the barrier adaptation laws, and thereby achieve the desired tracking performance. The dynamics of chaotic models are strongly dependent upon the initial conditions, parameters of the system, parametric uncertainty and external disturbances which are required to be controlled/synchronized by a robust nonlinear control technique. By designing the terminal sliding mode control approach combined with the adaptive control law, the tracking problem of the nth-order nonlinear dynamical system with unmodeled dynamics, parametric variations and external disturbances is investigated. Moreover, the application of the proposed method is studied using the color-image scrambling system. The scrambling keys are created by transmitter chaotic systems, where using the chaotic keys and scrambling techniques, the original color image is encrypted. The performance and applicability of the proposed design is assessed using two practical applications: a chaotic hyper-jerk system and a color image encryption system. The simulation and analytical results obtained with both systems confirmed the ability of the proposed control method to guarantee the finite time convergence of sliding surface, ensure chattering-free dynamics and avoid the singularity problems.
Metaheuristic Optimization for Three Dimensional Path Planning of UAV

Unmanned Aerial Vehicles (UAVs) are used in numerous applications including civil, military and rescue operations. The UAVs are expected to autonomously travel through a collision-free, shortest route from the start position to the goal position. In this paper, three optimization algorithms, Grey Wolf Optimization (GWO), Archimedes Optimization Algorithm (AOA), and Particle Swarm Optimization (PSO) are employed to find out an optimized flyable route in a three-dimensional environment for the UAV. From the simulated results, it is found that the GWO achieves the minimum cost and therefore, outperforms the other two algorithms. However, the minimum time required to obtain the path is produced by AOA which is also comparable with the time estimated by GWO. Furthermore, the GWO achieves 8.4% and 80.5% lower mean cost than AOA, and PSO, respectively. This validates that GWO performs remarkably in a long run and therefore, should be employed for this task.
Building a Multilingual Corpus of Tweets Relating to Algerian Higher Education

Nowadays, sentiment analysis on user-generated content on social media platforms has shown outstanding benefits in various fields such as marketing, politics, and medicine. Likewise, higher education institutions can draw advantages from the knowledge gained by sentiment analysis of student-generated content on social media to improve their policies and services. However, there has been no available social media corpus concerning Algerian higher education. In light of this, we provide Algerian higher education institutions with the first multilingual tweets corpus. This paper describes the undertaken steps for the corpus-building involving data collection, data preprocessing, and data annotation.
A New Deep Fuzzy Based MSER Model for Multiple Document Images Classification

Understanding document images uploaded on social media is challenging because of multiple types like handwritten, printed and scene text images. This study presents a new model called Deep Fuzzy based MSER for classification of multiple document images (like handwritten, printed and scene text). The proposed model detects candidate components that represent dominant information irrespective of the type of document images by combining fuzzy and MSER in a novel way. For every candidate component, the proposed model extracts distance-based features which result in proximity matrix (feature matrix). Further, the deep learning model is proposed for classification by feeding input images and feature matrix as input. To evaluate the proposed model, we create our own dataset and to show effectiveness, the proposed model is tested on standard datasets. The results show that the proposed approach outperforms the existing methods in terms of average classification rate.
ROS Simulation-Based Autonomous Navigation Systems and Object Detection

Autonomous robots are becomingDr. Swati Shinde Tanvi Mahajan Suyash Khachane Saurabh Kulkarni Prasad Borle popular and are being used in many industries, due to their autonomy features. They are just like humans who have the ability to make decisions on their own without any human help. As the need for such robots is increasing, our paper aims to present an ROS autonomous navigation software system for autonomous robots, which is capable of creating 2D and 3D maps of the Simulation environment, localizing the robot in that environment and further performing path planning of the robot along with object detection using ROS. Moreover, various algorithms used for creating maps along with detailed internal working of the packages used for path planning are being discussed in this paper.
Closed-Loop Motion Control of Robotic Swarms – A Tether-Based Strategy

Swarm robots can achieve effective task execution via closed-loop motion control. However, such a goal can only be realized through accurate localization of the swarm. Past approaches have focused on addressing this issue using external sensors, static sensor networks, or through active localization—requirements that may restrict the motion of the swarm or may not be achievable in practice. We present a tether-based strategy that achieves closed-loop swarm-motion control by using a secondary team of mobile sensors. These sensors form a wireless tether that allows the swarm to indirectly sense a home base or a landmark, and to compensate for the accumulated motion errors via a closed-loop control strategy. The proposed strategy is the first to use a tether of mobile sensors that can dynamically reshape and reconnect to various points in the environment to achieve closed-loop motion control. The novelty of the strategy is in its ability to adapt to any swarm motion considered, and to be applied to swarms with limited sensing capabilities and knowledge of their environment. The performance of the proposed strategy was validated through extensive experiments.