Reviewerly/exHarmony huggingface dataset:
Enhancing Reinforcement Learning Performance in Delayed Reward System Using DQN and Heuristics

This paper suggests and implements how to apply the reinforcement learning on delayed reward system which is known to be complex to apply the machine learning technology such as Q-learning. Such games as Tetris game is known to be a delayed reward system because of its characteristics of generating sparse reward in learning process. Tetris game requires the actor’s quick judgment ability and speed of response because the blocks must be stacked in an optimal location quickly, considering the random shape and rotation of appearing blocks. Also, since the number of cases is very large due to the various block types and order, if a human-being is playing the game, the performance is limited by simply relying on human memorization capability. Therefore, we applied a reinforcement learning implemented in this study for this delayed reward system. We find that the general legacy reinforcement learning method shows its limitation in improving the performance. Hence, we apply the heuristic to increase the decision accuracy as the weighting method of reward. As a result, we were able to obtain high scores in games. Although it is not yet possible to say that this heuristic(rule-based) approach has completely conquered the game. In several experiments, this hybrid reinforcement learning shows better playability than human in terms of learning speed, as well as high scores. In this paper, it is shown that general Q- learning is not suitable for delayed reward system. And a hybrid learning that adds prioritized experience replay tactics, and the related techniques and algorithms are introduced to increase the reinforcement learning performance.
Pointing, Pairing and Grouping Gesture Recognition in Virtual Reality

During a team discussion, participants frequently perform pointing, pairing, or grouping gestures on artifacts on a whiteboard. While the content of the whiteboard is accessible to the blind and visually impaired people, the referring deictic gestures are not. This paper thus introduces an improved algorithm to detect such gestures and to classify them. Since deictic gestures such as pointing, pairing and grouping are performed by sighted users only, we used a VR environment for the development of the gesture recognition algorithm and for the subsequent user studies.
An Optimization Framework for Migrating and Deploying Multiclass Enterprise Applications Into the Cloud

Enterprises can reduce the computational burden and costs substantially by migrating and deploying their partial or even whole applications to the cloud, so as to promote and realize their digital transformation. In this article, we study the following problems in the migration and deployment of enterprise applications: i) How the migration time factor influences application migration indirectly? ii) What is the optimal deployment strategy for multiple applications? In this regard, many existing schemes that aim to optimize the economic cost can neither model the optimal migration strategy nor the optimal deployment resource allocation appropriately for enterprise applications. To tackle these limitations, first, this article aims at minimizing migration time by allocating the bandwidth of the access links for applications migration and formulates a strictly convex optimization problem. After that, the article concentrates on modelling the deployment interactions for resource allocation between enterprise application and cloud physical machines as a non-convex optimization problem. The successive approximation method is used to approximate the problem into a series of strictly convex optimization problems and an algorithm is proposed to achieve the optimal resource allocation for applications deployment problem. Numerical results illustrate the effective performance of the proposed schemes of enterprise application migration and deployment in comparison with other methods.
A Quantum Approach for Tactical Capacity Management of Distributed Electricity Generation

Matching electricity demand and supply by decentralised generators will be very important in the near future, where more and more electricity has to be produced sustainably. Optimisation problems that address this problem often have quadratic objective functions or constraints, due to the quadratic nature of energy loss. Where classical solvers struggle with quadratic, often integer or binary, optimisation problems, quantum computing (inspired) solvers are well equipped for this kind of problems. In this paper, we present such an optimisation problem and the performance of the quantum annealer in solving this problem, in comparison with classical methods and commercial solvers. We show that the hybrid, quantum-classical, solvers provided by D-Wave can outperform classical solvers despite the small-scale of the available quantum hardware.
Quantum-Classical Image Processing for Scene Classification

Deep learning-based convolutional neural network (CNN) models are prominent in processing and analyzing sensor signal data such as images for classification. Data augmentation is a powerful technique used in training such models to avoid overfitting and improve accuracy. This letter proposes a data augmentation technique using a quantum circuit for image data. The proposed quantum circuit is suitable to implement on real hardware provided by IBM QX platform. In comparison with other classical data augmentation techniques, the proposed technique increased the prediction accuracy of the CNN from 68.65% to 76.03%. However, CNN models for image classification use many parameters during the training process. Quantum computers can efficiently handle large-scale data inputs using qubits for information processing. Hence, we also propose a hybrid quantum-classical CNN model (HQCNN) for scene classification. The proposed model uses a combination of CNN layers and quantum layers to process images. The proposed HQCNN reduces parameters used for training as quantum layers are used in the model. Our experimental results show that the proposed HQCNN can classify the scenes in UC Merced land-use dataset with an accuracy of 85.28%, compared to the other models.
Implementing Contract-for-Difference Arrangements for Hedging Electricity Price Risks of Renewable Generators on a Blockchain Marketplace

The dynamic nature of competitive electricity markets means that participants often resort to some form of derivative financial instrument. One such instrument is a contract-for-difference (CFD), usually available to renewable generators in certain electricity markets to enable them to hedge their price risk. Embracing CFD presents new risks such as counterparty credit, margining, third-party, legal, and process risks. Derivative instruments existing on blockchains have recently demonstrated potential as suitable hedging tools for minimizing the risks of renewable generators. This article applies this concept for the first time to hedge the price risk of renewable generators by implementing a novel decentralized finance instrument, an Ethereum blockchain marketplace governed by a smart contract to mediate between stakeholders mutually enrolled in bilateral CFD arrangements. The employed structure mitigates the underlying risks of traditional arrangements, underpinned by a suite of autonomous mechanisms.
Knowledge Graph Entity Type Prediction with Relational Aggregation Graph Attention Network

Most of the knowledge graph completion methods focus on inferring missing entities or relations between entities in the knowledge graphs. However, many knowledge graphs are missing entity types. The goal of entity type prediction in the knowledge graph is to infer the missing entity types that belong to entities in the knowledge graph, that is, (entity, entity type=?). At present, most knowledge graph entity type prediction models tend to model entities and entity types, which will cause the relations between entities to not be effectively used, and the relations often contain rich semantic information. To utilize the information contained in the relation when performing entity type prediction, we propose a method for entity type prediction based on relational aggregation graph attention network (RACE2T), which consists of an encoder relational aggregation graph attention network (FRGAT) and a decoder (CE2T). The encoder FRGAT uses the scoring function of the knowledge graph completion method to calculate the attention coefficient between entities. This attention coefficient will be used to aggregate the information of relations and entities in the neighborhood of the entity to utilize the information of the relations. The decoder CE2T is designed based on convolutional neural network, which models the entity embeddings output by FRGAT and entity type embeddings, and performs entity type prediction. The experimental results demonstrate that the method proposed in this paper outperforms existing methods. The source code and dataset for RACE2T can be downloaded from: https://github.com/GentlebreezeZ/RACE2T .
FNUG: Imperfect Mazes Traversal Based on Detecting and Following the Nearest-to-Final-Goal and Unvisited Gaps

Most of the traditional information-based unknown maze traversal techniques are based on trial and error, which are time-consuming and may trap the robot in infinite loops or dead ends. To address the above issues, this letter proposes a gap based approach for imperfect-unknown mazes traversal named Follow the Nearest-to-final goal and Unvisited Gap (FNUG) for the first time. The algorithm is computationally efficient, and has the ability to deal with loops and dead ends through the revisit check. The main part of this approach is finding out gaps around the robot by analyzing the depth scans, building a coordinate system based topological map in the form of a tree. Then a heuristic selection of the robot’s unvisited-neighbor gap for navigation based on its Euclidean distance from the final target location. Finally Dijkstra’s algorithm is used to find a path from robot’s location to the sub goal. Compared to existing sensor information-based methods, our proposed algorithm not only takes into account the current sensor data, the shape and size of the robot, but also all the sub goals visited so far. This is the key factor in this algorithm that prevents the robot from falling into a dead end or trapped in a loop. To prove the effectiveness of this algorithm, simulations and physical experiments were performed using using a 2-wheeled differential mobile robot.
College Election System using Blockchain

Blockchain is a distributed system that confirms security and reliability. It has started a new era of a solid and consensus system. Because of blockchain's emphasis on security, many other operations and processes are adopting the same reliable approach. Almost all processes and operations are now encouraged to be computed electronically in the digital Ethereum network that has been presented. In the current situation, we have seen what the conduction of elections has done to the worsening situation of the Covid-19 Pandemic. The proposed system uses an Ethereum network on a blockchain platform to implement a college voting system, making use of the Aadhar API. E-voting will help to support transparency and voters' trust to reduce corruption and unreliability in the voting processes. The use of this system will allow a voter to vote from home while also having the assurity that this process is completely secure and reliable due to the use of blockchain technology.
Multilingual and Language-Agnostic Recognition of Emotions, Valence and Arousal in Large-Scale Multi-domain Text Reviews

In this article we present extended results obtained on the multidomain dataset of Polish text reviews collected within the Sentimenti project. We present preliminary results of classification models trained and tested on 7,000 texts annotated by over 20,000 individuals using valence, arousal, and eight basic emotions from Plutchik's model. Additionally, we present an extended evaluation using deep neural multilingual models and language-agnostic regressors on the translation of the original collection into 11 languages.
Pixel Difference Function and Local Entropy-Based Speckle Reducing Anisotropic Diffusion

Speckles destroy the texture details of synthetic aperture radar (SAR) images, thereby constraining their high-precision application. Speckle suppression and edge preservation are two aspects that need to be balanced in despeckling. Although a conventional anisotropic diffusion filter can theoretically achieve this balance, it still triggers many edge losses. To better improve the balance, a novel anisotropic diffusion (AD) filter based on the pixel difference function and local entropy is proposed. The proposed filter utilizes a pixel difference function to update the original diffusion function of the AD filter and introduces local entropy to recover the edge loss from the ratio image generated by noisy and filtered images. In addition, a neighborhood weighting approach and a new adaptive iterative rule are proposed for better AD filtering. Simulated data and real SAR images were applied to evaluate the performance of the proposed algorithm. Experimental results show that the proposed filter both effectively smooths speckles and reduces edge loss. Furthermore, the effectiveness and superiority of the proposed method were confirmed by comparison with other state-of-the-art methods.
Score-Based Anomaly Detection for Smart Manufacturing Systems

We propose an anomaly detection framework that generates an anomaly score for the current observation of a task. The proposed framework can overcome two major problems in smart manufacturing systems, where a robot learns task execution by human expert demonstrations. First, it can function as a warning system to alert human operators when the robot goes into a significantly different state than those of demonstrations. Second, in order to prevent redundant samples, the proposed framework can be used as a guiding system for human experts throughout the recording of demonstrations for collecting novel states, which are different from already collected samples. Unlike parametric models, the proposed framework is better suited for behavioral cloning-adapted smart manufacturing applications in which the training dataset is continuously extended. We also propose four real-world datasets for different applications, e.g., car plugging and refrigerator production line inspection. To the best of our knowledge, it is the most comprehensive real-world dataset for anomaly detection for smart manufacturing systems with a robotic arm. We evaluate our method quantitatively on the real-world datasets and compare it with several standard baselines from the literature.
Abstracting Ontology-Driven Conceptual Models: Objects, Aspects, Events, and Their Parts

Ontology-driven conceptual models are widely used to capture information about complex and critical domains. Therefore, it is essential for these models to be comprehensible and cognitively tractable. Over the years, different techniques for complexity management in conceptual models have been suggested. Among these, a prominent strategy is model abstraction. This work extends an existing strategy for model abstraction of OntoUML models that proposes a set of graph-rewriting rules leveraging on the ontological semantics of that language. That original work, however, only addresses a set of the ontological notions covered in that language. We review and extend that rule set to cover more generally types of objects, aspects, events, and their parts.
A Cluster-Based Cooperative Co-Evolutionary Algorithm for Multiobjective Workflow Scheduling in a Cloud Environment

The cloud workflow scheduling problem has important applications in modern commercial and industrial areas. In the public cloud environment, the workflow suffers from security threats because of the multiple tenants and the distribution of computational resources. This paper models cloud workflow scheduling as a novel multi-objective optimization problem that aims to minimize execution time, cost, and risk. Due to the complexity of the considered problem, a multi-objective cluster-based cooperative co-evolutionary (CBCC) algorithm with several novel designs is proposed. First, a new initialization strategy is presented to generate potential non-dominated solutions. Based on the cluster-based multi-objective optimization framework, a novel collaboration model is proposed, and it adopts four populations to address the subproblems, respectively. Moreover, a diversification strategy is designed to maintain the diversity of the global archive. Furthermore, a problem-specific intensification strategy is designed to intensify the potential solutions. A comprehensive computational and statistical campaign was carried out to verify the performance of CBCC. The results show that the proposed CBCC outperforms several meta-heuristics adapted from closely related scheduling models in the literature by a significantly considerable margin. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper describes a novel approach called CBCC for minimizing the cost, time, and risk when scheduling a workflow in the cloud environment. CBCC seamlessly combines the cluster-based multi-objective optimization framework and several problem-specific components such as initialization, diversification, and intensification strategies. As the considered problem has not been previously addressed in the literature, five state-of-the-art algorithms for closely related problems, which include I_MaOPSO (improved many objective particle swarm optimization), EMS-C (evolutionary multi-objective scheduling for cloud), ch-PICEA-g (enhanced multi-objective co-evolutionary algorithm), VaEA (vector angle-based evolutionary algorithm), and DQN-based MARL (Deep-Q-network-based Multi-agent Reinforcement Learning) are adopted as baselines. The results demonstrate that CBCC significantly outperforms the baselines with a 95% confidence level.
A Statistical Analysis of Textual E-Commerce Reviews Using Tree-Based Methods

With the increasing interest in e-commerce shopping, customer reviews have become one of the most important elements that determine customer satisfaction regarding products.This demonstrates the importance of working with Text Mining.This study is based on The Women's Clothing E-Commerce Reviews database, which consists of reviews written by real customers.The aim of this paper is to conduct a Text Mining approach on a set of customer reviews.Each review was classified as either a positive or negative review by employing a classification method.Four tree-based methods were applied to solve the classification problem, namely Classification Tree, Random Forest, Gradient Boosting and XGBoost.The dataset was categorized into training and test sets.The results indicate that the Random Forest method displays an overfitting, XGBoost displays an overfitting if the number of trees is too high, Classification Tree is good at detecting negative reviews and bad at detecting positive reviews and the Gradient Boosting shows stable values and quality measures above 77% for the test dataset.A consensus between the applied methods is noted for important classification terms.
A 184-<i>μ</i>W Error-Tolerant Real-Time Hand Gesture Recognition System With Hybrid Tiny Classifiers Utilizing Edge CNN

This article proposes a low-power real-time hand gesture recognition (HGR) system with high recognition accuracy for smart edge devices. This design balances accuracy and power consumption by utilizing computation-efficient hybrid classifiers assisted with a majority voting scheme. By combining the recognition results of consecutive frames, the HGR system shows improved immunity to misclassification. In addition, the compressed input data before high-level processing dramatically reduce the on- chip memory and computational load. The proposed Edge-convolutional neural network (CNN) core with interactable processing engines reduces the memory accessing and the feature register toggling rate by 27% and 50%, respectively. The sequence analyzer based on majority voting improves the static and dynamic gesture recognition accuracy by ~7% and ~8% only with 9.4% hardware overhead. The test chip was fabricated in 65-nm CMOS technology, occupying the area of 1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 1.5 mm2. It consumes the lowest power of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$184~\mu \text{W}$ </tex-math></inline-formula> at 25 MHz and 0.6 V. The proposed HGR system can recognize six static gestures and 24 dynamic hand gestures with an average accuracy of 87.25%–95% and 85.4%–94.9%, respectively.
Whale Optimizer-Based Clustering for Breast Histopathology Image Segmentation

Breast histopathology image segmentation is a complex task due to indiscernibly correlated and noisy regions of interest. Breast histopathological images are composed of different types of cells. Some of these cells can be harmful for humans due to the presence of cancer. Under such circumstances, many segmentation techniques for automatic detection of cancer cells have been proposed considering clustering schemes. However, such clustering methodologies are sensitive to initial cluster centers, which promote false-positive solutions. This paper presents the use of the Whale Optimization Algorithm (WOA) for proper clustering segmentation of breast histopathological images to overcome clustering issues. Also, a rigorous comparative study is conducted among the proposed approach and several state-of-art Nature-Inspired Optimization Algorithms (NIOAs) and traditional clustering techniques. The numerical results indicate that the proposed approach outperforms the other utilized clustering methods in terms of precision, robustness, and quality of the segmented outputs.
A Blockchain-Based Solution for Electronic Medical Records System in Healthcare

Healthcare has come out as an emerging field for technologies like IoTs, cloud computing, big data, etc. lately. However, keeping the healthcare records secure and private is still a big issue. Due to this, its widespread adoption is something that might take a few more years. For past few years, blockchain technology has shown its capability to be a preferred technology to provide better security and privacy. Recent breakthroughs in various technologies have improved medical transactions, health insurance claims, and secure records keeping, with the help of its decentralized and distributed nature. In this paper, several algorithms and methods are proposed to improve limitations in the current healthcare system using blockchain technology. It further proposes architecture and tools to measure the performance of the system. Finally, the results and future directions for the research in the healthcare domain are discussed.