Reviewerly/exHarmony huggingface dataset:
Survey of latest technologies on Decentralized applications using Blockchain

In recent years, Websites and Web applications have been playing a significant role in everyday life. In the past decade, the number of websites progressed from three million to more than 1.7 billion. The majority of contributions to this number are produced by CMS (content management systems). Current content delivery and management services has many issues in securing data, and are vulnerable to cyberthreats. Hence, one of the methods to create apps transparent and flexible is by implementing Deoentralized applications (Dapps). This paper describes the major security issues of cms and propose an efficient scheme to build a cms dapp on the most familiar blockchain platform-Ethereum. The main Blockchain has 2 categories: partially DApp and fully DApp for the software architectures in DApps. The full and partial Dapps have their own advantages and disadvantages. Here in this paper, a software architecture for full and partial DApp focusing on simulating asset transactions for comparing both the DApps efficiency has been proposed.
SafePM

Memory safety violation is a major root cause of reliability and security issues in software systems. Byte-addressable persistent memory (PM), just like its volatile counterpart, is also susceptible to memory safety violations. While there is a couple of decades of work in ensuring memory safety for programs based on volatile memory, the existing approaches are incompatible for PM since the PM programming model introduces a persistent pointer representation for persistent memory objects and allocators, where it is imperative to design a crash consistent safety mechanism.
Interval Prediction of Photovoltaic Power Using Improved NARX Network and Density Peak Clustering Based on Kernel Mahalanobis Distance

Photovoltaic (PV) power forecasting can provide strong support for the safe operation of the power system. Existing forecasting methods are ineffective for grid scheduling decisions or risk analysis. The novel multicluster interval prediction method is proposed to consider the volatility and randomness of PV power output. First, this method utilizes the sparse autoencoder (SAE) and Bayesian regularized NARX network (BRNARX) for point forecasting of PV power. Second, density peak clustering improved by kernel Mahalanobis distance (KMDDPC) is applied to classify the dataset into multiple clusters, including forecasting error and meteorological factors. Finally, the joint probability density is established by multivariate kernel density estimation (MKDE) to accomplish the PV power interval prediction. The proposed hybrid method is applied for the interval prediction of PV power at Yulara, Australia. Comparative research of point forecasting is implemented to evaluate the machine learning and deep learning methods, with the proposed SAE-BRNARX under four different periods. Results shows that the average values of nRMSE, MRE, nMAE, and R2 for the four periods are 4.45%, 0.90%, −0.15%, 3.39%, and 95.93%, respectively. Moreover, the results of interval prediction obtained by the other interval prediction approaches are compared with the proposed KMDDPC-MKDE. It shows that the average values of PICP, PINAW, ACE, and nMPICD for four periods are 93.93%, 9.50%, 3.93%, and 7.10% at 90% confidence level, respectively. Outcomes demonstrate that the proposed method can obtain more accuracy, a higher coverage rate, narrower average bandwidth, and a closer distance between the middle of interval and actual value than other methods.
Adorned in Memes: Exploring the Adoption of Social Wearables in Nordic Student Culture

Social wearables promise to augment and enhance social interactions. However, despite two decades of HCI research on wearables, we are yet to see widespread adoption of social wearables into everyday life. More in-situ investigations into the social dynamics and cultural practices afforded by wearing interactive technology are needed to understand the drivers and barriers to adoption. To this end, we study social wearables in the context of Nordic student culture and the students' practice of adorning boiler suits. Through a co-creation process, we designed Digi Merkki, a personalised interactive clothing patch. In a two-week elicitation diary study, we captured how 16 students adopted Digi Merkki into their social practices. We found that Digi Merkki afforded a variety of social interaction strategies, including sharing, spamming, and stealing pictures, which supported meaning-making and community-building. Based on our findings, we articulate Memetic Expression as a strong concept for designing social wearables.
Discovering Message Templates on Large Scale Bitcoin Abuse Reports Using a Two-Fold NLP-Based Clustering Method

Bitcoin is currently a hot issue worldwide, and it is expected to become a new legal tender that replaces the current currency started with El Salvador. Due to the nature of cryptocurrency, however, difficulties in tracking led to the arising of misuses and abuses. Consequently, the pain of innocent victims by exploiting these bitcoins abuse is also increasing. We propose a way to detect new signatures by applying two-fold NLP-based clustering techniques to text data of Bitcoin abuse reports received from actual victims. By clustering the reports of text data, we were able to cluster the message templates as the same campaigns. The new approach using the abuse massage template representing clustering as a signature for identifying abusers is much efficacious.
Visual and Location Information Fusion for Hierarchical Place Recognition

Recognizing places of interest in an unfamiliar environment has been a common challenge faced by humans. This paper presents a novel hierarchical place recognition system capable of general outdoor place recognition including landmarks, commercial buildings, and business entities. We aim to achieve this by fusing visual and location information. Our hierarchical approach comprises place of interest detection, location-based filtering, image similarity score-based ranking and information retrieval components. The system leverages state of the art deep learning models for place detection and deep feature extraction. To evaluate our proposed system, we introduce a new dense dataset, referred to as Tokyo Outdoor Places, consisting of landmarks, commercial buildings, and business entities. Our proposed hierarchical system achieves 95.69&#x0025; recall on our new dataset. We believe our system can contribute in achieving smart city goals by providing access to information, enabling locals and tourists to navigate with ease.
Enhanced task attention with adversarial learning for dynamic multi-task CNN

Multi-task deep learning is promising to solve multi-label multi-instance visual recognition tasks. However, flexible information sharing in the task group might bring performance bottlenecks to an individual task. To tackle this problem, we propose a novel learning framework of multi-task Convolutional Neural Network (CNN) to enhance task attention through conditionally tuning the Task Transfer Connections (TTC) with adversarial learning. For the dynamic multi-task CNN, we set up a shared subnet to extract shared features across multiple tasks and a task discriminator shared by all layers to distinguish features of all subnets. The adversarial training is introduced between the shared subnet and the task discriminator to guide each task subnet to focus on its specific task. To apply adversarial learning to the complex labeling system of multiple tasks, we design an even-label strategy for the multi-task model with a shared subnet to make adversarial learning feasible for the complex labeling system of multiple tasks. As a result, the proposed model can constrain the shared subnet’s learning unbiased to any single task and achieve task attention for all task subnets. Experimental results of the ablation study and the TTC analysis validate the effectiveness of the proposed approach.
Fumeus: A family of Python tools for text mining with smoke terms

Synthesizing meaningful insights from voluminous textual datasets is complex and challenging. The task is especially difficult for sparse target classes. Recent works have proposed “smoke terms,” or machine-learned words and phrases prevalent in a target class. Smoke terms may be utilized to rank or sort text, or they may serve as features in follow-on machine learning models. This paper introduces Fumeus, a family of Python-based smoke term analysis tools. We provide functionality to generate new smoke terms and to use existing smoke term dictionaries to rank or sort datasets. These analyses have numerous academic, regulatory, and industry applications.
Characterization, verification and generation of strategies in games with resource constraints

With an increasing demand for ensuring the reliability and efficiency of distributed and interactive systems, the model-checking technique has been studied extensively, for which a game-based modeling of the system and a logical specification of the desired properties are two essential parts. Existing studies mainly focused on ideal systems that always guarantee optimal responses and have not considered several realistic aspects, for instance, the fact that actions can only be performed under sufficient resources. In this paper, we propose a game model with resource constraints and thereby a novel logic named LRC for such games. This logic enables the strategic reasoning of the likely strategies of the other participating entities and thus supports not only the characterization of the equilibrium of games with limited resources, but also strategic exploitation in multi-agent systems in which the available resources are restricted. Interesting properties of this logic are investigated, and a model-checking algorithm is presented. Being built upon the alternating-time temporal logic (ATL), LRC is shown to be more expressive than ATL without bringing additional complexity. In addition to determining the existence or absence of a collaborative strategy, we explore the generation of the team plan when a certain protocol is supposed to be followed.
Clustering Based on Continuous Hopfield Network

Clustering aims to group n data samples into k clusters. In this paper, we reformulate the clustering problem into an integer optimization problem and propose a recurrent neural network with n×k neurons to solve it. We prove the stability and convergence of the proposed recurrent neural network theoretically. Moreover, clustering experiments demonstrate that the proposed clustering algorithm based on the recurrent neural network can achieve the better clustering performance than existing clustering algorithms.
A Robust Oversampling Approach for Class Imbalance Problem with Small Disjuncts

Class imbalance is one of the important challenges for machine learning because of it's learning to bias toward the majority classes. The oversampling method is a fundamental imbalance-learning technique with many real-world applications. However, when the small disjuncts problem occurs, how to effectively avoiding the negative oversampling results rather than using clusters previously, remains a challenging task. Thus, this study introduces a disjuncts-robust oversampling (DROS) method. The novel method shows that the data filling of new synthetic samples to the minority class areas in data space can be thought of as the searchlight illuminating with light cones to the restricted areas in real life. In the first step, DROS computes a series of light-cone structures that is first started from the inner minority class area, then passes through the boundary minority class area, last is stopped by the majority class area. In the second step, DROS generates new synthetic samples in those light-cone structures. Experiments considering both real-world and 2D emulational datasets demonstrate that our method outperforms the current state-of-the-art oversampling methods and suggest that our method is able to deal with the small disjuncts.
Motion cues guided feature aggregation and enhancement for video object segmentation

Video object segmentation (VOS) aims to separate unknown target objects from various given video sequences. Although many recent successful methods boosted the performance of VOS, especially those using deep convolution neural networks (CNNs), it is still difficult to aggregate deep features as well as motion cues effectively, which can be important to associate valid information of adjacent frames in video sequences. To tackle this problem, we propose a simple yet effective feature optimization method for VOS based on motion information. To achieve this, we construct a two-branch deep network and use computed motion cues (i.e., optical flow) to jointly optimize global and local interframe correlation information. Additionally, a clustering-based feature enhancement module is proposed to further fuse motion information and enhance the feature saliency of the target area. Optimized feature maps show a significant performance improvement in the final VOS tasks, especially those with rapid target movement. Experiments on the DAVIS16, DAVIS17, YouTube-Objects and YouTube-VOS datasets demonstrate that our simple feature aggregation and enhancement method for VOS improves segmentation accuracy effectively and gains an impressive result compared to many state-of-the-art methods.
One-Step Out-of-Place Resetting for Redirected Walking in VR

Redirected walking (RDW) allows users to explore virtual environments in limited physical spaces by imperceptibly steering them away from obstacles and space boundaries. However, even with those techniques, the risk of collision cannot always be avoided. For such situations, resetting techniques have been proposed to provide an immediate adjustment of the physical walking direction of a user. Existing resetting techniques are either applied in-place, where the user changes orientation but stays in the same position or out-of-place methods where the user is guided to move from the current position to a safe location all while freezing the movement in the virtual world. While out-of-place methods have the potential to provide more freedom to user movements after resetting, current out-of-place methods do not provide enough guidance for the users to move to optimal locations. In this work, we propose a novel out-of-place resetting strategy that guides users to optimal physical locations with the most potential for free movement and a smaller amount of resetting required for their further movements. For this purpose, we calculate a heat map of the walking area according to the average walking distance using a simulation of the currently used RDW algorithm. Based on this heat map, we identify the most suitable position for a one-step reset within a predefined searching range and use this one as the reset point. Our results show that our method increases the average moving distance within one cycle of resetting. Furthermore, our resetting method can be applied to any physical area with obstacles. That means that RDW methods that were not suitable for such environments (e.g., Steer to Center) combined with our resetting can also be extended to such complex walking areas. In addition, we present a user interface to provide a similar visual experience between these methods, using a two-arrows indicator to help users adjust their position and direction.
Application of Multiagent Technology in Intelligent Distributed Sports Training Simulation System

Multiagent technology, as a conceptual model commonly used in intelligent systems, has now had a lot of related research, and it is also widely used in sports training simulation systems. This paper mainly studies the development of the sports training simulation system based on agent technology. This paper proposes the motion perception system of multiagent technology in sports training. In the research, this paper introduces the related concepts and methods of agent in detail. In order to better simulate the changes of human joints during exercise with the system, this article uses a variety of comparison methods. Among them, the experiment of the imaging analysis module is carried out through the simulation of the simulation system. The experimental results show that the role of multiagent in the simulation system is huge. In linear motion, the accuracy of joint extraction has reached more than 95%. The extraction of joints in the curve movement reached more than 80%, indicating that it can not only complete the relevant processing adaptively but also predict and correct the exercise process of the trainer. Therefore, in the research of sports training simulation system, more attention should be paid to the research of curve motion.
A Sentiment Classification Model of E-Commerce User Comments Based on Improved Particle Swarm Optimization Algorithm and Support Vector Machines

With the rapid increase of the number of Internet users and the amount of online comment data, a large number of referable information samples are provided for data mining technology. As a technical application of data mining, text sentiment classification can be widely used in public opinion management, marketing, and other fields. In this study, a combination approach to SVM (support vector machine) and IPSO (improved particle swarm optimization) is proposed to classify sentiment by using text data. First, the text data of 30,000 goods reviews and corresponding ratings are collected through the web crawler. Then, TFIDF (term frequency-inverse document frequency) and Word2vec are used to vectorize the goods review text data. Next, the proposed classification model is trained by the SVM, and the initial parameters of the SVM are optimized by the IPSO. Finally, we applied the trained SVM-IPSO model to the test set and evaluated the performance by several measures. Our experiment results indicate that the proposed model performed the best for text data sentiment classification. Additionally, the traditional machine learning model SVM becomes very effective after parameter optimization, which demonstrates that the parameters’ optimization by IPSO has successfully improved the classification accuracy. Furthermore, our proposed model SVM-IPSO significantly outperforms other benchmark models, indicating that it could be applied to improve the accuracy and efficiency for text data sentiment classification.
Making sense of the black‐boxes: Toward interpretable text classification using deep learning models

Abstract Text classification is a common task in data science. Despite the superior performances of deep learning based models in various text classification tasks, their black‐box nature poses significant challenges for wide adoption. The knowledge‐to‐action framework emphasizes several principles concerning the application and use of knowledge, such as ease‐of‐use, customization, and feedback. With the guidance of the above principles and the properties of interpretable machine learning, we identify the design requirements for and propose an interpretable deep learning (IDeL) based framework for text classification models. IDeL comprises three main components: feature penetration, instance aggregation, and feature perturbation. We evaluate our implementation of the framework with two distinct case studies: fake news detection and social question categorization. The experiment results provide evidence for the efficacy of IDeL components in enhancing the interpretability of text classification models. Moreover, the findings are generalizable across binary and multi‐label, multi‐class classification problems. The proposed IDeL framework introduce a unique iField perspective for building trusted models in data science by improving the transparency and access to advanced black‐box models.
Hand Gesture Recognition and voice, text conversion using

Individuals primarily communicate with one another. Blind and deaf people use sign language to communicate with others. These individuals have difficulty communicating their message to ordinary people. Deaf and blind people believe they are unable to communicate because of a lack of communication skills, and as a result, they are unable to express their emotions. Because most individuals aren’t educated in sign language, communicating in an emergency can be extremely challenging. As a consequence, the challenge may be solved by converting hand gestures into human-hearing sounds and text. Vision and non-vision approaches are two of the most commonly used methods for detecting hand movements or gestures. In a vision-based approach, a camera will be used for gesture detection, whereas sensors will be employed in a non-vision-based technique. In this study, a vision-based technique was used. This device detects and locates hand motions in order to keep a communication channel open with others. Using convolutional neural networks and artificial neural networks, this research develops a gesture recognition system. This study looks into the advantages and disadvantages of hand motion recognition.
EthInsurance: A Blockchain based alternative approach for Health Insurance Claim

In case of medical emergencies health insurance is extremely important in providing us with coverage for medical expenses. However, preventing data breaches and fraudulent activities involving health care data is the most difficult challenge in this sector. Transparency, which is a major issue in the health sector, is provided by Blockchain Technology. This paper uses blockchain to design a claim model that allows the system to maintain transparency between insurance companies. This proposed model eliminates the need for agents and enables direct contact between insurance companies, hospitals, and companies. In addition, it enables secure and efficient access to medical data by patients, providers, and other third parties while protecting patient privacy.
Intelligent recommender system based on quantum clustering and matrix completion

Abstract Faster than classical algorithms, quantum algorithms benefit from the superposition property of quantum information to offer significant speedup to complex algorithms. Therefore, quantum computing can be used to help machine learning algorithms by boosting their performance and accelerate the processing of time‐consuming ones. Clustering algorithms are very complex unsupervised learning algorithms. Indeed, the similarity calculation (distance) between input vectors is a resource‐consuming step, especially when working with large datasets. In this article, we propose a new better‐performing recommender system that operates as a combination of an adapted quantum K‐means algorithm and the singular value decomposition (SVT) algorithm. We integrate the developed quantum clustering algorithm to a prediction process of the proposed recommender system using matrix completion. To the best of our knowledge, no system with such details was proposed in the literature. The system was applied on the MovieLens dataset without a dimensionality reduction step and evaluated according to measures of information retrieval systems. The results of the quantum K‐means algorithm show that the quantum version leads to a logarithmic reduction of the time complexity compared to the classical algorithm. The proposed system has proved to be better than the previous tested ones in terms of precision and recall.