Reviewerly/exHarmony huggingface dataset:
A Hybrid YOLOv4 and Particle Filter Based Robotic Arm Grabbing System in Nonlinear and Non-Gaussian Environment

In this paper, we propose a robotic arm grasping system suitable for complex environments. For a robotic arm, in order to achieve its accurate grasp of the target object, not only the vision but also a certain tracking ability should be provided. To improve the grasp quality, we propose a robotic arm grasping system using YOLOv4 combined with a particle filter (PF) algorithm, which can be applied in a nonlinear and non-Gaussian environment. Firstly, the coordinates of the bounding box in the image can be obtained through the YOLOv4 object detection algorithm. Secondly, the coordinates in the world system can be obtained through the eye-to-hand calibration system. Thirdly, a PF model can be established based on the coordinate changes of the target object. Finally, according to the predicted output of the PF, the robotic arm and the target object can reach the specific position at the same time and complete the grab. As the target object, the bowl is applied to experiments for the sake of achieving a more convincing demonstration. The experimental results show that the robotic arm grasping system proposed in this paper can accomplish the successful grasp at a rate of nearly 88%, even at a higher movement speed, which is of great significance to robot applications in various fields.
Design and Hardware Implementation of a Simplified DAG-Based Blockchain and New AES-CBC Algorithm for IoT Security

Recently, to enhance the security of the Internet of Things (IoT), research on blockchain-based encryption algorithms has been actively conducted. However, because blockchains have complex structures and process large amounts of data, there are still many difficulties in using the conventional blockchain-based encryption algorithms in an IoT system that must have low power consumption and be ultra-lightweight. In this study, to address these problems (1) we simplified the conventional Directed Acyclic Graph (DAG)-based blockchain structure, and (2) we proposed a new Advanced Encryption Standard (AES)-Cipher Block Chaining (CBC) algorithm with enhanced security by periodically changing the secret key and initialization vector (IV) in the conventional AES-CBC encryption algorithm. Because the DAG, which is the conventional blockchain structure, randomly transmits data to multiple blocks, there may be overlapping blocks, and the quantity of transmitted data is not limited; thus, the time and power consumption for encryption and decryption increase. In this study, a simplified DAG was designed to address these problems so that packets can be transmitted only to three blocks, without overlapping. Finally, to verify the effectiveness of the algorithm proposed in this paper, an IoT system consisting of 10 clients and one server was implemented in hardware, and an experiment was conducted. Through the experiment, it was confirmed that when the proposed AES-CBC algorithm was used, the time taken and the amount of power consumed for encryption and decryption were reduced by about 20% compared to the conventional AES-CBC algorithm.
The stray sheep of cyberspace a.k.a. the actors who claim they break the law for the greater good

Abstract The development of cyberspace has brought about innumerable advantages for the mankind. However, it also came with several serious drawbacks; as cyberspace evolves, so does cybercrime. Since the birth of cyberspace, individuals, groups and whole nations have been engaging in computer-related offences of various significance and impact, trying to exploit systems’ vulnerabilities, disseminate malicious software and steal data or funds. The concept of a hacker has entered the collective consciousness and become an intrinsic element of popular culture. However, there are hackers, or rather, cyberspace actors, who challenge this common view. This paper presents three types of such people, namely hacktivists, members of cyber militias and Internet trolls. Although they all use the Internet to break the laws or rules, their internal motivations are not always utterly sinister; actually, some of them firmly believe that their actions are for the greater good. This paper is structured as follows: Firstly, the general profile of a hacker is presented. Then, the state of the art is outlined, concerning other papers dealing with the motivations behind cyber threat actors. Following that, the three aforementioned groups of cyberspace actors are contrasted with the profile of a ‘typical’ hacker. Then, the profiles of a typical representative for each of the group and their motivations are indicated, followed by the final conclusions.
Accessing Imbalance Learning Using Dynamic Selection Approach in Water Quality Anomaly Detection

Automatic anomaly detection monitoring plays a vital role in water utilities’ distribution systems to reduce the risk posed by unclean water to consumers. One of the major problems with anomaly detection is imbalanced datasets. Dynamic selection techniques combined with ensemble models have proven to be effective for imbalanced datasets classification tasks. In this paper, water quality anomaly detection is formulated as a classification problem in the presences of class imbalance. To tackle this problem, considering the asymmetry dataset distribution between the majority and minority classes, the performance of sixteen previously proposed single and static ensemble classification methods embedded with resampling strategies are first optimised and compared. After that, six dynamic selection techniques, namely, Modified Class Rank (Rank), Local Class Accuracy (LCA), Overall-Local Accuracy (OLA), K-Nearest Oracles Eliminate (KNORA-E), K-Nearest Oracles Union (KNORA-U) and Meta-Learning for Dynamic Ensemble Selection (META-DES) in combination with homogeneous and heterogeneous ensemble models and three SMOTE-based resampling algorithms (SMOTE, SMOTE+ENN and SMOTE+Tomek Links), and one missing data method (missForest) are proposed and evaluated. A binary real-world drinking-water quality anomaly detection dataset is utilised to evaluate the models. The experimental results obtained reveal all the models benefitting from the combined optimisation of both the classifiers and resampling methods. Considering the three performance measures (balanced accuracy, F-score and G-mean), the result also shows that the dynamic classifier selection (DCS) techniques, in particular, the missForest+SMOTE+RANK and missForest+SMOTE+OLA models based on homogeneous ensemble-bagging with decision tree as the base classifier, exhibited better performances in terms of balanced accuracy and G-mean, while the Bg+mF+SMENN+LCA model based on homogeneous ensemble-bagging with random forest has a better overall F1-measure in comparison to the other models.
CovidBERT-Biomedical Relation Extraction for Covid-19

Given the ongoing pandemic of Covid-19 which has had a devastating impact on society and the economy, and the explosive growth of biomedical literature, there has been a growing need to find suitable medical treatments and therapeutics in a short period of time. Developing new treatments and therapeutics can be expensive and a time consuming process. It can be practical to re-purpose existing approved drugs and put them in clinical trial. Hence we propose CovidBERT, a biomedical relationship extraction model based on BERT that extracts new relationships between various biomedical entities, namely gene-disease and chemical-disease relationships. We use the transformer architecture to train on Covid-19 related literature and fine-tune it using standard annotated datasets to show improvement in performance from baseline models. This research uses the transformer BERT model as its foundation and extracts relations from newly published biomedical papers.
Reducing Approximation Error with Rapid Convergence Rate for Non-Negative Matrix Factorization (NMF)

Non-Negative Matrix Factorization (NMF) is utilized in many important applications.This paper presents development of an efficient low rank approximate NMF algorithm for feature extraction related to text mining and spectral data analysis.NMF can be used for clustering.NMF factorizes a positive matrix A to two positive matrices W and H matrices where A = W H. The proposal uses k-means clustering algorithm to determine the centroid of each cluster and assigns the centroid coordinates of each cluster as one column for W matrix.The initial choice of W matrix is positive.The H matrix is determined with gradient descent algorithm based on thin QR optimization.The performance comparison of the proposed NMF algorithm is illustrated with results.The accurate choice of initial positive W matrix reduces approximation error and the use of thin QR algorithm in combination with gradient descent approach provides rapid convergence rate for NMF.The proposed algorithm is implemented with the randomly generated matrix in MATLAB environment.The number of significant singular values of the generated matrix is selected as the number of clusters.The error and convergence rate comparison of the proposed algorithm with the current algorithms are demonstrated in this research.The accurate measurement of execution time for individual program is not possible in MATLAB.The average time execution over 200 iterations is therefore calculated with an increasing iteration count of the proposed algorithm and the comparative results are presented.
Big data fuzzy C-means algorithm based on bee colony optimization using an Apache Hbase

Abstract Clustering algorithm analysis, including time and space complexity analysis, has always been discussed in the literature. The emergence of big data has also created a lot of challenges for this issue. Because of high complexity and execution time, traditional clustering techniques cannot be used for such an amount of data. This problem has been addressed in this research. To present the clustering algorithm using a bee colony algorithm and high-speed read/write performance, Map-Reduce architecture is used. Using this architecture allows the proposed method to cluster any volume of data, and there is no limit to the amount of data. The presented algorithm has good performance and high precision. The simulation results on 3 datasets show that the presented algorithm is more efficient than other big data clustering methods. Also, the results of our algorithm execution time on huge datasets are much better than other big data clustering approaches.
A Blockchain-Based Healthcare Platform for Secure Personalised Data Sharing

To facilitate personalised healthcare provision across Europe, we envision solutions that enable the secure integration and sharing of medical health records. These solutions should address privacy concerns, such as granular access control to personal data, establishing what should be accessible when and by whom, whilst complying with collective regulatory frameworks such as the European General Data Protection Regulation (GDPR) and adhering to international standards on how to manage information security. The proposed healthcare system design integrates technologies such as blockchain and scalable data lakes with adequate system routines to guarantee the secure access of confidential data. In this paper, we present the essential architectural components for the secure integration of medical records in a blockchain-based platform. We present a patient-centric data retrieval approach which incorporates a structured format to compose access rules.
Hybrid Decision Model for Evaluating Blockchain Business Strategy: A Bank’s Perspective

Banks attempt to invest in emerging financial technology (FinTech), such as blockchain, to enhance competitiveness. There is a great deal of literature on the technical and legal aspects of blockchain. However, there is little specific guidance on how banks can apply a holistic model to evaluate the blockchain-based business. This study proposes a hybrid decision model with confidence-weighted fuzzy assessments to address this valuable research topic. Supported by a group of seasoned experts, five major blockchain-based business models are evaluated for a domestic bank in Taiwan. The key findings contribute to understanding the importance of the involved factors and identifying the ideal business strategy for the bank. The result suggests that the most crucial dimension is policies and regulations, not the technical capability of banks.
A one decade survey of autonomous mobile robot systems

&lt;span lang="EN-US"&gt;Recently, autonomous mobile robots have gained popularity in the modern world due to their relevance technology and application in real world situations. The global market for mobile robots will grow significantly over the next 20 years. Autonomous mobile robots are found in many fields including institutions, industry, business, hospitals, agriculture as well as private households for the purpose of improving day-to-day activities and services. The development of technology has increased in the requirements for mobile robots because of the services and tasks provided by them, like rescue and research operations, surveillance, carry heavy objects and so on. Researchers have conducted many works on the importance of robots, their uses, and problems. This article aims to analyze the control system of mobile robots and the way robots have the ability of moving in real-world to achieve their goals. It should be noted that there are several technological directions in a mobile robot industry. It must be observed and integrated so that the robot functions properly: Navigation systems, localization systems, detection systems (sensors) along with motion and kinematics and dynamics systems. All such systems should be united through a control unit; thus, the mission or work of mobile robots are conducted with reliability.&lt;/span&gt;
BayCANN: Streamlining Bayesian Calibration With Artificial Neural Network Metamodeling

Purpose: Bayesian calibration is generally superior to standard direct-search algorithms in that it estimates the full joint posterior distribution of the calibrated parameters. However, there are many barriers to using Bayesian calibration in health decision sciences stemming from the need to program complex models in probabilistic programming languages and the associated computational burden of applying Bayesian calibration. In this paper, we propose to use artificial neural networks (ANN) as one practical solution to these challenges. Methods: Bayesian Calibration using Artificial Neural Networks (BayCANN) involves (1) training an ANN metamodel on a sample of model inputs and outputs, and (2) then calibrating the trained ANN metamodel instead of the full model in a probabilistic programming language to obtain the posterior joint distribution of the calibrated parameters. We illustrate BayCANN using a colorectal cancer natural history model. We conduct a confirmatory simulation analysis by first obtaining parameter estimates from the literature and then using them to generate adenoma prevalence and cancer incidence targets. We compare the performance of BayCANN in recovering these “true” parameter values against performing a Bayesian calibration directly on the simulation model using an incremental mixture importance sampling (IMIS) algorithm. Results: We were able to apply BayCANN using only a dataset of the model inputs and outputs and minor modification of BayCANN's code. In this example, BayCANN was slightly more accurate in recovering the true posterior parameter estimates compared to IMIS. Obtaining the dataset of samples, and running BayCANN took 15 min compared to the IMIS which took 80 min. In applications involving computationally more expensive simulations (e.g., microsimulations), BayCANN may offer higher relative speed gains. Conclusions: BayCANN only uses a dataset of model inputs and outputs to obtain the calibrated joint parameter distributions. Thus, it can be adapted to models of various levels of complexity with minor or no change to its structure. In addition, BayCANN's efficiency can be especially useful in computationally expensive models. To facilitate BayCANN's wider adoption, we provide BayCANN's open-source implementation in R and Stan.
Ethical Adversaries

Machine learning is being integrated into a growing number of critical systems with far-reaching impacts on society. Unexpected behaviour and unfair decision processes are coming under increasing scrutiny due to this widespread use and its theoretical considerations. Individuals, as well as organisations, notice, test, and criticize unfair results to hold model designers and deployers accountable. We offer a framework that assists these groups in mitigating unfair representations stemming from the training datasets. Our framework relies on two inter-operating adversaries to improve fairness. First, a model is trained with the goal of preventing the guessing of protected attributes' values while limiting utility losses. This first step optimizes the model's parameters for fairness. Second, the framework leverages evasion attacks from adversarial machine learning to generate new examples that will be misclassified. These new examples are then used to retrain and improve the model in the first step. These two steps are iteratively applied until a significant improvement in fairness is obtained. We evaluated our framework on well-studied datasets in the fairness literature - including COMPAS - where it can surpass other approaches concerning demographic parity, equality of opportunity and also the model's utility. We investigated the trade-offs between these targets in terms of model hyperparameters and also illustrated our findings on the subtle difficulties when mitigating unfairness and highlight how our framework can assist model designers.
Anaphoricity in emoji: An experimental investigation of face and non-face emoji

Emoji are widely used, but have received relatively little attention in psycholinguistic research. Upon encountering a message consisting of both text and emoji, readers presumably construct some link between emoji and text. Based on a psycholinguistic study on text-emoji relations, we argue for (at least) two types of emoji-text dependencies, related to referential dependencies known to exist in the linguistic domain, namely (i) the dependency between an expressive (e.g. wow, damn, f*king) and the individual whose opinion it expresses, and (ii) the dependency between a pronoun (or other pro-form) and its antecedent. We extend the discussion of these dependencies to emoji, and provide experimental data that face emoji resemble expressives in that they tend to be interpreted as expressing the opinion of a salient experiencer, while action emoji are interpreted based on principles of discourse coherence (e.g. discourse relations like explanation), similar to what coherence-based accounts of pronoun resolution predict.
Analyzing Open-Source Serverless Platforms: Characteristics and Performance (S)

Serverless computing is increasingly popular because of its lower cost and easier deployment. Several cloud service providers (CSPs) offer serverless computing on their public clouds, but it may bring the vendor lock-in risk. To avoid this limitation, many open-source serverless platforms come out to allow developers to freely deploy and manage functions on self-hosted clouds. However, building effective functions requires much expertise and thorough comprehension of platform frameworks and features that affect performance. It is a challenge for a service developer to differentiate and select the appropriate serverless platform for different demands and scenarios. Thus, we elaborate the frameworks and event processing models of four popular open-source serverless platforms and identify their salient idiosyncrasies. We analyze the root causes of performance differences between different service exporting and auto-scaling modes on those platforms. Further, we provide several insights for future work, such as auto-scaling and metric collection.
Employee Turnover Intention and Job Satisfaction

Today it became a huge challenge for Human Resource Managers to retain the employees for longer time of period and decrease the rate of employee turnover.The main research objective is to find out the correlation between job satisfaction and employee turnover intention in private hospital in Erbil.A quantitative method was used to analyze the current study.144 participants were involved in this study from private hospital in Erbil.The correlation between job satisfaction factor as independent factor and turnover as dependent factor, the value of R for the Job satisfaction is =.386** which indicates that turnover is a positive and weak correlation with turnover intention.The coefficients analysis for this study, the value Beta for turnover is = .386>0.01, which supported the research hypothesis.This study showed that the there is a positive correlation between job satisfaction and employee turnover.Finally, on the turnover intentions of private hospital personnel in Erbil, most of them would quit in case if they have a better opportunities.
People’s Fast Moving Detection Method in Buses Based on YOLOv5

To ensure the public's safety such as in buses, it is very important to accurately judge people's behaviors and give early warnings.If by watching the video surveillance manually, the cost will be very high, and it cannot be effectively popularized, so video automatic monitoring is preferred.For buses, its environmental space is closed as well as narrow, and at the same time, it is often in a non-stationary state, so traditional behavior detection methods cannot be used here as they are easily affected by moving environment and difficult to fulfill object behavior identification in real time.Aiming at this problem, for people's fast-moving in buses, a kind of detection method based on YOLOv5 is proposed in this paper.Firstly, the method detects people through one-stage object detection.Secondly, in order to obtain the person's movement trajectory quickly and accurately, an improved two-stage object matching algorithm is designed to track different people.Then, the speed curves of a person during normal activities and fast moving are compared.Finally, an abnormal alarm mechanism is constructed to realize the effective fast movement alarm.Surveillance video in the bus was used to test and evaluate the effectiveness of the method.Results show that the accuracy rate of our method can get 95.4%.
Video Summarization for Multiple Sports Using Deep Learning

This paper proposes a computationally inexpensive method for automatic key-event extraction and subsequent summarization of sports videos using scoreboard detection. A database consisting of 1300 images was used to train a supervised-learning based object detection algorithm, YOLO (You Only Look Once). Then, for each frame of the video, once the scoreboard was detected using YOLO, the scoreboard was cropped out of the image. After this, image processing techniques were applied on the cropped scoreboard to reduce noise and false positives. Finally, the processed image was passed through an OCR (Optical Character Recognizer) to get the score. A rule-based algorithm was run on the output of the OCR to generate the timestamps of key-events based on the game. The proposed method is best suited for people who want to analyse the games and want precise timestamps of the occurrence of important events. The performance of the proposed design was tested on videos of Bundesliga, English Premier League, ICC WC 2019, IPL 2019, and Pro Kabaddi League. An average F1 Score of 0.979 was achieved during the simulations. The algorithm is trained on five different classes of three separate games (Soccer, Cricket, Kabaddi). The design is implemented using python 3.7.