Reviewerly/exHarmony huggingface dataset:
Trust, Automation Bias and Aversion: Algorithmic Decision-Making in the Context of Credit Scoring

Algorithmic decision-making (ADM) systems increasingly take on crucial roles in our technology-driven society, making decisions, for instance, concerning employment, education, finances, and public services.This article aims to identify peoples' attitudes towards ADM systems and ensuing behaviours when dealing with ADM systems as identified in the literature and in relation to credit scoring.After briefly discussing main characteristics and types of ADM systems, we first consider trust, automation bias, automation complacency and algorithmic aversion as attitudes towards ADM systems.These factors result in various behaviours by users, operators, and managers.Second, we consider how these factors could affect attitudes towards and use of ADM systems within the context of credit scoring.Third, we describe some possible strategies to reduce aversion, bias, and complacency, and consider several ways in which trust could be increased in the context of credit scoring.Importantly, although many advantages in applying ADM systems to complex choice problems can be identified, using ADM systems should be approached with caree.g., the models ADM systems are based on are sometimes flawed, the data they gather to support or make decisions are easily biased, and the motives for their use unreflected upon or unethical.
Extended Artificial Potential Field Applied for Soccer Robot Players Path Planning

Mobile robots have recently attracted the attention of researchers due to their various potential in civilian applications. The essential technology needed for mobile robots is their navigation and path-planning. The aim of this paper is to carry out a comprehensive study on the Artificial Potential Field (APF) algorithm. This paper presents a straightforward and reliable path planning method for general mobile robots, focusing on Omni-directional soccer robot players. The (APF) method has been widely applied in static real-time path planning. In this study, we present the Extended APF (EAPF) method to solve some inherent shortcomings, such as the latency and inertia of the robots, local minima, and the non-reachability of the target (GNRON problem). We also propose a novel approach to use APF in highly dynamic environments. We added gaussian noise to the simulation’s data to further resemble the real-world environment, and then we proposed a simple filter to mitigate the incoming noise as well as estimating other necessary information from the environment’s raw data. Simulation results show that the proposed method has promising feasibility and efficiency in real-time path planning and can effectively avoid obstacles.
Few-shot activity learning by dual Markov logic networks

In Human activity recognition (HAR), a large amount of data may have no labels, so it is necessary to realize effective and credible data calibration under Few-shot learning (FSL). This paper proposes a new method for calibrating unlabeled data by using two models to improve the credibility of data labels further. Markov logic network (MLN) is used as the basic model. On the one hand, the construction of knowledge can reduce the dependence on the amount of data. On the other hand, the relationship between the actions can be effectively expressed. Specifically, we pre-train two MLN models using two unmatched training datasets. Then, these models are used to infer the possible labels of unlabeled data simultaneously. When labels are inconsistent, the most likely label will be selected and used to retrain the models. In order to ensure the credibility of the selected label, the selection process adopts a dual-model cross-validation method. This method uses the least square method to determine models’ weights according to the test results. Finally, it gives the possible probability of the labels under the common prediction of the models. Experimental results showed that the dual-model design method is better than the single model method in terms of time efficiency and better than the single model, TextCNN, TextLSTM, Transformer, and other models in terms of label credibility.
A Hybrid of Rule-based and HMM-based Part-of-Speech Tagger for Indonesian

Aksara is an Indonesian NLP tool that conforms to Universal Dependencies annotation guidelines. So far, Aksara can perform four tasks: word segmentation, lemmatization, POS tagging, and morphological features analysis. However, one of its weaknesses is that it has not solved the word sense disambiguation problem. This work's objective is to build a hybrid of rule-based and Hidden Markov Model (HMM) based POS taggers that utilized the output of Aksara's rule-based POS tagger and solved the ambiguity problem using HMM and the Viterbi algorithm. We use the bigram and trigram model to train HMM. Our hybrid model is evaluated using a 10-fold cross-validation method and achieves an acceptable result with the trigram model slightly better. Trigram model managed to get 86.62% accuracy and an average F1-score of 82.32%, while the bigram model managed to get 86.47% accuracy and an average F1-score of 81.55%. The experiments also show that the hybrid model of rule-based and HMM-based is better than the HMM-based model alone, with a margin of 2.03% of accuracy.
Detecting Memory-Related Bugs by Tracking Heap Memory Management of C++ Smart Pointers

The smart pointer mechanism, which is improved in the continuous versions of the C++ standards over the last decade, is designed to prevent memory-leak bugs by automatically deallocating the managed memory blocks. However, not all kinds of memory errors can be immunized by adopting this mechanism. For example, dereferencing a null smart pointer will lead to a software failure. Due to the lack of specialized support for smart pointers, the off-the-shelf C++ static analyzers cannot effectively reveal these bugs.In this paper, we propose a static approach to detecting memory-related bugs by tracking the heap memory management of smart pointers. The behaviors of smart pointers are modeled during their lifetime to trace the state transitions of managed memory blocks. And the specially designed checkers are used to check the state changes according to five collected bug patterns. To evaluate the effectiveness of our approach, we implement it on the top of the Clang Static Analyzer. A set of handmade code snippets, as well as nine popular open-source C++ projects, are used to compare our tool against four other analyzers. The results show that our approach can successfully discover nearly all the built-in bugs. And 442 out of 648 reports generated from the open-source projects are true positives after manual reviewing, where the bugs of dereferencing null smart pointers are most frequently reported. To further confirm our reports, we design patches for Aria2, Restbed, MySQL and LLVM, in which seven pull requests covering 76 bug reports have been merged by the developers up to now. The results indicate that pointers should always be carefully used even after migrated to smart pointers and static analysis upon specialized models can effectively detect such bugs.
Software—A New Open Access Journal on Software Engineering

Software (ISSN: 2674-113X) [...]
A New Biomedical Image Denoising Method Using an Adaptive Multi-resolution Technique

In the world of digital image processing, image denoising plays a vital role, where the primary objective was to distinguish between a clean and a noisy image. However, it was not a simple task. As a consequence of everyone's understanding of the practical challenge, a variety of methods have been presented during the last few years. Of those, wavelet transformer-based approaches were the most common. But wavelet-based methods have their own limitations in image processing applications like shift sensitivity, poor directionality, and lack of phase information, and they also face difficulties in defining the threshold parameters. As a result, this study provides an image de-noising approach based on Bi-dimensional Empirical Mode Decomposition (BEMD). This project's main purpose is to disintegrate noisy images based on their frequency and construct a hybrid algorithm that uses existing de-noising techniques. This approach decomposes the noisy picture into numerous IMFs with residue, which were subsequently filtered independently based on their specific properties. To quantify the success of the proposed technique, a comprehensive analysis of the experimental results of the benchmark test images was conducted using several performance measurement matrices. The reconstructed image was found to be more accurate and pleasant to the eye, outperforming state-of-the-art denoising approaches in terms of PSNR, MSE, and SSIM.
Learning Local Recurrent Models for Human Mesh Recovery

We consider the problem of estimating frame-level full human body meshes given a video of a person with natural motion dynamics. While much progress in this field has been in single image-based mesh estimation, there has been a recent uptick in efforts to infer mesh dynamics from video given its role in alleviating issues such as depth ambiguity and occlusions. However, a key limitation of existing work is the assumption that all the observed motion dynamics can be modeled using one dynamical/recurrent model. While this may work well in cases with relatively simplistic dynamics, inference with in-the-wild videos presents many challenges. In particular, it is typically the case that different body parts of a person undergo different dynamics in the video, e.g., legs may move in a way that may be dynamically different from hands (e.g., a person dancing). To address these issues, we present a new method for video mesh recovery that divides the human mesh into several local parts following the standard skeletal model. We then model the dynamics of each local part with separate recurrent models, with each model conditioned appropriately based on the known kinematic structure of the human body. This results in a structure-informed local recurrent learning architecture that can be trained in an end-to-end fashion with available annotations. We conduct a variety of experiments on standard video mesh recovery benchmark datasets such as Human3.6M, MPI-INF-3DHP, and 3DPW, demonstrating the efficacy of our design of modeling local dynamics as well as establishing state-of-the-art results based on standard evaluation metrics.
Handwritten Character Recognition using Deep Neural Networks

The preliminary work performed in this manuscript is to recognize Handwritten English Characters using a multilayer perceptron. The standard EMNIST dataset of handwritten English characters is used here. The preprocessing of images included Binarization and reshaping into 784 (28x28) binary pixels. The model was trained using a dataset of 80,000 characters. The validation set had 20,000 characters other than those of the training set. The recognition accuracy on the Training set is 98.05%, and that on the validation set is 91.46%.
A Novel Approach to Manage Ownership and VAT Using Blockchain-Based Digital Identity

Blockchain is a revolutionary technology due to its security and transparency . A digital identity system based on blockchain technology can play an important role in securing the asset ownership information and protecting it against any discrepancy. However, there has been no work that combines the advantages of blockchain technology and an individual’s biometric information in creating a digital identity that can be used for ownership system, thereby preventing any fraud in ownership system. In this paper, a Blockchain-based Ownership and Value Added Tax (VAT) management system have been proposed depending on a digital identity system that is implemented on blockchain technology using individuals’ biometric information. The Ownership system along with VAT management was implemented using Ethereum smart contract. The experimental results of testing our system indicate that an intruder cannot perform any modification illegally to ownership data in the system. Any adversarial attempt is aborted instantly and thereby the security of citizen’s ownership information is ensured. Moreover, appropriate amount of VAT is automatically assigned to the owner while changing the ownership of suitable products. Our proposed system can be very useful for protecting the digital ownership information of citizens of a country and ensuring fair payment of VAT for suitable products.
A spatial co-location pattern mining approach based on column calculation

Spatial co-location pattern mining aims to discover correlations between spatial features. A co-location pattern corresponds to a subset of spatial features whose instances are frequently located in spatial neighborhoods. Most co-location pattern mining approaches calculate the prevalence based on table instances, but the time and space costs of generating table instances are enormous. To address this problem, this paper presents a novel co-location pattern mining approach based on column calculation, which only searches for participating instances of features in a pattern without having to generate the table instance. Furthermore, this paper designs the instance search space pruning, the candidate participating instance verification, and the prevalence beforehand awareness technologies to speed up the search of participating instances. The CPM-Col method is proposed on this basis, and its complexity, accuracy, and completeness are addressed. Extensive experiments are conducted on real and synthetic datasets, and experimental results show that the CPM-Col algorithm has better performance and scalability than seven other baseline algorithms, especially with a performance gain of several times or even orders of magnitude. Moreover, the effectiveness of the proposed optimization strategies is verified experimentally.
A Rapid Approach of Computing Multi-axis Tool Accessible Space for General Cutter Model by Utilizing Programmable Graphics Pipeline

Computation of tool accessible space (TAS) is key to interference-free toolpath planning for multi-axis machining complex parts. However, most existing solutions are difficult to guarantee efficiency and accuracy simultaneously, and are only applicable to certain types of tools. Hence, this article proposes a rapid and accurate TAS computation approach for general cutters based on programmable graphics pipelines of modern graphics-processing unit (GPU). With this approach, a concept of MID (maximum interference distance)-space that quantitatively describes interference states between obstacles and a general cutter model is first proposed. Then, an MID model between obstacles and the general cutter model is presented. And a GPU-enhanced MID computing algorithm is developed based on new features of graphics pipelines. Moreover, an algorithm for batch computing TASs is presented based on GPU instancing. Experimental studies show that compared with traditional GPU-based methods, since our approach enables TASs computation of all cutter contact points to be completed in batches by running the graphics pipeline only once, the computation efficiency is improved by nearly 11 times. Besides, since the accuracy of our approach does not rely on the resolution of GPU rendered image, and also because the actual MID is identified precisely, the accuracy can be guaranteed very effectively. More encouragingly, our approach is general for various cutters, including special-shaped cutters, which greatly facilitate generating interference-free toolpath as well as optimizing tool postures globally for multi-axis machining complex parts.
On the Fairness of Generative Adversarial Networks (GANs)

Generative adversarial networks (GANs) are one of the greatest advances in AI in recent years. With their ability to directly learn the probability distribution of data and then sample synthetic realistic data. Many applications have emerged, using GANs to solve classical problems in machine learning, such as data augmentation, class imbalance problems, and fair representation learning. In this paper, we analyze and highlight the fairness concerns of GANs. In this regard, we show empirically that GANs models may inherently prefer certain groups during the training process and therefore they’re not able to homogeneously generate data from different groups during the testing phase. Furthermore, we propose solutions to solve this issue by conditioning the GAN model towards samples’ groups or using the ensemble method (boosting) to allow the GAN model to leverage distributed structure of data during the training phase and generate groups at an equal rate during the testing phase.
Prediction of Sports Aggression Behavior and Analysis of Sports Intervention Based on Swarm Intelligence Model

In the process of sports, athletes often have aggressive behaviors because of their emotional fluctuations. This violent sports behavior has caused many serious bad effects. In order to reduce and solve this kind of public emergencies, this paper aims to create a swarm intelligence model for predicting people's sports attack behavior, takes the swarm intelligence algorithm as the core technology optimization model, and uses the Internet of Things and other technologies to recognize emotions on physiological signals, predict, and intervene sports attack behavior. The results show the following: (1) After the 50-fold cross-validation method, the results of emotion recognition are good, and the accuracy is high. Compared with other physiological electrical signals, EDA has the worst classification performance. (2) The recognition accuracy of the two methods using multimodal fusion is improved greatly, and the result after comparison is obviously better than that of single mode. (3) Anxiety, anger, surprise, and sadness are the most detected emotions in the model, and the recognition accuracy is higher than 80%. Sports intervention should be carried out in time to calm athletes' emotions. After the experiment, our model runs successfully and performs well, which can be optimized and tested in the next step.
Detection and Moving Object Tracking in images using an improved Kallman Filter (KF) by an Invasive weed optimization algorithm

Nowadays, Moving Object Tracking in real time is a new paradigm that has a great impact on video surveillance to identify and track objects, and due to the constant change in the motion of the object and changing the size of the scene, obstruction, changes in appearance and changes in movement and brightness of one It is one of the important research fields and a very important task in the field of automated security automation monitoring systems. On the other hand, object tracking in a film is a matter of estimating positions and other related information about the movement of objects in the film’s visual sequences. The first step in such systems is to detect a moving object in the film. The second step is to track the detected object. In this paper, Moving Object Tracking is performed using the improved Kalman Filter (KF) method. The algorithm is successfully applied to standard video datasets. The Kalman Filter detects an object by assuming the initial state and estimating the sound covariance, and provides an efficient method for calculating the state estimation process by improving its initial parameters using the Invasive weed optimization (IWO) algorithm. Experimental results will be compared on MATLAB software and the proposed algorithm will be compared with the basic article algorithm in terms of performance and accuracy.
Lightweight Network Traffic Classification Model Based on Knowledge Distillation

Deep learning have been extensively applied to network traffic classification. This technology reduce much manual design and archive high accuracy in complex and highly variable networks. The existing deep learning approaches usually require abundant space and computing resources to improve the accuracy. However, for on-line network traffic classifications, increments in latency and instability incurred by the high costs of the model make it unsuitable for the case. A promising tool to deal with the challenge is knowledge distillation, which produces space-and-time efficient models from high-accurate large models. In this paper, we propose a light-weight encrypted traffic classification model based on knowledge distillation. We adopt the LSTM structure and apply knowledge distillation to it. The distilling loss is focal loss, which can effectively solve the imbalance in the number of samples and different degrees of difficulty in classification. To enhance the learning efficiency, we design an adaptive temperature function to soften the labels at each training stage. Experiments show that compared with the teacher model, the recognition speed of the student model is increased by 72%, the accuracy of the student model decreased by only 0.45% to 99.52%. Our model achieves both high accuracy and low latency for on-line encrypted traffic classification compared with the state of the art.
Game-Theoretic Analysis of an Exclusively Transaction-Fee Reward Blockchain System

Miners in a blockchain system are typically rewarded in two ways - through a fixed block reward, and by the transaction fees that are voluntarily offered by its users. As the available space inside a block is limited, users must compete against each other by submitting higher fees to obtain this limited resource. In this paper, we model blockchain transaction inclusion as a time-sensitive dynamic game, where users base their fees depending on both what their competitors in the network are offering, and by their own urgency of having their transactions approved. We then investigate the effect that mempool congestion (the aggregate size of transactions waiting to be confirmed) and different block sizes have on the fees users would be willing to pay. Our analysis concludes that miners have no rational reason to artificially limit the block size, which is in direct contrast with previous research findings. Instead, we find that increasing the block size in relation to a growing mempool both lowers the individual fees that users have to pay, and increases the total in fees collected, which raises not only the utility of the miners, but also of the regular users of the blockchain system.
Analysis and Implementation of Course Quality Optimization based on Cloud Computing

This article discusses the role of Internet networks and technologies in improving the quality of lessons, the concept and definition of cloud classroom, cloud classroom students' learning objectives and learning resources from the network, communication with teachers and other students through the network and the fact that the process of using it as a way to build one's own knowledge is presented, that the traditional online education system cannot solve the problem of overloading data in the learning mode, and so on and so forth. in problem solving this article offers an online learning system in a cloud classroom under a system of personal recommendations. The proposed system adopts an algorithm of recommendations for co-filtering, which helps to identify potential desires of users and thus complements the recommendations more precisely.
Crime Scene Reconstruction Based on a Suitable Software: A Comparison Study

Crime scene animation has been a topic discussed by many authorities from various countries. Implementing the crime scene animation in solving the crime that took place in Malaysia would be more helpful. It is beneficial because it would present the case to those who have little or no understanding of it or the crime. With the help of an Unmanned aerial vehicle (UAV), those in the courtroom would be able to revisit the crime scene without physically attending the scene. With the help of software, the crime scene animation would be much appreciated by the courtroom member. This paper will conduct a practical demonstration to display three different software picked to reconstruct the crime scene. This demonstration would allow the reader to determine the proper software and add an appropriate crime scene reconstruction.