Reviewerly/exHarmony huggingface dataset:
An optimal visual servo trajectory planning method for manipulators based on system nondeterministic model

Abstract When a manipulator captures its target by a visual servo system, uncertainties can arise because of mechanical system and visual sensors exist error. This paper proposes an intelligent method to predict the successful rate for a manipulator to capture its target with motion and sensor errors. Because the mapping between the joint space of the manipulator and the Cartesian space at the end of the manipulator is nonlinear, when there is a bounded error of the manipulator’s joint, the error range of the end motion is constantly changing with the different joint positions. And at the same time, the visual servo camera will also measure the target from different positions and postures, so as to produce measurement results with different error ranges. The unknown time-varying error property not only greatly affects the stability of the closed-loop control but also causes the capture failure. The purpose of this paper is to estimate the success probability of different capture trajectories by establishing the nondeterministic model of manipulator control system. First, a system model including motion subsystem and feedback subsystem was established with system error described by Gaussian probability. And then Bayesian estimation was introduced into the system model to estimate the executing state of the predefined trajectory. Linear least quadratic regulators (LQR) control is used to simulate the input correction in the closed-loop control between motion subsystem and feedback subsystem. At last, the successful probability of capturing the target is established by the Gaussian distribution at the end point of the trajectory with geometric relationship calculation between tolerance range and error distribution. The effectiveness and practicability of the proposed method are proved by simulation and experiment.
A Two-Level Rectification Attention Network for Scene Text Recognition

Scene text recognition is a challenging task in the computer vision field due to the diversity of text styles and the complexity of the image backgrounds. In recent decades, numerous text rectification and recognition methods have been proposed to solve these problems. However, most of these methods rectify texts at the geometry level or pixel level. The former is limited by geometric constraints, and the latter is prone to blurring the text. In this paper, we propose a two-level rectification attention network (TRAN) to rectify and recognize texts. This network consists of two parts: a two-level rectification network (TORN) and an attention-based recognition network (ABRN). Specifically, the TORN first rectifies texts at the geometry level and then performs a pixel-level adjustment, which not only eliminates the geometric constraints but also renders clear texts. The ABRN’s role is to recognize text in the rectified images. To improve the feature extraction ability of our model, we design a new channel-wise and kernel-wise attention unit, which enables the network to handle significant variations of character size and channel interdependencies. Furthermore, we propose a skip training strategy to make our model converge smoothly. We conduct experiments on various benchmarks, including regular and irregular datasets. The experimental results show that our method achieves a state-of-the-art performance.
High Precision Eye Tracking Based on Electrooculography (EOG) Signal Using Artificial Neural Network (ANN) for Smart Technology Application

Electrooculography (EOG) signal is the potential difference between the cornea and the retina of the eye. The voltage amplitude changes when the eye moves in various directions. This change produces a distinct EOG pattern when the eye moves in a particular direction. Therefore, by monitoring the EOG signal, it is possible to track the eye movement. The EOG based eye-tracking technique can be extended to maneuver smart wheelchairs for neurodegenerative disease patients. For a successful operation of such a smart wheelchair, an accurate classification of the EOG signal is required. In this experimental study, we collected two channel EOG signals in the laboratory from multiple individuals and propose an Artificial Neural Network (ANN) based method to differentiate among the nine classes of EOG signals: up, down, left, right, down-left, down-right, up-left, up-right, and blink. This wide range classification would be suitable to perform complicated tasks in smart technology platform. Our model can successfully predict the eye movement from the statistical properties and dominant frequency of the measured EOG signal with an accuracy, precision, recall, and F1 score of 99%. This is a significant improvement over past studies conducted by various researchers for the same purpose and to the knowledge of the authors, such a high accuracy has not been previously achieved for the nine classes of EOG signals mentioned earlier. The proposed model is compatible for real-time smart applications based on eye movements.
Epistemological Challenges

Epistemology is concerned with the preconditions and goals of knowledge, helping us to understand how knowledge is generated. However, in recent years, the availability of Big Data and the advancement of Data Science (DS) methods have led to heated discussions as well as to a divided scientific community. On the one hand, there are those who believe that knowledge can now be generated exclusively from data, rendering theory no longer necessary, while the opposing voices, on the other hand, do not want to rely solely on the results that algorithms extract from the data. As such, this chapter discusses both perspectives and warns against taking a one-sided position. Rather, the goal is to understand the advantages of both sides and to profit from the synergies between the two scientific approaches.
Defining principles for preserving privacy in digital forensic examinations

As digital devices play an increasing role in criminal investigations, where in some cases the data they contain may describe events where few other sources of information exist, there is an increasing concern regarding potential privacy invasion caused by their examination. ICO, 2020, the Information Commissioner's Office called for the greater evaluation and scrutiny of data extraction and examination processes deployed by investigating authorities with regard to smartphone data. In doing so, a light was shone on the wider issues and balancing act of maintaining data privacy whilst still allowing for an effective investigation to be conducted by law enforcement. This article proposes a set of ten Privacy-Preserving Data Processing Principles (PPDPP) for consideration by those conducting the digital forensic extraction and examination of data from a digital device. These principles define conduct that is indicative of privacy-preserving, where it is encouraged that those undertaking device investigations demonstrate evidence of adherence to the spirit of them.
Similarity-based Gray-box Adversarial Attack Against Deep Face Recognition

The majority of adversarial attack techniques perform well against deep face recognition when the full knowledge of the system is revealed (white-box). However, such techniques act unsuccessfully in the gray-box setting where the face templates are unknown to the attackers. In this work, we propose a similarity-based gray-box adversarial attack (SGADV) technique with a newly developed objective function. SGADV utilizes the dissimilarity score to produce the optimized adversarial example, i.e., similarity-based adversarial attack. This technique applies to both white-box and gray-box attacks against authentication systems that determine genuine or imposter users using the dissimilarity score. To validate the effectiveness of SGADV, we conduct extensive experiments on face datasets of LFW, CelebA, and CelebA-HQ against deep face recognition models of FaceNet and InsightFace in both white-box and gray-box settings. The results suggest that the proposed method significantly outperforms the existing adversarial attack techniques in the gray-box setting. We hence summarize that the similarity-base approaches to develop the adversarial example could satisfactorily cater to the gray-box attack scenarios for de-authentication.
Epidemic Exposure Tracking With Wearables: A Machine Learning Approach to Contact Tracing

The recent pandemic revealed weaknesses in several areas, including the limited capacity of public health systems for efficient case tracking and reporting. In the post-pandemic era, it is essential to be ready and provide not only preventive measures, but also effective digital strategies and solutions to protect our population from future outbreaks. This work presents a contact tracing solution based on wearable devices to track epidemic exposure. Our proximity-based privacy-preserving contact tracing (P3CT) integrates: 1) the Bluetooth Low Energy (BLE) technology for reliable proximity sensing, 2) a machine-learning approach to classify the exposure risk of a user, and 3) an ambient signature protocol for preserving the user’s identity. Proximity sensing exploits the signals emitted from a smartwatch to estimate users’ interaction, in terms of distance and duration. Supervised learning is then used to train four classification models to identify the exposure risk of a user with respect to a patient diagnosed with an infectious disease. Finally, our proposed P3CT protocol uses ambient signatures to anonymize the infected patient’s identity. Extensive experiments demonstrate the feasibility of our proposed solution for real-world contact tracing problems. The large-scale dataset consisting of the signal information collected from the smartwatch is available online. According to experimental results, wearable devices along with machine learning models are a promising approach for epidemic exposure notification and tracking.
Dual-Level Adaptive and Discriminative Knowledge Transfer for Cross-Domain Recognition

Unsupervised domain adaptation is an appealing technique to learn robust classifiers for unlabeled target domain by borrowing knowledge from well-established source domain. However, previous works mainly suffer from two limitations: 1) the classifier trained on labeled source data may be prone to overfitting the source distribution, lowering its performance on the target domain; 2) the adaptation process will be misled by conditional distribution matching using hard pseudo labels of target samples. This paper presents a Dual-Level Adaptive and Discriminative (DLAD) classifier learning framework, in which transfer classifier and distribution adaptation can be mutually beneficial for effective knowledge transfer. Specifically, we aim to achieve a domain-level adaptive classifier by considering structural risk minimization (SRM) on both domains and performing weighted distribution adaptation, which facilitates joint classifier learning in a semi-supervised manner. To further achieve a class-level discriminative classifier, we explicitly leverage unlabeled target data to promote classifier learning based on class probabilities, which refines the decision boundary to be more discriminative for unlabeled target data. To the best of our knowledge, DLAD is the first attempt to consider the principle of SRM on the target domain, which significantly boosts the discriminative power of transfer classifier and yields a tighter generalization bound. Experimental evaluations on several standard cross-domain datasets show that DLAD significantly outperforms other competitive methods.
GPU-based acceleration of evolutionary induction of model trees

Evolutionary algorithms (EAs) are naturally prone to parallel processing. However, when they are applied to data mining, the fitness calculations start to dominate and the typical population-based decomposition limits the parallel efficiency. When dealing with large-scale data, the scalable solution may become a real challenge. In this article, we propose a GPU-based parallelization of evolutionary induction of model trees. Such trees are a special case of decision tree (DT) that is designed to solve regression problems. The evolutionary approach allows not only a robust prediction but also to preserve the simplicity of DTs. However, the global approach is much more computationally demanding than state-of-the-art greedy inducers, and thus hard to apply to large-scale data mining directly. A parallelized induction of model trees (with univariate tests in the internal nodes and multiple linear regression models in the leaves) requires a carefully designed decomposition strategy. Six GPU-supported procedures are designed to successively: redistribute, sort and rearrange dataset samples, next, calculate models and fitness, and finally gather the results. Experimental validation is performed on real-life and artificial datasets, using various (low- and high-end) GPU accelerators. Results show that the GPU-supported solution enables time-efficient global induction of model trees on large-scale data, which until now was reserved for greedy methods. The obtained speedup is very satisfactory (even up to hundreds of times). The solution is scalable for datasets of different sizes and dimensions.
Meta-Learning for Offensive Language Detection in Code-Mixed Texts

This research investigates the application of Model-Agnostic Meta-Learning (MAML) and ProtoMAML to identify offensive code-mixed text content on social media in Tamil-English and Malayalam-English code-mixed texts. We follow a two-step strategy: The XLM-RoBERTa (XLM-R) model is trained using the meta-learning algorithms on a variety of tasks having code-mixed data, monolingual data in the same language as the target language and related tasks in other languages. The model is then fine-tuned on target tasks to identify offensive language in Malayalam-English and Tamil-English code-mixed texts. Our results show that meta-learning improves the performance of models significantly in low-resource (few-shot learning) tasks1. We also introduce a weighted data sampling approach which helps the model converge better in the meta-training phase compared to traditional methods.
Sentiment Analysis on Telemedicine App Reviews using XGBoost Classifier

In recent years, companies have widely used sentiment analysis with machine learning classification algorithms to help business decision-making. Sentiment analysis helps evaluate customer opinions on a product in goods or services. Companies need this opinion or sentiment to improve the performance, quality of their products, and customer satisfaction. Machine learning algorithms widely used for sentiment analysis are Naive Bayes Classifier, Maximum Entropy, Decision Tree, and Support Vector Machine. In this study, we propose an approach of sentiment analysis using a very popular method, Extreme Gradient Boosting or XGBoost. XGBoost combines weak learners into an ensemble classifier to build a strong learner. This study will focus on the reviews data of the most popular telemedicine application in Indonesia, Halodoc. This study aims to examine the people’s sentiment towards telemedicine applications in Indonesia, especially during the COVID-19 pandemic. We also showed a fishbone diagram to analyze the most factors the users complained about. The data we have are imbalanced; however, XGBoost can perform well with 96.24% accuracy without performing techniques for imbalanced data.
Joint Reason Generation and Rating Prediction for Explainable Recommendation

Most recommendation systems focus on predicting rating or finding aspect information in reviews to understand user preferences and item properties. However, these methods ignore the effectiveness and persuasiveness of recommendation results. Consequently, explainable recommendation, namely providing recommendation results with recommendation reasons at the same time, has attracted increasing attention of researchers due to its ability in fostering transparency and trust. It is lucky that some E-commerce websites provide a kind of new interaction box called Tips and users can express their comments on items with a simple sentence. This brings us an opportunity to realize explainable recommendation. Under the supervision of two explicit feedback, namely rating and textual tips, we can implement a multi-task learning model which can provide recommendation results and generate recommendation reasons at the same time. In this paper, we propose an Encoder-Decoder and Multi-Layer Perception (MLP) based Explainable Recommendation model named EMER to simultaneously implement reason generation and rating prediction. Items title contains significant product-related information and plays an important role in grabbing users attention, so we fuse it in our model to generate recommendation reasons. Numerous experiments on benchmark datasets demonstrate that our model is superior to the state-of-the-art models.
A Convolutional Network-Based Intelligent Evaluation Algorithm for the Quality of Spoken English Pronunciation

Aiming at the problems of long time consumption and low accuracy of traditional spoken English pronunciation quality assessment algorithms, a convolutional network-based intelligent assessment algorithm for spoken English pronunciation quality is proposed. The convolutional neural network structure is given, the original data of the spoken English pronunciation voice signal are collected by multisensor detection, and the spoken English pronunciation voice signal model is constructed. Based on audio and convolutional neural network learning and training, it realizes the feature selection and classification recognition of spoken English pronunciation. The PID algorithm is used to extract the emotional elements of spoken English at different levels to achieve accurate assessment of the quality of spoken English pronunciation. The experimental results show that the average correct rate of spoken English pronunciation of the algorithm in this paper is 94.58%, the pronunciation quality score is 8.52–9.18, and the detection time of 100 phrases is 2.4 s.
Scrum Method in Help-desk Ticketing and Project Management System

Trust, convenience, and user satisfaction are important for service providers or services. Especially in the field of technology, application service providers are very concerned about these three things for their users. Especially when there is a problem with the application, users will need communication with the provider, response and fast resolution of the problem. Application development is also an important factor for providers to improve services to users. PT. Ihsan Solusi Informatika is a company in the field of technology as a provider of banking system services and sharia-based savings and loan cooperatives. Of course, expect good service for users. Reporting management and problem solving as well as project management or system development are needed to improve services. This researcher aims to build a helpdesk ticketing and project management application using the web-based scrum method. The application is built with HTML, Javascript and Python programming languages. Application testing uses the black-box testing method to show the application is running as needed. The results of the study show that the application built is beneficial for users and of course for service providers to improve services with well-documented reporting and are immediately accepted by all service provider members so that it has an impact on the efficiency of internal performance. This application provides information about reporting problems reported by users, and there is a comment feature for communication between users and service providers.
A Unifying View of Estimation and Control Using Belief Propagation With Application to Path Planning

The use of estimation techniques on stochastic models to solve control problems is an emerging paradigm that falls under the rubric of Active Inference (AI) and Control as Inference (CAI). In this work, we use probability propagation on factor graphs to show that various algorithms proposed in the literature can be seen as specific composition rules in a factor graph. We show how this unified approach, presented both in probability space and in log of the probability space, provides a very general framework that includes the Sum-product, the Max-product, Dynamic programming and mixed Reward/Entropy criteria-based algorithms. The framework also expands algorithmic design options that lead to new smoother or sharper policy distributions. We propose original recursions such as: a generalized Sum/Max-product algorithm, a Smooth Dynamic programming algorithm and a modified versions of the Reward/Entropy algorithm. The discussion is carried over with reference to a path planning problem where the recursions that arise from various cost functions, although they may appear similar in scope, bear noticeable differences. We provide a comprehensive table of composition rules and a comparison through simulations, first on a synthetic small grid with a single goal with obstacles, and then on a grid extrapolated from a real-world scene with multiple goals and a semantic map.
Coarse-to-Fine Spatio-Temporal Information Fusion for Compressed Video Quality Enhancement

With the successful application of deformable convolution in aligning different video frames, it has also been used in video compression artifact reduction. The existing methods based on deformable convolution only apply 2D convolutional layers to generate the features for predicting alignment offsets, which is inaccurate due to limited receptive field. In this letter, we propose a new end-to-end network called Coarse-to-Fine Spatio-Temporal Information Fusion (CF-STIF) for compressed video quality enhancement by predicting better offsets with a larger receptive field. Specifically, several 3D convolutional layers are first to roughly fuse the spatio-temporal information in the video sequence, and then a Multi-level Residual Fusion Module (MLRF) is developed to generate global and local fused fine features from different levels for predicting deformable offsets. Thanks to the inherent advantages of 3D convolution and multi-scale strategy, the receptive field is greatly increased in both spatial and temporal dimensions, so that information from neighboring frames can be efficiently aggregated. In the end, the enhanced frame is derived by the proposed reconstruction module (REModule). Both qualitative and quantitative experimental results show that the proposed CF-STIF performs better than the state-of-the-art approaches.