Reviewerly/exHarmony huggingface dataset:
FATS: Feature Distribution Analysis-Based Test Selection for Deep Learning Enhancement

Deep Learning has been applied to many applications across different domains. However, the distribution shift between the test data and training data is a major factor impacting the quality of deep neural networks (DNNs). To address this issue, existing research mainly focuses on enhancing DNN models by retraining them using labeled test data. However, labeling test data is costly, which seriously reduces the efficiency of DNN testing. To solve this problem, test selection strategically selected a small set of tests to label. Unfortunately, existing test selection methods seldom focus on the data distribution shift. To address the issue, this paper proposes an approach for test selection named Feature Distribution Analysis-Based Test Selection (FATS). FATS analyzes the distributions of test data and training data and then adopts learning to rank (a kind of supervised machine learning to solve ranking tasks) to intelligently combine the results of analysis for test selection. We conduct an empirical study on popular datasets and DNN models, and then compare FATS with seven test selection methods. Experiment results show that FATS effectively alleviates the impact of distribution shifts and outperforms the compared methods with the average accuracy improvement of 19.6% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 69.7% for DNN model enhancement.
Crafting Targeted Universal Adversarial Perturbations: Considering Images as Noise

The vulnerability of deep neural networks to adversarial perturbations has been demonstrated in a large body of research. Compared to image-dependent adversarial perturbations, universal adversarial perturbations(UAPs) is more challenging for indiscriminately attacking the model inputs. However, there are few studies on generating data-free targeted UAPs and the targeted attack success rate of the latest method remains unsatisfactory. Not only that, fewer studies have implemented their approach on Transformers and its efficacy remains uncertain. Therefore, a novel method denoted as Denoising Targeted UAP (DT-UAP) is proposed in this paper that considers the training input as the noise, and takes the input of the last layer into calculation. Specifically, the proposed method minimizes the distance between perturbations and adversarial examples, then incorporates a targeted loss function to generate targeted universal adversarial perturbations for different DNNs and Transformers based on different proxy datasets. DT-UAP has achieved an average improvement of 5% to 10% in terms of both fooling rate and targeted fooling rate comparing to the most recent method for generating targeted universal adversarial perturbation with proxy dataset for DNNs. Additionally, DT-UAP has also achieved a targeted attack success rate of over 80% on Transformers such as MaxVit and SwinTransformer.
Enhancing Privacy Preservation with Quantum Computing for Word-Level Audio-Visual Speech Recognition

In this paper, we investigate the effectiveness of using quantum machine learning for privacy protection in audiovisual speech processing. Quantum machine learning has made significant theoretical advancements and has been shown to possess natural advantages in privacy protection over conventional techniques. Here, we first apply quantum circuits to a word-level audio-visual speech recognition task. We then propose a novel metric, an inter-class intra-class similarity ratio, for measuring the privacy-protecting capabilities of quantum circuits. Finally, we conduct an in-depth analysis of the differences in privacy protection between quantum privacy methods and traditional methods, evaluating their working principles, strengths, and limitations. Experiments results on the LRW data set show that the quantum privacy-preserving approach performs well in word-level speech recognition tasks, demonstrating excellent privacy-preserving capabilities through selective retention of features.
FPGA-Based Human Detection System using HOG-SVM Algorithm

Human detection plays a crucial role in the field of computer vision. Novel methods using new techniques, such as Convolutional Neuron Networks or Recurrent Neuron Networks, can improve accuracy but occupy a large hardware footprint when implemented on FPGA. In contrast, human detection based on the HOG-SVM algorithm has much lower computational complexity, leading to a more hardware-efficient implementation on FPGA. In this paper, we proposed a novel human detection system using a HOG-SVM module in combination with Direct Memory Access (DMA) on Xilinx FPGA. The design was implemented on Xilinx FPGA Development Kit ZCU106 with direct memory access to reduce the CPU load and improve the overall system performance. The proposed system with a low hardware footprint can be utilized in vision-based IoT edge devices.
Human-Following Control of Omnidirectional Autonomous Mobile Robot by Integrating Sensor Information

In Japan, super-aging is progressing rapidly, and it is necessary for the target people, including healthy people, to be monitored by robots. Therefore, in this paper, we develop a small robot that can move in all directions and can follow people in an indoor environment. This robot has an infrared thermal sensor, a geomagnetic sensor, and five ranging sensors. In addition, by adopting an omni-wheel for the drive system, it is possible to move and enter narrow spaces. In particular, the posture control of the robot is realized by the geomagnetic sensor, and the stable straight running is achieved. In addition, we confirmed that the infrared heat sensor can measure human body temperature and show that it can be applied to human tracking control method. We define three modes according to the distance to the object in the human-tracking algorithm, and clarify that each mode works well in the tracking motion.
Security Verification of RISC-V System Based on ISA Level Information Flow Tracking

Software attacks that exploit the hardware security vulnerabilities of processors have become new breakthrough points for hackers, which pose severe threats to hardware security and trust. This paper proposes a novel system security verification method based on instruction set architecture (ISA) level information flow tracking (IFT), which is capable of modeling and checking security properties in both software and hardware designs. We use RISC-V system as a demonstration, which is a lately developed open-source ISA widely used in Internet of Things and is facing severe security threats due to the universal interconnection of devices. By developing RISC-V software and hardware IFT models, security properties including confidentiality and integrity can be verified and vulnerabilities can be detected. Experimental results show that the proposed verification method can detect software security threats and hardware security vulnerabilities in the RISC-V processor design.
Segmentation and observation of hand rehabilitation exercises by supporting of acceleration signals

In recent years, there has been growing interest in using sensors worn on patients’ bodies to assist doctors in monitoring and evaluating rehabilitation therapy. In this study, we toward an assisted tool that aids in the rehabilitation assessments of patients with impaired hands. The proposed method automatically spots the patient’s exercises using utilizing accelerometers and displays the results on the collected video. This tool helps doctors to monitor and evaluate the patient’s progress more concentrated and easily. The proposed method utilizes wavelet signal transformations, and signal segmentation by Sliding Window and Short-time Energy techniques. These techniques are not only simple calculations but also offer promising results. In the experimental results, the exercises, which are set up in real clinical situations, have been segmented with 87% of accuracy and 80% of sensitivity rate. These results confirm a potential solution to improve the efficiency and effectiveness of the rehabilitation process using the automatic assisted application.
Software Quality Assurace

Abstract: Software Quality Assurance (SQA) plays a pivotal role in the software development lifecycle, ensuring the delivery of high-quality software products. This abstract provides a comprehensive overview of the latest advancements in Software Quality Assurance methodologies, practices, and tools. The evolving landscape of software development, characterized by agile methodologies, DevOps practices, and continuous integration, has necessitated a reevaluation and enhancement of SQA processes. The abstract begins by elucidating the fundamental principles of SQA, emphasizing its critical role in detecting and preventing defects throughout the software development process. It explores the shift-left approach, where quality assurance is integrated early in the development cycle, and the benefits of this approach in terms of cost reduction and accelerated time-tomarket. Furthermore, the abstract delves into the integration of artificial intelligence (AI) and machine learning (ML) techniques in SQA processes. The use of AI-driven testing tools for automated test case generation, anomaly detection, and predictive analysis is explored as a means to enhance the efficiency and effectiveness of quality assurance activities. The abstract also discusses the challenges and ethical considerations associated with the use of AI in SQA. Additionally, the abstract highlights the importance of comprehensive test automation strategies in modern SQA. It outlines the advantages of continuous testing, test-driven development (TDD), and behavior-driven development (BDD) methodologies in ensuring robust software quality. The abstract also addresses the challenges of maintaining test automation suites and advocates for a balanced approach that combines automated and manual testing. The abstract concludes by emphasizing the need for a holistic and adaptive SQA framework that aligns with the dynamic nature of software development. It calls for ongoing research and collaboration in the SQA domain to address emerging challenges and capitalize on new opportunities for improving software quality
Quantifying Psychological Sophistication of Malicious Emails

Malicious emails (including phishing, spam, and scam) are significant attacks. Despite numerous defenses to counter them, they remain effective because our understanding of their psychological properties is superficial. This motivates us to investigate the psychological sophistication, or sophistication for short, of malicious emails. For this purpose, we propose an innovative framework of two pillars: Psychological Techniques (PTechs) and Psychological Tactics (PTacs). We propose metrics and grading rules for human experts to assess the sophistication of malicious emails through PTechs and PTacs. To demonstrate the usefulness of the framework, we conduct a case study based on 200 malicious emails assessed by four independent graders.
Students’ Perception on Using Storytelling in Developing Socio-Cultural Competence

The research aimed at finding out students’ perception of using storytelling as one of the techniques used in English class in order to develop students’ sociocultural competence. The research was conducted under mixed-method research. The sample of the research was English students at IAIN Bukittinggi who had taken storytelling classes and English students at UIN Imam Bonjol Padang who had joined an English activity (storytelling) held by the English students association of English study program. Data collection was done by distributing questionnarires and conducting interview. The instruments of the research were a questionnaire and an interview guide. The data were analyzed using quantitative analysis for the data from the questionnaire and qualitative analysis for the data from the interview. The research finding shows that the students agree that they have improved their ability in sociocultural competence, at least in five aspects. They state that they are good in these five elements; (1) students’ attitude toward sociocultural competence, (2) students’ knowledge of sociocultural competence, (3) students’ ability to interpret and relate cultural issues, (4) students’ ability in discovery and interaction, and (5) students’ ability of critical cultural awareness. Thus, it can be stated that storytelling is a good technique that can be used to develop students’ sociocultural competence based on students’ perceptions.
Do Self-translating Poets Have Equally Distributed Equivalent Words in the Target and Original texts? A Corpus Examination of Yu Guangzhong’s Poems

This article examines the utilization of high-frequency words in Yu Guangzhong’s self-translated poetry through a corpus-driven analysis. The objective is to explore the presence of equivalence and inequivalence in the translations executed by Yu Guangzhong himself. The utilization of modal verbs, lexical bundles, and keywords has been analyzed in various contexts, such as novels and speeches. However, studies that compare disparities in the use of high-frequency words between the source and target languages within poetry translation are scarce. The original and self-translated poetry corpora of Yu Guangzhong have been constructed at the word level to enhance the original texts’ co-occurrence and corresponding translations. In the comprehensive self-translation of his eighty-five poems, Yu Guangzhong encounters challenges in achieving an equitable distribution of equivalent words between the target and original texts. It is observed that most high-frequency words in Yu Guangzhong’s original and target texts lack equivalence in use and meaning. Although two of the three poems analyzed individually mainly achieve equivalence, this discrepancy might be attributed to the translator’s utilization of literal and word-for-word translations.
The Conflict and Coordination Between Data Sharing and Intellectual Property Protection in the Era of Big Data

Computer technology has constantly innovated since the computer first appeared in the last century. Cloud computing, the Internet, and other technologies are increasingly important in human life. Relying on the combination of computers and the Internet, the scale of data is increasing rapidly, the speed of data exchange is gradually accelerating, and the traditional information exchange mode is broken. The information exchange mode based on extensive data exchange has steadily become the mainstream, and the era of big data has come. Due to the changes brought about by new technologies, the original intellectual property law can no longer cope with new problems. How to solve the conflict between data exchange and intellectual property protection in the era of big data has become a vital issue. This paper focuses on the analysis of the connotation of data exchange in the age of big data, the conflict with intellectual property rights, and how to coordinate the contradiction between the two; it explains the conflict of property rights and personal rights brought about by extensive data exchange, and blends the scale, content, and technology of intellectual property rights in extensive data exchange. This paper aims to discuss the boundary of extensive data exchange and intellectual property protection, balance social interests, alleviate social contradictions, and provide feasible ideas for renewing intellectual property rights.
The Application and Potential of Artificial Intelligence Participating in College Teaching Evaluation

The dominance could be applied to many more areas as new artificial intelligence (AI) advances. There are great defects in the traditional evaluation methods of higher education. The evaluation of people is dominant, resulting in excessively subjective results and wasting human resources. These issues adversely affect many stakeholders. Therefore, the combination of the objectivity of artificial intelligence and the traditional method of teaching evaluation has an exploratory significance for the development of teaching and emphasizes the technical ability that the new artificial intelligence can realize. The purpose of this study is to explore the potential of a new method for artificial intelligence in higher education evaluation. Based on the analysis of existing experimental case reports and related studies by the method of literature synthesis, it is certain that the new artificial intelligence will help improve the quality of teaching evaluation in higher education. This has a positive impact on all fields, including schools, teachers, and students. The teaching objectives of the school can be better and more efficiently accomplished under the evaluation system. This study confirms the feasibility of incorporating artificial intelligence into university evaluation.
Sparse Linear Discriminant Analysis Based on lq Regularization

Linear discriminant analysis plays an important role in feature extraction, data dimensionality reduction, and classification. With the progress of science and technology, the data that need to be processed are becoming increasingly large. However, in high-dimensional situations, linear discriminant analysis faces two problems: the lack of interpretability of the projected data since they all involve all p features, which are linear combinations of all features, as well as the singularity problem of the within-class covariance matrix. There are three different arguments for linear discriminant analysis: multivariate Gaussian model, Fisher discrimination problem, and optimal scoring problem. To solve these two problems, this article establishes a model for solving the kth discriminant component, which first transforms the original model of Fisher discriminant problem in linear discriminant analysis by using a diagonal estimated matrix for the within-class variance in place of the original within-class covariance matrix, which overcomes the singularity problem of the matrix and projects it to an orthogonal projection space to remove its orthogonal constraints, and subsequently an lq norm regularization term is added to enhance its interpretability for the purpose of dimensionality reduction and classification. Finally, an iterative algorithm for solving the model and a convergence analysis are given, and it is proved that the sequence generated by the algorithm is descended and converges to a local minimum of the problem for any initial value.
Action Quality Assessment Model Using Specialists’ Gaze Location and Kinematics Data—Focusing on Evaluating Figure Skating Jumps

Action quality assessment (AQA) tasks in computer vision evaluate action quality in videos, and they can be applied to sports for performance evaluation. A typical example of AQA is predicting the final score from a video that captures an entire figure skating program. However, no previous studies have predicted individual jump scores, which are of great interest to competitors because of the high weight of competition. Despite the presence of unnecessary information in figure skating videos, human specialists can focus and reduce information when they evaluate jumps. In this study, we clarified the eye movements of figure skating judges and skaters while evaluating jumps and proposed a prediction model for jump performance that utilized specialists’ gaze location to reduce information. Kinematic features obtained from the tracking system were input into the model in addition to videos to improve accuracy. The results showed that skaters focused more on the face, whereas judges focused on the lower extremities. These gaze locations were applied to the model, which demonstrated the highest accuracy when utilizing both specialists’ gaze locations. The model outperformed human predictions and the baseline model (RMSE:0.775), suggesting a combination of human specialist knowledge and machine capabilities could yield higher accuracy.
Da Alta Complexidade à rede intersetorial: caminhos dos usuários acompanhados ambulatorialmente em um Hospital Escola

The purpose of this article is to highlight the characterization of users monitored in the outpatient service of Vascular Surgery dressings at the University Hospital Professor Polydoro Ernani de São Thiago and to analyze how users access the intersectoral network of social services in the metropolitan region of Florianópolis/SC. The research is qualitative, exploratory and descriptive, with application of interviews. The results showed that access to the services of the intersectoral network was restricted due to the Covid-19 pandemic, as well as the lack of knowledge, on the part of users, of the legislation and core activities of the network services. It is concluded from the study that most users access the health care network, but do not access the intersectoral network of the metropolitan region of Florianópolis.
DCNNLFS: A Dilated Convolutional Neural Network with Late Fusion Strategy for Intelligent Classification of Gastric Histopathology Images

Gastric cancer has a high incidence rate, significantly threatening patients' health. Gastric histopathology images can reliably diagnose related diseases. Still, the data volume of histopathology images is too large, making misdiagnosis or missed diagnosis easy. The classification model based on deep learning has made some progress on gastric histopathology images. However, traditional convolutional neural networks (CNN) generally use pooling operations, which will reduce the spatial resolution of the image, resulting in poor prediction results. The image feature in previous CNN has a poor perception of details. Therefore, we design a dilated CNN with a late fusion strategy (DCNNLFS) for gastric histopathology image classification. The DCNNLFS model utilizes dilated convolutions, enabling it to expand the receptive field. The dilated convolutions can learn the different contextual information by adjusting the dilation rate. The DCNNLFS model uses a late fusion strategy to enhance the classification ability of DCNNLFS. We run related experiments on a gastric histopathology image dataset to verify the excellence of the DCNNLFS model, where the three metrics Precision, Accuracy, and F1-Score are 0.938, 0.935, and 0.959.