Reviewerly/exHarmony huggingface dataset:
Attention Mechanism and Detection Box Information Based Real-time Multi-Object Vehicle Detection

Ensuring both the accuracy of vehicle target detection and meeting real-time requirements is crucial in traffic videos. The YOLOv5s target detection framework, known for its accuracy and efficiency, has attracted attention in academic circles. However, there are still some features that can be optimized. First of all, the detection subnet of the YOLOv5s framework cannot smoothly convert complex feature maps into relatively sparse target prediction boxes. To solve this, we integrate a self-attention-based gating mechanism into the detection subnet, forming the YOLOv5s-SAG network. Secondly, the loss function of CIoU used by YOLOv5s pays insufficient attention to the overlapping area of the detection frame, which can be used as metric for measuring target detection effectiveness. We add the loss term of area ratio to CIoU to further improve the modeling ability. Finally, the current multi-class Non-Maximum Suppression algorithm can cause high overlap of multi-class detection frames. To improve it, we propose a multi-class CS-NMS algorithm based on category suppression. Experimental results show an approximately 8% improvement in the mAP50 index on the UA-DETRAC dataset compared with YOLOv5s. The proposed algorithm also achieves better detection results compared to mainstream target detection algorithms and meets the real-time requirements of traffic video analysis.
Hardware-implemented Photonic Neural Network for High-throughput and Low-latency Image Transmission

We propose a photonic neural network and its experimental demonstration that enables high-throughput and low-latency image transmission by implementing most processing in optical domain, reducing latency of computation by more than four orders of magnitude.
FKeras: A Fault Tolerance Library for Keras

We present FKeras, an open-source tool that uses Hessian information to quickly find which parameters in a neural network are sensitive to radiation faults, reducing the usual 200% resource overhead needed to protect them.
[Good Practices Used in School Media Extracurricular Activities

This paper aims to present good practices used in the lessons related to the implementation of extracurricular activities – “School media”. These practices are based on innovative approaches, working with digital tools and using hybrid learning. Through these methods, students' active participation in the club and in school life is encouraged, their digital competences and language skills are enhanced, and their career orientation is supported. Their communication skills are developed. During the classes, students gain experience that prepares them for the challenges of the modern information society.
ANALISIS SPASIAL PERUBAHAN PENGGUNAAN LAHAN DI KABUPATEN PURBALINGGA

The rapid development of the Purbalingga Regency region, supported by the establishment of increasingly complex regulations and significant population growth, has significantly impacted the demand for facilities and infrastructure development; this aims to realize development plans and meet the increasing needs of society. Changes in land use are an important factor that needs to be analyzed and planned carefully. This research aims to understand changes in land use in Purbalingga Regency. The method used involves interpreting Landsat images with predictive modeling of land use changes using Annual Neural Network and Cellular Automata (ANN-CA) to describe land use changes between 2009 and 2022 using data techniques observation. The research results show that the most dominant land uses in Purbalingga Regency include gardens, rice fields, forests, and settlements. The most striking change in land use is the transformation from forest and agricultural land to residential land. The residential land area 2009 was around 6,294.48 hectares, which increased significantly to around 15,183.88 hectares in 2022.
Use of sentiment analysis for capturing hospitalized cancer patients' experience from free-text comments in the Persian language

Abstract Purpose Today, the Internet provides access to many patients' experiences, which is crucial in assessing the quality of healthcare services. This paper introduces a model for detecting cancer patients' opinions about healthcare services in the Persian language, both positive and negative. Method To achieve the objectives of this study, a combination of sentiment analysis (SA) and topic modeling approaches was employed. All pertinent comments made by cancer patients were collected from the patient feedback form of the Tehran University of Medical Science (TUMS) Cancer Institute (CI) in Iran, from March to October 2021. Conventional evaluation metrics such as accuracy, precision, recall, and F-measure were utilized to assess the performance of the proposed model. Result The experimental findings revealed that the proposed SA model achieved accuracies of 89.3%, 92.6%, and 90.8% in detecting patients' sentiments towards general services, healthcare services, and life expectancy, respectively. Based on the topic modeling results, the topic "Metastasis" exhibited lower sentiment scores compared to other topics. Additionally, cancer patients expressed dissatisfaction with the current appointment booking service, while topics such as "Good experience," "Affable staff", and "Chemotherapy" garnered higher sentiment scores. Conclusion The combined use of SA and topic modeling offers valuable insights into healthcare services. Policymakers can utilize the knowledge obtained from these topics and associated sentiments to enhance patient satisfaction with cancer institution services.
A Trusted Edge Resource Allocation Framework for Internet of Vehicles

With the continuous progress of information technique, assisted driving technology has become an effective technique to avoid traffic accidents. Due to the complex road conditions and the threat of vehicle information being attacked and tampered with, it is difficult to ensure information security. This paper uses blockchain to ensure the safety of driving information and introduces mobile edge computing technology to monitor vehicle information and road condition information in real time, calculate the appropriate speed, and plan a reasonable driving route for the driver. To solve these problems, this paper proposes a trusted edge resource allocation framework for assisted driving service, which includes two stages: the blockchain generation stage (the first stage) and assisted driving service stage (the second stage). Furthermore, in the first stage, a delay-and-throughput-oriented block generation model for the mobile terminal is designed. In the second stage, a balanced offloading algorithm for assisted driving service based on edge collaboration is proposed to solve the problems of unbalanced load of cluster mobile edge computing (MEC) servers and low resource utilization of the system. And this paper optimizes the throughput of blockchain and delay of the transportation network through deep reinforcement learning (DRL) algorithm. Finally, compared with joint computation and communication resources’ allocation (JCCR) and resource allocation method based on binary offloading (RAB), our proposed scheme can optimize the delay by 7.4% and 26.7%, and support various application services of the vehicular networks more effectively.
Programmable Logic Controller Block Monitoring System for Memory Attack Defense in Industrial Control Systems

Cyberattacks targeting industrial control systems (ICS) are becoming more sophisticated and advanced than in the past. A programmable logic controller (PLC), a core component of ICS, controls and monitors sensors and actuators in the field. However, PLC has memory attack threats such as program injection and manipulation, which has long been a major target for attackers, and it is important to detect these attacks for ICS security. To detect PLC memory attacks, a security system is required to acquire and monitor PLC memory directly. In addition, the performance impact of the security system on the PLC makes it difficult to apply to the ICS. To address these challenges, this paper proposes a system to detect PLC memory attacks by continuously acquiring and monitoring PLC memory. The proposed system detects PLC memory attacks by acquiring the program blocks and block information directly from the same layer as the PLC and then comparing them in bytes with previous data. Experiments with Siemens S7-300 and S7-400 PLC were conducted to evaluate the PLC memory detection performance and performance impact on PLC. The experimental results demonstrate that the proposed system detects all malicious organization block (OB) injection and data block (DB) manipulation, and the increment of PLC cycle time, the impact on PLC performance, was less than 1 ms. The proposed system detects PLC memory attacks with a simpler detection method than earlier studies. Furthermore, the proposed system can be applied to ICS with a small performance impact on PLC.
Analisis Kesalahan Siswa Kelas IV SDN Serang 8 dalam Menyelesaikan Soal Cerita pada Materi Pengukuran

This research described the students' errors in solving story qustions in grade IV SDN Serang 8. Most students find it difficult to interpret the purpose of the calculation questions in the form of stories and often get less scores because of the errors students make when students answer calculation questions in story form. This study aimed to find out what are the causes and errors made by students, then find out how the solutions can be given and carried out by students or teachers. This research used a qualitative approach with the descriptive type of research. By using content analysis methods, namely research by examining the text of documents or books to draw conclusions based on the context of their use. The data collection procedure was carried out by giving several research instruments in the form of 4 mathematical story questions about measurement and conducting interviews to produce oral data from participants. The findings of this study indicate that there are several facts that cause this occurrence of students’ errors. They are students did not understand the concept, students made calculation errors, some students did not answer correctly, and students did not answer in a complete way until the final answer. Researcher produced data on the level of ability of grade IV SDN 8 students. There are 3 students who have high ability level, 10 students have medium ability level, and 7 students have low ability level. This research was expected to help to provide solutions so that students are able to solve story problems correctly and according to the procedure for solving them.
Mechanisms to Address Different Privacy Requirements for Users and Locations

The significance of individuals' location information has been increasing recently, and the utilization of such data has become indispensable for businesses and society. The possible uses of location information include personalized services (maps, restaurant searches and weather forecast services) and business decisions (deciding where to open a store). However, considering that the data could be exploited, users should add random noise using their terminals before providing location data to collectors. In numerous instances, the level of privacy protection a user requires depends on their location. Therefore, in our framework, we assume that users can specify different privacy protection requirements for each location utilizing the adversarial error (AE), and the system computes a mechanism to satisfy these requirements. To guarantee some utility for data analysis, the maximum error in outputting the location should also be output. In most privacy frameworks, the mechanism for adding random noise is public; however, in this problem setting, the privacy protection requirements and the mechanism must be confidential because this information includes sensitive information. We propose two mechanisms to address privacy personalization. The first mechanism is the individual exponential mechanism, which uses the exponential mechanism in the differential privacy framework. However, in the individual exponential mechanism, the maximum error for each output can be used to narrow down candidates of the actual location by observing outputs from the same location multiple times. The second mechanism improves on this deficiency and is called the donut mechanism, which uniformly outputs a random location near the location where the distance from the user's actual location is at the user-specified AE distance. Considering the potential attacks against the idea of donut mechanism that utilize the maximum error, we extended the mechanism to counter these attacks. We compare these two mechanisms by experiments using maps constructed from artificial and real world data.
Quantum-Inspired Optimization for Task Scheduling in Software Development Projects

Software project development, characterized by numerous tasks and several engineers, necessitates effective project scheduling and personnel allocation for successful and timely completion. Tackling the inherent complexities of Software Project Scheduling (SPS) including personnel quality requirements and capability constraints is of paramount importance for software companies. This study aims to engineer robust project scheduling to enhance task completion efficiency, reduce resource waste, and ensure the punctual delivery of project milestones. Our proposed solution models the problem as a Software Project Scheduling Problem (SPSP), subsequently transformed into a Quadratic Unconstrained Binary Optimization (QUBO) model using quantum-inspired techniques. This model is then solved using a digital annealing device. We examine our proposed quantum-inspired method's effectiveness in solving SPSP through this experimental implementation, comparing its performance with the Simulated Annealing (SA) algorithm. The experimental findings reveal that the objective function, encapsulating the weighted sum of all job personnel costs and job end times, produced superior outcomes under the Digital Annealing (DA) algorithm compared to the SA algorithm. Moreover, the DA algorithm demonstrated greater stability and reduced runtime as the volume of input data expanded.
TransRacer: Function Dependence-Guided Transaction Race Detection for Smart Contracts

Smart contracts are programs that define rules for transactions running on blockchains. Since any qualified transaction sequence within the same block can be orchestrated by the blockchain miner, unexpected results may occur due to data races between transactions (called transaction races). Surprisingly, transaction races in smart contracts have not been fully investigated. To address this, we propose TransRacer, an automated approach and open-source tool that employs symbolic execution to detect transaction races in smart contracts. TransRacer analyzes function dependencies to identify transaction races hidden in specific contract states. It also generates witness transactions that can trigger such races. The experimental results on 50 real-world smart contracts show the effectiveness and efficiency of TransRacer: it detects 426 races in 255.9 minutes, including 149 race bugs leading to inconsistent states.
An Integrated Shape-Texture Descriptor for Modeling Whole-Organism Phenotypes in Drug Screening

Schistosomiasis is a parasitic disease with global health and socio-economic impacts. The World Health Organization (WHO) and National Institutes of Health (NIH) list it among diseases for which new treatments are urgently required. Drug discovery for Schistosomiasis typically involves whole-organism phenotypic screening. In such an approach, the parasites are exposed to different chemical compounds, and systemic phenotypic effects captured via microscopy (video or still images) are analyzed to identify promising molecules. Changes in parasite phenotypes tend to be multidimensional, involving changes in shape, appearance and behavior, and time-varying. In many image representation frameworks, shape and appearance are measured independently and their inter-correlation can be lost. In this paper, we propose an integrated shape-texture descriptor called the skeleton-constrained shortest band (SCSB) that extends the family of shape context descriptors well known in computer vision. We examine how SCSB can be used to measure temporally varying shape and appearance changes occurring as a consequence of chemical action and compare its performance with other members of the shape context family.
Optimizing LUT-Based Quantum Circuit Synthesis Using Relative Phase Boolean Operations

LUT-based synthesis methods have recently been proposed as a way to synthesize quantum Boolean circuits in a qubit constrained environment. Other results have also shown that allowing a relative phase when implementing quantum Boolean circuits yields an advantage in T-count without additional ancilla qubits, which is advantageous in the fault-tolerant quantum computing paradigm. We propose a method that utilizes both LUT-based synthesis and relative phase quantum Boolean circuits which minimize the T-count while minimizing the ancilla required to implement them. We leverage recent results regarding relative phase versions of Boolean functions and Shannon's decomposition to propose a novel method to synthesize arbitrary Boolean functions up to a relative phase. We then utilize this method to synthesize Boolean logic inside each LUT as a relative phase quantum Boolean circuit and show that the resulting quantum circuit has a clear advantage in T-count over more naive methods.
Research on design and control method of lightning protection detection robot for wind turbine

With the large-scale and high-power of wind turbines, as well as the remote location, harsh environment and high altitude of wind farms. Lightning weather seriously affects the normal operation of wind turbines. In order to solve this problem, this paper proposes a technology of UAV carrying manipulator. To solve this problem, this article proposes the idea of using drones to carry a robotic arm and transport cables to the blade tip for clamping. The tip grounding resistance of the fan is measured. Analyze whether the lightning protection system is in normal operation. This paper summarizes the unmanned lightning protection detection technology, wind resistance technology and cooperative control of wind turbines. The advantages and disadvantages of various technologies are analyzed and compared, and the problems that need further research are pointed out. The stability of the carrier arm UAV structure and the remote control of the high-altitude manipulator will be the research difficulties in this field. It provides a reference for further research on lightning protection unmanned detection of wind turbines.
Co-Guiding for Multi-Intent Spoken Language Understanding

Recent graph-based models for multi-intent SLU have obtained promising results through modeling the guidance from the prediction of intents to the decoding of slot filling. However, existing methods (1) only model the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unidirectional guidance</i> from intent to slot, while there are bidirectional inter-correlations between intent and slot; (2) adopt <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">homogeneous graphs</i> to model the interactions between the slot semantics nodes and intent label nodes, which limit the performance. In this paper, we propose a novel model termed Co-guiding Net, which implements a two-stage framework achieving the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mutual guidances</i> between the two tasks. In the first stage, the initial estimated labels of both tasks are produced, and then they are leveraged in the second stage to model the mutual guidances. Specifically, we propose two <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">heterogeneous graph attention networks</i> working on the proposed two <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">heterogeneous semantics-label graphs</i> , which effectively represent the relations among the semantics nodes and label nodes. Besides, we further propose Co-guiding-SCL Net, which exploits the single-task and dual-task semantics contrastive relations. For the first stage, we propose single-task supervised contrastive learning, and for the second stage, we propose co-guiding supervised contrastive learning, which considers the two tasks' mutual guidances in the contrastive learning procedure. Experiment results on multi-intent SLU show that our model outperforms existing models by a large margin, obtaining a relative improvement of 21.3% over the previous best model on MixATIS dataset in overall accuracy. We also evaluate our model on the zero-shot cross-lingual scenario and the results show that our model can relatively improve the state-of-the-art model by 33.5% on average in terms of overall accuracy for the total 9 languages.
Automatic Short Answer Grading System Based on Semantic Networks and Support Vector Machine

In education, exams are used to asses students’ acquired knowledge; however, the manual assessment of exams consumes a lot of teachers’ time and effort. In addition, educational institutions recently leaned toward distance education and e-learning due the Coronavirus pandemic. Thus, they needed to conduct exams electronically, which requires an automated assessment system. Although it is easy to develop an automated assessment system for objective questions. However, subjective questions require answers comprised of free text and are harder to automatically assess since grading them needs to semantically compare the students’ answers with the correct ones. In this paper, we present an automatic short answer grading method by measuring the semantic similarity between the students answer and the correct answer. A semantic network was built to represent the relationship between the words of the two texts to calculate semantic similarity. Representing a text as a semantic network is the best knowledge representation that comes closest to human comprehension of the text, where the semantic network reflects the sentence's semantic, syntactical, and structural knowledge. Several features were extracted from the semantic network and used as input to train the support vector machine (SVM) model to predict the degree of the targeted semantic similarity. The proposed method was tested on the Mohler dataset that is publicly available online. The obtained results were evaluated and reported in terms of Pearson correlation and root mean squared error (RMSE) where it achieved 0.63 and 0.83 respectively. The proposed method outperformed all previous methods on the used dataset.
Evaluating Contribution of Training Samples for Differentially Private Machine Learning

Differential privacy is applied to machine learning for privacy protection because of its formal privacy guarantee. Differentially private algorithm needs to inject enough noise to limit the overall privacy disclosure risk of the training set. In the case of unbalanced data sets, however, this privacy protection intensity is excessive for most datasets, leading to significant loss of utility. In this paper, we adjust the training set by assessing the risk of disclosure of data privacy and its contribution to model accuracy to improve the utility of differentially private ML. Specifically, we quantify the risk of data privacy disclosure through membership inference attacks and evaluate the contribution of data to improving model accuracy through ablation experiments. Our experiments show that when the training set of the model is unbalanced, the model accuracy can be substantially improved by adjusting the training set to our assessment method under the same budget of privacy.