Reviewerly/exHarmony huggingface dataset:
Fine-grained Similarity Matching with a Similarity Filtration Pyramid for Code Search

As more and more open-source code libraries are made public, code search is becoming increasingly important to software development. Early code search models mainly relied on the textual similarity between source code and natural language queries. They lack a deep understanding of the semantics of the query and source code. Developers began to apply deep learning techniques to code search models. However, these models either train code and query statements separately to obtain global correspondences, ignoring identifiers and vocabulary's interactive relationship. Either the interaction between the code and the query is not fully utilized for more accurate matching. To solve this problem, this paper proposes a new code search model FspCS. Firstly, fine-grained similarity calculation is carried out for the extracted code features and query features, which can explore the interaction matching between each code feature and each query feature. Then, we propose a global vector to represent the global similarity between code and query, which can capture various possible similarities from local to global via a similarity pyramid in code features and query features, respectively. The attention fusion module is further developed to integrate these alignments effectively by selectively attending to the significant and representative alignments and meanwhile casting aside the interferences of nonmeaningful alignments. We evaluate the performance of FspCS on two existing large-scale datasets. Our proposed model outperforms the state-of-the-art methods.
Combining Multiple Inputs to a Delay-line Reservoir Computer: Control of a Forced Van der Pol Oscillator System

The Reservoir Computing (RC) paradigm is a supervised machine learning approach that makes use of the inherent processing capacity of dynamical systems. Using the system's transient response to an external input, delayed chaotic systems offer rich dynamics for information processing, and have therefore been recognised as ideal systems for reservoir computing. A distinctive feature of delay-line reservoirs is their single-input/single-output structure, which makes them efficient for physical implementation. However, this also presents a significant limitation to multi-input tasks, as the sequence of information in the time-multiplexed input stream is not obvious. Here, we propose enhancing the input masking process used in delay-feedback RCs to mix multiple inputs in the time domain. We investigate two approaches: ‘interleaved’ and ‘sequential’, of injecting multi-input signals into a delay-line reservoir without modifying its topology. Further, we propose a novel task for RC, which inherently requires multiple inputs, to evaluate our approach: the control of a forced Van der Pol oscillator system. We use the trained reservoir as a controller to regulate the nonlinear dynamics of the Van der Pol system by constraining its trajectory to a circle. We find that, with careful choice of model parameters and offset masking scheme, the ‘sequential’ method outperforms the ‘interleaved’ method on this task.
A Novel End-to-End Transformer for Scene Graph Generation

An image usually contains not only visual information but also higher-level semantic information. Nevertheless, previous computer vision algorithms, such as target detection and image classification, use only the visual features of the image alone. Recently, the explosion of scene graphs in computer vision has led to the challenge of generating structured scene graphs with rich semantic information. This paper proposes a one-stage query-based end-to-end Transformer model and generates scene graphs using the Hungarian matching algorithm. We develop an anti-bias reasoner module to reduce the impact of the unbalanced data distribution. Time-division training strategy is proposed to improve model training efficiency and speed up model convergence while improving model training performance. Experiments on the large-scale dataset Visual Genome were conducted in order to confirm the validity of our method. Compared with the existing state-of-the-art method, our method guarantees inference speed while maintaining acceptable performance and is more suitable for tasks with high real-time performance. Our work demonstrates that the one-stage method has great potential for exploration in scene graph generation.
A Security Frame work for Data Storage in Cloud Computing by Using Cryptographic Approaches

Cloud computing has revolutionized the way data is stored, processed, and accessed. However, with the widespread adoption of cloud services, ensuring the security and privacy of data has become a paramount concern. This paper presents a comprehensive security framework for data storage in cloud computing, leveraging cryptographic approaches. The proposed framework aims to protect data confidentiality, integrity, and availability throughout its lifecycle in the cloud. It explores various cryptographic techniques, including encryption, access control mechanisms, and key management protocols. Additionally, the paper discusses the challenges and considerations associated with implementing cryptographic security measures in cloud storage and provides recommendations for a robust and efficient security framework.
Survey on Different Morphology Detection Techniques

There is a lot of data traffic that translates and transports data in the digital environment of the World Wide Web. The data is presented as files and photos. Data can morph, so it's important to detect these instances. The suggested method will identify modified photographs and alert the user to the validity of the images. In recent years, the research community has paid a great deal of attention to the problem of morph attack detection. To accurately detect morph attacks, various studies have been done in this area and various methods have been used. Although enough morph images are not readily available for research purposes, a variety of face databases are used to create morph image databases. Attack detection via face morphing is difficult. Automated border control gates utilize both manual inspection and automatic categorization methods to identify morphing attacks. It is vital to comprehend how a machine learning system can recognise altered faces and the most pertinent facial regions.
AMD: Adaptive Masked Distillation for Object Detection

As a general model compression paradigm, feature-based knowledge distillation allows the student model to learn expressive features from the teacher counterpart. In this paper, we mainly focus on designing an effective feature-distillation framework and propose a spatial-channel adaptive masked distillation (AMD) network for object detection. More specifically, in order to accurately reconstruct important feature regions, we first perform attention-guided feature masking on the feature map of the student network, such that we can identify the important features via spatially adaptive feature masking instead of random masking in the previous methods. In addition, we employ a simple and efficient module to allow the student network channel to be adaptive, improving its model capability in object perception and detection. In contrast to the previous methods, more crucial object-aware features can be reconstructed and learned from the proposed network, which is conducive to accurate object detection. The empirical experiments demonstrate the superiority of our method: with the help of our proposed distillation method, the student networks report 41.3%, 42.4%, and 42.7% mAP scores when RetinaNet, Cascade Mask-RCNN and RepPoints are respectively used as the teacher framework for object detection, which outperforms the previous state-of-the-art distillation methods including FGD and MGD.
Evaluation of Classification Algorithms Framework Domain-Specific Language: the case of finance-accounting domain

This short paper presents preliminary results on the evaluation of a Classification Algorithms Framework Domain-Specific Language (CAF DSL) that was previously developed. In our previous work, a domain-specific language to provide a framework for choosing-evaluating classification algorithms but with a system-level and simplified interface approach was presented. However, the evaluation that took place was restricted to data scientists and language engineers. In this work, the evaluation of the same DSL but by finance-accountant professionals (domain-experts) took place in a small scale. The DSL was presented and feedback regarding its usability and learnability took place. Future plans consist of scaling this attempt further and extending it to different domains such as health care. The long-term plan consists of bringing AI – Classification algorithms to professionals and to the wider public.
HeadSense: Visual Search Monitoring and Distracted Behavior Detection for Bicycle Riders

Distracted riding behavior is one of the main causes of bicycle-related traffic accidents, resulting in a large number of casualties and economic losses every year. There is an urgent need to address this problem by accurately detecting distracted riding behaviors. Inspired by the observation that distracted riding behaviors induce unique head motion features that respond to the rider’s attention, we present the HeadSense, a helmet-based system that not only monitors the visual search episode of the rider but also detects distracted riding behaviors. Specifically, HeadSense leverages the inertial motion unit (IMU) to recognize distracted behaviors such as using smartphones, attracting to the roadside element, and abreast riding. We designed, implemented, and evaluated HeadSense through extensive experiments. We conducted experiments with 19 participants inside the university’s campus. The experimental results show that HeadSense can achieve an overall accuracy of 86.14% while monitoring visual search episodes. Moreover, HeadSense can detect the occurrence of distracted riding behaviors with an average precision of up to 85.04%.
A Study on the Fun and Challenges of MR Games Targeting on Marble Games

We investigated the possibility of the MR game using the MR marble game implemented on HoloLens2. Since the average value of the items "interestingness", "replayability", and "would you recommend it to friends" was 7.5 or more, it was found that MR games could be used for entertainment.
A Study on Adult‘s fear of Cyber ​​Sex Crimes: Focusing on the Relationship between Perceptions of Punishment Criminal Psychology

As the cyber world, such as the surrealism-oriented metaverse, is approaching, you will have a new experience that is different from before. On the contrary, this means that crimes like real life will be done as if they are real in cyberspace. Furthermore, it also means that cyber crimes that transcend crimes in the real world can occur by reflecting the characteristics of online. The degree of fear of crime can vary depending on the type of fear of crime. Real crimes can be stopped through punishment, but cyber crimes are difficult to punish depending on their methods and characteristics. Therefore, the perception of punishment that individuals have can directly affect their fear of crime. The resolution of crimes such as cybersex crimes is not initiated by criminal justice agencies, but rather by reports from victims or witnesses, and these reports are determined by various factors. Assuming that the actions of victims or witnesses are not rational, and that psychological mechanisms work, it is necessary to conduct research that reflects the characteristics of cyberspace. Therefore, this study investigates the perception of celerity, severity, and certainty, which are the 3 key elements of criminal justice authorities' punishment for cyber sex crimes, and considers the fear of cyber sex crimes through the frame of these three levels of awareness based on the theoretical background. Psychological fear of perceived crime was measured. In this study, the subjects, methods, and main analysis results showed that only the severity of punishment among the perceptions of punishment predicted cyber sex crime fear. Therefore, this study suggests a plan to lower the fear of cyber sex crimes through strict punishment by strengthening the sentencing judgment for cyber sex crimes based on the research results.
Continuity Rotation Representation for Head Pose Estimation without Keypoints

In this paper, we present an improved end-to-end head pose estimation method in an unconstrained environment, which transforms the Head Pose Estimation(HPE) problem into a problem of directly predicting continuous 6D rotation matrix parameters belongs 3D Special Orthogonal Group(SO(3)). The method uses RepVGGplus-L2pse as the backbone, followed by one FC layer to output the results, model be trained on 300W-LP. The improved Root Mean Square Error of Geodesic Distance(RSME_GD) is used as the loss function to enhance the accuracy. The experiments on the two public face datasets AFLW-2000 and BIWI show that the results measured by Mean Absolute Error of Vectors (MAEV) are improved by 19.68% and 13.98% respectively compared with the original SOTA method.
Insulator Defect Detection Model Based on YOLOv5-SS

To solve the problem of slow speed and low accuracy in detecting insulator defect detection in high-voltage transmission lines under complex scenarios, a lightweight insulator defect detection algorithm based on YOLOv5 is proposed. Firstly, to reduce the floating point operations (FLOP) and improve the feature representation performance, the shufflenetV2 module is introduced into the YOLOv5 neck network; To speed up the bounding box regression and improve the localization accuracy, SIoU_Loss is then used as the bounding box regression loss function. The proposed algorithm was validated on data sets with complex backgrounds for both normal and self-detonation defective insulators. Compared with YOLOv5 algorithm, the results show that the detection speed of YOLOv5-SS is increased by 36%, the model size is reduced by 54.1%, and the detection accuracy is improved by 0.5%.
A Method for Extracting Documents for Theft Crime Based on Deep Learning

In order to assist judicial staff in analyzing the sentencing of the case, this article uses the named entity identification approach to extract the sentencing elements from the theft records. This study adopts the BERT named entity recognition approach, primarily employing the pre-trained model of BERT-WWM-EXT and CHINESE, and extracts the named entity recognition dataset based on stolen papers in Fuzhou City in recent years. The F1 value of the experimental model findings was 70.05%, which essentially made it possible to extract the punishment components from the theft records. However, the F1 value of experimental data is also lower than the reference evaluation index of Chinese Machine Reading Comprehension Data (CJRC) for the judicial area published online due to manual labeling errors, data processing errors, a small number of datasets, and machine restrictions.
ORS-Net: A Ship Detector for SAR Image Based on Oriented R-CNN

Synthetic aperture radar (SAR) is of significance in the field of marine monitoring. SAR collects reflection imaging of marine ship targets to radar beams and records a variety of information such as radiation phase, vibration frequency, and reflection intensity of the target area. Due to the complex sea surface environment, traditional vision methods cannot effectively identify weak ship targets. Deep learning methods, especially two-stage detectors, e.g. Oriented Region Convolution Neural Network (R-CNN) have given solutions to address such challenges, which have shown certain advantages when facing the problem of complex ocean scenes and weak ship targets. However, the presence of a large amount of coherent noise and speckle noise in SAR images makes the two-stage deep learning model approach still deficient in SAR image processing tasks, which leads to the degradation of typical model detection accuracy. In this paper, an enhanced Oriented R-CNN detector, namely ORS-Net, is proposed to balance between high efficiency and high performance, by augmenting both the backbone network and feature fusion network, and employing various optimization strategies in the training and inference stages. To improve the model's ability to extract feature information from SAR image ship targets, two backbone feature extraction network structures are optimized and improved. One is Dilated ResNet improved based on dilated convolution, and the other is Deep Swin-Transformer based on unstructured model tuning. In addition, the FPN structure is extended by embedding dilated convolution modules in the feature fusion network to ensure efficient feature fusion. Finally, the model is optimized in various aspects during the training and inference phases to further improve its performance. It is experimentally demonstrated that ORS-NET achieves 53.79 mAP, which is better than the current mainstream general-purpose rotating object detectors. ORS-Net can perform the SAR image ship object detection task with high quality.
Dynamic Trust Evaluation Algorithm for Blockchain Empowered FinTech

With the help of new technologies such as artificial intelligence, blockchain, cloud computing, and data science, financial technology (FinTech) has promoted the rapid application of the financial industry. Blockchain technology can better maintain the security and integrity of FinTech data and reduce the security risk of financial data. This paper proposes a dynamic trust evaluation algorithm for blockchain empowered FinTech (DTEBF). In the DTEBF algorithm, the blockchain is used to record the relevant data and trust evaluation in the process of block setting, which is conducive to the verification of the trust degree issued by the trust holder by the FinTech users and the safe and reliable transmission of FinTech data. The simulation experiment shows that DTEBF algorithm can better improve the security and reliable application of FinTech trust mechanism, and has advantages in improving the accuracy of trust calculation and potential applications.
Semi-supervised Zero-Shot Learning in Retinex decomposition-based Low-Light Image Enhancement

The images recorded by people are often subject to multiple degradation challenges due to the shooting environment and shooting equipment. In low-light scenes, images acquired by non-professional equipment are usually accompanied by a large amount of noise, and their contrast and color also have large deviations relative to normal images. Existing low-light enhancement methods are either limited by the number of low-light paired datasets, which tend to overfit the available data, or have difficulty achieving better performance when not using paired data. We propose a Semi-supervised Zero-Shot Learning low-light enhancement method based on Retinex decomposition, which can achieve better decomposition ability on existing paired data with multiple exposure conditions through supervised training, and also improve the overall network generalization using Zero-shot learning method. With a well-designed loss function, our method uses supervised training in the decomposition phase, which makes full use of paired images under different illumination conditions, and Zero-shot training in the illumination enhancement phase, which relies entirely on the unsupervised loss function to guide the enhancement of the illumination components. Our experiments demonstrate that our method has surpassed traditional augmentation methods as well as partially unsupervised methods, and is more generalizable than existing supervised methods.
An Improved Quad-Array Trie Algorithm for Website Sensitive Word Detection

With the development of the Internet and news media, numerous information releasing websites are emerging one after another, and the importance of website content supervision is also increasing day by day. Due to the convenience of the Internet, the speed of public opinion dissemination and fermentation is extremely fast. If only relying on manual monitoring, it is difficult to detect problems at the first time. Most of the existing sensitive word detection solutions probe sensitive words in the website when the content is published, but the sensitive words will be changing with current events, and the detection at the time of content publishing can no longer meet the needs of public opinion monitoring. In order to improve the efficiency of sensitive word detection, the advantages of two deterministic finite automata (DFA) algorithms (AC and DAT) are combined in this paper, and an improved multi-pattern string matching algorithm (IQAT, Improved Quad-Array Trie) is presented. The empirical results demonstrate that the proposed algorithm substantially improves the detection performance and memory consumption over existing methods.
A Robust Joint-training Graph Neural Networks Model for Event Detection with Noisy Labels

Events are the core element of information in descriptive corpus. Many progresses have been made in Event Detection (ED) to detection and extraction of key events information from massive unstructured texts, However, it is still a challenge to detect event information from data with unavoidable noisy labels. A robust Joint-training Graph Convolution Networks (JT-GCN) model is proposed to meet the challenge of ED tasks with noisy labels in this paper. Specifically, we first employ two Graph Convolution Networks with Edge Enhancement (EE-GCN) to make predictions simultaneously. A joint loss combining the detection loss and the contrast loss from two networks is then calculated for training. Meanwhile, a small-loss selection mechanism is introduced to mitigate the impact of mislabeled samples in networks training process. These two networks gradually reach an agreement on the ED tasks as joint-training progresses. Corrupted data with label noise are generated from the benchmark dataset ACE2005. Experiments on ED tasks has been conducted with symmetry label noise on different level. The experimental results show that the proposed model is robust to the impact of label noise and superior to present models for ED tasks.
Research on adaptive formation control of mobile robot based on improved virtual spring method

A robot formation control algorithm based on virtual spring model is proposed to solve the problem of path planning and formation keeping in unknown environment. By improving the design of target attraction, obstacle repulsion and interaction force between robots, the formation control is more secure and stable. Furthermore, the leader-follower formation transformation problem is studied. According to the objective function of minimum completion time, minimum total energy consumption and minimum total spring deformation, an adaptive formation transformation strategy is designed. This strategy can choose formation transformation mode adaptively according to the environment characteristics, such as adjust the parameters of the formation and reconfigure the formation, so that the robot formation can complete the task in a short time and energy consumption. Finally, the effectiveness of the proposed method is verified by simulation and real experiment.
Развитие читательских компетенций учащейся молодежи: книжный клуб как инструмент продвижения чтения в вузе

The article is devoted to the analysis of changes taking place in the practice of reading of modern students. Conclusions about the nature of these changes are based on a survey of young students conducted by the author and on the results of an associative experiment. As a tool for promoting reading at the university, a book club has been proposed, whose activities allow expanding the reading experience of young people and mastering new forms of reading practice. The work of the club effectively complements the educational process and provides new opportunities for intellectual and creative self-expression of students in the educational space of the university.