Reviewerly/exHarmony huggingface dataset:
Generalized Hamming Weights of Linear Codes From Quadratic Forms Over Finite Fields of Even Characteristic

The generalized Hamming weight of linear codes is a natural generalization of the minimum Hamming distance. They convey the structural information of a linear code and determine its performance in various applications, and have become one of important research topics in coding theory. Recently, Li (2021) and Li and Li (2022) obtained the complete weight hierarchy of linear codes from quadratic forms over finite fields of odd characteristic by analysis of the solutions of the restricted quadratic equation in its subspace. In this paper, we further determine the complete weight hierarchy of linear codes from quadratic forms over finite fields of even characteristic by carefully studying the behavior of the corresponding restricted quadratic forms to the subspaces of the field, and complement the results of Li and Li.
Dual Educational System of Professional Training of Future Skilled Workers

The article reveals the main ways of designing professional training for future skilled workers in dual education. This form is a methodological characteristic of vocational education; it involves the coordinated interaction of the educational and industrial spheres of training qualified personnel with a certain profile within the framework of different organizational forms of education. The main objectives of introducing elements of the dual form of training are to eliminate the main shortcomings of traditional forms and methods of training future skilled workers, to bridge the gap between theory and practice, education and production, and to improve the quality training qualified personnel, taking into account the requirements of employers within framework new organizational-different forms of training. Justified structural components of the dual form of education are revealed: organization of cooperation between politicians, business, and social partners; development of a legislative framework for the recognition of national standards for the quality of vocational education; in-service training for students; involvement of qualified production personnel in pedagogical activity (instructors, teachers); implementation of institutional research and consulting (monitoring the quality of the provision of educational services in the field of vocational education, updating educational standards).
Self-attention-based Human Activity Detection Using Wearable Sensors

Analyzing human activity through wearable sensors can assist applications connected to context—vigilance and health care. The proposed approach utilizes convolutional and recurrent modeling to express the space–time-dependent constraints from information provided by numerous sensors. We construct a neural network layout, a combination of convolutional neural network and recurrent neural network, which focuses on spatiotemporal characteristics provided by sensor time series data with short listing and learning crucial points by executing a self-attentive procedure. We exhibit validation of our designed strategy on the WISDM dataset and indicate that the self-attention technique achieves a remarkable amendment in presentation over deep neural networks through the union consisting of convolutional and recurrent network architectures. We also present that our designed algorithm offers a statistic performance improvement over other designed approaches for the information (dataset) under consideration. The presented technique allows precise interpretation of activity from numerous body part sensors through various time intervals. We evaluated our technique through various classification methods namely: MLP Classifier, gradient boosting, Random Forest Classifier, Conv2D–LSTM, and self-attention mechanism (proposed). The simulation results show that our proposed method achieved the highest accuracy of 89.58% out of all other methods. We compared the effects of the designed architecture scheme with a baseline constructed through ConvNN and RecNN models, and with earlier designed approaches. We figured out that our self-attention model outperformed the baseline model with high difference among all the considered metrics.
Web API Recommendation Based on Self-Attentional Neural Factorization Machines With Domain Interactions

With the advances of service computing technology, there has been a significant focus on enhancing the relevance or accuracy of Web API recommendation for Mashup creation. In the multiple established works, factorization machines based models portray increased performance compared with traditional content or collaborative filtering techniques with regard to accuracy, which still has great room for improvement. Thus, this article takes a further step to propose a more expressive factorization machines model, i.e., Self-Attentional Neural Factorization Machines with Domain Interactions for Web API recommendation. Specifically, we introduce the concept of domain interaction to reduce the calculation complexity and capture the common characteristics of all features in one domain. Then, we leverage an implicit attention mechanism, that is, self-attention, to identify the importance of different feature interactions, instead of using explicit attention. Moreover, to promote expressive capability, deep neural networks are integrated into the transformed feature interactions following the self-attention propagation, capturing low and high order representations. In this approach, the importance of feature interactions can contribute to both low and high order relations. Multiple studies on a practical dataset indicates the inherent benefits of the method proposed in this study over the baselines for Web API recommendation.
Reversible Data Hiding of JPEG Image Based on Adaptive Frequency Band Length

JPEG images are widely used on the Internet. Histogram shifting reversible data hiding (RDH) methods based on quantized DCT (discrete cosine transform) coefficients are a research focus for JPEG images. Among them, frequency band selection is a key step that affects the performance of JPEG image RDH. In the existing algorithm, frequency band selection is to evaluate the embedding performance of the whole frequency band. But after DCT block sorting, the distribution of expand AC (alternating current) coefficients (Coefficients that can carry data) is in the front of the frequency band, so the performance evaluation of the whole frequency band will produce errors. This paper proposed an adaptive frequency band length JPEG image RDH method, which determines the used length of the frequency band while selecting the frequency band, so that can effectively reduce the invalid shift. Instead of selecting the whole frequency band length in a fixed mode for performance evaluation, the optimal combination of frequency band lengths will be selected according to the embedding performance of different lengths of each frequency band. Then, a united solution mechanism is used to solve the optimal frequency band length, and the frequency band selection is also completed while solving the frequency band length. Experimental results show that our algorithm outperforms existing state-of-the-art methods in terms of marked image visual quality and file size increment.
Multi-Granularity Interaction and Integration Network for Video Question Answering

Video question answering, aiming to answer a natural language question related to the given video, has gained popularity in the last few years. Although significant improvements have been achieved, it is still confronted with two challenges: the sufficient comprehension of video content and the long-tailed answers. To this end, we propose a multi-granularity interaction and integration network for video question answering. It jointly explores multi-level intra-granularity and inter-granularity relations to enhance the comprehension of videos. To be specific, we first build a word-enhanced visual representation module to achieve cross-modal alignment. And then we advance a multi-granularity interaction module to explore the intra-granularity and inter-granularity relationships. Finally, a question-guided interaction module is developed to select question-related visual representations for answer prediction. In addition, we employ the seesaw loss for open-ended tasks to alleviate the long-tailed word distribution effect. Both the quantitative and qualitative results on TGIF-QA, MSRVTT-QA, and MSVD-QA datasets demonstrate the superiority of our model over several state-of-the-art approaches.
A New Smart Contract Anomaly Detection Method by Fusing Opcode and Source Code Features for Blockchain Services

Digital assets involved in smart contracts are on the rise. Security vulnerabilities in smart contracts have resulted in significant losses for the blockchain community. Existing smart contract vulnerability detection techniques have been typically single-purposed and focused only on the source code or opcode of contracts. This paper presents a new smart contract vulnerability detection method, which extracts features from different levels of smart contracts to train machine learning models for effective detection of vulnerabilities. Specifically, we propose to extract 2-gram features from the opcodes of smart contracts and token features from the source code using a pre-trained CodeBERT model, thereby capturing the semantic information of smart contracts at different levels. The 2-gram and token features are separately aggregated and then fused and input into machine-learning models to mine the vulnerability features of contracts. Over 10,266 smart contracts are used to verify the proposed method. Widespread reentrancy, timestamp dependence, and transaction-ordering dependence vulnerabilities are considered. Experiments show the fused features can help significantly improve smart contract vulnerability detection compared to the single-level features. The detection accuracy is as high as 98%, 98% and 94% for the three vulnerabilities, respectively. The average detection time is 0.99 second per contract, indicating the proposed method is suitable for automatic batch detection of vulnerabilities in smart contracts.
Towards Improved Power Management in Cloud GPUs

As modern server GPUs are increasingly power intensive, better power management mechanisms can significantly reduce the power consumption, capital costs, and carbon emissions in large cloud datacenters. This letter uses diverse datacenter workloads to study the power management capabilities of modern GPUs. We find that current GPU management mechanisms have limited compatibility and monitoring support under cloud virtualization. They have sub-optimal, imprecise, and non-intuitive implementations of Dynamic Voltage and Frequency Scaling (DVFS) and power capping. Consequently, efficient GPU power management is not widely deployed in clouds today. To address these issues, we make actionable recommendations for GPU vendors and researchers.
Patterns of Legislators' Communication on Social Media: The Case of Israel

We researched Twitter adoption by Israeli legislators and their use of the platform to spread populist messages. Using a neural network approach, we examined a unique dataset of Israeli lawmakers' Twitter activity from 2013 to 2022. Our analysis reveals a consistent rise in tweet productivity among Knesset members. Interestingly, Twitter usage is more significant among political underdogs, especially extreme and opposition parties. Populist tendencies on Twitter are observed across the political spectrum, with opposition status being a strong predictor. The study suggests that the Prime Minister's legal issues may have contributed to the prevalence of populist-style messaging. Additionally, right-wing parties in Israel have experienced a substantial increase in the use of populism. This research highlights the evolving communication style in Israeli politics, the shift from mainstream actors supporting liberal democracy to endorsing populism, and the growing influence of the radical right.
Agile Framework as a Key to Information Management Systems Delivery

The article examines the nature of the agile framework, and explores the peculiarities of its management. Project oversight and controls are key for delivering expected project and service outcomes. Building consistency and learning from each other helps to improve every project. The objective of our research, based on the agile framework, is to provide a set of practices for project teams to deliver success and promote information management systems. It leads to reliable delivery based on project management competencies, agile working methods, scaled agile framework techniques, and project-based service provision. The proposed framework defines what work needs to be done and how it can be accomplished at the engagement. The framework is comprehensive, covering critical areas that impact solution delivery, including strategy, planning, mobilization, management, development, and operations which is essential to consider information management systems specific. The research is based on the following engagements: to promote consistency across engagements worldwide with a common language and standard approach; to support the industrialization approach with a disciplined process for delivering high-quality solutions across multiple locations, multiple workforces, and multiple cultures; to enhance reliable, high quality and on-time delivery of engagements by providing a field-tested approach gleaned from current subject matter experts' experiences.
Movie Description Model for Media Retrieval Services

As the variety of media content increases, consumer services are being asked for more ways to access them. For a better consumer experience, more media attributes should be revealed to connect consumers with media content. Various methods have been studied to extract complicated contexts in media content like entity detection and image/video captioning. Among them, this paper focuses on the movie description. A movie description model manifests comprehensive descriptions of movies concerning story context. Thus, consumer services can provide rich information about movies to users. This paper proposes a novel movie description model with the story background to generate detailed descriptions. The story background entities are included in the movie script during the pre-production stage. These entities have a significant effect on the scene portrayed. We define the story background using the location and time information of the scenes. The models with shot structures extract the story background from a keyframe of each scene to generate the scene description. In experiments with the LSMDC dataset, the proposed model achieves 0.0141 of BLEU@4 and 0.1313 CIDEr, which is about 9% over the baselines. Qualitatively, the description generated through the proposed model provides richer contextual information compared to previous studies.
Smoothed-Truncated-Sine (STS) Pattern for Accuracy Improvement in Sinusoidal Fringe Projection Profilometry

Sinusoidal patterns are widely employed in fringe projection profilometry for high-accuracy measurements. However, in practice, random factors such as camera noise will seriously affect the accuracy of sinusoidal patterns. This paper presents a smoothed-truncated-sine (STS) pattern design method that can achieve higher accuracy than sinusoidal patterns under the same noise. By insensitive harmonic modulation, the STS pattern can achieve a higher signal-to-noise ratio (SNR) to reduce the random error and maintain the amplitude of sensitive harmonics low to restrain the systematic error. First, the error mechanism is analyzed, and the error model is established for the phase shifting algorithm, taking into account the resistance of the phase shifting algorithm to part of high-order harmonic errors. Second, a STS pattern is proposed and optimized based on the error model. The parameters of the STS pattern are optimized to achieve a high SNR and a small phase error, resulting in improved performance of the multiple-step phase shifting algorithm, especially when the number of steps is large. Simulations and experiments demonstrate that the proposed method achieves higher accuracy than sinusoidal patterns and that the maximum error reduction ratio exceeds 20% if the number of steps is sufficiently large in sinusoidal fringe projection profilometry.
3D-Net: Monocular 3D Object Recognition for Traffic Monitoring

Machine Learning has played a major role in various applications including Autonomous Vehicles and Intelligent Transportation Systems. Utilizing a deep convolutional neural network, the article introduces a zero-calibration 3D Object recognition and tracking system for traffic monitoring. The model can accurately work on urban traffic cameras, regardless of their technical specification (i.e. resolution, lens, the field of view) and positioning (location, height, angle). For the first time, we introduce a novel satellite-ground inverse perspective mapping technique, which requires no camera calibrations and only needs the GPS position of the camera. This leads to an accurate environmental modeling solution that is capable of estimating road users' 3D bonding boxes, speed, and trajectory using a monocular camera. We have also contributed to a hierarchical activity/traffic modeling solution using short- and long-term Spatio-temporal video analysis to understand the heatmap of the traffic flow, bottlenecks, and high-risk zones. The experiments are conducted on four datasets: MIO-TCD, UA-DETRAC, GRAM-RTM, and Leeds-Dataset including various use cases and traffic scenarios.
Portrait Interpretation and a Benchmark

We propose a task we name Portrait Interpretation and construct a dataset named Portrait250K for it. Current researches on portraits such as human attribute recognition and person re-identification have achieved many successes, but generally, they: 1) may lack mining the interrelationship between various tasks and the possible benefits it may bring; 2) design deep models specifically for each task, which is inefficient; 3) may be unable to cope with the needs of a unified model and comprehensive perception in actual scenes. In this paper, the proposed portrait interpretation recognizes the perception of humans from a new systematic perspective. We divide the perception of portraits into three aspects, namely Appearance, Posture, and Emotion, and design corresponding sub-tasks for each aspect. Based on the framework of multi-task learning, portrait interpretation requires a comprehensive description of static attributes and dynamic states of portraits. To invigorate research on this new task, we construct a new dataset that contains 250,000 images labeled with identity, gender, age, physique, height, expression, and posture of the whole body and arms. Our dataset is collected from 51 movies, hence covering extensive diversity. Furthermore, we focus on representation learning for portrait interpretation and propose a baseline that reflects our systematic perspective. We also propose an appropriate metric for this task. Our experimental results demonstrate that combining the tasks related to portrait interpretation can yield benefits. Code and dataset will be made public.
Few-Shot Data Augmentation for Industrial Character Recognition

The task of industrial character recognition is to extract character content on the surface of the workpiece in the industrial production process. Limited training data, incomplete available character categories and non-standardized character styles encountered in actual production have led to a significant reduction in the recognition performance of deep learning-based methods, such as scene text recognition and Optical Character Recognition (OCR). In this paper, we propose an augmentation strategy suitable for industrial character recognition based on the Generative Adversarial Network (GAN). The strategy consists of two modules, a character detection module and a synthetic data generation module. The results show that the augmentation strategy achieves best generation results. Recognition network utilizing the augmentation dataset generated by the strategy can achieve the best results on four types of industrial datasets.
Swin Transformer with Multi-Scale Residual Attention for Semantic Segmentation of Remote Sensing Images

Semantic segmentation of remote sensing images usually faces the problems of unbalanced foreground-background, large variation of object scales, and significant similarity of different classes. The FCN-based fully convolutional encoder-decoder architecture seems to have become the standard for semantic segmentation, and this architecture is also prevalent in remote sensing images. However, because of the limitations of CNN, the encoder cannot obtain global contextual information, which is extraordinarily important to the semantic segmentation of remote sensing images. By contrast, in this paper, the CNN-based encoder is replaced by Swin Transformer to obtain rich global contextual information. Besides, for the CNN-based decoder, we propose a multi-level connection module (MLCM) to fuse high-level and low-level semantic information to help feature maps obtain more semantic information and use a multi-scale upsample module (MSUM) to join the upsampling process to recover the resolution of images better to get segmentation results preferably. The experimental results on the ISPRS Vaihingen and Potsdam datasets demonstrate the effectiveness of our proposed method.
Research on Phoneme Recognition using Attention-based Methods

A phoneme is the smallest sound unit of a language. Every language has its corresponding phonemes. Phoneme recognition can be used in speech-based applications such as auto speech recognition and lip sync. This paper proposes an end-to-end deep learning model called Connectionist Temporal Classification (CTC) and attention-based seq2seq network that consists of one bi-GRU layer in the encoder and one GRU layer in the decoder, for recognizing the phonemes in speech. Experiments on the TIMIT dataset demonstrate its advantages on some other seq2seq networks, with over 50% improvements after applying the attention mechanism.
Network Bandwidth Prediction Method Based on Hidden Markov model in High-speed Railway

In the context of the full commercial use of 5G, high-speed rail passengers have higher and higher requirements for wireless network service quality. However, in the current high-speed rail 5G network streaming media transmission, due to the fast moving speed, the base station is frequently switched, and the user bandwidth does not match the streaming media bit rate, resulting in a poor user network experience and a poor streaming media experience. In view of the above problems, this paper focuses on the bandwidth prediction of network users in the high-speed rail environment, and proposes a bandwidth prediction algorithm High speed 5G Environment Bandwidth Predict(H5EBP) based on the hidden Markov model in different states of the high-speed rail. So as to improve the user's streaming media experience. After comparative evaluation with other existing bandwidth prediction algorithms, H5EBP can greatly improve the accuracy of bandwidth prediction, thereby improving the user's streaming media experience.