Reviewerly/exHarmony huggingface dataset:
Fine-Grained Dynamic Fast Metrics for Industrial Control Terminal

The current mainstream security-proof schemes are based on static protection, which can only guarantee the integrity of program code and cannot defend against dynamic attacks that only change the execution flow of devices. The existing defense methods against dynamic attacks are often overhead and difficult to practically applied on industrial IoT devices with limited computing power and storage space. To this end, this paper investigates and analyzes the current research status of dynamic security detection schemes for devices in industrial control scenarios, and based on this, proposes a dynamic fast metric remote authentication scheme based on sliding windows in a targeted manner. The program execution flow graph PEG is obtained through static analysis, and the program execution branch streams are extracted according to the program dependency graph analysis, and partial metrics are performed on the executive branch streams, and the metric values are stored in the benchmark library as the basis for comparison of actual program execution. The trustworthy management platform compares the actual obtained metric values with the metric values in the benchmark library to determine the trustworthy status of the device. Finally, based on the experimental results, it is verified that this scheme is feasible for the metric of program execution and can be well applied to industrial scenarios.
Semantic Tagging Algorithm for Tibetan La Case Function Words Oriented to Classification of La Case

Automatic classification of Tibetan La case () is a challenging and significant task in Tibetan natural language processing. Compared with other short text classification tasks, the automatic classification of La case depend more on the semantic information of context and function words of La case. Based on the definition about La case classification task, this paper designs a semantic labeling algorithm for Tibetan La case function words by analyzing their usage and semantic features. When training La case classification model, calling this algorithm can integrate the semantic information about La case function words, thus increasing the diversity of input features and enriching the semantic information of embedded vectors. Experiments on eight commonly used short text classification models show that the classification accuracy (ACC) after fusing the semantic information on La case function words is improved by 2.1 percentage points on average, which verifies the effectiveness and universality of the algorithm.
Design and Realization of Real-Time Motion Target Detection System Based on ZYNQ

Motion target detection algorithms have been maturely implemented on traditional PCs, but they are large in size and high in power consumption. In order to be used in spacerestricted scenarios, a motion target detection system based on ZYNQ hardware acceleration is designed. First of all, we customized the QDMA IP core for the problem that the AXI VDMA IP core provided by XILINX requires multi-parameter configuration, which is not flexible enough to be used. This IP core can be hooked to the bus to realize the read and write access to the memory address space, compared with the VDMA IP core, the custom IP core only needs to set 50% of its configuration items to read and write data. Secondly, the system is modularized by combining the characteristics of ZYNQ’s hardware and software synergy. The algorithm module including grayscale processing, median filter, image difference, morphological filter is processed by the programmable logic side which is good at parallel computing for pipeline operation, and the camera configuration, system memory read/write control is processed by the ARM side which is good at serial execution of complex programs, which not only improves the processing speed of the algorithm but also significantly shortens the development time compared with a single processing method. Experimental tests show that the system can effectively detect moving targets in real time within a certain distance, and compared with the traditional PC platform using the same processing algorithms, it can still run smoothly at a resolution of more than 30fps in $1280 \times720$ under the condition that the power consumption is only 1/22 of the CPU power consumption (2.039W).
PLiNIO: A User-Friendly Library of Gradient-Based Methods for Complexity-Aware DNN Optimization

Accurate yet efficient Deep Neural Networks (DNNs) are in high demand, especially for applications that require their execution on constrained edge devices. Finding such DNNs in a reasonable time for new applications requires automated optimization pipelines since the huge space of hyper-parameter combinations is impossible to explore extensively by hand. In this work, we propose PLiNIO, an open-source library implementing a comprehensive set of state-of-the-art DNN design automation techniques, all based on lightweight gradient-based optimization, under a unified and user-friendly interface. With experiments on several edge-relevant tasks, we show that combining the various optimizations available in PLiNIO leads to rich sets of solutions that Pareto-dominate the considered baselines in terms of accuracy vs model size. Noteworthy, PLiNIO achieves up to 94.34% memory reduction for a <1% accuracy drop compared to a baseline architecture.
Neuro-Symbolic Empowered Denoising Diffusion Probabilistic Models for Real-Time Anomaly Detection in Industry 4.0: Wild-and-Crazy-Idea Paper

Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. As these technologies become more interconnected and interdependent, Industry 4.0 systems become more complex, which brings the difficulty of identifying and stopping anomalies that may cause disturbances in the manufacturing process. This paper aims to propose a diffusion-based model for real-time anomaly prediction in Industry 4.0 processes. Using a neuro-symbolic approach, we integrate industrial ontologies in the model, thereby adding formal knowledge on smart manufacturing. Finally, we propose a simple yet effective way of distilling diffusion models through Random Fourier Features for deployment on an embedded system for direct integration into the manufacturing process. To the best of our knowledge, this approach has never been explored before.
Use of Digital Twins for the Technological Equipment Condition Monitoring

The article describes the application of digital technology on the example of an AC electric motor in solving the problem of an electromechanical converter condition monitoring. In the course of creating mathematical and computer models, the existing methods of technological equipment condition monitoring were analyzed. Based on them, the optimal method was chosen for determining the current value of variables that are not easily accessible for direct measurement, followed by the construction of a complete mathematical and informational model. The paper describes the functional scheme of a digital twin of a three-phase electromechanical AC converter, and presents a process control system. The validity of the developed system for monitoring the condition of the equipment and the digital twin of the AC electric motor was confirmed by subsequent analysis of the compliance of the passport and received calculation data.
Assessment of Feasibity of Neural Networks to Increase Image Clarity

The article describes the analysis of applying classical metrics for assessing the image quality with view of making a decision on a sucsessful solution of the SISR task. It presents the classification of the existing metrics for the assessment of images in general and of their clarity in particular. The majory of practical applications and app solutions in the present use generative and convolutional neural networks for solving the task of increasing the resolution of images. As a mechanism for enhancing the image resolution and clarity the neural netrworks of various architecture were implemented and used in the frame of the work: BSRGAN, SwinIR, ESRGAN, as well as the interpolation algorithm. The assessment of the images being restored was conducted through the subjective and objective analysises. The widely spread objective metrics for assesing the effectiveness of enhansing the resolution are the classical metrics PSNR and SSIM. It is shown that the application of the above metrics is not sufficiently accurate. The article cosideres such quality evaluation metrics as BRISQUE, NIQE, PIQUE, FDL and metrics that can be obtained based on the image similarity assesment. The results obtained make it possible to state that a really correct assessment of restored (enhanced) images should be conducted through a complex analysis with the use of several metrics of various focuse.
Development and Research of a Quantum Control System for the Computational Process and the Execution of Algorithms

Currently, the theory of quantum computing is actively developing. Despite the fact that a quantum computer has some features, any algorithm designed for a classical computer can be implemented for a quantum one. The relevance of the topic of quantum computers is confirmed by the fact that even now banking systems, cryptography and encryption of information require enormous resources of computing systems, these are just some examples, but the most important and vivid ones that relate to the everyday life of a huge part of the world's population. A hardware computing core has been developed and simulated, which is the main part of an accelerator based on field-programmable gate array with computer aided design Altera Quartus. Considering the obtained time dependence of the number of cycles required for the impact of a single-qubit quantum gate on a quantum register on the number of qubits and parallel arithmetic logic units in the hardware computing core, when simulating quantum computing, a method was developed to determine the increase in hardware performance and ways to improve the performance of an accelerator based on a hardware computing core were developed. based on field-programmable gate array.
The Control of the Metadata Processing in Anonymous Communication Network Devices

The paper deals with the problems of extracting identification and authentication information in anonymous networks built using the long-range energy-efficient radio communication technology. It is shown that the existing approaches to metadata processing require a large number of metadata decryption operations to be performed in the receiver for each packet. The reduction of the average number of data decryption operations through the use of cryptographic key sorting algorithm can increase the performance of the target class of device and to reduce their power consumption. The method based on the choice of decryption keys depending on the packet arrival intensity from the source is proposed. For the practical implementation of the method an original algorithm for determining the source instantaneous intensity has been developed. It ensures the minimization of the volume of intermediate results and the implementation of the principle of obsolescence of the values of the observed intensity. The implementation of the method and the algorithm makes it possible to reduce the average number of decryption operations for each packet of the considered network by 25% to 75% depending on the uniformity of the source intensities. Simulation modeling showed that when we use the algorithm for determining the intensity of receiving packets from sources, the observed number of decryption operations differs from the theoretically calculated one by no more than 20%. This difference depends on the intensity ratio of the most active source to the total intensity of all sources forming data packets for the receiver.
Buffer Overflow Vulnerability Detection Based on Static Analysis-assisted Symbolic Execution

Buffer overflow is a common exploitable high-risk vulnerability that can lead to arbitrary code execution, thus effective detection of buffer overflow vulnerability is of utmost importance for developers, enabling timely remendiation. Symbolic execution is a useful approach to detect it. In this paper, we propose VulDetection to improve the system resource utilization of symbolic execution through static analysis. Firstly, VulDetection uses static analysis techniques to extract the hotspot code segments in the program. Secondly, it uses the hotspot code segments to guide symbolic execution to explore only the vulnerability-related code parts. The experimental evaluation encompasses four vulnerable programs from the EDB vulnerability repository. The obtained experimental results demonstrate that VulDetection can improve the efficiency of vulnerability detection.
Traffic Object Detection System Based on YOLOv5 for V2V Communications

The deep learning techniques on the base of YOLOv5 architecture have been shown to make a traffic objects detection system for V2V communications to ensure traffic safety and traffic flows prediction. According to the detection data the detector allows to estimate the traffic object speed and the coordinates of movement. To improve the accuracy of calculations, the distortion correction algorithm (in geometric optics), the Chang method and the Kalman filter are used. To set the detector to work in online operation mode, an algorithm has been developed that receives video stream from open city surveillance cameras with the processing of a m3u8 playlist. The developed detector can be used on "connected" vehicles to assess the situation on the roads and ensure safety traffic.
Automatic Check-out within Generalized Zero-shot Learning Setting

Automatic Check-Out (ACO) aims to accurately forecast the presence and counts of each product in any given product combination. This paper concentrates on the ACO problem within generalized zero-shot Learning (GZSL) setting. Many recent GZSL studies have adopted a feature generation approach. However, these methods treat each class synthesis process independently, ignoring the rich dependency structure of different classes and their compositions. We propose a generative model that captures the common structure between classes and a classifier which optimizes classification boundaries by imposing a multi-class Deep SVDD constraint on seen classes. The evaluations are performed on the large-scale RPC dataset during the experiments.
Unsupervised Keyphrase Extraction Method Based on Graph Centrality

Existing textual keyword extraction mainly uses unsupervised methods that are more generalised and less costly and only require the selection of a method for evaluating keywords, such as the frequency and location of this word, by which possible keywords are extracted; however, this statistical-based method does not make use of the meaning of keywords in context. Due to the popularity of pre-trained language models (PLM), embedded-based methods have gained more application in unsupervised keyword extraction (UKE) tasks. We designed a new UKE approach to jointly model the feature information of candidate keyphrase at both sentence and phrase levels by PLM. First extract key sentences as a summary of the source document, obtain the candidate keyphrase from the summary sentences by syntactic analysis and lexical annotation and calculate the node centrality of the candidate keyword graph by fusing the candidate node position information to obtain the correlation information between the candidate word nodes; We also introduce an attention mechanism to enhance the capture of local feature information by computing for each candidate keyword its attention value in the above text. We evaluate our approach on three public commonly used keyword extraction datasets. Through the experimental analysis can be known that our model outperforms most models compared to the existing state-of-the-art(SOTA) methods.
A Research on Video-based Multi-person Upper Limb Keypoint Detection in Complex Backgrounds

The hand is the most expressive part of the human body, and humans are always actively getting information from gestures and other biometric features in a specific way combined in real time to make judgments. Few of the previous human pose estimation methods are specifically designed for the human upper body. In this paper, We propose a relatively lightweight method based on ViT for extracting keypoints on the upper limbs of the human body. The COCO-WholeBody dataset is used for training and evaluation to test the robustness of our model in complex backgrounds. The results show that our upper limb detection network achieves 75.63AP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> . With our method, the coordinates of human upper limb points in videos and pictures can be extracted quickly and easily to provide data support for subsequent posture analysis tasks.
Generating Conceptual Semantic Vectors Based on Key Phrase Extraction Techniques

In this article, we present a novel approach for representing text as fixed-length semantic vectors. Building upon previous research, we propose a modified method that combines key phrase extraction techniques with clustering techniques to generate concept-based, low-dimensional directed representations of documents. To evaluate the effectiveness of the resulting vectors, we conducted experiments using them in an aggregation task against a set of robust baselines following a similar approach. Our experimental findings demonstrate that the proposed modification significantly enhances the quality of the vector representation of documents. Specifically, the assembly accuracy improved by at least 3% on the V1 scale, validating the efficacy of our approach. This research contributes to the field of natural language processing by offering an efficient method for representing text as semantic vectors, thereby enabling more accurate and comprehensive analysis of textual data.
Hyperspectral Recovery of RGB Images Based on Progressive Dense Residual Networks

This chapter investigates deep learning-based hyperspectral image reconstruction algorithms and proposes a lightweight, end-to-end learning-based framework for progressive dense residual networks. Driven byjoint data and prior, the framework is able to efficiently process feature and gradient information, and progressively decouple features to achieve high-quality hyperspectral image reconstruction. Experimental results show that the method has obvious advantages in qualitative and quantitative comparisons compared with other algorithms.
Evaluation of the Geographic Video Synopsis Effect Based on Eye Movement Data

To quantitatively analyze the visual effects of geographic video synopsis from a user perception perspective, this paper combines eye-tracking experiments with questionnaires. It introduces eye movement metrics and task objectives to construct an evaluation model for expressing the effectiveness of the geographic video synopsis. Comparing the geographic video synopsis method with the conventional video synopsis method and the video-geographic scene fusion method, an evaluation of objective eye movement data and subjective questionnaire evaluation outcomes follows. The experimental results reveal that the three techniques differ significantly. In comparison to two other methods, the geographic video synopsis approach has higher search efficiency, lower information comprehension difficulty, higher job accuracy, and noticeably better synopsis expression effectiveness.
Offensive Chinese Text Detection Based on Multi-Feature Fusion

To purify the online environment, it is essential to identify objectionable content, including offensive texts. However, some offensive texts are expressed in a more subtle manner, making it difficult to detect their literal characteristics. To enhance the effectiveness of detecting offensive Chinese text, we propose a multi-feature fusion-based method. First, we combine the word vectors obtained from Wobert with the character vectors obtained from ALBERT. The attention mechanism assigns greater importance to key features within the word vectors. Next, we merge the fusion vector with the sentence vector generated by ALBERT, which encompasses contextual semantics and syntactic information. This results in a new fusion vector that captures information at the character, word, and sentence levels. Finally, we employ a fully connected layer to process the three-level fusion vector and obtain the detection outcome. Experimental results demonstrate that this approach provides a comprehensive characterization of offensive text by fusing information from multiple levels. It substantially enhances the detection performance for offensive Chinese text.
Neural Network Alternate Incremental Learning Algorithm for Intelligent Human Activity Recognition System

In this paper, we present an alternate incremental learning algorithm for neural networks in the context of human activity recognition. The algorithm aims to address the challenges of adapting to new tasks while preserving knowledge of previously learned tasks. The proposed algorithm involves two main states: functioning and sleeping. In the functioning state, the neural network learns new tasks by adjusting the weights in the output layer while preserving the existing knowledge. However, as training progresses, the network may face difficulties in finding a balance between old and new tasks. To overcome this, the network transitions to the sleeping state. In the sleeping state, the network undergoes two phases. In the first phase, the network is trained using backpropagation, where random values are assigned to the weights connecting the input and hidden layers. Pseudo-examples are generated to enhance adaptation. In the second phase, the extreme learning machine is used to further train the network based on the reconstructed matrix P, which represents the network's knowledge. Experimental results show that the proposed algorithm achieves promising performance. The MSE values decrease significantly in the sleeping state, indicating improved adaptation and generalization capabilities. However, there is a slight increase in MSE when the network learns new knowledge, suggesting the need for further investigation and optimization. Thus, the alternate incremental learning algorithm demonstrates potential for improving the performance of neural networks in human activity recognition systems, providing a framework for adaptive learning and knowledge preservation. Future research should focus on fine-tuning the algorithm.
Chinese Summary Generation Algorithm Based on Contrastive Attention Mechanism

Generative summarization based on the seq2seq model has gained widespread adoption due to the advancements and efficacy of the sequence-to-sequence framework. However, these models encounter challenges in handling out-of-vocabulary words and may produce summaries with factual inaccuracies. This paper addresses these issues by proposing a summarization generation model, MBC, which is built upon the Transformer as the baseline model. Additionally, it incorporates the BERT pre-trained model, pointer generation network, contrastive attention mechanism, and coverage loss. The model employs a pointer generation network to augment its vocabulary coverage capabilities by generating new terms. To enhance the representation of key information from the source text, a contrastive attention mechanism is employed during summary generation. In addition, by introducing coverage loss, the model can reduce repetitive content in the summary. To assess the model’s performance, this paper employs ROUGE values as evaluation metrics and conducts comparative experiments with other models using the LCSTS dataset. The experimental results demonstrate that, in comparison with other models, this model is more adept at attending to salient information within the source text and generating summaries of greater accuracy.