Reviewerly/exHarmony huggingface dataset:
Research on a Technology of Mail Anti-virus Gateway for Industrial Internet

With the rapid rise of the Industrial Internet, emails that convey a large amount of information require a strong network security environment. Aiming at the increasingly complex mail virus prevention and control work under the background of the industrial Internet, this paper designs and implements a mail anti-virus gateway system. First, design the overall process and structure of the mail virus processing system; then, design an extended mail receiving and sending protocol on the basis of the public framework, thereby ensuring the convenience of the system for use in different roles and environments. In the detection and filtering part of mail virus, an anti-virus engine containing multiple sub-function modules is designed to detect and filter viruses in mail text and attachments. Through the function test of 1000 kinds of virus mails, it is found that the mail anti-virus gateway system designed in this article can detect and filter most types of mail viruses. Compared with the virus detection on the client and server, the anti-virus gateway system has better performance And efficiency.
Intelligent Video Monitoring for Real-Time Detection and Recognition of Elderly Falls on the Embedded Platform

With the ageing of the social population, in order to avoid elderly people living alone at home because of accidental falls and not timely treatment, this article put forward a computer vision based on the elderly fall detection and recognition system. Using the YOLOv3-tiny algorithm and DeepSORT algorithm as the elderly detection and tracking stage, the system uses the FastPose algorithm and Spatio-Temporal Graph Convolution (ST-GCN) network to load the key points of the identified elderly human body and classify the elderly's actions. Then, Ascend MindX SDK is used to reason the model and deployed on the Atlas 200DK hardware platform so as to realize the purpose of real-time human motion detection. The comparison of the test results to those of other detection algorithms and the YOLOv3-tiny network model demonstrates that the detection algorithm has a good recognition effect.
Voice-Face Cross-Modal Association Learning Based on Deep Residual Shrinkage Network

Establishing associations between voices and faces has grown in popularity in recent years, but current voice-face cross-modal association methods face challenges such as limited feature extraction capability and insufficient semantic associations. To address the aforementioned issues, we propose a voice-face cross-modal association learning method based on a deep residual shrinkage network. First, a deep residual shrinkage block is added to the dual-stream residual network to improve network training efficiency and acquire more discriminative embedded features. Then the multi-similarity loss function is used in metric learning to tap into the connections between voice and face modalities and enhance the network's robustness and generalization ability. In voice-face cross-modal verification, cross-modal matching, and cross-modal retrieval tasks, experimental findings indicate that our method improves accuracy by about 2% over existing baseline methods.
Research on Text Generation Model of Natural Language Processing Based on Computer Artificial Intelligence

This paper proposes a text generation system model for natural language processing based on artificial intelligence technology. In this model, pre-trained dynamic word vector ALBERT was used instead of the traditional BERT reference model for feature extraction to obtain word vectors. Through ontology, the factors that affect the semantic similarity of concepts, such as node density, node depth and node hierarchy order, are improved. The semantic distance, the relationship between concepts, the attribute of concepts and the level of concepts are considered comprehensively. The experiment on the public data set proves that the BLEU value and manual evaluation index of the algorithm on the test set are significantly improved compared with the baseline model. The effectiveness of the algorithm is proved by the system.
Modulation Recognition Method of Cyclic Stationary Signal Based on Graph Theory

Aiming at the existing shortcomings of recognition algorithms based on signal cyclic spectra, such as incomplete feature extraction and poor robustness against noise, a signal recognition method based on a deep residual network and noise reduction cyclic spectrum is discussed in this paper. The Wiener filtering is used to preprocess the two-dimensional cyclic spectrum of the signal. Then the preprocessed signal is input into the modified 50-residual layer network for feature extraction and identification of the modulation type of the signal. Experimental results report that the proposed method can effectively identify eight different modulation types of cyclostationary signals, with the recognition accuracy reaching 96% when SNR is greater than −2dB. Furthermore, the recognition performance of the proposed method is better than some existing deep learning recognition algorithms, such as AlexNet, GoogleNet, etc.
Neural Machine Translation of Mauritian Creole to English Using Transfer Learning

The discipline of Language Translation to convert from a source language to a target language has been attempted to be automated for quite some time. Neural Machine Translation (NMT) is a field where a source language is automatically converted to a target language without much human intervention using Deep Learning (DL) techniques. Neural Machine Translation has gained appeal due to its swift and reliable accuracy for automatic language translation. However, due to ambiguous syntax and sentence logic structure, it is not a trivial task that can be solved straightforwardly, making this an extant field of research. Till now, a colossal amount of research and funding has been invested in this field. This paper attempts to translate text from Mauritian Creole Language to English using Bi-Directional Long Short-Term Memory (LSTM) with the Bahdanau Attention mechanism by using transferable knowledge (Transfer Learning) of French to English translation, as French and Mauritian Creole are almost similar in language structure. A model to accomplish the translation has been proposed and validated using appropriate sample data to determine the accuracy of the translation.
Resource Aware Client Selection for Federated Learning in IoT Scenarios

Machine learning optimizes performance in many embedded applications. A weak point of many learning solutions is the intensive use of data and computational resources required for training the model. By default, client devices send data to a solution developer's server to execute the training process in a more computationally powerful environment. However, this approach can compromise the client's privacy, as data is transmitted to third parties for processing. Federated learning solves this problem by training the model on the client devices, thus without sharing data. The trained models are then aggregated on the server to create a generalized version that can run on every client. The federated learning protocol involves selecting which clients will participate in each training round, with selection criteria focused on maximizing the number of clients per round, controlling fairness, lowering round discards, and managing resources. However, existing selection algorithms neglect the minimization of battery consumption, which is critical in scenarios where clients have limited resources. In this paper we propose a client selection mechanism for a federated learning protocol that considers energy, processing capacity, and network quality as determinant criteria for decision. Compared to a state-of-the-art selection technique, our algorithm saves resources while maintaining the model's accuracy.
ML-DPR: A Meta-Learning-Based Model for Domain-Adaptive Dense Passage Retrieval

Dense Passage Retrieval (DPR) models have demonstrated substantial advances in retrieving relevant passages from large document collections. These models employ encoders to generate vector representations of documents and queries. However, DPR models tend to produce low-quality sentence representations when applied across domains. Collecting domain-specific data and training specialized models for each domain is expensive and time-consuming. In this paper, we propose a meta-learning model which acquires knowledge from each domain-specific dataset (meta-task) and can generalize across domains, including finance, education, and others. We further apply knowledge distillation to transfer the knowledge from an interaction-based BERT model to a representation-based BERT model, improving performance. Experiments on five domains from the DPR dataset demonstrate that our method achieves the state-of-the-art performance.
A2SNet: An Adaptive Scale Selection Network for Crowd Counting in the Wild

As deep learning is widely used in diverse computer vision applications, the study of crowd counting has achieved superhuman performance. However, current methods tend to directly concatenate multi-scale features from different branches to reduce the error of counting. Such an approach hurt the multi-scale feature representation learning, therefore hard to distinguish the priority of different features. Different from previous studies, we propose a novel Adaptive Scale Selection Network (A2SNet) that learns multi-scale features to adaptively balance weights. Our approach contains multiple branches: one branch uses Adaptive Scale Selection (ASS) module for multi-scale features extraction, and the second branch employs Efficient Channel Attention (ECA) module to further learn subtle features. The proposed ASS is a parallel structure, which consists of coarse branch (i.e., large-scale learning) and fine branch (i.e., small-scale learning). Lastly, we reveal A2SNet’s effectiveness on standard crowd benchmarks and show remarkable results.
An Application Comparison of GAN-based Image Translation Methods

Image translation is an important and challenging area of computer vision. It aims to design models that translate source-domain images to target-domain images with applications such as data enhancement, style migration and super-resolution. Until 2016, researchers have successfully implemented generative adversarial networks to image translation using the findings of deep learning techniques for visual generation tasks. In this paper, we first review and analyze the literature in the field of GAN-based image translation and summarize common normalization techniques and model judging metrics. In accordance with the mapping connection between the model inputs and outputs, self-supervised and unsupervised methods to image translation models are then split into categories. The strengths and weaknesses of these models are then analyzed, and a short explanation of how each model performed on various evaluation indicators. Finally, several potential future research issues in this field are discussed.
Realizing Predictive Maintenance in Production Machinery Through Low-Cost IIoT Framework and Anomaly Detection: A Case Study in a Real-World Manufacturing Environment

Digital transformation and predictive maintenance are still some of the key challenges faced by the industrial sector as it moves towards Industry 4.0. However, the lack of resources, experienced personnel, and the absence of digital-savvy culture in manufacturing companies, often hinder the adoption of the required technologies. In this context, maintenance is a required process to ensure the smooth and continuous operation of the production, but to this day, engineers are still required to physically inspect equipment on site. By applying digital technologies, a predictive maintenance framework will enable the industry to maximize the life of the equipment and to act on time in order to prevent failures and thus reduce costs and optimize maintenance efficiency. In this effort, integrating such technologies and the required modern computing devices for monitoring legacy industrial equipment remains a challenge. Therefore, in this paper, we present a low-cost IIoT system for an AI-based predictive maintenance of machinery in a real-world industrial environment. In specific, a data collection system, consisting of a Raspberry Pi device and several sensors (namely, an accelerometer, temperature sensors and a microphone), was designed and developed. The collected data was then utilized to feed a deep-learning LSTM-autoencoder model for anomaly detection through a semi-supervised learning approach. Despite the small amount of data collected, the model is able to effectively detect anomalies. Finally, ways in which the existing system can be improved are proposed.
Design of College English Modular Teaching Management System Based on .NET Technology

In recent years, with the further deepening of the reform of English education in universities, “student-centeredness” has become the main form advocated by major institutions. The modular education management approach of English language focuses on examining students' daily performance, emphasizing the cultivation of their application ability, thereby improving their learning efficiency and quality. This article provides a detailed introduction to the .NET framework and analyzes the development requirements of the English process-oriented teaching management system. It proposes the main functions and database design of the modular teaching management system based on .NET technology, and explains the implementation effects of some modules. The aim is to provide important references and guidance for the development of similar management systems.
Garbage Image Classification Algorithm Based on Swin Transformer*

By comparing the accuracy of VGG16, ResNetl01 and Swin Transformer on the garbage classification dataset, it is proved that Swin Transformer has an advantage in garbage classification tasks. Experimental results show that, with the self-attention mechanism and multi-scale feature modeling capabilities, Swin Transformer exhibits higher accuracy than traditional convolutional neural network algorithms. The accuracy of the Swi Transformer algorithm reaches 97%, which is 2.9 % higher than VGG16 and 1.1 % higher than ResNetl01.
Multi-Class Industrial Texture-less Object Detection Method in Cluttered and Occluded Scenes

in industrial scenes, template matching algorithms have the advantages of robustness and fastness, but most of them tend to focus on single-class object detection. When facing multi-class object detection tasks, the performance degrades significantly. Deep neural networks are naturally suitable for this task, but rotation regression is still a problem that has yet to be solved perfectly, making it challenging to meet the requirements of industrial robot sorting and grasping scenes. We proposed a hybrid two-stage method called key-pair matching (KPM) algorithm, which can perform robust, fast, and accurate detection of multi-class industrial workpieces. In the first stage, we choose one off-the-shelf CNN detector to predict the horizontal bounding boxes to solve the classification and coarse localization problems in complex scenarios, in the second stage, features based on key-shapes primitives are used to calculate translations and rotations. Experiments show that our proposed method outperform the other comparison methods regarding detection performance and localization accuracy under reasonable runtime, making it suitable for industrial objects detection in cluttered and occlusion scenes.
Fast Identification of Stacked Steel Pipes based on Improved NCC Algorithm

In order to solve the problem that the rotation-invariant NCC algorithm has a large number of operations in each pyramid layer in the matching of rotating targets, this paper proposes a method to accelerate the algorithm by combining Hough transform with pyramid layering strategy. In the coarse matching stage at the top of the pyramid, Hough transform was introduced to detect the straight line in the search image to calculate the angle of the steel pipe, so as to set the angle constraint. This method omits the selection of rotation step size in the top layer of the pyramid and the subsequent estimation of rotation angle, which can improve the matching speed of the algorithm and the real-time performance of stacked steel pipe detection. The experimental results showed that this improved template matching algorithm can improve the recognition speed of stacked steel pipes with a high accuracy rate. This research has certain theoretical significance and application value for improving the visual recognition and positioning speed of stacked steel pipes.
(POSTER) Smart Shoe for Fall Detection Application

Real-time automatic fall detection is effective to mitigate the post-impact of fall injuries, especially in case of the elderly people as immediate detection transpires into a faster response. This paper proposes a methodology that utilizes subject's acceleration, angular velocity, pitch, and row as features and generates fall decisions based on processing and comparing feature data from multiple time frames. The developed algorithm uses features extracted from a single sensor unit consisting of 3 axis accelerometer and gyroscope integrated into shoes with processing units. In real-life evaluation, 5 healthy subjects performed 160 falls and 650 activities of daily living. Our methodology achieved a sensitivity of 94.38%, specificity of 98.58%, and accuracy of 96.30%. Evaluating using the SisFall public dataset sensitivity of 91.37%, specificity of 95.28%, and accuracy of 90.73% was achieved.
Research on Power Image Enhancement Algorithm Based on Image Recognition Technology

UAV line inspection greatly improves the efficiency of transmission line inspection. If the digital image collected by computer is processed, the efficiency can be further improved, and image enhancement processing is an important process of computer image processing Combined with the characteristics of power line digital gray image, two different gray image enhancement algorithms using random mathematical model and fuzzy mathematical model are discussed, and the application effects of these algorithms are compared This paper analyzes the shortcomings of the existing image enhancement algorithms in the application of power line gray image, presents an improved image enhancement algorithm based on fuzzy mathematical model, and uses an example to verify its better applicability.
A No Code XAI Framework for Policy Making

Explainable AI (XAI) techniques are used for understanding the internals of the AI algorithms and how they produce a particular result. Several software packages are available implementing XAI techniques however, their use requires a deep knowledge of the AI algorithms and their output is not intuitive for non-experts. In this paper we present a framework, (XAI4PublicPolicy), that provides customizable and reusable dashboards for XAI ready to be used both for data scientists and general users with no code. The models, and data sets are selected dragging and dropping from repositories While dashboards are generated selecting the type of charts. The framework can work with structured data and images in different formats. This XAI framework was developed and is being used in the context of the AI4PublicPolicy European project for explaining the decisions made by machine learning models applied to the implementation of public policies.
Acconotate: Exploiting Acoustic Changes for Automatic Annotation of Inertial Data at the Source

Smart infrastructures often intend to provide personalized context-aware services for their residents. These context-aware services, in turn, often rely on sophisticated machine learning algorithms which need vast volumes of costly annotated sensor data. State-of-the-art automated annotation frameworks try to solve this problem by generating annotated sensor data obtained from personal wearables. However, most of these approaches - (a) either need visual data from the environment or (b) can only work for environments with a single resident. This paper discusses the design of a first-of-its-kind framework Acconotate which can automatically generate annotated data from dual resident smart environments without requiring any visual information. Acconotate achieves this by exploiting the typical transitions present in complex human activities first to solve the critical problem of the user-to-activity association and then use that to annotate the sensor stream available from both the users. Rigorous evaluation with two real-life datasets collected in two diverse scenarios shows that Acconotate can successfully generate annotated sensor data over the edge without human intervention.
Feature Selection via Minimal Covering Sets for Industrial Internet of Things Applications

High stakes decision making requires that any decision support systems must be able to come up with plausible explanations about the decisions they propose to the user. Several popular approaches to explaining black-box AI systems, such as neural networks, focus either on highlighting the features that matter the most in one particular decision as in the SHAP models, or on developing a local to the particular instance data model that is explainable by nature, such as a decision tree. ML systems that are by default explainable and/or interpretable, such as decision trees, or rule-based systems do not require such third-party approaches, as they are themselves explainable. Nevertheless, presenting a consistent (small) set of features to the users as explanations for any given proposed decision can increase the confidence of the users towards the reliability of the system. For this reason, we have developed a system that given a set of rules that hold on a training dataset, finds a minimal cardinality set of features that are used in a set of rules that together cover the entire training dataset. We develop a parallel heuristic algorithm for finding such a minimal variables set, and we show it outperforms all state-of-the-art optimization solvers for finding the solution to a MIP formulation of the problem. Experiments with data from use cases applying AI in public policy decision making as well as in medical use cases show that the proposed small set of features is sufficient to explain all the cases in the test dataset via rules containing only variables from the proposed set of features.
A New Effective Kernel-Based k-Nearest Neighbor Classification Method

The Z-KNN model is upgraded to the proposed model based on a Gaussian kernel. In essence, the Gaussian kernel provides a way to gauge how similar samples are, which allows for better clustering of similar samples in a space that portrays “similarity” and is thus linearly separable. Based on the Euclidean distance, the Z distance can be seen as an enhancement of the Euclidean distance. In contrast to Euclidean distance, Z distance makes the distance between data belonging to distinct classes larger than between those belonging to the same class. The input data are transferred to a high-dimensional feature space by a Gaussian kernel method, and nonlinear mapping and Z-KNN are performed in this space. Experiments demonstrate that the proposed model produces superior classification results on nonlinear data sets.