Reviewerly/exHarmony huggingface dataset:
Detecting Faults at the Edge via Sensor Data Fusion Echo State Networks

The pervasive use of sensors and actuators in the Industry 4.0 paradigm has changed the way we interact with industrial systems. In such a context, modern frameworks are not only limited to the system telemetry but also include the detection of potentially harmful conditions. However, when the number of signals generated by a system is large, it becomes challenging to properly correlate the information for an effective diagnosis. The combination of Artificial Intelligence and sensor data fusion techniques is a valid solution to address this problem, implementing models capable of extracting information from a set of heterogeneous sources. On the other hand, the constrained resources of Edge devices, where these algorithms are usually executed, pose strict limitations in terms of memory occupation and models complexity. To overcome this problem, in this paper we propose an Echo State Network architecture which exploits sensor data fusion to detect the faults on a scale replica industrial plant. Thanks to its sparse weights structure, Echo State Networks are Recurrent Neural Networks models, which exhibit a low complexity and memory footprint, which makes them suitable to be deployed on an Edge device. Through the analysis of vibration and current signals, the proposed model is able to correctly detect the majority of the faults occurring in the industrial plant. Experimental results demonstrate the feasibility of the proposed approach and present a comparison with other approaches, where we show that our methodology is the best trade-off in terms of precision, recall, F1-score and inference time.
Key point localization and recurrent neural network based water meter reading recognition

Due to the complicated arrangement of the pipes in the narrow space leads to random orientation of the mechanical water meter dial meanwhile its digit wheels are accompanied by arbitrary angle rotation, which makes the detection and recognition of meter reading more difficult. Even the latest visual network technology cannot deal with the challenges. In this paper, two special visual task networks are being closely cooperated to solve above issues. First, a professional water meter detection method is proposed by redesigning and retraining a human joints detection network to accurately locate four key points of reading region. Based on key points the distorted reading region will be geometric corrected by using homography relation to reduce the interference from shooting angle and improve accuracy of subsequent digit recognition. Then, a water meter reading recognition method is proposed by modifying a recurrent block convolutional network. The robustness of digit recognition is improved by block recognition and transcription of reading region features. During transcription stage, we add new recognition markers and probability vectors between each digit in dictionary to solve the issue of digit wheels rotations. Finally, our method achieves more robust water meter detection in harsh environment and higher recognition accuracy. Experimental results showed that our method can get better performance in detection efficiency (6.15 fps) and accuracy (95.30%) compared with recent related works and closer to the level of practical application. • It focuses on the practical living application of water meter reading recognition in complex real scenes. • We redesign and train a human joints detection network to locate the key points of water meter for robust reading region detection. • A recurrent neural network based digital character sequence recognition method is designed to solve robust water meter reading recognition in lossy scene. • Our network can get better performance in detection efficiency (6.15 fps) and accuracy (95.30%) of water meter reading.
Pixel Processor Arrays For Low Latency Gaze Estimation

We demonstrate the use of a Pixel Processor Array (PPA) vision sensor for achieving rates of over 10,000 Hz, with processing latency below 0.1 ms, in a gaze tracking application, performing image processing directly upon the sensor itself with minimal external computation. Each pixel processing element of the PPA sensor is capable of light sensing, data storage and computation, allowing various visual processing to be performed efficiently at the point of light capture. We demonstrate how information such as pupil location, size, and LED reflections upon the eye&#x0027;s surface can be extracted by the PPA, along with a simple gaze tracker using pupil location. By extracting such information directly on-sensor, the data needing to be transferred from sensor to external processing is reduced from entire images to a hand-full of contextual bytes, this provides significant saving in terms of power, time and allows for frame-rates far exceeding traditional camera sensors.
The Increasing Frequency of Terms Denoting Political Extremism in U.S. and U.K. News Media

The term political extremism is commonly used to refer to political attitudes considered to be outside the ideological mainstream. This study leverages computational content analysis of big data to longitudinally examine (1970–2019) the prevalence of terms denoting far-right and far-left political extremism in more than 30 million written news and opinion articles from 54 news media outlets popular in the United States and the United Kingdom. We find that the usage of terms denoting right and left political extremism has been increasing across news media outlets in both countries. This trend is particularly stark for far-right-denoting terms, which have been growing in prevalence since at least 2008. Most U.S. and U.K. news media outlets tend to use far-right-denoting terms substantially more often than they use far-left-denoting terms. The rising prevalence in news media of terms denoting political extremism is strongly correlated with similar growing usage of terms denoting prejudice and social justice discourse.
The Construction of the Development Mode of School-Enterprise Cooperation in Higher Vocational Education with the Aid of Sensitive Neural Network

In-depth school-enterprise cooperation can promote the transformation of higher vocational colleges’ school-running concept and talent training mode, deepen the teaching reform of higher vocational colleges, and improve the employment rate of higher vocational colleges. In the context of school-enterprise cooperation, general higher vocational schools face huge challenges and opportunities in employment guidance. Under the school-enterprise cooperation model, the internship management of students during the internship process is a joint management behavior between the school and the enterprise, which is of great significance to deepen the school-enterprise cooperation. This paper changes the traditional analysis mode of school-enterprise cooperation in higher vocational education and applies sensitive neural network to the construction of school-enterprise cooperation development model in higher vocational education. On the basis of the sensitivity of Adaline neurons, this paper further gives the definition of the sensitivity of the Madaline network structure, deduces the sensitivity calculation of the Madaline network structure without too many constraints, and constructs an intelligent model. Through experimental research, we know that the school-enterprise cooperative development model of higher vocational education based on sensitive neural network proposed in this paper has good results.
The Relationship between Cloud Computing and Deep Learning towards Organizational Commitment

This article provides an integrative research model that integrates environmental, organizational, and technical capacity components to analyze theinfluence of cloud computing and deep learning adoption on company commitment. This study examines firms at various business factors to identify critical organizational characteristics that positively drive influence cloud computing appraisal and deep learning. In this context, a literature review on technological innovation features is conducted to recognize potential gaps in ongoing research. Cloud computing is altering the way goods are developed, allowing for greater collaboration between corporate IT and other business divisions such as sales, finance, and forecasting, and encouraging increased customerinvolvement, even to the point of collaboratively designing products with customers. The unique insights provided by the findings can aid businesses in improving their cloud computing usage and deeplearning. Big data and machine-learning capabilities are becoming more available from cloud computing, in addition to primary processing and storage. Due to the relationship between deep learning and the cloud, the firm gains competitive advantages that speed up processes and procedures, placing it on par with the market&#x0027;s key competitors and making it more strategically competitive and technologically well-organized. However, deep learning with the cloud cantransform business in productivity and efficiency to fulfil its commitment. This collaboration increases the company&#x0027;s ability to identify commercial opportunities by offering a comprehensive, well-defined, aggressive, and limitless (thanks to the cloud) database to manageand use strategically. Furthermore, the relationship between deep learning and cloud computing has acommercial impact since it enables automatic transaction identification and allocation, anomalydetection, and the ongoing development of predictive solutions. A cloud environment learns from the data itgathers, makes predictions, and fixes possible issuesbefore the user even realises they exist. These developments lead the company to recognise that cloudcomputing and deep learning are becoming increasingly entwined.
GraphCite: Citation Intent Classification in Scientific Publications via Graph Embeddings

Citations are crucial in scientific works as they help position a new publication. Each citation carries a particular intent, for example, to highlight the importance of a problem or to compare against results provided by another method. The authors’ intent when making a new citation has been studied to understand the evolution of a field over time or to make recommendations for further citations. In this work, we address the task of citation intent prediction from a new perspective. In addition to textual clues present in the citation phrase, we also consider the citation graph, leveraging high-level information of citation patterns. In this novel setting, we perform a thorough experimental evaluation of graph-based models for intent prediction. We show that our model, GraphCite, improves significantly upon models that take into consideration only the citation phrase. Our code is available online1.
MARS: A Cross-Platform Mobile AR System for Remote Collaborative Instruction and Installation Support using Digital Twins

This paper describes a multi-user, mobile, cross-platform AR system that allows real-time remote collaboration utilizing the digital twinning concept. Thanks to cloud services, the users can collab-oratively manipulate and exchange information using digital twin realized as detailed multi-part 3D models. We also discuss design requirements and task analysis captured using engineering design methodology and the usability verification of our system using a heuristical approach.
PSRMTE: Paper submission recommendation using mixtures of transformer

Nowadays, there has been a rapidly increasing number of scientific submissions in multiple research domains. A large number of journals have various acceptance rates, impact factors, and rankings in different publishers. It becomes time-consuming for many researchers to select the most suitable journal to submit their work with the highest acceptance rate. A paper submission recommendation system is more critical for the research community and publishers as it gives scientists another support to complete their submission conveniently. This paper investigates the submission recommendation system for two main research topics: computer science and applied mathematics. Unlike the previous works (Wang et al., 2018; Son et al., 2020) that extract TF–IDF and statistical features as well as utilize numerous machine learning algorithms (logistics regression and multiple perceptrons) for building the recommendation engine, we present an efficient paper submission recommendation algorithm by using different bidirectional transformer encoders and the Mixture of Transformer Encoders technique. We compare the performance between our methodology and other approaches by one dataset from Wang et al. (2018) with 14012 papers in computer science and another dataset collected by us with 223,782 articles in 178 Springer applied mathematics journals in terms of top K accuracy (K=1,3,5,10). The experimental results show that our proposed method extensively outperforms other state-of-the-art techniques with a significant margin in all top K accuracy for both two datasets. We publish all datasets collected and our implementation codes for further references.1
The Theory and Method of Data Acquisition of Mixed Traffic Popular People and Nonmotor Vehicles Based on Image Processing

In road mixed traffic, pedestrians and nonmotor vehicles have a great impact on the driving of motor vehicles. This kind of influence not only threatens the road traffic safety but also leads to the increase of delay and the decrease of traffic capacity. The purpose of this paper is to study the theory and method of data acquisition of mixed traffic popular people and nonmotor vehicles based on image processing technology. Aiming at the problem that the basic state space model solves the phenomenon of “failure” such as mutual interference between mixed objects, this paper proposes a KF tracking model based on a fuzzy matching method to realize the effective and accurate tracking of mixed traffic objects. The experimental results show that, after extracting the morphological features of the detected pedestrian and nonmotor vehicle images and using the method of pattern recognition to classify, recognize, and count the mixed traffic objects, through the comparison of the two trajectory lines, we can see that the tracking accuracy of the algorithm is high under the mutual interference of pedestrian and nonmotor vehicle. Excluding the detection error, the pedestrian tracking error is less than 10 pixels, the average error is 2.366 pixels, the maximum error of nonmotor vehicle tracking is 19 pixels, and the average error is 2.5 pixels.
Semantic Image Inpainting with Multi-Stage Feature Reasoning Generative Adversarial Network

Most existing image inpainting methods have achieved remarkable progress in small image defects. However, repairing large missing regions with insufficient context information is still an intractable problem. In this paper, a Multi-stage Feature Reasoning Generative Adversarial Network to gradually restore irregular holes is proposed. Specifically, dynamic partial convolution is used to adaptively adjust the restoration proportion during inpainting progress, which strengthens the correlation between valid and invalid pixels. In the decoding phase, the statistical natures of features in the masked areas differentiate from those of unmasked areas. To this end, a novel decoder is designed which not only dynamically assigns a scaling factor and bias on per feature point basis using point-wise normalization, but also utilizes skip connections to solve the problem of information loss between the codec network layers. Moreover, in order to eliminate gradient vanishing and increase the reasoning times, a hybrid weighted merging method consisting of a hard weight map and a soft weight map is proposed to ensemble the feature maps generated during the whole reconstruction process. Experiments on CelebA, Places2, and Paris StreetView show that the proposed model generates results with a PSNR improvement of 0.3 dB to 1.2 dB compared to other methods.
A Cascade Framework for Privacy-Preserving Point-of-Interest Recommender System

Point-of-interest (POI) recommender systems (RSes) have gained significant popularity in recent years due to the prosperity of location-based social networks (LBSN). However, in the interest of personalization services, various sensitive contextual information is collected, causing potential privacy concerns. This paper proposes a cascaded privacy-preserving POI recommendation (CRS) framework that protects contextual information such as user comments and locations. We demonstrate a minimized trade-off between the privacy-preserving feature and prediction accuracy by applying a semi-decentralized model to real-world datasets.
Decomposition and replacement: Spatial knowledge distillation for monocular depth estimation

Knowledge distillation has become a key technique for making smart and light-weight networks through model compression and transfer learning. Unlike previous methods that applied knowledge distillation to the classification task, we propose to exploit the decomposition-and-replacement based distillation scheme for depth estimation from a single RGB color image. To do this, Laplacian pyramid-based knowledge distillation is firstly presented in this paper. The key idea of the proposed method is to transfer the rich knowledge of the scene depth, which is well encoded through the teacher network, to the student network in a structured way by decomposing it into the global context and local details. This is fairly desirable for the student network to restore the depth layout more accurately with limited resources. Moreover, we also propose a new guidance concept for knowledge distillation, so-called ReplaceBlock, which replaces blocks randomly selected in the decoded feature of the student network with those of the teacher network. Our ReplaceBlock gives a smoothing effect in learning the feature distribution of the teacher network by considering the spatial contiguity in the feature space. This process is also helpful to clearly restore the depth layout without the significant computational cost. Based on various experimental results on benchmark datasets, the effectiveness of our distillation scheme for monocular depth estimation is demonstrated in details. The code and model are publicly available at : https://github.com/tjqansthd/Lap_Rep_KD_Depth.
Dynamic threshold strategy optimization for security protection in Internet of Things: An adversarial deep learning‐based game‐theoretical approach

Abstract As mobile communications, the Internet, databases, distributed computing, and other technologies continue to develop, the Internet of Things (IoT) has emerged as prevalent technique. However, attacks on security and sensitive data in IoT occur frequently, and these attacks often evade intrusion detection systems strategically by mutating their traffic. To prevent security threats and sensitive data leakage, we propose a game approach based on adversarial deep learning to optimize a dynamic security threshold strategy. We introduce a mobile edge computing framework and utilize a game model to describe the adversarial interaction between the two participants. To solve the complexity of the game problem to gain dynamically randomized adversarial attacks, we present a column generation (CG) framework, which uses a feedforward neural network to quantify data flowing through IoT devices. Considering the limited resources of IoT devices, we calculate an optimal response to cyberattacks via a particle swarm optimization algorithm, aiming to reduce the false alarm rate. The adversarial dynamic threshold (ADT)‐based column generation (CG‐ADT) algorithm generates the set of detection threshold and the probability. Finally, we present the results of experiments conducted to demonstrate the effectiveness and robustness of the proposed dynamic threshold scheme for sensitive data security protection in IoT and its suitability for implementation in production systems.
Attribute-based encryption with outsourced computation for access control in IoTs

There are many terminal devices with constrained computing resources in the IoT environment. The traditional lightweight attribute-based encryption schemes adopt third-party outsourcing computing to meet the lightweight access control requirements of constrained devices, but the correctness of outsourcing computing results is difficult to guarantee. To solve this problem, a lightweight attribute-based encryption scheme based on blockchain technology was proposed to realize outsourcing decryption based on the blockchain. However, the computational complexity of encryption process is much higher than that of the decryption process in attribute-based encryption schemes. This paper extends and improves the related work to further achieve outsourcing encryption based on blockchain. The proposed method can effectively reduce the burden of encryption computation on the user side.
Improving Information Cascade Modeling by Social Topology and Dual Role User Dependency

In the last decade, information diffusion (also known as information cascade) on social networks has been massively investigated due to its application values in many fields. In recent years, many sequential models including those models based on recurrent neural networks have been broadly employed to predict information cascade. However, the user dependencies in a cascade sequence captured by sequential models are generally unidirectional and inconsistent with diffusion trees. For example, the true trigger of a successor may be a non-immediate predecessor rather than the immediate predecessor in the sequence. To capture user dependencies more sufficiently which are crucial to precise cascade modeling, we propose a non-sequential information cascade model named as TAN-DRUD (Topology-aware Attention Networks with Dual Role User Dependency). TAN-DRUD obtains satisfactory performance on information cascade modeling through capturing the dual role user dependencies of information sender and receiver, which is inspired by the classic communication theory. Furthermore, TAN-DRUD incorporates social topology into two-level attention networks for enhanced information diffusion prediction. Our extensive experiments on three cascade datasets demonstrate that our model is not only superior to the state-of-the-art cascade models, but also capable of exploiting topology information and inferring diffusion trees.