Reviewerly/exHarmony huggingface dataset:
Self Attention Guided Depth Completion using RGB and Sparse LiDAR Point Clouds

We address the problem of completing per pixel dense depth map using a single RGB image and the sparse point cloud of the scene. Depth prediction from RGB image is a hard problem and while dense point clouds obtained from LiDAR sensors can be used in addition to RGB image, the cost of such sensors is a significant barrier. Having LiDAR sensors which capture sparse point clouds is a reasonable middle ground. We propose a novel architecture which incorporates geometric primitives and self attention mechanisms, to improve the prediction. The motivation of self attention is to capture the correlations between scene and object elements, e.g. between the right and left window of car, early on in the network. While that for using geometric primitives is to have a high level clustering cue to enable the network to exploit similar correlations. In addition, we enforce complimentarity in the predictions made with RGB and sparse LiDAR respectively, this forces the two corresponding branches to focus on hard areas which are not already well predicted by the other branch. With exhaustive experiments on KITTI depth completion benchmark, NYU v2 and Matterport3D we show that the proposed method provides state-of-the-art results.
Design of NNEF-PyTorch Neural Network Model Converter

There are various types of inference engines based on neural networks. These engines define their own neural network representation models. Because each inference engine uses a different neural network representation model, there is a difficulty in sharing the trained data with each other. To solve this interoperability problem, a standard neural network model format called NNEF (Neural Network Exchange Format) has been proposed. If the result data trained through the training engine is stored in a standard neural network model format and a neural network model converter is provided for each inference engine, interoperability among the training engines and the inference engines can be increased. In this paper, we designed a NNEF-PyTorch neural network model converter for PyTorch, one of the neural network engines for neural network training and inferencing. To verity the converter, we show that experimental neural network model written in NNEF can be executed on PyTorch engine after the converter transformed the model into PyTorch model.
Yetişkinler Çevrimiçi Sosyal Ağlarda Neden Bebek Gibi Konuşur? Edimsel bir Yüz Aracı Olarak Bebek Konuşması

This study aims to explore the motives and pragmatic functions of baby talk in adultto- adult communication in the online space. By concentrating on a recent communicative trend of using scripted baby talk in Turkish language by the Turkish social media users, we seek to answer why adults collectively adopt a speech register which is primarily used in adult-infant communication in their online socializations. Drawing on the Goffmanian notion of face and the theory of (im)politeness, the study argues that baby talk among adults in the online space functions as a powerful multidirectional and multifunctional face device addressing the notion of face in diverse directions and communicative goals. The most significant pragmatic functions of adult baby talk are found to be attacking/threatening one’s face, responding to face attacks and lastly enhancing/boosting one’s face. Face attacks were observed to occur through imposing sarcasm, mock-politeness, verbal aggression and insult while face boosting communicates affection, admiration and love. We also suggest that new affordances of the online space lead to the transformation of baby talk as a register among adults and its recontextualization as a tool for a new way of online language socialization.
Monitoring Method for devices of Server Racks Using Linear Modules

In the server rooms of data centers, operation and management are the responsibility of administrators. Facility management of the data centers is an important task for the administrators. A regular check of devices of server racks is essential in facility management. Natural disasters and pandemics can affect the work of administrators. For example, administrators may not be able to reach the data center. Therefore, we considered that remote monitoring of server racks would be effective. This paper proposes a method to monitor the devices of server racks by installing a linear module with a web camera in server racks. LED indicators of the devices monitor the status of the devices of server racks. We experimented with a monitoring system that implements the proposed method and discussed the monitoring method and improvements.
Digital System Design for Quantum Error Correction Codes

Quantum computing is a computer development technology that uses quantum mechanics to perform the operations of data and information. It is an advanced technology, yet the quantum channel is used to transmit the quantum information which is sensitive to the environment interaction. Quantum error correction is a hybrid between quantum mechanics and the classical theory of error-correcting codes that are concerned with the fundamental problem of communication, and/or information storage, in the presence of noise. The interruption made by the interaction makes transmission error during the quantum channel qubit. Hence, a quantum error correction code is needed to protect the qubit from errors that can be caused by decoherence and other quantum noise. In this paper, the digital system design of the quantum error correction code is discussed. Three designs used qubit codes, and nine-qubit codes were explained. The systems were designed and configured for encoding and decoding nine-qubit error correction codes. For comparison, a modified circuit is also designed by adding Hadamard gates.
Data-driven Construction of Rural Persona Based on Text Mining and Knowledge Association

The implementation of rural revitalization strategy in China makes “rural tourism” become one of the key industries of rural development. A comprehensive mining of the characteristics of various villages is helpful to promote the overall development of rural economy and realize the modernization of agriculture and rural areas. Based on big data and relevant policy texts, this paper develops a labelling framework, and proposes a deep mining of the rural themes and labels by integrating the deep learning-based text classification and LDA topic mining. In the study, a knowledge graphs of 4,814 entities and 7,369 relationships including villages, scenic spots and featured products is constructed using multi-source heterogeneous data, and we also realizes label fusion and system construction. Through the visualization and analysis of the rural panoramic feature persona, the development modes and the features of villages are described in different aspects. By the construction of rural personas, the study presents the typical features of key villages in rural tourism in China from multiple perspectives, which provides a strong data support for building the recommendation system of online tourism websites and is of great value for promoting the rural tourism.
Identifying HRM Practices for Improving Information Security Performance

This article focuses on identifying key human resource management (HRM) practices necessary for improving information security performance from the perspective of IT professionals. The Importance-Performance Map Analysis (IPMA) via SmartPLS 3.0 was employed and 232 samples were collected from information technology (IT) professionals in 43 organizations. The analysis identified information security training, background checks and monitoring as very important HRM practices that could improve the performance of organizational information security. In particular, the study found training on mobile devices security and malware; background checks and monitoring of potential, current and former employees as of high importance but with low performance. Thus, these key areas need to be improved with top priority. Conversely, the study found accountability and employee relations as being overly emphasized by the organisations. The findings raised some useful implications and information for HR and IT leaders to consider in future information security strategy.
Clustering and Multimodal Analysis of Participants in Task-Based Discussions

Participants in task-based conversational interactions are clustered using outcomes of interest that include task performance, satisfaction ratings, and demographic traits. Each cluster is described in terms of the member participants’ common characteristics, and we perform participant outlier detection as well. We extract multimodal features of the conversational interaction and analyze how the participant groups differ in terms of these features.
FINN: Feature Interaction Neural Network for Person-Job Fit

Person-Job Fit is one of the most critical steps in the recruitment chain. Existing studies on the Person-Job Fit problem either heavily depend on manually generated features or only focus on learning good representations of resumes and jobs based on the textual features. To this end, with the comprehensive consideration of numerical, categorical, and semantic features, we propose a novel end-to-end Feature Interaction Neural Network (FINN) to predict the matching scores between jobs and resumes. The key idea is to learn the latent vector of interacting signals in the type of categorical and semantic features. Specifically, we design several special operators to capture the interacting signals of categorical features between jobs and resumes. Meanwhile, the convolutional neural networks are introduced to model the interactions based on a word-level similarity matrix. We validate the effectiveness of model FINN by comprehensive experiments on a large-scale real-world dataset, which consists of 452,214 jobs and 651,711 resumes. Compared with several classical supervised learning methods, the proposed model can achieve an improvement of 1.96%-24.46% in the metric accuracy, F1 and AUC.
Towards Autonomous Parking using Vision-only Sensors

Existing autonomous parking solutions usually require special signs, pre-built maps or accurate ranging sensors to achieve reliable perception of the parking environment, but these methods are difficult to popularize because they either require preconditions or are expensive for production cars. In this paper, we propose a vision-only autonomous parking solution based on only six cameras. Through the appropriate depth estimation algorithms, our method obtains the pixel level depth of the image, and constructs a dense point cloud, so as to realize the fine perception of the parking environment. An improved Radon transform based parking space detection method are applied for better parking space detection method. Our proposed method achieves processing speed of above 5 Hz on a intermediate level computing platform. Furthermore, we demonstrate the practicability of the proposed system in real-world parking lots.
Exploring the impact of similarity index on the accuracy of a phishing site detection model using machine learning

Abstract In this paper, the problem of phishing site detection using machine learning is discussed. The main goal is to study the effectiveness of various binary classification models when extracting only lexical features from a URL. Special attention has been given to the analysis of features obtained from the domain by calculating the similarity index using the whitelist. After training and testing the models, accuracy metrics were calculated and the results were compared. The lexical features that have the greatest weight for the classification of URLs are highlighted, and the advantages and disadvantages of this approach are described.
Guiding Robot Model Construction with Prior Features

Virtually all robot control methods benefit from the availability of an accurate mathematical model of the robot. However, obtaining a sufficient amount of informative data for constructing dynamic models can be difficult, especially when the models are to be learned during robot deployment. Under such circumstances, standard data-driven model learning techniques often yield models that do not comply with the physics of the robot. We extend a symbolic regression algorithm based on Single Node Genetic Programming by including the prior model information into the model construction process. In this way, symbolic regression automatically builds models that compensate for theoretical or empirical model deficiencies. We experimentally demonstrate the approach on two real-world systems: the TurtleBot 2 mobile robot and the Parrot Bebop 2 drone. The results show that the proposed model-learning algorithm produces realistic models that fit well the training data even when using small training sets. Passing the prior model information to the algorithm significantly improves the model accuracy while speeding up the search.
Cross-view Matching Neural Network for Remote Sensing Images

In the research of remote sensing image processing, cross-view image matching has always been a researching focus. Due to the changing views of the sensor's observation, the objects feature often have large differences from different views, which lead to the failure of matching method based on image descriptors. Therefore, cross-view remote sensing image matching is a challenging work. We proposed a two-step cross-view image matching method based on deep neural networks: First, the pre-trained Faster RCNN object detection model is used to detect the drone images (search images) and we will get the targets region images through the detection results. Then, the optimized Siamese neural network model is used between the detection results (region images) and the satellite images (template images) to get the results of matching confidence. In this paper, comparative experiments were implemented on the university-1652 dataset, and the experimental results show that our method has excellent results on this benchmark database. The effectiveness of proposed method was demonstrated by evaluated on varying images, the R@10/%, and AP are 88.02% and 67.23% repectively.
A Tribute to José António Tenreiro Machado (1957–2021): Life and Work

José António Tenreiro Machado (Figure 1) left us unexpectedly on 6 October, the day of his 64th birthday [...]
Remote sensing, SWIR, Raman and XRD applications in Pesovets epithermal system mapping, Panagyurishte ore district, Bulgaria

Remote sensing UAV based study combined with field mapping, SWIR, XRD Raman and XRF tests for mineral detection outlined advanced argillic alteration domains in the Pesovets silica cap to demonstrate quick approach for epithermal gold exploration targeting and evaluation. As and Ti increasing trend toward epithermal high-sulphidation Cu-Au mineralization could be employed as a proximal path finder.
IR-MF-SVMe: Image Retrieval using Multiple Features Extraction with Support Vector Machine ensemble

Image representation is a widespread strategy of image retrieval based on appearance, shape information. The traditional feature representation methods ignore hidden information that exists in the dataset samples; it reduces the discriminative performance of the classifier and excludes various geometric and photometric variations consideration in obtaining the features; these degrade retrieval performance. Hence, proposed multiple features fusion and Support Vector Machines Ensemble (IR-MF-SVMe); an Image Retrieval framework to enhance the performance of the retrieval process. The Color Histogram (CH), Color Auto-Correlogram (CAC), Color Moments (CM), Gabor Wavelet (GW), and Wavelet Moments (WM) descriptors are used to extract multiple features that separate the element vectors of images in representation. The multi-class classifier is constructed with the aggregation of binary Support Vector Machines, which decrease the count of false positives within the interrelated semantic classes. The proposed framework is validated on the WANG dataset and results in the accuracy of 84% for the individual features and 86% for the fused features related to the state-of-the-arts.
Marine Vehicle Tracking using Agile Maritime IoTs

Marine surveillance deals with the complex problem of modeling and estimating natural and human processes while operating with limited information acquisition resources. The complexity of the problem and the scarcity of resources, such as power, processing, and communications, requires agile distributed sensing and computing that provides scalability, diversity in information acquisition, and adaptive allocation of limited resources. This work deals with tracking multiple marine vehicles using data from heterogeneous sensors. Accurate tracking is achieved by a synergy between a network of agile maritime IoTs, with limited processing resources, and a fusion center, with computationally intensive capability. The IoTs can reconfigure their elementary processing operations settings to produce informative yet low volume measurement statistics. The fusion center handles heterogeneous data fusion and solves the complex problem of vehicle state estimation. We demonstrate that by configuring its agile distributed computing capabilities, the proposed system provides significant savings in processing and communications resources without deteriorating tracking performance.
Higher Accuracy Entity Recognition of Power Grid through Bert-BiLSTM-CRF

Power grid enterprises collect a large number of data resources in the process of production management, including management documents, technical standards, project application documents, experimental reports, technical reports, etc. The location and retrieval speed of related text file data determine the efficiency of problem solving. Some related work has built a semantic search system in power field based on knowledge map [1], but only some preliminary ideas and methods are given. This paper introduces a power text entity recognition technology based on deep transfer learning Bert bilstm CRF, which can get better accuracy without a large amount of manual intervention. The experimental results show that the accuracy of the model in system, technology, equipment and other professional fields is greatly improved compared with the traditional annotation method.
Robust Federated Learning with Adaptable Learning Rate

Federated learning(FL), one of the most popular distributed machine learning algorithms, allows participants to collaborate on updating models without sharing local data. At the same time, FL is vulnerable to malicious attacks due to the secrecy of the data. Backdoor attacks, as one of many malicious attacks, attempt to add a backdoor to the global model, which will lead to misclassification after activation. Backdoor attacks are difficult to detect. To defend against backdoor attacks, we designed a defense method deployed on the central server side. The method autonomously adjusts the learning rate based on the received parameter signs form all participants to reduce the impact of malicious updates. It is shown experimentally that the method can significantly reduce the impact of backdoor attacks while maintaining high model accuracy on clean datasets when the correct threshold is set.
Development of safety diagnostic robot for industrial plant environment

In this paper, a robot for safety diagnosis is presented to solve the problem of safety diagnosis at industrial plant sites. Safety diagnosis is essential for industrial plants because accidents at industrial plants cause huge economic losses as well as casualties. However, safety diagnosis is difficult due to various dangers such as suffocation and gas explosions in industrial factories. Therefore, we have developed a robot that performs safety diagnoses instead of humans. We designed a mobile robot with various environmental sensors for safety diagnosis and autonomous navigation and installed autonomous mission performance software to enable unmanned safety diagnosis. When a robot receives a mission from the manager, it plans suitable for the mission and moves autonomously through an autonomous navigation algorithm and conducts safety diagnosis at the target point. It also visually shows the current state through the user interface and informs the user of the current diagnostic results. In this paper, we tested robots in an industrial plant environment and confirmed that robots can replace humans.
Grouping Synchronous to Eliminate Stragglers with Edge Computing in Distributed Deep Learning

With the development of artificial intelligence(AI) applications, a large number of data are generated from mobile or IoT devices at the edge of the network. Deep learning tasks are executed to obtain effective information in the user data. However, the edge nodes are heterogeneous and the network bandwidth is limited in this case, which will cause general distributed deep learning to be inefficient. In this paper, we propose Group Synchronous Parallel (GSP), which uses a density-based algorithm to group edge nodes with similar training speeds together. In order to eliminate stragglers, group parameter servers are responsible for coordinating communication of nodes in the group with Stale Synchronous Parallel and aggregating the gradients of these nodes. And a global parameter server is responsible for aggregating the gradients from the group parameter servers to update the global model. To save network bandwidth, we further propose Grouping Dynamic Sparsification (GDS). It adjusts the gradient sparsification rate of nodes dynamically based on GSP so as to differentiates the communication volume and makes the training speed of all nodes tend to be the same. We evaluate GSP and GDS’s performance on LeNet-5, ResNet, VGG, and Seq2Seq with Attention. The experimental results show that GSP speedups the training by 45% ~ 120% with 16 nodes. GDS on top of GSP can make up for some test accuracy loss, up to 0.82% for LeNet-5.
An improved Rapidly-exploring Random Tree Approach for Robotic Dynamic Path Planning

Aiming at solving the issue that the existing Rapidly-exploring Random Tree (RRT) algorithm cannot well replan the paths to avoid dynamic obstacles for robotic manipulator autonomously and rapidly in complex cluttered environments, three-dimensional reconstruction of the global dynamic scene around the robotic manipulator is carried out based on RGB-D visual sensor in this paper. A Bi-RRT-Star dynamic path planning approach based on improved exploring function with goal direction is proposed, which is improved from connection strategy, heuristic intensive exploring, and adjacent nodes expansion. On this basis, a multi-step expansion strategy with heuristic greedy is presented. Finally, the relevant evaluation indices of the proposed approach are verified in Virtual Robot Environment Platform (VREP) software. The simulation results show that in comparison with Bi-RRT and RRT-Star algorithms, the proposed method has a higher success rate in dynamic path planning online with less planning time and lower trajectory cost. In addition, a realistic experiment is designed to make UR robotic manipulator avoid human arm random motions dynamically. The experimental results show that the proposed method successfully realizes that robotic manipulator can avoid continuous moving obstacles of human arm online smoothly, comprehensively verifying the effectiveness and superiority.
Investigation on the Influence of Virtual Reality Displays on People’s Interaction

Virtual reality is a computer simulation system developed based on computer graphics and human-computer interaction technology. With the progress of science and technology, virtual reality has made great progress and brought changes to the way of social production and communication. This paper summarizes the development history of virtual reality and related display technology, and then analyzes the main application of virtual reality display technology. The application of virtual reality display technology in social contact was investigated by means of questionnaire survey. The results show that virtual reality display technology has been widely used in young people’s social contact and enhanced their experience in social contact.