Reviewerly/exHarmony huggingface dataset:
Simulation of athlete gait recognition based on spectral features and machine learning

The current technology related to athlete gait recognition has shortcomings such as complicated equipment and high cost, and there are also certain problems in recognition accuracy and recognition efficiency. In order to improve the efficiency of athletes’ gait recognition, this paper studies the different recognition technologies of athletes based on machine learning and spectral feature technology and applies computer vision technology to sports. Moreover, according to the calf angular velocity signal, the occurrence of leg movement is detected in real time, and the gait cycle is accurately divided to reduce the influence of the signal unrelated to the behavior on the recognition process. In addition, this study proposes a gait behavior recognition method based on event-driven strategies. This method uses a gyroscope as the main sensor and uses a wearable sensor node to collect the angular velocity signals of the legs and waist. In addition, this study analyzes the performance of the algorithm proposed by this paper through experimental research. The comparison results show that the method proposed by this paper has improved the number of recognition action types and accuracy and has certain advantages from the perspective of computation and scalability.
A Survey of Research on Image Data Sources Forensics

The development of technologies such as smart terminals and mobile Internet has made image data one of the most important forms of data in the Internet and personal storage media, and has grown at an alarming rate. As the most effective expression of information, image data can record various information when image content appears, and it can play an unparalleled role in restoring the truth of things. Therefore, the aim of efficiently and accurately identify the source of image is to determine the device that generated the data. It is an effective means of clustering data from the same device, and become a key step in helping to understand the full content. It is one of the core technologies for conducting electronic data forensic evidence. On the basis of summarizing and analyzing the image generation process, this paper analyzes the data shape and acquisition steps of the potential image generation device information, and then obtains the method of image data source identification. It also summarizes the existing related technologies and methods, comparative analysis of their applicability and potential development direction.
Real-time Vision-Language-Navigation based on a Lite Pre-training Model

Vision-Language-Navigation (VLN) is a challenging task that requires a robot to autonomously move to the destination based on visual observation following humans' natural language instructions. This paper presents a lite model based on the pre-training method, which can deal with real-time VLN task. Unlike previous traditional methods, our model achieves better performance and generalization thanks to adopting pre-training method. We introduce factorization and parameter sharing based on the PREVALENT model. These two lightweight approaches cause a 75% reduction of embedding parameters and a 77% reduction of the whole model parameters. About 17% of training time and 72.2% inference time are saved. At the same time, the performance of the original model was maintained, with a success rate (SR) and a success rate weighted by path length (SPL) consistent with the original model on the seen validation set (Seen Val) and a slight performance loss of about 1%-2% on the unseen validation set (Unseen Val).
Clustering of consecutive numbers in permutations under Mallows distributions and super-clustering under general p-shifted distributions

Let Al;k(n)⊂Sn denote the set of permutations of [n] for which the set of l consecutive numbers {k,k+1,⋯,k+l−1} appears in a set of consecutive positions. Under the uniform probability measure Pn on Sn, one has Pn(Al;k(n))∼l! nl−1 as n→∞. In one part of this paper we consider the probability of clustering of consecutive numbers under Mallows distributions Pnq, q>0. Because of a duality, it suffices to consider q∈(0,1). We show that for qn=1−c nα, with c>0 and α∈(0,1), Pnq(Al;kn(n)) is of order 1 nα(l−1), uniformly over all sequences {kn}n=1∞. Thus, letting Nl(n)= ∑k=1n−l+11Al;k(n) denote the number of sets of l consecutive numbers appearing in sets of consecutive positions, we have limn→∞EnqnNl(n)= ∞,ifl<1+α α;0,ifl>1+α α.. We also consider the cases α=1 and α>1. In the other part of the paper we consider general p-shifted distributions, Pn{pj}j=1∞, of which the Mallows distribution is a particular case. We calculate explicitly the quantity liml→∞lim infn→∞Pn{pj}j=1∞(Al;kn(n))=lim l→∞lim supn→∞Pn{pj}j=1∞(Al;kn(n)) in terms of the p-distribution. When this quantity is positive, we say that super-clustering occurs. In particular, super-clustering occurs for the Mallows distribution with fixed parameter q≠1.
Digital Economy: Overview of Definition and Measurement Criteria

Nowadays, the rapid development of information technology has opened up new development trends for countries. The concept of “Digital Economy” appears and is mentioned as an inevitable trend in the future. However, due to the advancement of technology platforms which results in such quick changes and applications in many fields that the concept of digital economy has not yet been defined accurately and universally, nor yet been clarified which activities are included in the measurement for the digital economy. The objective of this study is to give an overview of digital economy concepts and ways of measuring digital economy used by countries around the world. Further studies can be screened and adjusted to create a set of criteria for measuring digital economy for Vietnam.
Quality of Experience Evaluation for Streaming Video Using CGNN

One of the principal contradictions these days in the field of video i s lying between the booming demand for evaluating the streaming video quality and the low precision of the Quality of Experience prediction results. In this paper, we propose Convolutional Neural Network and Gate Recurrent Unit (CGNN)-QoE, a deep learning QoE model, that can predict overall and continuous scores of video streaming services accurately in real time. We further implement state-of-the-art models on the basis of their works and compare with our method on six public available datasets. In all considered scenarios, the CGNN-QoE outperforms existing methods.
A logic of defeasible argumentation: Constructing arguments in justification logic

In the 1980s, Pollock’s work on default reasons started the quest in the AI community for a formal system of defeasible argumentation. The main goal of this paper is to provide a logic of structured defeasible arguments using the language of justification logic. In this logic, we introduce defeasible justification assertions of the type t : F that read as “t is a defeasible reason that justifies F”. Such formulas are then interpreted as arguments and their acceptance semantics is given in analogy to Dung’s abstract argumentation framework semantics. We show that a large subclass of Dung’s frameworks that we call “warranted” frameworks is a special case of our logic in the sense that (1) Dung’s frameworks can be obtained from justification logic-based theories by focusing on a single aspect of attacks among justification logic arguments and (2) Dung’s warranted frameworks always have multiple justification logic instantiations called “realizations”. We first define a new justification logic that relies on operational semantics for default logic. One of the key features that is absent in standard justification logics is the possibility to weigh different epistemic reasons or pieces of evidence that might conflict with one another. To amend this, we develop a semantics for “defeaters”: conflicting reasons forming a basis to doubt the original conclusion or to believe an opposite statement. This enables us to formalize non-monotonic justifications that prompt extension revision already for normal default theories. Then we present our logic as a system for abstract argumentation with structured arguments. The format of conflicting reasons overlaps with the idea of attacks between arguments to the extent that it is possible to define all the standard notions of argumentation framework extensions. Using the definitions of extensions, we establish formal correspondence between Dung’s original argumentation semantics and our operational semantics for default theories. One of the results shows that the notorious attack cycles from abstract argumentation cannot always be realized as justification logic default theories.
Blockchain-based Scheme for Authentication and Capability-based Access Control in IoT Environment

Authentication and access control techniques are fundamental security elements to restrict access to critical resources in IoT environment. In the current state-of-the-art approaches in the literature, the architectures do not address the security features of authentication and access control together. Besides, they don't completely fulfill the key Internet-of-Things (IoT) features such as usability, scalability, interoperability and security. In this paper, we introduce a novel blockchain-based architecture for authentication and capability-based access control for IoT environment. A capability is a token which contains the access rights authorized to the device holding it. The architecture uses blockchain technology to carry out all the operations in the scheme. It does not embed blockchain technology into the resource-constrained IoT devices for the purpose of authentication and access control of the devices. However, the IoT devices and blockchain are connected by means of interfaces through which the essential communications are established. The authenticity of such interfaces are verified before any communication is made. Consequently, the architecture satisfies usability, scalability, interoperability and security features. We carried out security evaluation for the scheme. It exhibits strong resistance to threats like spoofing, tampering, repudiation, information disclosure, and Denial-of-Service (DoS). We also developed a proof of concept implementation where cost and storage overhead of blockchain transactions are studied.
THE USE OF MUSEUM FOR CULTURAL DAKWAH PURPOSES

This paper discusses the museum as a means of cultural da’wah. There are 2 main problems namely; how to interest visitors to the museum as a cultural propaganda suggestion and how is the intention of the museum visitors as a means of cultural propaganda? This study uses quantitative research types with analysis of cultural anthropological research and the science of da'wah. To measure the progress of this cultural propaganda can be seen through the level of visitor interest in Islamic artifacts and the level of visitor visits. The method and sample conducted a survey of 25 visitors using a questionnaire.&#x0D; The results found that the level of interest on Islamic artifacts and culture that are influenced by Islam with intention of visiting visitors in the museum showed the interest and intentions of cultural da’wah because of intense communication according to the communication patterns or da’wa patterns prevailing in the museum where a person or group of people came, observed, studied and understood everything related to Islam and culture that has been influenced by Islam through artifacts in the Central Sulawesi Museum over and over again.
Towards Applying Virtual Reality Techniques in Fostering Blended Learning of the Construction Technology

Effective learning of building technology is stemming from persistent practices in transforming information from construction drawings to appropriate responses in compliance with the industry standards and the government requirements. This type of training has been delivered in a conventional fashion involving formal lectures and studios-based practice sessions. Nevertheless, learners usually lack construction site experience and demand for more intensive guidance to understand the learning materials they received in classes. Unfortunately, this issue is more critical as professional trainers or academics in this area with sufficient industry experience is not readily available. Devising more effective training in construction technology shall be invaluable for the learners, trainers and the construction industry itself. Research findings suggest that virtual reality (VR) environment engender learners to make more references to critical thinking and higher-order cognition in compare to conventional teaching methods. In engineering education context, VR improves learners’ understanding about spatial arrangements and the link between two-dimensional and three-dimensional environment. A literature review that identified the types of VR techniques in application to construction education and professional training was conducted. Strengths and weaknesses of these applications were compared. The findings suggest that not all VR techniques are effective on fostering blended learning with integration among real-life construction site environment. A more effective form of VR technique should address the limitation of hardware, class size and conditions.
Algorithm Research of Magnetometer Assisted Inertial Navigation System for Mobile Robots

Mobile robots have broad application prospects in military, industrial, agricultural, commercial, transportation, and logistics fields. The core problem of mobile robot lies in the autonomous navigation ability of mobile robot. Inertial navigation system based on MEMS sensor is one of the research hotspots in the field of inertial navigation in recent years and one of the main research directions in the future. Aiming at the positioning of the mobile robot, this paper adopts the magnetometer assisted inertial navigation system, the navigation system consisting of a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer. The heading is calculated with three sensors, the velocity error is compensated, and calculate the position. In this paper, an Extended Kalman Filter for mobile robot navigation system is proposed to improve the position accuracy of the navigation system. Experiments are carried out to verify and analyze the filter with the experimental results.
Infrared Image ROI Encryption Method Based on Lorenz System

In order to improve the security of monitoring system and the speed of full image encrypting, the concept of Region of interest encryption for infrared image is advanced in the paper. Based on the Lorenz system, the ROI encryption methods are put forward to. Compared to the matrix transform ROI encryption, the Lorenz ROI encryption has better performance on operation speed, security and encryption effect.
Learning Local Feature Descriptors for Multiple Object Tracking

The present study aims at learning class-agnostic embedding, which is suitable for Multiple Object Tracking (MOT). We demonstrate that the learning of local feature descriptors could provide a sufficient level of generalization. Proposed embedding function exhibits on-par performance with its dedicated person re-identification counterparts in their target domain and outperforms them in others. Through its utilization, our solutions achieve state-of-the-art performance in a number of MOT benchmarks, which includes CVPR'19 Tracking Challenge.
Application of complex systems in neural networks against Backdoor attacks

Through the success of artificial neural networks (ANNs) in different domains and their increasing computational complexities, third parties and MLaaS (machine learning as a service) has been vastly used to do the training procedure. Hence the high possibility for malicious training recently caused intense researches centered on making these ANNs robust against various types of attacks such as backdoors. Backdoor attacks makes the ANN to behave normally on clean data but causes targeted misclassification in presence of the trigger. In this paper we provide the first investigation about the influence of applying complex systems such as random and scale-free networks instead of fully-connected structures on the robustness of feed forward neural networks (FFANNs) against backdoor attacks.
MAST: Myo Armband Sign-Language Translator for Human Hand Activity Classification

As Computer Science has grown into an encompassed field in various scientific areas, the need for developing a computer aided and artificially intelligent device has become more important especially in the medical field. Artificial Intelligence (AI) plays a vital role not only in accelerating and optimizing common tasks but also in performing tasks that humans are incapable of. This paper presents a Myo Armband Sign-Language Translator (MAST), which is a novel algorithm to translate a hand's gestures into medical sign language using a Myo armband sensor which collects muscles' electromyography signals and then to classify them using an enhanced version of a dynamic random forest. Our experimental results indicate that a systematic fine tuning of MAST parameters leads to an accuracy improvement of 13% over the state-of-the-art scheme such as SCIKIT's random forest. Other comparison results show an improvement of over 20% compared to a popular classification scheme such as Support Vector Machines (SVM) and a deep learning technique such as Convolutional Neural Network (CNN).
Integrated Augmented Reality Feedback for Cochlear Implant Surgery Instruments

In this article, we present a visualization system to provide assistance in cochlear implant surgery which can be seamlessly integrated within the devices that are currently used in surgery. The system is intended to improve tool alignment in positioning and during insertion, with the aim of reducing the problems encountered during perimodiolar electrode array insertion. Our system is composed of a semi-autonomous hand-held surgical tool, coupled with an optical tracker to monitor the tool position and an operating microscope. The microscope live view is overlaid with guidance information in the form of augmented reality to assist the surgeon in positioning the surgical tool and maintain that position during insertion. Our approach shows promising results in tool alignment, which are comparable to the state of the art.
NIR Image Colorization Using SPADE Generator and Grayscale Approximated Self-Reconstruction

Near infrared (NIR) images are robust to ambient light and contain clear textures in low light condition. In this paper, we propose NIR image colorization using spatial adaptive denormalization (SPADE) generator and grayscale approximated self-reconstruction. Compared with traditional image to image translation methods, the proposed NIR colorization pursues photorealism rather than generative diversity. The challenge of this task is NIR-RGB mis-registration in training data. We address this problem by separately extracting NIR texture and RGB color with an end to end SPADE based model. Moreover, the proposed method facilitates a more precise synthesis with a given low light RGB reference image. Experiments on an open NIR-RGB dataset verify that the proposed method effectively preserves NIR textures and RGB colors in the synthesized results and outperforms the baselines in terms of visual quality and quantitative assessments.
Improvement of insurance agents performance using data mining in OV agency

Abstract One problem that occurs in the insurance industry is the high turnover rate of insurance agents. It makes many companies must pay a large fee for the recruitment and training of new agents. OV Agency had high insurance agent turnover rate. This company wants to improve the agents performance to avoid the turnover. This research aims to identify prospective insurance agents who have better performance at recruitment process by using classification task of data mining. There are three classification methods were used . They were decision tree (J48), Neural Network (MLP) and Naive Bayes. Based on the result, MLP has the best average accuracy value 84.2% and ROC area value 0.87 (good classification). MLP was chosen to predict the 25 insurance agent candidates and 3 agents were selected because they entered class A (better performance) in all performance criteria. The prediction results show that three agents are men, aged 25-32 years old, divorced, senior high school education and master degree, work status in other companies, and have work experience of 0-3 years. The OV Agency can consider these result in new agent recruitment process.
Performance Characteristics of NTRU and ECC Cryptosystem in context of IoT Environment

With the increased use of Internet-of- Things (IoT) devices for daily activities, the necessity of improving the speed and performance of such devices also arise. The devices produce sensitive data at a massive and rapid rate. Since the infrastructure available with such small-scale devices are constrained, an algorithm which provides better security performance and is faster within the constraints is necessitated. This paper discusses two asymmetric algorithms NTRU (Nth Degree Truncated Polynomial) and Elliptic Curve Cryptography (ECC), which are widely known to be secure. ECC is a popular algorithm and widely used because of its low-key size. However, it can be easily broken by quantum computers. NTRU is one of the most recognized algorithms that can resist even quantum attacks. It has less computational complexity due to simple polynomial operations. The need is for an algorithm to provide ample security while taking the time of the computational complexities into consideration. The performance of ECC and NTRU have been evaluated based on key generation time, encryption time, and decryption time. Text data provided by the small-scale IoT devices has been considered for analysis. The analysis gives a deeper view of both algorithms.
Spatial and Channel Attention Modulated Network for Medical Image Segmentation

Medical image segmentation is a fundamental and challenge task in many computer-aided diagnosis and surgery systems, and attracts numerous research attention in computer vision and medical image processing fields. Recently, deep learning based medical image segmentation has been widely investigated and provided state-of-the-art performance for different modalities of medical data. Therein, U-Net consisting of the contracting path for context capturing and the symmetric expanding path for precise localization, has become a meta network architecture for medical image segmentation, and manifests acceptable results even with moderate scale of training data. This study proposes a novel attention modulated network based on the baseline U-Net, and explores embedded spatial and channel attention modules for adaptively highlighting interdependent channel maps and focusing on more discriminant regions via investigating relevant feature association. The proposed spatial and channel attention modules can be used in a plug and play manner and embedded after any learned feature map for adaptively emphasizing discriminant features and neglecting irrelevant information. Furthermore, we propose two aggregation approaches for integrating the learned spatial and channel attentions to the raw feature maps. Extensive experiments on two benchmark medical image datasets validate that our proposed network architecture manifests superior performance compared to the baseline U-Net and its several variants.