Reviewerly/exHarmony huggingface dataset:
Improved YOLOv5 for Dense Wildlife Object Detection

Wildlife plays a very important role in the ecological balance of the earth. With the rapid development of computer vision, we can use object detection techniques to count the number of wildlife for their better conservation; however, some wildlife live mainly in groups, so the datasets are often distributed in a dense state. It is difficult in dense environments since boxes for different objects should be preserved and duplicate detections should be suppressed at the same time. It is challenging to detect dense wildlife. To solve the problem, we propose an improved model based on YOLOv5. Firstly, we add a Convolutional Block Attention Model(CBAM) to enable the network to extract the richer wildlife features. Secondly, Distance Intersection over Union-Non Maximum Suppression(DIoU-NMS) is used to solve the problem of multiple-objects overlap in wildlife detection to reduce the redundancy of the wildlife. Finally, Efficient Intersection over Union Loss(EIoU Loss) is used to speed up the convergence of the loss function. The experimental results show that our model achieved good performance on the public dataset Wildlife. Compared with the basic YOLOv5s, it reaches 94.2% in mAP@0.5 and achieves average increments by 2.1% . It improves the application and practicability of object detection technology in dense wildlife detection, which is encouraging and meaningful.
Ontology-Based Representation of an Artificial Neural Networks

The paper considers a method for combining knowledge bases and artificial neural networks to solve complicated problems. A model for artificial neural networks (ANN) representation and actions for their processing in the knowledge base is required and is justified. The subject domains and associated ontologies for the following concepts are used to represent this model: a) ANN; b) actions for ANN processing. This page describes these subject domains and ontologies.
Consensus Algorithms for Blockchain

A consensus algorithm is an essential component of a blockchain, responsible for reaching an agreement among decentralized nodes. It also determines the performance and characteristics of an application. With more than 2,000 different cryptocurrencies currently in use, we face an ever-growing list of consensus algorithms. Furthermore, the inherent complexity of consensus algorithms and their rapid evolutions make it hard to assess their suitability for blockchain applications. Understanding the pros and cons of a consensus algorithm is crucial in designing new blockchain services and developing more advanced algorithms. We propose a framework with comprehensive criteria to evaluate consensus algorithms in terms of performance, security, and decentralization. In addition, we present the operational mechanisms and analyze the characteristics of mainstream consensus algorithms, namely, proof-based algorithms such as Proof of Work (PoW) and Proof of Stake (PoS), and vote-based algorithms with Byzantine Fault Tolerance (BFT). The algorithms are evaluated based on our proposed framework to provide a better understanding. We hope this article leads us to identify research challenges and opportunities of consensus algorithms.
VLSP 2021 - ASR Challenge for Vietnamese Automatic Speech Recognition

Recently, Vietnamese speech recognition has been attracted by various research groups in both academics and industry. This paper presents a Vietnamese automatic speech recognition challenge for the eighth annual workshop on Vietnamese Language and Speech Processing (VLSP 2021). There are two sub-tasks in the challenge. The first task is ASR-Task1 focusing on a full pipeline development of the ASR model from scratch with both labeled and unlabeled training data provided by the organizer. The second task is ASR-Task2 focusing on spontaneous speech in different real scenarios e.g., meeting conversation, lecture speech. In the ASR-Task2, participants can use all available data sources to develop their models without any limitations. The quality of the models is evaluated by the Syllable Error Rate (SyER) metric.
Chemisim: A Web-based VR Simulator for Chemistry Experiments

In developing countries, high schoolers rarely have opportunities to conduct chemical experiments due to the lack of facilities. There-fore, chemistry experiment simulation is an alternative environment for students to do the chemistry lab assignments. Despite the need of creating virtual simulations to expand the application usability, it is challenging to synthesize a realistic environment given the limited computing resources. In this paper, we propose Chemisim, a highly realistic web-based VR laboratory simulation for students with high quality and usability. In particular, we make use of the fluid simulation system to mimic real chemical reactions. The imple-mented simulation was based on the chemistry assignments in the national education system, consulted by chemical teachers. Then we deployed the simulator on the web to promote a wide range of students usage. The system was evaluated by collecting and analyzing feedback from chemical teachers based on four criteria, namely, convenience, realism, functionality, and preferences. Our experimental findings address educational challenges and produce innovative technical solutions to solve them in developing countries.
An Analysis of Human Perception of Partitions of Numerical Factor Domains

In Machine learning (ML), several discretization techniques and mathematical approaches are used to partition numerical data attributes. However, cut-points retrieved by discretizing techniques often do not match with human perceived cut-points. Therefore, understanding the human perception for discretizing the numerical attribute is important for developing an effective discretizing technique. In this paper, we conduct a study of human perception of partitions in numerical data that reflects best the impact of one independent numerical attribute on another dependent numerical attribute. We aim to understand how expert data scientists and statisticians partition numerical attributes under different types of data points, such as dense data points, outliers, and uneven random points. The findings lead to an interesting discussion about the importance of human perception under distinct kinds of data points for finding partitions of numerical attributes.
Human Activities Recognition Based on FA-FlowNet and Multi-channel Weighted Fusion

Two-stream convolutional neural network (CNN) is one of the hot spots in human activities recognition from videos. In this paper, a new two-stream CNN is proposed by improving the optical flow extraction neural network and presenting a novel fusion method. Specifically, the proposed two-stream CNN is composed of dynamic-data-stream subnetwork (DDS-SN), static-data-stream subnetwork (SDS-SN), and fusion network. The input of DDS-SN is optical flow image sequence generated by the proposed FA-FlowNet from the video segments. And the input of SDS-SN is RGB image sequence of the video segments. In both DDS-SN and SDS-SN, the long-term recurrent convolutional network with GoogleNet is adopted to create the predicted recognition results. After that, the proposed multi-channel weighted fusion method is applied to integrate the results of DDS-SN and SDS-SN. The performance improvements of two-stream CNN by introducing the developed technologies, including FA-FlowNet, multi-channel weighted fusion method, and GoogleNet, have been demonstrated by experimental results.
Nested Densely Atrous Spatial Pyramid Pooling and Deep Dense Short Connection for Skeleton Detection

The skeleton shows the local symmetry and the shape/topology of the object, and it is utilized for human pose recognition, road detection, text detection, and the representation of industrial parts. However, the size of the skeleton is variable, which makes high-level feature representation difficult. Existing methods only attempt to integrate multilevel features but ignore the extraction of high-level features and multiscales of contextual information that are helpful for the skeleton detection task. Thus, the contributions of this article include two aspects. The first contribution is to propose a nested densely atrous spatial pyramid pooling method that connects the atrous convolutions with different dilation rates in a nested cascade mode, which can provide the multiscale denser contextual information, a larger receptive field, and more local features. The second one is to propose a deep dense short connection (DDSC) that explores the role of features at different levels in the task of skeleton detection. DDSC adopts concatenation to fuse high-level semantic information with shape information. The proposed method is evaluated on four common datasets, and the experimental results show the effectiveness of the proposed method.
Design and Implementation of YANG Model Performance Evaluation Platform for Transport Network Management Interface

With the development of northbound transport network management interface technology, the performance of interface is more and more concerned. The standard of evaluating the performance consist of many factors like function completeness, structure complexity, syntax accuracy, reliability and so on. However, how to manage and evaluate certain network interface models is always a perplexing problem. At present, most evaluation methods of network interface performance still rely on expert experience after deployment and use, resulting in a lot of waste of time and cost. Since the generation of YANG model provides a new idea for operating unified model of different network architectures and standardization of platform interfaces, it is necessary to establish an interface model performance analysis and evaluation tool to measure the performance of YANG models of transport network management interface. This report raises a transport network northbound management interface evaluation system. The system supports users evaluating certain network interface immediately. This evaluation system reduces reliance on expert experience for interface performance evaluation and the cost of time compared with traditional methods.
Physical Traces and Materialization of Songs for Individuals’ Music Participation in Cafés: The Design and Field Studies of Camue

Expressing and sharing an individual’s music in public spaces are part of making creative culture. We designed a frame-shaped song ticket shredding speaker, called Camue, to investigate how the materialization of songs and their physical traces can facilitate music playing and sharing experiences in public spaces. Camue was deployed in two cafés for three weeks, providing music playback experience to 41 customers and 10 recruited participants. The findings showed the materialization of songs and physical traces affected users to motivate and engage their participation. The exposure of users’ actions when playing songs by putting a tangible paper ticket into Camue helped users to mediate between their personal music taste while considering the public ambience of the café. Our findings imply new insights into how the co-creation of physical remnants can trigger meaningful interpretations, and what factors are needed for designing artefacts that enable better music expression of individuals in public.
Chronos: Accelerating Federated Learning With Resource Aware Training Volume Tuning at Network Edges

Due to the limited resources and data privacy issue, last decade witnesses the fast development of Distributed Machine Learning (DML) at network edges. Among all the existing DML paradigms, Federated Learning (FL) would be a promising one, since in FL, each client trains its local model without sharing the raw data with others. A community of clients with the same interest can join together to derive a high-performance model by periodically synchronizing the parameters of their local models under the help of a coordination server. However, FL will encounter the straggler problem at network edges, and hence the synchronization among clients becomes inefficient. It slows down the convergence speed of learning process. To alleviate the straggler problem, we propose a method named Chronos that accelerates FL with training volume tuning in this paper. More specifically, Chronos is a resource aware method that adaptively adjusts the amount of data used by each client for training (i.e., training volume) in each iteration in order to eliminate the synchronization waiting time caused by the heterogeneous and dynamical computing and communication resources. In addition, we theoretically analyze the convergence of Chronos in a non-convex setting and utilize the results for the algorithm design of Chronos in return to guarantee the convergence. Extensive experiments show that compared with the benchmark algorithms (i.e., BSP and SSP), Chronos significantly improves convergence speed by up to 6.4×.
Apple in My Eyes (AIME): Liveness Detection for Mobile Security Using Corneal Specular Reflections

The demand for contactless biometric authentication has significantly increased during the COVID-19 pandemic and beyond to prevent the spread of Coronavirus. The global pandemic unexpectedly affords a greater opportunity for contactless authentication, but iris and facial recognition biometrics have many usability, security, and privacy challenges, including mask-wearing and presentation attacks (PAs). Mainly, liveness detection against spoofing is notably a challenging task as various biometric authentication methods cannot efficiently assess the real user’s physical presence in unsupervised environments. Although several face anti-spoofing methods have been proposed using add-on sensors, dynamic facial texture features, and 3-D mapping, most of them require expensive sensors and substantial computational resources, or fail to detect sophisticated 3-D face spoofing. This article presents a software-based facial liveness detection method named Apple in My Eyes (AIME). AIME is intended to detect the liveness against spoofing for mobile device security using challenge-response testing. AIME generates various screen patterns as authentication challenges, then passively detects corneal-specular reflection responses from human eyes using a frontal camera and analyzes the detected reflections using lightweight machine learning techniques. AIME system components include challenge and pattern detection, feature extraction and classification, and data augmentation and training. We have implemented AIME as a cross-platform application compatible with Android, iOS, and the Web. Our comprehensive experimental results reveal that AIME detects liveness with high accuracy at around 200-ms against different types of sophisticated PAs. AIME can also efficiently detect liveness in multiple contactless biometric authentications without any costly extra sensors nor involving users’ active responses.
Secured information sharing using Laplace transform and hyperbolic tangent function

Information security is very much important in this internet world, especially in Electronic communications such as system security, smart card, mobile communications etc. Cryptography is based on transformation of multiple rounds of transformation of messages in the form of plain text as input into encrypted text message. Through suitable mathematical technique, secrecy of the information is maintained. This paper proposes a Laplace and its inverse transform technique with a suitable function for encryption and decryption of a message.
VocabulARy replicated: comparing teenagers to young adults

A critical component of user studies is gaining access to a representative sample of the population researches intend to investigate. Nevertheless, the vast majority of human-computer interaction (HCI) studies, including augmented reality (AR) studies, rely on convenience sampling. The outcomes of these studies are often based on results obtained from university students aged between 19 and 26 years. In order to investigate how the results from one of our studies are affected by convenience sampling, we replicated the AR-supported language learning study called VocabulARy with 24 teenagers, aged between 14 and 19 years. The results verified most of the outcomes from the original study. In addition, it also revealed that teenagers found learning significantly less mentally demanding compared to young adults, and completed the study in a significantly shorter time. All this at no cost to learning outcomes.
CA-Free Real-Time Fuzzy Digital Signature Scheme

Digital signature has been playing a very important role in electronic money transactions while memorizing and storing keys for digital signatures brings a great burden to users. In recent years, certifying authority (CA) has been attacked frequently, and many users have suffered losses. In this article, we discuss the existing digital signature technologies, analyze their disadvantages, and propose a novel fuzzy digital signature scheme, which has the characteristics of immediacy and no need for CA. Initially, our scheme uses fuzzy extractors to generate digital signatures seamlessly from biometric or other noise data, since its information entropy is sufficient to ensure security. Secondly, we reduce the dependence of digital signatures on third parties and the threats and delays caused by them. Furthermore, We also conducted the computational security analysis of the proposed scheme to illustrate its security level. Finally, the comprehensive experimental evaluation elaborates that our scheme is reliable and practical for real scenarios, ensuring the information measured by the user is untraceable.
A Benchmark and Empirical Analysis for Replay Strategies in Continual Learning

With the capacity of continual learning, humans can continuously acquire knowledge throughout their lifespan. However, computational systems are not, in general, capable of learning tasks sequentially. This long-standing challenge for deep neural networks (DNNs) is called catastrophic forgetting. Multiple solutions have been proposed to overcome this limitation. This paper makes an in-depth evaluation of the memory replay methods, exploring the efficiency, performance, and scalability of various sampling strategies when selecting replay data. All experiments are conducted on multiple datasets under various domains. Finally, a practical solution for selecting replay methods for various data distributions is provided.
View Invariant Spatio-Temporal Descriptor for Action Recognition From Skeleton Sequences

Recently, Human action recognition based on 3D Skeleton sequences has gained significant interest in the computer vision field. However, skeleton sequences are sensitive to noises, viewpoint variations and similar movements. To sort out these problems, this paper proposes a View Invariant Spatio-Temporal Descriptor (VISTD) that encodes the positions of skeleton joints in each frame of an action sequence. VISTD is a combination of View Invariant Skeleton Joint Descriptor (VISJD) and Spatio-Temporal Skeleton Joint Descriptor (STSJD). Further, we employ a standard Convolutional Neural Network (CNN) architecture for classification. To analyze the impact of fusion methods, the results of CNN model are fused with different fusion rules. Extensive simulation experiments carried out over proposed model through UWA3DII dataset and NTU-RGB+D dataset. The comparison of experimental results with existing methods explores the superiority.
Design of Keyword Extraction System for Aviation Safety Information Based on ArcGIS Technology

In order to save a lot of time and energy for civil aviation staff, but also improve work efficiency and ensure the quality of safe production, a keyword extraction system based on information security is designed. First, the safety information related to civil aviation is collected. The keyword extraction technology is performed for the collected information. The word segmentation and feature values are refined. Then, the extracted keywords are defined and classified , and a new keyword library is buildded. Finally, the visualization of keywords are upgraded. The time and space elements are added. and a method of combining graphics and text is adoptted. The experimental results show that this system is more helpful for information readers to read, understand and memorize about the subject of the entire information, thereby ensuring that civil aviation workers can work in an orderly and faster manner.
Analysis and application of reservation of scenic spot based on relationship graph

In recent years, the reservation data of scenic spots in Gansu Province has increased dramatically. This paper cleans and integrates these scattered data, refines the correlation relationship between them, constructs a relationship map, and inputs it into the constructed recommendation system, in order to achieve the accurate recommendation for tourists to reserve scenic spots. Firstly, this paper constructs the relationship map between tourists’ characteristics and scenic spots based on the real data of scenic spots reservation in Gansu Province. Next, the paper divides the data set into training set and test set to train the model of recall algorithm. Then, the paper compares the clustering effect of different clustering algorithms to find out the most efficient algorithm to get Top-N recommendation. Finally, this paper uses two sorting algorithms to sort the scenic spots in the recommendation list respectively, and thus achieve the scenic spot recommendation for tourists.
Defect Detection for High Voltage Transmission Lines Based on Deep Learning

For UAV inspection of high-voltage transmission line defect detection, a cascading defect detection framework of component location and segmentation is proposed. This method solves the problem of time-consuming, laborious and inaccurate analysis of images by the naked eye of electrical personnel. Firstly, the YOLO v4 object location algorithm is used to locate the key components. Secondly, the localized key components are cropped, and then the images are reconstructed by SRGAN with super-resolution. Finally, the Mask-RCNN defect segmentation channel is designed. A feature reuse structure based on the ResNet-34 network is proposed to ensure accuracy while reducing the parameter redundancy. At the same time Point Set strong a priori anchor frame optimization mechanism approach is introduced, to achieve pin-missing and insulator self-shattering segmentation. The experimental results show that the mAP of the localization scheme is improved by up to 5.2% compared with other mainstream methods. The precision of the optimal defect segmentation scheme reaches 87.2%. The staff will be assisted by the proposed method which provides technical support for the intelligent level of the power system in this paper. It has important practical significance for the construction of smart grid.