Reviewerly/exHarmony huggingface dataset:
Inference in cylindrical models having latent Markovian classes—With an application to ocean current data

Spatial direction vector data can be represented cylindrically by linear magnitudes and circular angles. We analyze such data by using a hierarchical Markov random field model with latent discrete classes and conditionally independent cylindrical data given the classes. The structure of a Potts model segments the spatial domain, and each class defines a cylindrical density that represents a specific structure. We consider two types of cylindrical distributions; the Weibull sine-skewed von Mises distribution, which is skewed in the circular part, and the generalized Pareto-type wrapped Cauchy distribution, which is heavy-tailed in the linear part. In this setting, we develop a statistically efficient block composite likelihood method for parameter estimation. The method is shown to provide much faster convergence than an expectation–maximization approach. However, the convergence is less stable for the block composite likelihood method, and we suggest a hybrid estimation approach for practical use. We apply the approach to study ocean surface currents in the Norwegian Sea. The models are able to describe the currents in terms of interpretable local regimes of two–three classes. Scoring rules are used to measure predictive performance of the two cylindrical densities. Results indicate that there is clearly skew angular components, and possibly also some heavy tails in magnitude.
Blockchain in EHR

Blockchain technology is in an initial growth phase, with tremendous potential to be used by various industries and research organizations. The privacy, security, decentralization, and confidentiality features of blockchain have promoted its application for extensive use in the health care sector. Recently, the electronic health record (EHR) has faced issues related to the security, privacy, and confidentiality of data. This chapter discusses the use, application, and challenges of blockchain to resolve the problems of the EHR, and proposes a framework for using blockchain technology to resolve them. This chapter also discuss the conceptualization of blockchain technology through four tiers: Blockchain V1.0, V2.0, V3.0, and V4.0. The benefits and limitations of the use of blockchain in various sectors are also discussed. Finally, the chapter presents challenges in the projects being developed using blockchain technology.
A Comparative Study of Neural Network for Text Classification

This With the popularity of artificial intelligence in recent years, Natural Language Processing (NLP) technology has also become the focus of research. NLP technology's unique machine translation and text sentiment analysis functions can prevent people from experiencing poor language communication when travelling abroad and help artificial intelligence understand people's language better. This article has made corresponding practice and analysis for the critical requirement of “text classification” in NLP. In the experiment, we used the Internet Movie Database (IMDB) film review forum as the dataset. Recurrent Neural Network (RNN) and the corresponding variants of RNN (Long Short Term Memory (LSTM)) are analyzed and compared from the theoretical aspect. Moreover, we introduced a bidirectional mechanism to optimize RNN and reduce the influence of parameter changes on model training by comparing specific neural network structures. We found the benefits of LSTM in text classification applications compared with RNN and simple neural networks by comparing experiments. Besides, we explored the role of the bidirectional mechanism for RNN. Finally, we create a two-way LSTM model for text classification model and obtain the model training results indicating less overfitting and less loss than other structures.
Digital Image Processing Based Detection of Rheumatoid Arthritis using Pythagorean Theorem

Digital image processing has procured huge significance in analysis and diagnosis of diseases in this era of technology. Detection of rheumatoid arthritis is a challenge in the situation when experts are unavailable. Rheumatoid Arthritis (RA) is a disorder of the immune system, which attacks our own tissues and causes joint pain, swelling, and stiffness. To detect RA, rheumatologists recommend a blood test, named “Rheumatoid Factor”. In laboratories, this test is performed manually by technicians, where the presence of experts is necessary, and chances of human errors and mistakes exist. We proposed a method that will identify the Rheumatoid Arthritis by applying digital image processing approaches. This proposed method is based on pre-processing and finding the region of interest. To find the performance of proposed method the blood samples are taken in medical Laboratory and experimental results showed that the efficiency of this method is 98%.
Approximation Algorithms for the Single Robot Line Coverage Problem

The line coverage problem is the task of servicing a given set of one-dimensional features in an environment. Its applications include the inspection of road networks, power lines, and oil and gas lines. The line coverage problem is a generalization of the standard arc routing problems, and is NP-hard in general. We address the single robot line coverage problem where the service and deadhead costs are distinct and asymmetric. We model the problem as an optimization problem that minimizes the total cost of travel on a given graph. We present approximation algorithms to obtain bounded solutions efficiently, using the minimum cost flow problem. We build the main algorithm in stages by considering three simpler subproblems. The subproblems are based on the structure of the required graph, i.e., the graph induced by the features that require servicing. We first present an optimal algorithm for the case of Eulerian graphs with only required edges. Next we consider general graphs, not necessarily Eulerian, with only required edges and present a 2-approximation algorithm. Finally, we consider the general case with both required and non-required edges. The approximation algorithm is dependent on the Asymmetric Traveling Salesperson Problem (ATSP), and is bounded by $$\alpha (C) + 2$$ , where $$\alpha (C)$$ is the approximation factor of the ATSP algorithm with C connected components. Our upper bound is also an improvement over the existing results for the asymmetric rural postman problem.
Audio to Video: Generating a Talking Fake Agent

People have been interested in creating avatars of their own, to expose themselves in an exaggerated version or to hide themselves. From time to time, it is for the reason that they are embarrassed of their visual appearance or just because they just don’t want to be seen. However, there are certain times that the other side on the network wants to see a real-looking talking person. Especially with the rise of using online services, particularly online education, it has become more important to engage the user to the system, more than ever. This paper will propose an end-to-end pipeline on the way to controlling a single fake image (a face that does not exist) by using an audio as the input, and propose a chain of methods to create a Talking Fake Agent (TFA), that is meant to “puppet” the image through a voice with no real face in front of it. As a result, it is strongly believed that it will increase the users’ engagement to the system and increase their overall satisfaction.
Mitigating IoT Security Challenges Using Blockchain

There is an increase in the usage of the Internet of Things (IoT) devices which focus on, efficiency and automation of different tasks to minimize the user intervention during COVID-19 pandemic. These IoT devices incur seamless excessive data exchange, Therefore, there is need for reinforcing the security, authentication and privacy to be more resilient to the various types of attacks which are the key concern for many organizations, especially cloud based networks carrying sensitive data. This paper presents a review of the challenges facing the IoT ecosystem along with the attack vectors threatening the IoT environment. A proposed solution for defending security threats found in IoT using blockchain technology is also discussed.
Image Registration Improved by Generative Adversarial Networks

The performances of most image registrations will decrease if the quality of the image to be registered is poor, especially contaminated with heavy distortions such as noise, blur, and uneven degradation. To solve this problem, a generative adversarial networks (GANs) based approach and the specified loss functions are proposed to improve image quality for better registration. Specifically, given the paired images, the generator network enhances the distorted image and the discriminator network compares the enhanced image with the ideal image. To efficiently discriminate the enhanced image, the loss function is designed to describe the perceptual loss and the adversarial loss, where the former measures the image similarity and the latter pushes the enhanced solution to natural image manifold. After enhancement, image features are more accurate and the registrations between feature point pairs will be more consistent.
Noise Robust Hard Example Mining for Human Detection with Efficient Depth-Thermal Fusion

Identity-preserving human detection is important for the privacy-protecting applications. IPHD [1] is a newly collected identity-preserving dataset that only contains depth and thermal images, which have much less information than RGB images. While less information and weakly labeled ground-truth boxes make it difficult to locate the objects correctly. In this paper, we adopt an efficient depth-thermal fusion approach to combine these two different inputs and enhance the representation. Moreover, a noise robust hard example mining algorithm is proposed to deal with weakly labeled data. The experiments show that our single model with single scale testing can get the AP=88.1 at IoU=0.5, which is a significant improvement compared with other competition results.
FT-RCNN: Real-time Visual Face Tracking with Region-based Convolutional Neural Networks

Visual face tracking is one of the most important components for face analysis in mobile applications and in video surveillance systems. In this paper, we propose an efficient face tracker called FT-RCNN, short for Face Tracking with Region-based CNN, that is based on the Faster-RCNN framework. A simple yet effective tracking branch is proposed to enable the framework to jointly perform face detection and tracking. To address the problem of insufficient training data for face tracking, we propose a novel pairwise training strategy that enables us to train face tracker by leveraging existing face detection datasets, thus eliminating the need to collect and annotate video data specifically for face tracking. Furthermore, we devise a novel loss function, termed Pair-hard Triplet Cosine Loss, that employs a pair-hard triplet mining strategy to increase the discriminative power of our face tracker. We evaluated the proposed tracker on popular video face datasets including MobiFace, ChokePoint and YouTube Face. The results have shown that FT-RCNN outperforms state-of-the-art face trackers and runs at real-time speed. We plan to release the source code for reproducible results in the future.
High-efficiency continuous-variable quantum digital signature protocol for signing multi-bit messages

Abstract A quantum digital signature (QDS) is guaranteed by the laws of quantum physics and can provide unconditional security to authenticate classical messages. Previous QDS protocols that shared secret keys could be realized using discrete-variable quantum key distribution (DV-QKD), for which the DV-QKD relied on extreme requirements like single-photon detection and extremely weak pulses of light. QDS protocols are mainly focused on how to sign a one-bit message rather than multi-bit messages. In this paper, we present a continuous-variable quantum digital signature (CV-QDS) protocol for signing multi-bit messages. The CV-QDS protocol has better compatibility with existing communication systems and the encoding strategy for multi-bit messages is simpler than before. We hope the continuous-variable protocol could further improve the performance of QDS.
Design and Implementation of Computer Network Information Security Protection Based on Secure Big Data

In the current development of information technology, big data has become one of its important features. Moreover, with the rapid development of big data related technologies, the threat degree of computer information in China is increasing. Under the background of big data, information data grows rapidly, which makes it easier for people to obtain valuable information. However, people are faced with more and more information security problems, and it is increasingly difficult to maintain information security. In this paper, based on the security big data, the factors affecting the information security of computer network are analyzed, and the specific countermeasures are studied based on the background of big data. The purpose is to provide reference for relevant personnel and improve the stability and security of data information.
Artificial Intelligence Platform for Low-Cost Robotics

The rise of Artificial Intelligence (AI) and robotics in the past decade has created various career opportunities in many industries such as robotics, manufacturing and healthcare. Thus, skills such as AI and robotics will play a critical role in the coming years. This project focuses on introduction of an AI platform for Low-cost robotics using MATLAB. The graphical based interface simplifies the task of design and implantation of machine learning functionalities such as object/pattern recognition and classification. The application offers three types of learning methods such as Machine learning, Deep Learning and Transfer Learning. Currently, the end devices are Arduino based and raspberry pi processors. The communication can be wired or wireless. The MATLAB app is tested using an Arduino based Robot (DFRobot Turtle 2WD) and a Raspberry pi-based robot to achieve object recognition and voice recognition.
On the dynamical system generated by the Möbius transformation at prime times

We study the distribution of the sequence of elements of the discrete dynamical system generated by iterations of the Möbius map $$x \mapsto (ax + b)/(cx+d)$$ over a finite field of p elements at the moments of time that correspond to prime numbers. In particular, we obtain nontrivial estimates of exponential sums with such sequences.
Practical Software Diversification Tool Chain for Dissimilar Redundancy Architecture

With the rapid development of the information society, there are corresponding tool solutions for various daily problems. Aiming at the complex generation process and inconvenient operation of heterogeneous components in a security defense system constructed with dissimilar redundancy, in this paper, we propose a practical basic software application isomerization tool chain, which integrates a series of key technologies of heterogeneous structures. At the same time, corresponding automated tests are provided for users to conduct comprehensive evaluation. Experimental evaluation shows that the tool chain will be able to provide heterogeneous component support for security defense technologies based on dissimilarity redundancy construction and improve the security of software systems.
Using Deep 3D Features and an LSTM Based Sequence Model for Automatic Pain Detection in the Wild

Automatic pain detection is an important problem in diagnostic and therapeutic applications. In this paper, we aim to develop a computational framework to automatically detect pain in videos in the wild. The videos in the wild vary with respect to gender, age, ethnicity and even other qualitative attributes like upbringing. Previous systems focused on methodologies confined to one particular dataset that is hard to generalize for the population in the wild, or based on invasive methods that collect data using many physiological sensors and induced stressors. We propose a method to automatically detect pain in videos using state-of-the-art expression recognition system along with deep learning. We curated a dataset of 194 videos in the wild with pain and non-pain. We used a sliding window strategy to obtain a fixed-length input sample for the LSTM (Long Short Term Memory) network. We then carefully concatenate the network output of every segment to generate a video-level output. The proposed end-to-end framework can predict binary classification label (pain/non-pain) at video level. Our method achieves promising results on the dataset we collected.
Methodological Support of Additional Professional Education: Simulation and Labor Costs Estimation

The purpose of the study is to assess the labor costs of teachers to create programs of additional professional education. The SIM-UML system is used to build visual and simulation models of additional professional education processes. As a result of modeling, the estimation of labor costs in manual and automated versions is obtained.
Research on the Application of Data Visualization Design in CBA League News

In the context of the era of intelligence, the capacity and function of data is expanding, and is playing an irreplaceable role in the field of sports news. This paper studies the data expression mode of sports news. From the point of user needs and the characteristics of visual data, this paper discusses the methods and suggestions of data visualization design in CBA league news, in an attempt to form an intuitive, efficient and convenient visual form of sports news, which can promote the multi-dimensional development of visual symbols in sports news, and promote the progress of data analysis and prediction. The data visualization has become the core competitiveness of sports news.
Improved Auxiliary Particle Filter for SINS/SAR Navigation

In order to obtain the relatively appropriate importance density function and alleviate the problem of particle degradation, a new improved auxiliary particle filter algorithm is proposed. After calculating the auxiliary variable, the adaptive regulator is employed to obtain the state estimation. So, the latest measurement information is efficiently utilized to establish a better importance density function in the importance sampling process. Then, the process of particle weights’ adaptive adjustment and random-weighted calculation can keep the diversity of particles and improve the filter precision; thus, it can better solve the filter problem of nonlinear system model error and noise interference. The simulation and analysis result show that the proposed algorithm can optimize the filter performance and improve the calculation precision in the positioning of the SINS/SAR integrated navigation system, compared with the other two existing filters.
A Systematic Hybrid Smart Region based Detection (SRBD) Method for Object Detection

Traditional object Detection methods are the most rudimentary and have many testing issues in computer vision, since they endeavor to find object models from hefty number of predefined classifications in naturalistic images. From object indicators and scene classifiers, the image presentation constructs complex groups which join various low-level picture highlights with significant level setting. This paper analyzes various details of general object detection methods like object proposal generation, detection frameworks, object extraction, context modeling methods and Region of classification. Also using a brief architecture of object detection with its algorithm techniques, and deep learning methods based on object detection frameworks were studied. This paper proposed A Systematic Hybrid Smart Region Based Detection Method (SRBD) for Object Detection and attempts to overcome the drawbacks of the existing systems like Faster R-CNN, SSD and Yolo.
Enhanced Performance and Privacy via Resolver-Less DNS

The domain name resolution into IP addresses can significantly delay connection establishments on the web. Moreover, the common use of recursive DNS resolvers presents a privacy risk as they can closely monitor the user's browsing activities. In this paper, we present a novel HTTP response header allowing web server to provide their clients with relevant DNS records. Our results indicate, that this resolver-less DNS mechanism allows user agents to save the DNS lookup time for subsequent connection establishments. We find, that this proposal saves at least 80 ms per DNS lookup for the one percent of users having the longest round-trip times towards their recursive resolver. Furthermore, our proposal decreases the number of DNS lookups and thus improves the privacy posture of the user towards the used recursive resolver. Comparing the security guarantees of the traditional DNS to our proposal, we find that resolver-less DNS achieves at least the same security properties. In detail, it even improves the user's resilience against censorship through tampered DNS resolvers.
Two-Stage Body-Part Attention Network for Detecting Human-Object Interactions

Human-Object Interaction (HOI) detection is a computer vision task that can help computers understand the scene of still images. While too many human-object pairs usually appear in images, which results in the complexity of the HOI detection network. In this paper, we used the two-stage framework in the HOI detection method to reduce complexity. Besides, based on the idea that the features of each body part have different degrees of importance for human-object interaction detection. We combined the body-part attention mechanism with the two-stage network to use relative information of human parts and object. In the test, we provided extensive experiments to compare the proposed network with the existing human-object interaction detection networks. The experiment on the V-COCO dataset shows that our approach compares favorably with the existing HOI detection methods.
Electricity market transaction model design combining blockchain and machine learning

With the deteriorating environment and the issuance of various national policies, green energy has begun to rise and become an important part of the energy market. The traditional energy trading model is a centralized management model, and new energy is not suitable for the current situation due to its wide distribution. Some traditional transaction models, so we use blockchain technology to solve this problem. Aiming at the problems of transaction methods, energy price competition and how to implement the blockchain in energy transactions, the K-nearest neighbor algorithm in machine learning is used to realize the automatic auction at the same time, and the ACO algorithm competition based on multiple time scales is studied. The game realizes the legality of using smart contracts to restrict market transactions, solves the problem of distributed green energy grid connection, and combines with appropriate incentive mechanisms to increase the utilization rate of green energy and promote the rational development of ecology and human life.
AI for Humanity: The Global Challenges

This chapter summarizes four global challenges for AI: health, education, the environment, and science. In each area, AI has enormous potential to enhance human well-being, yet very substantial obstacles remain in both basic research and global deployment. Beyond these four areas, we ask whether reliance on AI to solve our problems is a viable strategy for humanity.
Industry Tip: Picking the Minimum Process Noise Variance for Your NCV Track Filter

When tracking maneuvering targets with a nearly constant velocity (NCV) Kalman filter with discrete white noise acceleration, the selection of the process noise variance is complicated by the fact that the process noise errors are modeled as white Gaussian, while target maneuvers are deterministic or highly correlated in time. In this industry tip, guidance on picking the minimum process noise variance for the NCV Kalman filter is provided.