Reviewerly/exHarmony huggingface dataset:
Research on Action Recognition Method Based on Weighted DTW Algorithm

Abstract Action recognition technology is an important part of artificial intelligence. In order to improve the rate of action recognition, this paper designs an action recognition method based on Weighted DTW algorithm. Firstly, the Kinect2.0 is used to obtain the three-dimensional data of the human joint points. Then the quaternion method is used to define the action sequences. The weight of joint is calculated according to the participation in different types of action. Then the improved DTW algorithm is used to design the action recognition experiment. The experimental results show that the action recognition algorithm designed in this paper has better recognition rate and timeliness than traditional DTW algorithm and F-DTW algorithm.
Deep Neural Networks in the View of Ecological Holism

Abstract In the view point of ecological holism, deep neural networks can be view as a machine cognitive agent with overall multi-level nested structure, the representation learning and problem-solving ability of machine cognitive agent with multi-modal perception function is also the results from the perception and judgment of ambient situation environment. In the paper, a machine cognitive method based on ecological holism was deconstructed. Then, two core questions to characterize learnability of AlphaGo were answered, one is how to explain the characteristics of the hierarchical gradient and update parameters between different layers of networks? Two is how to understand the effective ability to choose the drop sampling at the strongest level of game of Go. These core controlling structural and functional roles proposed in this study may be further improved to a new type of artificial intelligence law of machine cognitive system.
On the Adaptive Selection of the Cutoff Distance and Cluster Centers Based on CFSFDP

Aiming at deficiencies of clustering by fast search and find of density peaks (CFSFDP) algorithm in the manual selection of the cutoff distance and cluster centers, this paper proposes an adaptive CFSFDP (A-CFSFDP) algorithm, which is based on kernel density estimation and anomaly detection. Firstly, the distribution characteristics of data points are analyzed by using non-parametric kernel density estimation and the optimal value of the cutoff distance is adaptively selected by iteration. Secondly, the idea of anomaly detection is adopted and by calculating the weight of the cluster center for each point, anomaly points can be detected as cluster centers of each cluster. Finally, the idea of local density within the cluster is introduced and by computing it for each data point, the optimized division of cluster core and cluster halo can be achieved. The simulation results show that the proposed algorithm can not only avoid the subjective factors in the selection of cutoff distance and cluster centers compared to CFSFDP, but achieve better performances in robustness and accuracy of clustering.
Boundary Matched Human Area Segmentation for Chroma Keying Using Hybrid Depth-Color Analysis

In modern Security and Surveillance technologies, the significance of human detection and segmentation becomes having much importance. In addition to security systems, image/video editing applications demand semantic segmentation of foreground objects. For such high-quality applications, the foreground object's boundary pixels need to be matched accurately. The proposed method aims for an automated segmentation scheme to detect and segment the human area from an image or video frame and to paste it in another frame/image using image processing techniques using a hybrid analysis of color and depth information. The approach provides a high-quality human factor segmentation scheme that can be used in Chroma keying operations in advanced multimedia editing applications. Depth based analysis along with a series of post-processing stages is employed. The video frames are taken using an RGB-Depth sensors. Hybrid depth and image analysis are used to segment the foreground human subjects semantically. The multi-level segmentation using Chan-Vase active contour detection, grow-cut segmentation and a Trimap based matting approaches have been used to achieve a fair segmentation accuracy. The results are evaluated using standard metrics and compared with state-of-the-art automated chroma keying techniques. The qualitative analysis shows the efficiency of the proposed hybrid depth based chroma keying scheme. It can be concluded here that the visual quality has been substantially attained by this method.
The effects of “economic crisis” and “financial crisis” searches conducted in google on dollar/TL parity and a causality analysis

The increase of dollar parity in the short term in Turkey has brought the need to conduct research about the reasons of this situation. Google, which is a very common application for search, is considered as a platform for those who wonder about it. Therefore, it is also a matter of curiosity that the searches will have an impact on the dollar/TL parity or the dollar/TL parity will have an impact on the searches. This work has been carried out to determine the relationship between the “economic crisis” searches made in Turkey, “economic crisis” and “financial crisis” searches made in the world, and the dollar/TL parity. The number of searches for “economic crisis” made in Turkey, and the number of searches for “economic crisis” and “financial crisis” made in the world, and dollar/TL parity values are used as variables in the application part. Google Trends application was used to find the search numbers. The daily search numbers and dollar/TL parity between the dates of January 1, 2018 and March 22, 2019 were used. Finally, it was aimed to examine the causality relationship between the variables and find out whether they trigger each other or not.
Downsampling wearable sensor data packets by measuring their information value

Long-Short Term Memory models (LSTMs) are data-driven routines that classify Human Activity Recognition (HAR) with minimum human input. The price to pay for analysing large sequences of on-body sensor measurements with LSTMs are high processing power and battery requirements. In this paper, we recognize that sensor data packets have differing information value to classify HAR and propose to quantify it with cross entropy (CrossEn), Kullback Leibler (KL) divergence and sample entropy (SampEn). Both, CrossEn and SampEn have the potential to guide dropping redundant data packets without compromising HAR. However, we do not find substantial improvements in dropping rates when downsampling by CrossEn and SampEn over computationally cheaper random and uniform alternatives. Our results show that the KL divergence, evaluated at training time is equivalent to the classification accuracy criteria that involves a testing set. The computational requirements to compute the KL in real-time could well guide sensor node design to downsample wearable measurements near the user.
Digital Content Refinement by Collecting Partly Unreliable Attributes over a Network

The concept of refining digital content downloaded over a network is introduced. If a content item consists of a lot of preliminarily defined attributes, then content refinement can be executed by collecting a number of partly unreliable releases of the same content item from different information sources. In the refinement process, each release of the content item is divided into attributes of signifieds or signifié and weighted majority voting is applied to determine the most probable signifier or significant of the attributes. In addition, results of a simple experiment are presented to show the advantages of content refinement.
Global Optimization for Stochastic Programming Via Sequential Monte Carlo Sampling

This paper proposes a continuous-time optimization algorithm for stochastic programming. We introduce the sequential Monte Carlo sampling scheme to do with the high dimensionality of the stochastic disturbance and the switching gradient Lagevin dynamics to reduce the computational requirement on evaluation of the gradient on large data set. Our algorithm is in continuous-time and can seek the global minimizer of stochastic programming problems. We fit our analysis into the framework of two-time-scale systems and propose a stochastic averaging approach to analyze the convergence of our optimization algorithm. With the help of Dirichlet form, Fredholm alternative and Poincare inequality and by means of the tool of Fokker-Planck equation, we provide a rigorous theoretical foundation for the analysis and design of the global optimization algorithm. The importance of our algorithm on the one hand stems from serving the application-oriented purpose such as dealing with high-dimensionality and reducing computational complexity, and on the other hand originates in providing some theoretically interesting techniques which is of direct applicability for global optimization problems.
A Probability Adaptive Sampling-based Algorithm for Obstacle Avoidance Motion Planning Problems

In recent years, with the increasingly complex robot application environments, the research of robotic autonomous obstacle avoidance motion planning is one of the key technologies to improve its intelligence. In this paper, we present an improved algorithm based on Rapidly-exploring Random Tree (RRT) algorithm, which is called Probability Adaptive RRT (PARRT). We analyze the defects of the original algorithm in detail from the perspective of Voronoi diagram. For some complicated occasions, especially when the robot is surrounded by obstacles, the efficiency of the original algorithm will be greatly reduced. For this problem, we proposed an expansion probability adaptive method. By dynamically adjusting the extending probability of nodes near obstacles, the invalid node expansion is reduced effectively and the planning speed is greatly improved. Finally, we experiment in both two-dimensional space and the joint space of manipulators. The planning results show the improved effect of the proposed algorithm.
Object Tracking via Multi-layer Convolutional Features with Adaptive Correlation Weighting

Object tracking is an important task in computer vision and artificial intelligence. Correlation filter trackers have been extensively studied due to their high efficiency, but most of them are limited by the hand-crafted features. Although some works have introduced deep learning to solve the problem, the corresponding methods tend to use the fully-connected feature and cannot fully utilize the rich spatial and semantic information in different levels of deep features. Therefore, a novel tracking method is proposed via multi-layer convolutional features with adaptive correlation weighting. In this study, the convolution feature in low level are extracted to describe the spatial structure information, while the high level is exploited to capture the semantic information of the target. In particular, this paper proposes an adaptive correlation weighting scheme based on peak-to-sidelobe ratio to measure the discriminative ability at each level, so as to realize the final tracking. Extensive experiments demonstrate that the proposed method outperforms several existing methods.
Research on Pedestrian Recognition Based on DM642 Driver Assistance System on Vehicle

Road pedestrian recognition is a core issue in the field of automotive safety, the pedestrians captured by the video cameras installation on the road ahead of the vehicle. To determine and mark pedestrians and estimate the potential risks according to the image processing and pattern recognition technology. Taking measures to protect pedestrian in the vehicle have important applications in driver assistance. The paper collected road ahead of the vehicle video information in the DM642 hardware platform, extracted the lane edge line by improved Hough transform method, and the extraction of candidate regions according to the features of pedestrians itself, using the Adaboost method of training the classifier. The results show that the classification accuracy rate reached 92%, conform to the real-time performance of the system requirement. With the development of DSP technology, pedestrian's collection, processing, recognition as one of the embedded pedestrian recognition system has a broad research value and business prospects.
Fractal Features Combined with Local Phase Information in Texture Analysis

This paper concerns processing, analysis and classification of fully-textured images, which contain both natural stochastic texture (NST) and structured information (SI). Whereas the fractal attributes of Gaussianity and self-similarity, that characterize purely NST, are fully represented by the spatial spectrum, and the corresponding spatial phase of the NST is random, the structural, edge-and-curve-type, information is mostly represented by the local spatial phase. We therefore decompose textured images into two layers, corresponding to the NST and SI, respectively. We show that the extent to which the texture obeys the fractal property of self-similarity, can be assessed by the mutual information that exists between scales. The phase of this layer, in the spatial Fourier domain, is random and exhibit approximately uniform distribution. We confirm this random behavior using the Kolmagorov-Smirnov hypothesis testing framework. We proceed to construct a classification space that combines features/dimensions that characterize the two layers of the stochastic (fBm-type spectrum) and deterministic (phase) attributes of partial spatial information, and address the implementation of this space in classification of fully-textured images encountered in modeling and analysis of images in various fields by providing an example.
How Securely Are OAuth/OpenID Connect Implemented in Japan?

When a website authenticates users, it does so for a so-called social login in electronic commerce (EC) site. A social login is used for a social media account, such as Facebook, Google, and Twitter. In such a case, the website uses OAuth and OpenID Connect. However, the implementation of a website might involve privacy concerns or be vulnerable to the attacks. In this paper, by crawling the login pages of 500 Japanese EC sites and tracing the authentication flows, we investigate the implementation status of social logins and their security against cross-site request forgery. We observed 28 websites that acquired more user permissions from SNS than necessary, or were vulnerable as a result of improper implementation.
Caracterisation of Dementia by 3D Analysis of Cerebral Structures

Dementia is one of the main health challenges facing our generation. It is the third leading cause of death, after heart disease and cancer. Detection of dementia from neuroimaging data such as MRI through machine learning has been a subject of intense research in a recent year. Although many works of literatures have developed many approaches to characterize this disease and to classifier its stages automatically, currently there is no specific technique to confirm with certainty the diagnosis of dementia. Brain regions are important for prediction of dementia stages. Some of the researchers were focused on the segmentation of the grey matter, others are focused on the cortical thickness. This paper investigates how to characterize and to predict dementia stages from a 3D MR image Database. Data provided by OASIS BRAIN includes a cross-section of 175 subjects aged 60 to 96 years. The approach relies on many steps. It started with applying data preprocessing which includes registration and segmentation of the hippocampus with a Bayesian probabilistic approach based on the Markovian modeling. The second step is to extract some features from the hippocampus. Finally, the prediction of dementia stages is done with a supervised learning algorithm: Artificial Neural Network. The results evince that the proposed approach can be used to characterize and predict the stages of dementia with over 95% accuracy, considerably higher than that of conventional basic methods. This study confirms that to extract geometric descriptors from the hippocampus can effectively solve the dementia characterization problem.
A Multi-Copy Approach to Quantum Entanglement Characterization

An accurate evaluation of the performance of quantum entanglement sources-based on various physical realizations and designed for application in numerous quantum protocols-requires a thorough characterization of the ensemble of the generated output quantum states. This presents a great challenge, as numerous measurements, such as full state tomography, need to be performed on the ensemble. This incentivizes us to reduce the overall duration of the characterization procedure, so that time is saved and the effect of setup drift reduced. For many physical realizations of the quantum states, including polarization and path-encoded qubits, a traveling salesman problem of finding the optimal order of tomographic projections can be found and solved [1]. The achieved tomography speedup, found and experimentally verified [2,3] for up to six-qubit systems, is seen to increase with the size of the quantum system in question, see Fig. 1(a). For three-qubit systems already, the time spent on setting the measurement projections can be halved. A step-by-step workflow [4] is introduced to suit various tomographic scenarios.
Semantic Search Engine for Daily Work Reports Integrating Heterogeneous Ontologies

Abstract In recent years, Knowledge Transfer is one of the problems of business management. Companies are required to create a mechanism to transfer knowledge of experts to the next generation. Submitting work reports is one of examples of efforts to convert tacit knowledge to explicit knowledge. There are many companies letting their employees submit work reports after completion of their work. However, most of data is just accumulated, but it is not fully reused. Factors obstructing utilization of work report data are below; First, data of work reports is enormous and there are scattered descriptions concerning important business knowledge so that it is impossible to acquire business knowledge by reading work reports efficiently. Second, since work reports are plaintext, their structure cannot be seen so it is impossible to access the necessary information immediately. Therefore, in this research, we constructed a system that supports knowledge transfer by utilizing work report data. Specifically, we constructed domain ontologies that define the meaning of terms related to business knowledge and performed information extraction on work reports by using the domain ontology and sentence pattern matching. Then, based on the result of information extraction, a set of texts including business knowledge was selected. Finally, we constructed a system that provides business knowledge to users by using work report data as a knowledge source.
New paradigm for battery life management in data centers

In this paper new paradigm for battery life management in data centers is presented. A method for remaining useful life (RUL) estimation of batteries based on the voltage drop under full load. In the following study real time data of batteries deployed in a datacenter is used. Dataset has historic values of 3100 batteries over one year, having features like voltage, resistance and temperature. There is very little research been done on this topic the main reason is the unavailability of battery data used in the datacenter. The drop of voltage under full load of a battery have been considered as the performance parameter as most of the battery management systems use these parameters as a remaining useful life of a battery. We propose a new paradigm for battery life management to avoid complete ups failure for data center. We used real dataset obtained from datacenter and verified our results with success in the datacenter.
Mediating touch

Touch is back. After over a century of dominance by audiovisual technology, marketers, and technology designers are now pushing the benefits of touch in devices as diverse as videogame controllers ...
Gradually Global Pruning for Object Detection Network

State-of-the-art object detection methods employ the deep convolutional neural Network (CNN) to achieve excellent results on several public datasets. However, most of these methods depend on complex backbone networks which limit the application in many real-world scenarios. In this paper, a novel gradual global pruning is presented to remove the redundant filters for object detection models, which can largely reduce the model size of the network while maintain the network accuracy. Firstly, the importance of individual filter is evaluated globally across all the network layers by calculating the sum of its absolute weights. Then, a progressive sparsity control function is proposed to gradually reduce the proportion of pruning in the implementation process. Finally, this paper finetunes the pruned network to restore its accuracy. The experimental results on Pascal VOC2007 dataset show that the proposed method can drastically reduce the model size of the network with negligible loss in detection accuracy.
Tri Layer Model for Color Image Ciphering Through DNA Assisted 2D Chaos

In this modern digital world, there are many advancements that are made in the fields of networking. These days many multimedia messages, in the form of images are shared online for communicating with each other. The images thus send are vulnerable to attacks which are both internal (noise attack etc.) and external (hackers). An algorithm for securing these multimedia messages have been proposed here. The key employed in the proposed scheme is made from three different 2D chaotic maps. A colour image is split into RGB planes, it undergoes a 3-level confusion-double diffusion using Deoxyribo Nucleic Acid (DNA). A novel methodology has been approached to substantiate it over the currently existing encryption standards. The ability to withstand brute force attack, noise attack, chosen plain text attack of the method are to be tested.
Early trends and transformation

The electronic resources librarian (ERL) position was first documented in 1992, becoming increasingly common in academic libraries in the late 20th and early 21st centuries. However, the position was not well defined with considerable variations in actual responsibilities across libraries. Some ERLs handled only licensing for electronic serials whereas others had responsibility for the entire life cycle of electronic resources. Organizational structures also varied quite a bit with ERLs to be found in so many different departments or units such that an ERL might work in collection development or collection management, acquisitions, technical services, reference, other public services, systems, or even in dedicated electronic resource management units. Finally, some libraries never adopted the title at all.
Information Retrieval System and Knowledge Base on Diseases Using Variables and Contexts in the Texts

In the field of disaster prevention, it is crucial to provide the general public with appropriate integrated information before, during, and after the disasters. However, in such situations, individuals are not capable of establishing clear opinions owing to inconsistent and diverse information from multiple sources. As individuals receive varied inconsistent information from several sources, they are puzzled about the credibility of such information. This situation is defined as a belief drift in our study, and it must be alleviated by providing appropriate information. To create a system that provides the required information to individuals with drifting beliefs, we acquired reliable information from medical doctors and implemented the knowledge base and retrieval system to bridge the gap between professionals and nonprofessionals. We focused on certain blood diseases, collected large texts, and extracted the terms representing contexts and variables, and the medical doctors evaluated the relationships among these diseases. In this study, we discussed the design and the implementation of the proposed system and demonstrated its performance.