Reviewerly/exHarmony huggingface dataset:
System Architecture of a Smart Fall Detection System

With advances in medicine and healthcare systems, the average life expectancy of human beings has increased to more than 80 years. As a result, the demographic old-age dependency ratio (people aged 65 or above relative to those aged 15–64) is expected to increase, by 2060, from ~28 to ~50% in the European Union and from ~33 to ~45% in Asia []. Therefore, the percentage of people who need additional care is also expected to increase. Geriatric health care, which pertains to care for the elderly, has gained a lot of prominence in the recent years, with specific focus on fall and sleep apnea detection systems because of their impact on public lives. In the recent years, there has been widespread application of Internet of things (IoT) and machine learning in the geriatric healthcare domain because of the potential cost reduction such technologies can bring in. In this paper, we present the architecture and design of an end-to-end geriatric healthcare system, with focus on wearable device based fall detection using machine learning. We explain the major components of the system architecture, under a certain deployment scenario, and present the communication protocol between these system components. We also present the salient aspects of the multi-channel variable time-division multiple access (multi-channel V-TDMA) MAC protocol designed to suit the requirements of such a system. This protocol combines the strengths of both standard time-division multiple access (TDMA) that is modified to support flexibility and frequency-division multiple access (FDMA).
Formation of a Scientific and Educational Model of Musical-Psychological Anthropology

The article presents an understanding of the value attitudes and methodological approaches to the construction of the educational discipline “Musical-psychological anthropology”, developed by the author and tested in the educational process at the Faculty of Musical Arts of the Institute of Fine Arts of Moscow Pedagogical State University. The directions of development in the discipline content the ideas and aspirations by E. B. Abdullin are outlined. The connection of the actual problems of pedagogy and psychology of music education with the experience of setting tasks for the project activities of graduate students in the course of Musical-psychological anthropology is shown. Some research results of graduate students which contribute to the development of Music-Psychological Anthropology as an interdisciplinary field and academic discipline at a university, focused on current problems of music education, as well as on the development of professional reflection of music teachers are presented.
A Fast Method to Calculate the Parameter of Operational Capability Index Model

Due to the complexity of war and the variability of operational requirements, the parameters of any mathematical model to study warfare need to be adjusted according to the actual situation of war. Operational capability index model is an effective formula to evaluate the capability of combat system, and the current parameter calculation method of this model has a high time complexity. Based on the characteristic topology analysis, this paper presents a fast method to fast determine model parameter. Compared with the parameter calculation method based on genetic algorithm, the accuracy is similar while the speed is greatly improved.
Stereoscopic Image Quality Assessment Based on Human Visual System Properties

Modelling the behavior of Human Visual System (HVS) is the ultimate target of Image Quality Assessment (IQA). The hierarchical structure of HVS and different HVS models are introduced firstly in this chapter. And some classical IQA methods based on the hierarchical structure of HVS are discussed in detail. Visual attention, as one of the most important mechanisms of the HVS, is clarified clearly and some Stereoscopic Image Quality Assessments (SIQA) methods based on visual saliency are also presented. In the end of this chapter, Just Noticeable Difference (JND) model and corresponding IQA methods are introduced.
Vision-Based Robot Hand Using Open Source Software

The robot hand can plan grasp movements based on the position of the target object and the motion of the robot hand. The position of the target object is recognized from the image captured by a camera mounted on the robot arm, and the motion of the robot hand is estimated from the inertial measurement unit (IMU). We also adopt a variety of mechanisms to determine the target object among the objects detected in the camera scene. Experiments are conducted to verify the validity of control system. The experiments proved that the developed system can support the user to grasp the target object.
Distributed Parameter Estimation Using Invariant Manifold Approach

A novelty of distributed parameter estimation strategy for a class of nonlinear system with a time-varying parameter is proposed in this paper. The approach relies upon the concepts of invariant manifold and cooperative persistent excitation condition, does not require a priori knowledge of the time-varying parameters. In addition, it is shown that the parameter estimation error is not only bounded but also can converge to a small neighborhood of the origin for sufficiently large value of gain. A numerical simulation example is presented to demonstrate the effectiveness of the proposed method.
RBS: A New Comparative and Better Solution of Sorting Algorithm for Array

In modern technology or practical application, all things are gradually arranged. Therefore, in this paper, we attempt to establish a new sorting algorithm and compare it with some other standard sorting algorithms by their execution times. We designed and developed a methodology that can be executed on random data, ordering data, and some specific data (data means integers value), which can provide the best result for Right Bounding Sort (RBS). The methodology is comparing the array's left value with the rightmost value. Under a particular condition, these two values are swapped, then left index increase, and after completing the first step, the right index decrease, and it continues up to array size. Though there are many sorting algorithms invented for sorting an array with specific time complexity, space complexity, and other factors, they have some limitations because there is no sorting that is perfect for all various types of data. Some are good for small ranges of value, some are vast ranges of data, some are stable, and some are unstable. This algorithm performs with Big-O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) time complexity and Big-O (1) space complexity. We see that our new sorting algorithms provide better solutions than some other existing sorting algorithms like the bubble sort, insertion sort, cycle sort, etc. On the experimental dataset in respect of time complexity.
Preparing for the future of work: Building an informal learning community about intelligent technologies in poor neighborhoods

Abstract As intelligent machines continue to shape current and future work, low‐income families are expected to be the most negatively affected. To sustainably help low‐income families be better prepared for a future of working with intelligent machines, we are building an informal learning community in a poor neighborhood where we engage children and parents through a robotics club. We report our community building progress and the interviews we conducted with the children and their parents.
Generalized Group–Subgroup Pair Graphs

Abstract A regular finite graph is called a Ramanujan graph if its zeta function satisfies an analog of the Riemann Hypothesis. Such a graph has a small second eigenvalue so that it is used to construct cryptographic hash functions. Typically, explicit family of Ramanujan graphs are constructed by using Cayley graphs. In the paper, we introduce a generalization of Cayley graphs called generalized group–subgroup pair graphs, which are a generalization of group–subgroup pair graphs defined by Reyes-Bustos. We study basic properties, especially spectra of them.
Earthquake Forecasting by Parallel Support Vector Regression Using CUDA

Earthquakes are a devastating natural hazard that can wipe out thousands of lives and cause economic loss to the geographical location. Seismic stations continuously monitor and gather data regarding the vibration and movement of the ground at a particular site. The collected data is processed by the model to forecast the occurrence of earthquakes in the Caribbean region. This paper presents a Parallel Support Vector Regression (PSVR) model to forecast earthquakes using Graphic Processing Unit (GPU). In the implementation of a PSVR using GPU, Computing Unified Device Architecture (CUDA) framework is utilized, which is a famous programming structure for General Purpose Computing on GPU. This newly computed PSVR model shows considerable improvement in training speed and achieved an accuracy of 92% when compared with Scikit Learn and LibSVM library on Central Processing Unit (CPU) and GPU.
Learning deep kernels in the space of monotone conjunctive polynomials

Dot-product kernels is a large family of kernel functions based on dot-product between examples. A recent result states that any dot-product kernel can be decomposed as a non-negative linear combination of homogeneous polynomial kernels of different degrees, and it is possible to learn the coefficients of the combination by exploiting the Multiple Kernel Learning (MKL) paradigm. In this paper it is proved that, under mild conditions, any homogeneous polynomial kernel defined on binary valued data can be decomposed in a parametrized finite linear non-negative combination of monotone conjunctive kernels. MKL has been employed to learn the parameters of the combination. Furthermore, we show that our solution produces a deep kernel whose feature space consists of hierarchically organized features of increasing complexity. We also emphasize the connection between our solution and existing deep kernel learning frameworks. A wide empirical assessment is presented to evaluate the proposed framework, and to compare it against the baselines on several categorical and binary datasets.
Super-resolution Convolutional Neural Networks

ASTER network has the characteristics of high precision and fast speed in text recognition of natural scenes. However, the network is significantly less effective at identifying low-resolution images. To tackle this problem, this paper introduces the super-resolution reconstruction of text images before ASTER network. First, the improved residual network is used to reconstruct the image with super-resolution. The batch normalization (BN) layer and DESENE layer are added after the convolutional layer of the residual network (ResNet), termed as modification ResNet block (MRB). According to experimental results, the highest recognition rate is achieved when the number of MRB is 3. Second, the convolutional layer and ReLu layer are added before and after the MRB module to obtain image features and improve image resolution. The clear image from the reconstructed network is then sent to the ASTER network. Finally, an end-to-end network, Super-resolution convolutional neural networks for scene text recognition (SRTR), is established. The monitoring function of the network adopts MSELoss. A large number of experiments are conducted on standard experimental data sets SVTP, CUTE, ICDAR2015. From the experimental data, we can see that our model is superior to other methods in scene text recognition.
Analyzing the impact of experiential pedagogy in teaching socio-cybersecurity

This paper presents results from efforts to integrate cybersecurity into social science courses, for the purposes of improving the cybersecurity awareness of non-computer science students. The inst...
AWMF: All-Weighted Metric Factorization for Collaborative Ranking

This paper contributes improvements on both the defect of dot product and the imbalance of the datasets in matrix factorization. Above all, matrix factorization is still the most widely used technology in the recommendation system. However, its dot product does not follow triangle inequality, which restricts the improvement of its effect. We take inspiration from the distance factor of metric learning, and convert the determinants of user-item relevance from the size of the dot product to the distance of the metric factorization. Furthermore, the number of positive examples is much smaller than the negatives in most datasets. Such an unbalanced scenario will affect the accuracy of recommendations. Inspired by the positive semidefinite matrix of the popular Mahalanobis distance in the field of metric learning, we have fully considered the interaction information between users and items and propose the concept of all-weighted matrix. Finally, the combination of the two improved techniques proposed the All-Weighted Metric Factorization (AWMF) method, which is applied to the personalized ranking task. We have done scientific and adequate experiments on three common datasets, and the results outperform several baselines on different evaluation indicators.
Detection of Customer Interested Garments in Surveillance Video using Computer Vision

One of the basic requirements of humans is clothing and this approach aims to identify the garments selected by customer during shopping, from surveillance video. The existing approaches to detect garments were developed on western wear using datasets of western clothing. They do not address Indian garments due to the increased complexity. In this work, we propose a computer vision based framework to address this problem through video surveillance. The proposed framework uses the Mixture of Gaussians background subtraction algorithm to identify the foreground present in a video frame. The visual information present in this foreground is analysed using computer vision techniques such as image segmentation to detect the various garments, the customer is interested in. The framework was tested on a dataset, that comprises of CCTV videos from a garments store. When presented with raw surveillance footage, the proposed framework demonstrated its effectiveness in detecting the interest of customer in choosing their garments by achieving a high precision and recall.
Design and Implementation of an Automated Test Framework for Electric Power Data Network based on the Unified Resource Scheduling

Traditional Electric Power Data Network (EPDN) device tests are implied manually with the testing resources utilized separately. Such way of testing is of low efficiency and is difficult for the management of device resources. With the rapid increase of testing tasks in the future, improving the test efficiency and the resource utilization is of great importance to the development of EPDN. In this article, we propose a novel automated test framework for EPDN. In particular, we abstracts the related knowledge from data of real world experimental resources and testing business, based on the extracted knowledge, we combine virtualization technology with automated testing technology and software quality management, and proposes an automated test framework with unified scheduling of testing resources. The proposed framework is evaluated with the task of route flapping testing. The provided test flow and test results prove the feasibility and the effectiveness of the proposed framework.
Representative Negative Instance Generation for Online Ad Targeting

Online ad targeting can be formulated as a problem of learning the relevance ranking among possible audiences for a given ad. It has to deal with the massive number of negative,i.e., non-interacted, instances in impression data due to the nature of this service, and thus suffers from data imbalance problem. In this work, we tackle this problem by improving the quality of negative instances used in training the targeting model. We propose to enhance the generalization capability by introducing unobserved data as possible negative instances, and extract more reliable negative instances from the observed negatives in impression data. However, this idea is non-trivial to implement because of the limited learning signal and existing noise signal. To this end, we design a novel RNIG method (short for Representative Negative Instance Generator) to leverage feature matching technique. It aims to generate reliable negative instances that are similar to the observed negatives and further improves the representativeness of generated negatives by matching the most important feature. Extensive experiments on the real-world ad targeting dataset show that our RNIG model has achieved a relative improvement of more than 5%.
Signature Partitioning Using Selected Population-Based Algorithms

Dynamic signature is a biometric attribute which is commonly used for identity verification. Artificial intelligence methods, especially population-based algorithms (PBAs), can be very useful in the dynamic signature verification process. They are able to, among others, support selection of the most characteristic descriptors of the signature or perform signature partitioning. In this paper, we focus on creating the most characteristic signature partitions using different PBAs and comparing their effectiveness. The simulations whose results are presented in this paper were performed using the BioSecure DS2 database distributed by the BioSecure Association.
CES2Vec: A Confidentiality-Oriented Word Embedding for Confidential Information Detection

Confidential information firewalling with text classifiers is to recognize the text containing confidential information whose publication might pose a threat to national security, business trade, or personal life. Word embedding is a component of the detector and plays an important role. Existing word embeddings, e.g., Word2Vec, fail to learn a clear task classification boundary, i.e., the confidential polarities of words are opposite but the embedding vectors of the words are close to each other. We propose a confidentiality-oriented word embedding, CES2Vec, for confidential information detection. We embed confidentiality into semantics to catch both of them together, which can learn the word embedding with a clear task classification boundary. We use real-world data from WikiLeaks and conduct the comparison experiments of our CES2Vec and popular methods. The experimental results show that our proposed method is better than the previously reported methods in detecting confidential information.
Enabling Live State-of-Health Monitoring for a Safety-Critical Automotive LiDAR System

In the next few years, modern vehicles will integrate the next level of Advanced Driver-Assistance Systems (ADAS) such as Light Detection and Ranging (LiDAR) which will be one of the key enabler for autonomous driving. Autonomous driving will be in charge for controlling the vehicle without any inputs of a passenger. This requires highly robust and reliable components and systems. In general, mechanical defects are detectable through vibrations or noise changes but for semiconductor components these capabilities are not available. Semiconductor components fail silently and abrupt without any prior information and this could lead to fatal accidents when systems fail during autonomous driving phases. In this publication, we are introducing a novel state-of-health monitoring system for automotive LiDAR system that is capable to economically record the component history and automatically processes these data to the statistical Failure-In-Time (FIT) Rate that is primarily used in the Automotive domain such as in the "ISO 26262 - Road Vehicle Safety" standard.
Deontic Logic with Action Types and Tokens

A new characterization of the deontic operators of permission and prohibition is introduced based on a distinction between action types and action tokens. The resulting deontic action logic constitutes a hyperintensional system providing resources for a fine-grained study of the basic deontic notions. The logic is proved to be complete with respect to an appropriate semantics, where models include both possible worlds and action tokens, and the philosophical significance of the distinction is demonstrated by showing that a number of puzzles afflicting current accounts of the deontic operators find intuitive solutions in the new framework.
Methodologies of Audio-Visual Biometric Performance Evaluation for the H2020 SpeechXRays Project

Biometric recognition is nowadays widely used in different services and applications, making the user authentication easier and more secure than the traditional authentication system. Starting from this idea, the EU SpeechXRays project H2020 developed and evaluated in real-life environments a user recognition platform based on face and voice modalities. Since the proposed biometric solution was evaluated in real-life environments where biometric data recorded was not accessible because of the General Data Protection Regulation GDPR, the ground truth of the conducted evaluation was not available. To correctly report the performance evaluation, some methodologies were proposed to detect the errors caused by the absence of ground truth. This paper describes the biometric solution provided by the project and presents the biometric performance evaluation carried out in three real-life use case pilots on more than 2 000 users.
Application of Lid Technology Concept in the Planning and Design of New Sponge City

with the process of urbanization, the cavernous bodies, such as rivers and lakes, are replaced by many new materials in the construction of cities. The percolation rate of cities has been decreasing. The application of lid technology will change the natural hydrological cycle and the storage capacity of the water system in the city, so as to enhance the urban stormwater management and improve the ecological environment of the city.
Automatic Action Extraction for Short Text Conversation Using Unsupervised Learning

Collaboration tools are important for workplace communication. The amount of conversation data produced in workplaces are increasing rapidly, placing a burden on workers. There is a necessity to analyze large amounts of data automatically to extract actionable information. Multiple studies were conducted on action extraction to identify actions such as promises and requests. Most of these studies used supervised learning methods. The key problem discussed in this paper are (i) the automatic extraction of action types from short text conversations in collaboration tools such as Twitter and Slack, and (ii) leveraging large amounts of data using unsupervised learning. Data labelling is an important issue when dealing with large datasets for training and extending the corresponding algorithms across different actions and domains. In this paper, we propose an unsupervised learning approach using a combination of relation extraction techniques and word embedding to leverage large amounts of data. The action2vec model is created to identify specific actions from short text of conversation data. We have evaluated our unsupervised method against supervised learning and the results are comparable. The action type extractor is integrated with Slack to provide assistance for action type extraction. Thus, the contributions of this paper include an unsupervised learning method to utilize large amounts of data, an automatic extraction of action types from short text and the integration of our approach with state of the art collaboration tools.