Reviewerly/exHarmony huggingface dataset:
Classification of Health Literacy and Cognitive Impairments Using Higher-Order Kinematic Parameters of the Sit-to-Stand Movement From a Monostatic Doppler Radar

To develop daily monitoring systems that can identify elderly people with cognitive functioning and health literacy impairments, this study examined elderly participants during the sit-to-stand (STS) movement by using a Doppler radar to gather kinematic information for assessing any associations between those data and factors related to both cognitive functioning and health literacy. More specifically, we used Doppler radar systems to measure kinematic parameters related to the STS movement among 170 community-dwelling elderly participants aged 65 years and older, who were classified into good or bad health literacy groups and cognitive-healthy or cognitive- impairment groups based on the results of conventional paper-based tests designed to assess health literacy and cognitive functioning, respectively. The Doppler radar measured kinematic parameters for not only velocity, acceleration, and jerk (the time derivative of acceleration), but also higher-order derivative parameters: snap (the time derivative of jerk) and crackle (the time derivative of snap). Intergroup differences between the measured kinematic parameters were then compared using Welch's t-test. The results revealed that health literacy impairments were associated with velocity, acceleration, and jerk, while cognitive impairments were associated with the higher-order derivative parameters, snap and crackle. These findings show that the Doppler radar is an effective tool to distinguish impairments between health literacy and cognitive functioning based on the extraction of higher-order derivative parameters during the STS movement. The results should lead to the development of unconstrained monitoring systems that can detect early signs of impairments in both cognitive functioning and health literacy among the elderly.
Generating the Voice of the Interactive Virtual Assistant

This chapter introduces an overview of the current approaches for generating spoken content using text-to-speech synthesis (TTS) systems, and thus the voice of an Interactive Virtual Assistant (IVA). The overview builds upon the issues which make spoken content generation a non-trivial task, and introduces the two main components of a TTS system: text processing and acoustic modelling. It then focuses on providing the reader with the minimally required scientific details of the terminology and methods involved in speech synthesis, yet with sufficient knowledge so as to be able to make the initial decisions regarding the choice of technology for the vocal identity of the IVA. The speech synthesis methodologies’ description begins with the basic, easy to run, low-requirement rule-based synthesis, and ends up within the state-of-the-art deep learning landscape. To bring this extremely complex and extensive research field closer to commercial deployment, an extensive indexing of the readily and freely available resources and tools required to build a TTS system is provided. Quality evaluation methods and open research problems are, as well, highlighted at end of the chapter.
Smart Human Resource Management System to Maximize Productivity

Human resource is one of the most valuable assets in an organization. They are bounded to develop the unique and dynamic aspects that strengthen their competitive advantage to persist in an always changing market environment. In order to recruit a quality candidate for an organization, reducing human involvement and verifying details of the candidate is important in recruitment process. Furthermore, having an idea about how well or poor the employees perform, and how likely the employee attrition can occur is vital in human resource management process. This paper is an attempt to introduce smart human resource management system that can maximize the productivity of an organizational environment using machine learning and blockchain technologies. The end goal of this research is a smart human resource management system that reduces human judgment, time in the candidate selection process and predicts employee performance and attrition to motivate current employers to maximize productivity with minimal financial loss in the workplace environment. Skill assessment and resume classification have been done using unsupervised learning algorithms and natural language processing after extracting raw data from employee resumes using Object Character Recognition. Candidate details verification is done by comparing the hashes of the records which are stored in the blockchain. Employee performance and attrition are predicted using supervised machine learning classification techniques with high accuracy and the result of the final performance is generated as a score for each employee considering the multiple attributes that has been standardized and regulated by some specifically considered e-competence frameworks.
DISCO: Distributed Control Plane Architecture for Resource Sharing in Heterogeneous Mobile Edge Cloud Scenarios

This paper presents a novel control plane protocol designed to enable cooperative resource sharing in heterogeneous edge cloud scenarios. While edge clouds offer the advantage of potentially lower latency for time critical applications, computing load generated by mobile users at the network edge can be very bursty as compared with aggregated traffic served by a data center. This motivates the design of a shared control plane which enables dynamic resource sharing between edge clouds in a region. The proposed control plane is designed to exchange key compute and network parameters (such as CPU GIPS, % utilization, and network bandwidth) needed for cooperation between heterogeneous edge clouds across network domains. The protocol thus enables sharing mechanisms such as dynamic resource assignment, compute offloading, load balancing, multi-node orchestration, and service migration. A specific distributed control plane (DISCO) based on overlay neighbor distribution with hop-count limit is described and evaluated in terms of control overhead and performance using an experimental proto-type running on the ORBIT radio grid testbed. The prototype system implements a heterogeneous network with 18 autonomous systems each with a compute cluster that participates in the control plane protocol and executes specified resource sharing algorithms. Experimental results are given comparing the performance of the baseline with no cooperation to that of cooperative algorithms for compute offloading, cluster computing and service chaining. An application level evaluation of latency vs. offered load is also carried out for an example time-critical application (image analysis for traffic lane detection). The results show significant performance gains (as much as 45% for the cluster computing example) vs. the no cooperation baseline in each case at the cost of relatively modest complexity and overhead.
Bayesian Analysis of Finite Populations under Simple Random Sampling

Statistical methods to produce inferences based on samples from finite populations have been available for at least 70 years. Topics such as Survey Sampling and Sampling Theory have become part of the mainstream of the statistical methodology. A wide variety of sampling schemes as well as estimators are now part of the statistical folklore. On the other hand, while the Bayesian approach is now a well-established paradigm with implications in almost every field of the statistical arena, there does not seem to exist a conventional procedure—able to deal with both continuous and discrete variables—that can be used as a kind of default for Bayesian survey sampling, even in the simple random sampling case. In this paper, the Bayesian analysis of samples from finite populations is discussed, its relationship with the notion of superpopulation is reviewed, and a nonparametric approach is proposed. Our proposal can produce inferences for population quantiles and similar quantities of interest in the same way as for population means and totals. Moreover, it can provide results relatively quickly, which may prove crucial in certain contexts such as the analysis of quick counts in electoral settings.
A Job Interview Dialogue System with Autonomous Android ERICA

We demonstrate a job interview dialogue with the autonomous android ERICA which plays the role of an interviewer. Conventional job interview dialogue systems ask only pre-defined questions. The job interview system of ERICA generates follow-up questions based on the interviewee’s response on the fly. The follow-up questions consist of two kinds of approaches: selection-based and keyword-based. The first type question is based on selection from a pre-defined question set, which can be used in many cases. The second type of question is based on a keyword extracted from the interviewee’s response, which digs into the interviewee’s response dynamically. These follow-up questions contribute to realizing natural and trained dialogue.
The Relevance of the Source Language in Transfer Learning for ASR

This study presents new experiments on Zyrian Komi speech recognition. We use Deep-Speech to train ASR models from a language documentation corpus that contains both contemporary and archival recordings. Earlier studies have shown that transfer learning from English and using a domain matching Komi language model both improve the CER and WER. In this study we experiment with transfer learning from a more relevant source language, Russian, and including Russian text in the language model construction. The motivation for this is that Russian and Komi are contemporary contact languages, and Russian is regularly present in the corpus. We found that despite the close contact of Russian and Komi, the size of the English speech corpus yielded greater performance when used as the source language. Additionally, we can report that already an update in DeepSpeech version improved the CER by 3.9% against the earlier studies, which is an important step in the development of Komi ASR.
Automatic Database Index Tuning Using Machine Learning

Index tuning is considered as a powerful technique used to improve the database performance by ensuring the swift data access. Automating the index tuning by using Machine Learning (ML) algorithms will open up new research avenues to address the database performance challenges. ML also alleviates the performance tuning technique's dependency on the schema by adjusting the database to the changing business needs. The proposed research work presents a model to perform index selection by applying an ML classification algorithm on the dataset constructed by using the query's execution history. One of the proposed model's distinguishing characteristics is using the column's future usage rate as a feature in the Dataset, that makes the selectedindex to be adaptable for future query patterns.
Skew cyclic codes over 𝔽q[u,v,w]/〈u2 − 1,v2 − 1,w2 − 1,uv − vu,vw − wv,wu − uw〉

In this paper, we study skew cyclic codes over the ring [Formula: see text] where [Formula: see text]; [Formula: see text] is an odd prime. Here, the structural properties, generating polynomials and idempotent generators of the skew cyclic codes are obtained. Also, we show that the Gray images of skew cyclic codes of length [Formula: see text] over [Formula: see text] are skew 8-quasi-cyclic codes of length [Formula: see text].
Bitcoin Price Prediction Using Deep Learning and Real Time Deployment

The rapid development of digital currency especially crypto currency during the last decade is the most controversial development in the modern global economy. Because of the high volatile market and fluctuations in Bitcoin price, it has led a lot of confusion among the investors. This paper proposes the novel method of the construction of prediction model using deep learning approach. The proposed approach was found to be more accurate than the Machine learning models used for prediction as the deep learning model consider the non-linear nature of price. The results verify the applicability of model and give a direction to investors on how deep learning techniques can be used in decision making.
Identification of Degrading Effects in the Operation of Neighboring Photovoltaic Systems in Urban Environments

As photovoltaics technologies have emerged as one of the most promising renewable energy resources in urban environments, monitoring and maintaining of such systems have gained significance. In order to support reliable system operation during the projected in-field operation lifetime, effective strategies for identifying potential problems in photovoltaic systems operation are needed. In this paper, novel methods for the identification of degrading effects in the operation of neighboring photovoltaic systems are presented. The proposed methods are applicable for identifying panel aging properties, soiling effects, and the operation of photovoltaic systems under different shading scenarios. Since the proposed methods are based on the cross-correlation of the operation of neighboring systems, they are particularly suitable performance assessment in urban environments. The proposed identification methods are integrated according to the adopted fog computing model, providing a scalable solution capable of uniform integration into the distributed applications for monitoring and maintenance of photovoltaic systems in urban areas. The details regarding the implementation of the identification methods in the form of data processing services and service operation and dependencies are also provided in this paper. The identification methods, integration concept, and related service operation are verified through the presented case study.
Research on Scene Fusion and Interaction Method Based on Virtual Reality Technology

Abstract The construction of a virtual interactive model based on VR technology breaks through the single mode of traditional virtual reality helmets, through optical attitude sensing, immersive cave virtual reality, multi-person network collaboration, 9D interactive media applications, and mobile terminal interaction. The organic integration and application of technologies have improved the user’s interactive experience, and the inheritance and development of local folk culture and the continuous research on intangible cultural heritage can be a useful exploration. However, the development cost of the entire design is relatively high, and there is no unified construction standard and policy support for such projects in China, especially the lack of effective support from the network application platform, which makes it difficult to implement online, and the reproducibility is low.
Metadata Aggregation via Linked Data: Results of the Europeana Common Culture Project

Digital cultural heritage resources are widely available on the web through the digital libraries of heritage institutions. To address the difficulties of discoverability in cultural heritage, the common practice is metadata aggregation, where centralized efforts like Europeana facilitate discoverability by collecting the resources’ metadata. We present the results of the linked data aggregation task conducted within the Europeana Common Culture project, which attempted an innovative approach to aggregation based on linked data made available by cultural heritage institutions. This task ran for one year with participation of twelve organizations, involving the three member roles of the Europeana network: data providers, intermediary aggregators, and the central aggregation hub, Europeana. We report on the challenges that were faced by data providers, the standards and specifications applied, and the resulting aggregated metadata.
AnoGAN-Based Anomaly Filtering for Intelligent Edge Device in Smart Factory

Maintenance of production equipment and controlling products quality through data analysis are the main issues of smart factory. During production, detected data for analysis is showing abnormal data more than normal data. Therefore, there is lots of energy consumption for analysis, cost, and saving of data. Edge Device which applied deep learning algorithm is able to solve this problem. In this paper, a framework for data filtering method before data analysis is proposed through Anomaly detection using single board computer (SBC). Using Nvidia Jetson nano and desktop computer to compare and analyze the two virtual environments to determine the framework of optimum anomaly data filtering. AnoGAN is a deep learning model utilized for anomaly detection.
Automated Extraction of Causal Relations from Text for Teaching Surgical Concepts

Effective teaching of surgical decision making requires providing students with a deep understanding of the domain so that they have the ability to make decisions in novel situations. This means providing them with a thorough understanding of causal relations between actions and their possible effects in the context of various states of the patient as well as previous actions. Intelligent tutoring systems to teach surgical decision making thus require such domain knowledge, but there are currently no medical ontologies that encompass it. While it is possible to engineer the needed ontologies by hand, this requires a large effort for every new domain to be covered. In this paper we explore the possibility of automatically extracting causal relations from textbooks on surgery. Specifically, we adapt the spaCy NLP tool for this task and apply it to a collection of fifteen textbooks on endodontic root canal treatment, which is one of the most challenging areas of dental surgery. Since the main purpose is to extract knowledge for teaching, we focus on actions that can lead to surgical mishaps. We evaluate the precision and recall of the extracted relations using a gold standard prepared by a pair of dental surgeons.
The Failure Forecast Method applied to the GPS and seismic data collected in the Campi Flegrei caldera (Italy) in 2011-2020.

Episodes of slow uplift and subsidence of the ground, called bradyseism, characterize the recent dynamics of the Campi Flegrei caldera (Italy).In the last decades two major bradyseismic crises occurred, in 1969/1972 and in 1982/1984, with a ground uplift of 1.70 m and 1.85 m, respectively.Thousands of earthquakes, with a maximum magnitude of 4.2, caused the partial evacuation of the town of Pozzuoli in October 1983.This was followed by about 20 years of overall subsidence, about 1 m in total, until 2005.After 2005 the Campi Flegrei caldera has been rising again, with a slower rate, and a total maximum vertical displacement in the central area of ca.70 cm.The two signals of ground deformation and background seismicity have been found to share similar accelerating trends.The failure forecast method can provide a first assessment of failure San Francisco (CA), 1-
Application of Information and Communication Technologies in Solving Geometric Problems

In today's information age, the use of information and communication technologies in teaching geometry is one of the key factors in improving the quality and efficiency of education. To increase the motivation of students to study geometry and ensure an intersubject connection between geometry and computer science, geometry lessons need to use not only video projectors, presentations, historical materials downloaded from social networks, but also educational software in mathematics. The ability to use computer mathematical tools is available in Microsoft Office programs. Solving math problems in Microsoft Office programs requires students to have knowledge of computer science and information technology.
Toward Intelligent Workplace: Prediction-Enabled Proactive Planning for Human-Robot Coexistence on Unstructured Construction Sites

Construction robot path planning is critical for safe and effective human-robot collaboration in future intelligent workplaces. While many studies developed methods to generate paths for construction robots, very few, if any, have integrated the worker trajectory prediction on the jobsite. The objective of this research is to find a safe and efficient robot path, meanwhile, taking into account the predicted movement of construction workers. To this end, we propose a context-aware Long Short-Term Memory (LSTM)-based method to predict worker's trajectory. Based on the predicted trajectory, the A* and Dynamic Window Approach (DWA) are used to find an optimal path for the robot. The efficiency and effectiveness of the proposed method are manifested by simulated and field experiments. The proposed method will contribute to the body of knowledge for prediction-based construction robots path planning and provide the potential to be integrated into existing robot platforms to enhance their performance.
Coupled Digital Image Correlation and Peridynamics for Full-Field Deformation Measurement and Local Damage Prediction

Digital image correlation (DIC) measurement technique and peridynamics (PD) method have been applied in specific fields extensively owing to their respective advantages in obtaining full-field deformation and local failure ... | Find, read and cite all the research you need on Tech Science Press
The Santa Cruz sluicing data set

This report describes a new research resource: a searchable database of 4,700 naturally occurring instances of sluicing in English, annotated so as to shed light on the questions that have shaped research on ellipsis since the 1960s. The paper describes the data set and how it can be obtained, how it was constructed, how it is organized, and how it can be queried. It also highlights some initial empirical findings, first describing general characteristics of the data, then focusing more closely on issues concerning antecedents and possible mismatches between antecedents and ellipsis sites.
FactorNet: Holistic Actor, Object, and Scene Factorization for Action Recognition in Videos

The ability to recognize human actions in a video is challenging due to the complex nature of video data and the subtlety of human actions. Human activities often get associated with surrounding objects and occur in specific scene contexts. Existing action recognition systems are incapable of separating human actions from representation biases, like co-occurring objects and underlying scene, which often dominate subtle human actions. In this paper, we address the issue of factorization of human actions into the activity performed by the actor, co-occurring objects, and underlying context to mitigate the influence of representation biases when they are irrelevant to the action in consideration. We propose a deep neural network architecture, denoted by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FactorNet</i> , for efficient action recognition in videos with long temporal duration. We design an attention mechanism that separates an actor from the associated objects and co-occurring scene followed by capturing long-range temporal context. We perform a comprehensive set of experimentation on six benchmark datasets to show the efficacy of our architecture. To train a model using recent video-based action datasets certainly capture and leverages such bias. The supervised representation may not be competent to new action classes. We therefore design a new dataset, known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FactNet</i> , which consists of activity-object-scene related actions that occur in day-to-day applications. Dataset Link: FactNet.