Reviewerly/exHarmony huggingface dataset:
CLASSIFICATION OF METHODS AND ALGORITHMS FOR DETECTION OF FALLS IN OLDER ADULTS

Approximately 30% of older adults fall at least once a year and 50% of that number will fall twice.Likewise, the number of falls that an older adult may suffer rises with increasing age.Falls have a high morbidity and mortality rate and are considered a major public health problem.It is estimated that 7% of hospital visits by older adults are the result of a fall and 40% of these require hospitalization.This article presents some of the main detection methods and algorithms used for fall detection and discusses their advantages and disadvantages.Each of these methods and algorithms directly or indirectly requires varying processing, connectivity, storage, and portability capabilities and provides varying degrees of accuracy.Based on this analysis, an experimental development for fall arrest in older adults will be proposed that aims to achieve 95% accuracy using a minimally invasive wearable sensor.
Users’ search performance prediction in cross-device search

Users’ search performance indicates the effectiveness and success with which users’ information needs are met, which is calculated based on the relevance judgment by users themselves. This study proposed to explore the prediction of users’ search performance in the context of cross-device search. A user experiment was performed to collect users’ relevance judgments and search behaviors in cross-device search. Based on users’ relevance judgments, users’ search performance was evaluated by calculating the percentage of valid clicks, effective search time, nDCG@n, and satisfaction. A simple linear regression model was adopted to train the prediction model. The final results showed that a combination of users’ search performance in pre-switch sessions and their search behavior in post-switch sessions can attain the best prediction accuracy. Important features to predict users’ search performance in cross-device search shed light on improving search systems to aid users in completing the task efficiently.
Certification of HCD Professionals in Japan

The certification program of HCD-Net started for responding to the demand of the society for the usability professionals who have a certain level of skills and knowledge. A competence model was first established and the framework for assessing the expertise was then formed. The program that started in 2009 has been recognized as the unique system and more than 300 professionals are now playing the leading role in the area of HCD
Foundations of Bayesian Inference for Complex Statistical Models

In this virtual workshop a variety of recent developments in Bayesian high-dimensional and nonparametric statistics were discussed in depth. There were 12 in depth talks delivered via zoom in the afternoons (to allow for US attendance), and several informal evening time meetings on wonder.me, where follow up discussions of the most important mathematical developments took place.
Security Metrics

It has always been a struggle for information security professionals to measure “the effectiveness” of IT security controls. In the 1980s, “orange books” were released for security professionals as a standard to set basic requirements for assessing the effectiveness of security controls built into a computer system (Figure 7-1), which was a part of the Rainbow Series published by the US National Computer Security Center. The most well-known orange book is the Trusted Computer System Evaluation Criteria (TCSEC) and its European counterpart, Information Technology Security Evaluation Criteria (ITSEC). The Common Criteria for Information Technology Security Evaluation (in short, ISO/IEC 15408) replaced those books. It created a framework for defining security levels for systems (hardware, software, or firmware) and determining acceptable testing methods and certifying system compliance to defined assurance levels.
Clustering for the Neophyte: An R Shiny App for Self-Organizing Maps

This article provides an outline of clustering, key stages in creating self-organizing maps for purposes of clustering, instructions on how to use a free online R Shiny app that constructs self-organizing maps for data provided by users, and interpretations of the graphics produced.
A Preliminary Discussion on the Smart Large Unit Teaching of Inorganic Chemistry Based on MOOCs: An Example from Weak Electrolyte Dissociation Balance

A smart large unit teaching based on massive open online courses (MOOCs) is constructed through the full use of the functions of online MOOCs and large unit teaching.It integrates the concept of large units into MOOC learning, pre-class testing, and classroom discussion tasks, to help students understand and apply the law of weak electrolytes balance in aqueous solutions.The results of the learning evaluation prove that this kind of smart large-unit teaching can improve learning efficiency and students' willingness to learn, and help improve students' core literacy.
THE USE OF THE MEASUREMENTS REPRODUCTION PRINCIPLE IN THE PROCESSING OF FLIGHT NAVIGATION INFORMATION

The use of the measurements reproduction principle in the flight navigation systems for information interconnecting by methods of federated filtering is considered. This principle makes it possible to use measurements coming from external sources of navigation data when using the same inertial navigation system as independent information from two inertial navigation systems, which greatly simplifies the processing of information with the use of federated filtering methods with minimal loss in accuracy.
Digital Twins for the Intelligent Detection of Malicious Activities in Urban Spaces

The S4AllCities project has progressed rapidly during the last twelve months since it began in 2020 for the development of three distinct digital twins that collectively augment intelligence concerning cyber and physical security monitoring in smart urban spaces. These respectively specialize on; a) Distributed Edge Computing IoT (DEC-IoT); b) Malicious Actions Information Detection System (MAIDS); and c) Augmented Context Management System (ACMS) (S4AllCities, 2020). These three twins are built under a distributed System of Systems (SoS) architecture. Further, they each acquire real-time observations of both cyber and physical spaces while processing data for the critical extraction of knowledge at their levels. The extracted knowledge, represented as “events” at each of the respective twins levels, is communicated across the S4AllCities SoS Apache Kafka communication client/ server protocols. These respectively specialize in advancement of situation awareness at their levels. Namely, for the intelligent edge processing of observations in the urban space under the DEC-IoT; the detection of unusualness and understanding of cyber and human behavior under the MAIDS; while augmenting all awareness for the final release of threat alerts and proposed regulated responses (ACMS). In this paper, we will introduce the S4AllCities SoS overall architecture and the three twins high level functions. Then we will focus on describing our development of the MAIDS sub-modules and their functions under the De-Facto Joint Director of Laboratory (JDL) data fusion framework. The JDL framework efficiently enables the intelligent monitoring, detection and interpretation of the potential presence of threats and/or attacks in urban spaces. These attacks are either of cyber, physical, or both malicious nature. The well-known Endsley model for the cognitive advancement of situational awareness is mapped into the JDL framework in the context of critical decision support on cyber-physical surveillance in urban spaces. The JDL is much more adaptive for big data processing, machine learning, context knowledge modelling and augmented situational awareness of the cyber-physical space.
The effect of motion sickness on presence and user experience for head-mounted virtual reality

The purpose of this study is to analyse and evaluate the effect of motion sickness on presence and user experience for the virtual reality system of 3D head-mounted display integrated with a six-axis dynamic platform. According to literature review, we identified three characteristics of motion sickness, presence, and user experience based on two views of sensorial mode and cognitive mode. We conclude there are some associations among motion sickness, presence, and user experience based on two perspectives. In the experimental results, there is a significant negative relationship in the sensorial mode from the sensory disorder of motion sickness to the sensory fidelity of presence, while a significant positive relationship from the sensory fidelity of presence to the perceived attractiveness of user experience. In the cognitive mode, there is a significant positive relationship from the involvement of presence to the perceived ergonomic quality of user experience.
Use of Support Vector Machine to Check Whether Process Metrics are as Good as Static Code Metrics

There can be some faults, while the development of the software. If these faults are not recognized and corrected at any early stage of software development, then it leads to software failure and increase in development cost. So, the most important task is to deal with these software faults. Various models have been proposed to detect the fault proneness of the software using static code metrics. In this paper, we have used support vector machine with poly-kernel and normalized poly-kernel to predict the fault proneness, and both static code metrics and process metrics have been used as independent variables. AUC measure is used as performance evaluation measure to predict the fault proneness. We have also analyzed the results using 3D bar graph and Friedman test. The result of this study shows that in case of both kernels, process metric has positive impact on predicting fault proneness, or we can say that process metric is as good as static code metrics.
The Use of Sodium Polyacrylate for Oil-Water Demulsification Process

Oil-water emulsion formation is common in oil fields. The emulsion is a problem which causes an increase in production costs and a decrease in oil quality. Therefore, a demulsifier formulation is needed to overcome these problems. The use of household waste, particularly disposable diapers, is a promising alternative. One of the substances contained in diapers is sodium polyacrylate super absorbent polymer, known as sodium polyacrylate. The substance takes 450 years and 500 years to decompose in the sea and on land. Sodium polyacrylate has inert and hydrophilic properties with the working principle of absorbing and trapping water which is expected to reduce the water content in oil-water emulsions which are mostly produced from old oil wells. In this experiment, diapers were used to separate the emulsion in light crude oil samples which were tested with brine with 20% and 70% salinity at 50oC. Centrifuge was used to speed up the demulsification process within 1 to 3 minutes. It can be concluded that sodium polyacrylate contained in diaper waste can reduce the water content of the emulsion. In addition, the demulsification process by sodium polyacrylae was more stable and higher in brine with a salinity of 70% compared to brine with a salinity of 20%.
Profil Belajar Mahasiswa S2 Pendidikan Bahasa Indonesia (Gaya Belajar, Aktivitas Belajar, Dan Kebiasaan Belajar)

Learning activities can be achieved by a learner or student if he pays attention to various internal factors, such as learning styles or types, learning activities, and study habits. The purpose of this study is to describe the learning profile of Indonesian Language Education Master's Degree students based on learning style factors, learning activities, and study habits. The scope of this research includes (1) learning style factors include visual, auditory, and kinesthetic types, (2) learning activity factors include visual, oral, auditory, motor, writing, and mental activities, and (3) study habits factors, includes regularity in learning includes planning, implementing processes, and completing tasks and remedial, discipline, using guidelines in learning, and concentration of mind in learning. This study uses a descriptive method and a simple quantitative approach. Data collection using a questionnaire technique includes factors of learning style, learning activities, and study habits. The research sample used the total affordability sampling technique, namely all 29 respondents of Masters in Indonesian Language Education FKIP Bengkulu University in the odd semester of 2018/2019. Data analysis was carried out quantitatively using the mean and percentage formula by describing the results qualitatively. Based on the results of the study, the study profiles of S2 Indonesian Language Education students, FKIP Bengkulu University, achieved an average score of 3.43 in the high category and the highest contribution was achieved starting from learning style factors (41.3%), learning activities (30.4%), and study habits. (by 28.3%). This means that the student learning profile has been carried out at the level of a good learning process. In particular, that this learning profile is achieved sequentially starting from the best achievement, namely factors (1) learning style with a tendency sequence starting from the auditory type, kinesthetic, and visual type, and (2) learning activities with a tendency sequence starting from the motor element. , writing, auditory, verbal, visual and mental, and (3) habits with tendencies sequentially starting from the elements of regularity, discipline, and concentration in learning.
Menilai Kecenderungan Nilai Antar Mata Pelajaran Menggunakan Association Rule Pada Rapidminer

The focus of this research is trying to assess the tendency of scores between subjects using the association rule method using Rapidminer. The Association Rule method itself is often called Market Basket Analysis. The results showed that there was a tendency that students with good grades in English had good Mathematics scores with a 100% confidence level with a support value of 0.333
On-line Hybrid Perceptual and Cryptographic Image Hashing System

Vision systems became safety critical in modern &#x0027;Advanced Driver Assistance Systems&#x0027;, where software-controlled high-definition cameras and digital signal processors are the core components. Software verification using &#x2018;Hardware-In-Loop&#x2019; is a growing need as a result. In this paper a new hybrid perceptual hashing system for on-line digital video images processing is presented. The system can detect frozen images, mono-color images and may be used in image comparison as well. The design uses minimal hardware and storage while maintaining a high operational frequency. The system performance is analyzed and functionally verified on an HDL implementation using an FPGA.
Research on University Teaching Quality Evaluation and Guarantee System Based on Block Chain Technology

Influenced by the COVID-19 epidemic in the past two years, colleges and universities at home and abroad have adopted a combination of online and offline teaching reform, education informatization and intelligent talent training methods, which have become the focus of research for educators. The teaching quality is related to the quality of talent cultivation, and the intelligence, fairness and accuracy of the teaching evaluation system are particularly important. And block chain technology is decentralized and safe and reliable features, so the development of the technology based on big data and chain blocks obeys the law of education development of teaching evaluation system, to solve the shortage of the current appraisal system, and realize the sharing of teaching resources, integrate and optimize the teaching resources, promoting the standardization and standardization of teaching resources construction, To promote the construction and better development of disciplines in colleges and universities.
AI For Medical Imaging Informatics: Where Have We Missed Explainability?

When we consider AI for healthcare, infectious disease outbreak is no exception.The talk will begin with machine learning models that help in not only predicting but also detecting abnormalities due to infectious diseases such as Pneumonia, TB, and Covid-19.I will open my talk with infectious disease prediction models and unexploited data, where we will learn that predictive analytical tools are close to garbage-in garbage-out (at least for Covid19).I will then cover multimodal learning and representation based on both shallow learning (handcrafted features) as well as deep learning (deep features) that typically apply on medical imaging tools.Like in computer vision, I will open an obvious question, how big data is big in addition to common techniques: data augmentation and transfer learning.With all these facts, as most of models are limited to education and training, I will end up my talk with the statement "ML innovation should not be limited to building models."What we need is #ExplainablableAI in #ActiveLearning framework.
Scalable Fuzzy Systems

In this paper a new class of fuzzy systems called scalable fuzzy (SF) systems are proposed.The SF design is built upon the idea of extending the conventional fuzzy logic approach to linguistic variables to all numbers.This leads to a new set of infinite continuous rule-base and membership functions which are located on all rational numbers and are defined based on scale, position, and input variables.The consequent of rules in the Takagi-Sugeno form are then modified, and a mathematical solution based on the convolution theorem is employed for SF modeling purposes.The discrete form of SF systems is developed, and its application is exemplified using several case studies.
Reconfigurable analog computing architecture based on planar optical waveguides

The all-optical/optoelectronic hybrid intelligent computing with high speed and low power consumption characteristics has emerged as a promising solution for the bottleneck of Moore's Law. However, the low deployment flexibility and low integration limit the further development of optical computing systems, and it is urgent to match the new artificial intelligence theory with the physical characteristics of light itself. In this paper, we proposed a reconfigurable computing architecture based on planar optical waveguide, called wave network, which can be used for hardware development of optical switches, optical logic devices or neural networks. Inspired by the neuronal perceptron model, the elements of wave network are analyzed from the point of view of basic pixel units. Wave network has the processing capability of pulse input and time sequence input. To achieve low-power weight mapping, non-volatile materials with CMOS compatiblity characteristics are introduced above the waveguide (etc. phase change materials, multiferroics materials, and resistive switching materials). This hybrid structure provides a large-scale, low-cost, rapid development platform for the new photonic computing paradigm. And the simulation results show that the properties of the waveguide remain stable at different geometric scales and have the potential of scaling down.
Development of a fast binocular structured light measuring system using composite fringe projection

With the development of three-dimensional (3D) shape measurement technology, fringe projection has become an effective and reliable measurement method for its great performance on the robustness against environmental disturbance and ease of operation. Traditional method of fringe projection measurement using one projector and one camera needs multiple fringe patterns to project to get absolute phases. However, it always demands fast measuring speed in the condition of online measurement. In this work, one more camera is introduced to establish a binocular structured light system to extend the field of view. In addition, to improve the measurement speed, a triangular wave is supplemented to the sinusoidal fringe to form a superposed fringe to assist phase unwrapping with only three images projected. In the phase unwrapping process, geometric constraints are used to find the corresponding points in the images captured by left and right cameras, and then the unique corresponding points are selected by the embedded triangular waves and the period order is determined. Several experiments are performed and the results shows that the proposed method takes only one third of the time compared with the conventional measurement and thus can realize a fast 3D measurement by structured light.
Phase information based track-before-detect of maneuvering weak multi-targets

For the problem of the detection and tracking about multiple maneuvering fluctuating weak targets in radar systems, a track-before-detect algorithm based on interactive multi-model multi-Bernoulli filtering (IMM-MBTBD) is proposed. In a track-before-detect algorithm, the squared modulus likelihood ratio (SLR) is usually utilized to match the possible trajectories of targets. In this method, however, only the amplitude information is considered and the phase information is ignored, which leads to a decrease in filter performance. In this paper, we use the complex likelihood ratio (CLR) instead of the SLR in the IMM-MB-TBD and preserve the spatial coherence of the phase to realize the detection and tracking about multiple amplitude fluctuation targets with the Swerling 0,1, and 3 fluctuations. Furthermore, in order to accommodate to the situation where the prior information of target births is unknown and overcome the difficulty of detecting a weaker target and a stronger target at the same time when the targets fluctuate, an adaptive birth algorithm based on measurement likelihood ratio is proposed. Simulation results show that at low signal-to-noise ratio, the proposed IMM-MBTBD algorithm provides better performance in estimating of the state and number of targets.
An overview of dimensions and dimensions badge

Purpose Research assessment has long been important for directing research funding, rationalizing research organizations and enhancing productivity, including concentrating on specialized subjects. But due to a lack of data, research assessment procedures centered on simple indicators that solely included publications and their citation counts. The Dimensions is one such prodigy of technological evolution like the internet in discovering the research data metrics. Design/methodology/approach This paper outlines Dimensions, the emergence of Dimensions by partnering with various development partners into a single robust platform and provides directions on implementing a free tool for research insights: Dimensions badge. Findings The Dimensions platform for research insights pulls together data on financing, publications, policy, patents and grants. Originality/value This tool is freely available to libraries worldwide.
Paratexts and the meaning of the brand

Chapter 4 discusses the idea of brands as sources of meaning. The discussion again refers to several important consumer research papers published in the 1908s, 90s and 2000s, and also brings us up to date with more recent work. Meaning is treated as a socially constructed phenomenon that subsists in consumer culture and is not directed hegemonically by brand management. This contrasts with the common idea in the brand management literature that what a brand means can be controlled by brand management and inserted hypodermically into the minds of consumers. The chapter explains that paratextuality allows us to make sense of the totality of brand communication as the sub-categories of the promotional mix collapse into each other in the era of media convergence. In particular, paratextuality allows us to understand that consumer brands are unwritten primary texts, the meanings of which are brought into being by their associated paratexts.