Reviewerly/exHarmony huggingface dataset:
Out-of-the-Box Deployment to Support Research on In-Home Care of Alzheimer’s Patients

COVID-19 has caused many disruptions in conducting smart health research. Both in-lab sessions and in-home deployments had to be delayed or canceled because in-person meetings were no longer allowed. Our research project on “in-home monitoring with personalized recommendations to reduce the stress of caregivers of Alzheimer’s patients” was affected. To enable continued research without any person-to-person contact, we created an out-of-the-box deployment solution. The solution is multifaceted and deals with everything from technical adjustments, deployment documentation, EMA additions, additional monitoring software, use of videos, Zoom and TeamViewer, budget changes, new logistics, and changes to IRBs. This article briefly describes the purpose and design of the original system and then articulates the necessitated changes. We also provide lessons learned and an initial evaluation of the effectiveness of the solutions after the changes. The evaluation surveys the opinions of seven people that assembled, initialized, and deployed our system in home environments. We believe that the various solutions we developed can be applied to other similar projects, and will be helpful to new projects even when personal contact returns.
An MRF-Based Intention Recognition Framework for WMRA with Selected Objects as Contextual Clues

To mitigate the physical burden of disabled people, we propose an approach that a robot could perceive the implied action intentions of disabled people by their selected objects. This article presents a framework for recognizing and learning human intentions based on selected household objects and interaction history. First, the intention network is modeled based on Markov random field (MRF) to connect the selected objects and daily activities. Second, the q-learning algorithm is added to provide the intention network with the function of adapting to the user’s intention preference. Then, we build the wheelchair-mounted robotic arms (WMRA) with a green laser pointer as human-robot interaction (HRI). Finally, we demonstrate the feasibility of the intention recognition framework by evaluating a scene comprised of objects from 11 categories, along with 7 possible actions, 36 single-object intentions, and 24 multiple-object intentions. We achieve approximately 70% reduction in fewer sessions than the Recursive Bayesian Incremental Learning and achieve approximately 87.5% and 86.2% reduction in interactions overall on recognizing multiple-object intention, respectively.
Abnormal detection of artificial intelligence based on hierarchical timing memory algorithms

With the advent of the information age and the popularization of computers, computer technology has been greatly developed in this era. Many people began to take advantage of computers and develop computer performance, so a variety of algorithms were proposed. The main algorithm used in this paper is the newly proposed hierarchical timing memory algorithm. Because it is useful for detecting errors, we can use it to detect anomalies. Therefore, the purpose of this paper is to use the hierarchical timing memory algorithm to detect anomalies in artificial intelligence. After observing the possible anomalies of artificial intelligence, this paper records it, experiments it with strated timing memory algorithm and observes the advantages and disadvantages of strated timing memory algorithm and other algorithms for anomaly detection. The experimental results show that the abnormal detection efficiency of strated timing memory algorithm is better.
Infrared image of electrical equipment enhancement using grey wolf adaptive nonlocal mean denoising and adaptive fuzzy enhancement

In this paper, the infrared image of power equipment contains noise, blur and so on, which makes it impossible to accurately judge and locate the infrared fault. An infrared image enhancement algorithm based on grey wolf adaptive non local mean denoising and adaptive fuzzy enhancement is proposed. Firstly, grey wolf adaptive non local mean denoising is used to denoise the initial infrared image, and then NSCT transform is performed. After the transform, grey wolf algorithm is used to optimize the fuzzy parameters of high-frequency components, and then enhancement is performed; the low frequency component is linearly enhanced. Then the NSCT inverse transform is performed. After the algorithm verification, it is shown that the algorithm is effective in infrared image denoising and enhancement, and the evaluation index also verifies the effectiveness of the algorithm.
The Effect of Mind Mapping Techniques on Writing Text Descriptions for Grade 7 Junior High School Students

Teaching subject of English is a compulsory. English as a subject in school immediately improved all four of the abilities. The four abilities are: speaking, listening, reading and writing. Nevertheless, there are still a lot of students of 7th Grade in SMPN 1 Jabon Sidoarjo, especially at 7 E Class, who cannot reach minimum passing grade (KKM) caused by some lack in English lesson, specifically in writing ability of Descriptive text. Common obstacles students are facing are for example: Students cannot create their writing concept of descriptive about people, thing, places, and animals. This study attempted to answer the following research question (1) is there any significant effect of mind mapping technique on students’ writing of descriptive text at seventh grade of SMPN 1 Jabon Sidoarjo. In carrying this research, the researcher formulated the objectives of the study as follows: To find out the effect of mind mapping technique on students’ writing of descriptive text at 7th grade of SMPN 1 Jabon Sidoarjo. Research method: The research design is pre-experimental research with quantitative approach, The population of this research were all students of 7E SMPN 1 Jabon Sidoarjo, The sample was seventh E class consisting of 32 students, The research instrument used in this research was test and the data collected through test was analyzed using T-test formula. The result showed that different of mean score of students’ writing before being taught using mind mapping technique was 57. Meanwhile, the mean score of students’ writing after being taught using mind mapping technique was 86,59. The finding showed that the mind mapping technique was effective to use in teaching writing descriptive text for it could improve students’ writing ability. This research has benefit in English Education study, especially for further research to enrich the resource
Person Re-identification Scheme Using Cross-Input Neighborhood Differences

Intelligent CCTV-based surveillance is becoming an essential element in smart cities. Despite the recent explosion of CCTV installed for security purposes, its monitoring still depends on people. Person re-identification is a technique to find an image in disjoint camera views that contains the previously detected pedestrian. Conventional methods for person re-identification used the similarity based on hand-crafted features and their performance heavily relies on lighting or camera angle. In recent years, deep learning-based methods have shown good performance in person re-identification. However, deep learning-based models using two input images have a limitation that they cannot detect similarities and differences between images simultaneously. In this chapter, we propose a model that calculates similarities and differences between images simultaneously by extracting features from the input of three images and reconstructing the extracted feature map.
Robust Incremental Kalman Predictor

Under certain environmental conditions, when the measurement equation of the system is not verified or calibrated, the use of the measurement equation will often produce unknown system errors, resulting in large filtering errors. Similarly, when the noise variance of the system is uncertain, the performance of the filter will deteriorate, and even cause the filter divergence. The introduction of incremental equation can effectively eliminate the unknown measurement error of the system, so that the state estimation of system under poor observation condition with unknown measurement error can be transformed into the state estimation of incremental system. In this paper, a robust incremental Kalman predictor is proposed for linear discrete systems with unknown measurement error and unknown noise variance. A simulation result shows the effectiveness and feasibility of the proposed algorithm.
Dynamic and Adaptive Monitoring and Analysis for Many-task Ensemble Computing

Applications are not what they used to be. Modern HPC applications are increasingly a mix of heterogeneous tasks and services – both internal and external. This imposes new constraints and requirements on application and performance monitoring, which is fundamentally different from single-task monitoring. Runtime decisions are needed, and critically rely on monitored information to determine what to do. Thus, online monitoring and analytics must be first-class requirements of all applications and the runtime environments that support their execution. This position paper explores the implications of novel application requirements on future monitoring and profiling subsystems, in the context of ensemble computing.
A Structured Light Small Field Three-Dimensional Measurement System Based on Three-Frequency Heterodyne Method

As the demand for industrial inspection continues to increase, structured light 3D reconstruction technology is increasingly developing towards miniaturization and high precision in the field of industrial inspection. Aiming at the disadvantages of the small field of view and close-range detection systems, such as high cost, large size and heavyweight, a set of low-cost, high-precision, and high-resolution 3D reconstruction system suitable for measuring small objects with structured light is designed. In this paper, a microscope structured light system which composed of a USB camera and a MEMS programmable projection module with advantages that low-cost, high-accuracy, and high-resolution was proposed. A four-step phase-shifting algorithm and multi-frequency heterodyne algorithm are applied to decode the absolute phase value. Finally to calculate the 3D coordinate by using the correspondence between the absolute phase value and the actual 3D object point (The correspondence can be acquired by system calibration). Experiments show the accuracy of the system can achieve 4\(\mu \)m, and the measuring height range is 1.5 mm. Summarily, this system could greatly satisfy the requirement for the high precision 3D reconstruction of the micro parts.
Anomaly Detection Algorithm Using a Hybrid Modelling Approach for Energy Consumption Time Series

Many energy time series captured by real-time systems contain errors or anomalies that prevent accurate forecasts of time series evolution. However, accurate forecasting of load time series and fluctuating renewable energy feed-in as well as subsequent optimisation of the dispatch of controllable generators, storage and loads is crucial to ensure a cost-effective, sustainable and reliable energy supply. Therefore, we investigate methods and approaches for a system solution that automatically detect and replace anomalies in time series to enable accurate forecasts. Here, we introduce a hybrid anomaly detection system for energy consumption time series, which consists of two different neural networks (Seq2Seq and autoencoder) and two more classical approaches (entropy, SVM classification). This network is able to detect different types of anomalies, namely, outliers, zero points, incomplete data, change points and anomalous (parts of) time series. These types are defined for the first time mathematically. Our results show a clear advantage of the hybrid modelling approach for detecting anomalies in previously unknown energy time series compared to the single approaches. In addition, due to the generalisation capability of the hybrid model, our approach allows very good estimation of energy values without requiring a large amount of historical data to train the model.
Text Mining Approach for Identifying Research Trends

With the increase of unstructured data, the issues connected with automatic text processing, the categorization of documents and the discovery of topics have become objects of growing interest. In order to improve the process of grouping and processing research publications, we would like to propose a method based upon natural language processing. It is based on text mining technologies which aim to identify key tendencies in documents. It processes the content of publications by clustering and identifies the topics of each identified group. This analysis helps by identifying key tendencies as well as discovering emerging new areas of research. Publications from the research literature database, Scopus, were used to test the approach. The topic of the publications is “the application of digital technologies in the logistics business”. The experiments were completed using the RapidMiner Studio software.
Inside Blockchain and Bitcoin

Blockchain and Bitcoin have both become a very important topic in academia as well as in the economic world. This is due to the main characteristics of this system: transparency, anonymity, security, and low transactional costs. This paper studies the blockchain network, with special attention to Bitcoin. Its uses, advantages, and challenges are all presented, with special focus in instability and volatility, trust and security, cyberattacks, and hacking as well as illegal activities. Within the conclusion, the main future research trends are also analyzed.
Future of Intelligent IoT

More detail about the proposed prediction algorithm, as well as the many components that comprise our design, is given here. A Moving Window Regression with Adaptive Weights is an example of a regression model (AMWR).
A New Deep Hierarchy for Underwater Image Reconstruction

The diminishing land resources compel us to pay more attention to the Marine. The underwater image restoration therefore plays an increasingly important role. The early works usually rely on image super-resolution reconstruction. This family of methods yet suffer from the phenomenon of gradient dispersion, resulting in poor restoration performance. To solve this problem, this paper proposes a new deep neural network. The dense block structure and the adaptive mechanism are combined in a unified framework. The high-frequency representations can be then exploited. Moreover, the best weights of each channel can be determined automatically. Multiple comparative studies are performed. The experimental results prove that the proposed method could solve the gradient dispersion effectively and ignore the redundant information simultaneously.
Detecting renewal states in chains of variable length via intrinsic Bayes factors

Markov chains with variable length are useful parsimonious stochastic models able to generate most stationary sequence of discrete symbols. The idea is to identify the suffixes of the past, called contexts, that are relevant to predict the future symbol. Sometimes a single state is a context, and looking at the past and finding this specific state makes the further past irrelevant. States with such property are called renewal states and they can be used to split the chain into independent and identically distributed blocks. In order to identify renewal states for chains with variable length, we propose the use of Intrinsic Bayes Factor to evaluate the hypothesis that some particular state is a renewal state. In this case, the difficulty lies in integrating the marginal posterior distribution for the random context trees for general prior distribution on the space of context trees, with Dirichlet prior for the transition probabilities, and Monte Carlo methods are applied. To show the strength of our method, we analyzed artificial datasets generated from different models and one example coming from the field of Linguistics.
The pedagogical principles of Lia Oxinoit

The article reveals pedagogical principles of Lia Oxinoit, a renowned piano teacher from the Republic of Moldova who made a great contribution to the children’s piano education of the country. A vast teaching experience at the “C. Porumbescu” Republican Boarding Music School in Chișinău allowed her to devise her own original methods of educational and professional influence upon students, based on the principles of progressive and educative teaching, constientiousness and dynamic behavior of teaching-learning process, lucidity and accessibility of the musical material being studied. From this point of view, L. Oxinoit’s concerts of the class are described, with an emphasis on their artistic value and importance in the cultural life of Chișinău. A conclusion is drawn that Lia Oxinoit’s pedagogical principles were the foundation of her successful work in the domain of educating new gernerations of pianists.
Translating Patent, Legal and Medical Documents from English into Indonesian in PT STAR Software Indonesia

ABSTRACTThis final report is written based on the internship activity implemented in PT STAR Software Indonesia, started from January 20 until March 13, 2020. The objective of this final report is to describe the activities during the internship period. The writer’s main activity is translating various patent, legal and medical documents from English into Indonesian. In an effort to accomplish every translation project, the writer applied some translation steps, including analyzing, transferring and restructuring.Meanwhile, there were several problems that appeared during the internship activity. Those problems were dealing with the difficult terms, poor quality of translation tool’sdisplay and incomplete features of translation tools. In order to overcome the problems, the writer conducted more research and utilized other tools to support translation activity. Eventually, the writer asked for suggestions from the supervisor, project manager and his internship partners by having a discussion session to settle every problem.
Fuzzy Logic Systems

The word “fuzzy” means imprecisely defined, confused, and vague. However, fuzzy systems benefit from mathematical formulation to determine their output. This chapter deals with type-1 fuzzy systems as well as interval type-2 fuzzy systems in which different parts of these fuzzy systems are explained.
A framework for analysing state-abstraction methods

Abstraction has been used in combinatorial search and action planning from the very beginning of AI. Many different methods and formalisms for state abstraction have been proposed in the literature, but they have been designed from various points of view and with varying purposes. Hence, these methods have been notoriously difficult to analyse and compare in a structured way. In order to improve upon this situation, we present a coherent and flexible framework for modelling abstraction (and abstraction-like) methods based on graph transformations. The usefulness of the framework is demonstrated by applying it to problems in both search and planning. We model six different abstraction methods from the planning literature and analyse their intrinsic properties. We show how to capture many search abstraction concepts (such as avoiding backtracking between levels) and how to put them into a broader context. We also use the framework to identify and investigate connections between refinement and heuristics—two concepts that have usually been considered as unrelated in the literature. This provides new insights into various topics, e.g. Valtorta's theorem and spurious states. We finally extend the framework with composition of transformations to accommodate for abstraction hierarchies, and other multi-level concepts. We demonstrate the latter by modelling and analysing the merge-and-shrink abstraction method.
Facial Recognition UX*

This case study discusses the case of an industry-academic partnership with the NEC Foundation, a global leader in facial recognition technology, and a UX and usability BIPOC researcher investigating applications of the technology in emergency response and university settings. The research collaboration aims to provide formative user experience data to help the industry understand the experience of potential users to provide developers and marketers with user-centered, evidence-based guidelines for facial recognition deployment. Research questions include the following: How do service providers and consumers in these areas perceive collecting biometric data in higher education and fire services (which is the primary emergency response service for small and mid‐sized communities), as it pertains to their sense of ethics, privacy, data ownership, consent, and justice? The project investigates user attitudes, concerns, and, per value theory, estimations of tradeoffs between these domains. The case study shares preliminary findings from the work and plans for further investigation. Finally, the case study approaches a preliminary framework for industry-academic collaborations on UX and tech ethics. Partnerships with industry can render research in communication design immediately applicable. Even when they expose fault lines between industry and academia, they offer an essential service to the community and methods of advocacy and accountability that benefit users.
Expressive Facial Characters Based on Indonesian Compound Sentence Using Multinomial Naïve Bayes Classifier

Facial expressions are necessary for effective communication. In the field of animated facial films and game production, expressive facial characters are needed so that dialogue scenes can take place naturally as a human. To develop expressive facial characters like humans, these characters must be able to recognize emotions. Emotions from a text can be identified by classifying the text. This research aims to build an expressive facial character animation with the process of mapping dominant emotion classes from Indonesian sentences using the Multinomial Naive Bayes (MNB) model. A compound sentence is a sentence that has two or more clauses. The relationship between clauses is indicated by the presence of conjunctions. The classification process can produce complex emotional class probabilities (not just one emotion class). Emotion classes resulting from the classification process have different probability values. Therefore, a dominant threshold equation is needed to determine the dominant emotion classes. The dominant emotion classes of a compound sentence can consist of one emotion class. The combination of dominant emotion classes is called compound expression. In the development of 3D facial animation, the visualization of compound facial expressions is determined by the value of the associated Action Units (AUs). The results of this research indicate that MNB model can be used to map emotion classes based on the Indonesian compound sentence and the dominant emotion class can be determined using the dominant threshold equation. The dominant emotion classes as the basis for the formation of compound facial expressions.
Two Level Document Image Classification

Classifying documents is an important process for organizations that are responsible for keeping a large number of documents in a digital archive. In this paper, a two-level method was used to classify approximately 253 class of documents. In the first stage, the documents were visually classified. In the second stage, unclassifiable documents were tried to be classified using natural language processing (NLP). Training set was created by classifying approximately 28.000 documents by hand. Studies were carried out on single-page documents. Performances were measured by making experiments on the full, 1/2 and 1/3 of document using AlexNet for image classification, bag-of-words and LSTM algorithm for NLP. The performances of the study were measured according to different options. The success rate was 75% and 85% at the end of first and second stages respectively