Reviewerly/exHarmony huggingface dataset:
Fingeryeyes; Social Scripts; Flow (Three Poems Exploring Digital Affect)

Anthropology and HumanismVolume 46, Issue 1 p. 138-144 Poetry First Prize Fingeryeyes; Social Scripts; Flow (Three Poems Exploring Digital Affect) Susan Wardell, Corresponding Author susan.wardell@otago.ac.nz orcid.org/0000-0001-6567-0468 Social Anthropology Programme, University of Otago, Floor 2 Richardson Building, Leith Street, Dunedin, New ZealandSearch for more papers by this author Susan Wardell, Corresponding Author susan.wardell@otago.ac.nz orcid.org/0000-0001-6567-0468 Social Anthropology Programme, University of Otago, Floor 2 Richardson Building, Leith Street, Dunedin, New ZealandSearch for more papers by this author First published: 29 June 2021 https://doi.org/10.1111/anhu.12324Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Volume46, Issue1Special Section: Society for Humanistic Anthropology 2020 Writing AwardsJune 2021Pages 138-144 RelatedInformation
Affection of Java Design Patterns to Cohesion Metrics

The idea of code quality assessment is well known for a long time; class connectivity metrics were proposed by community several years ago and have not become generally applicable practice in industrial programming. The objective of the study, part of which we present in this paper, is to critically analyze the metrics available for today: Are they completely unusable, or considering their specifics can be useful helpers for software specialists. Specifically, we try to answer the questions: Are there any connection between design patterns and cohesion metrics, and how these patterns affect metrics if they do.
Adaptive Textile Mold System for Double Curvature

Free form architecture with complex geometry are one of the most striking trends in contemporary Architecture and design, enriching the spatial and expressive diversity of Architecture. These shapes are also one of the major challenges for architects, engineers and the building industry, creating new challenges for manufacturer of building components, because of the difficulty of manufacturing double curvature shapes at reasonable prices and quality. CAM manufacturing processes have limitations, their stage of development is not the same of geometric manipulation, made through CAD tools, that constitutes a research field with high potential. The proposed technique is based on the manipulation of elastic textile membranes by arduino motors, the points of the surface are transferred to the textile membranes using the CAD 3D data. The precast components are assembled using the Brick Warp System which is an post-stressed constructing process, the combination of these systems produces a solid solution, that allows materialize structural morphogenesis principals.
An Big Data Analysis Approach Based on Frequent Change Structure Mining

To improve the data support level and mining performance of traditional big data analysis methods, this paper designs a data model based on data mining algorithm. Firstly, the data model based on data mining algorithm is established. Then the tree model is used as the data storage standard to construct the frequent change structure mining framework, and different feature subsets are obtained according to the original feature space of the data. Finally, the simulation experiment is used to test, the results show that under the same experimental conditions, the F1 value, scalability and algorithm complexity of the design method are better than the traditional method, which shows that the mining performance of the design method has been significantly improved.
Effects of Data Corruption on Network Identification Using Directed Information

Complex networked systems can be modeled and represented as graphs, with nodes representing the agents and the links describing the dynamic coupling between them. The fundamental objective of network identification for dynamic systems is to identify causal influence pathways. However, dynamically related data streams that originate from different sources are prone to corruption caused by asynchronous time-stamps, packet drops, and noise. In this article, we show that identifying causal structure using corrupt measurements results in the inference of spurious links. A necessary and sufficient condition that delineates the effects of corruption on a set of nodes is obtained. Our theory applies to nonlinear systems, and systems with feedback loops. Our results are obtained by the analysis of conditional directed information (DI) in dynamic Bayesian networks. We provide consistency results for the conditional DI estimator that we use by showing almost-sure convergence.
Forward Propagation Neural Network Weighting Analysis as a Model Estimation Method

One of the main parameters that affect training process of feed-forward neural networks and final quality of the model is weights initialization. In this article different methods for analyzing weights matrices, which allow evaluation of the initial initialization quality, are presented. The considered methods make it possible to simplify parameters of weights initialization selection and increase the efficiency of neural networks training. All results are presented in the framework of regression problem.
Proposed technical activity on requirement engineering process

Abstract Requirement Engineering (RE) aims to provide assurance that a software product can meet user needs. The RE phase is a stage that is carried out early in the software development phase of the System Development Life Cycles (SDLC). The main problem in software development is the accuracy of the requirements specifications generated in the RE phase. Problems due to non-conforming specifications can result in software become of poor quality, development time become longer, and development costs become high. To minimize the obstacles that occur, in this article will be given about the techniques carried out in the RE process including elicitation, analysis, documentation, validation, and management. The techniques are given focus more on the implementation of technical activities and artifacts that are used (if any) in each RE process. Artifacts in the RE process can be used as a tool to simplify the verification process and validation of conformity between the requirements specifications produced with the expectations desired by the user. The resulting artifacts are using UML notation which consists of flow modelling using activity diagrams and prototype modelling using use case diagrams. Based on the proposed technical activities, the RE process can improve the quality of the software, the development time is according to the planned schedule, and the development costs according to the budgeted.
Hybrid Deep Neural Network Acoustic Model for Taiwanese Speech Recognition

Taiwanese is one of the main languages spoken by Taiwanese. Taiwanese was commonly used language in Taiwan before, but now it is facing the dilemma of declining. Therefore, maintaining traditional culture in the ever-changing trend is a major issue. In this study, we developed the Taiwanese speech recognition system which the Kaldi Toolkit was used to implement. The Taiwanese corpus was collected by Taiwan Taiwanese National Reading Competition and Classmate Recording, and about 11 hours of audio files were collected in total. Because the training data is a small dataset, two data augmentation methods are used to increase the training data, so that the acoustic model can be more robust and more effective training. One method is speed perturbation, which speeds up the original data by 1.1 times and slows it down by 0.9 times. Another method is to use multi-condition training data to simulate the reverberation of the original speech and add background noise. The background noise includes music, speech, and noise. The acoustic model is trained for different hybrid deep neural network architectures which can use the advantages of each neural network by hybrid different neural networks, such as CNN-LSTM-TDNN. In the experimental results, the CER in the domain of language modeling reaches 3.95%, and the CER of the online decoding test is 3.06%. Compared with other research on Taiwanese speech recognition of similar dataset size, the recognition results are better than other studies.
EarRecorder: A Multi-Device Earable Data Collection Toolkit

Earables are a hot topic in wearable research. An increasing number of off-the-shelf devices support collecting sensor data for different use cases. Following this trend, we introduce EarRecorder – a unifying, open-source app that connects to multiple earables (eSense, Cosinuss°, regular earphones). The app can collect data from multiple sensor streams at the same time and lets users label the data during recording. Two user studies allowed us to optimize the usability of the app further and to validate its functionalities. In the future, we hope to include new earables with open APIs and establish a community that advances the app further.
Digital Image Separation With Component Analysis

Image fusion and its separation is a frequently arising task in image processing field . In this Paper we have described image fusion and it’s Separation using Component Analysis technique. Fused Image Separation is very typical task so we have described component analysis technique based on Peak signal to noise ratio and Signal Interference ratio.
Risorse e applicazioni computazionali per l’accesso ai beni culturali: il Corpus CHerIDesCo

The paper presents CHerIDesCo - Cultural Heritage - Italian Description Corpus, a domain-specific linguistic resource designed for the training and testing of novel NLP tools in the Cultural Heritage field. The corpus has been developed by the UNIOR NLP Research group as a part of the SMACH project, a three-year project funded by the National Operative Program to pursue the Smart Specialization Strategy defined by the EU. The project aims at improving language-based human-computer interaction in the Cultural Heritage domain through the development of innovative applications for multilingual access to the contents based on semantic language technologies. In particular, the paper describes the design of the CHerIDesCo corpus, the annotation procedures, and the platforms where the resource has been uploaded. As pointed out in the conclusion, this linguistic resource can be exploited in several NLP tasks (e.g., NER - Named-Entity Recognition, NEL - Named-Entity Linking, and Topic Modeling).
Ubiquitous Body: Effect of Spatial Arrangement of Task's View on Managing Multiple Tasks

This research proposes a concept of the "ubiquitous body system", a new concept of using multiple body parts at various places simultaneously. This system enables users to access any number of the bodies at a time, and make physical intervention to any locations regardless of the current location where user's natural body is. However, when the number of ubiquitous bodies becomes larger, it becomes more difficult to see or manage all of those tasks.
Adaptive algorithm for mobile robot movement control

Abstract The paper reviews the goal of controlling mobile robot movement along the planned path. It proposes an adaptive robot movement control algorithm, allowing to increase the precision of holding the robot within the prescribed path under the conditions of unstable navigation field. The adaptation of the algorithm to jamming environment is in redistributing weights of the weighted sum of estimates given by the ultrasound navigation system (USNS) and wheel pulse transducers (WPT). When precision of the USNS reduces affect of the WPT on the final estimate increases while affect of the USNS decreases. Such a redistribution is carried out when the system is operating due to changing elements of a measurement noise matrix in Kalman filter [1] with use of which a mobile robot heading angle is estimated. Corresponding elements of the measurement noise matrix increase along with increase of deviation of the mobile robot trajectory from a straight line. The extent of deviation calculated as the standard error of a regression is defined by means of the statistical analyses involving the last n measurements contained in buffer store of the USNS. The obtained results demonstrate significant benefit of the developed mobile robot heading estimation algorithm in comparison with the conventional filtering algorithm basing on using the classical Kalman filter with the measurement noise matrix whose elements are fixed.
Face and Hand Gesture Recognition System for Controlling VLC Media Player

In human-computer interaction or sign language interpretation, recognizing hand gestures and face detection become predominant in computer vision research. The primary goal of this proposed system is to create a system, which can identify hand gestures and facial detection to convey information for controlling media player. For those who are deaf and dumb sign language is a common, efficient and alternative way for talking, by using the hand and facial gestures we can easily understand them. Here hand and face are directly use as the input to the device for effective communication purpose of gesture identification there is no need of an intermediate medium.
Implementasi Metode SAW dalam Penilaian Kinerja Tenaga Harian Lepas

The performance assessment of Civil Servants at the Bengkulu Province Communication, Informatics, and Statistics Service (Diskominkotik) process has utilized an e-performance application. However, the assessment of daily workers' performance is not included, which means evaluating their performance and competence would take a long time. This study aims to determine daily workers to get the best performance rewards using the Simple Additive Weighting (SAW) method. The research method used was the waterfall method, supported by data from observations, interviews, and questionnaires. Unified Modeling Language (UML) was used to analyze and design the system and in the coding stage, Microsoft Visual Studio 2010 software connected to a MySQL database was used, and in the testing stage, a Black-box testing approach was employed. The results showed that the implementation of the SAW method in the decision support system application could speed up the process of evaluating the performance of daily workers at Bengkulu Province Diskominkotik. Furthermore, daily workers’ performance appraisal became more objective because it involves the participation of many parties so that the results of the best daily worker employee performance appraisal can be accepted by various parties.
Exploring Relationships Between Distractibility and Eye Tracking During Online Learning

More than half of students think their attention is easily shifted when they’re learning online. Distractibility, to a certain extent caused by visual stimuli is the main impact to decrease their academic performance. In addition, eye-tracking technology has been widely applied to explore distractibility in many “look” tasks, such as reading, viewing advertisements, and watching online videos as well as measure the efficiency of visual cognition. Therefore, this paper aimed to discuss the relationship between distractibility with eye movement indices and academic performance. Fifty high school students (30 girls) were recruited to complete experiment that was divided into two groups, which are the experimental group with distractions and controls with no one. The result showed that three of traditional eye movement indices were significantly correlated with distractibility ( $$p < 0.05$$ ). Then we introduced the network accessibility model and the gaze transformation entropy to create two composite indexes according to the complexity and directivity of distractibility characteristics. The result revealed that the two composite indexes are significantly correlated with distractibility ( $$p < 0.05$$ ). Finally, we constructed the mapping model about eye movement metrics about distractibility and online learning performance with a machine learning algorithm. The result ration was $$R^{2} = 0.799,$$ and the error was $$Re < 0.1$$ , which proved the model was feasible and accessible. The research from the perspective of distractibility can provide valuable support for physiological indicators testing tools of academic performance and highlights the applications of eye movement dynamics.
Research of semantic role labeling based on long short-term memory neural networks

Abstract Semantic Role Labeling (SRL) is a shallow semantic analysis in the field of NLP, and a relatively basic and important step. Traditionally, SRL has been performed based on the results of syntactic analysis and has problems such as over-reliance on feature engineering. With the development of deep learning, many neural network models for NLP have been proposed and SRL tasks can be performed well by neural networks. Among these, long short-term memory networks form a very good fit with the SRL task by virtue of their sequence-based features. In this paper, in order, we first analyze the SRL task based on grammatical analysis and neural networks, then the SRL task based on LSTM and its improved models, then the dataset and model evaluation of the SRL task, and finally conclude and look forward.
A survey to analyze the mainstream and future face alignment algorithms

Face Alignment, focusing on detecting facial landmarks of input faces, has been widely applied in criminal investigation and security verification system. However, face alignment keeps an incredibly challenging task in computer version due to occlusion, pose variation and unsuited initial shape. Many related methods have been proposed to tackle these problems. Many traditional methods, such as Face Alignment by Coarse-to-Fine Shape Searching, focus on initial face shape optimization. Recent studies pay more attention to making advantages of deep learning approaches such as cascaded classifiers, convolutional neural networks and multitask learning methods, which have already achieved outstanding performance on prevalent datasets. In this paper, we summarize the mainstream face alignment models and analyze the corresponding advantages and shortages. Then, we also discuss further application and development of face alignment algorithms in the future.
Pervasive Smart Objects: Framework for Extending Smart-Object Services

Although various types of smart objects have already been developed, the use of these devices has not yet penetrated widely into people's everyday lives. Therefore, we constructed a design framework to increase the pervasiveness of smart-object services. This study consisted of two parts: defining a design space for smart objects, and developing a design framework using the defined design space. First, we analyzed 26 smart-object products to obtain the design space for smart-object services. These products were cited from various research projects regarding smart objects and were used to identify the features of smart objects. As a result, we obtained five dimensions: information acquisition method, perception, information provision by device, information conversion level, and target of service. Afterward, we performed a case study based on the design space, with the mindset of widening the applicability of the case study, and developed a design framework with the design space as the core to increase the pervasiveness of smart-object services. The resulting framework consists of four phases. Based on the application of this framework to other case studies, we then obtained valuable insights regarding the use of this design framework.
Methods of identifying touched areas have been wiped properly

According to the CDC, COVID-19 most commonly spreads during close contact. Although the cleaners are disinfecting the public places where they are expected to have been contacted, it is still difficult to know exactly which areas have been touched and wiped out. We devised combined methods of detecting touched and wiped areas by people, using both thermal and imaging cameras. It detects the changes of temperature when people touched or wiped in about 3 seconds. To improve the accuracy, we utilize object recognition for humans from imaging cameras as a method of filtering. All marks are tracked and calculated, to disclose which areas are still remaining as touched areas, revealing the information to anyone who wants to know.
Use of fingerprint biometric in the workplace during COVID times: a critical viewpoint

Introduction: The evidence for a relationship between dietary patterns and uric acid concentrations is scanty.In the state of Assam, a diverse group of the community lives with different food habit.This study has aimed to evaluate and compare a relationship between Serum Uric Acid level in relation to dietary intake and alcohol consumption among the different community of Kamrup district of Assam.Materials and methods: The present study was conducted among Ahom, Adivasi, Bodo, Bengali, Karbi, Manipuri and Marwari communities in Greater Kamrup District of Assam.Samples were collected by stratified random sampling technique.Serum uric acid level in relation to dietary intake and alcohol consumption were evaluated and compared.Results: Uric acid level of Bodo and Ahom community is found higher in comparison to Manipuri, Bengali, Adivasi and Marwari.High Uric acid level is absent among of vegetarian category whereas nonvegetarian who take alcohol have significant high uric acid level.Conclusion: Balance dietary habits need to be placed in the context of overall health promotion; disease prevention and disease treatment with appropriate attention to nutritional needs.
A Novel Chinese Reading Comprehension Model Based on Attention Mechanism and Convolutional Neural Networks

This paper presents a novel machine reading comprehension model based on deep learning techniques in Chinese environment. In our manner, the training process can be performed using a general-level GPU, and the convergence of the training process can be accelerated for a shorter period of time. In the architectural design, two main constituting parts include Self-Attention Mechanism and Convolutional Neural Networks. To enhance the interaction between an article and questions, we carry out the operation of Context-Query Attention twice, so that our model is more effectively for acquiring the information of the questions related to the article and converges faster while training. In the experiment, we adopt the Delta Reading Comprehension Dataset for model evaluation in Chinese environment. The experimental results reveal that our model is able to reach the accuracy of 64.9% for EM and 79.0% for Fl. The convergence time is less than 1 hour using the Titan XP GPU, and the memory usage is comparatively lower. The training performance is about 3 times faster than other models with state- of-the-art architecture.
Automated system of planning calculations of Gosplan of the USSR and technological restrictions of implementing ECM into the state management in the USSR

The article deals with analysis of Pilot Project of the Automated system of planning calculations of State Planning Committee [Gosplan] of the USSR (ASPC) [ASPR] and of organizational and technological restrictions whereunder it was developed. It is shown that it maintained the basic features of the projects of USCCN and USAS [OGAS] (both earlier described in historical references), while the introduced alterations were caused not by the Soviet bureaucracy's opponency to the very idea of computerization of the planning (nowadays, this rendering is dominating in the historiography) but by organizational and engineering problems which expressly came into view by the end of the 1960s.