Reviewerly/exHarmony huggingface dataset:
Mobile AR-Based Assistance Systems for Order Picking – Methodical Decision Support in the Early Phases of the Product Life Cycle

To support order picking assistance systems are in use, as employees depend on a constant supply of information to guide them through the work process. In addition to conventional assistance systems such as picking lists or handhelds, the first Augmented Reality-based systems are already used, which allow virtual insertions into the field of vision. Since these systems enable new forms of interaction and the choice of the forms of interaction has a significant influence on the choice of hardware, the process-based design of the optimal interaction between humans and the system must be made early in the development process. Based on sequence analyses and process models of current picking processes, we have methodologically investigated different forms of interaction between human and assistance systems, depending on the work process. For this purpose, we simulated the use of an AR-based assistance system using the Wizard of Oz method based on a representative example process of order picking and derived suitable interaction concepts from this. Furthermore, we identified the requirements of the system users by creating personas. In this way, we were able to make a process step-dependent selection of suitable forms of interaction, which is the basis for a requirement-based AR hardware decision. The general procedure derived from this allows a transfer to other use cases to methodically support the hardware selection for an AR-based assistance system depending on the selected interaction concept. In this way, well-founded decisions for the design of such an industrial assistance system can already be made in an early phase of the product development process without high development effort.
T-S fuzzy systems optimization identification based on FCM and PSO

Abstract The division of fuzzy space is very important in the identification of premise parameters, and the Gaussian membership function is applied to the premise fuzzy set. However, the two parameters of Gaussian membership function, center and width, are not easy to be determined. In this paper, based on Fuzzy c-means (FCM) and particle swarm optimization (PSO) algorithm, a novel T-S fuzzy model optimal identification method of optimizing two parameters of Gaussian function is presented. Firstly, we use FCM algorithm to determine the Gaussian center for rough adjustment. Then, under the condition that the center of Gaussian function is fixed, the PSO algorithm is used to optimize another adjustable parameter, the width of the Gaussian membership function, to achieve fine-tuning, so as to complete the identification of prerequisite parameters of fuzzy model. In addition, the recursive least squares (RLS) algorithm is used to identify the conclusion parameters. Finally, the effectiveness of this method for T-S fuzzy model identification is verified by simulation examples, and the higher identification accuracy can be obtained by using the novel identification method described compared with other identification methods.
An Estimation Method of Visibility Level on Winter Road Based on Multiple Features in CCTV Images

Since drivers acquire a lot of information with eyes, the estimation of the level of visibility (visibility level) on the winter roads contributes toward enhancing traffic safety. Therefore, this paper proposes a method for estimating the visibility level on the winter roads from closed-circuit television (CCTV) images. In the proposed method, the visibility level of each image is estimated based on features acquired via Fourier transform and Convolutional Neural Network (CNN). Specifically, by constructing support vector machines (SVMs) for each feature, the probabilities of the visibility level are calculated. Furthermore, based on a comparison of the calculated probabilities of SVMs, the proposed method estimates the visibility level accurately. Experimental results show the effectiveness of the proposed method.
Data processing scheme based on blockchain

Abstract In the white paper written on Bitcoin, a chain of blocks was proposed by Satoshi Nakamoto. Since then, blockchain has been rapidly developed. Blockchain is not only limited to the field of cryptocurrency but also has been extensively applied to the Internet of Things, supply chain finance, electronic evidence storage, data sharing, and e-government fields. Both the public chain and the alliance chain have been extensively developed. In the data processing field, blockchain has a particularly good application potential. The Square Kilometre Array (SKA) is a proposal consisting of a joint venture of more than ten countries, resulting in the world’s largest synthetic aperture radio telescope. In the SKA, the processing scale of the data is large, and it consists of several data processing nodes. The data will be processed in the cloud computing mode. Taking the SKA under consideration, this report proposes a data processing scheme based on blockchain for the anti-counterfeiting, anti-tampering and traceability of data. Furthermore, the authenticity and integrity of the data are assured. The primary aspects include data distribution, data operation and data sharing, which correspond to the data reception, data algorithm processing and result sharing of data operation in the SKA. With this process, the integrity, reliability and authenticity of the data are guaranteed. Additionally, smart contracts, homomorphic hashing, secure containers, aggregate signatures and one-way encrypted channels are implemented to ensure the intelligence, security and high performance of the process.
Extractive Text Summarization System for News Texts

In today's conditions, it is difficult to obtain information quickly and efficiently due to the size of the data. There are various text documents on the internet and a good extraction algorithm is essential to have the most relevant information from them. Long texts can be boring sometimes. So, readers are eager to get the main idea of the text or any useful information. For this reason, the importance of automatic summarization systems is understood. Text summarization systems can be considered as abstractive summarization or extractive summarization. While abstractive systems produce a summary with new sentences, extractive systems make a selection of sentences from the text used and combine them and present them as a summary. Creating a successful summarization algorithm increases in direct proportion to the success of applying text mining techniques. Text summary systems provide a summary of the text to the user by scoring words and sentences in the main text using various methods and combining high ranked sentences as a result of the process. In this context, many scoring methods have been used. In our study, news data sets are used. The algorithm used is based on extraction and has been evaluated using a task-independent method. After evaluation, the two highest scores taken are ROUGE-1 with 0.68 score and ROUGE-S with 0.54 score. Through all evaluation steps, Precision, Recall and F-Measure values are also specified to see the steps clearly.
Ecologically Valid Multimodal Data Collection

This research effort focuses on combining affordable sensing technologies to understand individual behavioral patterns, health outcomes, and personal environments. We deployed our own environmental quality monitor along with established mobile sensing and experience sampling techniques in a study of more than 50 student participants for 3 months in central Texas. We report on our remote deployment, descriptive statistics of the collected data, and results from an initial exploratory analysis. Our novel dataset allows us to study individuals as well as groups and draw ecologically valid conclusions since participants were not isolated from their environments or routines.
Personalized Word Recommendation System Using Sentiment Analysis

Online Social Networks is a place where a user is truly free to express himself or herself, and it is observed to be so. Social networks are used by users for not only socializing but to buy or sell products. This user behavior is quite adamant from the type of comments they post on different social networks. The word recommendation system is still not personalized rather generalized for all different websites. It can, however, be personalized by the use of sentiment analysis and the model here has done the same here. The model has used subjectivity and polarity for making a personalized recommender system by analyzing the behavior and classifying them. This provided the model with two greater ways to recommend, i.e., recommending on the basis of the user as prevalent from their comments and also by the topics been discussed by them.
Effectiveness of Scientific Learning Guided Inquiry Devices Based on Real Media to Improve Understand Concept and Skills Process of Science Students

This study aims to develop a science learning device with a guided inquiry model which assisted by real media in improving students’ conceptual understanding and science process skill. The research model uses the 4D (4-D Model) with the stages of define, design, develop and disseminate. Analysis of the results of the effectiveness using the N-gain equation shows that learning devices can improve students' conceptual understanding and science process skills in light material and optical devices. The average percentage of N-gain for students’ conceptual understanding on each sub-materials: shadows on a flat mirror, the nature of shadows on curved mirrors, the nature of shadows on lenses, and optical devices was shown an increase in the percentage of N-gain. This was caused by guided inquiry models assisted by real media which make students directly conduct learning activities and observations. In the aspect of science process skills, the average N-gain in the aspect of predicting and applying concepts has a high N-gain value. The model which used in this research help students to find a concept through experiments and students were directly involved in finding solutions for the problems
Improving the Transferability of Adversarial Examples with the Adam Optimizer.

Convolutional neural networks have outperformed humans in image recognition tasks, but they remain vulnerable to attacks from adversarial examples. Since these data are produced by adding imperceptible noise to normal images, their existence poses potential security threats to deep learning systems. Sophisticated adversarial examples with strong attack performance can also be used as a tool to evaluate the robustness of a model. However, the success rate of adversarial attacks remains to be further improved in black-box environments. Therefore, this study combines an improved Adam gradient descent algorithm with the iterative gradient-based attack method. The resulting Adam Iterative Fast Gradient Method is then used to improve the transferability of adversarial examples. Extensive experiments on ImageNet showed that the proposed method offers a higher attack success rate than existing iterative methods. Our best black-box attack achieved a success rate of 81.9% on a normally trained network and 38.7% on an adversarially trained network.
Phonology and Online Media Used in Enhancing Listening Skill

This study examines how phonology and online media used to enhance students’ listening skill. The data were collected for 5 months starting from April to August 2020 in Islamic State Institute of Tulungagung (IAIN Tulungagung). The data were collected through questionnaire and interview. It involves college students who study and major English and have the relevant information about the research theme. It consist of 196 students of 3rd semester in IAIN Tulungagung. The finding reveals that there are some perspectives of students about the use of phonology and online media to enhance listening skill such as: the importance of online learning English media, online learning English media towards students’ attitudes in learning, the effect of online learning English media towards inferential listening, and media enhancing inferential listening skill. It was also gained students’ responses towards phonology used to enhance listening skill.
A Study on Artificial Intelligence Technologies and its Applications

Artificial Intelligence (AI) helps computers to learn from experience, adjust to new stimuli, and perform tasks of a human nature. It works by combining large amounts of data with fast, iterative processing and smart algorithms, allowing the program to learn from patterns or features in the data automatically. We address AI and various subfields of AI in article. In this paper, we are studying the types, applications, tools. In addition, few examples of existing Internet of Things services with AI working behind them are discussed in this context.
Experience of the semantic technologies use for intelligent Web encyclopedia creation (on example of the Great Ukrainian Encyclopedia portal)

We consider the conceptual principles of the Great Ukrainian Encyclopedia development and analyze the main advantages of its portal version – e-VUE. This portal version requires the relevant technological platform with high-level expressiveness and efficiency. Development of e-VUE with complex knowledge structure causes the study of modern Web-technologies and matching their capabilities with the specific requirements of encyclopedia. Therefore we consider in details the project idea and purposes, the specifics of e-VUE information representation (number and volume of articles, content types and sources, relations between content elements and their properties, business processes of article publication, design requirements) and processing (navigation, requests, integration and matching means) to estimate the Wiki-technology feasibility use (namely - opportunities and restrictions of MediaWiki technological platform). Also attention is paid to the means of information security ensuring and semantic-based establishment of content access rights. The results of such analysis show that MediaWiki with semantic plugins can be used as a base for e-VUE portal construction but needs in extension of its functionality based on modern knowledge management tools and formats of their interoperable representation. Now we are oriented on use of ontological analysis and Semantic Web standards (OWL, RDF, SPARQL). In terms of the Semantic Web, e-VUE is a distributed database with heterogeneous types of information objects that can be used both by humans and by external software (e.g. by intelligent agents). The main principles of e-VUE knowledge base organization are grounded on Wiki categories and semantic properties of Semantic MediaWiki that are used for formal representation of the e-VUE typical information objects (TIO). Such approach allows to express TIO relations, properties and characteristics that can be used by logical inference and semantic retrieval to improve the navigation between portal elements and integration of data from various Wiki pages.
Round-Optimal and Communication-Efficient Multiparty Computation

Typical approaches for minimizing the round complexity of multiparty computation (MPC) come at the cost of increased communication complexity (CC) or the reliance on setup assumptions. A notable exception is the recent work of Ananth et al. [TCC 2019], which used Functional Encryption (FE) combiners to obtain a round optimal (two-round) semi-honest MPC in the plain model with a CC proportional to the depth and input-output length of the circuit being computed—we refer to such protocols as circuit scalable. This leaves open the question of obtaining communication efficient protocols that are secure against malicious adversaries in the plain model, which we present in this work. Concretely, our two main contributions are: 1) We provide a round-preserving black-box compiler that compiles a wide class of MPC protocols into circuit-scalable maliciously secure MPC protocols in the plain model, assuming (succinct) FE combiners. 2) We provide a round-preserving black-box compiler that compiles a wide class of MPC protocols into circuit-independent—i.e., with a CC that depends only on the input-output length of the circuit—maliciously secure MPC protocols in the plain model, assuming Multi-Key Fully-Homomorphic Encryption (MFHE). Our constructions are based on a new compiler that turns a wide class of MPC protocols into k-delayed-input function MPC protocols (a notion we introduce), where the function that is being computed is specified only in the k-th round of the protocol. As immediate corollaries of our two compilers, we derive (1) the first round-optimal and circuit-scalable maliciously secure MPC, and (2) the first round-optimal and circuit-independent maliciously secure MPC in the plain model. The latter MPC achieves the best to-date CC for a round-optimal malicious MPC protocol. In fact, it is even communication-optimal when the output size of the function being evaluated is smaller than its input size (e.g., for boolean functions). All of our results are based on standard polynomial time assumptions.
An Intelligent Fall Detection Design for Mobile Health-Care Applications

With increased aging of the population, concerns regarding the safety of home life continue to increase. However, the reduction in the number of elderly people who are injured in unforeseen events at home is the primary design issue. Additionally, smart homes currently use different fall detection strategies to improve security. Among these strategies, mobile fall detection is widely used in mobile devices to reduce the incidence of accidents. The mobile application processor system-on-a-chip within mobile devices uses intelligent thermal monitoring techniques, such as dynamic voltage frequency scaling (DVFS) mechanisms that provide a beneficial low-power consumption design and overheating protection mechanisms. Consequently, it is necessary to establish a DVFS-aware mobile fall detection design.
Deep Surface Normal Estimation on the 2-Sphere with Confidence Guided Semantic Attention

We propose a deep convolutional neural network (CNN) to estimate surface normal from a single color image accompanied with a low-quality depth channel. Unlike most previous works, we predict the normal on the 2-sphere rather than the 3D Euclidean space, which produces naturally normalized values and makes the training stable. Although the depth information is beneficial for normal estimation, the raw data contain missing values and noises. To alleviate this problem, we employ a confidence guided semantic attention (CGSA) module to progressively improve the quality of depth channel during training. The continuously refined depth features are fused with the normal features at multiple scales with the mutual feature fusion (MFF) modules to fully exploit the correlations between normals and depth, resulting in high quality normals and depth with fine details. Extensive experiments on multiple benchmark datasets prove the superiority of the proposed method.
Simplification of RNN and Its Performance Evaluation in Machine Translation

In this paper, we study on simplification of RNN and propose new structures which enable faster learning to improve the performance while reducing the number of learning parameters. We construct 4 types of RNNs with new gated structures and call these new RNNs “SGR (Simple Gated RNN)”. SGR have two or one gate and weight or no weight for input. Comparison studies are performed to verify the effectiveness of our proposal. As a result of machine translation in relatively small corpus, compared with LSTM and GRU, our proposed SGR can realize higher scores than LSTM and GRU. Furthermore, SGR can realize faster learning approximately 1.7 times than GRU. However, with the increase of learning layers and weights for input, the learning scores of SGR seems not increase as much as we expected, which should be studied in our future work. It is necessary to analyze in more detail a performance in larger dataset and a performance difference due to multi-layering, weight for input and the number of gates.
A novel image matching algorithm based on PCA and SIFT

Abstract In order to solve the problem of low matching precision and slow matching speed of image matching algorithm based on classical SIFT, a new image matching algorithm based on PCA, SIFT and improved RANSAC is proposed. Firstly, the SIFT feature was extracted from images; Secondly, principal component analysis is used to reduce the dimension of SIFT feature descriptor, from 128 to 20; then, the EUCLIDEAN distance is used for feature matching; finally, an improved RANSAC algorithm is proposed to eliminate the mismatched feature points. Experimental results show that the proposed algorithm improves the accuracy and speed of image matching.
A Gramian approach to entanglement in bipartite finite dimensional systems: the case of pure states

It has been observed that the reduced density matrices of bipartite qudit pure states possess a Gram matrix structure. This observation has opened a possibility of analysing the entanglement in such systems from the purely geometrical point of view. In particular, a new quantitative measure of an entanglement of the geometrical nature, has been proposed. Using the invented Gram matrix approach, a version of a non-linear purification of mixed states describing the system analysed has been presented.
Quality Control for Hybrid Unicast and Multicast Video Transmission Systems

The video streaming platforms have recently evolved to provide diversified services, including live video, video on demand, and TV shows premiere. The traditional unicast method may cause a waste of bandwidth when many users watch the same video contents at the same time. In this study, we realized a hybrid unicast and multicast adaptive streaming system. The system uses ROUTE (Real-time Object delivery over Unidirectional Transport) combined with the MPEG-DASH (Dynamic Adaptive Streaming over HTTP) to simultaneously satisfy the needs of unicast and multicast users. We use the SHVC (Scalable High-efficiency Video Coding) to generate high-quality video of different bitrates. The QoE (Quality of Experience) function is measured by bitrate, PSNR, and server bandwidth. The proposed adaptive algorithm dynamically allocates the bitrates of unicast and multicast users and achieves optimal average viewing quality. Experimental results show that the proposed algorithm effectively improves the system's QoE under various bandwidth models compared to the conventional method. In the hybrid multicast and unicast scenario, the PSNR for the stable, incremental, and decreasing bandwidth models are increased by 0.79 dB, 4.92 dB, and 7.88 dB, respectively. The average quality switching frequency of users is also decreased.