Reviewerly/exHarmony huggingface dataset:
Robustness of entanglement as an indicator of topological phases in quantum walks

How to reveal topological phases and their boundaries is an intriguing issue in various systems. Entanglement, which plays a fundamental role in quantum information, has been found profoundly related to topological phases. However, experimentally exploring this relation is precluded by the limited ability to obtain entanglement in many-body systems. In this work, we propose and experimentally demonstrate that the robustness of entanglement, quantified by the von Neumann entropy, can be used to reveal the topological phase with winding number <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi class="MJX-tex-caligraphic" mathvariant="script">W</mml:mi> </mml:mrow> <mml:mo>=</mml:mo> <mml:mn>1</mml:mn> </mml:math> and topological phase with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi class="MJX-tex-caligraphic" mathvariant="script">W</mml:mi> </mml:mrow> <mml:mo>=</mml:mo> <mml:mn>0</mml:mn> </mml:math> in quantum walks. With the different robustness of entanglement against perturbations of a parameter, the phase boundaries between the distinct topological phases can be further determined. As a result, our work not only offers a new perspective for quantum walks but also exhibits the deep connection between entanglement and topological physics.
Development of Media Three-dimensional (3D) Visualization using Virtual Reality on Chemistry Education

Abstract This study aims to develop and test feasibility three-dimensional (3D) visualization of media using virtual reality in chemistry learning . The development model used in this study is Research and Development (R&amp;D) model. This study implemented the stages of planning, preliminary development of the product, preliminary field testing, main product revision adapted from Borg and Gall. The development of the learning media in this study was validated by media expert, material expert, peer reviewers, and subject teachers, and was tested to 23 students. The results of this study revealed that three-dimensional (3D) media using virtual reality is feasible to use as learning media for high school students. The percentages of validation results are 74% by media and material experts, 76% by chemistry teachers, 84% by peer reviewers, and 77% by students. Overall, the average total percentage score of feasibility of the three-dimensional media is 78% categorized to ‘good’. In conclusion, the developed three-dimensional (3D) learning media using virtual reality is feasible to use in the chemistry learning process for the material of reaction rate.
Automated Decision-Making no an Economical Option of Layout for the Outdoor Switchgear of Substations with the Highest Voltage of 35-220 kV

The design of the electrical part of switchgear substations is mainly carried out without taking into account the economic components and projects in this case must comply only with regulatory documents. The exclusion of economic factors from consideration when designing switchgears for substations is also due to the lack of time for a design engineer involved in the development of large-scale project documentation, and additional calculations under the conditions suitable for the end of the project execution period completely exclude the possibility of integrated accounting of all factors when choosing a switchgear circuit. The paper provides a description of the algorithm for selecting the appropriate option for the layout of an open distribution device for substations of 35-220 kV. The algorithm is based on the method of direct enumeration according to the criterion of the minimum reduced costs for the construction, repair and operation of the switchgear of substations. Possible variants of substation switchgear schemes and layouts are determined on the basis of the requirements of regulatory documents and ensure the required reliability, economy and safety. As the initial data for the determination of capital investments in the construction of electrical installations, the enlarged cost indicators are used. The algorithm is implemented in the original software "ORU CAD". The reported study was funded by RFBR according to the research project No 1837-00115.
HRec: Heterogeneous Graph Embedding-Based Personalized Point-of-Interest Recommendation

POI (point-of-interest) recommendation as an important location-based service has been widely utilized in helping people discover attractive locations. A variety of available check-in data provide a good opportunity for developing personalized POI recommender systems. However, the extreme sparsity of check-in data and inefficiency of exploiting unobserved feedback pose severe challenges for POI recommendation. To cope with these challenges, we develop a heterogeneous graph embedding-based personalized POI recommendation framework called HRec. It consists of two modules: the learning module and the ranking module. Specifically, we first propose the learning module to produce a series of intermediate feedback from unobserved feedback by learning the embeddings of users and POIs in the heterogeneous graph. Then we devise the ranking module to recommend each user the ultimate ranked list of relevant POIs by utilizing two pairwise feedback comparisons. Experimental results on two real-world datasets demonstrate the effectiveness and superiority of the proposed method.
Video Variants for CrowdRE: How to Create Linear Videos, Vision Videos, and Interactive Videos

In CrowdRE, heterogenous crowds of stakeholders are involved in requirements elicitation. One major challenge is to inform several people about a complex and sophisticated piece of software so that they can effectively contextualize and contribute their opinions and insights. Overly technical or boring textual representations might lead to misunderstandings or even repel some people. Videos may be better suited for this purpose. There are several variants of video available: Linear videos have been used for tutorials on YouTube and similar platforms. Interactive media have been proposed for activating commitment and valuable feedback. Vision videos were explicitly introduced to solicit feedback about product visions and software requirements. In this paper, we describe essential steps of creating a useful video, making it interactive, and presenting it to stakeholders. We consider four potentially useful types of videos for CrowdRE and how to produce them. To evaluate feasibility of this approach for creating video variants, all presented steps were performed in a case study.
Providing automated grading and personalized feedback

Practicing helps students reinforce the concepts that they have learned. It is equally important that personalized feedback are given to the students to help the student to learn from their mistakes. Traditionally, marking assessments and providing personalized feedback are carried out manually by teachers. However, both of these tasks become increasingly difficult as class size becomes large. To cope with this problem, we utilize natural language processing and machine learning techniques to mark essay type assessments and to help teachers provide personalized feedback promptly. The scheme determines the correctness of students' answers according to the meaning of the answers instead of comparing the words in the answers with the ones in the specimen answer. The scheme partitions the students' answers into groups according to their semantic meanings. Teachers provide feedback to a few answers in each group, and this feedback covers the typical issues demonstrated by the answers in the group. The feedback is propagated to the other answers in the same group. The proposed schemes showed reasonable marking accuracy.
A Time-Aware CNN-Based Personalized Recommender System

Recommender system has received tremendous attention and has been studied by scholars in recent years due to its wide applications in different domains. With the in-depth study and application of deep learning algorithms, deep neural network is gradually used in recommender systems. The success of modern recommender system mainly depends on the understanding and application of the context of recommendation requests. However, when leveraging deep learning algorithms for recommendation, the impact of context information such as recommendation time and location is often neglected. In this paper, a time-aware convolutional neural network- (CNN-) based personalized recommender system TC-PR is proposed. TC-PR actively recommends items that meet users’ interests by analyzing users’ features, items’ features, and users’ ratings, as well as users’ time context. Moreover, we use Tensorflow distributed open source framework to implement the proposed time-aware CNN-based recommendation algorithm which can effectively solve the problems of large data volume, large model, and slow speed of recommender system. The experimental results on the MovieLens-1m real dataset show that the proposed TC-PR can effectively solve the cold-start problem and greatly improve the speed of data processing and the accuracy of recommendation.
Pulsar candidate selection using pseudo-nearest centroid neighbour classifier

ABSTRACT A typical characteristic of the pulsar candidate classification task is the class imbalance between true pulsars and false candidates. This imbalance has negative effects on traditional classification methods. In this study, we introduce a strategy using a scatter matrix-based class separability measure to estimate the harmfulness of class imbalance on pulsar candidate classification. The measure quantitatively describes the damage of the imbalanced situations on the pulsar candidate classification problem and provides some priori information to guide us to select an appropriate data processing method and to construct an effective classifier. After that, we present a non-parametric data exploration technique, a pseudo-nearest centroid neighbour classifier (PNCN), to identify credible pulsar candidates from pulsar survey data sets. The PNCN algorithm can effectively resolve the class imbalance problem and is applicable to data streams. The proposed algorithm is tested on High Time Resolution Universe Pulsar Survey (HTRU) 2 (obtained by an analysis of HTRU Medium Latitude data) and LOTAAS 1 (obtained from the LOFAR Tied-Array All-Sky Survey). The experimental results show that the proposed classifier can excellently identify the pulsars with high performance: the precision and the recall on HTRU 2 are 92.3 per cent and 83.1 per cent, and those on LOTAAS 1 are 97.4 per cent and 95.6 per cent, respectively; the false positive rate (FPR) on HTRU 2 is 0.7 per cent, on LOTAAS 1 is 0.03 per cent, which is an order of magnitude lower than the corresponding FPR obtained in Lyon et al. (2016) and Tan et al. (2018).
Variational level set method for image segmentation with simplex constraint of landmarks

Landmarks are prior image features for a variety of computer vision tasks. In the image processing domain, research on image segmentation methods has always been a significant topic. Due to the image characteristics of heterogeneous nature, lack of clear boundaries, noise and so on, accurate segmentation of the image is still a challenge. In this paper, utilizing a level set framework and the simplex constraint, preferred image point landmarks are combined into a variational segmentation model to enforce the contour evolve with prior points. Then the alternating minimization algorithm of the proposed model is designed, meanwhile the landmarks constraints are doubled ensured with simplex projection. Finally, experiments on many synthetic and real-world images were implemented. Comparing with other state-of-the-art segmentation variational models, the most striking result to emerge from the data is that the proposed method has higher segmentation performance. Benefiting from appropriate point landmarks, the proposed segmentation method can tackle noisy, weak edges and corrupted area images effectively and robustly.
Boolean Functions for Homomorphic-Friendly Stream Ciphers

The proliferation of small embedded devices having growing but still limited computing and data storage facilities, and the related development of cloud services with extensive storage and computing means, raise nowadays new privacy issues because of the outsourcing of data processing. This has led to a need for symmetric cryptosystems suited for hybrid symmetric-FHE encryption protocols, ensuring the practicability of the FHE solution. Recent ciphers meant for such use have been introduced, such as LowMC, Kreyvium, FLIP, and Rasta. The introduction of stream ciphers devoted to symmetric-FHE frameworks such as FLIP and its recent modification has in its turn posed new problems on the Boolean functions to be used in them as filter functions. We recall the state of the art in this matter and present further studies (without proof).
A Model for Deriving Trust and Reputation on Blockchain-Based e-Payment System

E-commerce has become a crucial part of our life allowing us to buy products, request services, and transfer money easily with a press of a button. As such, establishing immutable trust and reputation of entities that are resilient to manipulation by the malicious are critical in today’s online systems. In this work, we propose a model for calculating trust and reputation using the values stored on blockchain ledger. The model is applied to blockchain-based online payment systems which have a characteristic of immutability by preventing data manipulation. The model normalizes user evaluations based on each user’s personal evaluation criteria that changes over time. In addition, the model derives reputation of, and trust between, users by applying psychological factors. We evaluate our model using not only simulated transaction data but also on real Bitcoin transaction-based dataset to show that our model is able to derive stable values from immutable transactions on blockchain-based online payment systems. Our model has been built into a live commercial blockchain service platform, and new application developments are underway.
A case-based reasoning system for interior design using a new cosine similarity retrieval algorithm

During concept design stage, a case-based reasoning (CBR) system is good for searching the most suitable interior design drawing case as a preliminary concept drawing design aid to bridge the design requirements gap between the consigner and the designer. In the case base of the system, features of the pre-stored design drawing cases could be characterized by interval, nominal, ordinal and/or ratio scale data. However, the cosine similarity measure used for case reasoning scarcely deals with mixed measurement scales of interval, nominal, ordinal and ratio scale types. Therefore, the objective of this study has been twofold. The first goal was to propose a new cosine similarity retrieval algorithm for case reasoning, in which mixed measurement scales are considered and applied. The second goal was to propose a CBR system and apply the new cosine similarity retrieval algorithm to the search engine of the CBR system for interior design drawing retrieval. Finally, numerical experiments were carried out to demonstrate the capability and effectiveness of the proposed similarity matching algorithm on the case-based reasoning system.
GRU-based Attention Mechanism for Human Activity Recognition

Sensor data based Human Activity Recognition (HAR) has gained interest due to its application in practical field. With increasing number of approaches incorporating feature learning of sequential time-series sensor data, in particular the deep learning based ones has performed reasonably in uniform labeled data distribution scenario. However, most of these methods do not capture properly the temporal context of time-steps in sequential time-series data. Moreover, the situation becomes worse for imbalanced class distribution which is a usual case for HAR using body-worn sensor devices. To solve this issues, we have integrated hierarchical attention mechanism with recurrent units of neural network in order to obtain temporal context within the time-steps of data sequence. The introduced model in this paper has achieved better performance with respect to the well-defined evaluation metrics in both uniform and imbalanced class distribution than the existing state-of-the-art deep learning based model.
Blockchain-Based Communication and Data Security Framework for IoT-Enabled Micro Solar Inverters

It is expected that the conventional micro solar inverters can be revolutionized as a result of further investigation of the emerging Internet of Things (IoT) technologies and cloud computing services by providing continuous monitoring, data exchanging, and optimal operation in smart grid environments. However, there are imminent concerns and challenges regarding cybersecurity from cyber-attacks since the inverters through the IoT devices are always connected to networks such as Internet. The paper explores how blockchain technology could be used for potentially ensure communication and data security of the IoT -enabled micro solar inverters.
Real-time phase step measurement using the volume enclosed by a surface algorithm in self-calibrating phase-shifting interferometry

This manuscript presents a simple, and very efficient method capable of measuring in real-time the phase steps from an interferogram varying with the time. Within a range (0,2π) given by the phase steps of two fixed reference interferograms. The phase differences among the dynamic and reference interferograms are computed by an algorithm of type self-calibrating generalized phase-sifting interferometry based on geometric concept of volume enclosed by a surface. This proposal has the property to accept or reject the measured data, and its calibration process is very easy, which are very important advantages, so it is a very practical method. Theoretical model is described, verified with a numerical simulation, and applied to measure experimentally the phase steps in three optical tables with different conditions.
Development of Tank-Based Military Robot and Object Tracker

Indonesia is a country whose economy is developing very fast. According to World Bank data, Indonesia is the country with the largest economic development in Southeast Asia. Unfortunately, the threat of terrorism in Indonesia is very large, therefore it requires a smart system such as military robots. Today's vision-based robot technology has also been used in the development of autonomous military robots. A sophisticated military robot is a robot that is needed by the military/police because it can be deployed to the battlefield or the eradication of terrorism in a remote or autonomous manner. This system is needed to reduce the remaining casualties from the army, and this combat robot system can also be operated at any time with more numbers than regular soldiers and with minimal operator needs. In this paper, we propose a prototype of a tank-based military robot with object detection and tracking and turrets for simulation of shooting the enemy target based on the computer vision. The object tracker will detect an upper body of the target using a library in OpenCV and the tank will track until it reaches the best position to shoot the target. The methods explained, and experimental results were presented.
The design of the structure of the software system for processing text document corpus

One of the most difficult tasks in the field of data mining is the development of universal tools for the analysis of texts written in the literary and business styles.A popular path in the development of algorithms for processing text document corpus is the use of machine learning methods that allow one to solve NLP (natural language processing) tasks.The basis for research in the field of natural language DATA ANALYSIS AND INTELLIGENCE SYSTEMS
What is an ecological approach and how can it assist in understanding ICT take‐up?

Abstract This paper argues that ecological frameworks offer a powerful way of explaining the take‐up of information and communication technology (ICT) in schools. Past research has focused on teachers and their willingness and readiness to use ICT but this has not satisfactorily explained the use (or non‐use) of ICT. Gaps in this research can be addressed by an ecological approach which shifts attention from the teacher to the context in which ICT is expected to be used. Such an approach, following Bronfenbrenner and others, looks at the relationship of individuals to the system in which they act, a relationship which is seen as interdependent. An ecological perspective explains how take‐up of ICT is influenced by the degree to which different levels of a system, eg, micro, meso, exo and macro levels, are aligned. It is recommended that researchers and policy makers should pay greater attention to context when advocating the use of ICT.
Towards Enhancing RadViz Analysis and Interpretation

RadViz plots are commonly used to represent multidimensional data because they use the familiar notion of 2D points for encoding data elements, displaying the original data dimensions that act as springs for setting the x andy coordinates. However, this intuitive approach implies several drawbacks and produces misleading visualizations that can confuse the user, even while analyzing a single data point. The paper attacks this problem following the well known idea of changing the order of the dimensions and introducing ancillary visualizations to mitigate some of RadViz drawbacks. In particular, the paper defines the notion of point optimal disposition of the dimensions for a single data point, generalizes this concept to a set of data points, and proposes effective heuristics for dealing with the intractable problem of exploring all the (n-1)!/2 dispositions of the dimensions along the RadViz circumference. Additional views, visual quality metrics, and a circular grid superimposed on the RadViz complement the attribute reordering strategy and provide abetter understanding of the actual plot of the data elements.
Multiscale Cooperative Differential Evolution Algorithm

A multiscale cooperative differential evolution algorithm is proposed to solve the problems of narrow search range at the early stage and slow convergence at the later stage in the performance of the traditional differential evolution algorithms. Firstly, the population structure of multipopulation mechanism is adopted so that each subpopulation is combined with a corresponding mutation strategy to ensure the individual diversity during evolution. Then, the covariance learning among populations is developed to establish a suitable rotating coordinate system for cross operation. Meanwhile, an adaptive parameter adjustment strategy is introduced to balance the population survey and convergence. Finally, the proposed algorithm is tested on the CEC 2005 benchmark function and compared with other state-of-the-art evolutionary algorithms. The experiment results showed that the proposed algorithm has better performance in solving global optimization problems than other compared algorithms.