Reviewerly/exHarmony huggingface dataset:
GO: Out-Of-Core Partitioning of Large Irregular Graphs

Single-PC, disk-based processing of large irregular graphs has recently gained much popularity. At the core of a disk-based system is a static graph partitioning that must be created before the processing starts. By handling one partition at a time, graphs that do not fit in memory are processed on a single machine. However, the multilevel graph partitioning algorithms used by the most sophisticated partitioners cannot be run on the same machine as their memory requirements far exceed the size of the graph. The popular memory efficient Mt-Metis graph partitioner requires 4.8× to 13.8× the memory needed to hold the entire graph in memory. To overcome this problem, we present the GO out-of-core graph partitioner that can successfully partition large graphs on a single machine. GO performs just two passes over the entire input graph, partition creation pass that creates balanced partitions and partition refinement pass that reduces edgecuts. Both passes function in a memory constrained manner via disk-based processing. GO successfully partitions large graphs for which Mt-Metis runs out of memory. For graphs that can be successfully partitioned by Mt-Metis on a single machine, GO produces balanced 8-way partitions with 11.8× to 76.2× fewer edgecuts using 1.9× to 8.3× less memory in comparable runtime.
Computational psychometric approach for assessing mathematical problem-solving skills

In this paper, we described a computational approach in educational assessment and illustrated it with an interactive textbook of mathematics for undergraduate students. To maintain the validity of assessment results, we used evidence-centered design since data collection in psychometrics is aligned with a theoretical framework. However, the results of computational methods can be used as collateral information to extend psychometric models.
スマートフォン内蔵カメラを用いたEye Glance入力インタフェースの検討

The purpose of this study is to develop a line-of-sight input interface using the eye-glance input method, which uses a glance eye movement and method of estimating the eye-control movement by analyzing the image of the area near the eye-ball. The motion was discriminated by taking an image using the built-in camera of a smartphone to rapidly look at the four corners of the smartphone and calculate the moving distance of the eyeball, which was divided into horizontal and vertical components using the optical flow function in Open CV library. In the experiment, we obtained data by asking the subjects to look at the reciprocate of quick gaze movements at each of the four corners and the center of the screen. As a result of data analysis, characteristics of waveforms were obtained for the movements looking at the four corners, and we could determine which of the four corners the users were looking at. Furthermore, by distinguishing between camera shake and line-of-sight movement using the variance value, the discrimination rate was 90% or more even when held in the hand. It was shown that the Eye Glance movement can be measured with a smartphone as well as a PC.
A Proposal for Clothing Size Recommendation System Using Chinese Online Shopping Malls: The New Era of Data

Research was conducted in this study to design data-based size recommendation and size coding systems specifically for online shopping malls, expecting to lighten the burden of holding excessive inventories often caused by the high return rate in these online malls. The recommendation system has been implemented focusing mainly on size extraction and recommendation functions along with a UI (user interface). For the former function, data are necessary to extract customers’ sizes and, for instance, the system to be used in China adopts their Chinese standard body size GB/T (Chinese national standard) considering that there are a variety of body types in their substantial population. The system shows the most similar size dataset among the body size GB/T dataset to the customer once he/she inputs his/her height and weight. Each GB/T data was entered after categorizing it according to the proportion between height and weight. For the latter function, size recommendation, size coding was performed first for all the clothes by the shop owner by entering individual size data. The clothes providing the most suitable fit for the customer are recommended by the selection of that which has the smallest deviation between coded clothes size and the customer body data after performing a series of comparative calculations. To validate the effectiveness of the extraction, a method that checks whether the difference between extracted size and the body size that has been measured remains within the error range of 4cm was used. The result showed there to be an approximate 88% matching rate for women and a slightly lower accuracy of 80% for men. Moreover, the error rate was relatively smaller for the upper half clothing such as shirts, jackets, and blouses or one-piece dresses. Such a result may have been generated since the GB/T data were actually the average data entered 10 years prior without categorizing nationalities, ages, and body types in detail. This research emphasized the necessity of a database containing a more segmented human body size data, which can be effective for extracting and recommending sizes more accurately as the latest ones continue to accumulate.
Space–Time Codes From Sum-Rank Codes

Just as rank-metric or Gabidulin codes may be used to construct rate–diversity tradeoff optimal space–time codes, a recently introduced generalization for the sum-rank metric—linearized Reed–Solomon codes—accomplishes the same in the case of multiple fading blocks. In this paper, we provide the first explicit construction of minimal delay rate–diversity optimal multiblock space–time codes as an application of linearized Reed–Solomon codes. We also provide sequential decoders for these codes and, more generally, space–time codes constructed from finite field codes. Simulation results show that the proposed codes can outperform full diversity codes based on cyclic division algebras at low SNRs as well as utilize significantly smaller constellations.
Digitalization in the system of methods of scientific knowledge

The study aims to clarify the methodological status of digitalization in the framework of the philosophical theory of knowledge. The subject of the study: explication of the instrumental and methodological nature of digitalization. The problem is solved by employing the categorical and conceptual apparatus of philosophy of science, particularly in paradigm analysis. A comparative analysis of the concepts of “information” and “digitalization” is carried out. Information within the framework of classical scientific rationality coincides with the phenomenon of absolute-relative knowledge. In non-classical rationality, it represents a movement of meaning and expresses the value-valued nature of information technology. In postnonclassical science, the phenomenon of information is associated with intrasystemic communications. Digitalization as a mode of data transmission is incorporated into the deductive and inductive logic of classical science and leads to an essential limitation of the source base. In modern science, data selection entails the creation of systematic factual meaning, and digitalization coincides with analytical activity. In addition to general logical functions, digitalization points to new perspectives on such types of systems methodology as modeling and design. In relation to the information as a methodology, digitalization acts as a meta-methodology. That is, the digital world is defined as an observing reality. In the framework of digitalization, information is identical to knowledge in its postnonclassical meaning. In digitalization, the process of cognition is incorporated into reality itself, forming an intersubjective field of meaning. In this context, digitalization serves as an inter-paradigmatic method of scientific knowledge. Digitalization refers to the rational side of cognition and is a type of systematic methodology and general logical methods.
Evaluation of Schema Languages: RNG and WXS on Interface Complexity Metric

A complex interface can disorient the user in a mild situation while designing web-based application. Thus, measure should be taken to reduce the complexity and provide reasonable qualities (simplicity, generality and usability) to the user. This paper evaluates Interface Complexity Metric (ICM) between two xml schema languages: World Wide Web Consortium XML Schema (WXS) and Regular Language for Next Generation (RNG) to weigh human insight on the factors affecting effort for comprehending schema documents; taking into account elements and attributes of xml documents; acquired from Web Services Description Language (WSDL). It is probable to lure decisions about the professed qualities; that out of one hundred and twenty (120) schema documents implemented in rng and wxs; rng has the lowest complexity values for almost all the schema documents when compared with wxs because rng exhibits better presentation with high degree of simplicity, generality and usability qualities than wxs. Therefore, this is benefited to developers in assisting them in writing less lengthy code and simplifying software modularization.
Detection and Recognition of Infra mammary Fold on MLO-view Mammograms

As positioning issues occur during mammography, it degrades the mammogram quality and influences radiologists to screen breast lesions on the mammogram. Therefore, quality assessment is a significant task before mammogram screening. This paper introduces a method for the detection and recognition of inframammary fold on mediolateral-oblique-view mammograms. In the detection process, the proposed method computes pixel-wise curvatures to the contour of a breast. The pixels with the same signs of curvatures form several curves, and an inframammary fold is one of those curves with positive curvature. Subsequently, we compute two features based on the detected inframammary fold and develop a multi-decision framework-based classifier for inframammary fold recognition. The experiment results will demonstrate the proposed method outperforms the compared approaches.
Local motion blur detection by Wigner distribution function

The movement of targets often lead to a noticeable motion blur in the local part of an image. Traditional methods mainly based on separating the blur part and performing Fourier transform are often affected by window effect due to the deviation of edge segmentation in the case of complex background and edge occlusion. Rather than applying a more sophisticated edge extraction algorithm, this paper presents a simple method which can guarantee the accuracy of PSF extraction and reduce the precision requirements in edge segmentation. The proposed method is based on Wigner distribution of image and can reduce the impact of local sampling and processing. With this method, the requirement of sampling interval can be reduced from the whole targets to the size of motion pixels. Besides, the application of Wigner distribution in Light Field can make it possible in the application of computational optical imaging.
A multi-representation re-ranking model for Personalized Product Search

In recent years, a multitude of e-commerce websites arose. Product Search is a fundamental part of these websites, which is often managed as a traditional retrieval task. However, Product Search has the ultimate goal of satisfying specific and personal user needs, leading users to find and purchase what they are looking for, based on their preferences. To maximize users’ satisfaction, Product Search should be treated as a personalized task. In this paper, we propose and evaluate a simple yet effective personalized results re-ranking approach based on the fusion of the relevance score computed by a well-known ranking model, namely BM25, with the scores deriving from multiple user/item representations. Our main contributions are: (1) we propose a score fusion-based approach for personalized re-ranking that leverages multiple user/item representations, (2) our approach accounts for both content-based features and collaborative information (i.e. features extracted from the user–item interactions graph), (3) the proposed approach is fast and scalable, can be easily added on top of any search engine and it can be extended to include additional features. The performed comparative evaluations show that our model can significantly increase the retrieval effectiveness of the underlying retrieval model and, in the great majority of cases, outperforms modern Neural Network-based personalized retrieval models for Product Search.
IoT-Assisted Hybrid Intelligent Learning Architecture Based on Digital Education in a Diverse Society

The existing research paradigm has caused the research of educational theory to be criticized for lack of logic and scientificity. With the gradual deepening and refinement of the development of smart education platforms, the educational process can be quantified, and the research paradigm of educational science has moved from a sampling model to a full-sample model, and it has become a real empirical science. This paper combines the Internet of Things technology to construct a hybrid intelligent learning system and applies it to digital education in a diverse society to construct a functional structure of the extraction system. Moreover, starting from the actual situation of intelligent education, this paper combines a simulation system to implement an IoT-assisted hybrid intelligent learning architecture and evaluates the teaching effect of the system. Through experimental research, it can be known that the IoT-assisted hybrid intelligent learning framework based on social diversified digital education has certain effects and has a certain reference effect for the development of subsequent intelligent education.
Open-source multi-channel Smart Arbitrary Waveform Generators (SAWG) for quantum information processing

Trapping ions, performing qubit operations and quantum multi-qubit gates require complex pulse spectra on several frequency bands. The Smart Arbitrary Waveform Generators (SAWG) Sayma and Phaser are designed to trap control and generate the Short-time Fourier transform (STFT) pulses. They have quad channels of over 1.2 GS/s (Giga Samples per second) 16-bit DACs and a few MS/s (Mega Samples per second) ADCs. These modules are dedicated to implementing qubit operations, both optically and electronically. The Sayma and Phaser modules are part of the Sinara family - a modular, open-source measurement and control hardware ecosystem dedicated to quantum applications that require deterministic high-resolution timing. The hardware is controlled and managed by the ARTIQ open-source software platform, which provides nanosecond timing resolution and sub-microsecond latency via a high-level programming language. We present the Sayma and the Phaser construction and obtained characteristics.
Using blockchain technology for software identity maintenance

Recently, the number of entities in the digital world has increased, due to technologies like 5G and the Internet of Things (IoT). Therefore, in a software application, it is important to be able to identify and maintain entities in an interoperable, efficient, and secure way. For enhancing the identity management domain, blockchain technology has been proposed as a possible solution. We explore how blockchain technology can be used to maintain digital identities and to address some of the issues in the domain. We present the results that we obtained so far, as well as future directions and expected results for our research.
Notes on quantum coherence with $$l_1$$-norm and convex-roof $$l_1$$-norm

In this work, we evaluate quantum coherence using the $$l_{1}$$ -norm and convex-roof $$l_{1}$$ -norm and obtain several new results. First, we provide some new general triangle-like inequalities of quantum coherence, with results better than existing ones. Second, for some special three-dimensional quantum states, a method for calculating the convex-roof $$l_{1}$$ -norm is presented. Lastly, we offer distinct upper bounds in the $$l_{1}$$ -norm measure of coherence based on the quantum state itself.
Reactive synthesis from interval temporal logic specifications

In this paper, we deal with the synthesis problem for Halpern and Shoham's interval temporal logic HS extended with an equivalence relation ∼ over time points (HS for short). The definition of the problem is analogous to that for MSO(ω,<). Given an HS formula φ and a finite set Σφ⋄ of proposition letters and temporal requests, it consists of determining, whether or not, for all possible valuations of elements in Σφ⋄ in every interval structure, there is a valuation of the remaining proposition letters and temporal requests such that the resulting structure is a model for φ. We focus on the decidability and complexity of the problem for some meaningful fragments of HS, whose modalities are drawn from the set {A(meets),A¯(metby),B(begunby),B¯(begins)} interpreted over finite linear orders and N. We prove that, over finite linear orders, the problem is decidable (Ackermann-hard) for and undecidable for AA¯BB¯. Moreover, we show that if we replace finite linear orders by N, then it becomes undecidable even for ABB¯. Finally, we study the generalization of to , where k is the number of distinct equivalence relations. Despite the fact that the satisfiability problem for , with k>1, over finite linear orders, is already undecidable, we prove that, under a natural semantic restriction (refinement condition), the synthesis problem turns out to be decidable.
A Quantum Approach to Walk on Networks

Some interesting findings can be achieved by modeling and visualizing networks which can include complex networks and large scale networks to name a few. These networks have been analyzed with the help of classical stochastic walks before. So, here in our proposed work, we provide the quantum version of the walk which basically involves making use of properties found in quantum computing. These properties can include such as quantum interference, no-cloning theorem, and restriction on intermediate measurement. In this proposed work, we present quantum circuit designs for discrete time random walk algorithms that operate on various networks.To demonstrate our ideas we have considered two different types of networks, the star network and an extended version of the star network.Though there exist a few research works related to quantum approach for random walk on networks, to the best of our knowledge, the ideas and approach we present here are unique. These ideas which we have discussed can be extended to more complex networks and systems as well. As an application of quantum walk we have shown, how results got after performing quantum walk can be used to identify critical nodes in such networks. Therefore, we have also provided some analysis of complex networks. Identification of critical nodes is important for robustness and survivability of the network.
Verifiable Secret Sharing Scheme Based on the Plane Parametric Curve

Verifiable secret sharing is a special kind of secret sharing.In this paper, A secure and efficient threshold secret sharing scheme is proposed by using the plane parametric curve on the basis of the principle of secret sharing.And the performance of this threshold scheme is analyzed.The results reveal that the threshold scheme has its own advantage of one-parameter representation for a master key, and it is a perfect ideal secret sharing scheme.It can easily detect cheaters by single operation in the participants so that the probability of valid cheating is less than 1/p (where p is a large prime).
Research on Instrument Position Detection Method Based on YOLOV4

With the advent of the era of Industry 4.0, more and more industrial production sites have introduced information technology and intelligent technology. Identification of indicator number in pointer instrument is one of the important applications of intelligent industry, and the instrument positioning is the first premise to realize the indicator number reading of pointer instrument. Accurate position detection in the image can provide a solid foundation for the correct reading of indicator number. This chapter focuses on the research of instrument position detection methods. Firstly, the basic theory of target detection technology is summarized. Secondly, the YOLOV4 target detection technology is introduced, and the improvement is proposed based on specific industrial scenarios. Finally, a simulation experiment is conducted to verify the effectiveness of the improved method proposed in this chapter.
Gesture Recognition of Subway Drivers Based on Improved Dense Trajectory Algorithm

Metro drivers need to make corresponding gestures when the train is in different operation states. The purpose is to confirm whether the display of each instrument on the console is normal, and then determine whether the train is in good running condition, whether the switch or signal on the line is working normally, and whether the door is closed normally. This information needs to be fed back to the train control center in time. Therefore, it is necessary to recognize the drivers' gestures in the surveillance video of the drivers' cab. This paper proposes a gesture recognition model based on the monitoring video of subway train cab, which combines Improved Dense Trajectory (IDT) algorithm, Fisher vector coding technology and Support Vector Machine (SVM) classification technology to realize feature extraction, feature coding and classification of video clips. The model recognizes the drivers' gestures in the actual train monitoring video clips, and the accuracy can reach 100%.
A Biological Inspired Cognitive Framework for Memory-Based Multi-Sensory Joint Attention in Human-Robot Interactive Tasks

One of the fundamental prerequisites for effective collaborations between interactive partners is the mutual sharing of the attentional focus on the same perceptual events. This is referred to as joint attention. In psychological, cognitive, and social sciences, its defining elements have been widely pinpointed. Also the field of human-robot interaction has extensively exploited joint attention which has been identified as a fundamental prerequisite for proficient human-robot collaborations. However, joint attention between robots and human partners is often encoded in prefixed robot behaviours that do not fully address the dynamics of interactive scenarios. We provide autonomous attentional behaviour for robotics based on a multi-sensory perception that robustly relocates the focus of attention on the same targets the human partner attends. Further, we investigated how such joint attention between a human and a robot partner improved with a new biologically-inspired memory-based attention component. We assessed the model with the humanoid robot iCub involved in performing a joint task with a human partner in a real-world unstructured scenario. The model showed a robust performance on capturing the stimulation, making a localisation decision in the right time frame, and then executing the right action. We then compared the attention performance of the robot against the human performance when stimulated from the same source across different modalities (audio-visual and audio only). The comparison showed that the model is behaving with temporal dynamics compatible with those of humans. This provides an effective solution for memory-based joint attention in real-world unstructured environments. Further, we analyzed the localisation performances (reaction time and accuracy), the results showed that the robot performed better in an audio-visual condition than an audio only condition. The performance of the robot in the audio-visual condition was relatively comparable with the behaviour of the human participants whereas it was less efficient in audio-only localisation. After a detailed analysis of the internal components of the architecture, we conclude that the differences in performance are due to egonoise which significantly affects the audio-only localisation performance.