Reviewerly/exHarmony huggingface dataset:
Research on fully parallel matrix algorithm of ternary optical computer for the shortest path problem

The shortest path is an extensive algorithm problem in graph theory. When faced with a huge amount of data in the shortest path problem, the problem with using traditional algorithms is the slow operation speed and high power consumption. To address these problems, this paper proposes a fully parallel matrix (FPM) algorithm. It uses the matrix multiplication principle and one-step modified signed-digit (MSD) adder, which can effectively implement parallel computing in ternary optical computers (TOCs). Finally, we compare clock cycles, and the results show that the TOC-based FPM algorithm can efficiently reduce the calculation time when solving the shortest path problem.
Modular Representation Underlies Systematic Generalization in Neural Natural Language Inference Models

In adversarial testing, we pose hard generalization tasks in order to gain insights into the solutions found by our models. What properties must a system have in order to succeed at these hard behavioral tasks? We argue that an essential factor is modular internal structure. Our central contribution is a new experimental method called 'interchange interventions', in which systematic manipulations of model-internal states are related to causal effects on their outputs, thereby allowing us to identify modular structure. Our work is grounded empirically in a new challenge Natural Language Inference dataset designed to assess systems on their ability to reason about entailment and negation. We find that a BERT model is strikingly successful at the systematic generalization task we pose using this dataset, and our active manipulations of model-internal vectors help us understand why: despite the densely interconnected nature of the BERT architecture, the learned model embeds modular, general theories of lexical entailment relations.
The Possibilities of Arithmetic of L.F. Magnitsky in Stimulating Professional Self-Development of Future Teachers

The preparation of future primary school teachers includes the analysis of textbooks and teaching aids in mathematics. The first domestic printed textbook on mathematics in Russia — “Arithmetic” by L.F. Magnitsky. The article describes the educational potential of this book as a source of methodological and mathematical knowledge, and as a means of stimulating professional self-development of students. The contents of a professionally oriented project aimed at introducing this book and its creator are disclosed.
A Reduction of Label Ranking to Multiclass Classification

Label ranking considers the problem of learning a mapping from instances to strict total orders over a predefined set of labels. In this paper, we present a framework for label ranking using a decomposition into a set of multiclass problems. Conceptually, our approach can be seen as a generalization of pairwise preference learning. In contrast to the latter, it allows for controlling the granularity of the decomposition, varying between binary preferences and complete rankings as extreme cases. It is specifically motivated by limitations of pairwise learning with regard to the minimization of certain loss functions. We discuss theoretical properties of the proposed method in terms of accuracy, error correction, and computational complexity. Experimental results are promising and indicate that improvements upon the special case of pairwise preference decomposition are indeed possible.
Robotopias: mapping utopian perspectives on new industrial technology

Purpose This paper maps utopian theories of technological change. The focus is on debates surrounding emerging industrial technologies which contribute to making the relationship between humans and machines more symbiotic and entangled, such as robotics, automation and artificial intelligence. The aim is to provide a map to navigate complex debates on the potential for technology to be used for emancipatory purposes and to plot the grounds for tactical engagements. Design/methodology/approach The paper proposes a two-way axis to map theories into to a six-category typology. Axis one contains the parameters humanist–assemblage. Humanists draw on the idea of a human essence of creative labour-power, and treat machines as alienated and exploitative form of this essence. Assemblage theorists draw on posthumanism and poststructuralism, maintaining that humans always exist within assemblages which also contain non-human forces. Axis two contains the parameters utopian/optimist; tactical/processual; and dystopian/pessimist, depending on the construed potential for using new technologies for empowering ends. Findings The growing social role of robots portends unknown, and maybe radical, changes, but there is no single human perspective from which this shift is conceived. Approaches cluster in six distinct sets, each with different paradigmatic assumptions. Practical implications Mapping the categories is useful pedagogically, and makes other political interventions possible, for example interventions between groups and social movements whose practice-based ontologies differ vastly. Originality/value Bringing different approaches into contact and mapping differences in ways which make them more comparable, can help to identify the points of disagreement and the empirical or axiomatic grounds for these. It might facilitate the future identification of criteria to choose among the approaches.
Enhancing Customer Service Using Chatbot Application Through Artificial Intelligence

In every service based or product based company customer services is considered to be an important sector to maintain customer relationship. This sector also consumes a lot of resources from the company both labor wise and money wise. In this sector the usage of resources are high due to the demand in the sector, A good company is defined how good is their customer service, Today most of the companies lack a good customer interaction, Hence to ease this process of customer services in this paper we propose to use A.I chatbot in the customer service sector. The result will be faster and more optimal customer service solutions.
Modelling Temporal Dynamics and Repeated Behaviors for Recommendation

Personalized recommendation has yield immense success in predicting user preference with heterogeneous implicit feedback (HIF), i.e., various user behaviors. However, existing studies consider less about the temporal dynamics and repeated patterns of HIF. They simply suppose: (1) a hard rule among user behaviors (e.g., add-to-cart must come before purchase and after view); (2) merge repeated behaviors into one (e.g., view several times is considered as view once only), thus failing to unveil user preferences from their real behaviors. To ease these issues, we, therefore, propose a novel end-to-end neural framework – TDRB, which automatically models the Temporal Dynamics and Repeated Behaviors to assist in capturing user preference, thus achieving more accurate recommendations. Empirical studies on three real-world datasets demonstrate the superiority of our proposed TDRB against other state-of-the-arts.
The Hotspots of Smart Education in China: Base on the Bibliometric Analysis and Knowledge Mapping

Abstract To explore the hotspots in the area of Smart education in China, 245 related articles form the China National Knowledge Infrastructure (CKNI) were analyzed by the visualization software of CiteSpace. The results show that: (1) the studies of smart education began in 2003 while there were sporadic papers published in the next ten years. There was an increasing trend year by year from 2013 to 2017 and the number of articles reached the historical peak in 2017 with 52 articles. Xianming Yang, Jiangsu Normal University and E-Education Research topped the lists of contributing authors, organizations and publications respectively. (2) “smart education”, “educational informationization”, “intelligent education” and “smart campus” were the top four largest clusters in the domain of smart education in China.
TermEval 2020: Shared Task on Automatic Term Extraction Using the Annotated Corpora for Term Extraction Research (ACTER) Dataset

The TermEval 2020 shared task provided a platform for researchers to work on automatic term extraction (ATE) with the same dataset: the Annotated Corpora for Term Extraction Research (ACTER). The dataset covers three languages (English, French, and Dutch) and four domains, of which the domain of heart failure was kept as a held-out test set on which final f1-scores were calculated. The aim was to provide a large, transparent, qualitatively annotated, and diverse dataset to the ATE research community, with the goal of promoting comparative research and thus identifying strengths and weaknesses of various state-of-the-art methodologies. The results show a lot of variation between different systems and illustrate how some methodologies reach higher precision or recall, how different systems extract different types of terms, how some are exceptionally good at finding rare terms, or are less impacted by term length. The current contribution offers an overview of the shared task with a comparative evaluation, which complements the individual papers by all participants.
CNN training for age group prediction in an illumination condition

CNN models trained with the given training dataset usually appear to show good accuracy in the testing with no added illumination. In this paper, we show that age group prediction in an added illumination condition results in a significant drop in accuracy. Our testing was performed on the color printed test photos captured through camera under office lighting condition. We also show the results of applying several possible training options to alleviate the accuracy drop such as using Grayscale or RGB images, severity of jitters of contrast and brightness on images, and the augmentation of training data.
EthScope: A Transaction-centric Security Analytics Framework to Detect Malicious Smart Contracts on Ethereum.

As one of the representative blockchain platforms, Ethereum has attractedlots of attacks. Due to the potential financial loss, there is a pressing needto detect malicious smart contracts and understand their behaviors. Thoughthere exist multiple systems for smart contract analysis, they cannotefficiently analyze a large number of transactions and re-execute smartcontracts to introspect malicious behaviors. In this paper, we urge for a transaction-centric security analytics frameworkfor Ethereum, which provides an efficient way to quickly locate suspicious onesfrom a large number of transactions and extensible way to detect malicioussmart contracts with analyst-provided scripts. We present the system design inthe paper, which solves three technical challenges, i.e., incomplete states,scalability and extensibility. We have implemented a prototype system namedEthScope to solve these challenges. In particular, the first component DataAggregator collects and recovers critical blockchain states. The secondcomponent Replay Engine is able to {replay} arbitrary and a large number oftransactions. The third component Instrumentation Framework exposes interfacesfor an analyst to dynamically instrument smart contracts and introspect theexecution of suspicious transactions. The comprehensive evaluation with sixtypes of attacks demonstrated the effectiveness of our system. The performanceevaluation shows that our system can perform a large-scale analysis onsuspicious transactions (more than 8 million ones) and has a speed up of around2,300x compared with the JSTracer provided by Go-Ethereum. To engage thecommunity, we will release our system and a dataset of detected attacks onhttps://github.com/zjuicsr/ethscope.
Type-I intermittency from Markov binary block visibility graph perspective

In this work, the type-I intermittency is studied from the optimized Markov binary visibility graphs perspective. We consider a local Poincaré map such as the logistic map that is a simple model for exhibiting this type of intermittency. To consider the acceptance gate as G≪0.01, we show that the transition between laminar and non-laminar zones in type-I intermittency takes distinct phases and regions. According to their behavioral characteristics, we call them as pure, switching, threshold, trapping, and transforming phases for the laminar zone and initial, terminal reinjection, and chaotic burst regions for non-laminar zone. We investigate their properties based on statistical tools such as the maximum and the mean length of the laminar zone and also length distributions of the laminar zone. For further investigation, we study degree distribution of the complex network generated by type-I intermittency time series and finally, predict various behaviors of phases and regions by proposed theoretical degree distributions.
Guía de métodos de investigación aplicados al entorno digital

In this chapter we will examine the different methods and research techniques, specially those of qualitative nature, which can be applied to a digital environment (and to digitalized research objects, since many historical sources have been digitized or about to be digitized).Specifically, we will examine the application and adaptation of ethnographic methods (observation, interviews, life histories), of content analysis and, from a historiographical point of view, of modern research currents such as prosopography and career research.We will devote some room to examine the nature of the digital, and digitized, document, to digital preservation, and to the building of digital corpora to be analyzed designing databases and applying CAQDAS software.
Copyright Protection for Online Text Information: Using Watermarking and Cryptography

Information and security are interdependent elements. Information security has evolved to be a matter of global interest and to achieve this; it requires tools, policies and assurance of technologies against any relevant security risks. Internet influx while providing a flexible means of sharing the online information economically has rapidly attracted countless writers. Text being an important constituent of online information sharing, creates a huge demand of intellectual copyright protection of text and web itself. Various visible watermarking techniques have been studied for text documents but few for web-based text. In this paper, web page watermarking and cryptography for online content copyrights protection is proposed utilizing the semantic and syntactic rules using HTML (Hypertext Markup Language) and is tested for English and Arabic languages.
Improved Sampling Design for Depth Profile Analysis of Marine Sediments Using Sector-Field – Inductively Coupled Plasma – Mass Spectrometry (SF-ICP-MS)

(2021). Improved Sampling Design for Depth Profile Analysis of Marine Sediments Using Sector-Field – Inductively Coupled Plasma – Mass Spectrometry (SF-ICP-MS) Analytical Letters: Vol. 54, No. 3, pp. 442-452.
Outlier Detection for Water Supply Data Based on Joint Auto-Encoder

With the development of science and technology, the status of the water environment has received more and more attention. In this paper, we propose a deep learning model, named a Joint Auto-Encoder network, to solve the problem of outlier detection in water supply data. The Joint Auto-Encoder network first expands the size of training data and extracts the useful features from the input data, and then reconstructs the input data effectively into an output. The outliers are detected based on the network’s reconstruction errors, with a larger reconstruction error indicating a higher rate to be an outlier. For water supply data, there are mainly two types of outliers: outliers with large values and those with values closed to zero. We set two separate thresholds, τ<sub>1</sub> and τ<sub>2</sub>, for the reconstruction errors to detect the two types of outliers respectively. The data samples with reconstruction errors exceeding the thresholds are voted to be outliers. The two thresholds can be calculated by the classification confusion matrix and the receiver operating characteristic (ROC) curve. We have also performed comparisons between the Joint Auto-Encoder and the vanilla Auto-Encoder in this paper on both the synthesis data set and the MNIST data set. As a result, our model has proved to outperform the vanilla Auto-Encoder and some other outlier detection approaches with the recall rate of 98.94 percent in water supply data.
EnQuad: A Publicly-Available Simulator for Quantum Key Distribution Protocols

Abstract In this paper, we present EnQuad Version 1.0: a high-speed and expandable simulator for Quantum Key Distribution (QKD) protocols. Surpassing available simulators, EnQuad does not only simulate a QKD stack, but also does security testing and guides the researcher on which reconciliation protocol should be used in his experimental setup. On the top of that, it recommends changes for the researcher to satisfy security or a given target key-rate if any of them is not already fulfilled. Although EnQuad V1.0 is concerned with depolarizing channels and Individual Intercept-and-Resend attacks, EnQuad is featured with 24 parameters and 9 modular functions so that it could be expanded to a wide range of QKD protocols. In addition, we validated EnQuad outcomes against a comparable simulator and against theory. Furthermore, a set of 11 experiments showed that EnQuad runs 6.12× to 12.2× faster than a comparable simulator. EnQuad was implemented in MATLAB and the code is available online.
Adaptive and Feature-Preserving Bilateral Filters for Three-Dimensional Models

Bilateral filtering is a well-known tool to denoise or smooth one-dimensional (1D) signals, two-dimensional (2D) images, and three-dimensional (3D) models.The bilateral weights help preserve the edges or features more effectively than unilateral weights.However, it is immensely difficult to configure the scale parameters of the convolutional kernel functions.To overcome the difficulty, this paper proposes adaptive, feature-preserving bilateral filters for 3D models by introducing automatic, adaptive scale parameters.Firstly, the feature scale was defined on 3D models, bridging up the gap between feature scale and scale parameter of the Gaussian functions.Next, the scale descriptor was proposed to adaptively configure the scale parameter for each face of the target 3D model, changing the traditional approach of adopting the same scale parameter for all faces.On this basis, a feature-preserving local filter was designed by introducing the adaptive scale parameters to the iterative local scheme, and a modified global filter, which is robust to irregular sampling during denoising, was designed based on the adaptive scale parameters.The excellence of our filters was proved through experiments on multiple synthetic and real-world noisy models, in comparison to the state-of-the-art filters.The research results lay a solid basis for feature preservation and noise removal of 3D models.
Hybrid Deep Neural Network-Based Cross-Modal Image and Text Retrieval Method for Large-Scale Data

In this paper, we propose the hybrid deep neural network-based cross-modal image and text retrieval method to explore complex cross-modal correlation by considering multi-layer learning. First, we propose intra-modal and inter-modal representations to achieve a complementary single-modal representation that preserves the correlation between the modalities. Second, we build an association between different modalities through hierarchical learning to further mine the fine-grained latent semantic association among multimodal data. The experimental results show that our algorithm substantially enhances retrieval performance and consistently outperforms four comparison methods.
Photon counting with resonant tunneling diodes: overview and recent developments

Fast and efficient single-photon detectors are of paramount importance for a broad range of applications, e.g. in optical quantum information technologies. A variety of novel and refined photon counting devices has resulted from the need of ever better performance. One such device is the resonant tunneling diode (RTD) single-photon detector. Compared to conventional single-photon detectors, RTDs provide several advantageous characteristics. Since in RTDs a tunneling current is gated by photogenerated charge carriers, they are inherently photon-number resolving, and the RTD currentvoltage characteristic’s negative differential conductance region allows for higher functionality, e.g. the operation as an optically triggered bistable switch. Here, we present an overview on RTD single-photon detectors with a focus on the stateof- the-art, its underlying physical mechanisms as well the device limitations, and recent developments. We compare different RTD device geometries and operation schemes for enhanced detection efficiencies and operation frequencies.
A Blind Watermarking Algorithm for Digital Image Based on DWT

Abstract In order to improve the robustness of digital image watermarking algorithms, the digital image blind watermarking algorithm proposed in this paper embeds watermark information in a reasonable position. First, scramble the watermark information and embed the random signal into the low-frequency domain of the image. On the one hand, it can effectively prevent attacks on the watermark system and enhance the security of the system, disperse as much as possible. In the watermark embedding process, a method based on the wavelet transform domain is used. The original image is not required to participate in the watermark extraction process, and the blind watermark function is implemented. In order to verify the effectiveness of the algorithm, the watermarked image is subjected to filtering, scaling, noise, cropping and rotation attacks, and the peak signal-to-noise ratio and normalized correlation coefficient are used to quantitatively evaluate the watermarked image. Experimental results show that the algorithm can resist a variety of attacks, and has good imperceptibility and robustness.
Connected Personas: Translating the Complexity of Older Adult Personal Health Information Management for Designers of Health Information Technologies.

Human-centered design (HCD) can be used to communicate research study findings to designers of health information technologies (HIT). We used the HCD approach to develop personas, scenarios, and design guidelines for designers with the aim that it would lead to new HIT designs that support the autonomy and health of older adults. The foundation of the personas, scenarios, and design guidelines was a study that focused on understanding how older adults manage their health information and the role that stakeholders play in that process. In this paper, we describe how we carried out a HCD approach and how it led us to expand the persona process to create a network of connected personas. The connected personas allowed us to show the complexities of personal health information management for older adults and emphasize the importance of relationships with family, friends, and providers.