Reviewerly/exHarmony huggingface dataset:
Atomic excitation for a two-level system driven by three input photons

In this paper, the master equations for a two-level system driven by three photons has been derived. Particularly, the incident photons are distributed in two input channels, namely, the first input channel contains a two-photon state, while another single-photon state is in the second input channel. The excitation probabilities of the two-level system are simulated with different bandwidths of input photons. The influence of the number of input photons and channels on the atomic excitation is concluded.
Behavior-based Anomaly Detection for Securing Smart Home Systems Automation

This paper presents a framework for securing Smart Home Systems (SHSs) automation control. When SHS devices are autonomously controlled by automation apps (called SmartApps), the framework makes sure that only legitimate ones are allowed to automate the SHS. To do so, control commands issued by the SmartApps during regular operation are captured to build a One-Class Support Vector Machine (OCSVM) model as a regular behavior baseline for each installed SmartApp. Then, deviations from this baseline (i.e., anomalous commands) should be detected and rejected, while regular observations (i.e., normal commands) should be considered as legitimate and allowed to be executed. Experimental evaluation conducted on real-world adapted SHS automation history shows the effectiveness of such a behavior-based approach.
Research on the Labelling Technology of Morphology and Syntax

This paper aimed the integration tagging and tree-bank transformation of morphology and syntax on the basis of phrase and syntax tree-bank, tagged the nested named entity in combination with the ontological linguistic clues. Finally, it integrates the named entity to carry out the integrative experimental analysis; according to the experimental results, both the accuracy rate and recall rate have been improved somewhat.
Characterizing the Security Threats of Disposable Phone Numbers

Many organizations require users to provide a phone number for verification when registering an account. Simultaneously, because of the convenience and security of SMS-based two-factor authentication, many organizations adopt this method to enable users to log in their accounts. On the other hand, many web service platforms provide a mass of disposable phone numbers for receiving SMS messages. The original intention of these platforms is to provide user privacy protection services; however, people know very little about the threat that this service poses to the organization’s security. In this paper, we collected data from 9 disposable phone platforms with high traffic in China. These data include 4,669 phone numbers and 30 million messages. These phone numbers come from 44 countries, and most of the phone number carriers are mobile virtual network operators in China. To the best of our knowledge, this is the first paper that discloses the OTA (Online Travel Agency) accounts registered by disposable phone numbers, which would leak a large number of passenger information. Furthermore, we discovered that cybercriminals use temporary OTA accounts to carry out airline seat spinning attacks. Among the organizations we surveyed, only 47% of the organizations’ security mechanisms can detect accounts that registered with disposable numbers. Our findings indicate that disposable phone numbers pose potential threats to cybersecurity, and new solutions are needed to address the threat.
MESSY2NEAT: A Dataset for Image Transfer from Messy Scenes to Neat Scenes by Adversarial Nets

Nowadays, generative adversarial networks (GANs) and its variants are widely used as image-to-image translation tools to generate vivid pictures. However, most of those research efforts focus on changing the images' style, and they are not involved with the location translation. That is, the object locations are kept consistent in original and target images. Accordingly, there is also a lack of datasets suitable for location conversion. In this paper, 1) we present a GANs-based image-to-image translation application involved with location translation. More specifically, we focus on making messy scenes to neat scenes. 2) For the new application scenario, a dataset named MESSY2NEAT is built, which contains six sub-datasets and more than 18 000 paired images. 3) On MESSY2NEAT, three popular GANs-based frameworks, including pix2pix, pix2pixHD, and CycleGAN, are employed to make messy scenes to neat scenes. Experiment results show that it is possible to perform image-to-image translation involved with the location translation using GANs, while more research efforts are needed to improve the results.
Social risks of corporate management in an educational organization

The main types of social management risks in Russian educational organizations are described in this article, sources of risks are indicated, ways to reduce the negative impact of corporate risks on the management in an educational organization are shown.
Masterclass on Information Visualization for Games Research

This CHI PLAY 2020 masterclass offers an introduction into information visualization and how it can assist in the analysis and communication of the wide variety of data gathered in games-related research. The goal is to provide an overview of easy-to-apply visualizations for both quantitative and qualitative data frequently used within the CHI PLAY community, including survey, textual, relational, and spatial data. It is intended for everyone interested in how to represent and communicate data in an easily-understandable way.
A Physical Layer Encryption Scheme Based on Chaotic Maps in OFDM Systems

Security issues of the physical layer in wireless communications have attracted extensive attention in recent years. Meanwhile, the key space and encryption mechanisms have great impact on the security performance. This paper proposes a novel physical layer encryption scheme based on multi-fold chaotic maps in orthogonal frequency-division multiplexing(OFDM) systems. High security is achieved by performing the chaotic maps on the phase and amplitude of quadrature amplitude modulation(QAM) symbols, and the transmitted subcarrier. The initial conditions of the chaotic maps serve as the secret keys. Mathematical analysis indicates that the proposed scheme can provide a huge key space of 1.5 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">98</sup> , which is much larger than that of the existing solutions. In addition, the transmission performance is evaluated over both AWGN channels and Rayleigh fading channels. The theoretical analysis and simulation results reveal that the proposed scheme significantly outperforms the existing solutions in terms of security. Meanwhile, it can achieve similar transmission performance over different channels.
Detection and Casing Analysis of Internal Partial Discharge for 35kV High Voltage Switchgear

As an important equipment of distribution network, the running state of switchgear are directly affect the reliability of power supply for users. In order to achieve the safety and reliability running of switchgear, the internal partial discharge of switchgear are researched by employing the AE, infrared thermal imaging detection method, ultraviolet imaging detection method and diagnostic test based on the station transformer. Base on the related testing data, the types and reason of partial discharge are analyzed that the partial discharge signal disappear after processing. It can provide a reference and basis for on-site detection through the multiple detection technology.
Develop Test and Implementation of Low Cost DC Power Data Logger

A low cost DC power data logger was designed. It could log data about generated solar energy from two solar systems. The power was measured indirectly. Calculation of power and energy was made every 255 ms. They were averaged and logged with interval one minute. The data logger was made in order to analyze the operation of different solar systems types: fixed mount vs. single axis, single axis vs. double axis, solar system with PWM vs. system with MPPT controller.
Interference Avoidance Scheme Based on Reinforcement Learning

This paper proposes an interference avoidance scheme based on reinforcement learning for the communication stations exposed to interference. Firstly, we design the signal to interference noise ratio-mean opinion score (SINR-MOS) as the reward function to obtain optimal channel handoff strategy through reinforcement learning. Then, two specific methods of interference avoidance are proposed by applying Q-learning algorithm, which enables an automatic avoidance by switching to a new idle channel when the station is interfered. Finally, it is proved by computer simulation experiments that the methods have a low miss detection rate and false alarm rate, showing good interference avoidance performance.
Concept and Application of E-service in Citizen Associations

Public demands regarding public services, especially the need for excellent service, has encouraged the government to create innovation. Di Indonesia innovation base leads to e-government. E-government is a medium of reciprocal interaction between the community and the government. This is done because of the many problems that arise in society, especially related to bureaucracy and length of service time. Besides, the available human resources have not been able to meet the services that come from the community, and the administrative services are not optimal, causing new problems. Therefore, to answer the above problems, in this case, the researcher makes innovation in the form of a system that can be used as a solution, especially regarding population data collection, public service letters, data collection of births, deaths, and population mutations. The system was built using the prototype method as a guide in carrying out system development. Then the researcher translates the system into a website called e-service. This is because the website not only functions as a medium for disseminating information but the existence of communication with users allows for development into a better system. The results of the study indicate that the system built can overcome the problems that arise. For example, shorten the bureaucracy, the timeliness of administration. So, the conclusion is that the system can be used as a solution to answer existing problems.
Aligning Users Across Social Networks via Intra and Inter Attentions

In recent years, aligning users across different social networks receives a significant attention. Previous studies solve the problem based on attributes or topology structure approximation. However, most of them suffer from error propagation or the noise from diverse neighbors. To address the drawback, we design intra and inter attention mechanisms to model the influence of neighbors in local and across networks. In addition, to effectively incorporate the topology structure information, we leverage neighbors from labeled pairs instead of these in original networks, which are termed as matched neighbors. Then we treat the user alignment problem as a classification task and predict it upon a deep neural network. We conduct extensive experiments on six real-world datasets, and the results demonstrate the superiority of the proposed method against state-of-the-art competitors.
Long-Term Interaction and Persistence of Engagement for Musical Interaction using a Genetic Algorithm

Current research in human-agent interaction primarily focuses on short term interaction and rarely addresses day to day use. We propose a prototype system based on a genetic algorithm that places long term interaction as the core design goal. The goal of this system is to develop stand alone long-term development and provide a platform for future post-processing of deep learning generations. This paper addresses these issues through the domain of musical interaction and improvisation, a field that incorporates dialogue-like interaction built on stylistic constraints. We contend that the objectives of continual knowledge development and building relationships are key to long-term human interaction, and design the genetic algorithm specifically around these concepts. Our eventual goal of the prototype is a future application of post processing for deep learning generative systems.
Dual Role Neural Graph Auto-encoder for CQA Recommendation

Matching between questions and suitable users is an appealing and challenging problem in the research area of community question answering (CQA). Usually, different from the traditional recommendation systems where a user has only a single role, each user in CQA can play two different roles (dual roles) simultaneously: as a requester and as an answerer. For different roles, users usually have varying interests and expertise in different topics and knowledge domains, which is rarely addressed in the previous methods. Besides, based on an explicit single link between two users, existing methods cannot capture implicit associations between their possibly similar roles. Therefore, in this paper, we propose the structure of a dual role graph and employ the link prediction approach to make CQA recommendation on the graph. Moreover, we develop a Dual Role Neural Graph auto-encoder (DRNGae) framework, which can: 1) encode the dual role graph structure to capture the implicit dual role correlation by propagating high-order information embeddings of graph neural network; 2) learn variable weights with the dual role feature preferences from dual role content information by self-attention mechanism; 3) reconstruct the graph structure to predict the possible interaction links. Experimental studies on real-world datasets verify our design and prove that our model achieves significantly better performance than baselines in link prediction (95.3% AUC, 96.2% AP on Citeseer dataset) and CQA recommendation (79.5% recall@25, 76.7% ndcg@25 on Yahoo! answer dataset).
Social Perspective of Suspicious Activity Detection in Facial Analysis

The world is witnessing an unprecedented growth of cyber-physical systems (CPS), which are foreseen to revolutionize our world via creating new services and applications in a variety of sectors such as environmental monitoring, mobile health systems, and intelligent transportation systems and so on. The information and communication technology (ICT) sector is experiencing significant growth in data traffic, driven by the widespread usage of smart phones, tablets, and video streaming, along with the significant growth of sensors deployments that are anticipated soon. This chapter describes suspicious activity detection using facial analysis. Suspicious activity is the actions of an individual or group that is outside the normally acceptable standards for those people or that particular area. In this chapter, the authors propose a novel and cost-effective framework designed for suspicious activity detection using facial expression analysis or emotion detection analysis in law enforcement. This chapter shows a face detection module that is intended to detect faces from a real-time video.
Analysis of Recommendation Systems Based on Neural Networks

Abstract As the amount of online information explodes, the recommendation system has evolved into an effective strategy to overcome the problem of information overload, which has become the focus of academic and industrial circles, and has led to other related research results. Through the recommendation methods, the author mines the items including information, service, and goods that the user is interested in, and recommends the results to the user in the form of a personalized list. In this paper, the author mainly examines and summarizes the progress of research on neural network based recommendation systems in recent years, as well as giving conclusions about the differences and benefits between that and traditional algorithms. In the end, the future trend of the recommendation systems based on neural networks is prospected.
Basketball sports neural network model based on nonlinear classification

With the continuous innovation of science and technology, the mathematical modeling and analysis of bodily injury in the process of exercise have always been a hot and difficult point in the research field of scholars. Although there are many research results on the nonlinear classification of the basketball sports neural network model, usually only one model is used, which has certain defects. The combination forecasting model based on the ARIMA model and neural network based on LSTM can make up for this defect. In the process of the experiment, the most important is the construction of the combination model and the acquisition of volunteer data in the process of the ball game. In this experiment, the ARIMA model is used as the linear part of the data, and LSTM neural network model is used to get the sequence of body injury. The results of the empirical study show that: it is reasonable to divide the injury of thigh and calf in the process of basketball sports, which is very consistent with the force point of the human body in the process of sports. The results of the two models predicting the average degree of bodily injury for many times are about 0.32 and 0.38 respectively, which are far less than 1. The execution time of the program for simultaneous prediction on the computer is about 1 minute, which is extremely effective.
Research on User Information Security in the Context of New Media

New media technology has reshaped the network communication mode, and every netizen has a great right of expression. At the same time, Internet users are also facing information security risks such as information disclosure, false information, hacker attacks, etc. Through the method of questionnaire survey, the author analyzes the information security problems faced by Internet users in the use of new media, and puts forward measures to improve Internet users’ new media literacy, strengthen the industry self-discipline of platforms and enterprises, and improve laws and regulations.
Ensemble of Algorithms for Multifocal Cervical Cell Image Segmentation

One of the main challenges for cell segmentation is to separate overlapping cells, which is also a challenging task for cytologists. Here we propose a method that combines different algorithms for cervical cell segmentation of Pap smear images and searches for the best result underlying the maximization of a similarity coefficient. We carried out experiments with three state-of-the-art segmentation algorithms on images with clumps of cervical cells. We extracted features such as coefficient of variation and overlapping ratios for each cell grouping and selected the most appropriate algorithm to segment each cell clump. For decision criterion, we identified the cell clumps of the training dataset and calculated the mentioned features. We segmented each clump by the algorithms and reckoned the Dice measure from each segmentation. Finally, we used the kNN classifier to predict the best algorithm among neighboring k-clumps by choosing the one with the largest number of wins. We validated our proposal on multifocal cervical cell images and obtained an average Dice around 76.6% without using a threshold value. These results demonstrated that the proposed ensemble of segmentation algorithms is promising and suitable for cervical cell image segmentation.
Using Blockchain and Smart Contracts to Ensure Genuine Media Assets

This article introduces the key concepts of blockchain and smart contracts and proposes a design to ensure the traceability of media assets in a potentially untrustworthy environment. In this particular use case, the distributed ledger keeps track of each operation of an asset that takes place from its capture through to publication, including editing. This information can be checked against the ledger to validate the intermediate steps required to produce the resulting asset. This design ensures the integrity of media assets, including reliable chronological dating, and the validation of sources while preserving anonymity. At each stage of the workflow, manual or automated verifications are recorded, which creates chains of trust without a central authority.
Research on visualization method of high dynamic range infrared image based on M-CLAHE

The analog signal output by the infrared detector is usually sampled as a 14-bit high-dynamic-range digital infrared image, which is not compatible with most 8-bit display systems and cannot be directly used for imaging display. Therefore, image compression quality is very important for image display .This paper studies and analyzes the mapping technology of the base layer, focusing on the traditional limited contrast histogram equalization method (CLAHE). Considering that the conventional CLAHE method is applied to infrared images, there will be a problem of excessive stretching of some uniform areas. a new algorithm for Contrasted Limited Adaptive Histogram Equalization based on uniform region test (M-CLAHE) is proposed. Based on the traditional CLAHE method, this method introduces a gradient operator to extract the contour boundary of the sub-region image, and quantifies the complexity of the image region to solve the above problems. The experimental results show that compared with the traditional CLAHE infrared image compression method, this method can more than double the visual indicators such as image contrast and average gradient compared with the traditional visualization method. This method has engineering portability, and it can be transplanted to the infrared movement to increase the image quality of most scenes by about twice.
Study Cases to Test the Neuro Fuzzy Hybrid Model

The sections in this Chapter mention each of the classifiers that have been designed, the structure and parameters used for fuzzy systems, as well as the type of fuzzy system and number of fuzzy rules.
Programming Ideas for Teen Librarians

It can be hard enough to come up with new, exciting, and relevant programming for teens in libraries, let alone in the societal climate we live in now, where technology and social trends are fast-paced and ever changing. This article attempts to help with this in a small way through providing an introduction to Makerspaces -- a trending new type of library service/programming
Exploring Wearable Output Modalities - Screens &amp; Beyond

For decades, wearable research has explored different output modalities (e.g. screens, lights, scents, thermal, vibrotactile, shape change) implemented in wearable form factors. With these output modalities, prior research addresses a wide range of issues from information representation to self-expression and social cohesion. However, the close proximity of wearables to our bodies makes their design a complex task that requires attention to functional, social, and cultural aspects. Wearables are not just practical artifacts (e.g. conveying concise information for the user or the observer) but have a potential effect on our appearance and social interactions. In this workshop, we look into informative aspects and social challenges around designing different output modalities on bodies. By bringing together researchers and practitioners, we see particular potential in providing a broader perspective on the practical and social aspects of designing wearable output modalities.