Reviewerly/exHarmony huggingface dataset:
On the Security of a Certificateless Public Verification Scheme for Cloud-Based Cyber-Physical-Social Systems

With the popularity of the cyber-physical-social system, increasing individuals would like to share their data from both cyberspace and physical space with others. This results in more and more data needed to be stored locally. Due to the high price of storing and maintaining those data, the user would like to resort to the cloud storage. Thus, the concept of cloud-based cyber-physical-social (CBCPS) systems are putted forward. In CBCPS systems, the user loses his/her physical control on the data after they are uploaded to the cloud. How to guarantee the data integrity becomes a challenging issue. Recently, Zhang et al. proposed a certificateless public verification (CLPV) scheme to ensure the data integrity in CBCPS systems. Zhang et al. claimed that their CLPV scheme is secure against various attacks and is provably secure in the random oracle model. However, in this paper, we propose a universal attack against Zhang et al.’s CLPV scheme to demonstrate that a malicious cloud server can modify even delete the user’s data without being found by the auditor or the user. The security analysis shows that Zhang et al.’s CLPV scheme is not secure enough for the cloud-based cyber-physical-social systems.
Development of a New Type of Personnel Attendance Machine

In order to detect the number of people in public places, thermopile is used to sense human heat and estimate the number of people present. The infrared thermopile is used as the sensor in the system. The thermopile produces a voltage difference which is proportional to the temperature difference between the environment and the human body. That is to say, if the energy of the infrared light incident in the current visible environment is different, the thermopile will output different voltage values. Accordingly, the number of people in the current visual environment can be detected. In the system design, the thermopile is composed of 8 * 8 lattice. By detecting the voltage change at each point, it is input to the microprocessor for processing through amplification, filtering, and A/D conversion. Compared with the number of image processing detectors, this method has a lower complexity. The actual measurement shows that the accuracy rate is over 95%, which can effectively detect the number of people in the visual area.
Methods of Efficiently Constructing Text-Dialogue-Agent System Using Existing Anime Character

Many surely dream of being able to chat with his/her favorite anime characters from an early age. To make such a dream possible, we propose an efficient method for constructing a system that enables users to text chat with existing anime characters. We tackled two research problems to generate verbal and nonverbal behaviors for a text-chat agent system of an existing character. In the generation of verbal behavior, it is a major issue to be able to generate utterance text that reflects the personality of existing characters in response to any user questions. For this problem, we propose the use role play-based question-answering to efficiently collect high-quality paired data of user’s questions and system’s answers reflecting the personality of an anime character. We also propose a new utterance generation method that uses a neural translation model with the collected data. Rich and natural expressions of nonverbal behavior greatly enhance the appeal of agent systems. However, not all existing anime characters move as naturally and as diversely as humans. Therefore, we propose a method that can automatically generate whole-body motion from spoken text in order to make it so that anime characters have human-like and natural movements. In addition to these movements, we try to add a small amount of characteristic movement on a rule basis to reflect personality. We created a text-dialogue agent system of a popular existing anime character using our proposed generation methods. As a result of a subjective evaluation of the implemented system, our models for generating verbal and nonverbal behavior improved the impression of the agent’s responsiveness and reflected the personality of the character. In addition, generating characteristic motions with a small amount of on the basis of heuristic rules was not effective, but rather the character generated by our generation model that reflects the average motion of persons had more personality. Therefore, our proposed methods for generating verbal and nonverbal behaviors and the construction method will greatly contribute to the realization of text-dialogue-agent systems of existing characters.
Sentence Relation Classification Using Deep Learning Experiments

There are existing problems of finding relation between sentences for quite a few years. A system that can find the relation between a phrase and a document can be used for search engines, article finder and many more. This paper is focused on the problem statement of deciding whether given two sentences or two set of sentences are unrelated or related and if related whether it is mutually agreeing, disagreeing or neutral relation will be the classification outcome. Specifically, an attempt to find the title body consistency of a news article is done in this paper. For which, a deep transfer learning-based approach is proposed where the problem of detecting title body consistency is taken from the viewpoint of textual entailment (TE) where the title is considered as a hypothesis and news body is treated as a text. The framework used is bi-directional long short-term memory (LSTM) network which is a type of Recurrent Neural networks in deep learning with experiments with transfer learning technique.
Interactive Design Exploration of Game StagesUsing Adjustable Synthetic Testers

Game designers take into account the wide range of play-styles and skill levels of players to create enjoyable experiences. One important step in the game design process involves playtests with professional testers; this process is time-consuming and expensive. Hence, there exist several methods to create synthetic testers to test a game automatically. However, one shortcoming is the lack of realistic-playing with different play-styles and skill levels. In this paper, we propose a game level authoring tool that incorporates synthetic testers, which enable the control of play-styles and skill levels. Furthermore, we utilize visualization techniques to help assess the difficulty level of each part of the stage. Our user studies confirmed that our tool was effective for designing game stages appropriate for a particular type of player.
Frequency-Domain Triangulation of Spatial Harmonic Motion for Single-Camera Operating Deflection Shape Measurement

Optical vibration measurement methods have, due to their non-contacting nature and high spatial resolution, steadily been gaining in adoption with the developments in high-speed camera technology in recent years. In high-frequency applications, however, where observed displacements of the vibrating structure are particularly small in amplitude, the relatively high noise-floor of high-speed camera measurements still presents a major limitation. Taking into account the properties of the vibration response of linear mechanical systems, the negative effects of noise on the displacement measurement uncertainty can be mitigated in the frequency domain.In this work, a novel method for frequency-domain triangulation of displacement data, acquired by a multiview high-speed camera system, is presented. For linear, time-invariant mechanical systems under stationary excitation, frequency-domain triangulation enables us to perform spatial vibration response measurements using only a single, moving high-speed camera. The measurement field-of-view can thus be extended for objects with complex 3D geometry, utilizing a relatively simple and cost-effective imaging system. The resulting spatial displacement spectra are used in full-field deflection shape identification.
The rencontre problem

Let Xk1k=1∞,Xk2k=1∞,…,Xkdk=1∞ be d independent sequences of Bernoulli random variables with success-parameters p1,p2,…,pd respectively, where d≥2 is a positive integer, and 0<pj<1 for all j=1,2,…,d. Let Sj(n)=∑i=1nXij=X1j+X2j+⋯+Xnj,n=1,2,….We declare a “rencontre” at time n, or, equivalently, say that n is a “rencontre time,” if S1(n)=S2(n)=⋯=Sd(n).We motivate and study the distribution of the first (provided it is finite) rencontre time.
PS-LDA: A Course Item Model for Tutorial Personalized Recommendation

With the development of educational big data, personalized tutoring has become an important research direction to help people find interesting learning resources. However, due to limitation of learning resources, especially for the resource in unfamiliar subject areas, it may bring data sparseness of users' learning matrix. In this paper, we propose PS-LDA, a potential probability generation model for course item on learning preferences and subject area aware. By considering the mix of these two factors, our model provides personalized guidance for designated users. Moreover, we present a top-k method for online recommendation by matching the results from P-LDA and S-LDA. Finally, the experiments on two real-life datasets can verify the effectiveness and efficiency of our model.
Intelligent Image Segmentation and Circular Rectangle Dichotomy Defogging Algorithm

The dark channel prior algorithm calculates the transmittance by dividing the dark channel with fog image into blocks during defogging. Due to the inaccuracy of the blocks, the image after defogging appears “halo”. In this paper, an improvement is made on the basis of the dark channel prior algorithm. When calculating the transmittance, the intelligent image segmentation ant colony and clustering method are used to segment the dark channel of the image with fog, so as to ensure the similarity or similarity of the pixel points in the same block. In order to improve the accuracy of atmospheric light value so as to improve the quality of the image, the circulation rectangle dichotomy method was used to divide the image when looking for the area with high fog concentration. First, the image was divided vertically and then horizontally, and the block area was cycled until it was less than the predetermined area threshold. At the end of the paper, the experiment simulation is given. Using the improved defogging algorithm, the image after defogging has better visual, fidelity and other indicators, which can meet the requirements of image defogging for clarity and information.
A Framework for Fast MapReduce Processing Considering Sensitive Data on Hybrid Clouds

In recent years, large-scale data needs to be analyzed and processed for various purposes. One of the methods for processing large-scale data is to use the cloud. However, general public clouds have security issues. Therefore, when handling confidential data, there is a method of processing with a hybrid cloud that consists of a public cloud and a private cloud which users can use safely. In order to use the two clouds efficiently and securely in this hybrid cloud, the distributed processing framework SEMROD (Secure and Efficient MapReduce Over Hybrid Clouds) was developed. However, SEMROD has two problems: communication load and deterioration of load distribution efficiency. In this paper, we propose a method called HFK-SEMROD (Hold First Key-SEMROD) to make the existing method SEMROD more flexible and faster. Moreover, we provide two additional options of the HFK-SEMROD, whose utilizations are selectable by job types. Finally, we compare its performance with SEMROD experimentally.
A Verifiable Secret Sharing Scheme without Using Multi-Party Computations

On information security area, it is important to guarantee data privacy, availability and integrity. Secret sharing schemes are known as technologies for protecting data privacy and attaining availability. In secret sharing schemes, n distributed informations of a secret value d, called as shares are constructed, and distributed those to n participants. Then, a group of t members for n participants reconstruct the secret value d by exchanging their shares each other. On the other hand, less than t shares cannot induce the secret value. Moreover, as secret sharing schemes for solving the issue for data integrity, there exist verifiable secret sharing schemes. In verifiable secret sharing schemes, we can also confirm the correctness of reconstructed secrets. In this paper, we focus on a previous verifiable secret sharing scheme and point out issues as necessity of multi-party computations to protect shares from malicious participants. Next, we propose a new verifiable secret sharing scheme without multi-party computations and prove the security of verifications by the proposed scheme.
Automating Hierarchical Subject Index Construction for Scientific Documents

Subject, or back-of-the-book index consists of significant terms with relevant page numbers of the text document, thus providing an easy access to its content. The paper describes methods developed for automating main stages of subject indexing for specialized texts: namely, term extraction, selection of the most important ones, detecting their reference pages, as well as recognizing semantic relations among selected index terms in order to structure them into hierarchy. The developed methods are intended for processing scientific documents in Russian and are based both on formal linguistics rules and unsupervised machine learning. Experimental evaluation of the methods have shown their sufficient quality to be built into computer subject indexing system.
Enhanced Blockchain Application for Pub-Sub Model

The asynchronous communication is required for the distributed systems, these are fulfilled using the middleware products and services. The publisher-subscriber method is one of them. It is used for sending messages and on the other hand, they are the receivers. Due to the involvement of the third party (broker), there were security flaws. Blockchain technology was introduced providing confidentiality, integrity authentication, etc. that too in a broker-less environment. In this paper a new system is being introduced having the cryptographic functions like DES and RNS are used for encryption of data and creating the public and private keys for end-to-end encryption for the new system with blockchain, i.e. the authority control based system. The improved hashing technique SHA2-512, is used. An identity-based encryption mechanism is also introduced in making this system more secure against the known attacks that previous systems failed to provide.
Human Emotion Classification: An Expression Specific Geometric Approach

Human face emotions are generally classified in six different expressions such as Anger, Disgust, Fear, Happiness, Sadness, and Surprise. The authors propose a novel method for selecting an expression specific set of salient landmark points out of 68 landmark points produced by applying an Active Appearance Model (AAM) on an input face image. The salient Landmark points are selected by training a MultiLayer Perceptron network using a Histogram oriented Gradient (HoG) feature of neighboring pixels of a Landmark point. Next, a shape signature vector is constructed by forming triangulation using those salient landmarks for each expression. This is trained with six Multilayered Perceptron (MLP) network for classification of each of the six basic expressions. The suggested algorithm is tested on CK+, JAFFE, MMI, and MUG database. The outcomes are found extremely promising.
Representing scientific knowledge for quantitative analysis of physical systems

Representation of scientific knowledge in ontologies suffers so often from the lack of computational knowledge required for inference. This article aims to perform quantitative analysis on physical systems, that is, to answer questions about values of quantitative state variables of a physical system with known structure. For this objective, we incorporate procedural knowledge on two distinct levels. At the domain-specific level, we propose a representation model for scientific knowledge, i.e. variables, theories, and laws of nature. At the domain-independent level, we provide an algorithm which, given a system S with known structure and a relevant scientific theory T, extracts a constraint network, whose variables are state variables of S defined by T, and whose constraints raise from relevant laws in T. The constraint network is then solved, to build a system of equations whose unknowns are the output variables of S. The proposed representation model and reasoning algorithm are evaluated by applying them to classic analysis examples.
Multi-frame Dimensionality-Reduction Difference Method for Extracting Key Frames of Video

Key frame extraction is very important for video data processing. This paper mainly studies how to extract the key frames of the video data when transforming the medical action video data into image data, so as to process and mine the medical action video data with image processing technology in the later period. Because of the difference of the doctor for some special action concerns, the existing algorithm of key frames can not extract the important frames selected by the doctors exactly, so this paper will review the traditional method to extract key frames, and use a new method of key frame extraction (multi-frame dimensionality-reduction difference method) to extract the key actions that clinical doctors pay attention to. Compared with the traditional method, this method can extract the frame image concerned by the doctor better.
Bat Algorithm Method for Automatic Determination of Color and Contrast of Modified Digital Images

This paper presents a new artificial intelligence-based method to address the following problem: given an initial digital image (source image), and a modification of the image (mod image) obtained from the source through a color map and visual attributes assumed to be unknown, determine suitable values for color map and contrast such that, when applied to the mod image, a similar image to the source is obtained. This problem has several applications in the fields of image restoration and cleaning. Our approach is based on the application of a powerful swarm intelligence method called bat algorithm. The method is tested on an illustrative example of the digital image of a famous oil painting. The experimental results show that the method performs very well, with a similarity error rate between the source and the reconstructed images of only 8.37%.
A physical model for intuiting linear regression

The application of linear regression to data analysis is traditionally a tedious calculation delegated to a computer. This article presents a physical model to facilitate an intuitive way of thinking about the slope of a fit. In this simple model, data points are envisioned as beads applying torques on a massless, rigid, horizontal rod. To illustrate how this model can help colleagues directly address special questions about fits, the model is applied to two situations in which there are repeated measurements of data. This rod-and-bead model also arrives at the coefficient of determination formula in a very straightforward way and makes the recollection of the least-squared slope formula and coefficient of determination much easier.
Methodology for Assessing the Level of Modern Teaching Staff Competitiveness

In modern economic (market) conditions, the factor of ensuring high competitiveness of teachers, as one of the most popular professions in society, is of particular importance. This explains the relevance of the study. However, scientific publications do not pay enough attention to the development of methods for assessing the level of competitiveness of teachers, and the available publications are usually descriptive. In the paper, the authors propose a methodology for assessing the level of competitiveness of teachers, based on a number of mathematical methods. The developed methodology can be used for quantitative assessment and self-assessment of teachers, as well as in the course of work of commissions for the certification of teachers in higher education system. It seems that the material will be useful for both beginners and experienced teachers, as well as all those who are connected with the issues of modern pedagogical science and practice.
Towards Intelligent Web Context-Based Content On-Demand Extraction Using Deep Learning

Information extraction and reasoning from massive high-dimensional data at dynamic contexts, is very demanding and yet is very hard to obtain in real-time basis. However, such process capability and efficiency might be affected and limited by the available computational resources and the consequent power consumption. Conventional search mechanisms are often incapable of real-time fetching a predefined content from data source, without concerning the increased number of connected devices that contribute to the same source. In this work, we propose and present a concept for an efficient approach for online content searching, takes advantage of a) the structure of data profiling employed at the related data source; and b) the learning algorithms that are used for extracting its common features and for generating a map of indices to data contents. This enables instant mapping of users requests to make the process as realtime as possible. The adopted learning algorithms main blocks are built to capture the semantic features in the targeted context of data sentences. We reviewed several learning approaches and compared their results based on the criteria of capturing the semantic features that appeared through the preliminary results. The preliminary results conclusively confirmed that employing the weighted recurrent neural networks and the GloVE pre-trained model paired with NMF topic modeling, yielded highly acceptable levels of Fl-score and prediction time.
Research on the Intelligent Image Recognition and Diagnosis Technology of Key External Insulation Devices Based on Deep Learning

Devices and their faults increasingly bring a great difficulty to grid operators. In order to solve troubles caused by equipment fault diagnosis, this paper conducts a research on the intelligent image recognition and diagnosis technology of key external insulation devices based on deep learning.
Research and Design of Student Party Member Development Recommendation Model Based on Grid Platform Deep Learning Mode

With the informationization of the development of party members in colleges and universities, the traditional management platform has been unable to meet the recommendation work of party members in colleges and universities. By analyzing the shortcomings of the existing party member management platform, this paper proposes a student party member development recommendation model based on the grid platform deep learning mode. Under the grid platform, the artificial intelligence method of deep learning training is used to set the development object. Different parameters and parameter weights are set, and repeated training makes the constructed model more optimized, thus obtaining the recommended queue, solving the manual operation mode of daily work, reducing the working time, improving the work efficiency, and making the whole process more intelligent.