Reviewerly/exHarmony huggingface dataset:
On Some Computational and Security Aspects of the Blom Scheme

We study some computational and security aspects of the (k, m)-Blom scheme. This key pre-distribution scheme is defined by a symmetric polynomial $$ f\left( {x_{ 1} ,x_{ 2} , \ldots ,x_{k} } \right) $$ of degree m in each variable over a field Fp and a set of user identifiers $$ r_{i} ,r_{i} \in F_{p} ,i\, = \, 1, \ldots ,n $$ . The pre-shared keys that are distributed to users are the coefficients of the polynomials $$ f\left( {r_{i} ,x_{ 2} , \ldots ,x_{k} } \right) $$ with distinct users identifiers ri, $$ i\, = \, 1, \ldots ,n $$ . Implementing Stinson’s proof of unconditional security of (2, m)-Blom scheme we get the proof of unconditional security of (k, m)-Blom scheme. We establish that for disclosing of the (k, m)-Blom scheme we do not need to use all coefficients of m + 1 compromised polynomials. We show that for disclosure, it is enough to use just $$ \left( {\begin{array}{*{20}c} {m + k} \\ m \\ \end{array} } \right) $$ certain coefficients - exactly as many coefficients as there are present in a non-redundant representation of the original polynomial. Additionally, we estimate the number of multiplications in the field Fp sufficient to compute a pre-shared key and the shared key $$ f\left( {r_{i} ,r_{ 2} , \ldots ,r_{k} } \right) $$ for communication within a privileged group of k users and to find the initial polynomial as well.
Novel of Web Search Strategy Based on Web Page Block Granularity Analysis Algorithm and Correlation Calculation Model

In this paper, the running parameters of crawler nodes are dynamically adjusted to make them more manageable and configurable. The running parameters include crawling speed, crawling depth, number of crawler threads. After processing and organizing the information, the search engine provides Web information query service for users, crawls continuously on Internet, collects web information. Then the information is understood, extracted, organized and processed by the indexer, and the index database is established. The paper presents novel of web search strategy based on web page block granularity analysis algorithm and correlation calculation model.
Structuring Digital Plane by Closure Operators Associated with n-ary Relations

We associate a closure operator with every n-ary relation ( $$n>1$$ an integer) on a given set. We focus on certain n-ary relations on the digital line $$\mathbb {Z}$$ and study the closure operators on the digital plane $$\mathbb {Z}^2$$ that are associated with special products of pairs of the relations. These closure operators, which include the Khalimsky topology, are shown to provide well behaved connectedness, so that they may be used as background structures on the digital plane for the study of digital images.
Target Pedestrian Tracking Algorithm Based on Occlusion Scene

Abstract The traditional incremental learning tracking algorithm tends to drift or lose track when the pedestrian is occluded and the background is similar to the pedestrian target. This paper proposes a pedestrian tracking algorithm based on improved incremental learning. According to the difference between the central position of the current observation model and that of the previous one, the reliability of the observation model can be judged, and then the forgetting factor can be adjusted, so that the observation model can adapt to the changes of pedestrian targets. The experimental results show that the improved algorithm can track the pedestrian target accurately and effectively in the occlusion scene.
Deep Feature Based on Convolutional Auto-Encoder for Compact Semantic Hashing

For content-based image retrieval, a good presentation is crucial. Nowadays, as deep learning models can be used to generate an excellent presentation, it has been extensively investigated and widely used in research systems and commercial production systems. However, the deep representation (deep feature) is still too large. Compared with directly using deep representation, binary code can reduce significant storage overhead. Meanwhile, the bit-wise operations for binary code can dramatically fasten the computation. There exist some schemes used to convert the deep feature to binary code, but all of them directly applied the last layer of the connection layers, which exhibit global feature and discriminating features. To achieve deep generative feature and avoid destroying the image locality, we aim to construct the binary hash code based on convolutional auto-encoders. Namely, we use the generative model to transform the local feature to binary code. The training process of our proposed model is decomposed into three stages. Firstly, the convolutional layers are trained using convolutional autoencoders, followed by the fully-connected layers training using Restricted Boltzmann Machine. Thirdly, we deploy a supervised similarity learning algorithm to learn close code for similar images.
An Abstractive Summarizer Based on Improved Pointer-Generator Network

Aiming at the problems of insufficient semantic understanding, fluency and accuracy of abstracts in the field of neural abstractive summarization, an automatic text summarization model is proposed. First, we introduce the decoder attention mechanism in the reference network, which effectively improves the ability to understand words and generate vocabulary words. Second, the ability to extract words from the original text is improved by using the multi-hop attention mechanism, which improves the ability of the model to process out-of-vocabulary words. The experimental results on the CNN/Daily Mail dataset show that the model performs well on the standard evaluation system and improves the summary accuracy and sentence fluency.
On the Electro-Optic Couple Systems of Quantum Station for Quantum Communication Based on Phase Shift Scheme

Like any other ordinary functions to which the calculus approach of different, for instance differentiation and integration, can apply, the function for a particle term as wave function in similar manner can also be subjected by the different dynamical variables as well that may be either continuous or discrete to gives the information. Using this particle, here as optical one, the concern for confidential communication from past can be resolved as they satisfy the property of non-clonable. Here with in this paper based on the phase shifting scheme for optical pulses, different types of electro-optic system (EOS) are discussed, which is essential for the quantum communication. These electro-optic couple systems for both Alice and Bob led to the establishment of quantum station at each, connected by optical fibre links, responsible for generation, controlling, measurement and detection. By the required process of all, the quantum communication which takes place in between the different stations even in the presence of active eavesdropper is secured, as any of its interfere get likely detected by channels of legitimate users.
A Robust Feature-processing Method for Age-invariant Face Recognition

Abstract In this paper, we present a novel feature-processing method for age-invariant face recognition so that it is robust to aging process. The main purpose of feature-processing is to remove aging effects while keep personalized properties stable simultaneously. In order to achieve this, we try to learn a space map and then encode the mapped feature to an age - invariant representation. In the encoding step, we introduce two kinds of constraints: the temporal constraint (local constraint) and boundary constraint (global constraint). We applied our feature-processing method to Cross-Age Celebrity Dataset (CACD). In order to verify the versatility of our method, we apply it to both high-dimensional LBP feature and deep feature. Results show that our feature-processing method works well on CACD and the face verification subset of CACD (CACD-VS).
Texts of religious nature with an extremist-terrorist focus as an object of hermeneutic research and a source of forensically significant information in judicial linguistic expertise

The relevance of the chosen research topic is due to the growing need of investigative and judicial practice in the research of speech works of religious discourse in connection with the rise of extremist sentiments in modern society. Despite the obvious need for expert practice in the development of scientific and methodological support for the examination of texts of religious content, to date they have been poorly studied in terms of their potential conflict. This led to the novelty of the studied topic and its practical significance. The purpose of the work is to study the problems of using special linguistic knowledge in cases of verbal religious extremism, namely the problems of the theoretical aspect of forensic examination of religious materials (texts of religious discourse) with extremist-terrorist focus. To accomplish the tasks, a corpus of sources on judicial linguistics (judicial verbal studies), judicial expertology, Russian and foreign legislation, international legal acts, and expert and judicial practice were used. The author gives his own concept of the relationship between the concepts of «verbal (verbal) extremism», «hate speech» and «verbal religious extremism», highlighting the range of articles of the Criminal Code of the Russian Federation containing offenses correlated with these phenomena. The paper examines the problem of competence of linguistic experts involved in this category of cases, as well as the problem of the limits of their competence.
Performance Evaluation of Data Stream Mining Algorithm with Shared Density Graph for Micro and Macro Clustering

We propose to solve the problem of micro clustering using the integration of data stream mining algorithms. Streaming data are potentially infinite sequence of incoming data at very high speed and may evolve over the time. This causes several challenges in mining large scale high speed data streams in real time. This paper discusses various challenges associated with mining data streams. Several algorithm such as data stream mining algorithms of accuracy and micro clustering are specified along with their key features and significance. Also, the significant performance evaluation of micro and macro clustering relevant in streaming data of shared density graph and clustering are explained and their comparative significance is discussed. The paper illustrates various streaming data computation platforms that are developed and discusses each of them chronologically along with their major capabilities. The performance and analysis are different radius activation functions and various number of radius applied to an data stream mining algorithm with shared density graph for micro clustering and macro clustering.
Impact of Cloud of Clouds in Enterprises Applications

In recent times, the cloud has become a leading technology demanding its functionality in every business. According to research firm IDC and Gartner study, nearly one-third of the worldwide enterprise application market will be SaaS-based by 2018, driving annual SaaS revenue to $50.8 billion, from $22.6 billion in 2013. Downtime is treated as the primary drawback which may affect great deals in businesses. The service unavailability leads to a major disruption affecting the business environment. Hence, utmost care should be taken to scale the availability of services. As cloud computing has plenty of uncertainty with respect to network bandwidth and resources accessibility, delegating the computing resources as services should be scheduled accordingly. This chapter proposes a study on cloud of clouds and its impact on a business enterprise. It is also decided to propose a suitable scheduling algorithm to the cloud of cloud environment so as to trim the downtime problem faced by the cloud computing environment.
Resource Management Techniques to Manage the Load Balancing in Cloud Computing

Load balancing is one of the vital issues in cloud computing that needs to be achieved using proper techniques as it is directly related to higher resource utilization ratio and user satisfaction. By evenly distributing the dynamic local workload across all the nodes in the whole cloud, load balancing makes sure that no single node is overwhelmed, and some other nodes are kept idle. Hence, the technique helps to improve the overall performance resource utility of the system which will lead to high user satisfaction and resource utilization ratio. It also ensures the fair and effective distribution of each and every computing resource in the distributed system. Furthermore, the various load balancing techniques prevent the possible bottlenecks of the system created by the load imbalance. Maximization of the throughput, minimization of the response time, and avoidance of the overload are the other major advantages of the load balancing. Above all, by keeping resource consumption at the minimum, the load balancing techniques help to reduce costs.
The Research Ethics Policy for the Effective Utilization of Research Equipment

Proposed in this article is a solution and guideline for scientific misconduct prevention through a research ethics policy proposal for the effective utilization of research equipment. First, the scientific misconduct classification system for research equipment utilization is discussed and presented. Then, the results of the survey carried out targeting research equipment scientists for the analysis of the recognition of scientific misconduct in research equipment utilization are presented. For this survey, the non-probability sampling method was utilized for 60 research equipment scientists. The results of the survey conducted among research equipment scientists showed significant correlations among the variables for all the questionnaire items. This paper proposes a research ethics policy for scientific misconduct prevention and for the effective utilization of research equipment through scientific misconduct classification in relation to research equipment utilization, and based on the survey results from the research equipment scientists.
Plane Motion and Structure from Two Separate Ellipse Correspondences under 2D Homography

This paper presents an algorithm for computing plane motion and structure from two separate, visible ellipse correspondences under 2D homography. First, using the common self-polar triangle, an analytic solution to the plane-induced homography is presented. Second, using Singular Value Decomposition of the homography and the visibility condition of the common poles, an analytic algorithm for the plane motion and structure problem is presented. The whole proposed method almost always gives a unique solution. The results of the synthetic and real experiments demonstrate the efficiency and effectiveness of the proposed method.
Software development to predict the level of paddy production using Gaussian Copula Marginal Regression

Abstract Appropriate decision making in rice production management is needed to support national food security efforts, especially in East Java which is the largest rice production province in Indonesia. This study aims to develop a web-based decision support system to predict rice production levels in five districts of rice production centers in East Java. A web-based decision support system is constructed to make information accessible and understandable. The method used in this study is Gaussian Copula Marginal Regression (GCMR), which is regression based on Copula. Predictor variables (rainfall) and response variables (harvested area) are identified their correlation using Copula correlation. Estimation of harvested area is constructed using GCMR model. The results showed that the GCMR model used was able to model the area of rice harvest in five districts of rice production centers in East Java. Furthermore, the system is also able to predict the level of rice production in the short term as decision support to help the authorities give consideration in taking policy related to agriculture and food security in East Java.
Opinion Spam Detection with Attention-Based Neural Networks

Today, significant impacts of comments on the web affect people decisions while they are about to choose a product. Unfavorable effect of spam attacks in these reviews follows heavy damages for customers and organizations. The majority of methods so far classify reviews to spam and non-spam groups. Therefore, most researches are done on feature learning techniques to enhance the classification performance. From another point of view, presence of huge amount of features makes text classification overwhelming. Attention mechanism has lately been used to improve neural networks performance on sequence modeling. Instead of mining all existing features, attention can enables the model to concentrate on most important parts of the data. To these ends, we applied an attention based deep structure for detecting deceptive reviews. This model contributes distinguishing between truthful and fake reviews and benefits an attentional part to engineering better features. Our proposed model accuracy and scalability is comparable regard to the other common models.
Rating the risk: how to assess the dangers of working with other organisations

The security of your organisation doesn't just depend on eliminating the vulnerabilities from your networks and your devices. In this highly interconnected world, your weakest point might be the other organisations with which you work, such as suppliers and partners. But how do you assess the risk of another organisation? As Jasson Casey, CTO at SecurityScorecard, explains in this interview, firms have difficulty judging their own risk. When it comes to analysing others, they definitely need help.
Simultaneous Face Detection and Head Pose Estimation: A Fast and Unified Framework

In this paper, we present a fast and unified framework for simultaneous face detection and 3D pose (pitch, yaw, roll) estimation of unconstrained faces using deep convolutional neural networks (CNN). Face detection is implemented with region-based framework as previous work like Faster RCNN. We model the pose estimation as a classification and regression problem: first divide continuous head poses into several discrete clusters, then adjust poses within each class with a class-specific regressor to achieve more accurate results. All classification and regressions for the two tasks are trained and tested simultaneously in one unified network. Our approach runs at 10 fps, which is the fastest implementation among the recently proposed methods as far as we know. Moreover, it is able to predict pose without using any 3D information. Extensive evaluations on several challenging benchmarks such as AFLW and AFW demonstrate the effectiveness of the proposed method with competitive results.
An Improved Ping-Pong Protocol Using Three-Qubit Nonmaximally Nonorthogonal Entangled States

Abstract We analyse the ping-pong (PP) protocol [K. Bostrom and T. Felbinger, Phys. Rev. Lett. 89 , 187902 (2002)] using different sets of partially entangled three-qubit states. Interestingly, our results show that the partially entangled nonorthogonal three-qubit states are more useful as resources in comparison to three-qubit maximally entangled Greenberger–Horne–Zeilinger (GHZ) states. The properties of orthogonal set of partially entangled states as resources for PP protocol, however, are similar to that of maximally entangled GHZ states – both the states are not preferable due to the vulnerability towards eavesdropping. On the other hand, partially entangled nonorthogonal basis set holds importance for transferring two-bit information, one each from a sender, to a single receiver. The protocol is further analysed for various eavesdropping attacks, and the results are compared with the use of two shared Bell pairs for two-bit information transfer. Surprisingly, the use of partially entangled nonorthogonal set of states is found to offer better qubit efficiency and increased security, as against the use of two separate maximally entangled Bell states with orthogonal basis. In addition, we also propose a mixed-state sharing protocol to further enhance the security of the PP protocol.
Generating Semantic Aspects for Queries

Large document collections can be hard to explore if the user presents her information need in a limited set of keywords. Ambiguous intents arising out of these short queries often result in long-winded query sessions and many query reformulations. To alleviate this problem, in this work, we propose the novel concept of semantic aspects (e.g., $${\langle }\{\textsf {michael\text {-}phelps}\}, \{\textsf {athens, beijing, london}\}, [2004,2016] \rangle $$ for the ambiguous query ) and present the xFactor algorithm that generates them from annotations in documents. Semantic aspects uplift document contents into a meaningful structured representation, thereby allowing the user to sift through many documents without the need to read their contents. The semantic aspects are created by the analysis of semantic annotations in the form of temporal, geographic, and named entity annotations. We evaluate our approach on a novel testbed of over 5,000 aspects on Web-scale document collections amounting to more than 450 million documents. Our results show the xFactor algorithm finds relevant aspects for highly ambiguous queries.
An Improved Paperless Process Model for Qualification Assurance in Higher Education

It is greatly necessary for higher education in Thailand to enhance the quality and competitiveness of education, placing a clear emphasis on the specialization in both general and specific fields of study. Supported by the consistent development of quality and standards admired by service users, the quality assurance has become an integral part of quality development of higher education as it can guarantee the enhanced quality and standards of institutional management from the most fundamental to highly advanced levels. Therefore, higher education is required to evolve new dynamic systems and mechanisms of quality assurance in an efficient and consistent manner. Educational personnel have been encouraged to keep pace with the advancement of the quality assurance to be part of their routine education-related tasks, inspired by the application of modern technology to ensure greater accountability and convenience in using the required information. Significantly, the quality assurance has been expected to reduce redundant costs and excessive resources for Thailand's education industry.
A Cost Optimal Information Dispersal Framework for Cloud Storage System

In cloud computing, secure storage and retrieval is of significant importance. Along with that, maintaining confidentiality, reliability and availability of data is also an important objective. This can be achieved by dispersing the data into pieces and storing them at different places. But with the increase in data reliability and availability, the cost of maintaining those pieces also increases, which users hesitate to pay. Thus, a strategy is required to maintain a balance between these objectives and cost paid by user. In this paper, efforts have been made to propose a cost optimal information dispersal framework for cloud storage systems that uses an optimization algorithm for the optimal cost expenditure and information dispersal algorithm for the secure storage and retrieval of data. A system architecture is also presented that tells the different components required for the implementation of this strategy.