Reviewerly/exHarmony huggingface dataset:
Position and Deformation-Checking Method Based on Structured Illumination. External Radiotherapy Use

The main goal of a radiotherapy treatment is to provide the prescribed radiation dose to the tumor while minimizing healthy tissue irradiation. During every fraction of treatment, correct placement of the patient on the treatment couch as well as early detection of any shape changes that the patient´s body may undergo are crucial, because both affect the dose distribution. Structured illumination by fringe projection is typically used for finding an object’s spatial dimensions by projecting a pattern on it and taking a photo from a specific location. In this paper we propose a different approach for this technique: given an object’s 3D surface, an image can be computed and projected with the objective of locating this object at a specific location; only when the object is at the exact previously chosen position and preserves its original shape and form, an undistorted fringe pattern will be observed. Verification of patient’s correct position on the treatment couch and the detection of shape changes nearby the radiation field are feasible using the structured illumination based method presented in this work, a potentially useful tool for external radiotherapy treatments.
Img-Shelter : Privacy Protection of Images In Online Social Networks Using Watermarking Scheme

Social networking sites are very useful in sharing information, making friends and keeping in touch with old friends. It is an online service, platform, or site that focuses on facilitating the building of social networks and social elation among peoples for sharing interests, activities, backgrounds, or real-life connections. But with the increasing demand of social networking sites (SNS) privacy and security concern have also increased. Protecting personal information privacy has become a controversial issue among online social network providers and users. Most social network providers have developed several techniques to decrease threats and risks to the users’ privacy. These risks include the misuse of personal information which may lead to illegal acts such as identity theft. This study aims to measure the awareness of users on protecting their personal information privacy, as well as the suitability of the privacy systems which they use to modify privacy settings. In this paper, categorize the picture as sensitive or normal. If it is sensitive means, perform copyrights algorithms. Then provide the permission to the receiver end for download the images in secure manner. Experimental result can be shows that in real time environments using C#.NET as front end and SQL SERVER as back end and comparative study of existing algorithms based on computational time and privacy rate.
Analysis of the Transformed Contour in the Writer Identification

This work shows an approach for the writer identification using off-line handwritten writing. The contour of handwritten words is calculated. That feature is transformed by a Hidden Markov Model, in order to get a hyperdimensionality of new features. Later, a support vector machine classifier is applied to analyze the grade of discrimination. The approach reaches an accuracy between 98, 60% and 100%, according to the type of word. This proposal presents a robust and novel system for the based-writing identification in comparison versus the state-of-the-art; and shows the study of its efficiency according to different off-line handwritten words.
A Polya Tree Based Model for Unmarked Individuals in an Open Wildlife Population

Many ecologicalDiana, Alex sampling schemes do not allow for unique marking of individuals. Instead, only counts of individuals detected on each sampling occasion areGriffin, Jim available. In this paper, we propose as novel approach for modelling count data in an open populationMatechou, Eleni where individuals can arrive and depart from the site during the sampling period. A Bayesian nonparametric prior, known as Polya Tree, is used for modelling the bivariate density of arrival and departure times. Thanks to this choice, we can easily incorporate prior information on arrival and departure density while still allowing the model to flexibly adjust the posterior inference according to the observed data. Moreover, the model provides great scalability as the complexity does not depend on the population size but just on the number of sampling occasions, making it particularly suitable for data-sets with high numbers of detections. We apply the new model to count data of newts collected by the Durrell Institute of Conservation and Ecology, University of Kent.
Exploration of Product Search Intents via Clustering of Query Clusters

We describe a system that organizes search results in the context of an exploratory product search session where the user is researching goods. Compared to existing approaches that use predefined categories to filter results by attributes, we organize information needs based on queries instead of documents. The idea is to organize queries around the same topic and produce a hierarchical representation of intents that describe information about a product from different perspectives. We present a prototype implementation using a real-world data set of 24M queries.
Bound for mixed exponential sums associated to binary good cyclic codes

In this paper, we give an improved bound for mixed exponential sums associated to good cyclic codes.
LWE-Based Single-Server Block Private Information Retrieval Protocol

The appearance of fully homomorphic encryption (FHE) scheme induces new ways to construct the single-server private information retrieval protocol. At PKC 2015, Hiromasa et al. proposed the first FHE scheme that encrypts matrices and supports homomorphic matrix addition and multiplication. (hereafter, referred to as HAO15 scheme). Motivated by their work, we construct a LWE-based single-server block private information retrieval protocol. To get almost optimal communication cost, we adopt the homomorphic-ciphertext compression technique proposed by Naehrig et al. And as an intermediate product, we give a homomorphic algorithm, with no need for the secret key, to check the equality between diagonal matrices that are encrypted under HAO15 scheme.
A New Temporal Deconvolutional Pyramid Network for Action Detection

Temporal action detection is a challenging task for detecting various action instances in untrimmed videos. Existing detection approaches are unable to localize the start and end time of action instances precisely. To address this issue, we propose a novel Temporal Deconvolutional Pyramid Network (TDPN), in which a Temporal Deconvolution Fusion (TDF) module in each pyramidal hierarchy is developed to construct strong semantic features of multiple temporal scales for detecting action instances with various durations. In the TDF module, the temporal resolution of high-level feature is expanded by a temporal deconvolution. The expanded high-level features and low-level features are fused by a fusion strategy to form strong semantic features. The fused semantic features with multiple temporal scales are used to predict action categories and boundary offsets simultaneously, which significantly improves the detection performance. Besides, a strict strategy for assigning label is proposed during training to improve the precision of temporal boundaries learned by model. We evaluate our detection approach on two public datasets, i.e., THUMOS14 and MEXaction2. The experimental results have demonstrated that our TDPN model can achieve competitive performance on THUMOS14 and best performance on MEXaction2 compared with the other approaches.
Analysis of the Problems Accounting Information Distortion for the Listed Companies

Abstract In this paper, the research results of scholars is referenced at home and abroad, the social reality is combined with, the problem of accounting information distortion of the listed companies is expounded, the background and related concepts of accounting information distortion are introduced, the performance and harms of accounting information distortion are summarized, the cause of accounting information distortion is raised, and the solutions which will avoid accounting information distortion is put forward effectively.
Design of the In-depth Intelligent Educational System Based on the English Learning

The traditional English teaching mode is based on the teachers’ teaching of the language knowledge, and attaches great importance to the training of the students’ language basis, but neglects the cultivation of the students’ communicative competence, which cannot meet the needs of the society for talents. Teachers should combine the intelligent comprehensive evaluation and the inquiry and form an interactive way of the cooperative learning. According to the differences in the students’ personalities and their different learning conditions, various learning activities can be constructed so that all the members of different groups can gain and grow. We can use the question discussion, the operation and activities, the mutual aid group, the chop splicing, and the role play and so on to carry out the cooperative learning.
Improving the competitiveness of Russian universities to the level of the world’s leading research and educational centers is the most important task of higher education

The article presents a brief history of the formation of educational institutions of higher education in the three hundred year history of Russian higher education, shows the participation of the European scientific school and its best representatives in the formation of Russian education and science, reveals the features of cooperation of Russian educational institutions with Western universities in the fields of science, education, student exchange. The authors pay attention to the contribution of great Russian scientists to the development of world science. Specific examples of the implementation of the Project 5-100 to improve the competitiveness of leading Russian universities to the level of the world’s leading scientific and educational centers are given.
Modelling the Privacy Impact of External Knowledge for Sensor Data in the Industrial Internet of Things

Some type of privacy-preserving transformation must be applied to any data record from Industrial Internet of Things (IIoT) before it is disclosed to the researchers or analysts. Based on the existing privacy models such as Differential Privacy (DP) and k-anonymity, we extend the DP model to explicitly incorporate feature dependencies, and to produce guarantees of privacy in a probabilistic form that generalize k-anonymity. We assume that additional (external) knowledge of these relations and models can be represented in the form of joint probability distributions, such as Mutual Information (MI). We propose an enhanced definition of DP in conjunction with a realisation for non-randomizing anonymizing strategies such as binning, reducing the extent of binning required and preserving more valuable information for researchers. This allows the formulation of privacy conditions over the evolving set of features such that each feature can be associated its own allowance for privacy budget. As a case study, we consider an example from the Industrial Medical Internet of Things (IMIoT). We have identified some challenges that are not completely addressed by existing privacy models. Unlike physiological measurements in conventional medical environments, IMIoT is likely to result in duplicate and overlapping measurements, which can be associated with different personally identifiable items of information. As an example, we present a model of sequential feature collection.
Applying Social Network Analysis Techniques to Architectural Smell Prediction

As a software system evolves, the amount and complexity of the interactions amongst its components is likely to increase, which negatively affects the system design structure and also its quality. For instance, certain modules might become coupled due to a new user feature being added or to suboptimal development decisions. Design degradation symptoms are often related to high coupling and unwanted dependencies, such as: cyclic dependencies or violations to design rules, amongst other architectural smells. Thus, the early detection of such symptoms is important for architects to: i) anticipate dependency-related design problems in different parts of the system, ii) assess possible situations of technical debt, and iii) proactively look for solutions to preserve the quality of the system. Although there are approaches that analyse design dependencies in code bases and flag smell occurrences, very few of them have dealt with the prediction of dependency relations amongst software components. This research hypothesises that a predictive approach can warn architects about dependency-related problems before they appear. To this end, a particular graph-based approach is social networks analysis (SNA), which has been used for modelling both nature and human phenomena. Specifically, SNA techniques can predict links that do not yet exist between pairs of nodes in a network. SNA applications have shown evidence that the topological features of dependency graphs can reveal interesting properties of the software system under analysis. Nonetheless, SNA techniques have not yet been extensively exploited in the Software Architecture community. In this context, the question that motivates this research is to what extent SNA can leverage on information from a software design (and its evolution over time) for inferring new dependencies and likely configurations of architectural smells out of those dependencies.
Professor Dr.-Ing. Werner Lorenz retired from university

"Professor Dr.-Ing. Werner Lorenz retired from university." Proceedings of the Institution of Civil Engineers - Engineering History and Heritage, 172(2), pp. 90–91
Data Classification by Reducing Bias of Domain-Oriented Knowledge Based on Data Jackets

In recent years, because of the worldwide trend of big data and AI, cross-disciplinary data exchange and collaboration is one of the social demands. However, data users do not always have sufficient knowledge about data, which prevents from exchanging and utilizing data. The meaning of words depends on the contexts even if the same words are used because of the different background knowledge. It is necessary to bridge the gap between the expertise of the data owners and the requests of data users. To avoid this contextual gap, we propose the classification system to support data users to discover the related categories of data which is learned by the semantic knowledge. We use Data Jackets as the summary of data, and the knowledge base of Wikipedia and word2vec in order to reduce the influence of domain-oriented knowledge. As a result of the experiment, we found that our proposed method got a higher accuracy rate of the classification tasks and the classification was similar to human recognition.
Discovering Association Rules Using R. A Case Study on Retail’s Database

Today, the high competitiveness in retail businesses requires them to seek new strategies to ensure their survival. To this end, organizations have understood that the data located in their transactional databases can be used as raw material to boost business growth, if they can be exploited properly. The research’s main objective is to apply Data Mining techniques for the discovery of association rules from purely commercial transactional data, taking as a study period 10-year in a household appliances and furniture retail entity. The selection’s phase and preparation data are described as well as its cost in man/hours. In the modeling phase, the Apriori and Eclat algorithms implemented in the arules package of the R tool were executed, where both the resulting associations and execution time were compared. The results show relevant patterns in the buying behavior of customers such as those that relate items and accessories’ prices.
Physically realizable adversarial examples for convolutional object detection algorithms

In our work, we make two primary contributions to the field of adversarial example generation for convolutional neural network based perception technologies. First of all, we extend recent work on physically realizable adversarial examples to make them more robust to translation, rotation, and scale in real-world scenarios. Secondly, we demonstrate attacks against object detection neural networks rather than considering only the simpler problem of classification, demonstrating the ability to force these networks to mislocalize as well as misclassify. We demonstrate our method on multiple object detection frameworks, including Faster R-CNN, YOLO v3, and our own single-shot detection architecture.
Using radial basis function neural networks (RBFNNs) to forecasting with fuzzy time series

we introduce study of a method to forecasting  fuzzy time series   relying on radial basis function neural networks in the process of lifting the misty during speculate process a few steps away from the use of  FCM function and the process of fuzzy clustering.been integrating these processes to reach the best results when compared to previous studies which has recording data for students at the University of Alabama. has been using MATLAB program to create clusters that are used as groups Millbh and drawing functions, as well as the use of Excel software to perform calculations were obtained a few error ratio of between the numbers of registered university mentioned when compared to previous studies.
Argumentation mining: classifying argumentation components with Partial Tree Kernel and Support Vector Machine for constituent trees on imbalanced persuasive essay

Argumentation Mining is a method that automatically identified an argument structure in a text document. The structure of this argument consists of several components that become very important to evaluate itself. This study builds a classification system of 3 classes of argumentation components on persuasive essays so that the data is multi-class using Tree Kernel which is part of the pre-processing and Support Vector Machine as a tool for grouping this text. This research is an adaptation of research conducted by Lippi and Torroni where they used 2 classes to get 74.6% precision, recall 68.4% and F1- score 71.4%, while this study used 3 classes of argument class and managed to get a precision value of 79%, recall 78% and F1-score 74% by using the Sampling Method to overcome the problem of the amount of data imbalance.
Interface for VR, MR and AR Based on Eye-Tracking Sensor Technology

Virtual Reality (VR) was a technology proposed in the 1950s, but VR devices and content have been actively spreading in recent years. However, due to the recent development of computing technology, a device capable of tracking user behavior in a small size has appeared, so that VR content that interacts with a user It was able to appear. In addition, since VR is extended to AR and MR, it can be used in all three virtual environments and requires efficient user interface (UI). In this paper, we propose a UI based on the Eye-tracking sensor technology. Eye-tracking sensor-based UI not only allows users to directly control the controller, but also dramatically lowers the time spent on simple operations, while also reducing the need for a dedicated controller by allowing a wide variety of controllers to be used in combination.
Quantifying Customer Review by Integrating Multiple Source of Knowledge

The recent emergence of a large volume of customer reviews on e--commerce web sites has raised concerns on the provision of intuitive and comprehensive reputation comparisons of feature dimensions. In this paper, we propose and implement a product reputation mining prototype system. A multiple-knowledge based F-O pair extraction model, which is the center piece of our work, is presented for conducting analyses toward deeper sentence-level comprehension of sentiments in customer reviews. Experimental results demonstrate the effectiveness of the proposed method.
Coincidence—a statistical description

The focus is on two classic descriptions of birthdays occurring on the same day. That is, the probability that two people selected at random from 23 have the same birthday and a specific probability a person’s birthday matches a specific day.
Control of Stochastic Systems Based on the Predictive Models of Random Sequences

This chapter is devoted to the development of the mathematical models of stochastic control systems based on the predictive models of random sequences. In particular, the linear algorithms of the forecast of a control object state for an arbitrary number of states of an investigated object and the values of a control parameter are obtained. In a mean-square sense, the algorithms allow one to get an optimal estimation of the future values of a forecasted parameter, in case true known values for an observation interval are used, provided that the measurements are made with errors. Using an arbitrary number of non-linear stochastic relations, a predictive model of a control system is obtained as well. The schemes that reflect the peculiarities of determining the parameters of a nonlinear algorithm and its functioning regularities are introduced in the work. Developed models allows one to take the peculiarities of the sequence of the change of control object parameters into full consideration and also to make full use of all known priori and posteriori information about the random sequence that was investigated. The algorithms obtained in this chapter can be used in different areas of human activity to solve a wide range of problems of the control of the objects of stochastic nature.
Robust and Invisible Image Watermarking for Presentation Slides in E-Learning

Problem of copyright infringement on presentation slides available online continued to increase over the next few years. Digital image watermarking is developed and used to discourage people from violating copyrighted contents in presentation slides. This paper presents a robust and invisible image watermarking method particularly developed for presentation slide in e-learning. By considering the entire area of presentation slide as a color image, the reflectance component from its blue color channel was extracted and used to carry the watermark. The watermark extraction is blindly achieved by predicting the original reflectance component from the guided image filter, and subtracting the result from the watermarked component. The performance of our proposed watermarking method in terms of wPSNR and BCR is evaluated, and compared with the two previous methods. Improved performance in terms of robustness against various attacks was also evaluated and shown.
Space Exploration and Education using Virtual Reality

Traditional methods of learning in the form of books and chalks are now-a-days being taken over by the e-Learning methods. For more understandability and memory power, Visual representation of any subject plays an important role. Thus, instead of teaching the subject capturing the information about space and the space objects traditionally, an environment is created in this product to enhance the feel of the overall theory which would end up grabbing more attention of the students increasing their grasping power. The simulation is made under the technology famously known as “Virtual Reality”, which helps one create an immersive environment. The environment allows one to move around in the space as well as open up the details about any specific planet. The external elements needing are a VR Headset and a Pointer. Also, VR compatible devices might prove necessary.