Reviewerly/exHarmony huggingface dataset:
GRD for R: An intuitive tool for generating random data in R

Statistics courses prove to be a common difficulty to many social sciences students.To address this problem, we developed a tool in the R programming language (R Core Team, 2018) that can be used to easily and quickly generate data to be analysed.The Generator of Random Data or GRD, allows the user to build datasets according to any design type, both within and between, with or without statistical effects and/or correlations.By default, GRD creates normally distributed data, but any type of distribution defined in R can be specified.With GRD, it is possible to generate samples of any size and see the benefit of larger sample sizes on the precision of statistical measures.The students of statistics can acquire better skills in analyzing custom-made datasets, skipping long data-acquisition processes.They can also experience first-hand concepts such as statistical power and type-I and type-II errors.Each sample being different, students can appreciate randomness at the tips of their fingers.
End user programing of intelligent agents using demonstrations and natural language instructions

End-user programmable intelligent agents that can learn new tasks and concepts from users' explicit instructions are desired. This paper presents our progress on expanding the capabilities of such agents in the areas of task applicability, task generalizability, user intent disambiguation and support for IoT devices through our multi-modal approach of combining programming by demonstration (PBD) with learning from natural language instructions. Our future directions include facilitating better script reuse and sharing, and supporting greater user expressiveness in instructions.
Endoscopic orientation by multimodal data fusion

To improve the feasibility of endoscopic inspection processes we developed a system that provides online information about position, orientation and viewing direction of endoscopes, to support the analysis of endoscopic images and to ease the operational handling of the equipment. The setup is based on an industrial endoscope consisting of a camera, various MEMS and multimodal data fusion. The software contains algorithms for feature and geometric structure recognition as well as Kalman filters. To track the distal end of the endoscope and to generate 3D point cloud data in real time the optical and photometrical characteristics of the system are registered and the movement of the endoscope is reconstructed by using image processing techniques.
Robust Multibit Image Watermarking Based on Contrast Modulation and Affine Rectification

In this paper, we present a robust multibit image watermarking scheme to undertake the common image-processing attacks as well as affine distortions. This scheme combines contrast modulation and effective synchronization for large payload and high robustness. We analyze the robustness, payload, and the lower bound of fidelity. Regarding watermark resynchronization under affine distortions, we develop a self-referencing rectification method to detect the distortion parameters for reconstruction by the center of mass in affine covariant regions. The effectiveness and advantages of the proposed scheme are confirmed by experimental results, which show the superior performance as comparing against several state-of-the-art watermarking methods.
Modelling the Behaviour of Context-aware Systems: State-of-the-Art Analysis and Introduction of a Customized UML Profile

The usage of smart devices in combination with the extensive deployment of sensors enables the development of systems that can recognize their environment and act accordingly. These so-called context-aware systems allow the automation of the user interactions by providing the desired functionality based on an understanding of the users' current context. During the development of such systems, the context-related system properties, the contextual functionalities, as well as the underlying contexts must be defined. UML models are a valuable tool for such tasks. In this publication, the previous research regarding the graphical representation of context-aware systems is analysed. It is shown that the modelling of contextual behaviour is still not addressed sufficiently. To counter this issue, contextual design templates are introduced. These templates allow the visualization of different system properties by combining the UML standard notation with a newly introduced notation for stating contexts. Further, a profile for the modelling of contexts and related system functionalities is presented.
No-Reference Image Quality Assessment: An Attention Driven Approach

In this paper, we tackle no-reference image quality assessment (NR-IQA), which aims to predict the perceptual quality of a test image without referencing its pristine-quality counterpart. The free-energy brain theory implies that the human visual system (HVS) tends to predict the pristine image while perceiving a distorted one. Besides, image quality assessment heavily depends on the way how human beings attend to distorted images. Motivated by that, the distorted image is restored first. Then given the distorted-restored pair, we make the first attempt to formulate the NR-IQA as a dynamic attentional process and implement it via reinforcement learning. The reward is derived from two tasks-classifying the distortion type and predicting the perceptual score of a test image. The model learns a policy to sample a sequence of fixation areas with a goal to maximize the expectation of the accumulated rewards. The observations of the fixation areas are aggregated through a recurrent neural network (RNN) and the robust averaging strategy which assigns different weights on different fixation areas. Extensive experiments on TID2008, TID2013 and CSIQ demonstrate the superiority of our method.
Multi-Modal Detection Fusion on a Mobile UGV for Wide-Area, Long-Range Surveillance

We introduce a self-contained, mobile surveillance system designed to remotely detect and track people in real time, at long ranges, and over a wide field of view in cluttered urban and natural settings. The system is integrated with an unmanned ground vehicle, which hosts an array of four IR and four high-resolution RGB cameras, navigational sensors, and onboard processing computers. High-confidence, low-false-alarm-rate person tracks are produced by fusing motion detections and single-frame CNN person detections between co-registered RGB and IR video streams. Processing speeds are increased by using semantic scene segmentation and a tiered inference scheme to focus processing on the most salient regions of the 43° × 7.8° composite field of view. The system autonomously produces alerts of human presence and movement within the field of view, which are disseminated over a radio network and remotely viewed on a tablet computer. We present an ablation study quantifying the benefits that multi-sensor, multi-detector fusion brings to the problem of detecting people in challenging outdoor environments with shadows, occlusions, clutter, and variable weather conditions.
Design of Admissions Examination System Based on Cloud Architecture

With the enrichment of admission scale and exam types,traditional hardware architecture of information system can't meet the requirements of existing system. Different systems and processes have different requirements for hardware facilities. If allocating a set of hardware for each type of system, not only a large amount of hardware resources will be wasted,but also a huge burden will be set on the system operation and maintenance. This paper redesign the infrastructure service layer of the admission examination system with cloud architecture. We use cloud computing[1] technology torealize the dynamic expansion and on-demand allocation of hardware resources, and provides an effective solution to meet the diverse requirements of the examination system.
Analysis of Speaker’s Voice in Cepstral Domain Using MFCC Based Feature Extraction and VQ Technique for Speaker Identification System

Automatic Speaker Recognition technology have been rapidly developed in recent years and facilely integrated with existing biometric system, which can be deployed in the identification systems to improve recognition and ensure security. An essential initial phase in Speaker Recognition (SR) system is the step of extracting accurate information from human acoustic signal that captures the unique characteristics of the speaker’s voice. One popular choice for features extraction is the short-term spectral characteristics. In this paper, we proposed to investigate the performance of the Mel frequency cepstral coefficient (MFCC) to extract features in training phase for text-dependent speaker identification system. In order to evaluate the reliability of the proposed MFCCs feature sets, we use the Vector Quantization (VQ) classifier based on the best Known Linde-Buzo-Gray (LBG), and results are reported for a dataset composed of eight subject (5 male and 3 female). Moreover, we also outline the influence of changing the codebook size to find the best identification rate. The results elucidate the influence of the codebook size on the identification rate for the text-dependent speaker identification system that yield an identification accuracy of 87.5% using codebook of size 8, 16, 32 and 64.
Analysis of Tensor-Based Image Segmentation Using Echo State Networks

In pixel classification-based segmentation both the quality of the feature set and the pixel classification technique employed may influence the accuracy of the process. Motivated by the potential of Structural Tensors in the extraction of hidden information about texture between neighboring pixels and the computational capabilities of Echo State Network (ESN), this study proposes a tensor-based segmentation approach using the standard ESN. The pixel features of the image are initially extracted by incorporating Structural Tensors in order to enrich it with information about image texture. Then the resulting feature set is fed into ESN and the output is trained to classify unseen pixels from the testing set. The effect of the two main parameters that impact the accuracy of ESN: reservoir size and spectral radius, was also evaluated. The results are promising when compared to recent state-of-the-art segmentation approaches.
Retrospective Analysis of the Reforms of the Content of General Secondary Education: How They Contribute to the Consolidation of Public Consciousness

The purpose of this article is to analyze the experience of reforming the education systems of Russia and Kazakhstan over the past three decades to identify appropriate areas of development of the education system in the face of the confrontation of globalization and the preservation of national interests of our countries. The following tasks are solved: to analyze the experience of the development of state educational standards; to evaluate the results of international research in the field of education; to assess the role of schools in the development of the state and the role of the state in the development of education in the face of the confrontation of globalization ideas and the preservation of national interests of our countries. The following methods were used: content analysis, statistical analysis, hermeneutic approach, method of historical and cultural research. Reforming the education system has led to fundamental changes in both the field itself and the public consciousness of our citizens. As a result, a part of the population approves of reforms, and another part does not accept them, referring to the fact that the former education system was one of the best in the world. The standards of general education, and then the standards of pedagogical education were largely modernized. This led to changes in their content. As a result, various educational systems emerged and the terms “competence” and “competence approach” were consolidated, which became the main components of the content of education, both general and professional pedagogical. The school itself is changing and the role of the teacher in it. However, to date society does not have an answer to the main question - what positive results did our entry into the world educational space give us. The authors propose to comprehensively analyze the carried out modernization in the field, draw conclusions and, if necessary, make corrections to the essence and content of the ongoing reforms in the system of both general secondary and higher pedagogical education.
Associative Representation and Processing of Databases Using DASNG and AVB+trees for Efficient Data Access

Today, we have to cope with a great amount of data – BIG data problems. The main issues concerned about BIG data are sparing representation, time efficiency of data access and processing, as well as data mining and knowledge discovery. When dealing with the big amount of data, time is crucial. The most of time for data processing in the contemporary computer science is lost for a various search operation to access appropriate data. This paper presents how data collected in relational databases can be transformed into the associative neuronal graph structures, and how searching operations can be accelerated thanks to the use of aggregation and association of the stored data. To achieve an extraordinary efficiency in data access, this paper introduces new AVB+trees which together with Deep Associative Semantic Neuronal Graphs which can typically allow for constant time access to the stored data. The presented solution allows representing horizontal and vertical relations between data and stored objects, expanding possibilities of relational databases and replacing various search operations by the specific graph structure. Another contribution is the expansion of the aggregation of the duplicates to all data tables which contain the same attributes. In such a way, the presented associative structures simplify and speed up all searching operations in comparison to the classic solutions.
DISDAIN: An Auto Content Generation VR Game

Objectives: To build a video game that implementing procedural content generation with a first-person shooter genre and using the newest gaming hardware technology, i.e.Virtual Reality.Methods: Some steps being incorporated in this study, starting from designing the video game, implementing the Procedural Content Generation and Grammars methods, implementing the game level generation, and evaluating the results using Game User Experience Satisfaction Scale (GUESS) and Procedural Content Generation (PCG) evaluation techniques.Findings: DISDAIN was successfully built using Grammars to generate the story and Room Generation Algorithm and Corridor Generation Algorithm to generate the maze-like game space.The game was evaluated by using the GUESS questionnaire which results on 77% satisfaction scale.Application/Improvements: The technique can be adapted to other heavy content games to reduce the production cost.
Data Security Scheme for a Trusted Third Party Platform Based on RSA One-time Key

In recent years, the possibility that cloud data is accessed illegally and the key is cracked in the process of storage is becoming increasingly. The paper proposes a data security scheme of a trusted third party platform based on RSA one-time pad encryption technology. It can use RSA one-time pad encryption technology to realize the function that encrypt the data in security. Then it manages the one-time key generation by the trusted third party platform. Finally, the simulation experiment detected the data security by using the MD5 algorithm and compared the digital fingerprint generated by the data before being attacked and after being attacked. The experimental results show that this scheme increases the workload of the key cracking, and reducing the probability of key being cracked and data being accessed illegally.
Enrich Existing Ontologies with New Knowledge from Existing Artifacts

This work investigates that how new terms or concepts will be enriched in an existing ontology from existing artifacts. As we know that daily new information is added in the system. Semantic web is used nowadays to extract the meaningful information from web. With the advent of such a large amount of information every day, generating new ontology from scratch is a very difficult task but it is easier to add the new information in the already existing ontology. If the ontology will be updated manually then there can be many problems like time consumption and error generation because there are very large files of ontology. To address this challenge we are going to purpose an approach that will enrich the existing Ontologies from existing artifacts. The main objective of this proposed approach is to extract the information from UML artifacts and existing Ontologies and then enrich the existing ontology with the new information which was not present before enrichment in the ontology. Various experiments and evaluation are conducted to support our proposed approach.
PRAN: A Provenance Based Model and Prototype to Strengthen Authentication

In this paper, we present the provenance based authentication (PRAN) model, and prototype that aims to strengthen password-based authentication of a system—ensuring legitimacy of a user’s identity by detecting anomalous user logins without the need for other obtrusive authentication factors. The PRAN model utilizes authentication provenance along with traditional methods. We evaluate the prototype with password stealing/sharing use cases. We observe that the PRAN prototype detects anomalous logins, and also legitimate logins thereby preventing applications from allowing anomalous user logins’.
Implementation issues in optimization algorithms: do they matter?

Two factors that have a major impact on the performance of an optimization method are (1) formal algorithm specifications and (2) practical implementations. The impact of the latter is typically ignored, although it defines the results measured in experiments. We present an in-depth study of algorithm implementation issues and ask questions such as Does optimizing the implementation of an optimization algorithm pay off? Do bugs matter? and Is using more complicated but also more efficient data structures worth the effort? The intuitive answer to all of these questions is yes, but there is little published evidence. To bridge this gap, we use one of the most studied combinatorial optimization problems – the Traveling Salesman Problem – as a test bed and implement two state-of-the-art approaches for solving it – the Lin-Kernighan Heuristic and an Ejection Chain Method. We investigate implementation effort and performance gain, in order to provide further insights to the above questions.
Software Quality Prediction Using Fuzzy Logic Technique

In the global market, the value of software quality is a major factor in software industries. In a majority of cases, software quality needs to be quantified and measured. Previously, many models like the ISO model, the McCall model, the Boehm model, etc., have been used for the quantification of the parameters which determine the software quality. The software quality can be judged by analyzing all the characteristics of the product. In this article, a new pragmatic model which is the modification of the existing ISO Model has been proposed in order for the quantification of the software quality factors in a more precise way. Because of the nature of the attributes which determine the software quality, a Fuzzy logic-based approach is considered to be a better technique among various existing machine learning techniques. Here, the simulink tool is used for constructing the running model which helps the net quality of the software.
Quantum information processing with macroscopic two-component Bose-Einstein condensates

Two component Bose-Einstein condensates (BECs) have been recently shown to be viable systems for storing and manipulating quantum information. Unlike standard single-particle qubits, the quantum information is duplicated in a large number of identical bosonic particles, thus can be considered to be a macroscopic qubit. The duplication of the quantum information makes them potentially more robust than conventional qubits, where all the quantum information is lost with a single error. It has been shown theoretically and experimentally that such ensembles can be used in many ways the same way as a standard qubit: they can be visualized on the Bloch sphere, and can be manipulated analogously to standard qubits. On the other hand, the BEC qubits do not have genuine interaction between each other and one of the main difficulties with such a system is how to effectively interact them together in order to transfer quantum information and create entanglement. Furthermore, the larger Hilbert space of the macroscopic bosonic system does not allow for unique mapping of standard quantum algorithms. However, in a few past years the main building blocks of quantum information processing and several quantum algorithms were translated to the BEC qubits. In this paper we give a brief colloquium of the current achievements and outline new perspectives of the use of BEC qubits and spin-coherent ensembles for quantum technologies.
Adaptive segmentation method of pressed character image based on Wellner algorithm

The image of pressed characters on the surface of metal workpieces in industry has obvious unimodal characteristics, for this feature, this paper proposes an adaptive segmentation method based on Wellner algorithm, this method is used to segment the pressed character image whose character gray value is similar to background gray value. Firstly, we use uniform illumination to capture grayscale images. Next, the Retinex algorithm is used to enhance the details of the character edge, the grayscale distribution range is expanded to improve the image contrast. Then, the bilateral filtering algorithm is used to filter the image noise. In this paper, the pixel gray value of a certain point is selected as the center, the row and column mean value of the pixel is calculated, at the same time, the mean value of the pixel gray value in the 8-connected region that it belongs to the pixel selected to be the center is calculated. The algorithm applies the “center-around” idea, the Wellner algorithm is improved with the mean value and the image pixel points are traversed to achieve image binarization. Finally, the final segmentation result is obtained by combining morphological operations. The verification experimental results show that the proposed method has good self-adaptiveness and accuracy for the gray-scale histogram image with unimodal characteristics.
Robust Steganography in Non-QRS Regions of 2D ECG for Securing Patients' Confidential Information in E-Healthcare Paradigm

The upsurge in the communication infrastructure and development in internet of things (IoT) has promoted e-healthcare services to provide remote assistance to homebound patients. It, however, increases the demand to protect the confidential information from intentional and unintentional access by unauthorized persons. This chapter is focused on steganography-based data hiding technique for ECG signal in which the selected ECG samples of non-QRS region are explored to embed the secret information. An embedding site selection (ESS) algorithm is designed to find the optimum embedding locations. The performance of the method is evaluated on the basis of statistical parameters and clinically supportive measures. The efficiency is measured in terms of embedding capacity and BER while key space measures its robustness. The implementation has been tested on standard MIT-BIH arrhythmia database of 2 mins and 5 mins duration and found that the proposed technique embarks the proficiency to securely hide the secret information at minimal distortion.