Reviewerly/exHarmony huggingface dataset:
Derivation of the Threshold Form of the Polytomous Rasch Model

Ordered categoriesordered categories are taken as analogous to physical measurementmeasurement with a continuumcontinuum partitioned by successive thresholdsthreshold. However, unlike physical measurements, the thresholdsthreshold which form the categories are not assumed to be equidistant. Thresholdsthreshold are defined by minimum proficiencies required to succeed at the thresholdsthreshold. The threshold form of the polytomous Rasch modelpolytomous Rasch model involves the hypothesis that the thresholdsthreshold are ordered. Threshold estimates from data may be disordered, in which case they reflect a problem with the empirical ordering of the categories.
Hiding Secret Data Using Image Steganography

Today’s large demand of internet applications requires data to be transmitted in a secure manner. Data transmission in public communication system is not secure because of interception and improper manipulation by eavesdropper. So, the attractive solution for this problem is Steganography, which is the art and science of writing hidden messages in such a way that no one, apart from the sender and intend recipient, suspects the existence of the message, a form of security through obscurity. Hiding Secret Data Using Image Steganography is the scheme of hiding the existence of secret information by concealing it into another medium such as image file. In this paper we mainly discuss different types of image steganographic methods, advantages and disadvantages. We focus in this paper on Hiding Secret Data Using Image Steganography, which has emerged as a prominent source of data hiding across novel telecommunication technologies such as covered voice-over-IP, audio conferencing, etc. The multitude of steganographic criteria has led to a great diversity in these system design techniques. In this paper, we review current digital image steganographic techniques and we evaluate their performance based on robustness, security and hiding capacity indicators. The primary goal of steganography is to reliably send hidden information secretly, not merely to obscure its presence. Steganography in today’s computer era is considered a sub-discipline of data communication security domain. Lately, new directions based on steganographic approaches started to emerge to ensure data secrecy. Rather than as a substitute to existing solutions, these approaches could achieve better data secrecy if combined with conventional security techniques. Modern techniques of steganography exploit the characteristics of digital media by utilizing them as carriers (covers) to hold hidden information.
Efficient Discrimination between Arabic Dialects

Background: With the explosion of communication technologies and the accompanying pervasive use of social media, we notice an outstanding proliferation of posts, reviews, comments, and other forms of expressions in different languages. This content attracted researchers from different fields; economics, political sciences, social sciences, psychology and particularly language processing. One of the prominent subjects is the discrimination between similar languages and dialects using natural language processing and machine learning techniques. The problem is usually addressed by formulating the identification as a classification task. Methods: The approach is based on machine learning classification methods to discriminate between Modern Standard Arabic (MSA) and four regional Arabic dialects: Egyptian, Levantine, Gulf and North-African. Several models were trained to discriminate between the studied dialects in large corpora mined from online Arabic newspapers and manually annotated. Results: Experimental results showed that n-gram features could substantially improve performance. Logistic regression based on character and word n-gram model using Count Vectors identified the handled dialects with an overall accuracy of 95%. Best results were achieved with Linear Support vector classifier using TF-IDF Vectors trained by character-based uni-gram, bi-gram, trigram, and word-based uni-gram, bi-gram with an overall accuracy of 95.1%. Conclusion: The results showed that n-gram features could substantially improve performance. Additionally, we noticed that the kind of data representation could provide a significant performance boost compared to simple representation.
A High-Efficient Infrared Mosaic Algorithm Based on GMS

The most important thing in infrared image mosaic technology is image registration technology. In order to adapt to the real-time requirement of the battlefield, the ORB algorithm in this paper is used for feature extraction. In order to obtain high-quality feature matching points, this paper proposes a new IGMS algorithm on the basis of GMS algorithm. The experimental results show that the correct point matching rate is increased by 8%. Using RNASAC to obtain the transformation matrix between the two images, and finally using the fade-in and fade-out fusion algorithm to obtain a complete wide-field military investigation map.
Application of Machine Vision in Pose Detection of Objects

The application of machine vision technology in pose detection of objects is studied. Subtraction of the morphological operation is used for rough localization and preliminary detection of the target area. Hough transform is used to detect the linear edge of the target. Under the condition of the known target template, the geometric parameters of the target are known, and the pose data of the target is obtained by using simple geometric relations.
The Idea of Measurement

AssessmentAssessment involves the engagement of an entity with some instrumentInstrument and the recording of observations of the engagement according to some protocol. MeasurementMeasurement involves some kind of transformation of assessments and is defined as the estimation of the amount of an unidimensional latent traitUnidimensional latent trait relative to a unitUnit . A scaleScale is a linear continuumContinuum partitioned into equal units, which provides the measurements, and scaling is the process of locating an entity on such a scaleScale . Arithmetic operations are justified with measurements.
FPGA Implementation of pAsynch Design Paradigm

Information leakage through physical channels is a major security vulnerability for embedded hardware. Power analysis is a well known technique used for side channel attacks. This paper overviews a new design paradigm, dubbed pAsynch, recently proposed by our group as an effective circuit level countermeasure against Power Analysis Attacks. pAsynch utilizes both the internal state and random signals to uniformly spread the information-carrying energy within the clock period. This paper is based on previous publications discussing pAsynch in detail and focuses on the practical implementation of pAsynch protection scheme on FPGA, overcoming some of the inherent challenges associated with the clocking scheme that is the basis of this technique. We show that pAsynch can provide h/w security with low overhead, making it suitable for energy/area constrained applications.
The Once-ler Problem: Introduction to Decision Trees

Abstract In the “Once-ler Problem,” the decision tree is introduced as a very useful technique that can be used to answer a variety of questions and assist in making decisions. This chapter builds on the “Lorax Problem” introduced in Chapter 19, where Bayesian networks were introduced. A decision tree is a graphical representation of the alternatives in a decision. It is closely related to Bayesian networks except that the decision problem takes the shape of a tree instead. The tree itself consists of decision nodes, chance nodes, and end nodes, which provide an outcome. In a decision tree, probabilities associated with chance nodes are conditional probabilities, which Bayes’ Theorem can be used to estimate or update. The calculation of expected values (or expected utility) of competing alternative decisions is provided on a step-by-step basis with an example from The Lorax.
Automatic Processing of Oral Arabic Complex Disfluencies

The present work is a part of the realization of the Arabic vocal server SARF (Bahou 2014). Indeed, in this paper we propose a numerical learning-based method for the oral Arabic complex disfluencies processing. The proposed method allows, from a pretreated and semantically labeled utterance, to delimit and label the conceptual segments of a spontaneous Arabic oral utterance. Then, it allows, from a segmented utterance, to detect and delimit the disfluent segments in order to correct them. The result of the implementation of this method is the Complex Disfluencies Processing Module (CDPM). For the evaluation of our CDPM, we found satisfactory results with an F-measure equal to 91.9%. After integrating the CDPM into the SARF system, we achieved an improvement of 11.88% in acceptable understanding and 3.77% in error rate. This improvement proves the effectiveness of the numerical learning-based method in the oral Arabic complex disfluencies processing.
Multi-party Quantum Communication Complexity on Composite Boolean-Valued Function

The performance of communication complexity depends on the selected computation model. Even on the specific model the quantum communication complexity is not always better than the classical one. This paper investigates the quantum communication complexity based on a multi-party computation model of the composite Boolean-valued function. On this model we design a quantum distributed algorithm to obtain the upper bound of quantum communication complexity. The result shows that the performance gap between quantum and classical communication complexity depends on the infinity order of function domain’s square root and users’ number. In the best situation the performance of the quantum communication complexity wins the quadratic level of advantage than the classical one. And sometimes the classical way is more efficient than the quantum one.
VujaDessin: A Sketch Learning Support System Using a Blurred Motif Object

Sketch is a creative activity that anybody can start casually and often used as training to begin a full-fledged painting. Since beginners sometimes feel difficulty to draw accurate sketch, various tutoring systems of sketch have been developed. However, some problems about seeing motif object have not been solved. First, they often have difficulty to grasp the entirety of the object and fail to draw balanced sketch. Second, they have difficulty to draw exactly what they saw due to labeling of conceptual meaning. We assumed that these problems are caused by their inappropriate way of seeing motif object: an excessive focus on detail of specific part. In order to solve these problems, we proposed sketch learning support system VujaDessin. This system prompts users to see the entirety of the motif object by blurring motif image when focus on detail parts is detected. We conducted user study of the system targeting two subjects to evaluate the system effectiveness. While the system did not provide positive effect to quality of outcome, we confirmed generated blur changed users seeing behavior. The system possibly encouraged users to see entire motif object including peripheral parts in a more exploratory way. Further system development and study is required in order to support beginners who strongly feel they are not good at drawing and confirm the long-term learning effect.
Double Semion Model as a Quantum Memory

To carry out a quantum error-correction protocol, we must first encode the quantum information we want to protect, and then repeatedly perform recovery operations that reverse the errors. Standard quantum error correcting codes [1–8] are based on a special class of codes, called concatenated codes, which enable us to perform longer quantum computations reliably, as we increase the block size.
Use of Gaussian Processes in System Identification

Gaussian processes are used in machine learning to learn input-output mappings from observed data. Gaussian process regression is based on imposing a Gaussian process prior on the unknown regressor function and statistically conditioning it on the observed data. In system identification, Gaussian processes are used to form time series prediction models such as nonlinear finite-impulse response (NFIR) models as well as nonlinear autoregressive (NARX) models. Gaussian process state-space (GPSS) models can be used to learn the dynamic and measurement models for a state-space representation of the input-output data. Temporal and spatiotemporal Gaussian processes can be directly used to form regressor on the data in the time domain. The aim of this article is to briefly outline the main directions in system identification methods using Gaussian processes.
An Improved Total Variation Denoising Model

Total variation denoising model is vulnerable to the influence of the gradient and often loses the image details. Aiming at this shortcoming, an improved total variation denoising model is proposed to recover the damaged additive Gaussian noise image. First, guided filtering and impulse filtering are used to preprocess noisy images; second, the adaptive norm parameter is selected by the edge detection operator; third, the horizontal and vertical weight values are selected by adaptive method; Finally, the image processed by non-local means filter replaces the noisy image to modify the fidelity term in the method. Experiments show that the improved total variation denoising model can remove the noise and can keep the texture and edge of the image better as well.
Comparing Deep Learners with Variability Grading for Cancer Detection on Limited Histopathology Dataset

State-of-the-art deep convolution neural networks (CNN) can be applied to various domains, including the grading of cancers in histopathology images, and are most promising approaches. However, it is well-known that CNNs require huge amounts of tagged images and resources to train and work well, and some prior works on cancer grading also achieved top accuracy by analyzing how cancer affects structures, such as cells, in terms of variability of characteristics. The aim of this work is to compare CNN-based classification of medical images with automated analysis of multiple structures. This is done experimentally, by implementing the alternatives and comparing classification accuracy on a public cancer grading dataset. The results show that a well-designed automated analysis of structures improved accuracy by 4% when compared with the best CNN result, showing that it is worth to study further and establish procedures based on that analysis.
Color image encryption algorithm based on hyperchaotic and security analysis

According to the problems of the traditional image encryption algorithm, such as cumbersome encryption process, small keyspace, and insufficient security, a color image encryption algorithm based on a four-dimensional hyperchaotic system and using “conversion-scrambling-diffusion” mode is used. The algorithm utilizes improved Chen’s hyperchaotic system. First, according to the color image correlation attribute, the gray code iteration number was calculated, and the gray code inversely transformed the pixel value. Subsequently, the chaotic sequence generated by the hyperchaotic system was used to convert the gray code converted pixel matrix. The global pixel position was scrambled, and then, the scrambled matrix was bit-operated to complete image diffusion. Finally, the ciphertext was obtained through matrix transformation. The algorithm can realize a picture and a key. Good image encryption algorithms should meet the requirements of large keyspace, key sensitivity, effective resistance to violent attacks, entropy attacks, plaintext attacks, and other attacks as well as real-time requirements. The simulation experiments calculate and analyze evaluation indexes, such as key sensitivity, histogram, image information entropy, including one-dimensional entropy and two-dimensional entropy, and adjacent element correlation. The information entropies are more than 7.99, and the keyspace is larger than 1086. The results prove that the encryption algorithm has robust security and antiattack ability.
A Novel DWTTH Approach for Denoising X-Ray Images Acquired Using Flat Detector

This paper proposes a new approach for denoising an X-ray flat detector image by combining Discrete Wavelets Transform (DWT) and the hard Thresholding method (DWTTH). The developed procedure can decrease noise for X-ray images to achieve a great quality of image at minimum X-ray dose. Noisy images are those acquired with low X-ray doses. For this purpose we have tested our DWTTH algorithm on one low X-ray dose image (Low_RX). The denoised image is compared to a standard X-ray dose image (S_RX). Images are acquired on a Pro-Digi phantom. We have focused our study on denoising the image with preserving contrast between regions. So, denoising procedure is applied on seven region of interest (ROI) selected on the two Pro-Digi X-ray images with different contrast. The proposed denoising DWTTH method is based on the combination of discrete wavelet transform and hard thresholding of energy coefficients of the approximation image issued from DWT applied on several decomposition levels. The denoised image is reconstructed by applying the inverse DWT. The DWTTH results are evaluated in terms of Contrast to Noise Ratio (CNR) and the Signal to Noise Ratio (SNR). These ratios are computed for each denoised ROI and are compared to those corresponding ROIs of S_RX image. The DWTTH method results show that the SNR and the CNR ratios are improved considerably compared to those obtained by the Wavelet Coefficient Magnitude Sum (WCMS), the soft thresholding and the conventional filtering methods.
Evaluating the User Experience: A Study on Children’s Interaction with Socio-enactive Artifacts in a Hospital Context

Digital technologies are currently present in many areas of our lives and are used for various purposes. The omnipresent technology scene through ubiquitous and pervasive technologies has brought new forms of interaction. These interactions have become spontaneous and natural, in the sense of being infiltrated in the physical landscape. Considering the new forms of interaction, we believe that quantitative measures mainly those related to pre-defined tasks are not enough to evaluate the current technological contexts. User eXperience (UX) brings with it new perspectives for evaluation and is now widely recognized as an aspect to be considered and understood in the creation of interactive technologies. In this study, we evaluated the UX of children aged 7 to 12 years old who undergo treatment at the Sobrapar Hospital (Brazilian Society for Research and Assistance for Craniofacial Rehabilitation). Two workshops were carried out with the inclusion of plush animals with ubiquitous and pervasive characteristics in a socio-enactive scenario. The results showed that adapted methods were able to capture essential aspects of children’s experience, such as emotions. In addition, the use of drawings for data collection was also useful, since the drawings represent greater spontaneity on the part of the children, validating the emotional responses given by the children through the Emoti-SAM and the Laddering interview. The triangulation of the data analyzed with the different methods was effective for the evaluation of children’s UX.
Education in the process of career guidance as a response to the needs of young adults

This paper presents the results of a theoretical-empirical research intended to examine the educational needs of young adults, originating from their concern regarding the contemporary traits of professional life. The framework for the analysis of the identified educational needs of the target group comprises statistical indicators, results of empirical research and consideration of the theoretical specificities of young adults' career development. The research sample consists of 238 final year students of bachelor, master and doctoral studies at the University of Belgrade, who are enrolled at faculties within various faculty groups. The results of the research indicate that students demonstrate educational needs that may be addressed by content and activities belonging to various areas of standards of career management skills, including the discovering and understanding of one's self, perceiving the possibilities of academic and professional life, and career creation.
Education and Assessment Using Ship Handling Simulator- Application for Analysis of Communication -

This study was made to determine index and standard model for education and assessment of Ship Handling Simulator. In this study, first the experiment using the BRM training scenario are carried out for professional ship’s officer and student. During the experiment, their communication are recorded by video camera and voice recorder. Next, the difference of communication between the navigator and student is analyzed by the data. From the result, the time of communication and time per minute of the navigator became more than them of the student. And Resource sharing and Cooperation action were defined as the index to assess communication and Evaluated Standards of Resource sharing, Positioning and Cooperation action were proposed as standard to evaluate communication. The concrete examples of assessment for communication were given. Finally, the way of effective education using the model of communication and the play back of operation by the navigator were proposed.
Applying Deep Convolutional Neural Network to Cursive Chinese Calligraphy Recognition

Calligraphy is one of the most important cultural art as well as writing tool in ancient China. Various writing styles have evolved over time in Calligraphy text, including Regular script, Clerical script, Semi-cursive script, Cursive script, and Seal script. In this study, we consider the cursive Chinese calligraphy recognition task as a variant of handwritten text recognition. We apply deep convolutional network approach to this recognition problem and achieve 84.6%, 92.6%, 93.7%, 96.7% average top1, top3, top5, top10 accuracy for 395 characters and 83.8%, 91.8%, 94%, 96.1% average top1, top3, top5, top10 accuracy for 632 characters. Our investigation indicates that text recognition tasks can be tackled by deep learning based approach even only when a limited number of training samples are available.
Towards Private and Efficient Ad Impression Aggregation in Mobile Advertising

In the secure mobile advertising, mobile users privately select advertisements of interest for displaying without exposing their preferences to the ad network. However, the strong privacy guarantee has uncovered limitations on gathering aggregated ad impression statistics for the ad network to enforce correct billing on the merchants who run their ad campaigns. Early efforts integrated cryptographic voting mechanism to address this challenge, which introduces additional bandwidth overhead on mobile devices due to the construction of the ballot proof. In this paper, we propose a private and efficient ad impression aggregation scheme in mobile advertising to protect the individual ad impression statistics while preventing the ad-fraud attack. The main idea of the proposed scheme is the design of an efficient cryptographic voting mechanism based on the compact hamming weight proof technique and additive homomorphic encryption. The proposed scheme has better bandwidth efficiency by reducing the ballot proof size from O(logN) to O(1), where N denotes the dimension of the ballot. Security analysis demonstrates the confidentiality of the individual impression statistics and the verifiability of the ballot proof under standard cryptographic assumptions. Experimental results consolidate that the proposed scheme is feasible for real-world implementations on mobile devices.