Reviewerly/exHarmony huggingface dataset:
A Study on the Development of the Psychological Assessment a Using Eye-Tracking: Focused on Eye Gaze Processing of Literacy Text

The purpose of this study is to examine the possibility of improving a self-epic diagnosis tool by using a gaze processing method in terms of literacy therapy. In this study, we used an eye-tracker measuring eye-position and eye-movement to read subjects' reactions to the critical region in a context. With this technology, we focused on the fixation and saccade to see what vocabulary subjects focused and how their gaze was shifted. We also explored to find a relationship between their eye-movement and their degree of sympathy by examining their physiological data. This pilot study showed a potential use of a physiological data in the epic of self-test in literacy therapy.
Recognition of Meitei Mayek Using Statistical Texture and Histogram Features

Recognition of handwritten characters is one of the challenging and interesting problems in recent trends considering the empirical aspect. The research work on Meitei Mayek (Manipuri Script) recognition is still in the infant stage due to its intricate patterns and being a regional language. Moreover, the language has been reinstated recently, and there is no standard database available for research work. Therefore, we attempt to develop a recognition system on our developed dataset of Meitei Mayek using statistical texture and histogram features. A total of 4, 900 samples of 35 letters of Meitei Mayek have been collected in 140 pages from 90 different people of varying age group. Feature extraction technique like Uniform Local Binary Pattern (ULBP) and Projection Histogram (PH) have been considered to evaluate and validate the developed dataset. Finally, recognition is performed using the K-nearest neighbor (KNN) classifier. The combination of ULBP and PH has given the recognition rate of 97.85%.
Developing Authoring Tools for Simulation-Based Intelligent Tutoring Systems: Lessons Learned

Intelligent tutoring systems have a long history of significantly improving student performance. Unfortunately, they also have a long history of being very expensive to develop while producing very short shelf lives. To address these deficiencies, we developed and evaluated an authoring suite known as the Rapid Adaptive Coaching Environment (RACE). While developing RACE, we frequently encountered tension between maximizing the power of the intelligent tutoring system that authors would produce and minimizing the level of effort associated with producing this tool. In this paper, we will attempt to highlight this tension as we consider the five core design challenges that we addressed in the RACE prototype. We will review the formal and informal evaluations conducted by the development team and discuss how those evaluations led to the maturation of the design.
Mahaviracharya Encryption Algorithm (MEA) with Modified Counter Mode and Comparing with AES Algorithm

Now a day's usage of internet is being increased day by day, as well as users also being increased. Online education, ecommerce and online banking are being increased in developing countries. Network security is playing a vital role to provide the security to the data in the internet. Even though there are number of algorithms, developing new algorithms for internet security is very important, because attacks are increased and processing speed is also increased. In this paper, we are discussing a new symmetric encryption algorithm with modified counter mode and comparing with Advanced Encryption Standard (AES) algorithm with ECB mode. For this comparison we are taking encryption time and decryption time as parameters with varies data sizes. The comparison results are summarized and highlighted the characteristics of the new algorithm.
A Graph Updating Method of Data Theft Detection Based on Rough Set

In recent years, the problem of data theft is becoming more and more serious. This paper provides a graph update method of data theft detection based on rough set DLD-RGU to prevent the leakage of sensitive data. This method builds a clustering model of sensitive text data by training samples, and then constructs a sensitive word graph structure, periodically calculates the importance of sensitive words and latent sensitive words to update the sensitive word graph, adapts to the change of incremental sensitive data, and improves the detection effect. Experiments on real data sets and simulation data sets show that DLD-RGU can adapt to sensitive content changes, update the sensitive word graph in time, and accurately identify the key words of confidential text containing sensitive content.
Based on vector flexible weighted morphological image edge detection

The classic gray-scale morphological operator has been widely used in image filtering, edge detection, image enhancement and image segmentation, etc. Morphology theory in color image processing was still in the stage of exploration in the field of application, the application of mathematical morphology in image processing is good or not is the key to the morphological gradient operator and structural elements. In view of the two key factors the vector flexible weighted morphological algorithm is put forward to detect image edge. Firstly build the weighted structural elements, if the pixel is the edge of the image to give greater weighted value, otherwise give small weighted value. Secondly reference gray-scale morphological duality theorem, the vector flexible gradient operator meets the duality theorem of color image. Vector flexible gradient operator is proposed to apply weighted structural elements to corrosion and dilation gradient operation to extract image edge. The experimental results show that the proposed vector flexible weighted morphological operators edge detection algorithm can effectively extract image edges, keep small image edge and can remove the image noise, verify the effectiveness of the proposed algorithm.
Oculus Rift Versus HTC Vive: Usability Assessment from a Teleportation Task

Virtual Reality (VR) technology has shown impressive growth in recent years, extending to industrial, military, and rehabilitation occupations. However, despite such growth, there is little research on the usability aspects associated with different VR devices. This paper investigates subjective and objective usability differences between two commercial Head-Mounted Display (HMD) systems, the HTC Vive and Oculus Rift, using a between-subjects experimental design on three teleportation task scenarios. Each scenario had a time limit of five minutes and sequentially increased in complexity. Objective usability was evaluated through performance measures, including per scenario effectiveness, time duration, total time duration, completion rate, and time-based efficiency. Subjective usability was evaluated by users after the three scenarios, via a questionnaire formed of ease of use, comfort, effectiveness, and visual quality subscales. The results, interpreted using Mann-Whitney U Tests, indicated significant differences between the HTC Vive and Oculus Rift: in terms of objective usability, Vive’s overmatch in scenario three effectiveness suggests harder tasks in the Rift may require additional aids; in terms of subjective usability, Vive’s overmatch in effectiveness questionnaire items suggests it is a preferred choice for a range of applications, as well as for learning real-world skills. In terms of significant Spearman’s rho correlations, more HMD comfort is aligned with completion rates, within the Vive; different contexts may lead to a reversal effect, where visual quality can either relate to negative or positive performance, depending on the headset; and overall, many different usability aspects positively relate to total time-based efficiency of the teleportation task.
A Bioinspired Model of Decision Making Considering Spatial Attention for Goal-Driven Behaviour

Cognitive architectures (CA) are currently used to approach computer systems’ behavior to human behavior and intelligence. Fundamental human capability is planning and decision-making. In that regard, numerous AI systems successfully exhibit human-like behavior but are limited to either achieving specific objectives or too heavily constrained environments, which makes them unsuitable in the presence of unforeseen situations where autonomy is required. In this work, we present a bioinspired computational model to undertake the autonomous navigation problem as a result of the interaction between planning and decision-making, spatial attention and the motor system. The proposed model is embedded in a greater cognitive architecture. In the case study developed, it is proposed and tested that the process of planning and decision-making plays an important role to carry out spatial navigation. In it, the agent must move through an unexplored maze from an initial point to a final point, which it accomplished successfully. The gathered results prompt us to continue working on the model that considers attentional information to guide the agent’s behavior, which is strongly supported by the concise selection of neuroscientific evidence related to the cognitive functions we provided.
Exploring Errors in Reading a Visualization via Eye Tracking Models Using Stochastic Geometry

Information visualizations of quantitative data are rapidly becoming more complex as the dimension and volume of data increases. Critical to modern applications, an information visualization is used to communicate numeric data using objects such as lines, rectangles, bars, circles, and so forth. Via visual inspection, the viewer assigns numbers to these objects using their geometric properties of size and shape. Any difference between this estimation and the desired numeric value we call the “visual measurement error”. The research objective of this paper is to propose models of the visual measure error utilizing stochastic geometry. The fundamental technique in building our models is the conceptualization of eye fixation points as might be determined by an eye-tracking experiment of viewers estimating size and shape of a visualization’s object configurations. The fixation points are first considered as a stochastic point process whose characteristics require comment before proceeding to the statistical shape analysis of the visualization. Once clarified the fixation points are reinterpreted as a sampling of the shape and size of the landmark configurations of geometric landmarks on the visualization. The ultimate end of these models is to find optimal shape and size parameters leading to minimum visual measurement error.
A Semi-automatic Methodology for Recognition of Printed Kannada Character Primitives Useful in Character Construction

Every character of the language having script is written using basic units called primitives. One or more number of primitives connected appropriately result in construction of a character. In the process of character construction identification of primitives is considered as high priority. Through this paper we propose a semi-automatic method for extracting features from primitives for their recognition and further Kannada characters’ construction. The primitives are recognized automatically by adopting the zone features and neighbor classifier. The feature vectors are obtained for all the primitives of Kannada character set and a knowledge base is created. We have used Euclidean distance measure to establish similarity between test input primitives and existing primitives present in the knowledge base for identifying the primitives in Kannada characters. The suggested methodology is tested for 11520 manually extracted primitive images. Average recognition accuracies observed is in the range of 75% to 100% for printed primitives. Application spreads in various verticals of automating literature like calligraphy, digitizing old manuscripts, multimedia teaching, Robot based assistance in handwriting, animation etc.
Recognition of Planar Object in Stack via Local Prediction Consistency

Abstract A novel method for recognition of planar object in stack via local prediction consistency is proposed in this paper. By checking the consistency of local predictions, an object can be recognized and located. The prediction method by local features under uncertain scale is emphatically discussed. According to the distribution prior for local predictions, the similarity scale can be determined and the consistent local predictions can be composited. A great number of experiments are carried out to prove the stability of this method.
Reversible Data Hiding in JPEG Images Based on Multiple Histograms Modification

The joint photographic experts group (JPEG) is the most popular image on the internet nowadays. Therefore, reversible data hiding (RDH), which can be used to ensure the widely used JPEG images’ originality and integrity, has attracted more and more attention. However, when performing RDH in JPEG images, besides the embedding capacity and visual quality, which have to be considered for uncompressed images, the storage size of the marked JPEG file should also be considered. In this paper, a new JPEG RDH scheme based on multiple histograms modification is proposed. Firstly, according to zero run length (ZRL) of the discrete cosine transform (DCT) coefficients, multiple DCT coefficient histograms are generated. Then, the message bits are embedded via the histogram shifting algorithm. In order to improve the efficiency of the proposed method, a new adaptive embedding strategy based on the ZRL and the number of zero coefficients in each 8 × 8 block is also proposed. Experimental results show that our method can effectively reduce the increase of image storage size caused by information embedding.
CHARM: Cord-Based Haptic Augmented Reality Manipulation

The recent trend of emerging high-quality Augmented Reality (AR) glasses offered the possibility for visually exciting application scenarios. However, the interaction with these devices is often challenging since current input methods most of the time lack haptic feedback and are limited in their user interface controls. With this work, we introduce , a combination of a belt-worn interaction device, utilizing a retractable cord, and a set of interaction techniques to enhance AR input capabilities with physical controls and spatial constraints. Building on our previous research, we created a fully-functional prototype to investigate how body-worn string devices can be used to support generic AR tasks. We contribute a radial widget menu for system control as well as transformation techniques for 3D object manipulation. To validate our interaction concepts for system control, we implemented a mid-air gesture interface as a baseline and evaluated our prototype in two formative user studies. Our results show that our approach provides flexibility regarding possible interaction mappings and was preferred for manipulation tasks compared to mid-air gesture input.
LibGuides: Ithaka S+R Faculty Survey: Results

This guide presents data gathered from the 2018 Ithaka S+R Local Faculty Survey fielded by the A-State Library
Scene Recognition via Bi-enhanced Knowledge Space Learning

Scene recognition is one of the hallmark tasks in computer vision, as it provides rich information beyond object recognition and action recognition. It is easy to accept that scene images from the same class always include the same essential objects and relations, for example, scene images of “wedding” usually have bridegroom and bride next to him. Following this observation, we introduce a novel idea to boost the accuracy of scene recognition by mining essential scene sub-graph and learning a bi-enhanced knowledge space. The essential scene sub-graph describes the essential objects and their relations for each scene class. The learned knowledge space is bi-enhanced by global representation on the entire image and local representation on the corresponding essential scene sub-graph. Experimental results on the constructed dataset called Scene 30 demonstrate the effectiveness of our proposed method.
Exploring ISBSG R12 Dataset Using Multi-data Analytics

This paper presents an exploratory study that applies three data analysis techniques: statistical analysis, data clustering, and visualization conducted to the ISBSG R12 data set. Both SPSS and RapidMiner are used to conduct the analysis. While statistical analysis main advantage is the summarization of data, the overall behavior of the data is lost, particularly the view of outlier values. The study applied two techniques in this regard using SPSS: correlation analysis and the general linear model using multiple variables. The statistical analysis showed a high significant level of relationship between and among the selected variables. In the data mining areas, the clustering technique and visualization used both SPSS and RapidMiner (RM). For the selected variables, the number of clusters is determined after several runs, in an attempt to diversify the one larger cluster into several sub-clusters. Finally, visualization technique demonstrates how it could show concentration and trends. Statistical analysis found high correlation between speed of delivery and manpower delivery rate, and the independent factors of industry type and development methodologies vs. the dependent variable of defect density. The clustering process highlighted the importance of variables related to work efforts and defects in forming the clusters. Major conclusions of the visualization charts revealed an inverse no-linear relationship between effort of analysis and design of total effort and speed of delivery form one side and total defects delivered. Overall, multiple view of data analytics is needed to arrive at a clear and consistent understanding of the underlying behavior of the data in a complex data set such as ISBSG.
Non-Functional Requirements In Health Information Systems:

In the development of information systems (IS) often non-functional requirements (NFR) are not considered as important as functional requirements. However, they remain essential to ensure software quality, as they are more critical than individual functional requirements. This research synthesizes the results of a systematic mapping research about NFR implemented in IS of the health area, where 488 primary studies were identified, of which 39 were included in data extraction. Among the most cited NFR, we highlight security and privacy, usability, reliability, and availability. The most commonly used requirement elicitation technique was the interview. It is hoped that the results will serve to assist in the quality of the development of software for the health area.
ESTIMATING CO2 EMISSIONS BY USING ENERGY INTENSITY DATA OF OECD COUNTRIES

It is discussed that economic development has an essential effect on the country’s CO2 emission which plays an important role in global warming. In this research well-known machine learning algorithm Extreme Learning Machine, ELM, is used to investigate the relationship between CO2 emission and energy intensity for countries in OECD. The results indicate a strong correlation and the method perform well for estimation.
n-means: Adaptive Clustering Microaggregation of Categorical Medical Data

Huge amount of information is managed and shared publically by the individuals and data controllers. Publically shared data contains information that can reveal identity of users, thus affecting privacy of individuals. To palliate these disclosure risks, Statistical Disclosure Control (SDC) methods are applied to the data before it is released. Microaggregation is one of the SDC methods that aggregate similar records into clusters, and then transform them into m indistinguishable records. K-means is a famous data mining clustering algorithm for continuous data, which iteratively maps similar elements into k-cluster until they all converge. However, adapting k-means algorithm for categorical multivariate is a challenging task due to high dimensionality of attributes. In this paper, we extend k-means clustering algorithm to achieve notion of microaggregation of structured data. Moreover, to preserve data utility, we extend fixed clustering nature of this algorithm to adaptive size clusters. For this purpose, we introduce n-means clustering approach that construct clusters based on the semantics of the datasets. In experiments, we proved significance of our proposed system by measuring cohesion of clusters and information loss for utility purpose.
Analysing heuristic subsequences for offline hyper-heuristic learning

A selection hyper-heuristic is used to optimise a number of well-known benchmark problems. The resulting sequences of heuristics and objective function values are used to create a database. The sequences in the database are broken down into subsequences and the concept of a logarithmic return is used to discriminate between "effective" subsequences, which tend to decrease the objective value, and "disruptive" subsequences, which tend to increase the objective value. These subsequences are then employed in a sequenced based hyper-heuristic and evaluated on unseen benchmark problems. The results demonstrate that the "effective" subsequences perform better than the "disruptive" subsequences across a number of problem domains with 99% confidence. The identification of subsequences of heuristic that can be shown to be effective across a number of problems or problem domains could have important implications for the design of hyper-heuristics.
A pipeline for mining association rules from large datasets of retailers invoices

The concept of massive data generation nowadays affects several domains such as marketing including electronic invoices (e-invoices) of large retailers, web access log files, healthcare, life sciences and so on. Datasets dimensions grow up, due to the availability of several cheap connected devices, such as mobile devices, RFID and wireless sensors networks, from which to collect data. Often, the collected data need to be gathered into a consistent, integrated and comprehensive form, to be used for knowledge discovery. Without adequately cleaning, transforming and structuring the data before the analysis, it is hard to mine useful knowledge. Thus, users by using data mining can extract knowledge from large invoices documents. In this paper, a pipeline for preprocessing and mining association rules from large retailers commercial documents has been proposed. The preprocessing provides merging, cleaning, formatting and summarization. The methodology can improve the quality of large retailers data by reducing the quantity of irrelevant data, making the remaining data suitable to mine association rules (ARM). Analyzing a real invoices dataset (provided by an Italian retailer) by using the proposed methodology, it was possible to extract 36 significant association rules, highlighting the customers' behavior in the purchase of goods.
Exploratory Data Analysis to Build Applications for Android Developer

In this paper, authors used the Exploratory Data Analysis (EDA) that embodies different patterns and find useful tidings from Google play store application (app) data. The intrinsic objective behind this is to analyze the features of the dataset in order to help the developers to understand the trends within the market and the end user needs towards the application, as well as the mechanism of App Store Optimization (ASO) that leads to enhancement of the popularity of the developer app.