Reviewerly/exHarmony huggingface dataset:
Model Checking for Gaze Pattern Recognition

Automated gaze pattern recognition has found applications in several settings: software evaluation, marketing, education, etc. Traditional algorithms apply string matching approaches to obtain patterns from scanpaths (sequences of eye fixations). Although string matching algorithms have been widely studied and can be highly efficients, which is of crucial importance in real time applications, only relatively simple patterns can be recognized. In the current paper, we propose the use of linear temporal logic (LTL) as an expressive pattern specification language. Expressive patterns involving temporality and loops can be succinctly expressed. These expressive patterns are detected by a model checking algorithm for LTL. The algorithm decides if a formula satisfies or not a given model. In our proposal, the scanpath represents the model and the formula represents the pattern. We illustrate this proposal with some experiments.
Multiellipsoidal extended target tracking with known extent using sequential Monte Carlo framework

In this paper, we consider a variant of the extended target tracking (ETT) problem, namely the multiel- lipsoidal ETT problem. In multiellipsoidal ETT, target extent is represented by multiple ellipses, which correspond to the origin of the measurements on the target surface. The problem involves estimating the target’s kinematic state and solving the association problem between the measurements and the ellipses. We cast the problem in a sequential Monte Carlo (SMC) framework and investigate different marginalization strategies to find an efficient particle filter. Under the known extent assumption, we define association variables to find the correct association between the measurements and the ellipses; hence, the posterior involves both discrete and continuous random variables. By expressing the measurement likelihood as a mixture of Gaussians we derive and employ a marginalized particle filter for the independent association variables without sampling the discrete states. We compare the performance of the method with its alternatives and illustrate the gain in nonstandard marginalization.
Nucleus Neural Network for Super Robust Learning.

Artificial neural networks which are inspired from the learning mechanism of brain have achieved great successes in many problems, especially those with deep layers. In this paper, we propose a nucleus neural network (NNN) and corresponding connecting architecture learning method. In a nucleus, there are no regular layers, i.e., a neuron may connect to all the neurons in the nucleus. This type of architecture gets rid of layer limitation and may lead to more powerful learning capability. It is crucial to determine the connections between them given numerous neurons. Based on the principle that more relevant input and output neuron pair deserves higher connecting density, we propose an efficient architecture learning model for the nucleus. Moreover, we improve the learning method for connecting weights and biases given the optimized architecture. We find that this novel architecture is robust to irrelevant components in test data. So we reconstruct a new dataset based on the MNIST dataset where the types of digital backgrounds in training and test sets are different. Experiments demonstrate that the proposed learner achieves significant improvement over traditional learners on the reconstructed data set.
Phonology and Writing: Can we look at written productions to “see the unseeable” in phonology?

Oral and written productions of language seem to correspond to ontologically separate entities. In this paper, we shall not argue against this basic assumption. However, it will be proposed that a careful examination of the writing systems and of particular written productions can provide phonologists with important information about the nature of phonological representations. Writing systems often originate in relevant intuitions about the nature of phonological units and phenomena and preserve the morphophonemic kinships between roots and words that are surfaced as phonetically distinct. The same can be said about the written productions of pre-school children and illiterate adults, strongly shaped by phonological intuitions rather than by orthographic convention. Bearing in mind that phonology, within the generative approach that is adopted here, is a form of knowledge, spelling can be accepted as a way of getting access to phonological knowledge. Therefore, our main point is that, in spite of the classical divide between spoken and written language, attention to writing can be useful for the understanding of the phonological level, too. The article includes two main parts: firstly, on Sections 2 and 3, we shall survey some general aspects of the relation between phonological and written representations; the second part consists mainly of Section 4 and attempts to illustrate some of the topics presented in Sections 2 and 3 with some data of a small-scale study with Portuguese pre-schoolers.
A multiple feature-based offline handwritten signature verification system

This paper proposes an efficient technique to develop an automated offline signature verification system that could help in crime prevention and biometric authentication systems. The technique proposed makes use of direction-based methods to compute a set of features that are taken together as a combination. The features include the geometric details of the different strokes that compose a signature and contours of the signature. It includes the two-step/three-step features, radical points, directions and transitions of strokes and contours, energy density and angles of strokes to name a few. A grid-based approach is applied to extract some of the features. Classification is done by using Support Vector Machine (SVM). Experiments are performed on a standard CEDAR database and a self-prepared database. The results speak for the efficiency of the proposed system that achieves accuracy level much better than many of the published works.
Impact of Supply Chain Governance on Financial Reporting: Evidence from Iraq

The importance of supply chain governance mechanism has grabbed the attention of academia due to massive corporate scandals in 21st century. The following study is aimed to investigate the supply chain governance mechanism in Iraqi corporations at first stage and develop the relationship between supply chain governance with quality of financial reporting at second stage. The sample of this study is consists of listed Iraqi companies between the time period of 2009 to 2016. A panel regression technique is applied to investigate the proposed framework by fulfill the objective of this investigation. Results revealed that board independence has a significant and positive role on financial reporting quality. Whereas, board size and CEO duality have a negative association with financial reporting quality. Furthermore, firm size and age was considered as control variables which influence the relationship of supply chain governance and quality of financial reporting.
The Need for Steganalysis in ImageDistribution Channels

The rise of social networks during the last 10 years has created a situation in which up to 100 million new images and photographs are uploaded and shared by users every day. This environment poses an ideal background for those who wish to communicate covertly by the use of steganography. It also creates a new set of challenges for steganalysts, who have to shift their field of work away from a purely scientific laboratory environment and into a diverse real-world scenario, while at the same time having to deal with entirely new problems, such as the detection of steganographic channels or the impact that even a low false positive rate has when investigating the millions of images which are shared every day on social networks. We evaluate how to address these challenges with traditional steganographic and statistical methods, rather than using high performance computing and machine learning. To achieve this we first analyze the steganographic algorithm F5 applied to images with a high degree of diversity, as would be seen in a typical social network.We show that the biggest challenge lies in the detection of images whose payload is less then 50% of the available capacity of an image.We suggest new detection methods and apply these to the problem of channel detection in social network. We are able to show that using our attacks we are able to detect the majority of covert F5 channels after a mix containing 10 stego images has been classified by our scheme.
Intellectual capital, social capital and collective intelligence

An exploratory analysis with qualitative focus combines examples of out of box legal researches with theoretical references on intellectual capital, social capital and collective intelligence. The analysed data consists of real cases, sellected from an exploratory qualitative study, on January and February 2016, mainly from Sao Paulo city, Brazil. This investigation has identified: (1) intellectual capital, produced by librarians and researchers, as products of their surveys; (2) existing social capital among Law librarians, expressed by means of their information research and diffusion, articulated throughout social media, discussion lists and networking; (3) collective intelligence, indicated by mapping information sources and survey strategies by means of cooperation and sharing.
Automatic Counting for Live and Dead Cells from Trypan Blue-Stained Images by Image Analysis Based on Adaptive K-Means Clustering

Computer-assisted image analysis can be employed to reduce the time consumed in the routine task such as cell counting.This study aimed to establish a method to perform this routine task based on an image analysis to automatically count live and dead cells after staining with trypan blue dye.Gray scale conversion and morphological operation were applied to the input images to enhance the image quality before image segmentation, then adaptive k-means clustering was applied to classify the groups of live and dead cells.Circular Hough transform and object labelling were carried out to identify the number of each cell type.The counting results from the proposed method were compared with the counting of three experts and the ImageJ software.The results showed that the proposed method had very high correlation with the results of the three experts in counting live cells (R 2 >0.95) and was better than the counting results achieved by ImageJ.The number of dead cells counted by our program was in good agreement with the experts' counting (R 2 >0.64).In conclusion, this study suggests that using new image analysis program can be confidently substituted for a manual counting in routine cell counting.
Fast Denoising of Sonar Image Based on Saliency Detection

Sonar image is inevitably contaminated by noise during the acquisition process, while the noise reduction algorithms with good performance are usually of high time complexity. The human visual attention mechanism usually guides the eyes to salient region and gives priority to those visual information. In view of this, saliency detection based on manifold ranking was introduced into sonar image processing, to divide the image into two parts:salient region and non-significant region. For the salient region with small proportion, block-matching and 3-D filtering (BM3D) algorithm was adopted to reduce noise and protect the main information of the image; for the non-significant background which was not very concerned, high efficiency mean filtering was used. On the collected sonar image data set, the present algorithm was compared with the classic MF and BM3D algorithms through the subjective and 2 objective evaluation indexes. The experimental results show that the efficiency of the present algorithm is much higher than that of BM3D, while the image visual effect is guaranteed, which is satisfied with the real-time application requirement of autonomous underwater vehicles.
Approaches To The Implementation Of The Out-Comes Control At The Digital University

The purpose of the study is to investigate how to apply the world-leading experience of knowledge testing to the development of an independent examination center in Plekhanov Russian University of Economics. In the article the authors consider the task of development of the information system inter-departmental (independent) examination (Certification) center of the University, which opens new opportunities for the use of the forms and methods of evaluation of educational achievements of students corresponding to the tendencies of modern information society development. The principal advantage of Prometric is much broader list of the vendors (more than 80) providing the examinations, however there is a number of tests which can be handed over only in VUE (such as Corel, Macromedia, Linux Professional Institute, etc.). Information system of IEC PRUE support monitoring of information base and examination processes from the side of divisions. In addition to statistical methods of data processing attraction the Data Mining methods are using. Information system IEC PRUE has developed analytical part with great variety of the following opportunities. The task has an innovative character in the field of higher education. The result is a knowledge base. This knowledge base can be considered as intellectual property of the University.
Ensemble Classifier System for Offline Ancient Tamil Character Recognition

Recognizing ancient Tamil characters enable archaeologists to reveal historical events in Chola period in 12 th century with huge efforts of the archeological experts.The future research in the field of archeology will have negative impact due to inefficiency of the manual procedure.Optical Character Recognition (OCR) functionality is used to recognize ancient Tamil Inscription in stones.OCR module of application i.e.Ensemble Learning is mainly focused in this paper.The collected inscriptions are preprocessed and segmented.During preprocessing the color images are converted to gray images and they are enhanced.The resultant images are applied for noise removal process using various filters.The output is applied for post processing steps to remove further noises includes opening, closing, erosion and dilation.Segmentation process is performed using bounding box and the features like orientation points & magnitude points are detected using SIFT algorithm.Characters are classified using Ensemble learning classifier and the classified characters are matched using Unicode.The exact Modern Tamil Character is mapped and their Recognition rate & Segmentation Rates are calculated and compared.
Building an e-learning recommender system using Association Rules techniques and R environment

To help the human mind in its process of selecting and filtering information, several recommendation systems have been developed in multiple application domains, such as e-commerce and e-tourism. Recommender systems are built to guide end users to easily find the most proper items on huge amount of data. In other words, this type of system tries to suggest items that best meet the preferences and needs of a user. It can be represented as a unit for predicting the behavior of a user by anticipating his next actions. In this article, we develop a courses recommender system dedicated to online learning environment. It aims to discover relationships between student’s courses activities using association rules method in order to help the student to choose the more appropriate learning materials. We also focus on the analysis of past historical data of the courses enrollments or log data. The article discusses particularly the frequent itemsets concept to determine the interesting rules in the transaction database. Then, we use the extracted rules to find the catalog of more suitable courses according to the learner’s behaviors and preferences. Next, we implement our system using the FP-growth algorithm and R programming language. Finally, the experimental results prove the effectiveness and reliability of the proposed system to increase the quality of student’s decision and orientate them during the learning process by providing most relevant pedagogical resources.
Improved Rerun Particle Swarm Optimization Algorithm with Harmony Search

One of the most crucial problem of the particle swarm optimization is that it can easily trap in local optima. There are some studies tried to resolve the drawback using the reset and mutation mechanism. Although it unraveled the problem well when the optimal position of benchmark function is at zeros in all dimensions. It cannot locate optima when the exact answer randomly shifted over the possible region. This study integrates Harmony Search (HS) to the rerun and reset mechanism to PSO. It performs well with selected benchmark functions, when the optimal position is randomly shifted in most of the test functions. Since it does not favor optimal point at zeroes when the optimal position is not shifted, the number of calls of the evaluation function is more than the other algorithms in some cases.
Quality of Service Aware Resource Allocation Using Hybrid Opposition-Based Learning-Artificial Bee Colony Algorithm

In the recent days the Cloud Computing is the top most challenging and upcoming topic and is more being used in corporate IT industry. The rapid growth of IT trade is strongly varying and is not sufficient enough to handle big data. In the recent trend is to transfer all types of data from desk top to huge data centers for computing. This model is quite different and services provided to the end user based on pay-as-you-use model to public and provide dynamic resource allocation for guaranteed services to the user. The proper allocation of resources is one of the major issues in cloud computing. The term resource management is the process of share out existing resources and helps to coordinate IT resources and application over the internet. The objective of the successful resource allocation is to minimizing the costs benefits for providers and achieving client happiness. One of the foremost issues in cloud is proper resource allocation. In this paper, hybrid OBL and ABC algorithm is used to find out reliable RA problems in cloud workflow. Usually RA is a troublesome optimization problem. In our work we aim to consider QoS requirements and issues. QOS parameter like execution time, response time, throughput, cost as taken in our algorithm and tries to allocate the resource in efficient manner. The algorithms were executed maximum 50 times with different VM and task. The proposed algorithm were compared with the existing PSO, GA and ABC and the Cloudsim simulation output prove that OABC algorithm performed improved against existing algorithms. The output shows better allocation to achieve QoS with customer satisfaction as well ensuring an optimum resources use.
GraphTQL: A visual query system for graph databases

End-users who are experts in an application domain need to retrieve data from databases in order to answer particular inquiries. Visual Query Systems (VQSs) facilitate this task; but, formulating complex queries is still challenging for end-users. In particular, VQSs for graph databases can be broadly classified into two groups: One group is based on data exploration and allow for direct manipulation of data graphs, but with limited query expressiveness. The other group, oriented to build visual sentences that represent the query, provide greater expressiveness, but they transfer complex language concepts and operations to the visual language. Thus, this research developed GraphTQL a visual query system that provides greater expressiveness and ease in the query formulation. GraphTQL uses a graph data model to depict the domain of interest at the conceptual level. A set of operators provides the means to transform schema and instance graphs, reducing them progressively to obtain the data of interest. The operators include implicit management of incomplete data; as well as clarification dialogues to guide users in order to express complex filter conditions and to select the paths which connect entities in the graph. The development of GraphTQL followed a user-centered design approach which focused on the medical domain. Thus, potential users’ contribution and feedback played a significant role in this work. A comparison with a graphical query interface for SPARQL showed better results for GraphTQL regarding the number of queries correctly formulated by users, the number of errors and the time spent for query formulation.
A Meta-model for Diverse Data Sources in Business Intelligence

With the manifestation and evolution of Internet, several new types of data have emerged (videos, images, audio files, documents…).These new types of data classed as unstructured are more and more used and exchanged between IT systems, therefore their exploitation in Business Intelligence (BI) systems will absolutely provide a gold mine of information that guarantee a better and rich decision-making.Unfortunately BI systems don't consider this sort of data and they are still limited to classical data sources: structured as Relational data source and semi-structured as XML files.Many research works separate the treatment and the design of a data warehouse that involves heterogeneous sources in order to avoid any problems of data integration and storage.However, the need for an approach that gathers diverse data sources still present.In this paper we appeal Model Driven Engineering (MDE) to propose a meta-model that assemble and describe all sort of structured, semi-structured and unstructured data sources such as relational, multidimensional, XML and NoSQL databases.Models conforming this meta-model will serve as an input for our BI process and for designing and modeling a data warehouse.
Sentiment and Stance Visualization of Textual Data for Social Media

Rapid progress in digital technologies has transformed the world in many ways during the past few decades, in particular, with the new means of communication such as social media. Social media plat ...
Syntax and Table Aware Parsing Based Naturalized Structured Query Language

A database is characterized as accumulation of data that is organized to access, manage, and update information effectively.All information is store in a database and there are numerous approaches to interface with the database to access our information.A client needs some specialized knowledge to extricate information from the database.They have to utilize Structured Query Language (SQL) for information definition, information manipulation, or information control.Although, the vast majority of the clients who need to separate information from a database are not technical experts.Hence, there is a tremendous communication gap between the database and its core client.With the advancement of Natural Language Processing (NLP), a client would now be able to converse with their database in their characteristic language without learning database language.The communication gap between the client and the database has begun to vanish with this stunning capacity.In this paper, the methodology addresses this issue by considering the structure of table and the syntax of SQL.The nature of the generated SQL query is fundamentally enhanced through finding out how to recreate content from column names, cells or SQL keywords; and enhancing the generation of WHERE clause by utilizing the column-cell relation.Analyses are directed on WikiSQL, a recently released dataset with the biggest query SQL sets.Our approach significantly improves the state-of-the-art precision, recall and F-measure from 76.5% to 82%.