Reviewerly/exHarmony huggingface dataset:
Validating Few Contemporary Approaches in Image Segmentation – A Quantitative Approach

In this paper, we present an extensive study and quantitative evaluation of six segmentation techniques on images of Berkeley Segmentation Database. Image segmentation plays a vital role in many computer vision applications and benchmarking such algorithms may assist research community in present and future research efforts in the field of image segmentation. Color space models, Hybrid color space and wavelet, Gradient Magnitude Techniques, K – means, C-Means & Fuzzy C-Means (FCM) and Edison’s Mean – shift approaches are evaluated using at least six metrics with respect to ground-truth boundaries of entire images in BSD 300/500 dataset images. The results stated here gives useful insights to above mentioned approaches and its significance in aligning upcoming research avenues in image segmentation.
Template-Based Video Search Engine

The exponential increase in video-based information has made it challenging for users to search specific video from a huge database. In this paper, template-based video search engine is proposed to improve the retrieval efficiency and accuracy of search engines. To begin with, the system splits the video sequence into eight key frames and then the fused image is created. The visual features like color and texture are extracted from the fused image and stored as complete feature set in a database. Now, the query clip is selected from the query database and then the template image is selected from the fused query image. The template query image features are compared with stored feature database using various similarity measures. The relevant retrieval experiments show that template-based video search engine using wavelet-based feature extraction gives better result in terms of average precision and recall using Euclidean distance as a similarity measure.
Exploring data- and knowledge-driven methods for adaptive activity learning with dynamically available contexts

Various aspects of human activity recognition have been researched so far and a variety of methods have been used to address them. Most of this research assumed that the data sources used for the recognition task are static. In real environments, however, sensors can be added or can fail and be replaced by different types of sensors. It is therefore important to create an activity recognition model that is able to leverage dynamically available sensors. To approach this problem, we propose methods for activity learning and activity recognition adaptation in environments with dynamic sensor deployments. In our previous work, we proposed sensor and activity context models to address sensor heterogeneity and also a learning-to-rank method for activity learning and its adaptation based on the proposed context models. However, most of the existing solutions, including our previous work, require labelled data for training. To tackle this problem and further improve the recognition accuracy, in this paper, we propose a knowledge-based method for activity recognition and activity model adaptation with dynamically available contexts in an unsupervised manner. We also propose a semi-supervised data selection method for activity model adaptation, so the activity model can be adapted without labelled data. We use comprehensive datasets to demonstrate effectiveness of the proposed methods, and show their advantage over the conventional machine learning algorithms in terms of recognition accuracy.
Conceptualising an Online Resource Platform for Business Students at Université des Mascareignes

Since the creation of the Université des Mascareignes in 2012, there has been an urge to develop online learning resources that students might need for their instruction and research during their university years. So far, resources abound in the form of textbooks, CDs and online resources like EBSCO along with an online library facility from Université de Limoges (Unilim, France). Business students in human resource management, banking and related disciplines need up-to-date resource material for their immediate use and application to their studies. This research purports the need to have a well-structured learning platform targeted specifically to student needs. By creating an online and intelligent platform, students might have access to the appropriate research undertaken by former students, resource material from local newspapers and journals and also reading material to supplement their work. Such creation of an online resource platform aims at improving the access to high-quality premium research material but also help in enhancing the quality of research. Through a demanding process of requiring updating and maintenance of resource material, the database will be a valuable tool for students and alumni alike.
Analysis of Feature Ranking Methods on X-Ray Images

Mycobacterium causes an infectious disease called tuberculosis which can be diagnosed by its various symptoms like fever, cough, etc. Tuberculosis can also be analyzed by understanding the chest X-ray of the patient which is revealed by an expert physician. The chest X-ray image contains texture and shape-based features which are extracted from X-ray image using image processing concepts. This paper presents implementation of various feature weighting methods on the extracted features of X-ray images. These feature weighting methods are analyzed using linear regression model and Linear Discriminant Analysis (LDA) model. The performance of various feature weighting methods is compared and found that the accuracy of weights by PCA using linear regression model is 98.75% which is better than other methods.
Pupil Size as Input Data to Distinguish Comprehension State in Auditory Word Association Task Using Machine Learning

In communication, it is very important for a speaker to understand the comprehension state of the speaking partner. In this study, the “comprehension state” is defined as whether or not the speaker’s message is clearly understood, which is difficult to accurately evaluate. This study aims to evaluate the comprehension state from the pupil size using machine learning. We conduct a word association task using elements that are similar to those used in conversations and measure the pupil size; this pupil size data is used as input data for machine learning. The results show that high accuracy is achieved by learning the low frequency components of the pupil size.
Pedagogical technologies in modern higher education: possibility and necessity

In the article, from the standpoint of the system analysis of the issue, the characteristics of the main modern pedagogical technologies used in higher educational institutions of Russia are given, the features, principles, forms, methods and means of educational activity are derived. Ideally, new developments should take place gradually and naturally, excluding the crisis breaking of existing educational theories.
Self-dual and complementary dual abelian codes over Galois rings

Self-dual and complementary dual cyclic/abelian codes over finite fields form important classes of linear codes that have been extensively studied due to their rich algebraic structures and wide applications. In this paper, abelian codes over Galois rings are studied in terms of the ideals in the group ring ${ GR}(p^r,s)[G]$, where $G$ is a finite abelian group and ${ GR}(p^r,s)$ is a Galois ring. Characterizations of self-dual abelian codes have been given together with necessary and sufficient conditions for the existence of a self-dual abelian code in ${ GR}(p^r,s)[G]$. A general formula for the number of such self-dual codes is established. In the case where $\gcd(|G|,p)=1$, the number of self-dual abelian codes in ${ GR}(p^r,s)[G]$ is completely and explicitly determined. Applying known results on cyclic codes of length $p^a$ over ${ GR}(p^2,s)$, an explicit formula for the number of self-dual abelian codes in ${ GR}(p^2,s)[G]$ are given, where the Sylow $p$-subgroup of $G$ is cyclic. Subsequently, the characterization and enumeration of complementary dual abelian codes in ${ GR}(p^r,s)[G]$ are established. The analogous results for self-dual and complementary dual cyclic codes over Galois rings are therefore obtained as corollaries.
Semantic relation classification through low-dimensional distributed representations of partial word sequences

By virtue of recent developments in machine learning techniques, higher-level information can now to be extracted from massive data. In this paper, we focus on extracting multiple semantic relations, using light-weight processing through the efficient low-dimensional expression of substrings in text data. We propose an approach to build features for relational classification consisted of only the low-dimensional vectors representing substrings between words called substring vectors [1]. In addition, we investigate the relationship between the numbers of dimensions and the obtained accuracies when nonlinear classifiers are applied. The experimental results show that, with simple features and small computational cost, our approach using relatively low-dimensional representations achieves a sufficiently high accuracy that is better than most existing approaches.
Study on the Theory and Practice of Data Visualization

Data visualization is a science and technology research on data visual form, which is originated in the 50’s of the twentieth Century. It went from scientific visualization, information visualization to data visualization. Based on computer graphics and pictures data visualization display and reveal the main information contained in data. Data visualization contains data acquisition, data analysis, data processing and data modeling. Based on psychology the motivation of data visualization is to expand the scope of visual perception which is easier to get information than other biological perceptions while the effect is better. Modern science and technology greatly extends the human sensory “arms”. Visual perception has very long “arm” and often “robs” other perceptions’ “business”. This paper introduces the basic concept and main content of data visualization, reveals the essence of data visualization. Through the “Agricultural planning visualization” case, the technology process of data visualization applied in production has been established, which can be described as 4 steps operated by order. The first step is to determine and decompose a target. The second step is data acquisition and processing. The third step is models designing and expression. The last step is video production.
Multi-Object Detection Using Modified GMM-Based Background Subtraction Technique

Detection of objects is the most important and challenging task in video surveillance system in order to track the object and to determine meaningful and suspicious activities in outdoor environment. In this paper, we have implemented novel approach as modified Gaussian mixture model (GMM) based object detection technique. The object detection performance is improved compared to original GMM by adaptively tuning its parameters to deal with the dynamic changes that occurred in the scene in outdoor environment. Proposed adaptive tuning approach significantly reduces the overload experimentations and minimizes the errors that occurred in empirical tuning traditional GMM technique. The performance of the proposed system is evaluated using open source database consisting of seven video sequences of critical background condition.
Design and Development of Image Retrieval in Documents Using Journal Logo Matching

This research focuses on the detailed study of document image retrieval form World Wide Web using the existing techniques, text and image information which are extracted in the database. Retrieval based on the user’s given keywords of journal papers. It fetches the information from the extracted folder and analyzing with logo matching technique using efficient SURF (Speeded-Up Robust Features) method. Precision, recall, f-measure, time, and memory are calculated to compute the accuracy of the given input. Logos are clustered using image descriptor methods using shape, edge, and color techniques. This paper proposes a new technique for logo matching in journal database depends upon the documents.
Multimedia Data Security With Recent Trends and Technologies

This chapter throws light on major multimedia data security techniques. It has become so essential in today's society which uses multimedia data in almost all walks of life. There are a lot of multimedia data transactions carried out every day. Thus, to ensure data security the techniques, steganography and watermarking are frequently used. In steganography and watermarking techniques, the data to be hidden is encrypted and fed into a transparent layer like documents, images, and it is decrypted at the receiving end by the recipient. Generally, steganographic communications are one-to-one while watermarking is one-to-many. Besides these techniques, certain other techniques are also used in the application for providing security to multimedia data. From this chapter, a detailed content about steganography and watermarking can be obtained.
Spenser’s Spell: Archaism and Historical Stylometrics

In the present essay, on Spenser’s orthography, we address the question of whether the orthography of Spenser’s texts, in early modern editions, is salient and therefore worth preserving in modern editions. Even when we face the bibliographic fact that Spenser’s texts seem not to have been regarded as so valuably idiosyncratic that early modern printers preserved them from adjustment, we may still seek to know whether the first editions of Spenser’s works are demonstrably and articulably idiosyncratic, whether orthographically, lexically, inflectionally, or syntactically. This essay formulates a method for statistically probing Spenser’s orthographic profile against what we demonstrate to be the variant, but coherent background of early printed English. We build a model of early modern orthographic change based on letter n-grams extracted from the 60,000 texts in the EEBO-TCP corpus. The n-grams yield some 30,000 features; we concentrate on a subset of the 200 most variant features, reducing the dimensionality of the data by means of principal component analysis (PCA). Situating Spenser’s texts against contemporary and near-contemporary texts, we demonstrate that, in general, Spenser’s corpus is not orthographically distinctive and propose that the impression of the linguistic distinctiveness of Spenser’s poetry is concentrated elsewhere. We conclude both with proposals for new tests that might enable us to isolate that distinctiveness and with a brief assessment of appropriate editorial responses to our investigations.
Performance Analysis of Image Enhancement Techniques for Mammogram Images

Mammography is a technique which uses X-rays to take mammographic images of the breast, but identifying abnormalities from a mammogram is a challenging task. Many Computer-Aided Diagnosis (CAD) systems are developed to aid the classification of mammograms, as they search in digitized mammographic images for any abnormalities like masses, microcalcification which is difficult to identify especially in dense breasts. The first step in designing a CAD system is preprocessing. It is the process of improving the quality of the image. This paper focuses on the techniques involved in preprocessing the mammogram images to improve its quality for early diagnosis. Preprocessing involves filtering the image, applying image enhancement techniques like Histogram Equalization (HE), Adaptive Histogram Equalization (AHE), Contrast-Limited Adaptive Histogram Equalization (CLAHE), Contrast Stretching, and Bit-plane slicing; filtering techniques like mean, median, Gaussian and Wiener filters are also applied to the mammogram images. The performance of these image enhancement techniques are evaluated using quality metrics, namely Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Contrast-to-Noise Ratio.
Curvelet Based Thinning Algorithm

Efficiency of a recognition algorithm depends upon availability of noiseless and classes of images with unique and differentiable features. Thinning plays an important part in building a recognition system for various types of images. It is mainly used in handwritten character recognition systems and Biometric systems. In Handwritten character recognition systems where there exist large variations within same class of images, thinning plays a vital role. Here curvelet based thinning algorithm is proposed which produces better results compared to other thinning algorithms. The capability of curvelet transform in estimation of directional features added to the strength of proposed work. Curvelet transform is combined with watershed algorithm, binarization and skeletonization to produce the final thinned output. The efficiency of the proposed work has been estimated in this work through detailed experimental analysis and comparisons with 7 efficient thinning algorithms. The measures used for comparison in this works are number of foreground pixels, PSNR (peak signal to noise ratio), MSE (mean square error), RMSE (root mean square error), reduction rate and thinness ratio.
Comparative Analysis of Our Association Rules Based Approach and a Genetic Approach for OLAP Partitioning

OLAP databases remain the first choice of enterprises to store and analyze huge amount of data. Thereby, to further enhance query performances and minimize the maintenance cost, many techniques exist, among which data partitioning is considered as an efficient technique to achieve this purpose. Although most of business intelligence tools support this feature, defining an appropriate partitioning strategy remains a big challenge. Hence, many approaches have been proposed in the literature. Nevertheless, most of them have been evaluated only in relational model. Therefore, we propose in this paper, a comparative study between our partitioning approach based on the association rules algorithm and a genetic based one. The study aims to compare the results of the aforementioned approaches in case of OLAP partitioning.
Uncertainties Revisited

In this chapter, the authors use actor-network theory (ANT) to explore the relations between uncertainties in co-design processes and the quality of participation. To do so, the authors investigate Latour's discussion uncertainties in relation to social processes: the nature of actors, actions, objects, facts/matters of concern, and the study of the social. To engage with the discussion on uncertainties in co-design and, more specific in infrastructuring, this chapter clusters the diversity of articulations of the role and place of uncertainty in co-design into four uncertainty models: (1) the neoliberal, (2) the management, (3) the disruptive, and (4) the open uncertainty model. To deepen the reflections on the latter, the authors evaluate the relations between the role and place of uncertainty in two infrastructuring processes in the domain of healthcare and the quality of these processes. In the final reflections, the authors elaborate on how ANT supported in developing a “lens” to assess how uncertainties hinder or contribute to the quality of participation.
Evaluation Theory for Characteristics of Cloud Identity Trust Framework

Trust management is a prominent area of security in cloud computing because insufficient trust management hinders cloud growth. Trust management systems can help cloud users to make the best decision regarding the security, privacy, Quality of Protection (QoP), and Quality of Service (QoS). A Trust model acts as a security strength evaluator and ranking service for the cloud and cloud identity applications and services. It might be used as a benchmark to setup the cloud identity service security and to find the inadequacies and enhancements in cloud infrastructure. This chapter addresses the concerns of evaluating cloud trust management systems, data gathering, and synthesis of theory and data. The conclusion is that the relationship between cloud identity providers and Cloud identity users can greatly benefit from the evaluation and critical review of current trust models.
bi-Byte correcting non-binary perfect codes

Etzion [T. Etzion, Perfect byte correcting codes, IEEE Trans. Inform. Theory 44 (1998) 3140–3146.] has classified byte error correcting binary codes into five different categories with respect to the size of the byte. If a code is partitioned into [Formula: see text] equal byte of size say [Formula: see text] then [Formula: see text] or the size of bytes is [Formula: see text]. Alternatively if bytes are of different size say, [Formula: see text] then [Formula: see text]. The result was further modified by Tyagi and Sethi [V. Tyagi and A. Sethi, [Formula: see text]-Byte correcting perfect codes, Asian-Eur. J. Math. 7(1) (2014) 1–8.] and the classification of bytes was given with respect to size of the byte as well as length of the burst. We call such codes as [Formula: see text]-byte correcting perfect codes. Our aim in this paper is to find the possibilities for the existence of [Formula: see text]-byte correcting non-binary perfect codes.
A Semantic Management Method of Simulation Models in GNSS Distributed Simulation Environment

In GNSS (Global Navigation Satellite System) Distributed Simulation Environment (GDSE), the simulation task could be designed with the sharing models on the Internet. However, too much information and relation of model need to be managed in GDSE. Especially if there is a large quantity of sharing models, the model retrieval would be an extremely complex project. For meeting management demand of GDSE and improving the model retrieval efficiency, the characteristics of service simulation model are analysed firstly. A semantic management method of simulation model is proposed, and a model management architecture is designed. Compared with traditional retrieval way, it takes less retrieval time and has a higher accuracy result. The simulation results show that retrieval in the semantic management module has a good ability on understanding user needs, and helps user obtain appropriate model rapidly. It improves the efficiency of simulation tasks design.
GPU Based Denoising Filter for Knee MRI

MRI is a popularly used technique for diagnosing muscle and skeletal disorders, especially of the knee. For accuracy in diagnosis, the rician noisy knee image needs to be filtered using efficient denoising algorithm. In recent years, the spatial neighborhood bilateral filter is being explored by researchers for its capacity to retain edges and tissue structures. It is noted that increase in image resolution slows down performance of the bilateral filter effectively discouraging its use. The research work proposes a cost-effective accelerated solution to the problem by implementing CUDA-based bilateral filter as applied to T2-weighted sagittal knee MRI slice. The work suggests use of GPU shared memory for optimized implementation and better speedup. The speedup achieved for 3.96 Mpixel knee MR image is 114.27 times more than that of its CPU counterpart. The results indicate average occupancy of 90.15% for image size of 6302 pixels, indicating effective parallelization. Also, over varying rician noise levels, the average PSNR achieved is 21.83455 dB indicating good filter performance.
Post-Quantum Security of IGE Mode Encryption in Telegram

IGE mode used in Telegram's customized protocol has not been fully investigated in terms of post-quantum security. In this letter, we show that IGE mode is IND-qCPA insecure by Simon's algorithm, assuming that the underlying block cipher is a standard-secure pseudorandom function (sPRF). Under a stronger assumption that the block cipher is a quantum-secure pseudorandom function (qPRF), IND-qCPA security of IGE mode is proved using one-way to hiding lemma.