Reviewerly/exHarmony huggingface dataset:
L 2 Normalized Data Clustering Through the Dirichlet Process Mixture Model of von Mises Distributions with Localized Feature Selection

In this chapter, we propose a probabilistic model based-approach for clustering L 2 normalized data. Our approach is based on the Dirichlet process mixture model of von Mises (VM) distributions. Since it assumes an infinite number of clusters (i.e., the mixture components), the Dirichlet process mixture model of VM distributions can also be considered as the infinite VM mixture model. Comparing with finite mixture model in which the number of mixture components have to be determined through extra efforts, the infinite mixture VM model is a nonparametric model such that the number of mixture components is assumed to be infinite initially and will be inferred automatically during the learning process. To improve clustering performance for high-dimensional data, a localized feature selection scheme is integrated into the infinite VM mixture model which can effectively detect irrelevant features based on the estimated feature saliencies. In order to learn the proposed infinite mixture model with localized feature selection, we develop an effective approach using variational inference that can estimate model parameters and feature saliencies with closed-form solutions. Our model-based clustering approach is validated through two challenging applications, namely topic novelty detection and unsupervised image categorization.
Map, Filter and Reduce

Python provides three functions that are widely used to implement functional programming style solutions in combination with collection container types. These functions are what are known as higher-order functions that take both a collection and a function that will be applied in various ways to that collection.
Sensor based Evolutionary Mission Planning

Robot mission planning deals with accomplishing a mission that requires the robot to visit a set of sites and carrying out specific tasks at each site, consisting of Boolean and temporal constraints. The Boolean constraints are like "Visit any one of the three coffee machines" and "Visit any two of the three instructors", while the temporal constraints typically ask the robot to carry some sub-missions in a sequential order and some others in parallel. Each mission can have a different number of sites and nature of constraints involved. Each mission is expressed in a generalized form using OR, AND, and THEN clauses. Further, the notion of sensors is added, wherein a resource for a mission may be unavailable and therefore the robot will have to plan an alternate (maybe longer) strategy, however the availability will only be known when the robot physically visits the mission site. The typical mechanisms to solve for such mission using formal languages and verification engines incur an exponential complexity. Therefore, this paper proposes an evolutionary algorithm to solve the problem, enabling the use of probabilistic optimality properties. However, calculating the expected path length with the prior probabilities of the sensor is also exponential complexity which causes the resulting algorithm to have an exponential complexity in terms of the number of sensors. A greedy heuristic cost function is devised that imitates human decision making, wherein the robot tries to solve the mission with the shortest length as well as maximize the probability of completion of the task. Further, each site is assumed to have an associated waiting time which is the expected time for which the robot will have to wait in the queue till the resource is available for use. Another greedy heuristic is used to align the sites in the crossover operation to approach a better solution faster. Experimental results confirm that the proposed solution performs well as compared to baseline optimistic and pessimistic approaches. The proposed algorithm is also tested on the Pioneer LX robot.
A Process for Fault-Driven Repair of Constraints Among Features

The variability of a Software Product Line is usually both described in the problem space (by using a variability model) and in the solution space (i.e., the system implementation). If the two spaces are not aligned, wrong decisions can be done regarding the system configuration. In this work, we consider the case in which the variability model is not aligned with the solution space, and we propose an approach to automatically repair (possibly) faulty constraints in variability models. The approach takes as input a variability model and a set of combinations of features that trigger conformance faults between the model and the real system, and produces the repaired set of constraints as output. The approach consists of three major phases. First, it generates a test suite and identifies the condition triggering the faults. Then, it modifies the constraints of the variability model according to the type of faults. Lastly, it uses a logic minimization method to simplify the modified constraints. We evaluate the process on variability models of 7 applications of various sizes. An empirical analysis on these models shows that our approach can effectively repair constraints among features in an automated way.
UPDATING SPIRITUAL TRADITION AT THE LEVELS OF OBJECTIVE AND SUBJECTIVE BEING

The article aims to analyze the ontological foundations of the spiritual tradition at the levels of its actualization to form a mechanism of its transfer from a potential to a factor state.In this context, the spiritual tradition is considered at the objective and subjective levels, i.e. at the national (social) and personal levels.As a result of the research based on ontological and epistemological approaches, at the objective level, the spiritual tradition is formed through the prism of forms of social consciousness and daily practice.At the subjective level, the spiritual tradition is formed through the inner qualities of the person: the acquired knowledge, moral and aesthetic ideals and spiritual qualities.It is concluded that on the basis of these levels it will be possible to form a mechanism of transformation of the spiritual tradition into a spiritual factor of society.The article is intended for students, postgraduates and university lecturers, humanitarian training centers teachers and upgrading courses trainers.
Efficient Transformation of Protein Sequence Databases to Columnar Index Schema

Mass spectrometry is used to sequence proteins and extract bio-markers of biological environments. These bio-markers can be used to diagnose thousands of diseases and optimize biological environments such as bio-gas plants. Indexing of the protein sequence data allows to streamline the experiments and speed up the analysis. In our work, we present a schema for distributed column-based database management systems using a column-oriented index to store sequence data. This leads to the problem, how to transform the protein sequence data from the standard format to the new schema. We analyze four different methods of transformation and evaluate those four different methods. The results show that our proposed extended radix tree has the best performance regarding memory consumption and calculation time. Hence, the radix tree is proved to be a suitable data structure for the transformation of protein sequences into the indexed schema.
Opinion Mining in Facebook Regional Discussion Groups

This paper presents the results a case study that apply opinion mining about health, security and education, using as source discussions in Facebook regional groups. The method used is quite different from other researches because it propose an approach to identify regional posts. Five different supervisioned learning algorithms was applied during the classification step. The results show that region's posts can be identified with this new approach.
Application of face recognition in large-scale event scenes-taking Tianjin National Games as an example

There are still many problems in the application of face recognition technology in specific environments. In order to improve the recognition accuracy of face recognition technology in large-scale event scenes, a face tracking algorithm is constructed based on improved ORB features in this paper. The algorithm uses the kernel function color histogram to model the target, which can perform well on target recognition during rotation or edge occlusion. The value of the face representation function is calculated by combining the texture feature and the color feature. By converting the function value into moment feature and combining it with the Bhattacharyya coefficient, the tracking performance of the target and the accurate detection of the target real scale improves. Taking the Tianjin National Games as an example, the intelligent security system based on face recognition was designed and implemented. The test results show that the proposed algorithm can reduce the face feature dimension effectively. When the control library is no more than 10,000 people, the false alarm rate is 1%, and the false negative rate is less than 5%. It provides a new recognition method for face recognition in the application of large-scale event scenes.
Image retrieval based on convolutional neural network and linear discriminate analysis

Abstract Image retrieval is a hot research topic in the field of computer vision image processing, and the user queries the image database for similar images and produces a list of recommendations. The paper firstly sets forth the research status of image retrieval, then the convolution neural network is briefly introduced. Due to the traditional image retrieval and recommendation system use manual extraction of image features is relatively cumbersome, and the retrieval accuracy is not high research status, the paper proposes an image retrieval method based on the improved convolutional neural network and linear discriminate analysis. Caltech256 and CIFAR-10 datasets were trained using the model in this paper, experimental, results show that the proposed method can effectively improve the performance of retrieval.
Facilitating Storytelling and Preservation of Mementos for the Elderly Through Tangible Interface

Mementos act as emotional companions that anchor stories. Current related research is not applicable for the elderly, as they focus more on digital mementos, while the elderly’s mementos are normally physical. Additionally, digital devices supporting capture and recording are generally inaccessible for them. The above makes elderly’s storytelling and preservation of mementos are still problematic. In response to this, a research prototype named Slots-Memento was designed and implemented. In the field study, six families were recruited. Semi-structured interviews and stories collected were transcribed, and thematic analysis was conducted, which form the foundation of insights on the research questions.
Existence, Uniqueness and Asymptotic Behaviour of Intensity-Based Measures Which Conform to a Generalized Weber’s Model of Perception

In this paper, we report on further progress on our study of “Weberized” metrics for image functions presented at ICIAR 2018. These metrics allow greater deviations at higher intensity values and lower deviations at lower intensity values in accordance with Weber’s model of perception. The purpose of this paper is to address some important mathematical details that were not considered in the ICIAR 2018 paper, e.g., (a) proving the existence and uniqueness of greyscale density functions $$\rho _a(y)$$ which conform to Weber’s model, (b) complete description of the dominant asymptotic behaviour of the density functions $$\rho _a(y)$$ for $$y \rightarrow \infty $$ and $$y \rightarrow 0^+$$ for the cases (i) $$0< a < 1$$ and (ii) $$a > 1$$ , (c) a method of computing the asymptotic expansion of $$\rho _a(y)$$ for $$y \rightarrow \infty $$ to any number of terms for the case $$0< a < 1$$ .
Digital competence through two courses at the Faculty of Arts: The Empirical Study

The empirical study analysed data from two courses on digital competence at the Faculty of Arts, Masaryk University.Data was obtained through multiple research tools -tests in the MU information system, selfevaluation questionnaire, Google Analytics and Smartlook.It deals with the topic of digital competences, which theoretically frames DigComp 2.1.It is a quantitative case study on a sample of 144 people.In our case study, we have identified the following research questions: 1) How do students evaluate themselves as part of their digital competencies?2) Is there a relationship between self-assessment and test results?3) Can competence domains or individual competencies be identified in which students are significantly better or worse?4) Can identify the relationship between user interactions with a website and test results and self-evaluation?The results of our research show a significant imbalance in how the students in digital competencies assessed: from 2.5 points out of 8 in programming to digital technology through cooperation with 4.7 points.At the same time, they suggest that students have significant problems with self-assessment as such.In terms of whole competence domains, students perceive themselves as the strongest in information and data literacy (4.5) and the weakest in problem-solving (3.4).
HMM Based Keyword Spotting System in Printed/Handwritten Arabic/Latin Documents with Identification Stage

In this paper, we propose a novel script independent approach for word spotting in printed and handwritten multi-script documents. Since each writing type and script needs to be processed using a specific spotting engine, the proposed system proceeds on two stages: First, a one-step identification method of the writing type and the script of the input image document. The identification system is based on HMM and does not need any additional resources (training, preprocessing, feature extraction) besides those used in the spotting step. Second, a specific word spotting method is used to detect any given keyword in document images. The proposed spotting system is lexicon-free, i.e., able to spot arbitrary keywords that are not required to be known at the training stage. The global system has been evaluated on mixed corpus of public databases such as KHATT, KAFD for Arabic script and ALTID, RIMES for Latin script. The experimental results on both document-level writing type and script type identification and keyword spotting confirm the effectiveness of the proposed scheme.
Comparison of Region Filling Algorithms Using Texture Synthesis Methodologies

This study aims to apply the region filling operations in an optimized way by using the methods of texture synthesis and carry out experimental analysis comparisons with the results obtained from those methods. The texture synthesis approaches employed in the study are "Exemplar Based Image In-painting", "Patch-Based Sampling", "Non-parametric Sampling" approaches respectively. The working methodology declared here follows four main stages. First, appropriate image data sets are selected. Then, the methodologies of texture synthesis to be utilized is determined. Afterwards, region filling operations are performed with the application of texture synthesis approaches on images. Finally, experimental results are obtained and evaluated.
Design of Security Monitoring System for Power Dispatching Control Cloud Platform

At present, cloud computing, as a strategic emerging industry in China, has greatly promoted the development of informatization. Although State Grid Corporation of China’s regulation cloud construction is steadily advancing, there is no effective security protection in the face of illegal access, virtual machine escaping, abnormal operation traceability, etc. In this paper, a security monitoring system based on the cloud platform is designed to achieve the comprehensive real-time monitoring of cloud platform security events of the hosts, virtual machines and cloud management software, timely identify of security threats with early warnings, and guarantee the operation of the cloud platform in security, through the investigating and in-depth research on the security monitoring technology of key components of the cloud platform.
Rule Modularity and Execution Control Enhancements for a Java-Based Rule Engine

Drools is a popular and well known Java-based production rule system. Production rule systems, like Drools, present challenges when applied to large and complex systems. This industrial paper evaluates the prior art with regards to rulebase modularity and execution control. It then looks at how that can be applied to Drools and what further enhancements can be made. Consideration is given to ensure the work is more palatable for developers in a Java-based rule environment. Venus and RuleWorks are identified as two differing \textit{state of the art} systems. Venus provides a decoupling of rules and data as well as a declarative guard based system, without life cycle callbacks. RuleWorks provides imperative subroutines with lifecycle callbacks.
Identifying Collaborative Aspects During Software Product Lines Scoping

The software product line engineering (SPLE) is a reuse strategy that allows software companies to save effort when they develop products with common features. There, the software product line scoping is one of most essential and complex activities because (1) a correct scope for the line has a high impact in its success and (2) it implies an interdisciplinary activity involving stakeholders with different visions about the products. In this paper, we report an exploratory study aimed to identify problems related to the collaborative work at scoping SPL in practice. We studied problems related to the participation and interaction of stakeholders in projects where groups of students must develop SPLs of serious video games for training employees in a company. Our study revealed problems related to low levels of communication, participants with different project objectives and stakeholders requesting different types of programs. Problems that are exacerbated by the staff rotation and inconveniences scheduling working sessions. In addition, our study revealed other problems regarding developers misunderstandings the artifacts related to the scope and their use in the further development activities. In this paper, we also present the first version of a collaborative method for SPL scoping, which seeks to combine scoping practices with collaborative patterns and thinkLets, with this combination we seek the effective participation of the required roles in this activity.
Image Enhancement of Finger Vein Patterns Based on the Guided Filter

To solve the problem that image enhancement of finger vein patterns based on traditional filtering methods fails to intuitively highlight the feature of edge protection, the experimental study model based on the guided filter is proposed. Through adding the comparison experiment between guided filter and bilateral filter, and doing the binary processing to the finger vein image after the process of the guided filtering and bilateral filtering, it can be found that some noises exist around the vein texture. In order to reduce or eliminate the interference, a traditional average filtering method is applied for denoising, which not only highlights the vein texture details but eliminates the interference in the post-processing, and at the same time, adjusting the filter parameters will cause a significant impact on the enhancement of finger vein image. A comparison experiment in false recognition rate between two filtering algorithms is conducted, and visual and numerical evaluations are performed on finger vein image after the process of enhancement and binarization; the result indicates that the guided filter has better edge protection feature and lower false recognition rate than the bilateral filter.
Examination paper text detection based on character discriminator

Examination paper text detection poses a challenging task of detecting text lines from examination paper with different sizes, low resolution, handwritten character and print character mixed, etc. In this paper, we propose a simple yet effective text detection method base on character discriminator (TDCD) to tackle the problem of examination paper text detection. We aim at detecting text lines of examination paper refer to high-quality character rectangles using the text line construction approach. To obtain the high-quality character rectangles, we firstly leverage canny filter and bounding rectangle tool for producing the bounding rectangles of examination paper connected regions. Then, the candidate rectangles generated by the merging of the bounding rectangles base on IOU. We finally propose a character discriminator (CD) to guide different candidate rectangles of a character to merge. Furthermore, we collect and annotate an examination paper image dataset EPDB-100. We conduct extensive experiments on EPDB-100. In particular, our TDCD achieves the F-measure score of 63.5%.
Profiling Dynamic Data Access Patterns with Bounded Overhead and Accuracy

One common characteristic of modern workloads such as cloud, big data, and machine learning is memory intensiveness. In detail, such workloads tend to have a huge working set and low locality. Especially, the size of working sets is rapidly growing so that cannot be fully accommodated by a DRAM based main memory. Worse yet, the cloud computing systems, which has been pervasive since few decades ago, are continuously reducing the size of DRAM per CPU and encouraging memory overcommitment. Consequently, efficient and effective out-of-core memory management is becoming more important. Though a number of memory management mechanisms for such situations have proposed, manual analysis and optimization are still required for optimal performance of each workload due to the wide variety of data access patterns. However, existing tools for memory access analysis are not appropriate to be used here because those are not designed for extraction of the dynamic data access pattern of modern workloads. When those tools are used for the purpose, those incur unacceptably high overheads for unnecessarily accurate analysis results. To mitigate this situation, we introduce a tool that is designed for the purpose. Basically, the tool employs a memory access tracking technique based on page table entry access bit, which incurs only minimal overhead. It also provides a technique for an effective tradeoff between profiling overheads and accuracy of the output by dynamically adjusting number of tracking regions. By adopting the technique, this tool can control the level of overheads and output accuracy in bounded range that user specified regardless of the size of target workloads. The overhead can be lowered even enough to be used for online target workloads while still providing useful quality of the extracted data access pattern. The main contributions of this paper are: 1) introduce of the data access patterns profiler tool designed for modern memory-intensive workloads, and 2) empirical memory access pattern analysis of various realistic workloads.
Causative purpose adjuncts in Estonian and Finnish: A non-linear approach

Abstract In this article, we examine the form and meaning of a subtype of causative adjuncts, purpose adjuncts , in Estonian and Finnish. The analysis is based on the micro-modular approach of Conceptual Semantics (Nikanne 1990, 2005, 2018; Pörn 2004; Paulsen 2011a, 2011b; Petrova 2011). It provides a non-linear approach to linguistic causation, alternative to the linear models. By the term ‘non-linear’ we mean the involvement of separate levels of conceptual structure that influence the causative reading, and a separation of causation from the temporal flow of the situation. We propose two causative operators for subordinate causative structures, BECAUSE OF and LEAD TO. According to their temporal structures, we separate two types of purpose adjuncts: reason-purpose adjuncts and avoidance adjuncts . The causative effect of purpose is in our analysis related to the notion of intentionality.