Reviewerly/exHarmony huggingface dataset:
AN IMPROVED ACTION KEY FRAMES EXTRACTION ALGORITHM FOR COMPLEX COLOUR VIDEO SHOT SUMMARIZATION

Key frame extraction is one of the critical techniques in computer vision fields such as video search, video identification and video forgeries detection. The extracted key frames should be sufficient key frames that preserve main actions in a video with compact representation. The objective of this work is to improve our previous action key frames extraction algorithm (AKF) by adapting a threshold which selects action key frames as final key frames. The threshold adaptation was achieved by using the mean value, the standard deviation, and the L1-norm instead of the comparison of user summaries evaluation method to obtain a fully automatic video summarisation algorithm, and by eliminating the conditions in selecting the final key frames to reduce the complexity of the algorithm. We have validated our proposed Improved AKF on complex colour video shots instead of the simple grey level video shots. The Improved AKF algorithm was able to extract a compact number of action key frames by preventing redundant key frames, reduce processing complexity, and preserve sufficient information about the main actions in a video shot. We then evaluated the Improved AKF algorithm with the-state-of-the-art algorithms in terms of compression ratio using Paul videos and Shih-Tang dataset. The evaluation results showed that the Improved AKF algorithm achieved better compression ratio and retained sufficient information in the extracted action key frames under different testing video shots. Therefore, the improved AKF algorithm is a suitable technique for applications in computer vision fields such as passive object-based video authentication systems
Data-Driven Machine Learning Techniques for Self-Healing in Cellular Wireless Networks: Challenges and Solutions

For enabling automatic deployment and management of cellular networks, the concept of self-organizing network (SON) was introduced. SON capabilities can enhance network performance, improve service quality, and reduce operational and capital expenditure (OPEX/CAPEX). As an important component in SON, self-healing is defined as a paradigm where the faults of target networks are mitigated or recovered by automatically triggering a series of actions such as detection, diagnosis, and compensation. Data-driven machine learning has been recognized as a powerful tool to bring intelligence into networks and to realize self-healing. However, there are major challenges for practical applications of machine learning techniques for self-healing. In this article, we first classify these challenges into five categories: (1) data imbalance, (2) data insufficiency, (3) cost insensitivity, (4) non-real-time response, and (5) multisource data fusion. Then, we provide potential technical solutions to address these challenges. Furthermore, a case study of cost-sensitive fault detection with imbalanced data is provided to illustrate the feasibility and effectiveness of the suggested solutions.
FLAGSHIP UNIVERSITIES’ PERSONNEL POTENTIAL DEVELOPMENT MECHANISMS IN RUSSIA

The article reviews the research on flagship universities’ personnel potential development mechanisms. The purpose of this review is to classify the forms, methods, and tools for the human resources development in flagship universities, as well as to assess the effectiveness of mechanisms for the human resources development with the help of 2016–2018 monitoring data of the universities effectiveness. According to the results of the study, the flagship universities’ development programs have been designed to present such forms of human resources development as developing effective contract systems, involving young scientific and pedagogical workers, improving continuous education systems, forming a personnel reserve, maintaining highly qualified personnel potential, and increasing the universities’ attractiveness as places of career development. These forms are implemented through methods and tools that are both invariant and specific. As for the end of 2017, there is no noticeable increase in the share of flagship universities’ teachers younger than 40, the arrays of values of this indicator being poorly correlated with the arrays of values of other important indicators. The authors’ study does not claim to be a complete description of the whole group of potential reference universities, as it covers the winners of 2015 and 2017 competitions. However, the research makes it possible to generate the basic directions of personnel potential development mechanisms efficiency increase in flagship universities. The problem of improving the mechanisms of flagship universities’ human resources development is solved by identifying best practices in forming mechanisms for the human resources development in flagship universities and by approaching to assessing their effectiveness in the context of different groups of these universities. This makes the article original and brand-new. The practical significance of the study lies in the methodological potential of the proposed classifications of forms, methods and tools for the development of flagship universities’ human resources, these classifications to be used in the practice of regional universities without special status. The article attempts at generalizing approaches to the human resources development of the universities in this country and at distinguishing on this basis certain ways to improve the effectiveness of HR-management mechanisms. It may be of interest to researchers of university governance mechanisms in the context of university development programs.
Variance of Average Surprisal: A Better Predictor for Quality of Grammar from Unsupervised PCFG Induction

In unsupervised grammar induction, data likelihood is known to be only weakly correlated with parsing accuracy, especially at convergence after multiple runs.In order to find a better indicator for quality of induced grammars, this paper correlates several linguistically-and psycholinguisticallymotivated predictors to parsing accuracy on a large multilingual grammar induction evaluation data set.Results show that variance of average surprisal (VAS) better correlates with parsing accuracy than data likelihood, and that using VAS instead of data likelihood for model selection provides a significant accuracy boost.Further evidence shows VAS to be a better candidate than data likelihood for predicting word order typology classification.Analyses show that VAS seems to separate content words from function words in natural language grammars, and to better arrange words with different frequencies into separate classes that are more consistent with linguistic theory.
A Problem Meta-Data Library for Research in SAT

Experimental data and benchmarks play a crucial role in developing new algorithms and implementations of SAT solvers. Besides comparing and evaluating solvers, they provide the basis for all kinds of experiments, for setting up hypothesis and for testing them. Currently – even though some initiatives for setting up benchmark databases have been undertaken, and the SAT Competitions provide a “standardized” collection of instances – it is hard to assemble benchmark sets with prescribed properties. Moreover, the origin of SAT instances is often not clear, and benchmark collections might contain duplicates. In this paper we suggest an approach to store meta-data information about SAT instances, and present an implementation that is capable of collecting, assessing and distributing benchmark meta-data.
RRTxFM: Probabilistic Counting for Differentially Private Statistics

Data minimization has become a paradigm to address privacy concerns when collecting and storing personal data. In this paper we present two new approaches, RSTxFM and RRTxFM, to estimate the cardinality of a dataset while ensuring differential privacy. We argue that privacy-preserving cardinality estimators are able to realize strong privacy requirements. Both approaches are based on a probabilistic counting algorithm which has a logarithmic space complexity. We combine this with a randomization technique to provide differential privacy. In our analysis, we detail the privacy and utility guarantees and expose the impact of the various parameters. Moreover, we discuss workforce analytics as application area where strong privacy is paramount.
PERCEPTION OF THE UNIVERSITY ORGANIZATIONAL CULTURE BY ACADEMICS VS. STUDENTS

There are a lot of studies devoted to organizational culture of the academics and students at the universities. However, these studies refer either only to the former or only to latter. This provides no opportunity for comparing the perception of organizational culture by academics and students of the same university and, hence, for conclusions regarding needed or possible changes of this culture. This research is supposed to eliminate this shortcoming. The widely known method of OCAI has been used for the study. The study has covered academics and students of all academic departments of Tomsk Polytechnic University. The traditional OCAI questionnaire has been adapted to the university specificity, as well as to the level of students’ organizational culture perception. The study shows that organizational profiles of academics and students are very similar, but the academics demonstrate heavier accent on hierarchical culture. Therefore, the regular staff of the university possesses a strong culture, which is very likely to be imposed on the university’s variable – students. The research also reflects the trend to enhancing hierarchical culture at the university, this tendency seeming rather dangerous. The research has taken place in a Russian technical university. It seems to be reasonable to do such a study in other kinds of universities in order to obtain a comprehensive picture of organizational culture in Russian higher school. The article describes the first experience of combined studying the academics’ and students’ organizational culture. Its results might orient university administrators and academics to paying special attention to their own organizational culture, since this culture is translated to society via values fostered in university graduates during their student’s life.
SISTEM INFORMASI PENJUALAN PRODUK BAJU PADA TOKO INYIAK PADANG BERBASIS WEB

Rapid technological developments directly or indirectly affect the trading system. At present, only by accessing the internet, we can choose the desired product, know the details of the product, and can make transactions with anyone without being limited by time and distance. The sales system at INYIAK Padang Shop is still manual, where the delivery of product information to consumers cannot be accessed directly. So a web- based sales system is needed. The method used in the development of this website is SDLC with a prototype model including needs analysis, prototype making, prototype evaluation, system coding, system testing, system evaluation and system usage. In making this system using the Sublime Text 3 text editor, XAMPP server, MySQL database and other supporting software that can facilitate the creation of this system. With the existence of this online sales information system, it can improve service systems, processing sales data and make it easier for people to get product information. The website can also create a system that is more effective and efficient in supporting the activities of this company.
Condition Analysis and Forecasting in the Fashion Industry

The work considers the problem of demand for the clothing industry's goods.It shows how this problem is connected with the mathematical problem of the partition of the set.Investment decisions depend on a diagnosis based on forecasting demand in individual product groups.These groups are characterized by a number of features and even in the simplest situations (3 attributes) lead to computationally complex situations.In this situation, the recursive partitioning method can be used.This is a method related to the construction of classification trees (regression).These methods are widely used in natural, technical and economic sciences.The main direction of their applications is to support decision-making processes.The article shows how to support the construction of classification trees.The paper proposes a practical solution to the problem using the method of random partitions.The proposed method can be a complement to the recursive partitions method, or used in some situations instead.The submitted method is a practical proposal to avoid the problem of computational complexity.The numerical example shows how to replace a population of about 52 trillion by a sample of only 100.The applied method was justified by an example of a less numerous population, where the result could be verified empirically by reviewing all possibilities.Such verification is not practically possible in the case of 20 product profiles.Such a number generates a number of partitions amounting to almost 52 trillion.The article also presents the estimation of the calculation time.These results are useful from a practical point of view, although they are not optimal.
Multilingual Factor Analysis

In this work we approach the task of learning multilingual word representations in an offline manner by fitting a generative latent variable model to a multilingual dictionary.We model equivalent words in different languages as different views of the same word generated by a common latent variable representing their latent lexical meaning.We explore the task of alignment by querying the fitted model for multilingual embeddings achieving competitive results across a variety of tasks.The proposed model is robust to noise in the embedding space making it a suitable method for distributed representations learned from noisy corpora.
Exploration Trends and Developments 2019

The 2019 edition of ETD (Mineral Exploration Trends and Developments) tracks the recent developments in exploration technology around the world.ETD has been published since 1965, and in that time h...
Examining the Relationship between Academic Leaders’ Communication Skills and their Social Media Usage

This study attempts to investigate the relationship between academic leaders' communication skills and their social media usage.To obtain data, the investigator ensured that surveys were sent to each of the four universities in the four geographical areas of Saudi Arabia.This survey instrument was randomly distributed to academic leaders of four universities in Saudi Arabia, and I received 848 surveys.The research showed that the overall average of leadership communication skills was 4.36, while the overall average of the use of social media was 4.30.The analysis of data reveals a statistically significant correlation between the level of academic leaders' communication skills and the level of their use of social media.The data shown above indicate that there was not a significant difference between social media usage in the workplace and leaders' communication skills regarding gender; social media platform; time on social media each day; workplace, personal, or business use; or positives and negatives of use.
Webpages Classification with Phishing Content Using Naive Bayes Algorithm

Phishing attacks cause people to be scammed and cheated because of the impossibility to visually detect fraudulent websites. As is known, the attack occurs from emails sent to collect or update information supposedly from an entity, there are also cases of phone calls or instant messages. There is ignorance of such attacks by people in general, which means that the user is not alerted, which means that he is not attentive to the digital certificates present on the page that authenticate the content of the same. For this reason, the web pages designed have required tools that counteract and alert the user of the “phished” webpages, which commit the theft of money from the account from which information has been provided.
Measuring crime: behind the statistics

This well-composed book is not quite technically written statistics book. Nevertheless, it is an excellent source for learning on how to handle the massive data especially about the crime incidence...
Proof-Net as Graph, Taylor Expansion as Pullback

We introduce a new graphical representation for multiplicative and exponential linear logic proof-structures, based only on standard labelled oriented graphs and standard notions of graph theory. The inductive structure of boxes is handled by means of a box-tree. Our proof-structures are canonical and allows for an elegant definition of their Taylor expansion by means of pullbacks.
A Comparative Analysis of the Zernike Moments for Single Object Retrieval

Zernike Moments has been popularly used in many shape-based image retrieval studies due to its powerful shape representation. However its strength and weaknesses have not been clearly highlighted in the previous studies. Thus, its powerful shape representation could not be fully utilized. In this paper, a method to fully capture the shape representation properties of Zernike Moments is implemented and tested on a single object for binary and grey level images. The proposed method works by determining the boundary of the shape object and then resizing the object shape to the boundary of the image. Three case studies were made. Case 1 is the Zernike Moments implementation on the original shape object image. In Case 2, the centroid of the shape object image in Case 1 is relocated to the center of the image. In Case 3, the proposed method first detect the outer boundary of the shape object and then resizing the object to the boundary of the image. Experimental investigations were made by using two benchmark shape image datasets showed that the proposed method in Case 3 had demonstrated to provide the most superior image retrieval performances as compared to both the Case 1 and Case 2. As a conlusion, to fully capture the powerful shape representation properties of the Zernike moment, a shape object should be resized to the boundary of the image.
VOICE VERIFICATION USING I-VECTORS AND NEURAL NETWORKS WITH LIMITED TRAINING DATA

This study proposes an approach to voice identification based on neural networks (DNN) for i-Vector.Modern voice identification systems based on DNN use large amounts of labeled training data.Using the LRE i-Vector Machine Learning Challenge restricts access to readyto-use i-Vector for learning and testing the voice identification system.This poses unique challenges in developing DNN-based voice identification systems, since optimized external interfaces and network architectures can no longer be used.We propose to use the training i-Vectors to train the initial DNN to identify the voice.Next, we present a novel strategy for using this initial DNN to strip the language labels of the inappropriate set from the development data.The final DNN for voice identification is trained using the original training data and the estimated out-of-set language data.We show that augmenting the training set with out-of-set labels leads to a significant improvement in voice identification performance.In this paper, we studied the possibility of using neural networks for speech identification.In particular, standard approaches to speech recognition were considered, the concept of an artificial neuron as an object used in speech identification was defined.A speech recognition option using a neural network was investigated, and steps were presented to perform this task.Accuracy using neural networks with limited learning data and a higher i-vector dimension is superior to others with a score of 92.1%.From this study, we can conclude that the size of the UBM and the dimension of the i-vector affect the accuracy of voice identification based on the i-vector.
HMD-TMO: A Tone Mapping Operator for 360 $$^\circ $$ HDR Images Visualization for Head Mounted Displays

We propose a Tone Mapping Operator, denoted HMD-TMO, dedicated to the visualization of 360 $$^\circ $$ High Dynamic Range images on Head Mounted Displays. The few existing studies about this topic have shown that the existing Tone Mapping Operators for classic 2D images are not adapted to 360 $$^\circ $$ High Dynamic Range images. Consequently, several dedicated operators have been proposed. Instead of operating on the entire 360 $$^\circ $$ image, they only consider the part of the image currently viewed by the user. Tone mapping a part of the 360 $$^\circ $$ image is less challenging as it does not preserve the global luminance dynamic of the scene. To cope with this problem, we propose a novel tone mapping operator which takes advantage of both a view-dependant tone mapping that enhances the contrast, and a Tone Mapping Operator applied to the entire 360 $$^\circ $$ image that preserves global coherency. Furthermore, we present a subjective study to model lightness perception in a Head Mounted Display.
NALDO: From natural language definitions to OWL expressions

Domain ontologies are pivotal for Semantic Web applications. The richness of an ontology goes in hand with its usefulness and efficiency. Unfortunately, manually enriching an ontology is very time-consuming. In this paper, we propose to enrich an ontology automatically by obtaining logical expressions of concepts. We present NALDO, a novel approach that provides an OWL DL (Web Ontology Language Description Logics) expression of a concept from two inputs: (1) the natural language definition of the concept and (2) an ontology describing the domain of this concept. NALDO uses as much as possible entities provided by the domain ontology, however it can suggest, when needed, new entities. The expressiveness of expressions provided by NALDO covers value and cardinality restrictions, subsumption and equivalence. We evaluate our approach against the definitions and the corresponding ontologies of the BEAUFORD benchmark. Our results show that NALDO is able to perform the correct identification of formal entities with an F1-measure up to 0.79.
Parallel Strongly Connected Components Detection with Multi-partition on GPUs

The graph computing is often used to analyze complex relationships in the interconnected world, and the strongly connected components (SCC) detection in digraphs is a basic problem in graph computing. As graph size increases, many parallel algorithms based on GPUs have been proposed to detect SCC. The state-of-the-art parallel algorithms of SCC detection can accelerate on various graphs, but there is still space for improvement in: (1) Multiple traversals are time-consuming when processing real-world graphs; (2) Pivot selection is less accurate or time-consuming. We proposed an SCC detection method with multi-partition that optimizes the algorithm process and achieves high performance. Unlike existing parallel algorithms, we select a pivot and traverse it forward, and then select a vice pivot and traverse the pivot and the vice pivot backwards simultaneously. After updating the state of each vertex, we can get multiple partitions to parallelly detect SCC. At different phases of our approach, we use a vertex with the largest degree product or a random vertex as the pivot to balance selection accuracy and efficiency. We also implement weakly connected component (WCC) detection and 2-SCC to optimize our algorithm. And the vertices marked by the WCC partition are selected as the pivot to reduce unnecessary operations. We conducted experiments on the NVIDIA K80 with real-world and synthetic graphs. The results show that the proposed algorithm achieves an average detection acceleration of 8.8 $$\times $$ and 21 $$\times $$ when compared with well-known algorithms, such as Tarjan’s algorithm and Barnat’s algorithm.