Miracle-dz/UniFAR-FTUs huggingface dataset:
LOGS ANALYSIS TO SEARCH FOR ANOMALIES IN THE FUNCTIONING OF LARGE TECHNOLOGY PLATFORMS

Today, with the widespread use of machine learning methods in various fields of human activity, the detection of rare events still remains one the most challenging tasks. This is due to the fact that there is very little information to learn computers to detect deviations from normal operation, although it has to deal with the processing of very large amounts of data that characterize the ongoing processes. This occurs, for example, in high energy physics, when searching for and studying new particles. The similar situation occurs when detecting pre-anomalous situations in the complex high-tech equipment operation. Logs are the only source of information to detect the processes running on such equipment, therefore many IT companies use them to analyze the functioning of their software and hardware technologies. This allows viewing the logs starting from very beginning to the point of failure completion, consistently figuring out the possible causes of the incident. In most companies, this process is not automated, because there is no single established approach to analyze logs of different configurations of stored metric values and different filling intensities. In addition, historical logs are not used to predict the sequence of events that lead to anomalies in the operation of any software technologies. The present article deals with the problem of detecting states and predicting the nearest behavior of large technological platforms by directional analysis of their logs. Usually, logs of large technology platforms represent data sets of very high dimensionality that does not allow modern algorithms in the allowable time limits to draw the necessary conclusions about the behavior of platforms and form sequence of control actions, if necessary. To solve this problem, the article compares the effectiveness of existing algorithms, traditionally used unsupervised learning, because the available data for learning are too small, as well as algorithms working with big data. Pilot implementations of all algorithms involved in solving the problem, performed in Python programming language, have been studied in a single environment. Based on their comparison, the most efficient algorithm was chosen, when recognizing different types of events based on real data. The solution of the chosen algorithm was implemented using Apache Spark framework. Additional investigation has shown that the selected algorithm can work in real time mode.
SUPER RESOLUTION RECONSTRUCTION OF IMAGE GRADIENT PROFILE SHARPNESS

-Single Image super resolution is an active and classic image processing problem, which aims to reconstruct a high resolution (HR) single image from a low resolution input image. Due to the several use of profile dictionary under-determined nature of this type of problem, an effective image prior is necessary to make the problem solvable, and to improve the quality of reconstruct super resoluted image. In this paper image super resolution algorithm is proposed based on gradient profile sharpness. Gradient Profile Sharpness (GPS) is an edge sharpness matrix which is extracted from two gradient description models, i.e. a Gaussian mixture model for the description of different kind of gradient profile. The proposed approach will generate superior HR image with better visual quality, lower reconstruction error than the input image. To improve the HR image pixel quality, we will be use some filters. And compare the PSNR values to the input image.
Automated Custom Named Entity Recognition and Disambiguation

: - Named Entity Recognition (NER) and Disambiguation are sub-tasks in Natural Language Processing (NLP) that seek to identify and classify named entities in the text into their designated categories. With recent advancements in Deep Learning it is possible to use attention mechanisms and recurrent networks in order to produce reliable NER predictions. The use of NER ranges from profanity detection to extracting meta-data from documents. However, the greatest shortcoming of the classical NER models is the limited number of predefined classes that are set in the task
AUTHORITY AND TRANSFERENCE: NOTES ON THE WHITE CASTLE, BY O. PAMUK

This is a bibliographical research, in essay format, that comments on the book of O. Pamuk O castelo branco. From it, it was sought to clarify the historical, cultural and epistemic conditions of the accomplishment of the analytic discourse, questioning the use of authority in analytical technique in comparison with other modalities of social ties. The categories of analysis adopted are Koyré's contributions to the construction of the modern scientific method, Lacan's elaborations on the subject of science, his discourse’s theory and the concepts of transference, uncanny and narcissism of small differences. It is emphasized that the Pamuk text has as its center the problematization of the feeling of the uncanny between two look-alikes in a relation strongly marked by the cultural distance, the rivalry and the admiration. It is then discussed the participation of the subject of the unconscious in the act of knowing and the influence of narcissism on the production of obstacles in the field of knowledge. It is emphasized that, by failing to intervene throughits own signifiers and Ideal of Self, the psychoanalyst evidences the relation of the subject with its master-signifiers and its object cause of desire. Psychoanalysis thus demonstrates that the social injunction to place oneself in the place of the other is crossed by the contradictions of the narcissism of small differences. In the end, it is questioned through the feeling of the uncanny as a motive of segregation and violence the manifestations of the other in me
Predicting the Symptoms of Cardio Vascular Disease using Machine Learning Technique

Heart disease is the top most reason for mortality in India. Cardio Vascular Disease is a type of heart disease and it refers to narrowed or blocked blood artery vessels that affect the flow of blood to and from the heart and rest of the parts of the human body. Globally 31% of the people died due to Cardio Vascular Disease per year. Blood pressure, diabetes, abnormal cholesterol increase, diabetes, hypertension are the risk factors for the Cardio Vascular Disease. This paper used a machine learning approach with C4.5 classifier to predict the reason for the Cardio Vascular Disease. The data used for this research was collected from private hospitals located in different districts of Tamil Nadu. The parameters which cause Cardio Vascular Disease are consulted with doctors and a dataset was formed with 18 parameters. Decision tree was obtained from the dataset using C4.5 algorithm and the reason for the cause of Cardio Vascular Disease was generated as rules from the Decision Tree. This extracted knowledge is used to predict the reason for Cardio Vascular Disease and prevents deaths due to heart disease
Effective Implementation of Pre-Processing Techniques in Machine Learning for Autism Spectrum Disorder

Autism Spectrum disorder (ASD) is a neurobiological developmental disorder is symbolize by means of the impairment of social interaction, stereotypic behaviours, and communiqué lack. Early deduction of ASD will enhance the fine of lifestyles of the affected person. The objective of the paper is to focus on the application of various Machine Learning strategies applied for the autism dataset for diagnosing ASD. In this study, the effective pre-processing techniques One-hot encoding, Splitting and Scaling are used to standardize the dataset and the Principal Component Analysis (PCA) evaluator method is applied for the best feature selection. This technique is investigated with various Machine learning techniques like Random Forest, SVM, Logistic Regression, KNN, Naive Bayes. Comparatively, the effective Pre-Processing technique with Random Forest model shows the better accuracy of 92% in diagnosing ASD. When with other metrics such as accuracy, precision, recall, F1-score, ROC, error rate.
Moth-Dolphin Optimization Algorithm: A Nature Inspired Technique

This paper proposes a moth-dolphin based nature inspired optimization algorithm. This algorithm embeds the communication behavior of dolphin to Moth. The Moth route towards moon through the moon light in an optimized manner. But each moth doesn’t follow optimized path to travel towards the moon. The communication among the moth enables them to follow the best path towards moon. Thus, moth-dolphin algorithm uses the communication between moths to enhance the performance. The Moth–dolphin is an optimization algorithm which uses the exploration as well as exploitation search to achieve the global optima. The performance comparison of proposed algorithm with the moth-flame optimization algorithm, modified particle swarm optimization algorithm, differential evaluation and non-dominated sorting genetic algorithm over unimodal as well as multimodal functions ensures better results
CATEGORIZATION OF TEXT WITH RNN, CNN AND HAN MODELS

In this paper, few distinctive machine learning algorithm strategies recurrent neural network (RNN), convolutional neural network (CNN) and hierarchical attention network has been used to classify the text and performances has been evaluated. Classification of text was executed on datasets having Frennch, Italian, Germman, and Englishh languages. The execution is entirely based on the Kera’s. It characterizes the time taken by each machine learning algorithm RNN, CNN, HAN for processing the text data and also, it epitomizes the training accuracy and validation accuracy of each machine algorithm on the dataset1, dataset2, dataset3 respectively.
Study of the Aging of a Concrete Reinforced by Alkali Resistant Glass Fiber in the Wet Environment

1 Laboratory of Research in Civil Engineering (LRCE), University of Biskra, BP 145 RP, Biskra 07000, Algeria 2 Department of Civil Engineering, University of Biskra, BP 145 RP, Biskra 07000, Algeria 3 Laboratory of Civil Engineering and Environment (LCEE), University of Jijel, Jijel 18000, Algeria 4 Laboratory of Physics of Thin Films and Applications, University of Biskra, BP 145 RP, Biskra 07000, Algeria
Reconstructing biogeographic history using phylogenetic-tree analysis of community structure

The dispersal of a community in a territory can be reconstructed using various more or less specific methods of data analysis. When the data are in the form of presence/ absence of species for the various units of territory, or in the form of frequencies of occurrence, they are analogous to the data used for reconstructing evolutionary trees. Furthermore, when one is looking for a treelike structure of dispersal, issued from a common trunk, the tree- reconstruction methods may represent an appropriate analytical tool. Four tree-reconstruction methods are found to be appropriate in their hypotheses. They lead to satisfactory trees when applied to a data set describing the geographic distribution of the stenohaline freshwater fishes of the Quebec peninsula, previously divided into 21 regions. For binary data, the method of Camin and Sokal and the polymorphism parsimony method were used with success, while the continuous character data maximum-likelihood method and the least-squares method of Cavalli- Sforza and Edwards were applied to frequency-of-occurrence data. When compared to a method of pairwise comparison of adjacent regions, it is shown that the tree-reconstruction methods provide the user with information that is not found otherwise. I also show that external infor- mation (i.e., geographic adjacencies, and the degree of asymmetry of the faunal vectors of regions connected by the tree), available in biogeographic data but not taken into account by the tree-reconstruction algorithms, must be considered to render the results meaningful. (Bio- geography; cladistic methods; freshwater fishes; methods of phylogenetic inference; postglacial dispersal; tree-reconstruction methods.) Biogeographic reconstruction of the dis- persal of a community through a territory can be studied by several methods, whose interest and complexity grow as more as-
Observations on the Captive Biology of the Southern Stingray

Abstract Complete life history information is essential for effective population management, but little is known about the demographic characteristics of the southern stingray Dasyatis americana. We describe life history characteristics of southern stingrays maintained in captivity from birth to 13 years. Size (disc width [DW]) at maturity ranged from 48 to 52 cm in males and from 75 to 80 cm in females; age at maturity was 3–4 years in males and 5–6 years in females. Individual growth was described using three models: the Gompertz function, von Bertalanffy growth function (VBGF), and logistic function. Analyses of DW at age and weight at age indicated that females were significantly larger at birth and reached larger maximum sizes than males. Because of the ability of the VBGF to fit the observed growth and that of previously reported maximum sizes, we favor its use to describe individual growth dynamics for this species. Using this model, we estimated that for males (n = 20), the asymptotic DW (DW∞) was.
GENETICALLY OPTIMIZED PRECODERS FOR HIGHER TRANSMIT DIVERSITY IN SINGLE USER MIMO

Wireless communications depends on multiple-input-multiple-output (MIMO) techniques for high data rates. Feedback of channel information can be used in pre-coding to use the strongest channel mode and improve MIMO performance. This paper proposes genetic algorithm to optimize codebook for Alamouti and OSTBC pre-coding technique to enhance the Performance of MIMO system. The Simulation and Result shows that proposed optimized codebook provides improved bit error rate than the conventional rotating codebooks
An investigation into the Teaching and Learning Process Towards Enhancing Learning: A Case Study of Mechanical and Production Engineering Department, Kyambogo University

This study was carried out to determine ways of improving the delivery of training in computing for mechanical engineers in the Department of Mechanical and Production Engineering, Kyambogo University, Uganda. The study employed the “Work Process Analysis tool to analyze the prevailing situation in the delivery of instruction, and the “Future Workshop procedures to identify the gaps in delivery of training and the most appropriate strategies to address the gaps. Evaluation of implemented strategies was carried out. The key stakeholders that participated in the study included students, the head of department, the department administrator, examinations coordinator, teaching staff and technicians. The Work Process Analysis was carried out by means of focused group discussion with the aim of identifying gaps in the teaching/learning process. It was found that more theory was being covered and less of practical work and inadequate relevance in relation to the real work environment. The most appropriate intervention strategies identified were; allocation of more time for practical work, relating instruction to the real work environment, group work, demonstrations, integration of information communication technology (ICT) in the teaching process, such as video tutorials for repetitive demonstrations. Implementation of the identified strategies was carried out. Evaluation of the implemented interventions was carried out. Additional practical time and group discussion were rated highest and ICT integration in the teaching/learning process was rated lowest. Group discussion has merits of occurring in a social context with free interaction, collaboration and feed back in the group which factors maximize the learners’ ability to construct meaning. Demonstration and particularly ICT integration received the lowest ratings. It should be remembered that ICT integration in the teaching/learning process depends on available resources and efficient infrastructure.
OPTIMIZATION OF MULTI-DEPOT PERIODIC VEHICLE ROUTING PROBLEM WITH TIME WINDOW

Logistics is one of today's most important industries. It is important to store and transport raw materials, products cost-effectively. The article presents a specific delivery task, the Multi-Depot Periodic Vehicle Routing Problem with Time Window. In case of the problem several customers must be visited and must satisfy their demands. The vehicles start their route from one of the several depots, visit some customers and return to the depot from which they started their route. The customers have time window, which means that they must be visited within a predefined time interval. The periodic keyword means that the customers must be visited not once, but periodically. This means, that a periodic time is given, and the number of visits of each customer within this periodic time are also known in advance. The goal is the minimization of the length of the route. This problem is solved in this paper with construction and improvement algorithms. The presented construction algorithms are the Nearest Neighbor, Insertion Heuristics and Greedy algorithm. The presented improvement algorithms are the Firefly Algorithm, Harmony Search, Particle Swarm Optimization, Simulated Annealing and Tabu Search algorithms. Based on the test results the improvement of construction algorithms gave better performance than improving randomly generated solutions.
A Study of the Relationship between Organizational Conflicts & Employees’ Intention to Leave with Special Reference toStaff Assistants in ABC Merchant Bank of Sri Lanka

Major HR objective of any organization is to maintain or retain employees in the organization. In other words, all organization expects to minimize the employee turnover to best possible level. This study mainly focused about the practical issue of employee turnover and organizational conflicts of ABC Merchant Bank. This study was conducted to investigate the relationship between organizational conflicts and employees’ intention to leave with special reference to staff assistants of ABC Merchant Bank of Sri Lanka. This research study is significant to Sri Lankan context as there are fewer studies have been conducted in this field. In order to fill that gap while finding the solutions to practical issue present study was conducted at ABC Merchant Bank of Sri Lanka PLC . The research analysis was based on the information collected from 102 staff assistant which were selected through simple random sampling. GJMBR-A Classification: JEL Code: J54 AStudyoftheRelationshipbetweenOrganizationalConflictsandEmployeesIntentiontoLeavewithSpecialReferencetoStaffAssistantsinABCMerchantBanko fSriLanka Strictly as per the compliance and regulations of: Global Journal of Management and Business Research: A Administration and Management Volume 17 Issue 4 Version 1.0 Year 2017 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: 2249-4588 & Print ISSN: 0975-5853
Fossil stomatopods (Crustacea: Malacostraca) and their phylogenetic impact

The Stomatopoda form a distinctive order within the Malacostraca. It is an ancient group of benthic, obligate carnivorous crustaceans. Various studies have focused on stomatopods, mainly in regard to their taxonomy, their complex behaviour patterns, and the advanced neurophysiology of their visual systems. The α-taxonomy of this group seems well established; their phylogeny, however, still remains unclear. Palaeontological information can contribute significantly to the resolution of this group's phylogeny. Fossil evidence has already indicated that the ancestors of stomatopods probably diverged from other malacostracans in the Devonian. The first true stomatopods appear in the Carboniferous. The Mesozoic stomatopod families Sculdidae and Pseudosculdidae were studied more than a century ago. These early investigations on fossil stomatopods are of excellent quality. Unfortunately these sources were underestimated in later work on fossil stomatopods. The Cenozoic fossil stomatopod record appears to be riche.
PLC Reducer – A Tool to Generate Possible Reductions in Coupling at Design Level

: Coupling and cohesion are the two prime attribute that define the quality of a software design. High coupling is an undesirable feature while higher cohesion is enviable property. A number of software quality parameters like maintainability, readability, understandability etc. are directly or indirectly related with coupling and cohesion and thus it become necessary to pay a great attention towards desirable degrees of coupling and cohesion during design phase of software development life cycle. In this paper, an algorithm PLCReducer is proposed that suggested the possibilities how the coupling can be reduced in a design and also generates a redesign for the designed software. A complete demonstration of algorithm functionality is shown for a project. Also, algorithm is applied on five different java based projects and the amount of coupling before and after applying the algorithm is shown in the paper.
Long-Term Skid Resistance Evaluation of GAC-16 Based on Accelerated Pavement Testing

In this paper, four antiskidding surface test sections were paved to investigate the long-term skid resistance of the improved dense-graded asphalt concrete in Guangdong Province (GAC) using diabase fine aggregate instead of limestone. Four test sections were tested by the accelerated loading equipment (MLS11, mobile load simulator). The reduction law of the long-term skid resistance of GAC-16 was analyzed based on the accelerated pavement testing results. Prediction models of the GAC-16 skid resistance were also established and verified. The evaluation indexes of the long-term skid resistance of the asphalt pavement were introduced, and the antiskidding durability of different sections was evaluated. Results show that the initial British pendulum number (BPN) and mean texture depth (MTD) of the asphalt pavement cannot completely characterize its long-term skid resistance. With increasing loading cycles, the attenuation law of the BPN and MTD of GAC-16 denotes a fast reduction during the early stage, which gradually stabilizes. The relation between the skid resistance index and accelerated loading cycles was analyzed by nonlinear fitting according to the least-squares-method principle. The attenuation law of the BPN and MTD of GAC-16 with loading cycles was in accordance with the exponential and logarithmic models, respectively. The long-term antiskidding performance of the asphalt pavement could be accurately characterized using a stable BPN, loading cycles while reaching a stable BPN, the initial MTD value, and the MTD reduction rate as the evaluation indexes of the skid resistance of asphalt pavement. Compared with limestone fine aggregate, diabase fine aggregate can improve the long-term skid resistance of the asphalt mixtures.
Identifying Destination Image of Rural Areas: The Case of Brookings, South Dakota

A clear understanding of destination image is crucial for developing effective marketing and positioning strategies. The purpose of the study is to examine the images of Brookings perceived by actual tourists. An online questionnaire was developed and sent to email subscribers of the largest local event center. A total of 344 valid responses were received. Overall tourists had positive perceptions of Brookings as a tourism destination. The study identified six Brookings’ image dimensions, including Outdoor Activities and Natural Scenery, Atmosphere, Tourism Infrastructure, Value for Money and Convenience, Historic Attractions, and College Town Style. The social and cultural environment is the most favored element in Brookings. As a college town, Brookings was differentiated from other rural tourism destinations. It is suggested that the city and the university work in partnership to increase visitation both to the campus and the community. To enhance Brookings’ image, destination marketers should focus on the low-rated image items and incorporate them in destination marketing materials.