Search results for: self organization feature map

Authors: Swarnadip Ghosh

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Sentiment Analysis is the process of detecting the contextual polarity of text. In other words, it determines whether a piece of writing is positive, negative or neutral.Sentiment analysis of documents holds great importance in today's world, when numerous information is stored in databases and in the world wide web. An efficient algorithm to illicit such information, would be beneficial for social, economic as well as medical purposes. In this project, we have developed an algorithm to classify a document into positive or negative. Using our algorithm, we obtained a feature set from the data, and classified the documents based on this feature set. It is important to note that, in the classification, we have not used the independence assumption, which is considered by many procedures like the Naive Bayes. This makes the algorithm more general in scope. Moreover, because of the sparsity and high dimensionality of such data, we did not use empirical distribution for estimation, but developed a method by finding degree of close clustering of the data points. We have applied our algorithm on a movie review data set obtained from IMDb and obtained satisfactory results.

Keywords: sentiment, Run's Test, cross validation, higher dimensional pmf estimation

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Authors: Yao-Hong Tsai

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This paper presents an efficient fusion algorithm for iris images to generate stable feature for recognition in unconstrained environment. Recently, iris recognition systems are focused on real scenarios in our daily life without the subject’s cooperation. Under large variation in the environment, the objective of this paper is to combine information from multiple images of the same iris. The result of image fusion is a new image which is more stable for further iris recognition than each original noise iris image. A wavelet-based approach for multi-resolution image fusion is applied in the fusion process. The detection of the iris image is based on Adaboost algorithm and then local binary pattern (LBP) histogram is then applied to texture classification with the weighting scheme. Experiment showed that the generated features from the proposed fusion algorithm can improve the performance for verification system through iris recognition.

Keywords: image fusion, iris recognition, local binary pattern, wavelet

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Authors: Handan Ertaş

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The aim of the study is to determine the relationship between organizational trust level and organizational justice of Municipality officials. Correlational method has been used via descriptive survey model and Organizational Justice Perception Scale, Organizational Trust Inventory and Interpersonal Trust Scale have been applied to 353 participants who work in Konya Metropolitan Municipality and central district municipalities in the study. Frequency as statistical method, Independent Samples t test for binary groups, One Way-ANOVA analyses for multi-groups and Pearson Correlation analysis have been used to determine the relation in the data analysis process. It has been determined in the outcomes of the study that participants have high level of organizational trust, “Interpersonal Trust” is in the first place and there is a significant difference in the favor of male officials in terms of Trust on the Organization Itself and Interpersonal Trust. It has also been understood that officials in district municipalities have higher perception level in all dimensions, there is a significant difference in Trust on the Organization sub-dimension and work status is an important factor on organizational trust perception. Moreover, the study has shown that organizational justice implementations are important in raising trust of official on the organization, administrator and colleagues, and there is a parallel relation between Organizational Trust components and Organizational Trust dimensions.

Keywords: organizational trust level, organizational justice perceptions, staff, Konya

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Authors: Abraham Undu, Stephen Akuma

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Websites are becoming a major component of an organization’s success in our ever globalizing competitive world. The website symbolizes an organization, interacting or projecting an organization’s principles, culture, values, vision, and perspectives. It is an interface connecting organizations and their clients. The university, as an academic institution, makes use of a website to communicate and offer computing services to its stakeholders (students, staff, host community, university management etc). Unfortunately, website designers often give more consideration to the technology, organizational structure and business objectives of the university than to the usability of the site. Website designers end up designing university websites which do not meet the needs of the primary users. This empirical study investigated the Benue State University website from the point view of students. This research was realized by using a standardized website usability questionnaire based on the five factors of usability defined by WAMMI (Website Analysis and Measurement Inventory): attractiveness, controllability, efficiency, learnability and helpfulness. The result of the investigation showed that the university website (https://portal.bsum.edu.ng/) has neutral usability level because of the usability issues associated with the website. The research recommended feasible solutions to improve the usability of the website from the users’ perspective and also provided a modified usability model that will be used for better evaluation of the Benue State University website.

Keywords: Benue State University, modified usability model, usability, usability factors

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Authors: Bahaa Eltahawy, Mikko Ylihärsilä, Reino Virrankoski, Esko Petäjä

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Sheet metal processing is automated, but the step from product models to the production machine control still requires human intervention. This may cause time consuming bottlenecks in the production process and increase the risk of human errors. In this paper we present a system, which automatically recognizes features from the CAD-model of the sheet metal product. By using these features, the system produces a complete model of the particular sheet metal product. Then the model is used as an input for the sheet metal processing machine. Currently the system is implemented, capable to recognize more than 11 of the most common sheet metal structural features, and the procedure is fully automated. This provides remarkable savings in the production time, and protects against the human errors. This paper presents the developed system architecture, applied algorithms and system software implementation and testing.

Keywords: feature recognition, automation, sheet metal manufacturing, CAD, CAM

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Authors: Michele Sinico

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The psychological present has an actual extension. When a sequence of instantaneous stimuli falls in this short interval of time, observers perceive a compresence of events in succession and the temporal order depends on the qualitative relationships between the perceptual properties of the events. Two experiments were carried out to study the influence of perceptual grouping, with and without temporal displacement, on the duration of auditory sequences. The psychophysical method of adjustment was adopted. The first experiment investigated the effect of temporal displacement of a white noise on sequence duration. The second experiment investigated the effect of temporal displacement, along the pitch dimension, on temporal shortening of sequence. The results suggest that the temporal order of sounds, in the case of temporal displacement, is organized along the pitch dimension.

Keywords: time perception, perceptual present, temporal displacement, Gestalt laws of perceptual organization

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Authors: Analise Borg, Paul Micallef

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Over the past few years, the online multimedia collection has grown at a fast pace. Several companies showed interest to study the different ways to organize the amount of audio information without the need of human intervention to generate metadata. In the past few years, many applications have emerged on the market which are capable of identifying a piece of music in a short time. Different audio effects and degradation make it much harder to identify the unknown piece. In this paper, an audio fingerprinting system which makes use of a non-parametric based algorithm is presented. Parametric analysis is also performed using Gaussian Mixture Models (GMMs). The feature extraction methods employed are the Mel Spectrum Coefficients and the MPEG-7 basic descriptors. Bin numbers replaced the extracted feature coefficients during the non-parametric modelling. The results show that non-parametric analysis offer potential results as the ones mentioned in the literature.

Keywords: audio fingerprinting, mapping algorithm, Gaussian Mixture Models, MFCC, MPEG-7

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Authors: Assma Azeroual, Karim Afdel, Mohamed El Hajji, Hassan Douzi

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Key frame extraction methods select the most representative frames of a video, which can be used in different areas of video processing such as video retrieval, video summary, and video indexing. In this paper we present a novel approach for extracting key frames from video sequences. The frame is characterized uniquely by his contours which are represented by the dominant blocks. These dominant blocks are located on the contours and its near textures. When the video frames have a noticeable changement, its dominant blocks changed, then we can extracte a key frame. The dominant blocks of every frame is computed, and then feature vectors are extracted from the dominant blocks image of each frame and arranged in a feature matrix. Singular Value Decomposition is used to calculate sliding windows ranks of those matrices. Finally the computed ranks are traced and then we are able to extract key frames of a video. Experimental results show that the proposed approach is robust against a large range of digital effects used during shot transition.

Keywords: FSDWT, key frame extraction, shot detection, singular value decomposition

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Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

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A crucial component of maintaining a customer-oriented business as in the telecom industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years. It has become more important to understand customers’ needs in this strong market of telecom industries, especially for those who are looking to turn over their service providers. So, predictive churn is now a mandatory requirement for retaining those customers. Machine learning can be utilized to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: machine learning, gradient boosting, logistic regression, churn, random forest, decision tree, ROC, AUC, F1-score

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Authors: Ali Abdi Kordani, Hamid Bigdelirad, Sid Mohammad Boroomandrad

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Operating Urban Bus Transit System is a phenomenon that has a major role in transporting passengers in cities. There are many factors involved in planning and operating an Urban Bus Transit System, one of which is selecting optimized number of stops and scheduling of bus fleet departure. In this paper, we tried to introduce desirable methodology to select number of stops and schedule properly. Selecting the right number of stops causes convenience in accessibility and reduction in travel time and finally increase in public preference of this transportation mode. The achieved results revealed that number of stops must reduce from 33 to 25. Also according to scheduling and conducted economic analysis, the number of buses must decrease from 17 to 11 to have the most appropriate status for the Bus Organization.

Keywords: number of optimized stops, organizing bus system, scheduling, urban transit

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Authors: Moshe Davidian, Shlomo Mark, Yotam Lurie

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With the rapid development of technology and the concomitant growth in the capability of Artificial Intelligence (AI) systems and their power, the ethical challenges involved in these systems are also evolving and increasing. In recent years, various organizations, including governments, international institutions, professional societies, civic organizations, and commercial companies, have been choosing to address these various challenges by publishing ethical codes for AI systems. However, despite the apparent agreement that AI should be “ethical,” there is debate about the definition of “ethical artificial intelligence.” This study investigates the various AI ethical codes and their key ethical values. From the vast collection of codes that exist, it analyzes and compares 25 ethical codes that were found to be representative of different types of organizations. In addition, as part of its literature review, the study overviews data collected in three recent reviews of AI codes. The results of the analyses demonstrate a convergence around seven key ethical values. However, the key finding is that the different AI ethical codes eventually reflect the type of organization that designed the code; i.e., the organizations’ role as regulator, user, or developer affects the view of what ethical AI is. The results show a relationship between the organization’s role and the dominant values in its code. The main contribution of this study is the development of a list of the key values for all AI systems and specific values that need to impact the development and design of AI systems, but also allowing for differences according to the organization for which the system is being developed. This will allow an analysis of AI values in relation to stakeholders.

Keywords: artificial intelligence, ethical codes, principles, values

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Authors: Ali Muhammad

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The study examines the consequences of decision making in Kuwait work organization. The framework used in this study proposes that participation in decision making improves organizational ethical climate, which in turn increases employee’s trust in supervisor and trust in the organization. Furthermore, the model suggests that allowing employees to voice their opinions positively effects their perceptions of organizational justice. Providing employees with the opportunity to participate in decision making (voice), enhances their perceptions of the fairness of those decisions. Allowing employees to express their opinions and feeling about decisions being made show that the organization respect appreciates their views. This feeling of respect and appreciation reflects positively on employee’s perception of justice. Survey data were collected from a sample of 292 employees working in Kuwaiti work organizations. Pearson correlation, non-parametric tests, and structural equation models were used to analyze the data. Results of the analysis show that participation in decision making enhances employee perception of ethical climate, which in turn increases perception organizational justice and organizational trust. Implications of the findings and directions for future research are discussed.

Keywords: participation in decision making, organizational trust, trust in supervisor, organizational justice, ethical climate

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Authors: Maryam Esfandiari, Bohdan Fridrich

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On December 28, 2017, a wave of protests erupted in several Iranian cities. Protesters demonstrated against the president, Hasan Rohani, and theocratical nature of the regime. Iran has a recent history with protest movements, such as Green Movement responsible for demonstrations after 2009 Iranian presidential election. However, the 2017/2018 protests differ from the previous ones in terms of organization and agenda. The events show little to no central organization and seem as being sparked by grass root movements and by citizens’ fatigue of government corruption, authoritarianism, and economic problems of the country. Social media has played important role in communicating the protests to the outside world and also in general coordination. By using content analyses, this paper analyzes the visual content of Twitter posts published during the protests. It aims to find the correlation between their decentralized nature and nature of the tweets – either emotionally arousing or efficiency-elicit. Pictures are searched by hashtags and coded by their content, such as ‘crowds,’ ‘protest activities,’ ‘symbols of unity,’ ‘violence,’ ‘iconic figures,’ etc. The study determines what type of content prevails and what type is the most impactful in terms of reach. This study contributes to understanding the role of social media both as a tool and a space in protest organization and portrayal in countries with limited Internet access.

Keywords: twitter, Iran, collective action, protest

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Manufacturing companies are facing global competition and enormous cost pressure. The use of machine learning applications can help reduce production costs and create added value. Predictive quality enables the securing of product quality through data-supported predictions using machine learning models as a basis for decisions on test results. Furthermore, machine learning methods are able to process large amounts of data, deal with unfavourable row-column ratios and detect dependencies between the covariates and the given target as well as assess the multidimensional influence of all input variables on the target. Real production data are often subject to highly fluctuating boundary conditions and unbalanced data sets. Changes in production data manifest themselves in trends, systematic shifts, and seasonal effects. Thus, Machine learning applications require intensive pre-processing and feature selection. Data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets. Within the used real data set of Bosch hydraulic valves, the comparability of the same production conditions in the production of hydraulic valves within certain time periods can be identified by applying the concept drift method. Furthermore, a classification model is developed to evaluate the feature importance in different subsets within the identified time periods. By selecting comparable and stable features, the number of features used can be significantly reduced without a strong decrease in predictive power. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. In this research, the ada boosting classifier is used to predict the leakage of hydraulic valves based on geometric gauge blocks from machining, mating data from the assembly, and hydraulic measurement data from end-of-line testing. In addition, the most suitable methods are selected and accurate quality predictions are achieved.

Keywords: classification, achine learning, predictive quality, feature selection

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Authors: Ping Tan, Xiaomeng Su, Yi Shen

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The motion intention in the motor imagery braincomputer interface is identified by classifying the event-related desynchronization (ERD) and event-related synchronization ERS characteristics of sensorimotor rhythm (SMR) in EEG signals. When the subject imagines different limbs or different parts moving, the rhythm components and bandwidth will change, which varies from person to person. How to find the effective sensorimotor frequency band of subjects is directly related to the classification accuracy of brain-computer interface. To solve this problem, this paper proposes a Minimum Distance to Riemannian Mean Classification method based on Non-Uniform Filter Banks. During the training phase, the EEG signals are decomposed into multiple different bandwidt signals by using multiple band-pass filters firstly; Then the spatial covariance characteristics of each frequency band signal are computered to be as the feature vectors. these feature vectors will be classified by the MDRM (Minimum Distance to Riemannian Mean) method, and cross validation is employed to obtain the effective sensorimotor frequency bands. During the test phase, the test signals are filtered by the bandpass filter of the effective sensorimotor frequency bands, and the extracted spatial covariance feature vectors will be classified by using the MDRM. Experiments on the BCI competition IV 2a dataset show that the proposed method is superior to other classification methods.

Keywords: non-uniform filter banks, motor imagery, brain-computer interface, minimum distance to Riemannian mean

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Authors: O. Y. Yurieva, O. V. Yurieva, O. V. Kiselkina, A. V. Kamaseva

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Since the early 2000s, there are some innovative changes in the civil service in Russia due to administrative reform. Perspectives of the reform of the civil service include a fundamental change in the personnel component, increasing the level of professionalism of officials, increasing their capacity for self-organization and self-regulation. In order to achieve this, the civil service must be able to continuously change. Organizational changes have long become the subject of scientific understanding; problems of research in the field of organizational change is presented by topics focused on the study of the methodological aspects of the implementation of the changes, the specifics of changes in different types of organizations (business, government, and so on), design changes in the organization, including based on the change in organizational culture. In this case, the organizational changes in the civil service are the least studied areas; research of problems of its transformation is carried out in fragments. According to the theory of resistance of Herbert Simon, the root of the opposition and rejection of change is in the person who will resist any change, if it threatens to undermine the degree of satisfaction as a member of the organization (regardless of the reasons for this change). Thus, the condition for successful adaptation to changes in the organization is the ability of its staff to perceive innovation. As part of the problem, the study sought to identify the innovation civil servants, to determine readiness for the development of proposals for the implementation of organizational change in the public service. To identify the relationship to organizational changes case study carried out by the method of "Attitudes to organizational change" of I. Motovilina, which allowed predicting the type of resistance to changes, to reveal the contradictions and hidden results. The advantage of the method of I. Motovilina is its brevity, simplicity, the analysis of the responses to each question, the use of "overlapping" issues potentially conflicting factors. Based on the study made by the authors, it was found that respondents have a positive attitude to change more local than those that take place in reality, such as "increase opportunities for professional growth", "increase the requirements for the level of professionalism of", "the emergence of possible manifestations initiatives from below". Implemented by the authors diagnostics related to organizational changes in the public service showed the presence of specific problem areas, with roots in the lack of understanding of the importance of innovation personnel in the process of bureaucratization of innovation in public service organizations.

Keywords: innovative changes, self-organization, self-regulation, civil service

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Authors: Robin Ratnasingham, Maher Elshakankiri

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This research report will look at how to make a smart washroom to increase public hygiene and cleanliness. The system would use IoT devices to pick up various activities in the washroom and notify the appropriate stakeholders or devices to regulate the condition of the washroom. As more people are required to physically go back to the office or school, ensuring a clean and sanitized washroom is even more important now than before. It would help prevent virus outbreaks and safeguard the organization from shutdowns or slowdowns in their business. A framework of the suggested smart washroom was introduced to help reduce the chances of a virus outbreak. Most organizations outsource renovation or implementation to an external party. Using the smart washroom framework, we looked at vendors that provide smart washroom solutions. There are IoT vendors that cannot match the framework, and there are vendors that can support the framework design. This segment is a niche market, and most of the devices are similar in their basic functions. However, all the vendors have unique characteristics to give them a competitive advantage over the rest of the IoT washroom companies. Ultimately, the organization would need to decide if they want to add IoT devices to enable smart capability or renovate the washroom to create a fluid IoT smart washroom design. The report would introduce an IoT smart washroom framework to help organizations design a cohesive preventive measure network for the daily maintenance routine. The framework is designed to help understand how to manage washroom cleanliness more efficiently and to provide guidance in achieving this goal. The leading result is eliminating potential viral outbreaks that could jeopardize the organization.

Keywords: IoT, smart washroom, public hygiene, cleanliness, virus outbreaks, safeguard

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Authors: Georgi Y. Georgiev, Matthew Brouillet

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This research applies the power of agent-based modeling to a pivotal question at the intersection of biology, computer science, physics, and complex systems theory about the self-organization processes in open, complex, non-equilibrium thermodynamic systems. Central to this investigation is the principle of Maximum Entropy Production (MEP). This principle suggests that such systems evolve toward states that optimize entropy production, leading to the formation of structured environments. It is hypothesized that guided by the least action principle, open thermodynamic systems identify and follow the shortest paths to transmit energy and matter, resulting in maximal entropy production, internal structure formation, and a decrease in internal entropy. Concurrently, it is predicted that there will be an increase in system information as more information is required to describe the developing structure. To test this, an agent-based model is developed simulating an ant colony's formation of a path between a food source and its nest. Utilizing the Netlogo software for modeling and Python for data analysis and visualization, self-organization is quantified by calculating the decrease in system entropy based on the potential states and distribution of the ants within the simulated environment. External entropy production is also evaluated for information increase and efficiency improvements in the system's action. Simulations demonstrated that the system begins at maximal entropy, which decreases as the ants form paths over time. A range of system behaviors contingent upon the number of ants are observed. Notably, no path formation occurred with fewer than five ants, whereas clear paths were established by 200 ants, and saturation of path formation and entropy state was reached at populations exceeding 1000 ants. This analytical approach identified the inflection point marking the transition from disorder to order and computed the slope at this point. Combined with extrapolation to the final path entropy, these parameters yield important insights into the eventual entropy state of the system and the timeframe for its establishment, enabling the estimation of the self-organization rate. This study provides a novel perspective on the exploration of self-organization in thermodynamic systems, establishing a correlation between internal entropy decrease rate and external entropy production rate. Moreover, it presents a flexible framework for assessing the impact of external factors like changes in world size, path obstacles, and friction. Overall, this research offers a robust, replicable model for studying self-organization processes in any open thermodynamic system. As such, it provides a foundation for further in-depth exploration of the complex behaviors of these systems and contributes to the development of more efficient self-organizing systems across various scientific fields.

Keywords: complexity, self-organization, agent based modelling, efficiency

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Authors: Farzane Sadatnia, Bahram Fathi

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Today, all public and private organizations have found that the management of the world for key information related to the activities of a staff and its main essence and philosophy, though they constitute the management information systems are very helpful in this respect the right to apply systems can save a lot in terms of economic organizations including reducing the time decision - making, improve the quality of decision making, and cost savings to bring information systems is a backup system that can never be instead of logic and human reasoning, which can be used in the series is spreading, providing resources, and provide the necessary facilities, provide better services for users, balanced budget allocation, determine strengths and weaknesses and previous plans to review the current decisions and especially the decision . Hence; in this study attempts to the effect of an information system on a review of the organization.

Keywords: information system, planning, organization, coordination, control

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Authors: Jia-Hong Lee, Mei-Yi Wu, Hsien-Tsung Kuo

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Texture is an important characteristic in real and synthetic scenes. Texture analysis plays a critical role in inspecting surfaces and provides important techniques in a variety of applications. Although several descriptors have been presented to extract texture features, the development of object recognition is still a difficult task due to the complex aspects of texture. Recently, many robust and scaling-invariant image features such as SIFT, SURF and ORB have been successfully used in image retrieval and object recognition. In this paper, we have tried to compare the performance for texture classification using these feature descriptors with k-means clustering. Different classifiers including K-NN, Naive Bayes, Back Propagation Neural Network , Decision Tree and Kstar were applied in three texture image sets － UIUCTex, KTH-TIPS and Brodatz, respectively. Experimental results reveal SIFTS as the best average accuracy rate holder in UIUCTex, KTH-TIPS and SURF is advantaged in Brodatz texture set. BP neuro network works best in the test set classification among all used classifiers.

Keywords: texture classification, texture descriptor, SIFT, SURF, ORB

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Authors: Venugopal Kummamuru

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Corporate Governance (CG) is of utmost importance for running a company ethically. It is essential for the growth and success of the corporation. It is intended to increase the accountability of an organization to the larger context of the business environment. The general principles of CG include and are related to Shareholder recognition, Stakeholder interests, and focus on Corporate Social Responsibility (CSR), Clear Board responsibilities, Ethical behavior, and Business transparency. Network Marketing Organizations (NMOs) focus on marketing through direct-sales using people who are associated with the organization but are not their employees. This paper tries to study the importance of Ethics and CSR in an NMO and suggest a basic guideline for CG in NMO(s). This paper could be used as a basis or starting point for conducting an in-depth research to understand the difference in CG practices between NMO(s) and other organizations and define a standard set of guidelines for CG practice.

Keywords: corporate governance, corporate responsibility, direct selling, network marketing

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Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

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Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by density-based time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., mean value, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one class classifier (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, a new anomaly detector strategy is proposed, namely one class classifier neural network two (OCCNN2), which exploit the classification capability of standard classifiers in an anomaly detection problem, finding the standard class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimation. The coarse estimation uses classics OCC techniques, while the fine estimation is performed through a feedforward neural network (NN) trained that exploits the boundaries estimated in the coarse step. The detection algorithms vare then compared with known methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and auto-associative neural network (ANN). In many cases, the proposed solution increases the performance with respect to the standard OCC algorithms in terms of F1 score and accuracy. In particular, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 96% with the proposed method.

Keywords: anomaly detection, frequencies selection, modal analysis, neural network, sensor network, structural health monitoring, vibration measurement

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Authors: Shahid Ali

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Leadership and management are essential components of running successful organizations. Both concepts are closely related but serve different purposes in the overall management of a company. Leadership focuses on inspiring and motivating employees towards a common goal, while management involves coordinating and directing resources to achieve organizational objectives efficiently. Objectives of Leadership and Management: Inspiring and motivating employees: A key objective of leadership is to inspire and motivate employees to work towards achieving the organization’s goals. Effective leaders create a vision that employees can align with and provide the necessary motivation to drive performance. Setting goals and objectives: Both leadership and management play a crucial role in setting goals and objectives for the organization. Leaders create a vision for the future, while managers develop plans to achieve specific objectives within the given timeframe. Implementing strategies: Leaders come up with innovative strategies to drive the organization forward, while managers are responsible for implementing these strategies effectively. Together, leadership and management ensure that the organization’s plans are executed efficiently. Contributions of Leadership and Management: Employee Engagement: Effective leadership and management can increase employee engagement and satisfaction. When employees feel motivated and inspired by their leaders, they are more likely to be engaged in their work and contribute to the organization’s success. Organizational Success: Good leadership and management are essential for navigating the challenges and changes that organizations face. By setting clear goals, inspiring employees, and making strategic decisions, leaders and managers can drive organizational success. Talent Development: Leaders and managers are responsible for identifying and developing talent within the organization. By providing feedback, training, and coaching, they can help employees reach their full potential and contribute effectively to the organization. Research Type: The research on leadership and management is typically quantitative and qualitative in nature. Quantitative research involves the collection and analysis of numerical data to understand the impact of leadership and management practices on organizational outcomes. This type of research often uses surveys, questionnaires, and statistical analysis to measure variables such as employee satisfaction, performance, and organizational success. Qualitative research, on the other hand, involves exploring the subjective experiences and perspectives of individuals related to leadership and management. This type of research may include interviews, observations, and case studies to gain a deeper understanding of how leadership and management practices influence organizational behavior and outcomes. In conclusion, leadership and management play a critical role in the success of organizations. Through effective leadership and management practices, organizations can inspire and motivate employees, set goals, and implement strategies to achieve their objectives. Research on leadership and management helps to understand the impact of these practices on organizational outcomes and provides valuable insights for improving leadership and management practices in the future.

Keywords: empowering, leadership, management, adaptability

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Authors: O. V. Jurieva, O. U. Jurieva, R. H. Yagudin, P. B. Chursin

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In the recent scientific researches on the problems of public service the idea of the use of innovative technologies of management of personnel processes is accurately formulated. Authors made an attempt to analyze the changes in the public service organizations and to understand how the studied situation is interpreted by the government employees themselves. For this purpose the strategy of sociological research was carried out on the basis of application of questionnaire developed by M. Rokich and focus group research. For the research purposes it was necessary to get to microlevel in order to include daily activities of employees of an organization, their life experience and values in the focus of the analysis. Based on P. Bourdieu's methodology, authors investigated the established patterns of consciousness and behavior of officials (doxa) and also analyzed the tendencies of re-thinking (change) of the settled content of values (heterodoxy) by them. The distinctive feature of the conducted research is that the public servants who have different length of service in the public service took part in the research procedure. The obtained data helped to answer the following question: what are the specifics of doxs of the public servants who work in the public civil service more than 7-10 years and what perception of values of civil service have junior experts whose work experience doesn't exceed 3 years. Respondents were presented by two groups: (1) public servants of the level of main positions in the public civil service of the Republic of Tatarstan. (2) Public servants of the level of lower positions in the ministries and departments of the Republic of Tatarstan. For the study of doxa or of the existing values of public servants, the research with use of the questionnaire based on M. Rokich's system is conducted. Two types of values are emphasised: terminal and instrumental, which are united by us in the collective concept doxa. Doxa: the instrument of research of the established patterns of consciousness and behavior which can either resist to changes in the organization or, on the contrary, support their implementation. In the following stage an attempt to deepen our understanding of the essence and specifics of doxa of officials by means of the applied sociological research which is carried out by focus group method is made. Information obtained by authors during the research convinces that for the success of policy of changes in the organizations of public service it is necessary to develop special technologies of informing employees about the essence and inevitability of the developed innovations, to involve them in the process of changes, to train and to develop the younger generation of civil servants, seriously to perceive additional training and retraining of officials.

Keywords: innovative technologies, public service organizations, public servants

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Authors: Marina Fomina

Abstract:

The paper focuses on the category of semantic subject within the framework of a functional approach to linguistics. The semantic subject is related to similar notions such as the grammatical subject and the bearer of predicative feature. It is the multifaceted nature of the category of subject that 1) triggers a number of issues that, syntax-wise, remain to be dealt with (cf. semantic vs. syntactic functions / sentence parts vs. parts of speech issues, etc.); 2) results in a variety of approaches to the category of subject, such as formal grammatical, semantic/syntactic (functional), communicative approaches, etc. Many linguists consider the prototypical approach to the category of subject to be the most instrumental as it reveals the integrity of denotative and linguistic components of the conceptual category. This approach relates to subject as a source of non-passive predicative feature, an element of subject-predicate-object situation that can take on a variety of semantic roles, cf.: 1) an agent (He carefully surveyed the valley stretching before him), 2) an experiencer (I feel very bitter about this), 3) a recipient (I received this book as a gift), 4) a causee (The plane broke into three pieces), 5) a patient (This stove cleans easily), etc. It is believed that the variety of roles stems from the radial (prototypical) structure of the category with some members more central than others. Translation-wise, the most “treacherous” subject types are the peripheral ones. The paper 1) features a peripheral status of spatial impersonal sentence models such as U menia v ukhe zvenit (lit. I-Gen. in ear buzzes) within the category of semantic subject, 2) makes a structural and semantic analysis of the models, 3) focuses on their Russian-English translation patterns, 4) reveals non-prototypical features of subjects in the English equivalents.

Keywords: bearer of predicative feature, grammatical subject, impersonal sentence model, semantic subject

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Authors: Norah Mohammed Alshahrani, Abdulaziz Almaleh

Abstract:

Autism Spectrum Disorder (ASD) is a condition related to issues with brain development that affects how a person recognises and communicates with others which results in difficulties with interaction and communication socially and it is constantly growing. Early recognition of ASD allows children to lead safe and healthy lives and helps doctors with accurate diagnoses and management of conditions. Therefore, it is crucial to develop a method that will achieve good results and with high accuracy for the measurement of ASD in children. In this paper, ASD datasets of toddlers and children have been analyzed. We employed the following machine learning techniques to attempt to explore ASD and they are Random Forest (RF), Decision Tree (DT), Na¨ıve Bayes (NB) and Support Vector Machine (SVM). Then Feature selection was used to provide fewer attributes from ASD datasets while preserving model performance. As a result, we found that the best result has been provided by the Support Vector Machine (SVM), achieving 0.98% in the toddler dataset and 0.99% in the children dataset.

Keywords: autism spectrum disorder, machine learning, feature selection, support vector machine

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Authors: Amin Ranjbar, Amir Arsalan Jalili Zolfaghari, Amir Abolfazl Suratgar, Mehrdad Khajavi

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This paper describes two stages of learning-based fault detection procedure in alternators. The procedure consists of three states of machine condition namely shortened brush, high impedance relay and maintaining a healthy condition in the alternator. The fault detection algorithm uses Wigner-Ville distribution as a feature extractor and also appropriate feature classifier. In this work, ANN (Artificial Neural Network) and also SVM (support vector machine) were compared to determine more suitable performance evaluated by the mean squared of errors criteria. Modules work together to detect possible faulty conditions of machines working. To test the method performance, a signal database is prepared by making different conditions on a laboratory setup. Therefore, it seems by implementing this method, satisfactory results are achieved.

Keywords: alternator, artificial neural network, support vector machine, time-frequency analysis, Wigner-Ville distribution

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Authors: Abdelghani Alidra, Mohamed Tahar Kimour

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Genetic algorithm must adapt themselves at design time to cope with the search problem specific requirements and at runtime to balance exploration and convergence objectives. In a previous article, we have shown that modeling and implementing Genetic Algorithms (GA) using the software product line (SPL) paradigm is very appreciable because they constitute a product family sharing a common base of code. In the present article we propose to extend the use of the feature model of the genetic algorithms family to model the potential states of the GA in what is called a Dynamic Software Product Line. The objective of this paper is the systematic generation of a reconfigurable architecture that supports the dynamic of the GA and which is easily deduced from the feature model. The resultant GA is able to perform dynamic reconfiguration autonomously to fasten the convergence process while producing better solutions. Another important advantage of our approach is the exploitation of recent advances in the domain of dynamic SPLs to enhance the performance of the GAs.

Keywords: self-adaptive genetic algorithms, software engineering, dynamic software product lines, reconfigurable architecture

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Authors: Nipun Agarwal, David Paul, Fareed Un Din

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Strategy development and implementation is the key to an organization’s success in today’s competitive marketplace. Many organizations develop excellent strategy but are unable to implement this strategy in order to succeed. The difference between strategic goals and its implementation is called strategic error. Strategic error occurs when an organization does not have structures in place to implement their strategy. Strategy implementation happens through projects and having a project management method that provides certainty and agility will help an organization become more competitive in implementing strategy. Numerous project management methods exist in theory and practice. However, projects mainly used the Waterfall method in the past that provides certainty in terms of budget, delivery date and resourcing. It is common practice now to utilise Agile based methods. However, Agile based methods do not provide specific deadlines and budgets. But provide agility in product design and project delivery, which is useful to companies. Both Waterfall and Agile methods in some forms are the opposites of each other. Executive management prefer agility in delivery projects as the competitive landscape changes frequently. However, they also appreciate certainty in the projects being able to quantify budgets, deadlines and resources that is harder for an Agile based method to provide. This paper attempts to develop a hybrid project management method that attempts to merge these Waterfall and Agile methods to provide the positives from both these approaches.

Keywords: strategy, project management, strategy implementation, agile

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Authors: Tariq Ali Gujar, Anita Hökelmann

Abstract:

Postural balance plays essential role throughout life in daily activities. Somatosensory, visual and vestibular inputs play the fundamental role in maintaining body equilibrium to balance the posture. The aim of this study was to find out electroencephalography (EEG) responses during balance activity of young people during Sensory Organization Balance Test. The outcome of this study will help to create the fitness and neurorehabilitation plan. 25 young people (25 ± 3.1 years) have been analyzed on Balance Master NeuroCom® with the coupling of Brain Vision 32 electrode wireless EEG system during the Sensory Organization Test. From the results it has been found that the balance score of samples is significantly higher under the influence of somatosensory input as compared to visual and vestibular input (p < 0.05). The EEG between somatosensory and visual input to balance the posture showed significantly higher (p < 0.05) alpha and beta activities during somatosensory input in somatosensory, attention and visual functions of the cortex whereas executive and motor functions of the cerebral cortex showed significantly higher (p < 0.05) alpha EEG activity during the visual input. The results suggest that somatosensory and attention function of the cerebral cortex has alpha and beta activity, respectively high during somatosensory and vestibular input in maintaining balance. In patients with balance impairments both physical and cognitive training, including neurofeedback will be helpful to improve balance abilities.

Keywords: balance, electroencephalography activity, somatosensory, visual, vestibular

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