Reviewerly/exHarmony huggingface dataset:
Erma Algorithm for Time Series Anomaly Detection and the Exploration of its Application in Financial Information System

The IT operations in financial industry has characteristics of wide variety of KPIs (Key Performance Indicators), large data volume, and high demand for business continuity and stability, bringing great challenges to business monitoring for finance enterprises. Conventional monitoring methods based on fixed thresholds are difficult to adapt to frequently changed business developments that abnormal false report and under-reports frequently occur, which leads to poor monitoring performance and huge manpower consumption. In view of the characteristics and current situation of the anomaly detection in above financial scene, this paper proposes a solution to apply the energy statistical algorithm (ERMA) to the financial scene for the first time, constructs a time series data set for anomaly detection with both the experience of business experts and algorithm experts are incorporated while bringing up the associated anomaly labeling criterion considering the difficulties of labeling in AIOps, and finally draw comparison between different intelligent algorithms of their performances of application in the financial industry. The results prove generally superior performance, high efficacy, and universality of Erma, guaranteeing the operational stability of IT system in financial industry, and exhibit capability of Erma for supporting real-time anomaly detection on data at million scale, which provides the possibility of developing a generic solution in the field.
Restoration Simulation of Image Quality Degradation of Nanosensors Based on Virtual Reality

The texture complexity of traditional sensor image degradation restoration methods is high and the restoration effect is reduced. For this reason, a virtual reality-based image quality degradation recovery method for nanosensors is designed in this paper. First, the image quality degradation model of nanometer sensor is constructed based on virtual reality technology. Then, the noise characteristics of the degraded image are analyzed. On the premise of retaining the original image information, the diffusion coefficients in the vertical and horizontal directions are calculated to obtain the expression of adaptive filter (ADF) in the image with noise, so as to complete the image denoising process. On the basis of texture complexity analysis, singular value decomposition detection and alpha channel calculation are completed, and image quality degradation recovery of nanosensor is achieved through synthesis operation. The experimental results show that the texture complexity of the recovered images is lower than 0.54, the average absolute error percentage of the recovered images is only 10%, and the P-R value is high, which fully demonstrates the effectiveness of the offered procedure.
Relational Analysis of Exercise Need, Health Awareness, and Health Promotion Behavior of University Adult Learners

초록·키워드 목차 오류제보하기 The purpose of this study was to analyze the relationship between adult learners" health awareness, health promotion behavior, and exercise desire. For this purpose, 189 students were sampled using the non-probability bias sampling method as the population of university adult learners. For data processing, correlation analysis between variables, multiple regression analysis, and multivariate ANOVA were performed through exploratory factor analysis and reliability analysis using SPSS 25.0. The research results are as follows. First, the ‘fun’ and ‘health’ needs of the exercise needs of adult learners had a significant effect on health perception. Second, in the health needs of adult learners, ‘fun’, ‘health’, and ‘body shape’ needs had a significant effect on health promotion behavior. Third, there was a significant difference in health information promotion behavior and exercise desire according to the type of exercise participation of adult learners. Therefore, the exercise desire recognized by adult learners is a variable that affects their health beliefs. Therefore, along with the search for ways to increase the motivation of exercise to lead a healthy life, the exercise participation type of ‘exercise together’ rather than ‘exercise alone’ exercise participation type. Through efficiency, basic data to help adult learners with correct health information has been provided. #University Adult Learners #Exercise Need #Health Awareness #Health Promotion Behavior AbstractⅠ. 서론Ⅱ. 연구방법Ⅲ. 결과Ⅳ. 논의Ⅴ. 결론 및 제언참고문헌
دور الثقافة التنظيمية في تعزيز عمليات إدارة المعرفة في الشركات العامة : (بالتطبيق على شركة الموانئ الهندسية)‎ الفترة من يناير 2010م إلى يوليو 2011 م

دور الثقافة التنظيمية في تعزيز عمليات إدارة المعرفة في الشركات العامة : (بالتطبيق على شركة الموانئ الهندسية)‎ الفترة من يناير 2010م إلى يوليو 2011 م&#x0D; The study aimed at investigating the role of organizational culture (OC) in consolidating the knowledge of management processes (KMP) in public limited companies (PLC) .The study used the analytical descriptive method.lt contained four themes..&#x0D; The study achieved the following results: - Implementation of Various elements of (OC) and (KMP)in ports Engineering Company ,scored medium level rate.&#x0D; - Significant relationship was found between all elements of (OC) and elements of (KMP) in (PEC).&#x0D; - The study provided several recommendations and the following are the most impornant: - Activation of knowledge management tools and approaches.&#x0D; - Difusing the culture of knowledge of the field
Outlier Detection Method for Flash Flood Disaster Monitoring Data based on Information Entropy

Abstract In the research of flash flood disaster monitoring and early warning, the Internet of Things is widely used in real-time information collection. There are abnormal situations such as noise, repetition and errors in a large amount of data collected by sensors, which will lead to false alarm, lower prediction accuracy and other problems. Aiming at the characteristic that outliers flow of sensors will cause obvious fluctuation of information entropy, this paper proposes a local outlier detection method based on information entropy and optimized by sliding window and LOF (Local Outlier Factor). This method can be used to improve the data quality, thus improving the accuracy of disaster prediction. The method is applied to data stream processing of water sensor, and the experimental results show that the method can accurately detect outliers. Compared with the existing detection methods that only use data distance to determine, the test positive rate is improved and the false positive rate is reduced.
A Fast 4K Video Frame Interpolation based on StepWise Optical Flow Computation and Video Spatial Interpolation

Recently, with the generalization of ultra-high-resolution displays and the beginning of terrestrial UHD TV, the demand for 4K video is spreading. However, the supply for 4k contents is still insufficient compared to the demand. To create the 4K video with high temporal resolution, this paper proposes video frame interpolation method for 4K video. Video frame interpolation with high spatial resolution requires a large receptive field of the convolution kernel due to its resolution and also consumes a lot of GPU memory and computation. To solve this problem, in this paper, a frame interpolation method that combines a stepwise optical flow computation method and video spatial interpolation is proposed. Experiment results show that the proposed method is not only objectively and subjectively superior to the existing method, but also significantly reduces the GPU memory and computational amount compared to the existing one.
TOWARDS SELF-CREATION THROUGH SELF-KNOWLEDGE AND THE RENEWAL OF THE WORLDVIEW

The aspects of updating the content of university programs are discussed, in the content of which it is necessary to emphasize the cognitive interests of students in the fields of cosmogenesis and anthropogenesis that expand the horizons of the worldview and meet the requirements of the progress of self-creative activity and the improvement of the Life of the Earth and the Universe
Smart Data for Goods and Vehicle Monitoring – Practical Considerations on Data Semantization

In the paper we present an original semantic data mining solution for Internet of Things. We designed an ontology for sensors used in the monitoring of goods and vehicles. Moreover, we developed a practical implementation for this solution based on the state of the art semantic tools, namely RDF triple stores with dedicated interfaces. However, due to number of undocumented limitations of these tools, this process turned out to require number of refactorizations to deliver a working solution. We describe these challenges and solutions we found. We believe our experiences are valuable for researchers and practitioners in the field, as production-ready development of such semantic solutions is far more complex than lab experiments. In fact, our work was developed for the deployment in a production environment, with a close cooperation with an IT company funding the project.
Extended isolation forest – application to outlier detection in geomagnetic data

Abstract The aim of this study is to present a method for detection of outliers in the time series of total intensity of geomagnetic field using Extended Isolation Forest algorithm. The method is consisted of three steps: 1) generation of additional features that take into account the regular daily variation and smooth behaviour of normal data, 2) detection of potential outliers based on ensemble of extended isolating trees and 3) subsequent refinement based on difference between the outlier and its replacement with interpolated value. Application of the method for detection of outliers in yearly time series of the total geomagnetic field at Ak-Suu and Kegety stations showed that the algorithm identifies both global and contextual outliers. Average classification metrics for the method are characterized as high and have the following values: precision 94.3%, recall 93.9% and F-score 94.5%, and probabilities of errors of the first and second kind are comparable to similar algorithms used for detection of outliers in magnetograms of different sampling rate.
The Emergence of Artificial Intelligence in Africa and its Impact on the Enjoyment of Human Rights

Abstract Meeting the rising need for development and tackling the attendant challenges in Africa requires pragmatic and innovative strategies. Although there is evidence that political governance is improving across the continent, these improvements are not meeting the expectations and needs of several sections of the society. Artificial Intelligence ( AI ) seems to be one tool with a great potential to address these current challenges. Just like every innovative technology, AI has both positive and negative aspects. This article examines the human rights implications of AI introduction into Africa in light of rights enshrined in the African Charter on Human and Peoples’ Rights.
“Do Not Fear Mistakes—There Are None”: Ethnography as Improvisation

I use the iconic album, Kind of Blue, to re‐frame a conversation around ethnography and innovation. Moving fast and breaking stuff is not how ethnography brings value to the business. Rather, we use our craft to move nimbly through the complex terrain of “users” in much the same way as jazz musicians improvise. We are grounded in our craft through practice, but technical mastery is not enough. Bounded by structures and constraints, we move nimbly because knowing the rules allows us to creatively push against boundaries. And ethnography never exists in a vacuum, even if we're the only anthropologist in the room. Instead, we riff off those who came before us, in community with those who innovate alongside us. The core value of ethnography is to improvise—to use our mastery of our craft to build on what came before, to make sense of it, and anticipate what comes next.
Beyond numerical rationality

This contribution analyzes Bildung under the auspices of digital capitalism. At the beginning, Bildung is characterized as a concept of human freedom. Subsequently, our society will be described as a capitalist society, which is primarily structured by economic progress based on technical innovations. This leads to the thesis that digitalization is also determined by economic goals, which will be explored by linking the phenomena of big data and subjectivation. In this way, it can be illustrated that big data is based on a numerical logic. This logic provides rational patterns of orientation but equally leads us to more control and self-management. Thus, a way can be shown how the logic of capitalism merges with subjective actions. Therefore, it is ultimately necessary to understand Bildung as a concept that allows us to claim our freedom even beyond rationality.
Non-aligned multi-view anomaly detection algorithms based on one-cluster clustering

Multi-view data exists widely in reality, and anomalies inevitably accompany it. Existing multi-view anomaly detection methods aim at the aligned multi-view scenes, and they cannot be competent for non-aligned multi-view scenes for that they are more challenging. Therefore, in this paper, we propose four kinds of non-aligned multi-view anomaly detection algorithm based on one-cluster possibilistic C-means (PCM), namely P1M (PCM, where ${\\rm~C}=1$). Different from the aligned multi-view scenes, the non-alignment feature causes the lack of correspondence between the samples in each view, which makes it impossible for us to utilize the local key information at the sample level in the aligned scenes. Therefore, we try to learn from the global information of each view. The key point of this paper is that we try to learn a transformation matrix for each view by means of an alternating optimization method, and project each view into a common subspace. Then the P1M is used to learn the memberships to the target class of all training data in the common subspace, and a threshold of the memberships is set, so as to realize anomaly detection. The experimental results on several UCI datasets show that our proposed method can effectively deal with anomaly detection in the non-aligned multi-view scenes.
An Automated Planning Approach for Scheduling Air Conditioning Operation Using PDDL+

Scheduling and planning of a hybrid (mixed discrete—continuous) system for a real-world problem are very challenging. Fortunately, PDDL+ shows a way to model such systems. An air conditioning system is an example of a hybrid system which provides thermal comfort to the occupants of any residence at an expense of energy. Scheduling the air conditioner with a wise plan can reduce the energy consumption without losing thermal comfort. In this work, we create schedules for air conditioning operations and predict the amount of energy required to execute the schedules for maintaining the thermally comfortable zone temperature. For this purpose, we design two hybrid domain models, namely linear durative model and process-event model. For optimized operations of an air conditioner, we implement those models with automated planning using PDDL+ and show the comparisons between two domains. For simulation, a state-of-the-art hybrid planner SMTPlan+ is used.
Selfreg: Regularization Method via High-level Feature

Regularization approach is a key technique to improve the generalization ability of models. The current dropblock method removes information pixels on images by covering black pixels or patches of random noise. Such deletion is undesirable because it leads to information loss and inefficiency in the training period. Therefore, we propose to use better noise than vacancies for feature regularization, we use high-level feature map of the network as perturbations, and these high-level feature additions avoid losing useful information compared to vacancy additions, and this approach is easier to implement. It is experimentally demonstrated that our method has more boosting and generalization capability than dropblock.
Information System Computer Dynamics Simulation of the Water Traffic Emergency Rescue by Vensim Software

With the increasing frequency of water traffic, the occurrence frequency of water traffic crisis is also increasing, and the emergency rescue work has been paid more and more attention. In this paper, based on the dynamic rescue process and static influencing factors of water traffic emergency rescue, the system dynamics (SD) method is used to construct the water traffic emergency rescue system model. Through literature review and field research, the dynamic rescue process mainly includes risk situation confirmation, emergency response, rescue operations and emergency aftermath; Static influencing factors mainly include emergency response system, emergency legal system, plan construction, favorable environment, personnel quality, and emergency resources. The size of model parameters is determined by practical cases and the model is verified by Vensim software. Simulation results show that: The emergency rescue system model based on the system dynamics can reveal the water traffic emergency rescue process and show the changing trend of the rescue level over time.
Opening science

Advances in technology have triggered the emergence of a new generation of science communication forms that are based on Open Science (OS), an umbrella for different movements whose goal is to promote transparency, reusability, and connectivity of research. It provides the infrastructure (platforms, tools, and services) to make scientific information freely available. OS can mean many different things—Open Access (OA) to publications, open research data, open peer review, open citations, open-source software, open collaboration, open notebooks, open educational resources, open books, and citizen science. This chapter focuses on the movements promoting (1) OA to publications, (2) Open peer review, (3) Open research data, and (4) OA to citations. It also looks at how preprints, academic social sites, and new communication formats, such as Registered Reports, Lab Protocols, and Study Protocols are challenging the traditional scientific publishing system.
EG-HRNet: An Efficient High-Resolution Network Using Ghost-Modules for Human Pose Estimation

As an essential task in predicting a human’s behavior, human pose estimation (HPE) plays a very important role in many real-time applications. However, existing HPE methods are still too large which severely prevents them to be used in resource-sensitive applications. In this paper, aiming to a significant reduction in computation complexity, we propose an efficient high-resolution HPE network using ghost-modules (EG-HRNet). Based on the HRNet architecture, the new EG-HRNet uses modified shuffle blocks as the inner blocks to replace the residual blocks. Meanwhile, we use the lightweight ghost bottleneck for a more efficient feature extraction and use the ghost modules in the fusion layers to replace the costly 1x1 point-wise convolutions. Finally, we use the distribution-aware coordinate representation of the keypoints to acquire more accurate heatmaps of the input images. The experimental results on the COCO keypoint detection dataset show that the new efficient EG-HRNet model has successfully reached a tender balance between the processing speed and the estimation accuracy.
Design of an Efficient Adder based on Residue Number System

In this paper, a new modified approach is implemented with gates which consists of OR, AND gates and RNS based ALU (Arithmetic and Logical Unit) is implemented. The technology used is Xilinx ISE 14.7 Verilog coding for these adders. The conventional structure consists of mux's where converting from one form to another form is required. The two techniques named, thermometer and one-hot coding are very useful. The disadvantages with the conventional approach are area, power and delay. In VLSI, the major factors are speed, are and delay. This paper attempts to reduce the power and maintain efficiency. The proposed research work is further extended to increase the bit size and design modular adder with various moduli i.e., extended the feature of coding which works for mod 5, 7, 11, and 13. In this, paper to understand the methodology modulo-7, modulo-8 based modular adders are designed with logic gates and a modified architecture is presented and a basic comparison is shown between binary and residue number system. The parameters of both the techniques are compared and the proposed methods shows best results compare to traditional methods.
Assessing the Robustness of Deep Q-Network Agents to Changes on Game Object Textures

The research in autonomous agents aspires to achieve Artificial General Intelligence, where agents, like humans, are able to understand concepts and learn how to solve tasks. We would like to observe this ability on game agents as well. Recent research on autonomous agents for game playing uses a combination of Deep Neural Networks and Reinforcement Learning algorithms. Commonly, Neural Networks present vision-based models, usually Convolutional Neural Networks (CNN). However, those models can undergo performance degradation when dealing with different pixel patterns, an issue that also happens with vision-based autonomous agents in games. Prior works have shown that CNN-based autonomous agents cannot reproduce the behavior learned in one scene when they are placed into a brand new version with different textures. In this work, we evaluate whether the agents educe high-level elements, such as enemy, foreground, and background. Instead of testing the agent in a completely different scene, we designed two experiments based on slight changes. In the first experiment, we change only a subset of the game objects. In the second experiment, the agents play in an interpolated version of two scenes. Even when changing only a single game object texture, the agents are not guaranteed to present good behavior. We show that, depending on the training scenario, the agents are not fully robust to generalize a high-level concept of game objects.
Explainability for Time Series Models

A time series model is a way of generating a multi-step prediction along a future time period. There are statistical models and machine learning-based models that can be deployed to generate forecasting for the future based on historical data. If the model predictions can be trusted or not, what is the degree of confidence someone can have about the predictions? Models that can be explained and models that cannot be explained are certain things we are going to cover in this chapter.
Mathematics and Bayesian Inference

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"Cyber-Security of Industrial Computer Systems" - Differentiaton as a Separate Discipline

The Faculty of Computer Systems and Technologies of the Technical University of Sofia develops methodology and curricula for teaching of Cyber-security in higher education following the basic principles, set out in the ACM and IEEE report "Computing Curricula 2020", namely: transition from defining knowledge-based curricula to competence training in computer education and expanding the scope of computer science education. The subject of the present work is the extension of the discipline "Cyber-security" (by the way, one of the main disciplines in the structure of CC2020) with the separation of the discipline "Cyber-security of Industrial Computer Systems". The paper analyzes the differences in the approach and interpretation of Cyber Threats, Cyber Defense and other elements of Cyber-security between the Information and Industrial Systems that justify this differentiation.