Reviewerly/exHarmony huggingface dataset:
A KPI Anomaly Detection Method Based on Fast Clustering

In the Artificial Intelligence for IT Operations scenarios, KPI (Key Performance Indicator) is a very important operation and maintenance monitoring indicator, and research on KPI anomaly detection has also become a hot spot in recent years. Aiming at the problems of low detection efficiency and insufficient representation learning of existing methods, this paper proposes a fast clustering-based KPI anomaly detection method HCE-DWL. This paper firstly adopts the combination of hierarchical agglomerative clustering (HAC) and deep assignment based on CNN-Embedding (CE) to perform cluster analysis (that is HCE) on KPI data, so as to improve the clustering efficiency of KPI data, and then separately the centroid of each KPI cluster and its Transformed Outlier Scores (TOS) are given weights, and finally they are put into the LightGBM model for detection (the Double Weight LightGBM model, referred to as DWL). Through comparative experimental analysis, it is proved that the algorithm can effectively improve the efficiency and accuracy of KPI anomaly detection.
Investigation of the Unsupervised Machine Learning Techniques for Human Activity Discovery

Human activity recognition has been considered as the main capability of an intelligent system in understanding of human activities. Human activity recognition focuses on classifying activities with predefined models learned from labelled data based on supervised or semi-supervised approaches. These approaches have assumed the availability of abundant labelled activity observations. In real-world scenarios, labelled activity observations are difficult to obtain given the undefined number of human activities and their wide variation between different subjects. The desirable approach is an un-supervised one in which an intelligent system can discover new activities from unlabeled observations. This work aimed to evaluate the performance of several clustering algorithms to effectively distinguish different daily activities for human activity discovery. Clustering algorithms used include k-means, spectral, hierarchical and BIRCH clustering. Activity observations were represented as a sequence of postures with 3D skeletal joint locations derived from the Kinect depth map, and then different clustering algorithms were applied to the data. The approach is evaluated on a lab recorded dataset and a publicly available dataset. Overall mean precision, recall and F1-score for both datasets were above 58%, 68%, 61% respectively. K-means and agglomerative clustering with ward linkage achieved highest precision, recall and f1-score on both datasets which demonstrated the potential of using clustering algorithms to distinguish and group different activities for activity discovery without using labeled data.
Memory-Efficient Minimax Distance Measures

Minimax distance measure is a transitive-aware measure that allows us to extract elongated manifolds and structures in the data in an unsupervised manner. Existing methods require a quadratic memory with respect to the number of data points to compute the pairwise Minimax distances. In this paper, we investigate two memory-efficient approaches to reduce the memory requirement and achieve linear space complexity. The first approach proposes a novel hierarchical representation of the data that requires only $$\mathcal {O}(N)$$ memory and from which the pairwise Minimax distances can be derived in a memory-efficient manner. The second approach is an efficient sampling method that adapts well to the proposed hierarchical representation of the data. This approach accurately recovers the majority of Minimax distances, especially the most important ones. It still works in $$\mathcal {O}(N)$$ memory, but with a substantially lower computational cost, and yields impressive results on clustering benchmarks, as a downstream task. We evaluate our methods on synthetic and real-world datasets from a variety of domains.
ON THE DETERMINATION OF LITERARY PLAGIARISM IN COPYRIGHT LAW

Ponte Academic JournalJun 2022, Volume 78, Issue 6 ON THE DETERMINATION OF LITERARY PLAGIARISM IN COPYRIGHT LAWAuthor(s): Ziyu ZuoJ. Ponte - Jun 2022 - Volume 78 - Issue 6 doi: 10.21506/j.ponte.2022.6.4 Abstract:Literary plagiarism is mainly presented as advanced plagiarism in the form of article laundering in contemporary times. Advanced plagiarism includes language-transformation (adapted) and content-collage (deconstructive) plagiarism. Copyright law mainly follows the principle of “access + substantial similarity - free-use defence” in determining literary plagiarism. However, there are still some defects in the judicial practices, which mainly show in the misunderstanding of the nature of literary plagiarism and the rigid understanding and application of basic theories of copyright law. The linguistic form of literature is not only the arrangement of words and sentences but also the structure of works. The idea of works in the popular sense is not consistent with the ideological connotation of the idea/expression dichotomy. The nature of work is the expression, but to protect the interests of the public domain, some expressions are not protected. There is no high or low level of originality; the so-called high or low originality refers to the work to bring readers the strength of aesthetic experience. It is suggested that copyright law follow four review principles to determine literary plagiarism. Download full text:Check if you have access through your login credentials or your institution Username Password
Hybridization of Artificial Potential Field and D* Algorithm for Mobile Robot of Path Planning in Dynamic Environment

Robot path planning has been paid much interest by many researchers to be utilized in many industrial applications. In order to attain accurate robot movements, the path planning methods are improved. In this paper, the artificial potential field has been enhanced to find the robot path that follows the dynamic goal and avoids the dynamic obstacle. The D* algorithm cost is utilized to add to the attractive potential equations, taking into consideration the dynamically changing goal point and robot environment. The essential functions of the prospered D*-based potential field method are solving the artificial potential field problems in generating the potential area and path, as well as obtaining the best path that achieves the whole motion criteria, especially the minimum distance. Simulation results of the implementation of the proposed D*-based artificial potential field demonstrate that the proposed method has promising potential for efficient robot path planning in following the dynamic goal and avoiding the dynamic obstacles with achieving the minimum distance and overcoming the potential field problems.
Actual Rating Calculation of the Zoom Cloud Meetings App Using User Reviews on Google Play Store with Sentiment Annotation of Bert and Hybridization of RNN and LSTM

The recent outbreaks of the COVID-19 forced people to work from home. All the educational institutes run their academic activities online. The online meeting app the “Zoom Cloud Meeting” provides the most entire supports for this purpose. For providing proper functionalities require in this situation of online supports the developers need the frequent release of new versions of the application. Which makes the chances to have lots of bugs during the release of new versions. To fix those bugs introduce developer needs users’ feedback based on the new release of the application. But most of the time the ratings and reviews are created contraposition between them because of the users’ inadvertent in giving ratings and reviews. And it has been the main problem to fix those bugs using user ratings for software developers. For this reason, we conduct this average rating calculation process based on the sentiment of user reviews to help software developers. We use BERT-based sentiment annotation to create unbiased datasets and hybridize RNN with LSTM to find calculated ratings based on the unbiased reviews dataset. Out of four models trained on four different datasets, we found promising performance in two datasets containing a necessarily large amount of unbiased reviews. The results show that the reviews have more positive sentiments than the actual ratings. Our results found an average of 3.60 stars rating, where the actual average rating is 3.08 stars. We use reviews of more than 250 apps from the Google Play app store. The results of our can provide more promising if we can use a large dataset only containing the reviews of the Zoom Cloud Meeting app
Improving the Efficiency of E-Healthcare System Based on Cloud

The human services supplier might be moving from paper records to electronic health records (EHRs) or might be utilizing EHRs as of now. EHRs permit suppliers to utilize data all the more successfully to improve the quality and productivity of your consideration; however, EHRs won't change the protection assurances or security shields that apply to your wellbeing data. This venture centers around creating secure cloud structure for developing and getting to believe registering administrations in all degrees of open cloud sending model. Along these lines, disposes of both inside and outside security dangers. These outcomes in accomplishing information classification, information honesty, confirmation, and approval, disposing of both dynamic and detached assaults from cloud arrange condition. Building up a safe cloud structure for getting is to believe figuring and capacity benefits in all degrees of open cloud organization model.
Exploring Unet Architecture for Semantic Segmentation of the Brain MRI Scans

In today’s digital world, machine learning is comprehended as the first step toward achieving automatization. Machine learning developed the deep learning, a Web of entangled neural networks which are now weaving the definition of modern medical sciences. Computer-aided diagnosis (CAD) is one of the by-products of these Webs which uses various algorithms for medical image processing and analysis. Semantic segmentation thus contributes in one of the processes in CAD. In this paper, we carried out various experiments on MRI scans of brain tumors and made an attempt to figure out a constructive and elementary method for segmentation of these scans. In particular, we aimed at semantic segmentation of medical images by exploring the well-known Unet architecture. The results obtained reached an accuracy and precision of more than 90% and a good estimated IOU score for each case. Unet was applied on images operated upon by k-means clustering and median filter.
A Smart Healthcare Imbalanced Classes Model Using Multi Conditional-Task GAN

The rapid development in the internet of things (IoT) produced a heavenland for attackers due to neglecting the security aspect in manufacturing. Intrusion Detection System has shown a great promise in anomaly detection traffic in the network. However, this lastest had problems because of imbalance data and the minority classes problem. This paper presents a new security model using Machine Learning. In particular, this model is based on a Multi Conditional-Task Generative Adversarial network (MCTGAN) to address the problem of class imbalance in anomaly detection System. The proposed approach use is illustrated by a case study: A Smart Healthcare System-based scenario.
Hands-Free Interaction Methods for Smart Home Control with Google Glass

Augmented reality glasses (AR glasses) provide their users with visual and sometimes also acoustic information without requiring their hands. This fact allows users to consume the information provided by AR glasses even in situations where they need their hands to perform an activity. However, if interaction with the AR glasses is also required, users will still need to use their hands most of the time. In order to prevent hindering the user in his or her actual activity, a well-designed human-machine interaction concept that does not involve the hands is desirable. One solution could be to develop interaction methods for AR glasses that do not involve the user's hands and to use the AR glasses not only as an output device but also as an input device. From those methods also people with physical limitation who rely on assistance systems, like a hand exoskeleton, could benefit. Therefore, we developed and evaluated alternative input methods without hands in assistive augmented reality (AR) contexts, in terms of accessibility. With 15 subjects we investigated 5 alternative interaction concepts for operating a smart TV without hands via a Google Glass. The results show that it is possible to develop hands-free input methods for the Google Glass that work quickly and reliably and achieve high user satisfaction. Thus, our user study also shows that it seems to be possible to use the Google Glass to control smart home and assistance systems, such as a hand exoskeleton.
Cyptocurrencies: development and impact on the world economy

Cryptocurrencies, also known as cryptocurrencies, are a new form of payment, similar to conventional money, but they operate through the Internet and facilitate buying and selling transactions in a faster and more efficient way. Over the years several types of cryptocurrencies similar to bitcoin have been developed, but with the advancement of technology others have emerged that have different objectives and operation, currently they are well known as a source of investment, as a business opportunity and forms of payment. The objective of this research is to know how cryptocurrencies have developed over the years and how they affect or benefit the world economy. The concepts and information that were analyzed and studied for the realization of the article were the definitions, history, advantages, disadvantages, characteristics among other variables in order to meet the objective and have a clearer idea of what cryptocurrencies are and what is their function and impact on the world economy. The methodology used in the research was documentary and bibliographic, which began with a brainstorming to be able to raise the keywords that would be necessary to search for scientific articles on the Google Scholar platform, then upload them to Google Drive to finally cite the articles with the help of the Mendeley application.
Generalized Net Model of Balanced Iterative Reducing and Clustering Using Hierarchies (Birch) with Intuitionistic Fuzzy Evaluations

Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) is a method for agglomerative cluster analysis. A Generalized net (GN) model of the BIRCH is constructed. The clustering procedure is estimated using intuitionistic fuzzy evaluations. The process monitoring is explained using the constructed GN model and calculated IFEs. The GN model of BIRCH with IFEs optimizes and estimates the standard clustering algorithm. The proposed method is implemented using Python programming language.
Facial Mask Impact on Human Age and Gender Classification

The human face contains important information enabling the social identification of the owner about the age and gender. In technical systems, the face contains a number of important information that enables the identification of a person. The COVID-19 pandemic made it necessary to cover the face with a mask and thus hide a significant part of information content in the face, important for social or technical purposes. The paper analyses how covering the face with a mask makes it difficult to identify a person in terms of age and gender determination. Analyzes with the employment of state of the art models based on deep neural networks are performed. Their effectiveness is investigated in the context of the limited information available, as with the case of the face covered with a mask.
Evaluation of Circuit Lifetimes in Tor

Tor is a popular anonymity network which achieves its anonymity by constructing paths over three Tor relays, so-called circuits. Multiple streams that correspond to TCP connections can be multiplexed over a single circuit. By default, circuits are used for about ten minutes before switching to new circuits. Once that time limit is reached the circuit cannot be used for any new streams. This time-window is called the maximum circuit dirtiness (MCD). This paper analyzes the consequences of changing the MCD for all clients in the network and provides data on how changing the MCD affects various metrics of the Tor network. Our analysis shows that reducing the MCD to a sane value has almost no impact on the clients. Neither performance nor anonymity of the clients are significantly affected by the MCD. On the relays however halving the default MCD reduces the memory usage by about 20% while maintaining the original throughput and no measurable increase in CPU usage. Raising the MCD shows the opposite effect and increases memory usage. By drastically reducing the MCD, a significant number of extra circuits are created. From a performance point of view, the MCD should be reduced. Building on this work, side effects on specific attacks on Tor should be investigated in future work.
UFIR Filtering for Uncertain WSN with Multiplicative Noise and Randomly Delayed Data

We address the problem of unbiased finite impulse response (UFIR) state estimation in wireless sensor networks (WSNs) with uncertain parameters in data transmission and insufficient knowledge of the model. Data are transmitted which one-step random delays having the Bernoulli distribution. The multiplicative noise is detected in the channel and observation, which are described by random variables. The state space model is converted to another without delay, but with delay-dependent matrices. Based upon this model, the UFIR filter is developed based on the transformed model. The Kalman filter (KF) and game theory $$H_{\infty }$$ filter are used as benchmarks. Based on GPS tracking of a moving vehicle, it is shown that the UFIR filter is more robust than the KF and game theory $$H_{\infty }$$ filter.
A Deep Neural Network as a TABU Support in Solving LABS Problem

One of the leading approaches for solving various hard discrete problems is designing advanced solvers based on local search heuristics. This observation is also relevant to the low autocorrelation binary sequence (LABS) – an open hard optimisation problem that has many applications. There are a lot of dedicated heuristics such as the steepest-descent local search algorithm (SDLS), Tabu search or xLostovka algorithms. This paper introduce a new concept of combining well-known solvers with neural networks that improve the solvers’ parameters based on the local context. The contribution proposes the extension of Tabu search (one of the well-known optimisation heuristics) with the LSTM neural network to optimise the number of iterations for which particular bits are blocked. Regarding the presented results, it should be concluded that the proposed approach is a very promising direction for developing highly efficient heuristics for LABS problem.
Fine-Grained Analysis of Similar Code Snippets

Code recommendation aims to help programmers in their coding endeavors by suggesting appropriate code snippets to complete their program. Code recommendation approaches such as code search or code repair may rely on code snippets or code templates extracted from existing projects to provide these suggestions. In this context, extracting and characterizing reusable and recurring code structures beforehand is thus essential. In this paper, we characterize recurring code structures through parametrizable code templates. Code templates can outline the common structure in code snippets along with their variation points, hence providing a convenient way to define their structural similarity. Pattern Structure is a mathematical data analysis framework for organizing objects depending on their similarity: it produces a structure supporting clustering, analysis, and knowledge discovery tasks. We propose an approach leveraging this framework and similarity defined through code templates to highlight and organize groups of similar snippets. The produced structure contains all relevant code templates as well as refinement relationships between them, and can be used to support both manual and automated analysis. We present a case study where we apply this approach to analyze snippets for the task of code sophistication, which consists of identifying and suggesting missing conditional paths in programs.
FINNger: To Ease Math Learning for Children using Hand Gestures by Applying Artificial Intelligence

Nowadays, kids have a startling capacity to make use of trending electronic devices like tablets, smart phones, gaming devices, etc. So, it has been eminently boosted by the adroitness of access of latest smart devices throughout the world. Also, it is a known fact that children are facing difficulty in learning mathematical-related subjects at pre-school. We focus broadly on alphabetization so that children can overcome from the difficulties in learning various subjects especially mathematics. In this work, we proposed a basis for instinctual application that provides a lot of ease while using technological applications and enhances the children knowledge, thereby implementing Jarvis March algorithm in artificial intelligence for the finger tip detection which can improve the capability to learn new concepts and also implementing a novel CNN to achieve a new technique so named FINNger.
Rethinking Customer Analytics: The Impact of Artificial Intelligence

With the triggering effect of Covid-19 pandemic, the role of digitalization has become a strategic target and expedited the digital transformation process. World’s direction to the digital future has therefore shaped the use of new-age technologies, such as internet of things, artificial intelligence (AI), machine learning and blockchain. In response to this evolvement of new-age technologies, a noticeable shift from data-driven analysis to technology-oriented applications has occurred, particularly addressing the significance of analytics and AI. These rapid advancements of AI applications influence the use of customer analytics whilst enhancing the importance both for the general understanding and individual behavior of customers, within the scope of customer analytics. Considering the embeddedness of these technologies on practical applications, this study acknowledges the high-impact role and power of AI. In this regard, the study concentrates AI applications from the perspectives of customer analytics. Furthermore, the task of AI, the level of intelligence of AI applications and how the information from customer analytics is obtained and exploited by these applications are discussed. Focusing on the practical case applications, the study suggests a taxonomical structure of AI and customer analytics.
Persepsi dan Pendampingan Belajar Anak Oleh Orang Tua Di Masa Pandemi Covid- 19

The purpose of this study was to (1) Describe the parents' perceptions of the implementation of learning from home by students, (2) To analyze the role of parents in learning assistance for students, (3) To analyze the factors that support and hinder parents in learning assistance for fifth-grade students of SDN Tegalarum 1 during the Covid-19 pandemic.Supporting factors for learning assistance by parents for learning from home: (1) the use of WhatsApp as a communication medium and a learning medium (2) The role of schools in providing guidance and consultation services for students and parents. As for the obstacles faced by parents during learning assistance at home: (1) cellphones are often brought to work while children at home do not have cell phones; (2) some parents don't have Android phones; (3) Many parents find it difficult about learning materials when accompanying children to learn; (3) additional internet quota fees; and (4) additional work for parents in accompanying children to learn.