Reviewerly/exHarmony huggingface dataset:
Competitive Analysis of the Top Gradient Boosting Machine Learning Algorithms

In this paper, we compare four state-of-the-art gradient boosting algorithms viz. XGBoost, CatBoost, LightGBM and SnapBoost. All these algorithms are a form of Gradient Boosting Decision Trees(GBDTs). They find wide usage across competitive machine learning contests like Kaggle, due to their flexibility and considerably faster training times. Since a typical vanilla GBDT is usually implemented as a black box model, our research makes an attempt to help improve the explainability of GBDTs. We performed an exhaustive 360 degree comparative analysis of each of the four algorithms by training and testing them on diverse datasets leveraging IBM's PowerAI <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sup> AC922 CPU. The analysis was performed using two approaches; One was by training the baseline algorithms on the datasets, and the other was by performing systematic hyperparameter optimization (HPO) using the HyperOpt framework. Although the HPO process is resource-intensive, the Power System architecture facilitated lower training times without compromising the algorithm's accuracy for each of the datasets. We present the accuracy scores and training times across the four datasets for both the aforementioned approaches. The results imply that despite interesting trends observed across all the datasets, there is no clear winner that excels equally in every aspect of performance
Resource Controllability of Business Processes Under Conditional Uncertainty

Abstract A current research problem in the area of business process management deals with the specification and checking of constraints on resources (e.g., users, agents, autonomous systems, etc.) allowed to be committed for the execution of specific tasks. Indeed, in many real-world situations, role assignments are not enough to assign tasks to the suitable resources. It could be the case that further requirements need to be specified and satisfied. As an example, one would like to avoid that employees that are relatives are assigned to a set of critical tasks in the same process in order to prevent fraud. The formal specification of a business process and its related access control constraints is obtained through a decoration of a classic business process with roles, users, and constraints on their commitment. As a result, such a process specifies a set of tasks that need to be executed by authorized users with respect to some partial order in a way that all authorization constraints are satisfied. Controllability refers in this case to the capability of executing the process satisfying all these constraints, even when some process components, e.g., gateway conditions, can only be observed, but not decided, by the process engine responsible of the execution. In this paper, we propose conditional constraint networks with decisions (CCNDs) as a model to encode business processes that involve access control and conditional branches that may be both controllable and uncontrollable. We define weak, strong, and dynamic controllability of CCNDs as two-player games, classify their computational complexity, and discuss strategy synthesis algorithms. We provide an encoding from the business processes we consider here into CCNDs to exploit off-the-shelf their strategy synthesis algorithms. We introduce $$\textsc {Zeta}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Z</mml:mi><mml:mstyle><mml:mi>E</mml:mi><mml:mi>T</mml:mi><mml:mi>A</mml:mi></mml:mstyle></mml:mrow></mml:math> , a tool for checking controllability of CCNDs, synthesizing execution strategies, and executing controllable CCNDs, by also supporting user interactivity. We use $$\textsc {Zeta}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Z</mml:mi><mml:mstyle><mml:mi>E</mml:mi><mml:mi>T</mml:mi><mml:mi>A</mml:mi></mml:mstyle></mml:mrow></mml:math> to compare with the previous research, provide a new experimental evaluation for CCNDs, and discuss limitations.
WITHDRAWN: Key challenges and research direction in cloud storage

Cloud computing has gained the importance in recent time because of its unique characteristics like elasticity, resilience in delivering compute, storage, network, and resource management in form of utility services over the Internet. Cloud computing area has attracted attention and interest due to the ever-increasing demand for reliable and cost-effective infrastructure and service delivery. This paper primarily focuses on the survey of issues and open challenges of cloud database in cloud computing storage. The aim of this survey is to provide a perspective to a researcher a general overview of issues, open challenges, and future trends in cloud storage with primarily focuses on cloud databases. The paper also tabulates comparison of different methods and approaches of researchers in tackling issues pertaining to data availability through data replication, data partitioning, data management, and data placement under the context of cloud enabled technologies.
Emergency Vehicle Detection in Traffic Surveillance Using Region-Based Convolutional Neural Networks

Emergency vehicles such as ambulances and fire engines get stuck in a heavy traffic may lead to the loss of valuable lives. To overcome this problem, a framework is proposed to detect emergency vehicle in heavy traffic, using Region-Based Convolutional Neural Network (RCNN) to take quick decisions. When an ambulance comes in a particular direction in a traffic signal, the controller detects and checks for the density of traffic and speed of the vehicle in a particular direction and also calculates the time taken by the emergency vehicle to cross the road. Based on the information received, the controller alerts the alternate roads, by displaying a red signal for a particular time in its path. Once the emergency vehicle passes away from the signal, the master control has to be reset for maintaining the normal traffic flow.
Big Data Cloud Storage Optimization Scheme based on PSO Algorithm

Abstract PSO algorithm, as one of the best swarm intelligence algorithms, has a strong optimization ability. It has obvious advantages to apply PSO algorithm to the optimization of resource scheduling strategy of cloud computing platform. Through the research on the application of PSO algorithm in cloud computing, effective methods are provided for the improvement of efficiency of cloud computing platform. This paper analyzes and studies the application of PSO algorithm optimized by cloud model in business scheduling of big data cloud storage platform.
Generation of synthetic 4 s utility-scale PV output time series from hourly solar irradiance data

The short-term characteristics of utility-scale PV variability become increasingly important for power system operation as PV penetrations grow. However, understanding how these characteristics and their aggregated impacts will change with new PV deployments is challenging given the limited and highly site dependent availability of high-resolution PV output data. This study proposes a methodology to generate a synthetic 4 s utility-scale PV output time series for a PV plant anywhere in a power system using hourly satellite-derived irradiance data, which is globally available, to select a set of 4 s output from models created from four PV plants operating within the Australian national electricity market (NEM). The method resamples 4 s clear sky PV output index variability from statistical distributions which are binned according to hourly clear sky index pairs of global horizontal irradiance and direct normal irradiance. The method is cross-validated against the observations from the four utility PV plants located in different climate zones. The monthly Kolmogorov–Smirnov Integral (KSI) tests on the modeled variability distributions show that they are not statistically different from the observed time series with most KSI values remaining under 80%. Finally, the method is applied to all 20 utility PV plants that were registered in the NEM as of 2019. The modeled result shows good agreement with the measured aggregated 4 s variability. Hence, our method can be usefully applied for modeling the short-term variability of future power system scenarios with high PV penetrations if at least some existing utility PV plant generation data are available.
Machine learning algorithm to identifies fraud emails with feature selection

Abstract One percent of the emails that come in each day are fraudulent. Promotion emails tend to offer products. The recipient’s email is recorded by a company or organization. Not all promotional emails are considered spam or hoaxes. When observed, incoming promotional emails provide the information that is needed. How to identify promotional emails including hoaxes or shipping with machine learning, known as algorithms, Support Vector Machines, Naïve Bayes, Decision Tree, Logistic Regression, Stochastic Gradient Descent, and Neural Network (MLP). Decision trees are effective tracing using a data structure consisting of vertices &amp; edges. A node (root, branch, leaf) can categorize incoming e-mail, including hoax or e-mail shipping. Previously, it was necessary to categorize the characteristics of the email. After searching the email, the promotional email grouping is continued. In Feature Extraction, the calculation of Gain and Entropy is used to determine the selection of features in the classification of promotional emails, fraudulent emails or hoaxes.
Off-Line Tamil Handwritten Character Recognition Based on Convolutional Neural Network with VGG16 and VGG19 Model

Automated character recognition is an evolving field of research that recognizes the character with the help of imaging technology. Deep learning pipeline have become a very popular method for various handwritten character recognition tasks. Over time, these network designs have mature to comprise many layers and are skilled of producing strong networks for character recognition tasks. Numerous Convolutional Neural Network (CNN) design have been planned such as LeNet, AlexNet, and GoogleNet depends on accuracy for various language character recognition. In this paper we in detail explore the usefulness of the VGG16 and VGG19 architecture on 25 class subset of the HP lab Offline Tamil isolated character dataset. We examine the benefits of transfer learning by using VGG network weights trained on the ImageNet dataset, which can provide better performance in the Tamil handwritten character recognition Task.
A New Self Adaptive Fuzzy Unsupervised Clustering Ensemble Based On Spectral Clustering

Nowadays, the goal of ensemble clustering approach, including the heterogeneity and huge number of variables, is to get the underlying structure based on the clustering quality results. In this context, we improve cluster ensemble approach based on cluster forest using Self-Adaptive Fuzzy C-Means (SAFCM) method to determine a good local base clustering. This proposed approach consists of two stages. To begin, unsupervised feature selection method is designed on the constructed of better variables on simulated data under the kappa metric. Next, we ameliorate the spectral clustering algorithm applied to find final grouping. Then, the SAFCM is used to obtain also the best number of K clusters and the optimal fuzzy exponent. This algorithm consists of three key steps: initially, a new algorithm is run which aims to get the best initial centroids values based on fuzzy density factor and distance. Next, new fuzzy cluster validity index (VI) is applied to evaluate the accurate number of the selected clusters. Finally, self adaptive FCM for the optimal value of Fuzzy weighting exponent m was proposed. To validate the effectiveness of the proposed clustering approach, we use eight datasets. Fur then, the performance of our system will be compared with several methods.
Light Weight Cryptography Based Encrypted Multiple Secret Share Creation for Biometrics Images

Owing to the rapid growth of information technologies, a rising need for cybersecurity and biometric technologies is increasingly evolving. Biometrics image protection is an important problem as digital images and medical details are distributed via public networks. This research work proposed a threshold-based share creation scheme for Biometrics images. To enhance the security level of the shares, each shares are encrypted by Light Weight Cryptography (LWC)-Stream Cipher method. To increase the stream cipher encryption efficiency, optimal keys are selected by Ant Lion Optimization (ALO) technique. The benefit of consuming stream ciphers is that the speed of execution is maximum over block cipher and less complex. The benefit of the suggested stream cipher approach is that the decoding of the keys in the keystream and the characters in the plain text denotes decrypted biometrics image will improve device reliability. From the implementation results proposed model achieves the maximum PSNR with the security of Biometrics images, compared to other existing techniques.
Towards Evolvable Ontology-Driven Development with Normalized Systems

Normalized Systems (NS) enables sustainable software development and maintenance using code generation of evolvable information systems from models of so-called NS Elements. To promote semantic interoperability with other conceptual models, RDF and OWL technologies can be used for knowledge representation in NS as it is common within the Semantic Web and Linked Open Data domains. Previous research resulted in initial NS-OWL bi-directional transformation and a prototype tool for its execution. In this extended paper, these efforts are further elaborated into an evolvable solution based on NS Expanders. The transformation utilizes RDF to encode all domain-specific structural knowledge of an NS model to ensure bi-directionality. In addition, it also maps entities of NS metamodel to OWL concepts to serve as an ontology for underlying data. Because of the metacircular NS metamodel, any NS model including the metamodel itself, can be transformed. Moreover, the transformation of application data to or from RDF is also possible. Having the NS metamodel, NS models, and potentially also data in RDF opens further research possibilities in terms of analysis and integrations. The use of NS Expanders caused that the solution can be easily extended and refined, e.g. when the metamodel is updated. The results of our research are expected to help with the design of real-world information systems, including the NS tooling and the metamodel.
Knowledge Extraction: Automatic Classification of Matching Rules

With the fast development of information technologies, more massive amounts of data are produced in cyberspace. Traditional web search methods cannot satisfy users’ demands timely and accurately, and it is an urgent task to develop big search techniques in cyberspace. MDATA (Multi-dimensional Data Association and Intelligent Analysis) is a knowledge representation model with temporal and spatial characteristics. Through the effective expression of temporal and spatial characteristics, it supports efficient updating of dynamic knowledge. Pattern matching is often used to extract the needed knowledge from massive data for constructing the MDATA. Pattern matching requires matching rules to acquire needed substrings from a string. In practical application scenarios, some matching rules can be divided into several categories. The same category of the matching rules has the same meaning, but with different expressions. Regular expressions can aggregate matching rules with consistent structure and strong regularity together. However, in practical scenarios such as cyber security knowledge, such homogeneous matching rules are rare, and most of them are random and disordered. For random matching rules, manually designing regular expressions to aggregate them becomes time consuming and laborious. In order to address the problem, we apply word embedding algorithm to automatic classifying matching rules. Word embedding is a kind of representation learning algorithms which is usually adopted in recommendation systems, relation mining, text similarity matching and so on. It can convert words into low-dimensional space vectors based on neural network models. However, word embedding algorithms take into account the relationship between semantic information and context, which needs a large number of data. When we only consider the matching rules in pattern matching, such data is insufficient to reflect the context relationship, which leads to the failure of deriving accurate results. In this chapter, we design an automatic classification method which only needs a small number of data to meet the practical requirement.
Case Study: The Corona Contact Tracing App in Germany

The long lasting COVID-19 pandemic is a burden on daily life and a serious health threat. One tool to control the pandemic’s spread is contact tracing. Contact tracing means identifying and notifying all previously contacted persons of an infected person. This helps interrupting infection chains. This case study explores the challenges the developers, architects, and politicians faced during the development of the German Corona-Warn-App, a digital contact tracing application utilizing Bluetooth Low Energy (BLE). Possible privacy implications are analyzed based on the study by the creators of the contact tracing protocol DP-3T. Furthermore, digital contact tracing solutions of other countries are explored. European countries which have not released an app can benefit from the lessons learnt. Especially the involvement of the open-source community turns out to be beneficial.
Development of competence “forecasting” as conditions of readiness for professional self-determination

The article presents the results of the study of the conditionality of the components of forecasting the regulatory-will qualities of the personality among men who are ready and not ready for professional self-determination. In the article, the author reveals the specifics of the competence of forecasting in higher education. In the article, the author identifies two areas in the framework of which the competence of forecasting is considered – as a personal quality and as a "through" skill required in any professional activity. As a result of empirical research, differences were revealed in the conditionality of readiness for professional self-determination, depending on the development of forecasting components. Thus, for students unready for professional self-determination, such components of self-regulation as "planning", "programming", "evaluating results", "flexibility", "independence", are caused by changes in mood, well-being, activity. For students ready for professional self-determination, these same components of self-regulation are determined by the strength of the intentions of young men, the development of volitional qualities, the ability of individual regulation of activity and all regulatory links. Making a conclusion, the author focuses on the role of a teacher at a higher educational organisation in the development of forecasting competence.
Multi-agent Fault-tolerant Reinforcement Learning with Noisy Environments

Multi-agent reinforcement learning system is used to solve the problem that agents achieve specific goals in the interaction with the environment through learning policies. Almost all existing multi-agent reinforcement learning methods assume that the observation of the agents is accurate during the training process. It does not take into account that the observation may be wrong due to the complexity of the actual environment or the existence of dishonest agents, which will make the agent training difficult to succeed. In this paper, considering the limitations of the traditional multi-agent algorithm framework in noisy environments, we propose a multi-agent fault-tolerant reinforcement learning (MAFTRL) algorithm. Our main idea is to establish the agent's own error detection mechanism and design the information communication medium between agents. The error detection mechanism is based on the autoencoder, which calculates the credibility of each agent's observation and effectively reduces the environmental noise. The communication medium based on the attention mechanism can significantly improve the ability of agents to extract effective information. Experimental results show that our approach accurately detects the error observation of the agent, which has good performance and strong robustness in both the traditional reliable environment and the noisy environment. Moreover, MAFTRL significantly outperforms the traditional methods in the noisy environment.
Initial results of Software Development Models based on Service Oriented Architecture

Organizations have challenges to innovate their software developments. Service Oriented Architecture is a style of Information Technology Architecture that is based on service orientation. The objective of the work is to show the results obtained from models related to software developments based on the Service Oriented Architecture for Organizations. A review is made to analyze articles published in prestigious journals during the last five years. 866 potential articles were extracted, from which 15 were selected based on inclusion and exclusion criteria. It is concluded that there are several software development models based on the Service Oriented Architecture for Organizations with positive results.
Decoding up to 4 Errors in Hyperbolic-Like Abelian Codes by the Sakata Algorithm

We deal with two problems related with the use of the Sakata’s algorithm in a specific class of bivariate codes (see [, , ]). The first one is to improve the general framework of locator decoding in order to apply it on such abelian codes. The second one is to find sufficient conditions to guarantee that the minimal set of polynomials given by the algorithm is exactly a Groebner basis of the locator ideal.
Location Spoofing Detection for Social Network Service using Camera Fingerprint

Check-in on location-based social network services have become a popular trend recently. Meanwhile, people may deceive their location to seek illegal violence. Making the data reliability of location-based social networks negatively affected. Hence, it is important to identify the authenticity of location. However, methods in literature rely heavily on user-generated content and check-in data, and these methods may be invalidated due to missing prior information or lacking rich training samples. To address this challenge, this paper proposes an innovative method for detecting the location spoofing based on the source of the image for specific scenarios. Specifically, we first extract the camera sensor fingerprint based on the images posted by an inquiry user via the well-designed denoising filter. Second, the authenticity of the location is tested by comparing the consistency of the residual noise from newly-posted images with location check-in and the unique camera sensor fingerprint from an inquiry user. Finally, we conducted wide experiments on the image database, which empirically showed that the method we proposed is effective and simple, that is, it need no prior information, but only sample images from the social networks.