Reviewerly/exHarmony huggingface dataset:
An Introduction to SAP SuccessFactors Talent Modules

Welcome to SAP SuccessFactors Talent! SAP SuccessFactors is a grouping of cloud-based modules across a common platform that spans the entire realm of HCM data storage, management, and reporting. Modules include Employee Central (EC) which acts as a core Human Resources Information System (HRIS), Onboarding (ONB), Learning Management System (LMS), Recruiting (RCM/RMK), Analytics and Planning (WFA/WFP), Compensation and Variable Pay (CMP/VarPay), and Jam, as well as a variety of Talent Management modules. SAP SuccessFactors Talent modules provide a grouping of state-of-the-art tools built on the SuccessFactors cloud platform for customers interested in storing and managing talent information in real time from anywhere in the world. These modules include Job Profile Builder (JPB), Goal Management (GM), Performance Management (PM), 360, Continuous Performance Management (CPM), Calibration, Career Development Planning (CDP), and Succession Management (SCM). Using these cloud-based tools, customers are able to quickly request an instance and work with SAP and/or a certified SAP SuccessFactors partner to configure that instance specifically to their needs. Since an instance is a fully operational system that only needs to be configured without coding, the cloud solution gets you up and running in less time and for much less capital investment than traditional on-premise solutions. This book will guide you through each module configuration, as well as using the configured modules so you can realize your talent solution quickly and effectively.
Text2Chart: A Multi-staged Chart Generator from Natural Language Text

Generation of scientific visualization from analytical natural language text is a challenging task. In this paper, we propose Text2Chart, a multi-staged chart generator method. Text2Chart takes natural language text as input and produces visualization as two-dimensional charts. Text2Chart approaches the problem in three stages. Firstly, it identifies the axis elements, known as x and y entities, of a chart from the given text. Next, it finds a mapping of x-entities with its corresponding y-entities. Subsequently, it generates a chart type among bar, line, or pie, which is suitable for the given text. Combination of these three stages is capable of generating visualization from the given statistical text. We have also constructed a dataset for this problem. Experiments show that Text2Chart performs best with BERT based encodings with LSTM models in the first stage to label x and y entities, Random Forest classifier in the mapping stage and fastText embedding with LSTM in the chart type prediction stage. In our experiments, all the stages show satisfactory results and effectiveness considering the formation of charts from analytical text, achieving a commendable overall performance.
Blockchain-Enabled NextGen Service Architecture for Mobile Internet Offload

The amalgamation of heterogeneous generations of mobile cellular networks around the globe has resulted in diverse data speed experiences for end users. At present, there are no defined mechanisms in place for subscribers of a mobile network operator (MNO) to use the services of third-party WiFi providers. MNOs also have no standardized procedures to securely interact with each other, and allow their subscribers to use third-party services on a pay-as-you-go basis. This paper proposes a blockchain-enabled offloading framework that allows a subscriber of a mobile operator to temporarily use another MNO or WiFi provider’s higher-speed network. A smart contract is employed to allow diverse entities, such as MNOs, brokers and WiFi providers, to automatically execute mutual agreements, to enable the utilization of third-party infrastructure in a secure and controlled manner. The proposed framework is tested using Ethereum’s testnet on the Goerli network using Alchemy and Hardhat. The analysis of the results obtained shows that the proposed technique helps mobile operators to offer improved user experience in the form of average speed and latency. The experiments show that the average time taken to deliver a 500 MB file is reduced from 10.23 s to 0.91 s for the global average scenario, from 6.09 s to 0.50 s for 5G, from 13.50 s to 0.50 s for 4G-LTE, from 41.11 s to 0.49 s for 4G, and from 339.11 s to 0.49 s for the 3G scenario. The results also show that, with WiFi offloading, users from all cellular generations can enjoy a similar quality of services, because delivery time ranges from 0.49 s to 0.91 s for offloaded experiments whereas for the non-offloaded scenario it ranges from 6.09 s to 339.11 s.
VR Classroom for Interactive and Immersive Learning with Assessment of Students Comprehension

The virtual classroom environment is created using virtual reality that enables multiple students to enter as if in a real class but with better learning environment. Conventional learning is currently limited in the current model of textbook teaching. An interactive and visual environment provided for learning enhances the rate at which the student grasps concepts. Even though many modern online teaching methods are available today, it is not possible to check whether a student is paying attention or not. Technology is evolving at a very fast rate, and this research is an apt integration of two modern technologies: machine learning and virtual reality, so as to increase the quality of education for students. A shared VR environment, optimised for learning, will be created. Students can wear a head-mounted display and select an avatar for themselves, which will be seen by other students and teachers. The VR environment is created using Unity3D software. Students will also have to wear an EEG scanner on their heads. The output of this scanner will be fed to the machine learning subpart. Neural networks are used to identify whether the student is paying attention or not. If a student is not paying attention, the teacher will be informed about it, with a message near the student’s avatar. It has many advantages over traditional learning techniques, like usage of multiple senses and inclusivity for differently abled students.
Cost optimization techniques in Cloud Computing: Review, Suggestions and Future Scope

In the present scenario, when the technology is changing at the rapid speed, Cloud computing getting more popularity with the span due to increase in its user base. In the present era, everyone is working on the Internet and wants to get the optimum results with minimum resources. Cloud computing has to face speed performance, security, load balancing, cost management and energy efficiency related problem to maintain its sustainability in the today’s cutthroat environment. Cost management is prime element in cloud computing to provide the services to the users on optimum rate. In the present era, various researchers have proposed the various approaches to reduce the cost especially in terms of energy consumption but there are various factors which become the prime concern in the cost management. This research reveals the all factors which cause the cost element in data centers and it also suggest some steps which can be taken to reduce the cost of data centers after the complete survey of various approaches given by the prominent researchers.
A Comparative Evaluation of Acceleration and Jerk in Human Activity Recognition Using Machine Learning Techniques

Acceleration data have been widely used to study human activity recognition. However, the acceleration data collected from the accelerometer do not consider the force of gravity. Thus, it has a difficulty in discriminating closely similar activities. Jerk, the derivative of acceleration, is able to describe the changes of body accelerations correctly and mutually exclusive from the sensor orientation. This study aimed to compare the performance of jerk compared to acceleration data. The dataset used to do the comparison was collected from a triaxial accelerometer built-in Samsung smartphone with a sampling frequency of 50 Hz attached to the waist of thirty subjects. The subjects performed walking on a flat surface, walking upstairs, walking downstairs, sitting, standing, and lying down. The feature data were then grouped into three categories: acceleration features, jerk features, and combined features of acceleration and jerk. The evaluation was done using k-Nearest neighbors (KNN), linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF). The results showed that jerk features performed worse than the acceleration features. However, the combined acceleration and jerk features yielded the highest accuracy with above 87% for all classifiers. The present findings show that acceleration is still better than jerk in recognizing the pattern in human activity. The present study is, therefore, increasing the understanding of acceleration data and its derivative in human activity recognition.
Big data and industrial innovation progress in Jiangxi Province incremental effect highlights enabling digital economy cultivation

Abstract With the advent of the Internet information age, most social and economic activities rely on data. As a new resource, big data technology plays an important role in the social digital economy. Therefore, it is not only the objective requirement of economic development, but also the value of big data resources to promote the close integration of big data and industry, and help the upgrading and development of industry with big data. In order to emphasize the strength of digital economy, this paper studies the gradual effect of big data and industrial innovation in Jiangxi Province, and carries out a series of studies. With the development of the times, the most popular Internet also changes with the development of the times. The Internet has changed from the IT era at the beginning to the new digital technology era. The creation of big data technology and the establishment of database have become the storage and source direction of new resources for all industries. At present, the digital economy is facing the opportunities and challenges of the “Internet plus” new market. Digital economy related enterprises should strengthen mutual cooperation among resources supply network organizations such as information and information, dissolve and reorganize production factors, strengthen the competitiveness among industrial economies, promote the optimization and upgrading of industrial economic structure, and expand the market space for which the industry can develop. However, the development of the Internet plus industry depends on the innovative investment and innovation power of the industry. In the era of big data, one of the ways to improve the overall level of the industry is innovation. This paper discusses how much influence big data shows in the cultivation of digital economy.
Digital Contact-Tracing in a Pandemic… We Need One with a Blended Approach

Governments across the world are rolling/planning to roll-out digital contact tracing (DCT) to manage the COVID-19 pandemic. However, deploying digital contact-tracing to curtail COVID-19 without recourse to existing structural inequalities, including digital divides, will lead to unintended consequences. Therefore, we propose that while the use of technology is welcomed, governments should bear in mind that exiting structural inequalities, including digital divides, could result in the exclusion of vulnerable people with a myriad of unintended consequences. Accordingly, governments must account for structural inequalities in the design and deployment of DCT to prevent more vulnerable people from falling through the cracks.
Progressive von Mises–Fisher Filtering Using Isotropic Sample Sets for Nonlinear Hyperspherical Estimation

In this work, we present a novel scheme for nonlinear hyperspherical estimation using the von Mises–Fisher distribution. Deterministic sample sets with an isotropic layout are exploited for the efficient and informative representation of the underlying distribution in a geometrically adaptive manner. The proposed deterministic sampling approach allows manually configurable sample sizes, considerably enhancing the filtering performance under strong nonlinearity. Furthermore, the progressive paradigm is applied to the fusing of measurements of non-identity models in conjunction with the isotropic sample sets. We evaluate the proposed filtering scheme in a nonlinear spherical tracking scenario based on simulations. Numerical results show the evidently superior performance of the proposed scheme over state-of-the-art von Mises–Fisher filters and the particle filter.
Extended Privacy Preservation of Health Official Document using blockchain

Currently, new models such as mobile clouds and data warehouses have been selected to handle records. This approach reduces costs and increases availability. However, these technologies face some problems due to uncertainties, such as data privacy, flexibility, network security, attacks, and missing transparency. In particular, sensitive applications data such as medical certificates, data distribution, and student certificate maintenance. It is essential to check the reliability of these applications. The number of scams on this issue is increasing day by day. Therefore, in this paper, we propose a blockchain-based model for the issuance and distribution of medical certificates. It reduces fraud on medical certificates such as birth, death, and sick leave due to the blockchain technical features such as immutability, transparency, security, and distribution. Furthermore, this application's vital purpose is to quickly analyze and estimate the birth and death rate due to blockchain symptoms. As part of the literature survey number of existing works were interpreted as part of the literature survey, but the maximum number of documents failed to show their implementation with the results. The main strength of the paper is the literature survey, results, and analysis. Distributed based public blockchain and metamask used to develop the proposed framework. The results show medical certificate generation and verification and the gas(cryptocurrency) consumption to store certificate credentials into a blockchain as a transaction.
Risks Analyzing and management in Software Project Management Using Fuzzy Cognitive Maps with Reinforcement Learning

Many projects fail each year simply because a risk has been misjudged, ignored or unidentified. An essential motivation for analyzing the risk of a project is to inform managers in order to reduce the risk, and therefore the loss of the project. Risk analysis can help identify the best actions that would reduce the risk and assess by how much. In the last decades, the Fuzzy Cognitive Map emerged as a powerful tool for modeling and supervising dynamic interactions in complex systems. There is two ways to construct them, the first way by experts of domain and the second way by learning method based on the historical of data. In this paper, we develop a new learning fuzzy cognitive maps based on a reinforcement learning algorithm so called Q-learning and we propose here a new formulation of kosko causality principle. This connection between fuzzy cognitive maps and reinforcement learning allows us to choose based on the historical of data learning process the best and the most important connections between concepts. In this work, we illustrate the effectiveness of the proposed approach by modeling and studying the analysis of project risk management as an economic decision support system.
Image registration method based on Generative Adversarial Networks

Image registration technology has been gradually applied in military and civil fields, such as unmanned aerial vehicle (UAV) target recognition, remote sensing image registration, 3D object reconstruction and so on. The registration accuracy of traditional registration algorithms, such as sift and surf, are not high and even mismatch, when the image content changes or affine transformation occurs. In order to solve this problem, an image registration method (IR-GAN method) based on Generative adversarial networks (GAN) was proposed, in view of the content change and affine transformation of images for training. At the same time, the sample images are expanded by translation, rotation, reflection, scaling, shearing, brightness transformation to improve the precision and accuracy of the registration algorithm. Simulation results show that our method is correct in principle and can improve the registration accuracy of both images with content changing and affine transform images.
The (Im)Possibility on Constructing Verifiable Random Functions

Abstract In this paper, we further explore the properties of the verifiable random functions in both a black-box and a non-black-box manner. The results are mainly following two parts: $\bullet $ Black-Box Barrier: It is set up for an impossibility result of black-box reduction from verifiable random functions to injective one-way functions and indistinguishability obfuscators, where the verifiable random functions are suggested to be domain-invariant (i.e. the support of the distribution of keys and the domain of the evaluation space are independent of the underlying building blocks). Our result illustrates how the non-domain-invariant constructions circumvent the black-box barriers for constructing verifiable random functions and sheds light on why it is so difficult to give a domain-invariant instantiation. $\bullet $ Non-Black-Box Construction: On the other hand, the verifiable unpredictable functions are constructed from a given primitive by a non-black-box technique called the hitting-set generator. To show it's a somewhat useful technique for constructing the verifiable unpredictable functions, we further derive a limitation of the black-box barrier by proving the barrier still holds between the given primitive and verifiable unpredictable functions. Our results not only analyse the properties of verifiable random functions theoretically, but also reveal the limitation of indistinguishability obfuscators in a black-box manner, and show the advantages by adopting non-black-box techniques.
Research Based on The Improved Grey Wolf Optimization Algorithm

Abstract With the continuous progress of machine learning methods, more and more methods are widely used in various fields. Grey Wolf Optimization Algorithm (GWO), as a kind of population algorithm, has become a research hotspot in recent years because of its good optimization ability. Similar to other population algorithms, gray wolf optimization algorithm itself has the problem of imbalance between global search and local search capabilities. In order to solve this problem, the following improvements are proposed to the standard gray wolf optimization algorithm: First, change the decrement method of the convergence factor and adopt a non-linear decrement method to meet the actual search process; At the same time, a weighting strategy is introduced to dynamically assign weights to the guide wolves. It can ensure that the population jumps out of the local optimal solution. In order to verify the effectiveness of the improved algorithm, an international general test function is selected for simulation. The simulation results show that the improved gray wolf optimization algorithm has faster convergence speed, higher solution accuracy and better stability.
The Influence of Work Ethics on Job Performance of Government Employees

Ethics is associated with a positive influence on employee performance. Those who strongly support the practice of work ethics make beneficial changes to the goals and values of the organization. This descriptive-comparative correlational study assessed the influence of work ethics on the level of the job performance of government employees. A researcher-made survey questionnaire administered to 367 employees identified thru proportional random sampling was used. The study revealed that the practice of work ethics is very good job performance is very satisfactory, which implies that the influence of work ethics on job performance is great. However, significant differences were found when employees were grouped as to their demographic profiles. A significant relationship between work ethics and job performance was discovered; however, most employees' performance is on compliance level only. Hence the need to strengthen ethical values. The findings provided baseline information for an enhanced human resource management program.
Autocorrelation and Linear Complexity of Binary Generalized Cyclotomic Sequences with Period pq

Ding constructed a new cyclotomic class <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mfenced open="(" close=")" separators="|"> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mtext> </mtext> <mo>,</mo> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </mfenced> </math> . Based on it, a construction of generalized cyclotomic binary sequences with period <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>p</mi> <mi>q</mi> </math> is described, and their autocorrelation value, linear complexity, and minimal polynomial are confirmed. The autocorrelation function <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>S</mi> </mrow> </msub> <mfenced open="(" close=")" separators="|"> <mrow> <mi>w</mi> </mrow> </mfenced> </math> is 3-level if <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>p</mi> <mo>≡</mo> <mn>3</mn> <mi mathvariant="normal">mod</mi> <mn>4</mn> </math> , and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>S</mi> </mrow> </msub> <mfenced open="(" close=")" separators="|"> <mrow> <mi>w</mi> </mrow> </mfenced> </math> is 5-level if <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mi>p</mi> <mo>≡</mo> <mn>1</mn> <mi mathvariant="normal">mod</mi> <mn>4</mn> </math> . The linear complexity <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"> <mtext>LC</mtext> <mfenced open="(" close=")" separators="|"> <mrow> <mi>S</mi> </mrow> </mfenced> <mo>&gt;</mo> <mfenced open="(" close=")" separators="|"> <mrow> <mi>p</mi> <mi>q</mi> </mrow> <mo>/</mo> <mn>2</mn> </mfenced> </math> if <math xmlns="http://www.w3.org/1998/Math/MathML" id="M8"> <mi>p</mi> <mo>≡</mo> <mn>1</mn> <mtext> </mtext> <mi mathvariant="normal">mod</mi> <mtext> </mtext> <mn>8</mn> </math> , <math xmlns="http://www.w3.org/1998/Math/MathML" id="M9"> <mi>p</mi> <mo>&gt;</mo> <mi>q</mi> <mo>+</mo> <mn>1</mn> </math> , or <math xmlns="http://www.w3.org/1998/Math/MathML" id="M10"> <mi>p</mi> <mo>≡</mo> <mn>3</mn> <mi mathvariant="normal">mod</mi> <mn>4</mn> </math> or <math xmlns="http://www.w3.org/1998/Math/MathML" id="M11"> <mi>p</mi> <mo>≡</mo> <mo>−</mo> <mn>3</mn> <mi mathvariant="normal">mod</mi> <mn>8</mn> </math> . The results show that these sequences have quite good cryptographic properties in the aspect of autocorrelation and linear complexity.