Reviewerly/exHarmony huggingface dataset:
High-Precision Content Classification Using Hierarchy

Content classification is the most fundamental form of holistic content understanding. It helps make your resources findable (F2) and connects them to other resources (I3). Content understanding represents each piece of content in the index. Relevance of content is a function of query and content understanding. Query understanding represents each search query as a search intent. Classification maps a document to one or more predefined categories. We can do so using hand-tuned rules or machine...
A Tracer Study of the Master of Management at Universitas Trisakti, Indonesia

This study aims to determine the profile of alumni of the Master of Management Study Program, Faculty of Economics and Business, Universitas Trisakti. The respondents to this study were Master of Management alumni from the Faculty of Economics and Business at Universitas Trisakti who graduated in 2020–2021. The type of research used is descriptive research with a quantitative approach. The data used are primary data collected through surveys by distributing questionnaires to respondents. The data is processed using SPSS software. There were 261 graduates of the Master of Management Faculty of Economics and Business Universitas Trisakti Master of Management Study Program for 2020–2021, but only 201 completed the complete tracer study. Questionnaires were distributed directly to alumni or alumni users whose whereabouts were known. The results of the study show that alumni stated that the majors taken were under the field of work. Overall, graduates were able to get their first job in less than six months. Alumni receive academic guidance, participate in research projects, and have the opportunity to become part of professional scientific networks. Graduates can communicate, work with teams, develop themselves, and utilize information technology. The results of the implementation of this tracer study can be used as material for evaluating and improving the leadership of MM FEB Trisakti University regarding graduate profiles and the abilities that graduates must have, both soft and hard, to be competitive in the world of work.
Effect of Corporate Charcteristics on Triple Bottom Line Reporting of Quoted Commercial Banks in Nigeria

The study investigated effect of ‘corporate characteristics (cc) measured by Firm Size (FS), Firm Age (FA) and Firm Leverage (FLEV) on Triple Bottom Line Reporting measured by Environmental Bottom Line Reporting (EBLR), Social Bottom Line Reporting (SBLR) and Return on Assets (ROA)’. An ex-post facto research design approach was adopted for the study. The population of the study comprises of 13 quoted commercial banks in Nigeria. The study used panel data from the period of eleven (11) years (2006-2015) and 13 quoted commercial banks were chosen as the sample size using purposive sampling technique. The study utilized secondary data obtained from the annual reports of quoted commercial banks listed on the Nigerian Stock Exchange (NSE) for the period 2010-2020. Content analysis was applied in measuring disclosure, descriptive statistics and panel regression Analysis using Eview version 8 was applied in testing hypotheses. The study result revealed that there was positive (t-statistics, 0.380644) but non-significant (p-value, 0.7040) effect of Firm Size on Environmental Bottom Line Reporting (EBLR) of commercial banks. Also there was a negative (t-statistics, -1.239531) but non-significant (p-value, 0.2172) effect of Firm Age (FA) on SBLR quoted commercial banks. Finally there was a positive (t-statistics, 3.131350) but significant (p-value, 0.021) association between Firm Leverage (FLEV) and ROA. This implies that the level of environmental &amp; social performance and reporting does not depend on the size or age of company. Based on the findings, the study recommended among others, that the Central Bank Nigeria and those charged with governance in commercial banks should initiate policies to improve sustainable social and environmental performance practices especially in the area of afforestation, erosion control, mother – child health care and pandemic sensitization and support.
Research on Service Composition Mechanism in Mobile Workflow System

Considering the impact of power consumption of mobile devices on the quality of service of mobile workflow, this paper introduces the “energy consumption” in the quality of service system, which provides a new basis for selecting member services during the execution of mobile workflow. A workflow service composition algorithm based on genetic algorithm and improved particle swarm optimization algorithm is proposed in this paper. The fitness function is constructed on the basis of the user's service quality requirements. The high quality solution obtained by genetic algorithm is used as the initial value of the particle swarm. The inertia weight and learning factor of the particle swarm are adaptively adjusted to improve the quality of the solution. The service composition scheme is obtained according to the final position vector of the particle swarm.
Guided Linear Upsampling

Guided upsampling is an effective approach for accelerating high-resolution image processing. In this paper, we propose a simple yet effective guided upsampling method. Each pixel in the high-resolution image is represented as a linear interpolation of two low-resolution pixels, whose indices and weights are optimized to minimize the upsampling error. The downsampling can be jointly optimized in order to prevent missing small isolated regions. Our method can be derived from the color line model and local color transformations. Compared to previous methods, our method can better preserve detail effects while suppressing artifacts such as bleeding and blurring. It is efficient, easy to implement, and free of sensitive parameters. We evaluate the proposed method with a wide range of image operators, and show its advantages through quantitative and qualitative analysis. We demonstrate the advantages of our method for both interactive image editing and real-time high-resolution video processing. In particular, for interactive editing, the joint optimization can be precomputed, thus allowing for instant feedback without hardware acceleration.
Healthcare Management using Blockchain

Healthcare management using blockchain technology refers to the use of blockchain to improve the efficiency, security and privacy of healthcare data management. Blockchain enables the creation of a decentralized, tamper-proof database that can be shared among healthcare providers, patients, and other stakeholders. This can enhance the coordination and sharing of healthcare data, streamline the administrative processes, reduce costs, and improve patient outcomes. This abstract provides an overview of healthcare management using blockchain, highlighting its benefits and challenges, as well as its potential applications in healthcare. The major reason that paves way for the exploration of blockchain in health care management is its capabilities to secure the information’s from cyber-attacks, fraud, and unauthorized access and allow an equitable and transparent data management without the need for a central authority or intermediary, making sure that patients have greater control over their own data. This method can also enable the secure exchange of health data between different healthcare systems and providers, promoting interoperability and improving patient care, by removing the need for intermediaries, blockchain can help streamline the administrative processes, reducing costs and improving the efficiency of healthcare delivery and tracking of medical products and devices throughout the supply chain, helping to prevent counterfeiting and ensuring patient safety. Overall, the use of blockchain in healthcare management has the potential to improve the quality of care, reduce costs, and increase patient satisfaction. However, there are also challenges to be addressed, such as regulatory compliance, interoperability, and scalability.
Research on the Application of VR in Education and Gaming

VR in virtual reality technology is a new technology applied in the field of education and gaming in recent years. This article describes the current development of VR in the field of education and games, and demonstrates the diversity of VR technology applications to promote a better understanding of VR technology. First of all, starting with education, this paper expounds that virtual reality education is designed based on educational and teaching objectives. we introduce the performance of virtual reality technology in the field of education. This section describes the important role of virtual reality technology in the field of medical education and classroom education. The challenges in the field of education were discussed. Then it expounds the practical exploration of VR in games and the value of VR in game development. Finally, we analyse some advantages and characteristics of VR games. The article concludes with a summary and outlook for the full text.
The Innovations Made on the RSA Cryptosystem Using the Eulers Totient Function

Cryptography has been invented for thousands of years as an important tool to safely transfer sensitive messages. Nowadays, cryptography has already become an essential component of our society. The RSA cryptosystem, a widely used cryptosystem in our current online system, is a safe but time-consuming method. Eulers totient function, in the modular arithmetic that the RSA is based on, plays an important role and has strong potential to be applied with other cryptosystems to make innovations on the RSA. This paper will introduce the potential proposed risk of hacking the RSA cryptosystem using Eulers totient function, and how this function can be applied itself or with other ciphers to create more optimized algorithms and methods based on the RSA cryptosystem. The analyzed results show that the current innovations made to the RSA cryptosystems are rarely balanced on time complexity and security levels and need improvements under most circumstances.
Reversible data hiding scheme based on improved interpolation and three-in-one intelligent strategy

Reversible data hiding method combined with interpolation is a widely used technique in secretly transmitting large collections of information through images. However, this approach can be subjected to inaccurate prediction because of using a single prediction formula for different textures of complex images. We proposed a new method using a circular predictor interpolation (CPI) for improved accuracy performance, which adjusts the predictive radius adaptively for different image textures and takes the coefficient of distance into consideration to increase image load capacity and visual quality. Additionally, to reduce unnecessary image distortion under low load conditions, the first part of the intelligent strategy modifies the scanning sequence in images so that the secret data is preferentially embedded into the pixel with less deviation. Meanwhile, an interpolation-embedding synchronization was created as the second part to allow the interpolation domain and embedding domain to be adjusted synchronously in light of the data capacity to be embedded. In addition, the third part of the intelligent strategy is to take the original image as a benchmark for steganographic pixel value rectification to improve the overall image quality and the overflow or underflow problems could be solved in this process. The outcomes of the experiments indicate that the CPI and the intelligent strategy realize a high payload and play an important role in improving visual quality compared with six advanced schemes.
A COPRAS-based Approach to Multi-Label Feature Selection for Text Classification

In this article, we present a novel approach for text classification feature selection using a pseudo-relation matrix constructed with ridge regression. This approach involves generating a matrix that captures the relationship between features and target variables and then using that matrix to create a multi-criteria decision making (MCDM) problem. We then utilize the Complex Proportional Assessment (COPRAS) method to rank features in order of importance. To assess the performance of our proposed algorithm, we conduct tests on ten real-world text datasets and compare our results to the existing methods. Our findings indicate that the proposed approach outperforms other methods, and we provide statistical significance to support our claims. Our proposed algorithm provides a viable answer for researchers looking for efficient and effective feature selection methods for text data analysis, which can lead to a better understanding and interpretation of the underlying text.
Performance Evaluation of Face Recognition Model in Deep Learning using Old Facial Photos: A Mathematical Modelling Using SVM Supervised Learning

Deep Learning has been a remarkable state-of-the-art method in any classification challenge, particularly in face recognition applications. In this paper, Feature Extraction in face recognition using Deep CNNs handpicked pre-trained CNN architectures such as InceptionV3, MobileNetV2, ResNet50, and VGG19 were experimentally explored. Initially, these architectures extracted important features from eight (8) classes of face photos with large age differences of ten (10) years from the present age of an individual. The features were processed with the application of a Support Vector Machine (SVM) classifier to enhance its performance. The evaluation of each model was based on average scores ofaccuracy, precision, recall, and f1-score. The results concluded an accuracy of 84.60%, a weighted precision of 85%, a weighted recall of 84.60%, and a weighted f1-score of 84.60% obtained by ResNet50.Further, ResNet50 has the highest obtained 98% generated ROC-AUC score. With the results presented, ResNet50 isrecommended for application development related to face recognition with the consideration of large age gaps of 10 years.
Rewarding Employee’s Value Creation Through Personalized Non-fungible Token (NFT)

Non-fungible tokens (NFTs) are one of the latest approaches to use blockchain technology to create uniqueness in the digital world. Unlike physical assets, claiming authenticity and credibility is tough in digital assets. COVID-19 era brought new lifestyles like remote working, work life balance, mental health awareness, etc., which created a new meaning of job satisfaction. Professionals started to look for new pastures where opportunities suited to their new lifestyle. This brought a daunting task to the organization to retain their best talents. Rising cases of job resignation among working professionals are becoming a huge concern for organizations. This article proposes a concept of introducing NFTs in organizations to increase motivation and recognition to enhance employee’s job satisfaction in the post-COVID-19 era. It also aims to draw attention to how NFTs combined with intelligence can create unique opportunities personalized to each employee, to foster trust and commitment to their work. The insights proposed can provide a new perspective on creating an organization’s work culture that creates meaningful rewards for employee’s value creation.
Pilot testing the SUCCESS peer mentoring program for students with concussion: the role of personas in mobile technology development

College students with concussion experience academic, cognitive and psychosocial challenges, yet frequently lack supports necessary for successful reintegration into school. Success in College after Concussion with Effective Student Supports (SUCCESS) is a virtual peer mentoring program designed to provide education, support and connection through a mobile application. The purpose of this study was to describe use of personas as components of mobile app development and conduct preliminary testing of SUCCESS using personas.Personas were developed from case studies and portrayed by college students trained as fictitious mentees. Mentors were blinded to use of personas. Eleven mentors completed measures pre and post a 4-week mentoring cycle. Mentors and personas interacted in the app via chat, video calls and sharing of educational materials. Measures included the Post-Concussion Symptom Scale (PCSS); PROMIS Self-Efficacy; Depression, Anxiety and Stress Scale (DASS); and a series of focus groups.Mentors suggested improvements to resolve instability of video calls, expand educational materials to address psychosocial functioning, and add structure to the mentoring relationship. Some preferences around communication, like groups chats and emoji keyboards, were not able to be addressed. As expected, PCSS scores were stable. DASS score (p = .04), especially depression (p = .03), decreased. PROMIS scores showed a trend towards growth (p = .057), although were not statistically significant.Use of personas allowed technical challenges and program refinements to be addressed before including students with subacute concussion in testing. Although continued development will address enhancement of communication modalities preferred by students, future efficacy testing of SUCCESS is warranted.Personas can be useful for the development of mobile applications, particularly those that include interaction between users, allowing for testing of complex usage scenarios to improve app functionality.Students who have recovered from concussion may benefit from providing peer mentoring to students with more acute injuries.Mentoring programs should have a clear structure that considers relationship closure as much as relationship building. Mobile apps can deliver just-in-time prompts that support both of these activities.Postsecondary students express a preference for augmenting written app-based communication with digital images such as emojis and gifs, considering these important towards relationship-building in virtual spaces.
Real‐time reconstruction of pipes using RGB‐D cameras

Abstract This paper presents a novel method that automatically reconstructs pipes in real time using an RGB‐D camera. The input image is decomposed into superpixels, and a pipe element, which is a 3D circle lying on the pipe surface, is generated for each superpixel. Over frames, the pipe elements are grouped into sequences and are finally modeled in parametric equations. The method is tested in daily settings, where the pipes may be curved and their radii may vary along the axes. The test results show that the method is able to reconstruct pipes efficiently, precisely and robustly.
Convex risk-bounded continuous-time trajectory planning and tube design in uncertain nonconvex environments

In this paper, we address the trajectory planning problem in uncertain nonconvex static and dynamic environments that contain obstacles with probabilistic location, size, and geometry. To address this problem, we provide a risk-bounded trajectory planning method that looks for continuous-time trajectories with guaranteed bounded risk over the planning time horizon. Risk is defined as the probability of collision with uncertain obstacles. Existing approaches to address risk-bounded trajectory planning problems either are limited to Gaussian uncertainties and convex obstacles or rely on sampling-based methods that need uncertainty samples and time discretization. To address the risk-bounded trajectory planning problem, we leverage the notion of risk contours to transform the risk-bounded planning problem into a deterministic optimization problem. Risk contours are the set of all points in the uncertain environment with guaranteed bounded risk. The obtained deterministic optimization is, in general, nonlinear and nonconvex time-varying optimization. We provide convex methods based on sum-of-squares optimization to efficiently solve the obtained nonconvex time-varying optimization problem and obtain the continuous-time risk-bounded trajectories without time discretization. The provided approach deals with arbitrary (and known) probabilistic uncertainties, nonconvex and nonlinear, static and dynamic obstacles, and is suitable for online trajectory planning problems. In addition, we provide convex methods based on sum-of-squares optimization to build the max-sized tube with respect to its parameterization along the trajectory so that any state inside the tube is guaranteed to have bounded risk.
Consumer complaints of consumer financial protection bureau via two-stage residual one-dimensional convolutional neural network (TSR1DCNN)

The Consumer Financial Protection Bureau (CFPB) is a government body responsible for safeguarding consumers from financial fraud and abuse. Managing customer complaints is one of the key tasks undertaken by the CFPB. However, the sheer volume of complaints received can overwhelm the bureau's resources, hindering prompt and efficient resolution. To address this challenge, we propose a novel approach called the Two-Stage Residual One-Dimensional Convolutional Neural Network (TSR1DCNN) to optimize the processing of consumer complaints at the CFPB. In this study, we conducted comprehensive experiments, including Ablation Experiment 1 (AE1) and Ablation Experiment 2 (AE2), to evaluate the effectiveness of our proposed TSR1DCNN model. AE1 involved removing the first Conv1D layer, while AE2 removed the Batch Normalization layer. These experiments allowed us to assess the impact of removing specific components on the overall performance of the model. Furthermore, we compared our TSR1DCNN model with other popular deep learning architectures, including 1DCNN, LSTM, and BLSTM, to provide a comprehensive analysis of our proposed approach. Using a dataset of 555,957 consumer complaints received by the CFPB, we trained and tested the TSR1DCNN model, as well as the ablated versions in AE1 and AE2, alongside the 1DCNN, LSTM, and BLSTM models. The results showed that the TSR1DCNN model achieved an impressive accuracy of 78.07% on the training set and 76.53% on the test set. In comparison, AE1 achieved an accuracy of 69.63% with a loss of 1.1207, while AE2 achieved an accuracy of 71.00% with a loss of 1.0583. The performance of the TSR1DCNN model outperformed the other deep learning architectures, including 1DCNN, LSTM, and BLSTM, indicating its superiority in handling consumer complaints effectively. These results demonstrate the superiority of the TSR1DCNN model over the ablated versions in AE1 and AE2, as well as its superiority over other commonly used deep learning architectures. By incorporating advanced neural network architectures such as 1DCNN, LSTM, and BLSTM, and considering the specific modules where our proposed method operates, we provide a promising solution for enhancing the efficiency and effectiveness of complaint-handling processes in organizations facing a large volume of complaints, such as the CFPB.
GWBNER: A named entity recognition method based on character glyph and word boundary features for Chinese EHRs

Electronic Health Records (EHRs) contain unprecedented volumes of data related to health such as diagnosis and treatment information. Based on the technologies of named entity recognition (NER), EHRs mining has become a research focus in health domain. Nevertheless, the complex medical entities are challenging to be recognized, especially in Chinese EHRs. In this paper, a Glyph and Word Boundary-based Named Entity Recognition (GWBNER) method is presented, which takes into account both the Chinese character glyph and word boundary features in Chinese EHRs. Specifically, the character glyphs are utilized to capture the character-level global structural features, and the word boundaries are adopted to extract the word-level local structural features in Chinese. Therefore, the medical entities are fully recognized with rich semantic features from diverse perspectives in Chinese EHR texts. Finally, we conduct extensive experiments to evaluate the novel approach, and the F1 scores of 0.879, 0.998 and 0.962 are achieved on three EHR datasets, respectively. The experimental results demonstrate the optimal performance of the GWBNER method compared with the state-of-the-art models in NER community.