Reviewerly/exHarmony huggingface dataset:
Design of Financial Management Talent Training Model under Demand Coupling Mechanism and IoT Applications

The financial environment of businesses and institutions has significantly changed due to the development of new technologies and the deepening of vocational education reform. The Financial Management Department is, in theory, responsible for the financial management of the company. Specialty benefits from both the accounting and finance specialties. The advanced stage of corporate financial activity management is cash-related management. As a result, the social demand subject should praise graduates of the financial management specialty. The subject of social demand is also a good motivation to raise proficient capacity necessities for graduates. Regardless, there is a huge hole among organic market for proficient capacity under the current ability preparing model, in light of the fact that numerous schools and colleges that set monetary administration claims to fame are uncertain of the expert capacity necessities of the social interest subject, monetary in nature. Graduates in executive positions are in a more humiliating position on the job market. In light of this, this paper proposes the basic idea of talent training model innovation in the financial management specialty, based on an analysis of the status and causes of professional ability supply and demand. The board specialises in determining talent training goals, revising professional talent training programs, respecting the central status of students, and engaging in innovative teaching activities. Using Sichuan University of Arts and Science as an example, this paper examines the current state of practice teaching of financial management in colleges and universities, incorporates course practice content into the national vocational and technical certificate, promotes the integration of professional courses and vocational qualification certificates, discusses the new mode of practice instructing of university-enterprise cooperation, and makes recommendations. With the quick movement of the economy and society, the force of science and innovation, similarly as steady getting the hang of; perception; and usage of the significance of data and capacity progression has never been more prominent. Schools are the establishment for propelling one’s capacities. China as of now has an enormous number of colleges, showing that 100 blossoms are growing and 100 distinct perspectives are contending. Each school has its own unmistakable character and attributes. Regardless, the mirror of market economy obviously mirrors the condition of the economy contamination, commitment, bad behaviors, and veritable financial discipline encroachment of endless high-situating insightful individuals, addressing a real test to school capacity improvement.
A Bayesian Network-Based Software Requirement Complexity Prediction Model

Software development has an inflated probability of project failure and the major reason for it is the poor requirement engineering process. Potential threats or risks related to requirements must be identified at the earlier stages of the development itself, so as to minimize the negative impact of subsequent affects. Researches reveal that VUCA risks, i.e., Requirement Volatility, Requirement Uncertainty, Requirement Complexity, and Requirement Ambiguity, are the basic sources of risks for other risks too. Complexity in requirements is one of the important factors affecting quality of the product. Computing and analysis of the product complexity in the requirement analysis phase of SDLC will give benefits in assessing the required development and testing efforts for the prospective software product. Failing to which, software designers and testers will need further clarification, thus slowing down the design and verification process. This paper attempts to establish a connection between the VUCA risks and propose a methodology to minimize requirement complexity. The various factors affecting requirement complexity are identified, in the requirement engineering phase. A Bayesian approach is proposed to predict the requirement complexity. The proposed model uses various complexity factors found through extensive literature review to manage requirement complexity of the software products.
Sanskriti—A Distributed E-Commerce Site Implementation Using BlockChain

Application for decentralised blockchain adoption has no end. Obstacle for deployments revolves around recognition, willingness to take the lead and overcoming a privacy risk obstacle. Our research work is an implementation of this application in the e-commerce sector. In the real-world scenario of e-commerce, small businesses have to face the adverse effects of centralised authorities and their monopoly, and it becomes really hard for them to register or advertise their products on centralised platforms. Security and privacy are also huge issues in case of centralised platforms. Therefore, we propose a decentralised e-commerce platform for fast KYC system which can be completed in simple and easy steps and can be done from any convenient place. We use a smart contract system which consists of a set of rules that parties in a smart contract agree to follow. Our proposed approach is both quick and secure that stores data in parts into MongoDB server and blockchain. This also improves user privacy. In case of any malicious entry into the server, the attacker will have his hands only on some and irrelevant data which cannot be decoded. Making this division will also reduce the cost of writing all data on blockchain. Along with these, the implementation supports INR payments as well for ease of access.
Question Answering and Text Generation Using BERT and GPT-2 Model

Question and answering system and text generation using the BERT and GPT-2 transformer is a specialized field of the information retrieval system, which a query is stated to system and relocates the correct or closet answer to a specific query asked by the natural language. The main aim of the QA system is to provide the short answer to a question rather than the list of possible relevant document and text generation is a language generation which focuses on the producing understandable test in English which can predict the next sentence or generate a text with all the raw content from previous words. The motivation for selecting this work is to provide a great relevance to find the answer, find answer to general knowledge type of question, find the answers for questions like Who? What? Where? How?, and Provide provide the shortest form of answer. The scope for the chosen work is to provide the solutions for the automation moderation in the websites to provide the exact and short information answers from the websites, like Stack Overflow, Reddit, Quora, provide the self-answering and find text. The method we are using for the QA and text generation system is a transformer architecture which consist of Encoder and Decoder which is a stack of encoder represents the BERT model and Decoder part is represented as the GPT-2 model.
The Occurrence of Platinum (Pt) and Skaergaardite (Pd Cu) in the Hutti Underground Gold Mines, Eastern Dharwar Craton, Karnataka, India

The novelty of this study consists, use of the Powder X-Ray Diffraction (PXRD) technique facility at CSIR-NGRI to report the occurrence of platinum and skaergaardite (Pd Cu) in the Hutti underground gold mines. This study utilized a quartz-bearing sample from the Hutti underground gold mines. One of the split samples were analyzed by PXRD. In this study, quartz samples are found to be associated with sulphides composed of pyrite, chalcopyrite, and arseno-pyrite with intense chemical alterations. The mineral phase of skaergaardite shows a strong intensity at 2θ 43.5° and confirmed with database of International Centre for Diffraction Data Powder Diffraction File — ICDD-2021-PDF4+ PDF00-057-0606 and native platinum, syn (Pt) Pt 0.0999 Sn 0.001 matched with PDF 04-001-3300. Further research work is planned on this interesting mineral association with primary gold ore in Hutti underground gold mine.
SDNMF: Semisupervised discriminative nonnegative matrix factorization for feature learning

As one of the most effective feature learning methods, Nonnegative Matrix Factorization (NMF) has been widely used in many scientific fields, such as computer vision, data mining, and bioinformatics. However, NMF is an unsupervised method that cannot fully utilize the label information of data. Thus, its performance is limited in some recognition and classification problems. To remedy this shortcoming, this paper proposes a Semisupervised Discriminative NMF (SDNMF) method. First, we design a Soft-Labeled NMF (SLNMF) model by introducing a soft-label matrix-based regression term into the original NMF, so that the relationship between the soft-label matrix and low-dimensional features can be constructed to improve the discriminative ability of low-dimensional features. Second, to effectively estimate the soft-label matrix, a Label Propagation (LP) model is adopted to fully explore the spatial distribution relationship between the labeled and unlabeled samples. Third, an Adaptive Graph Learning (AGL) model is proposed to exploit the geometric relationship of samples well, which could enhance the performance of LP. Finally, the above three models (i.e., SLNMF, LP, and AGL) are integrated into a unified framework for effective feature learning, which can not only effectively explore the structural relationship matrix between data, but also predict the labels for unknown samples. Moreover, an iterative optimization algorithm is presented to solve our objective function. The convergence and computational complexity analysis of the proposed SDNMF method are also provided. Extensive experiments are conducted on several standard data sets. Compared with related methods, the experimental results verify that the proposed SDNMF method achieves better performance.
Development of the cybercrime awareness scale (CAS): a validity and reliability study in a Turkish sample

Purpose Cybercrimes increase day by day in parallel to cyber-attacks and cyber-threats. Due to such an increase, required cybersecurity precautions against all kinds of cyber-attacks and cyber-threats should be taken by both organizations and individuals. This study aims to develop a reliable and valid measurement tool to measure cybercrime awareness levels of individuals. Design/methodology/approach In this study, a scale named as Cybercrime Awareness Scale (CAS) has been developed and psychometric properties of the scale have been tested by two separate studies. Findings The first study included a total of 500 respondents (294 female and 206 male). In the first study, factor structure of the proposed scale has been determined through an exploratory factor analysis. The results revealed a three-factor structure (i.e. information systems crimes, personal data crimes, and privacy and security). Cronbach's alpha reliability coefficients for the subdimensions were 0.95, 0.92, and 0.90, respectively. The sample of the second study consisted of 494 respondents (281 female and 213 male). The confirmatory-factor-analysis results revealed that three-factor structure is valid and optimal model. Further, the proposed scale demonstrated moderate concurrent validity results in relation to the Digital Data Security Awareness Scale. Originality/value Findings indicated that the CAS is a valid and reliable measurement tool to measure individuals' cybercrime awareness level. This study makes a novel contribution to the existing cybersecurity literature by providing the CAS, which is developed by following rigorous methodological standards. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-01-2022-0023/
Keep Your Distance: Determining Sampling and Distance Thresholds in Machine Learning Monitoring

Machine Learning (ML) has provided promising results in recent years across different applications and domains. However, in many cases, qualities such as reliability or even safety need to be ensured. To this end, one important aspect is to determine whether or not ML components are deployed in situations that are appropriate for their application scope. For components whose environments are open and variable, for instance those found in autonomous vehicles, it is therefore important to monitor their operational situation in order to determine its distance from the ML components’ trained scope. If that distance is deemed too great, the application may choose to consider the ML component outcome unreliable and switch to alternatives, e.g. using human operator input instead. SafeML is a model-agnostic approach for performing such monitoring, using distance measures based on statistical testing of the training and operational datasets. Limitations in setting SafeML up properly include the lack of a systematic approach for determining, for a given application, how many operational samples are needed to yield reliable distance information as well as to determine an appropriate distance threshold. In this work, we address these limitations by providing a practical approach and demonstrate its use in a well known traffic sign recognition problem, and on an example using the CARLA open-source automotive simulator.
Typed SLD-Resolution: Dynamic Typing for Logic Programming

The semantic foundations for logic programming are usually separated into two different approaches. The operational semantics, which uses SLD-resolution, the proof method that computes answers in logic programming, and the declarative semantics, which sees logic programs as formulas and its semantics as models. Here, we define a new operational semantics called TSLD-resolution, which stands for Typed SLD-resolution, where we include a value “wrong”, that corresponds to the detection of a type error at run-time. For this we define a new typed unification algorithm. Finally we prove the correctness of TSLD-resolution with respect to a typed declarative semantics.
Cloud-Based English Multimedia for Universities Test Questions Modeling and Applications

This study constructs a cloud computing-based college English multimedia test question modeling and application through an in-depth study of cloud computing and college English multimedia test questions. The emergence of cloud computing technology undoubtedly provides a new and ideal method to solve test data and paper management problems. This study analyzes the advantages of the Hadoop computing platform and MapReduce computing model and builds a distributed computing platform based on Hadoop using universities' existing hardware and software resources. The study analyzes the advantages of the Hadoop computing platform and the MapReduce computing model. The UML model of the system is given, the system is implemented, the system is tested functionally, and the results of the analysis are given. Multimedia is the critical link to realizing the optimization of English test questions. The proper use of multimedia test questions will undoubtedly become an inevitable trend in the development of English test questions in the future, which requires every worker on the education front to continuously analyze and study the problems arising from multimedia teaching, summarize the experience of multimedia teaching, and explore new methods of multimedia teaching, so that multimedia teaching can better promote the optimization of English test questions in colleges and universities and better serve the education teaching.
Bi-level optimization-based projective non-negative matrix factorization

Non-negative matrix factorization (NMF) is a powerful tool for image data analysis and has been utilized in a range of applications involving data classification, clustering, and image processing. Nevertheless, NMF and most of its variants are unsupervised learning algorithms and do not take account of the projection property of the basis image matrix. These flaws will have a negative impact on their classification performance. To resolve the mentioned drawbacks of NMF-based methods, this paper establishes a novel projective non-negative matrix factorization (PNMF) framework using the class-label information and deals with the model via a bi-level quadratic optimization (BLO) strategy. The convergence of the proposed BLO-PNMF algorithm is theoretically analyzed and proved in detail, and also verified by experiments. Our approach and the related state-of-the-art methods are evaluated and compared on three face databases. The experimental results indicate that the BLO-PNMF method can extract more localized features for image data representation and achieve superior performance in face recognition.
New Hybrid Methodology Based on Particle Swarm Optimization with Genetic Algorithms to Improve the Search of Parsimonious Models in High-Dimensional Databases

Our previous PSO-PARSIMONY methodology (a heuristic to search for accurate and low-complexity models with particle swarm optimization) shows a good balance between accuracy and complexity with small databases, but gets stuck in local minima in high-dimensional databases. This work presents a new hybrid methodology to solve this problem. First, we incorporated to PSO-PARSIMONY an aggressive mutation strategy to encourage parsimony. Second, a hybrid method between PSO and genetic algorithms was also implemented. With these changes, particularly with the second one, improvements were observed in the search for more accurate and low-complexity models in high-dimensional databases.
Companion Losses for Ordinal Regression

In Ordinal Regression (OR) class labels contain ranking information about the underlying samples and, thus, the goal is not only to minimize classification errors but also the rank distance of misclassified patterns. Thus, while class rankings are not metric values, they add a regression-like character to OR. Within this perspective, we propose here deep OR models built from losses which mix classification and regression components. While conceptually simple, our experiments will show their performance to be comparable to that of models representative of the state of the art in OR.
Stereo matching by using a new local descriptor based on sign and order in belief propagation algorithm

Stereo matching is still a challenging problem in computer vision. Belief propagation (BP) algorithm is one of the global methods to solve this problem which can lead to desired results if useful information is applied in data function of this algorithm. In most literatures, color information of pixels has been applied in data function of BP which its result is not very well. In this paper, a low complexity modified version of local descriptor based on contrast and ordering (LCO) of intensity differences is proposed which is known as the local sign and ordering (LSO). LSO, like its original descriptor, has some distinguished properties such as robustness against illumination change and preserving depth discontinuity which are the important challenges of stereo matching. Moreover, a combination of intensity values of pixels and LSO descriptor is presented. This combination of cost measures is utilized as data function of BP algorithm. In the post-processing of stereo matching, the left–right consistency check is applied to the final result, which is followed by median filter in order to refine disparity map. Performed experiments on the Middlebury dataset show that the results of the proposed method are competitive with the methods based on other descriptors used in BP algorithm. Furthermore, the proposed method has relatively good performance compared to the other methods rather than BP algorithm.
Game-theoretic hypergraph matching with density enhancement

Feature matching plays a fundamental role in computer vision and pattern recognition. As straightforward comparison of feature descriptors is not enough to provide reliable matching results in many situations, graph matching makes use of the pairwise relationship between features to improve matching accuracy. Hypergraph matching further employs the relationship among multiple features to provide more invariance between feature correspondences. Existing hypergraph matching algorithms usually solve an assignment problem, where outliers may result in a large number of false matches. In this paper we cast the hypergraph matching problem as a non-cooperative multi-player game, and obtain the matches by extracting the evolutionary stable strategies. Our algorithm exerts a strong constraint on the consistency of obtained matches, and false matches are excluded effectively. In order to increase the number of matches without increasing the computation load evidently, we present a density enhancement method to improve the matching results. We further propose two methods to enforce the one-to-one constraint, thereby removing false matches and maintaining a high matching accuracy. Experiments with both synthetic and real datasets validate the effectiveness of our algorithm.
Splicing forgery localization via noise fingerprint incorporated with CFA configuration

Noise is the inherent intrinsic fingerprint in digital images and is often used for forgery localization. Most noise-based methods assume that the noise is similar over the whole image and can be considered as white Gaussian noise. However, the noise is different in various regions, which degrade the performance of these noise-based methods. To reduce the impact of impractical assumptions, in this paper, we propose an effective noise fingerprint incorporated with CFA configuration for splicing forgery localization. The noise of interpolated pixels is expected to be suppressed after interpolation, and the relationship between the noise levels of adjacent acquired and interpolated pixels is only related to the interpolation algorithm, which is constant in the original image. We utilize a dual tree wavelet based denoising algorithm to extract the noise from the green channel and compute the standard deviation of the noise for acquired and interpolated pixels, respectively. The noise level of acquired and interpolated pixels are then obtained by the geometric mean of the noise standard deviations. Finally, the ratio of noise levels between acquired and interpolated pixels can be a fingerprint to locate tampered regions. Experiments conducted on publicly available databases demonstrate that the proposed approach outperforms previous methods for detecting splice tampering. Moreover, the proposed method is robust to Gaussian filtering and JPEG compression attacks.