Reviewerly/exHarmony huggingface dataset:
Calculus Logic Function in Tax Risk Avoidance in Different Stages of Enterprises

Abstract During the entire development process of corporate tax risks, these risks will show different tax characteristics, which will result in four stages of tax risk evolution: the incubation stage, the development stage, the deterioration stage and the outbreak stage. During the evolution of tax risks and the establishment of an early warning mechanism, enterprises are facing both internal and external environments. From the internal perspective, the company’s business management philosophy, the quality of its staff, and the tax risk management system and procedures of the company will affect the establishment of tax risk and early warning mechanisms; from an external understanding, the regional tax environment, the country’s financial legal system, and the company’s Social relations are also important factors that must be considered in constructing a tax risk early warning mechanism. From the perspective of supporting conditions, in order to optimize the corporate tax risk early warning mechanism, this article considers the following aspects: first, the establishment of an effective internal tax risk control system for enterprises; second, the establishment of a systematic and effective tax risk control procedures; third, Efforts to adapt to the macro environment and actively respond to tax risks; Fourth, adopt effective tax planning skills; Fifth, improve the quality of tax risk prevention for all employees. When discussing the realization of the goal of the tax risk early warning mechanism, the author uses calculus knowledge to make logical derivation; in the risk attitude and tax risk early warning selection, geometric figures are used to describe people’s behavioural guidelines intuitively; in the construction of the early warning mechanism, the scoring method Visually integrated the values of various tax risk warning indicators.
Tailoring Cognitive Mapping Analysis Methods to Different Management Styles of Collective Action by Handling Actor Reasoning Diversity

Collective action is needed to conceive and implement sustainability transitions within our society. Yet, it requires the coordination of a diversity of actors, each possessing their own reasoning. Integrating diversity, characterized by variety, disparity and balance, is thus a crucial methodological challenge to address, notably in order to enhance the quality of policy decisions concerning sustainability issues. Here we show how to tailor the way of collecting, analyzing and using actor reasoning diversity to the management style of collective action, characterized by the role the actors are allowed to play in defining collective action. To this purpose, we compare three design experiments using actor reasoning in the context of collective action aiming at developing sustainable food production: (1) considering actor reasoning in top-down decision-making (scallop fishery in Integrated Coastal Zone Management); (2) building consensus for a common strategy (quality in a sheep milk cooperative); and (3) fostering collective intelligence for individual and collective paths for action (agroecological transition of an agricultural territory). The diversity of actor reasoning in each of the design experiments was collected through cognitive mapping, a systemic representation tool adapted to represent actor reasoning. We propose a framework to adapt cognitive mapping methods to different management styles of collective action. In practice, adjusting the level and nature of the reasoning diversity considered at each step of a participatory process, through the way cognitive maps are collected and analyzed, is key to the tailoring of a cognitive mapping method to a management style of collective action. Overall, we show that the level of reasoning diversity considered in collective action should increase with actor involvement in analyses and decision-making.
Optimization Decision Tree Algorithm in Sports Video Tracking Technology

With the improvement of the level of sports competition, the requirements for sports training are also increasing. In the past, only relying on the coach's intuitive training method could not meet the needs of the stage sports training. However, the sports video tracking technology can extract the action features in the video, and according to the athlete's action features, it can help the coach to formulate reasonable training plan. The decision tree algorithm proposed in this paper is a typical C4.5 algorithm, and the genetic algorithm is used to optimize it. The unoptimized algorithm and the optimized algorithm are used to track the same sports video, and the performance of the two algorithms in processing video images is compared. Running time and tracking error, the C4.5 algorithm optimized by genetic algorithm has more advantages in video tracking.
MILEAGE: An Automated Optimal Clause Search Paradigm for Tsetlin Machines

The logic based underpinning of the Tsetlin Machine (TM) offers substantial benefits for compute-efficient inference. The caveat to this is the large memory footprint of the TM growing substantially as problem complexity increases. This paper presents a novel automated approach to find an optimum model complexity in terms of number of clauses for a given application. It proposes a run-time pruning system which interfaces with the TM model between training cycles and determines the inconsequential clauses. The clauses are iteratively removed till no degradation of accuracy is observed before commencing the next training cycle. Our approach retains important clause propositions developed at a higher model complexity and translates it to a smaller clause-sparse model while retaining the learning efficacy. We validate our approach using MNIST, Fashion-MNIST, Kuzushiji-MNIST and KWS6 where we demonstrate that compared to large model size, a similar accuracy gain can be achieved with fewer clauses resulting in significantly smaller training time. We demonstrate up to 63% compression in model size for the chosen training parameters.
International Currency Competition under the Background of Digitization and Its International Enlightenment to RMB

The emergence of digital currency has gradually shaken the status of traditional currency, people's traditional concept of currency is gradually being overturned, and traditional currency faces huge challenges and opportunities.With the increasingly fierce international currency competition, digital currency faces multiple challenges and opportunities.Under the background of digitization, the development of digital currency has had a huge impact on the existing international monetary system.On a global scale, central banks in different countries in the face of digital currency issuance have a more positive and open attitude, and this means the emergence and development of digital currency have a great probability of reconstructing the existing international monetary system.Our country's central bank leads the world in the research and development and issuance of digital currency, which not only enables more countries to pay attention to the RMB but also promotes the RMB to go global and further expands the circulation scale and quantity in the international trade market.
English to Hindi Machine Translation Using Sub-classed Model

Natural language processing (NLP) includes a component called machine translation that tries to bridge the communication gap between different social groups. The principle of translation in a language translation system is simple: a system analysis, the pre-translated sentences in the database to determine a suitable translation of an input sentence. As a result, the more pre-translated sentences in the database, the more accurate the language translation system will be. Language translation is one area in which machines clearly fall short of human cognitive abilities. One of the traditional approaches to solving the challenge of machine translation is statistical machine translation. This strategy necessitates large datasets and works effectively on grammar structured language pairs with a similar grammar structure. Neural machine translation (NMT) has evolved as an alternative method of addressing the same problem in recent years. We investigate various setups for putting up a neural machine translation system for the Indian language, Hindi, in this study. For English to Hindi translation, we tested six alternative architecture combinations of NMT and compared our results to those of traditional machine translation techniques. In this work, we also discovered that NMT takes a small quantity of data for training and so provides excellent translation for tens of thousands of training sentences. The objective of the MT mechanism is to automatically translate between two different natural languages, with neural machine translation (NMT) attracting interest since it provides reasonable translation accuracy in the context of context analysis and fluent translation.
How Severe Is Benchmark-Sensitivity in Video Self-supervised Learning?

Despite the recent success of video self-supervised learning models, there is much still to be understood about their generalization capability. In this paper, we investigate how sensitive video self-supervised learning is to the current conventional benchmark and whether methods generalize beyond the canonical evaluation setting. We do this across four different factors of sensitivity: domain, samples, actions and task. Our study which encompasses over 500 experiments on 7 video datasets, 9 self-supervised methods and 6 video understanding tasks, reveals that current benchmarks in video self-supervised learning are not good indicators of generalization along these sensitivity factors. Further, we find that self-supervised methods considerably lag behind vanilla supervised pre-training, especially when domain shift is large and the amount of available downstream samples are low. From our analysis we distill the SEVERE-benchmark, a subset of our experiments, and discuss its implication for evaluating the generalizability of representations obtained by existing and future self-supervised video learning methods. Code is available at https://github.com/fmthoker/SEVERE-BENCHMARK .
Poly-Connexivity: Connexive Conjunction and Disjunction

This paper motivates the logic PCON, an extension of connexivity to conjunction and disjunction, called poly-connexivity. The motivation arises from differences in intonational stress patterns due to focus, where PCON turns out to be a logic of intentionally stressed connectives in focus.
Three-Stage Heterogeneous Data Clustering Using Unsupervised Multiple Kernel and Extreme Learning Machine

In spite of momentous endeavors for regulated various portion ELM, hardly any distributions have tended to the solo case, which is more basic yet trying for handling the practical issue. Different Authors proposed, in particular, two-stage unaided numerous piece outrageous learning machine (TUMK-ELM) a quick unsupervised heterogeneous information learning algorithm to settled this issue on the other hand removes data from various sources and learns the heterogeneous information portrayal with closed structure arrangements, which empowers its very quick speed. With the low computational complexity, TUMK-ELM provides at each stage, and the emphasis of its two phases can be merged inside limited advances. This issue of two phase can be settled by three-Stage unsupervised multiple kernel clustering-based extreme learning machine (TMKC-ELM) for quick unsupervised heterogeneous data learning. This will catch the heterogeneous data from various sources through different portions and incorporate the heterogeneous data into an ideal piece through an iterative three-stage approach directed by an overall unsupervised evenhanded. Dataset will be preprocessed to eliminate filthy qualities in it. Information will be built from the various kernels, and pseudo-marks will be doled out using a clustering algorithm as indicated by a learned K-space through the optimal kernel. This research work will be an endeavor to propose an efficient three-stage unsupervised multiple kernels extreme learning approach.
Mining User Feedback For Software Engineering: Use Cases and Reference Architecture

App reviews can provide valuable information about user needs but analyzing them manually is challenging due to their large quantity and noisy nature. To overcome this problem, a variety of app review mining techniques have been proposed. So far, however, research in this area has paid little attention to the software engineering use cases of the mining techniques. This limits the understanding of their usefulness, applications and desired future developments. We address this problem by elaborating a reference model relating app review mining techniques to specific software engineering activities. In this paper, we present a unified description of software engineering use cases for mining app reviews and define a reference architecture realizing these use cases through a combination of natural language processing and data mining techniques. The use cases provide a novel systematic exposition of the envisioned applications and benefits of app review mining for software engineers. The reference architecture synthesises the diversity of research to realise these benefits and provide a general framework guiding the development and evaluation of future research and tools.
Deepfake Video Detection Methods using Deep Neural Networks

Nowadays, humans are dealing with incipient trouble known as deepfake videos, advanced with the usage of deep learning. Due to freely accessible deep fake technology equipment and inexpensive computational power, internet is flooded with fake media like fake images, videos, audios etc. Fake images and videos are causing threats to privacy, reputation and the very identity of common people. Researchers are taking efforts to develop tools using various Convolutional Neural Networks (CNNs) to automatically detect this fake media however the existing tools are not able to cope with the evolution of deep fakes. In this paper, 26 unique deep convolutional models are utilised for the task of deepfake video detection. The models can natively classify objects like table, face, humans, cars etc. However, the paper highlights the use of these models in detection of deepfake/manipulated images and videos by changing the top layer of the model with sigmoid layer hence, detecting artifacts in an image produced by Generative Adversarial Networks (GANs)[11]. Once the models are trained, the paper demonstrates the usefulness of model ensemble to improve the accuracy of proposed system and thereby making the system more reliable.
A Novel Image perceptual hashing algorithm based on frequency decomposition and LoG

research-article Share on A Novel Image perceptual hashing algorithm based on frequency decomposition and LoG Authors: Zihao Yang School of Computer Science and Technology, Jiangsu Normal University, China School of Computer Science and Technology, Jiangsu Normal University, China 0000-0001-6046-6433View Profile , Guosheng Hao School of Computer Science and Technology, Jiangsu Normal University, China School of Computer Science and Technology, Jiangsu Normal University, China 0000-0002-5962-0832View Profile , Xiaoyun Zhou School of Computer Science and Technology, Jiangsu Normal University, China School of Computer Science and Technology, Jiangsu Normal University, China 0000-0002-7144-072XView Profile , Wang Ruan School of Computer Science and Technology, Jiangsu Normal University, China School of Computer Science and Technology, Jiangsu Normal University, China 0000-0003-0263-9353View Profile Authors Info & Claims CSSE '22: Proceedings of the 5th International Conference on Computer Science and Software EngineeringOctober 2022Pages 300–305https://doi.org/10.1145/3569966.3570057Published:20 December 2022Publication History 0citation14DownloadsMetricsTotal Citations0Total Downloads14Last 12 Months14Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A/B Testing in the Small: An Empirical Exploration of Controlled Experimentation on Internal Tools

Previous research on A/B testing and continuous experimentation has mostly focused on large-scale settings with company-external customers. However, work efficiency and satisfaction for company co-workers may be highly related to internal tools and their fit-for-use. In this study, we therefore explore A/B testing for online services exclusively used by company co-workers. We A/B tested two versions of Customer Admin, a tool that helps 34 500 IKEA co-workers to interact with customer data. The study comprised i) stakeholder interviews to understand objectives and phrase the experimentation goals, and ii) A/B test execution where data was collected and processed from approximately 350 users for 33 days. While the user base is relatively large for this internal system, the primary metric data collected was too scarce to allow distinction between the two versions. However, secondary metrics and a user questionnaire suggest that the users are more efficient in the new menu design and that the users prefer it to the old. We conclude that A/B testing requires lots of data, making it less feasible for internal users, also for large, global organizations. Thus, we propose combining quantitative and qualitative evaluation of internal tools and propose further research on how to adapt A/B testing for smaller-scale settings.
Applications of Virtual and Augmented Reality for Practical Application Learning with Gamification Elements

Virtual reality and augmented reality have the potential to enhance and widespread practical learning environments in professional courses efficiently in a cost-efficient manner by limiting the costs of real resources substituting them with fixed costs from.VR/AR applications with virtual resources. There are advantages in the learning process, as practical, active and visual learning methods are more efficient and virtual and augmented reality can digitalize these procedures and replicate them at scale with different degrees of virtualization. In this work we aim to provide a framework that allows the creation of VR and AR experiences for learning or training proposes in a serious environment adding gamification elements to keep user engaged in the learning/training process. In the process gamification adaptation to VR/AR environment is demonstrated in real applications. The learning tasks in this approach are not necessarily changed or take advantage of new forms of interactions and guidance but aim to be replicated in a blend between virtual and real environments. In this regard, we hope to advance gamification application to account for more elements, such as VR/AR interaction, digital twins and digital aids in a learning application. In this article we detail possible scenarios for the application of virtual reality and augmented reality combined with machine learning in serious games and learning scenarios.
A one-dimensional superior logistic map based-image encryption

The importance of digital media has been increased in this pandemic; the media's security is a huge issue and this led to the development of cryptographic techniques that are secure and fast. Chaos-based image encryption has gained popularity due to its ergodic properties. This paper proposes a 1-dimensional chaotic logistic map cryptography based on superior iterations. Both the real and imaginary parts of the 1-dimensional map are used to generate the chaotic sequence. This chaotic sequence is further mixed with the key. The new sequence is used for scrambling and diffusion of pixel values of an image. The proposed scheme was evaluated using many measures like NPCR, UACI, MSE, PSNR and entropy. The simulation result shows that the cipher image's correlation coefficient is below 0.01. The entropy of the cypher picture is similar to its optimal value of eight; thus, the proposed technique is immune to statistical attack. The demonstrated result of NPCR and UACI shows that the proposed scheme is reliable and robust to differential crypto-analysis attacks. Gaussian and salt and pepper noise are added to the picture, the results show that the proposed scheme is resistant to occlusion attacks.
An Analysis The Implementation of Distance Learning System to Support The Teaching Process At SMA/SMK Level in the Region of North Maluku

Today learning in schools is starting to be adapted to the development of information technology, resulting in changes and shifts in the educational paradigm [1] This needs to be done so that the learning process does not seem less interesting, monotonous and boring so that it will hinder the transfer of knowledge [2]. At the beginning of 2020, the spread and transmission of the Corona Virus was increasingly spreading throughout the world. Rapid and massive transmission resulted in system changes in all aspects, both from social, economic, and educational aspects. Indonesia is no exception. To avoid the spread of this pandemic, various efforts have been made by the government to minimize the acceleration of the movement of the corona virus. Since the pandemic, the learning system in the world of education has undergone very significant changes. The government has explicitly issued a decision to implement distance learning by simultaneously utilizing technology so that the teaching and learning process continues. The big obstacle faced by most schools in North Maluku is that online learning support facilities are not yet ready. So that the majority of schools use teleconference applications as a medium to support distance learning. This research is intended to design an application that supports blended learning model learning. Blended learning is a learning method that combines two or more methods and approaches in learning to achieve process goals.
Perceptions and Difficulties of Software Engineering Students in Code Smells Refactoring

Code smells are bad code structures that can harm the quality and maintainability of software. To remove these bad structures, developers use refactoring techniques. Refactoring helps code be easier to understand and modify by eliminating potential problems and improving the software quality. Most refactoring activities are usually performed manually and undisciplined, which can cause code degradation. Currently, concepts, practices and tools of refactoring and code smells are little discussed in undergraduate computing courses. This problem is reflected in the software industry, which generally does not use refactoring practices to improve code readability and maintainability. In this context, we present in this paper an experience report on teaching the practice of code smells refactoring, investigating the following topics: (i) skills acquired in the practice of code smells refactoring, (ii) difficulties encountered in code smells refactoring and (iii) students’ perception of code smells refactoring practices. The study was carried out in two undergraduate classes with 20 students in the course of software quality. Our findings contribute to replicating the practice in other courses that want to delve into code smell refactoring and contribute to the use of refactoring practices and code quality evaluation in the industry.