Reviewerly/exHarmony huggingface dataset:
Opinion Mining For E-Commerce in Social Media Accounts

Abstract: The usage of the Internet has been growing rapidly, and in the field of natural language processing (NLP), sentiment analysis has become one of the most popular techniques. Using sentiment analysis, the emotional tone behind a body of text can be mined effectively for different occasions. Instagram has become a popular platform for people to buy and sell online. Nonetheless, some studies have found that frauds occurred as a result of buying and selling on the platform, such as when product quantity and specifications differed from what was claimed, a defective product was received, and other frauds involved Instagram sellers. Hence, trust is vital when customers are engaged in S-Commerce activities on Instagram, and there is a need for a trust model to evaluate the trustworthiness of sellers. Nowadays, people provide their feedback after a long time of usage only on social media platforms like Instagram. Therefore, these comments play a major role in helping people decide if the product will be beneficial for them over a long period of time. Mining such content to evaluate people’s sentiments can play a critical role in making decisions to keep the situation under control. {objective} Six different classifiers have been used to classify the data. The experiment achieved the highest accuracy of 76.7% with logistic regression. This study can be useful to determine the trustworthiness of the company.
Remote Audit Compared to Onsite Audit and the Capabilities Required in the View of Internal Auditor Practices

&lt;em&gt;The COVID-19 pandemic has made the business model change even faster. One of the things that must be adjusted is the organization that is engaged in auditing, especially internal audit. If in the past, the audit process was carried out with on-site visits, now it can be done with a new process called remotes audits. therefore, this requires new competence in conducting audits, especially how to conduct remote audits, but can still produce audit reports of the same quality as the previous reports. In practice, there are some similarities and differences in conducting remote audits compared to onsite audits. This paper will discuss the internal auditor's views regarding the similarities and differences between the remote audits and onsite audits processes from the view of auditors practitioners and what capabilities the auditor needs to deepen so that the remote audits process can run well. this study uses an explorative descriptive method involving 20 internal auditors as respondents, who were asked for their opinion in October 2022. The results of the study indicates that there is no difference in the planning process and reporting process. The only difference is in fieldwork, where remote auditing is technically more difficult. In addition, auditors are expected to deepen their knowledge in the field of digital and communication technology.&lt;/em&gt;
Study of a chassis path planning algorithm for a forest harvester

ABSTRACTIn this study, a global path planning method is proposed for the motion of the chassis of a forest harvester to satisfy the requirements of obstacle avoidance when the harvester is working in the forest. First, a distributed plane map is generated in accordance with the characteristics of forest environment. Subsequently, a Ms_BiRRT algorithm is proposed based on the principle of the random search tree algorithm for multi-stage target-guided global path planning, and the path is further optimized. The effectiveness of the Ms_BiRRT algorithm proposed in this study is examined through experimental simulation. Three types of map environments are designed based on the characteristics of the forest environment. The average planning time of the proposed algorithm is extended by 90.69% and 47.37%, the path length is shortened by 10.40% and 8.23%, the total number of nodes is reduced by 81.52% and 54.65%, and the node utilization rate increases from 18.47% and 44.12% to 85.46%, respectively, compared with classical RRT algorithm and two-way RRT algorithm. The path planning algorithm proposed in this study is capable of quickly reaching the target point in different environments, improving the convergence speed of the algorithm, and increasing the efficiency and stability of the forest harvester in the working process based on the initial path guidance and target guidance methods.KEYWORDS: Harvesting machineryintelligent forestryglobal planningrapidly-exploring random tree Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis study was supported by the national natural science foundation of China(31670719).
Research on Internet + "Double First Class" Construction Based on GH Model to Promote the Reform and Innovation of Tax Law--Teaching in Universities in Hubei

Under the background of the Internet + era, big data technology has been rapidly promoted and applied. With the arrival of the post epidemic era, the reform and innovation in the field of tax law teaching in universities in our province are more inseparable from the reform and integration of online and offline teaching modes. Big data technology has brought a profound impact on the reform of tax law teaching mode. Especially under the new situation that our province is vigorously promoting the construction of "double first-class", this topic combines the GH model proposed by the famous scholars Grossman and Helpman, and improves and innovates on the basis of this model to build a new model, so that the theoretical research and data model can be better integrated to promote the reform and innovation of tax law teaching in Hubei universities.
Potential verbal attacks in a television political talk show: A case of a host’s questioning

This study examined potential verbal attacks in questioning delivered by a host of a political talk show in Indonesia. It aims at describing a realization of the potential verbal attacks in questioning and identifying the patterns which provides the resource persons with an awareness of the verbal attacks delivered by the host to appropriately respond to them. The data were taken from the host questioning items from a political talk show dialogue ‘Mata Najwa’ in five different political topics entitled ‘Adu Kuat di Demokrat’, ‘Berebut Tahta di Tengah Wabah’, ‘Beres-Beres Kursi Menkes’, ‘Gaduh Tiga Periode’, and ‘Kritik Tanpa Intrik’. The research applied a qualitative method with a descriptive design by employing Elgin’s definition and verbal attack pattern, presuppositions, and the context in questioning referring to the information triggered by certain linguistic constructions of the participant's speech event. The results indicated that the host's questioning behavior during the political talk show potentially tends to result in verbal attacks. The realization of potential verbal attacks resulted in the form of interrogative sentences initiated by wh-question words, modalities, conjunctions, and auxiliary words and affirmative (statement) as a question that was getting more complex depending on the depth of information and presuppositions hidden in the host questioning. The complexity of the attack patterns used by the host affects the strategy used to break, reject, or attack the opinion or argument by countering, judging and criticizing, and denying the resource person's responses.
Object classification through unknown random diffusers using a single-pixel diffractive network and spectrum encoding

We report an all-optical object classification framework using a single-pixel diffractive network and spectrum encoding, classifying unknown objects through unknown random phase diffusers at the speed of light. Using this single-pixel diffractive network design, we numerically achieved a blind testing accuracy of 88.53%, classifying unknown handwritten digits through 80 unknown random diffusers that were never used during training. This framework presents a time- and energy-efficient all-optical solution for directly sensing through unknown random diffusers using a single pixel and will be of broad interest to various fields, such as security, biosensing and autonomous driving.
Polarization diffractive networks: performing multiple linear transformations using a polarization-encoded diffractive optical network

We report a polarization-encoded diffractive network to perform multiple arbitrary complex-valued linear transforms within a single diffractive processor. An array of pre-selected linear polarizers is placed between the trainable isotropic diffractive layers, and distinct complex-valued linear transformations are individually assigned to different combinations of input/output polarization states. A polarization-encoded diffractive network performs the target linear transforms with negligible error when N ≥ P x I x O, where N is the number of trainable diffractive features/neurons, I and O denote the number of pixels at the input and output fields-of-view, respectively, and P represents the number of target linear transforms.
Data class-specific imaging with all-optical erasure of undesired objects using diffractive computing

We present a diffractive camera that performs class-specific imaging of target objects, while all-optically and instantaneously erasing the objects from other classes during light propagation through thin diffractive layers, maximizing privacy preservation. We experimentally validated this class-specific camera design by 3D-printing the resulting diffractive layers (optimized through deep learning) and selectively imaging MNIST handwritten digits using the assembled camera system under terahertz radiation. The presented object class-specific camera is passive and does not require external computing power, providing a data-efficient solution to task-specific and privacy-aware modern imaging applications.
Datacenter optics: key component technologies for the next decade

In this presentation, we will review the key optical and networking technologies which drove the evolution of Google's datacenter networks over the past 15 years. We then discuss critical directions for continued scaling in a post-Moore's Law paradigm.
Exploring the role of verb frames in assessing semantic difficulty

This paper explores the role of verb frames in semantic difficulty.We built a textbook corpus based on textbooks in primary schools, and the grades of the textbooks were treated as semantic difficulty levels.The difficulty levels were furthered mapped onto the performance of verb frames.The study found that the verb-frame diversity is highly correlated with semantic difficulty level with statistical significance and that the distribution of verb frames may correspond to the skill theory (Fischer & Corrigan, 1981) in cognitive language development.The skill theory could be used to group verb frames by tiers and roughly predict their distributional trends.Thus, verb frames could be helpful as a semantic reference for textbook planning and readability assessment.
The Application of Modern Educational Technology in College Physical Education under the Background of Informatization

With the deepening of the current educational curriculum reform, modern teaching technology is a brand new educational mode produced based on the current historical background.On the one hand, modern educational technology is based on the inheritance of Chinese traditional PE teaching methods, and on the other hand, it improves and innovates and develops relevant teaching methods, making it get a comprehensive play.This article focuses on the development trend of modern educational technology to be deeply studied, and analyzes its relative advantages in the current university physical education teaching, and strive to further improve the current level of university physical education teaching, and promote its continuous development.
Convex-Hull-Directed Edge Routing Geometric Optimization

The realistic Digital Earth, whose surface is covered with terrain elevation and remote sensing image, brings terrain occlusion and image disturbance to 3D graph visualization. We propose a terrain-constrained 3D edge routing optimization problem to obtain the optimal edge with the shortest length and the fewest terrain feature points while keeping the convexity and terrain disocclusion. The main idea is to solve combinatorial optimization using a convex-hull-directed geometric technique. A convex hull capability model (CHCM) describes that the convex hull can not only extract topographic feature points, but also support graph aesthetic criteria. Under the guidance of CHCM, the optimization is solved by the two chains of geometric evolution: the first one extracts topographic feature polyline via an auxiliary closed convex hull on the 2D earth slice, and the second one uses convex-hull-based convex-preserving Bezier interpolation to further smooth the polyline as optimal edge. In addition, the lightness of the remote sensing image is adjusted in color space to reduce image interference. Simulation results show that our approaches can eliminate visual clutters such as piercing mountains and visual zigzags caused by terrain occlusion, in the experimental case, the number of 3D projected edge intersections is reduced by 42%, and topographic feature points only account for 24% of the total sampling points. The geometric and image-based compound framework will provide a general design solution for 3D graphic visualization on various realistic earth platforms.
Creativity of a future English language teacher as basis of professional improvement

The article considers the theoretical and methodological principles of studying creativity in psychological and pedagogical science. The understanding of creativity as an internal structure and set of meshanisms of search actions and creativity person as an individual is given. Creativity is defined as a productive activity, as aresult of which a new product is created, which is distinguished by novelty, originality. At the sam e time, creativity is described as a meaningful, useful, interesting, accessible and harmonious, effective solutions to problems, conflicts, as well as way to self actualization and self expression. Peculiarities of training a future creative foreign language teacher in the condition of education modernization are analyzed. The main features of a creative teacher are determined. The task facing a modern creative foreign language teacher are specified. Emphasis is placed on the need to use didactic games to develop the creative abilities of participants in the educational process. The main aspects of forming a creative active personality of a modern teacher are considered; psychological and pedagogical conditions of development of pedagogical creativity at students of pedagogical specialities are considered. It is proved that creative activity is an important condition for the development of modern foreign language teachers.
Analyzing speaker information in self-supervised models to improve unsupervised speech recognition

The quality of speech representation is the key to the success of unsupervised speech recognition. Self-supervised models contain a variety of audio information, and non-speech content information is not beneficial or even harmful to speech recognition, so removing speaker information can greatly improve the accuracy of unsupervised speech recognition. To effectively remove speaker information, this paper first analyzes the audio features extracted by wav2vec 2.0 and HuBERT self-supervised model qualitatively and quantitatively, and derives the mean value of each sentence as a representation of speaker information. The extracted speech representations are then speaker normalized to obtain valid features to be fed into the unsupervised speech recognition model for training. Experimental results on the TIMIT dataset show that the speaker normalization method can significantly reduce the error rate of unsupervised speech recognition.
Unmanned Driving Technology in Coal Mine Based on Semantic Information Method

Aiming at the transportation scheduling problem of unmanned trucks in open-pit mines, an optimization model for transportation scheduling of unmanned trucks in open-pit mines is established, taking the minimum sum of fuel cost, fixed start-up cost, fault maintenance cost and network base station construction and maintenance cost as the objective function, and taking mining amount, crushing amount in crushing field, truck number and truck transportation work as the bundle reduction conditions. By adjusting the position of the encapsulated individual and the optimal encapsulated individual through the parameter position adaptive updating mechanism, the search range of the solution space can be enlarged, so that the algorithm can jump out of the local optimum. This paper analyzes the influence of vehicle clearance on coordinated traffic at intersections under clearance theory, and puts forward the minimum vehicle clearance to meet coordinated traffic. Generate trajectories with behavioral semantics through hierarchical online selection of primitives; Based on the model predictive control algorithm, the motion control module fully considers the execution ability, tracking deviation and control stability of the platform drive motor, and updates the weight coefficient of the control objective function in real time with the help of the semantic attributes of the behavioral motion primitives given by the motion planning, so as to reduce the lateral error, heading error and speed error of the trajectory tracking. Finally, a real vehicle is verified by a medium-sized hybrid unmanned crawler platform, which proves that the proposed intersection coordinated traffic model is feasible in theory, reflecting that the traffic density is a factor that affects the transportation efficiency. Under the condition of meeting the production requirements, reduce the intersection conflict and improve the traffic capacity of the intersection by adopting large-load unmanned mine trucks as much as possible, reducing the number of unmanned mine trucks and the interference between equipment.
Recommending on graphs: a comprehensive review from a data perspective

Recent advances in graph-based learning approaches have demonstrated their effectiveness in modelling users' preferences and items' characteristics for Recommender Systems (RSS). Most of the data in RSS can be organized into graphs where various objects (e.g., users, items, and attributes) are explicitly or implicitly connected and influence each other via various relations. Such a graph-based organization brings benefits to exploiting potential properties in graph learning (e.g., random walk and network embedding) techniques to enrich the representations of the user and item nodes, which is an essential factor for successful recommendations. In this paper, we provide a comprehensive survey of Graph Learning-based Recommender Systems (GLRSs). Specifically, we start from a data-driven perspective to systematically categorize various graphs in GLRSs and analyze their characteristics. Then, we discuss the state-of-the-art frameworks with a focus on the graph learning module and how they address practical recommendation challenges such as scalability, fairness, diversity, explainability and so on. Finally, we share some potential research directions in this rapidly growing area.
Development of electrooculogram based human computer interface system using deep learning

The patients with diseases that cause severe movement disabilities was noticeably increasing. These disabilities made patients unable to carry out their daily activities or interact with their external environment. However, the existence of human-computer interfaces (HCI) gave those patients a new hope to be able to interact once again. HCI enabled these patients to communicate with their environment by recognizing the movement of their eyes. Eye movements are recorded by an electro-oculogram (EOG) through some electrodes that are put vertically and horizontally on the eyes. In this paper, EOG vertical and horizontal signals were analyzed to detect six eye movements (up, down, right, left, double blinking, and center). Three deep learning models namely convolution neural network (CNN), visual geometry group (VGG), and inception had been examined on filtered EOG signals. The experimental results reveal the superiority of the inception model in providing the best average accuracy 96.4%. Accordingly, a writing system is presented based on the detected movements.
Phenomenon of free painting: Comparison of the artistic images of the modernism era and children's painting

The relevance of the study is due to the significant changes in the content of education, in particular, art education; the need for a thorough study of modern methods of teaching and upbringing a child, analysis of the modern children’s needs, taking into account the psycho- physiological features of their development and periodization of the children's painting development. The aim of the article is to analyze the artistic images of modernism era artists and artistic images created by preschool children in the process of painting, to identify the common features, find out the analogies, describe manifestations and characteristics. Objectives: to analyze and characterize the phenomenon of free painting; compare the artistic images created by the modernism era artists and preschool children; consider the means of expression used by both groups of recipients, compare them, identify the common things, draw an analogy. The following research methods are used: comparison, which is the basis of such logical method as an analogy; comparison as a comparative-historical method that reveals the general and special features of the phenomena, different stages of development of the same phenomenon or different coexisting phenomena; analysis which helps to identify and compare levels of the development of the phenomenon under study, explore changes, identify trends. The method of formalization is also used, as it clarifies the content of thought by specifying its logical form.
Optimization method of aluminum electrolysis current efficiency based on LightGBM-TPE

The influencing factors of aluminum electrolysis production process are complex, and current efficiency is an important evaluation index. In order to study the influence of various parameters on the current efficiency in the aluminum electrolysis production process, a LightGBM-TPE current efficiency optimization model was established in this paper. First, the production data is preprocessed, and the industrial parameters are fitted using the LightGBM prediction model. Then, to further increase the model's prediction accuracy, the TPE optimization method is used to optimize the LightGBM hyperparameters. Finally, the optimization of current efficiency is realized through Optuna combined with TPE Bayesian optimization algorithm. The experimental results demonstrate that the model is capable of accurately identifying the realization conditions and process parameters of high current efficiency in the production process, as well as providing a parameter control foundation for the effective operation of the actual electrolytic aluminum production, ultimately achieving the goal of power consumption reduction.
Theoretical and methodological aspects of the formation of research competence of future teachers of physical education in the process of professional training

The purpose of the article is to analyze the theoretical and methodological aspects of the formation of research competence of future teachers of physical education in the process of professional training. The article is based on the use of both general and special pedagogical research methods. An important role for our work was played by the use of sociological (sociological-statistical) method. The modeling method made it possible to propose a separate project-model of the curriculum. Research competence is important, with its help the future specialist will be able to conduct their own projects and develop the latest methods and ways of working and learning.