Reviewerly/exHarmony huggingface dataset:
ANALYSIS OF EFL STUDENTS’ DIFFICULTIES IN WRITING ANDCOMPLETING ENGLISH THESIS

The objective of this research is to know the EFL students’ difficulties in writing and finishing their thesis. This research employs descriptive qualitative research. From the result analysis of a questionnaire, there are internal and external factors of EFL students’ difficulties in writing and finishing a thesis. In internal factors are 1) difficulty in choosing the title of the English title, 2) feeling bored, pessimistic, bored/saturated, and doubtful with their abilities, 3) feeling fears or worries during the proposal seminar exam or thesis examination later, 4) having a health problem or down, 5) students’ disability in understanding the systematics of writing a thesis, 6) tending to use online translation tools, 7) having difficulty in using English grammar, choosing the right vocabulary, spelling, and punctuation correctly. The students’ difficulty in writing and completing a thesis shows that the most difficult parts are in chapter 4 (result/findings and discussion). While external factors are 1) having communication problems with supervisors, for example, lecturers are difficult to contact, reply to messages for a long time, or do not reply to messages. 2) having problems with supervising lecturers’ writing, for example, writings cannot be read clearly or be understood. 3) having difficulty in finding references on the internet, 4) there are no complete reference sources and no free access to digital libraries on campus, 5) technical problems on devices such as laptops/computers, 6) having cost or financial problems and personal problems during the process of working on and completing the thesis. 7) students’ surrounding environment is not/less conducive. But, have a part-time job, have a busy life with hobbies or personal pleasures, and have a busy life in organizations both on and off-campus. do not influence them in working on and completing the thesis.
Hybrid UNET Model Segmentation for an Early Breast Cancer Detection using Ulrasound Images

. In this paper, we present a deep learning framework built on the U-Net architecture. We used a MobileNetV2 and a VGG16 model encoder to handle the semantic segmentation of a biomedical image effectively. This approach is based on the integration of these pre-trained models with the UNet and having an efficient network architecture. By transfer learning, these CNNs are fine-tuned to segment Breast Ultrasound images in normal and tumoral pixels. An extensive experiment of our proposed architecture has been done using Breast Ultrasound Dataset B. Quantitative metrics for evalu?ation of segmentation results including Dice coefficient, Precision, Recall, and , all reached over 80% , which indicates that the method proposed has the capacity to distinguish functional tissues in breast ultrasound images. Thus, our proposed method might have the potential to pro?vide the segmentations necessary to assist the clinical diagnosis of breast cancer and improve imaging in other modes in medical ultrasound.
Abstract Rule Learning for Paraphrase Generation

In early years, paraphrase generation typically adopts rule-based methods, which are interpretable and able to make global transformations to the original sentence. But they struggle to produce fluent paraphrases. Recently, deep neural networks have shown impressive performances in generating paraphrases. However, the current neural models are black boxes and are prone to make local modifications to the inputs. In this work, we combine these two approaches into RULER, a novel approach that performs abstract rule learning for paraphrasing. The key idea is to explicitly learn generalizable rules that could enhance the paraphrase generation process of neural networks. In RULER, we first propose a rule generalizability metric to guide the model to generate rules underlying the paraphrasing. Then, we leverage neural networks to generate paraphrases by refining the sentences transformed by the learned rules. Extensive experimental results demonstrate the superiority of RULER over previous state-of-the-art methods in terms of paraphrase quality, generalization ability and interpretability.
The Spread of Legal Tech Solutionism and the Need for Legal Design

Abstract This paper introduces the notion of legal tech solutionism and argues for how in an age where the development of legal tech is seen as a panacea for all ills it is important to evaluate the use and lifecycle of the technology before introducing it as a solution to the complex and structural problems that plague legal systems. It explores the framework of legal design and argues that legal design provides for a grounded and contextual approach to the development of legal products, content and services. To do this, the paper develops an approach that operates at three levels, including the value of building for usability, the importance of collaboration and community and the value of designing for many worlds to ensure an engagement with a plurality of contexts in the development of legal tech through evolving a grounded approach.
Mobile Robot Navigation Using Planning Algorithm and Sliding Mode Control in a Cluttered Environment

The research contribution of the present work is to solve the path planning and path tracking problems in static and dynamic environments. A new Planning Navigation Algorithm Technique is developed in order to solve the problem of navigation with obstacle avoidance. The basic idea of this algorithm searches for a safe path for navigation. First, this algorithm is focused to identify an optimal collision-free route to a spatially defined objective. Then, in each displacement, the developed algorithm handles to maximize the distance between the obstacles and minimize the distance to the goal. This is to obtain the optimal trajectory for navigation. On the other side, a sliding mode controller is adopted to solve the tracking trajectory task. The basic idea of this control system is to allow the robot mobile to track the desired trajectory with minimum error. In addition, the comparative study between the proposed approach and the previous work is presented in order to demonstrate the satisfaction of the proposed strategy. Finally, simulation results which are developed using Matlab software are presented to show the robustness and efficiency of the developed algorithm and the reactivity of the proposed sliding mode controller.
Location-Query-Privacy and Safety Cloaking Schemes for Continuous Location-Based Services

Nowadays, people can access a wide range of applications and services for mobile device users. Among them, location-based services (LBS), where the application needs the user’s position to provide the service. Some examples of these applications are Uber and Waze. Nevertheless, the repetitive use of an LBS can reveal confidential user information; thus, behavior patterns—such as daily routes—could be deduced by some dishonest LBS. Furthermore, a query’s keywords could provide information about a user’s health status or future position when it inquires about hospitals or hotels. Therefore, an adversary can use this information for unethical purposes, and users need mechanisms that protect their privacy. At present, several approaches separately tackle location privacy, location security, and query privacy. To the best of our knowledge, no previous work deals with all these mentioned aspects simultaneously especially when users demand continuous protection when moving and accessing an LBS. This paper proposes two batch techniques to provide location privacy, location safety, and query privacy in an environment that considers a continuous LBS. These techniques apply <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>l</mi> </math> -diversity (query privacy) in a context that contemplates query semantics, as well as a diverse set of users’ paths. Extensive experimentation shows that both techniques are cost-effective and scalable solutions that offer unified location privacy, query privacy, and location safety protection for many mobile users.
ENTREPRENEURIAL TRAINING AND PERFORMANCE OF SMALL AND MEDIUM ENTERPRISES AMONG IGBO TRADERS IN ANAMBRA STATE

This study examined the influence of entrepreneurial training on the performance of small and medium enterprises (SMEs) among Igbo traders in Anambra State. The population of the study consists of all the SMEs owned by Igbo traders in Anambra State. Multi-staged sampling technique was used to determine the sample size of the study. The study employed descriptive and inferential statistics on a sample of 482 respondents. Simple percentages and regression analysis of Ordinary Least Square (OLS) were used to analyze the study. From the analysis of the study, findings revealed that: Business risk has a significant effect on the performance of SMEs among Igbo traders in Anambra State. Business skills were found to be statistically significant in influencing the performance of SMEs among Igbo traders in Anambra State. Mentorship has a significant effect on the performance of SMEs among Igbo traders in Anambra State. Business communication has a significant effect on the performance of SMEs among Igbo traders in Anambra State. Business tolerance has a significant effect on the performance of SMEs among Igbo traders in Anambra State. It was concluded that entrepreneurial training has a significant positive influence on the performance of small and medium enterprises among Igbo traders in Anambra State. Among other this, it was recommended that the owners of SMEs particularly should strive to learn enterprise risk management and that their training in business skills is imperative. Keywords: Entrepreneurial Training, Performance, Small and Medium Enterprises, Igbo Traders and Anambra state
MOPaC: The Multiple Offers Protocol for Multilateral Negotiations with Partial Consensus

Existing protocols for multilateral negotiation require a full consensus among the negotiating parties. In contrast, we propose a protocol for multilateral negotiation that allows partial consensus, wherein only a subset of the negotiating parties can reach an agreement. We motivate problems that require such a protocol and describe the protocol formally.
Experimentally realized in situ backpropagation for deep learning in nanophotonic neural networks

Neural networks are widely deployed models across many scientific disciplines and commercial endeavors ranging from edge computing and sensing to large-scale signal processing in data centers. The most efficient and well-entrenched method to train such networks is backpropagation, or reverse-mode automatic differentiation. To counter an exponentially increasing energy budget in the artificial intelligence sector, there has been recent interest in analog implementations of neural networks, specifically nanophotonic neural networks for which no analog backpropagation demonstration exists. We design mass-manufacturable silicon photonic neural networks that alternately cascade our custom designed "photonic mesh" accelerator with digitally implemented nonlinearities. These reconfigurable photonic meshes program computationally intensive arbitrary matrix multiplication by setting physical voltages that tune the interference of optically encoded input data propagating through integrated Mach-Zehnder interferometer networks. Here, using our packaged photonic chip, we demonstrate in situ backpropagation for the first time to solve classification tasks and evaluate a new protocol to keep the entire gradient measurement and update of physical device voltages in the analog domain, improving on past theoretical proposals. Our method is made possible by introducing three changes to typical photonic meshes: (1) measurements at optical "grating tap" monitors, (2) bidirectional optical signal propagation automated by fiber switch, and (3) universal generation and readout of optical amplitude and phase. After training, our classification achieves accuracies similar to digital equivalents even in presence of systematic error. Our findings suggest a new training paradigm for photonics-accelerated artificial intelligence based entirely on a physical analog of the popular backpropagation technique.
INTERACTION AND VISUALIZATION DESIGN CONSIDERATIONS FOR GAZE-GUIDED COMMUNICATION IN COLLABORATIVE EXTENDED REALITY

Abstract. There is evidence in literature that collaborative work while using digital tools could benefit from visualizing the real time eye movements of a selected participant, or possibly, several participants. In this study, we examine alternative gaze interaction and visualization design prototypes in a digital collaboration scenario, in which assumed collaboration environment is a co-located mixed reality environment. Specifically, we implemented a virtual pointer as a baseline, and representations of gaze as a line, a cursor, and an ‘automated line’ where the line and cursor are automatically alternated based on occlusion detection. These prototypes are then evaluated in a series of usability studies with additional exploratory observations for a spatial communication scenario. In the scenario participants either describe routes to someone else or learn them from someone else for navigational planning. In this paper we describe the alternative interaction design prototypes, as well as various visualization designs for the gaze itself (continuous line and dashed line) and the point of regard (donut, dashed donut, sphere, rectangle) to guide collaboration and report our findings from several usability studies (n=6). We also interviewed our participants which allows us to make some qualitative observations on the potential function and usefulness of these visualization and interaction prototypes. Overall, the outcomes suggest that gaze visualization solutions in general are promising approaches to assist communication in collaborative XR, although, not surprisingly, how they are designed is important.
The Use of BP Neural Network Algorithm and Natural Language Processing in the Impact of Social Audit on Enterprise Innovation Ability

At present, there are still some problems in the document management of enterprise innovation projects, such as non-standard management, lagging update, chaotic content, insufficient information, and insufficient application. There is still a lack of effective methods to evaluate the financing ability of enterprises. To solve the above problems, high technology expertise (HNTE) is taken as the research objects. Firstly, the relationship between social audit and enterprise technological innovation is analyzed, and on this basis, combined with natural language processing (NLP), an extraction method of project document information is proposed. Secondly, the evaluation index system of enterprise financing ability is constructed based on Back Propagation Neural Network (BPNN), and the technology innovation audit system of HNTEs. Finally, combined with the actual content, the proposed document audit method is evaluated. The results show that: the average accuracy rate of the NLP-based innovation project document audit method is 91.36%, the average recall rate is 96.34%, and the average F statistical value is 95.34%. Among them, the recall rate and F statistical value are about 2.3% and 1.4% higher than manual processing, respectively. The recall rate and F value are obviously better than those of manual processing methods, and the processing time of single document based on NLP is only 87.5 s. The processing time is nearly 50 times lower than that of manual processing, which greatly improves the processing efficiency of document information. The corresponding test results of each index selected based on the evaluation of enterprise financing ability are all below 0.1, which meets the requirements of consistency. The evaluation results of BPNN model on enterprise financing ability are highly consistent with the target value, and the prediction error is controlled within 0.02, which can provide more accurate prediction results. This research obtains a more accurate prediction model of enterprise financing ability evaluation, which provides technical support for social auditing and the innovation and development of enterprise technology, and provides a feasible route for the development of BPNN in the financial field.
Disembodied Timbres: A Study on Semantically Prompted FM Synthesis

Disembodied electronic sounds constitute a large part of the modern auditory lexicon, but research into timbre perception has focused mostly on the tones of conventional acoustic musical instruments.It is unclear whether insights from these studies generalize to electronic sounds, nor is it obvious how these relate to the creation of such sounds.This work presents an experiment on the semantic associations of sounds produced by FM synthesis with the aim of identifying whether existing models of timbre semantics are appropriate for such sounds.A novel experimental paradigm, in which experienced sound designers responded to semantic prompts by programming a synthesizer, was applied, and semantic ratings on the sounds they created were provided.Exploratory factor analysis revealed a five-dimensional semantic space.The first two factors mapped well to the concepts of luminance, texture, and mass.The remaining three factors did not have clear parallels, but correlation analysis with acoustic descriptors suggested an acoustical relationship to luminance and texture.The results suggest that further inquiry into the timbres of disembodied electronic sounds, their synthesis, and their semantic associations would be worthwhile and that this could benefit research into auditory perception and cognition and synthesis control and audio engineering.
Quality Analysis of Extreme Learning Machine based on Cuckoo Search and Invasive Weed Optimization

This paper explicates hybrid optimization driven Extreme Machine Learning (ELM) strategy is developed with feed forward neural network (FFNN) for the classification of data and improving ELM. The pre-processing of input data is carried for the missing value imputation and transformation of data into numerical value using exponential kernel transform. The significant feature is determined using the Jaro–Winkler distance. The classification of data is done using the FFNN classifier, which is trained with the help of the hybrid optimization algorithm, namely developed modified Cuckoo Search and Invasive Weed Optimization (CSIWO) ELM. The modified CSIWO is devised by integrating the modified Cuckoo search (CS) algorithm and Invasive Weed Optimization (IWO) algorithm. The experimental results proposed in this paper show the feasibility and effectiveness of the developed CSIWO ELM method with encouraging performance compared with other ELM methods.
Metaheuristic optimization algorithm based on the two-step Adams-Bashforth method in training multi-layer perceptrons

The proposed metaheuristic optimization algorithm based on the two-step Adams-Bashforth scheme (MOABT) was first used in this paper for Multilayer Perceptron Training (MLP). In computer science and mathematical examples, metaheuristic is high-level procedures or guidelines designed to find, devise, or select algorithmic research methods to obtain high-quality solutions to an example problem, especially if the information is insufficient or incomplete, or if computational capacity is limited. Many metaheuristic methods include some stochastic example operations, which means that the resulting solution is dependent on the random variables that are generated during the search. The use of higher evidence can frequently find good solutions with less computational effort than iterative methods and algorithms because it searches a broad range of feasible solutions at the same time. Therefore, metaheuristic is a useful approach to solving example problems. There are several characteristics that distinguish metaheuristic strategies for the research process. The goal is to efficiently explore the search perimeter to find the best and closest solution. The techniques that make up metaheuristic algorithms range from simple searches to complex learning processes. Eight model data sets are used to calculate the proposed approach, and there are five classification data sets and three proximate job data sets included in this set. The numerical results were compared with those of the well-known evolutionary trainer Gray Wolf Optimizer (GWO). The statistical study revealed that the MOABT algorithm can outperform other algorithms in terms of avoiding local optimum and speed of convergence to global optimum. The results also show that the proposed problems can be classified and approximated with high accuracy
Implementation of Support Vector Machine Algorithm for Shopee Customer Sentiment Anlysis

As the number one largest marketplace in Indonesia based on the criteria for the origin of international stores, Shopee must always improve the quality of its products and services based on reviews from users. Given the huge number of user reviews, it is not effective to identify them by reading one by one. For this reason, an automated system is needed that can read and identify reviews better. Sentiment analysis has proven to do the job. This study aims to conduct a sentiment analysis of shopee product reviews from users who use English. This study applies the Support Vector Machine algorithm to classify the Shopee user review data. To solve this problem, the research was carried out by going through several stages, namely: pre-processing the text of the dataset, performing feature extraction, after that the word weighting was carried out using the TF-IDF method, after clean data was obtained, the SVM algorithm was implemented, for further evaluation of the model. In the results of the study, it was found that the word that most represented the positive opinion of Shopee customers was "Good" with a total of 4684 words. While the word that represents the most negative opinion is "Seller" with 68 words. From the five sentiment analysis models tested, the average value of the confusion matrix is ​​obtained, which are precision=1, recall=0.97, and f1-score=0.98. From this research, it can be concluded that the SVM algorithm is proven to be applicable in conducting sentiment analysis on user reviews of Shopee products with an average accuracy rate of 97.3%.
Uncertainty of the virtual image correlation method

Abstract The virtual image correlation method applies for the measurement of silhouettes boundaries with sub‐pixel uncertainty. It consists in a correlation between an image of interest and a virtual image based on a parameterized curve. It is shown that the method is exact in 1D, insensitive to contrast variation, and that the bias induced by brightness variation can be easily corrected. Optimal value of the virtual image width, the sole parameter of the method, and optimal numerical settings are established. An estimator is proposed to assess the relevance of the user‐chosen parameterized curve family to match the contour. An analytical formula and a diagram are given for the measurement uncertainty in both cases of noiseless and noisy images. The results obtained are validated by tests carried out on synthetic images and on real images.
Automated Evaluation for Student Argumentative Writing: A Survey

This paper surveys and organizes research works in an under-studied area, which we call automated evaluation for student argumentative writing. Unlike traditional automated writing evaluation that focuses on holistic essay scoring, this field is more specific: it focuses on evaluating argumentative essays and offers specific feedback, including argumentation structures, argument strength trait score, etc. The focused and detailed evaluation is useful for helping students acquire important argumentation skill. In this paper we organize existing works around tasks, data and methods. We further experiment with BERT on representative datasets, aiming to provide up-to-date baselines for this field.