Reviewerly/exHarmony huggingface dataset:
VLSP 2021 - vnNLI Challenge: Vietnamese and English-Vietnamese Textual Entailment

This paper presents the first challenge on recognizing textual entailment (RTE), also known as natural language inference (NLI), held in a Vietnamese Language and Speech Processing workshop (VLSP 2021).The challenge aims to determine, for a given pair of sentences, whether the two sentences semantically agree, disagree, or are neutral/irrelevant to each other. The input sentences are in English or Vietnamese and may not be in the same language. This task is important in identifying, from different information sources, the evidence that supports or refutes a statement. The identification of such evidence is subsequently useful for many information tracking applications, such as opinion mining, brand and reputation management, and particularly fighting against fake news.Through this challenge, we would like to provide an opportunity for participants who are interested in the problem, to contribute their knowledge to improve the existing techniques and methods for the task, so as to enhance the effectiveness of those applications.In the paper, we introduce a collection of Vietnamese and English sentences in the domain of health that we built to serve as a benchmarking dataset for the task. We also describe the evaluation results of systems participating in the challenge.
Exploring the Digital Divide among the Bhutanese Refugee Community during COVID-19: Engaged Research in Action

The digital divide proved a critical barrier to accessing information and healthcare during the COVID-19 pandemic and negatively impacted the Bhutanese refugee community. Moving beyond a technological model of the digital divide that highlights a lack of access to computers and the internet, we engaged the community to co-produce a dynamic approach that identifies the impact of socio-cultural and socio-environmental factors as well. Our paper reports on our community-academic research partnership and explores how the digital divide exacerbates health disparities in a midwestern Bhutanese refugee community. Combining the efforts of the community, anthropologists and social workers, this paper reports on the health disparities that confront the community as well as interventions designed to mitigate social inequities.
DyCC-Net: Dynamic Context Collection Network for Input-Aware Drone-View Object Detection

Benefiting from the advancement of deep neural networks (DNNs), detecting objects from drone-view images has achieved great success in recent years. It is a very challenging task to deploy such DNN-based detectors on drones in real-life applications due to their excessive computational costs and limited onboard computational resources. Large redundant computation exists because existing drone-view detectors infer all inputs with nearly identical computation. Detectors with less complexity can be sufficient for a large portion of inputs, which contain a small number of sparse distributed large-size objects. Therefore, a drone-view detector supporting input-aware inference, i.e., capable of dynamically adapting its architecture to different inputs, is highly desirable. In this work, we present a Dynamic Context Collection Network (DyCC-Net), which can perform input-aware inference by dynamically adapting its structure to inputs of different levels of complexities. DyCC-Net can significantly improve inference efficiency by skipping or executing a context collector conditioned on the complexity of the input images. Furthermore, since the weakly supervised learning strategy for computational resource allocation lacks of supervision, models may execute the computationally-expensive context collector even for easy images to minimize the detection loss. We present a Pseudo-label-based semi-supervised Learning strategy (Pseudo Learning), which uses automatically generated pseudo labels as supervision signals, to determine whether to perform context collector according to the input. Extensive experiment results on VisDrone2021 and UAVDT, show that our DyCC-Net can detect objects in drone-captured images efficiently. The proposed DyCC-Net reduces the inference time of state-of-the-art (SOTA) drone-view detectors by over 30 percent, and DyCC-Net outperforms them by 1.94% in AP75.
A Multi Objective Hybrid Collision-free Optimal Path Finder for Autonomous Robots in Known Static Environments

The most important field of robotics study is path planning. Path planning problem in general is an NP-complete problem. Though several attempts have been made using A*, PRM, RRT, and RRT* these algorithms explore too many nodes in the state space, not completely captured kinematic constraints, and are not optimal in real-time. In this paper, a Multi-Objective Hybrid Collision- free Optimal Path Finder (MOHC-OPF) is proposed which is an attempt to obtain a near-optimal solution by exploring fewer nodes compare to the above existing methods while considering kinematic constraints aiming to generate optimal drivable paths. The empirical study revealed that the proposed algorithm is capable of detecting static obstacles and finding a collision-free nearest-optimal, smooth and safe path to the destination in a static known environment. Multiple criteria, including path length, collision-free, execution time, and smooth path, are used to determine an optimal path.. The proposed algorithm shows efficiency in finding the shortest path length and execution time decreased in 90% of the experiments.
SNPERS: A Physical Exercise Recommendation System Integrating Statistical Principles and Natural Language Processing

As chronic diseases such as cardiovascular diseases are prevalent and progressively more common in young people, more and more college students are paying attention to exercising, even though they are busy studying. However, some college students are unmindful of their physique and their bodies’ targeted exercise. The exercise they do is either extensive but not refined or too homogeneous. We conducted a statistical analysis of 18,101 college students’ physical examination results. We found that students who exercise regularly but still did not achieve satisfactory results in one or more physical examination items had often exercised in the two unscientific ways mentioned above. This paper presents an intelligent recommendation system that integrates statistical principles and natural language processing, which improves traditional recommendation systems and could provide suitable and targeted exercise suggestions for college students. The R2 increased by about 27.72%.
DEVELOPMENT OF THE NATURAL LANGUAGE PROCESSING-BASED CHATBOT FOR SHOPPRITE SHOPPING MALL

Software-as-a-service (SaaS) solutions are frequently used to create chatbots, giving users the option to interact with them via desktop computers, mobile phones, and tablets. To increase customer accessibility, a chatbot is being developed for customers. The user can choose their own convenient language because the device offers text or audio support. This project's goal was to develop a chatbot for the Shopprite Shopping Mall. The chatbot's goal is to converse with the client in a clever, accurate, and timely manner using natural language processing. Customers can use this feature to communicate with the bot and ask questions about specific things they want to buy and the price before visiting the mall.Customers can access the chatbot from portable mobile devices or laptops at any time, making it possible to offer a round-the-clock online service. The discomfort customers feel when they visit the Shopprite shopping mall to look for things only to discover that they are either unavailable or out of stock will be lessened by the results of this study. The following methodologies were used to carry out the work: React.js to create the chatbot's front-end and admin login page; Spacy and React.ai to train the chatbot's NLP section andE-commerce datasets for the chatbot and MySQL to manage and create the data structure that will house the e-commerce datasets.It is recommended that new capabilities be added to the chatbot, such as the delivery of purchased things to a customer's home, more training phrases to give the chatbot a better social outlook, automated item addition to the chatbot database, and even adding a barcode reader option. Testing the chatbot using a bigger dataset would also be helpful.
Quality Judgment of 3D Face Point Cloud Based on Feature Fusion

3D face point cloud quality judgment is an important task in 3D face recognition and 3D construction, as an auxiliary task of face recognition to predict the quality of a given image. However, projection-based quality judgment methods always ignore the value of multi-view 2D image features and 3D point cloud features. We propose a Feature Fusion Network (FFN) to address the above problems. We first preprocess the 3D point cloud obtained by the CCD camera, cut out the face area, and then use the point cloud data for multi-angle rotation and the corresponding 2D plane depth map projection as input. Secondly, Dynamic Graph Convolutional Neural Network (DGCNN) was trained for point cloud learning and ShuffleNet was trained for image learning. Then, the middle layer features of the two network modules were extracted and concat to fine-tune the whole network. Finally, three fully connected layers were used to realize the five-class classification of the 3D face point cloud (excellent, ordinary, stripe, burr, deformation). The proposed FFN achieved the classification accuracy of 83.7%, which was 5.8% higher than that of ShuffleNet and 2.2% higher than that of DGCNN. The experimental results show that concat depth map features and point cloud features can achieve the complementary effect between different features.
Designing an user-centric framework for perceptually-efficient streaming of 360&amp;#xB0; edited videos

This paper presents a set of developments for the design of a user-centric framework for perceptually efficient streaming of 360-degree edited videos.First, we introduce a software for annotation of regions of interest (RoI) in 360-degree videos with the goal of creating datasets for the training of machine learningbased visual attention computational models and for performance evaluation studies.Based on this software, we designed a subjective experiment to evaluate its usability and to create a dataset of annotated 360-degree videos.Then, to showcase an application of this dataset, we present a preliminary comparative analysis between saliency maps generated by a well-known visual attention computational model and RoI maps created from the dataset.Finally, we describe our efforts to investigate 360-degree video editing techniques that can improve the user's quality of experience (QoE).In particular, we show some preliminary results on a subjective experiment designed to evaluate perceived QoE, expressed in terms of "comfort" and "presence," for 360-degree videos that were statically edited (that is, off-line editing).The particular editing technique focuses on RoI alignment in the same scene (i.e., intra-scene alignment of distinct RoIs) for purposes of reducing the users' head movements.We look at both instantaneous (i.e., "snap-change") and gradual rotation editing, and we present the mean opinion scores (MOS) for the different cases, in different video categories.
Traffic sign recognition based on YOLOX in extreme weather

With the rise of machine learning and deep learning, the accuracy and speed of traffic sign recognition in the direction of computer vision have been continuously improved in recent years, but most of the literature and research are based on clear unobstructed This paper studies the problem of traffic sign recognition that becomes blurred in rainy and snowy weather, fog and haze weather, and the speed of the vehicle is too fast. The newly proposed YOLOX model is used to train and test the data set. The final experimental results show that the accuracy of using the YOLOX model to identify pictures after deblurring is improved, which is better than other models, but the types of pictures are not rich enough. The similarity with the real traffic scene needs to be improved.
TTT/ik: Learning Accurate Mealy Automata Efficiently with an Imprecise Symbol Filter

Active automata learning algorithms like TTT infer accurate automata models of black-box applications and thereby help developers to understand the behavior of increasingly complex cyber-physical systems. However, learning systems with large input alphabets often takes very long or is not feasible at all. We present TTT/ik, an extension of TTT that exploits an imprecise symbol filter that uses imprecise prior knowledge of state local alphabets to learn accurate Mealy automata more efficiently. We show across seven realistic case studies that our method almost always dramatically reduces queries and symbols even with imprecise knowledge, while still being accurate, thus, greatly improving runtime in practice.
Analisis Kualitas Website Menggunakan Metode Webqual dan Importance Performance Analysis (IPA) Pada Website Pondok Pesantren Al-Hidayah Pringsewu

The web has turned into an inseparable part of human existence in this computerized age. With the web, all the ideal data can be obtained effectively and quickly. However, the quality of the website needs to be evaluated to improve the information provided by the website. The purpose of this study was to analyze the quality of the website of the Al-Hidayah Islamic Boarding School. Website quality analysis using the Webqual method is a measurement of website quality based on end user perceptions and Importance Performance Analysis (IPA) is a technique for identifying the quality measurement attributes of the product. Based on the results of the Webqual Index calculation on the Al-Hidayah Islamic Boarding School website, it can be seen that the service interaction dimension gets the highest index value. While the information dimension (information) gets the lowest index value. Then the Importance Performance Analysis on the website there are several attributes that are in quadrant I which are the main priority for improving the quality of the website to increase user satisfaction, the information dimension is an indicator of timely information, relevant information. In the service interaction dimension, it is a safe indicator of personal information, feature facilities can be accessed properly, thus providing easy input or suggestions for the school.
APPLICATION OF MAGNETOMETRY IN MANTO-TYPE COPPER DEPOSIT EXPLORATION, CASE STUDY: MEYAMI, IRAN

This study has aimed to introduce a novel strategy for exploring the Manto-Michigan copper deposits, considered a principal copper resource after the porphyry type. Faults and crushed zones have a prominent role in this deposit type, and so we hope to detect unique patterns in magnetic responses that provide a helpful indicator to determine the mineralized zone. Accordingly, we want to test the magnetometry in Manto-type exploration. We performed a magnetometry survey in the Dochileh copper deposit that other researchers have claimed to be a Manto-Michigan type with a distance of 10 meters between survey lines and a spacing of 5 meters among stations on each profile. After processing the required maps, the residual magnetic map does not show any typical dipole magnetic anomaly, but some linear trends exist. One of those linear trends belongs to a faulted and crushed zone with a length of almost 2.5 km and different widths between 50 and 250 meters. The previous mineralized zones indicated by other prospectors who relied more on geological evidence in the Dochileh area have mostly stayed inside this negative value on the residual map. Four new boreholes were made in the negative anomaly to evaluate our hypothesis, and the derived cores confirmed the native copper, malachite, and cuprite mineralization.
Implementation of Apriori Algorithm for Music Genre Recommendation

Music interest is diverse yet enticing to be a part of knowledge discovery. It influences how people feel, study, work, etc. A lot of things are to be considered in producing brand new music with its correlation to its genre. We have already collected the dataset that we can utilize in this research, which is the history of every song listened to by several users in a total of 20.000 records from a million song dataset. This study implements the Apriori algorithm which can handle a large amount of data while simplifying the data to create a recommendation system where the result is a pattern from the music genre according to the interests of each user with the help of the RapidMiner tool. The purpose of this research is that the pattern which has been found can become a reference for music producers in terms of making or distributing their brand-new music. The result of the best combination of genres states that listeners of the rock genre will also hear the pop genre with a combination frequency of 50, support value of 21.2%, and confidence value of 51%.
On the application of wavelet transform and Huffman algorithm to Yorùbá language syntax text files compression

Most algorithms of data compression were developed with English language as target text syntax. However, this paper approaches the problem of Yor?b? text files compression via the use of Discrete Wavelet Transform (DWT) and Huffman algorithm. Text files in Yor?b? language syntax are first converted into signal format that are then decomposed using DWT. The decomposed ASCII code representation of the text files are subsequently encoded using Huffman algorithm. Twenty different variants of DWTs taken from four families of wavelet filters (Haar, Daubechies, Symlets and bi-orthogonal) are considered to select the optimal DWT for Yor?b? text files compression. Furthermore, experiments are carried out in the proposed compression scheme with six different Yor?b? text files extracted from the open sources as input data sets. It is found that out of the twenty variants of DWT investigated, sym6 gives the best output for effective Yor?b? text files compression, due to its relatively high compression ratio, high compression factor and lowest compression error. Thus, sym6 as a wavelet transform is suitable for lossy text compression algorithm meant for Yor?b? language syntax text files.
Content Aware and Other Case Studies: Museum of Synthetic History

With the rise of artificial intelligence and related computational tools in everyday dealings with knowledge organisation, production, and distribution, incl.for example archives and historyrelated applications, we're concerned whether these computational methods 'colonize' and fundamentally change our common approaches to what constitutes studying and knowing a subject matter.We will unpack upon these concerns, looking at phenomena such as a lack of completion and categorisation in biodiversity archives, or new methods of creating artificial fossils as ways of filling gaps within historical datasets and potentially narratives.We also call back into how ontological architectures of computer science have emerged and how they defined ways in which knowledge is accessed.Via the examples of various case studies and thought experiments, the paper tries to examine the initial concern and predict its potential consequences, building upon the question as to what degree machine-learning-based approaches can augment our methods of analysis not just in history but in cultural behaviours.In other words, how might computational models of ontology be producing an epistemological shift within the quality of knowing by imposing a knowledge system of references, linked nodes, hashtags, and databases that are never entirely complete in representing subjects they are set to define.Thus, asking if we shall hold on to our approaches of comprehension of things and their emergence or instead succumb to the generative, on-demand, a click away, always-at-yourfingertips forms of knowing and comprehending?
Towards Usable Neural Comment Generation via Code-Comment Linkage Interpretation: Method and Empirical Study

Code comment is important to facilitate code comprehension for developers. Recent studies suggest to generate comments automatically with deep learning, in particular, based on neural machine translation models. However, such a promising Neural Comment Generation (NCG) technique suffers from unsatisfactory performance, as well as poor usability, i.e., developers cannot easily understand and modify the auto-generated comments. This paper suggests that a proper interpretation of how the comments are generated can significantly improve the usability of NCG approaches. We propose a novel model-independent framework, namely CCLink, to interpret the auto-generated comments. CCLink generates a set of code mutants and obtains their corresponding comments. Based on these data, several contribution mining algorithms are designed to infer the key elements in code that contributes to the generation of the key phrases in the comments. The links between code and its auto-generated comment can thus be constructed. This in turn allows CCLink to visualize the links as the comment interpretations to developers. It greatly facilitates manual verification and correction of the comments. We examine the performance of CCLink with different contribution mining algorithms, NCG approaches, and real-world datasets. We also conduct an empirical study on 32 experienced Java programmers to evaluate the effectiveness of CCLink. The results show that CCLink is promising in making NCG more usable with a proper interpretation of the auto-generated comments.
Korean Drama Scene Transcript Dataset for Emotion Recognition in Conversations

Understanding emotions in conversation is a challenging task as the sentences often have an implied meaning which is not generally understood in isolation. Efficient use of contextual information is important for emotion recognition in conversations. Many of the published datasets provide contextual information for situations such as text-based online messaging, chatbots, and movie dialogues. However, such dialogue-based datasets are collected by selecting the ideal conversational situations and thus do not include many variations in dialogue length and number of participants. Therefore, such datasets may not be applicable for emotion recognition in text-based movie transcripts, where scenes contain variations in the number of speakers and length of the spoken sentences. We present a conversation dataset based on the Korean TV show transcripts for analysis of the emotions in presence of scene context. Korean Drama Scene Transcript dataset for Emotion Recognition (KD-EmoR) is a text-based conversation dataset. We analyze three classes of complex emotions namely euphoria, dysphoria, and neutral in the scenes of TV Drama to build a publicly available dataset for further research. We developed a context-aware deep learning model to classify the emotions utilizing speaker-level context and scene context and achieved an F1 score of 0.63 on the proposed dataset.