Reviewerly/exHarmony huggingface dataset:
Improvement of the RF-Based and XGBoost-Based Visitor Data Prediction for Colocated Museums

Over recent years, data analysis and prediction have been crucial for museum curators to predict the number of visitors and plan how to improve their service. In this research, we investigate and analyze the factors that affect the number of visitors. We also propose a museum visitor prediction method based on the Random Forest (RF) model and the XGBoost model using historical data from 2016 to 2020 from three museums under the supervision of the National Science Museum Thailand. All of these museums are in the same area and therefore, to improve the prediction performance, our proposed method combines the data from the three museums. More specifically, the result of one museum is used to help predict the visitor number of the other museums. Originally, the RF and XGBoost models have 59.14% and 58.08% average values for accuracy, respectively. With our proposed method, the average accuracy values increase to 72.04% and 74.02%. We also observe that the XGboost model performs better and consumed less computational time than the RF model. In addition, the XGBoost model also performs better than the RF model with 96.77% accuracy after reducing the number of visitor classes from five to three.
GenGen: Analysing the Performance of Transformer-based Encoder-Decoder in a General Natural Language Generation Paradigm

The Natural Language Processing [NLP] community has branched off in different directions, making windfall gains within each individual task but relatively slow progress in a general paradigm. In this paper, we attempt a comprehensive analysis of the performance of transformer-based encoder-decoder setups, when both individually and jointly trained on tasks spread across four domains – question answering, natural language inference, sentiment analysis and summarisation. The proposed model is trained both separately and jointly on these tasks, experimenting with varied training strategies. The results of our experiments will highlight the downsides like decreased performance, and upsides, such as performance gains of upto 114% on the MultiNLI dataset when jointly trained. We prove that the model accumulates generalised knowledge, which is applicable to language in general, as opposed to specific tasks. Also, this research work tests the zero-shot capabilities of the proposed model in unseen domains for achieving a EM score of 66.490 without any fine-tuning and an EM score of 86.324 with fine-tuning on the SNLI dataset.
The Use of YouTube Motivates Students in Improving their Arabic Listening Skills

As one of the global media sites that is a video sharing platform, YouTube's role in the field of education also has an impact on the development of education in general.Thus, this study aims to identify the level of YouTube use and the level of motivation in learning Arabic listening skills using YouTube among secondary school students.The findings of this study indicate that YouTube is beneficial in motivating students to acquire Arabic listening skills.Using a descriptive quantitative research methodology, the study of 195 respondents of Form Four students in three SMKA schools in Selangor was conducted using a questionnaire distributed through Google Form and analysed using SPSS version 26.The study found that the level of YouTube use and the level of students' motivation to learn Arabic listening skills using YouTube were high.Although the effectiveness of the use of YouTube in improving the Arabic listening skills of high school students has not yet been confirmed, these findings give the impression that it has the potential to improve their listening skills.However, the video content used as learning material must meet the objectives of the skills learned so that its use can provide optimal benefits to students.
SnappView, a Software Development Kit for Supporting End-user Mobile Interface Review

This paper presents SnappView, an open-source software development kit that facilitates end-user review of graphical user interfaces for mobile applications and streamlines their input into a continuous design life cycle. SnappView structures this user interface review process into four cumulative stages: (1) a developer creates a mobile application project with user interface code instrumented by only a few instructions governing SnappView and deploys the resulting application on an application store; (2) any tester, such as an end-user, a designer, a reviewer, while interacting with the instrumented user interface, shakes the mobile device to freeze and capture its screen and to provide insightful multimodal feedback such as textual comments, critics, suggestions, drawings by stroke gestures, voice or video records, with a level of importance; (3) the screenshot is captured with the application, browser, and status data and sent with the feedback to SnappView server; and (4) a designer then reviews collected and aggregated feedback data and passes them to the developer to address raised usability problems. Another cycle then initiates an iterative design. This paper presents the motivations and process for performing mobile application review based on SnappView. Based on this process, we deployed on the AppStore "WeTwo", a real-world mobile application to find various personal activities over a one-month period with 420 active users. This application served for a user experience evaluation conducted with N1=14 developers to reveal the advantages and shortcomings of the toolkit from a development point of view. The same application was also used in a usability evaluation conducted with N2=22 participants to reveal the advantages and shortcomings from an end-user viewpoint.
Mixed Breed Ensemble Method for Mushroom Classification

In the field of data analytics, machine learning and deep learning plays a crucial role. In machine learning one of the popular techniques is classification. In recent times, to improve the accuracy of a model, they combined different base model algorithm results into one solution using ensemble learning. Ensemble learning is the finest option to improve the accuracy of classification task. This proposed model adopts heterogeneous ensemble learning technique to classify the mushroom by combining different base classification algorithms like support vector machine, K-Nearest Neighbor and Random Forest classifier. Performance evaluation shows that proposed model outperforms homogeneous ensemble classifier with high accuracy.
Automated Exam Proctoring using Support Vector Machine and Histogram of Oriented Gradients

Online learning has grown exponentially since the Covid-19 outbreak. Educational organizations are forced to stop their regular curriculum. Using remote communication software, students are able to pursue their educational curriculums. This paper proposes a multimedia exam proctoring system based on face recognition and object capturing systems. System hardware includes a webcam only. Dlib is used for facial detection and face landmark detection. The OpenCV Caffe model is then utilised to appropriately locate and sketch the face. YOLOv3 is used to detect objects. Onfocus event is used for tab detection. By combining all these, an exam proctoring system is developed which can classify if the student is found cheating and is then informed to the examiner.
An Efficient Framework for Load Balancing using MapReduce Algorithm for Bigdata

A humanoid body of data is what is meant as “Big Data,” which refers to the enormous amount of data that exists in the world today. In order to cope with such a large range of and rapidly changing data, the techniques of Big Data Analytics must be employed. Accurately identifying and implementing current challenges is a challenge. Data is increasing at an exponential rate, according to the most current discoveries in the field of big data. Processing enormous amounts of data on computing clusters need a powerful computing paradigm like Hadoop and MapReduce. It is possible to process terabytes of data in a short period of time using the MapReduce framework. If you want to get the most out of big data, you need to plan meticulously. The Scheduling Technique can reduce starvation, increase resource utilization, and distribute jobs to available resources. The MapReduce technique, which can be implemented on a single node or across numerous, is useful when dealing with enormous amounts of data. The Hadoop MapReduce architecture has spawned a slew of scheduling algorithms, all of which differ substantially in their design, behaviour, and approach to addressing various issues. MapReduce makes it possible to organize analysis talents in order to analyze large amounts of data. System load and task tracking speed must be taken into account for this type of software. Make sure that the task is assigned to the node with the fastest task tracker in relation to the other data nodes.
LGST-Drop: label-guided structural dropout for spatial–temporal convolutional neural networks

Region dropout regularization strategies have proven to be highly effective at improving the generalization performance of convolutional neural networks (CNNs) in a variety of computer vision tasks including image classification, object detection, and semantic segmentation because these strategies enable models to focus on a wider range of image region information. However, for action recognition, models need to be able to extract not only useful spatial information but also important temporal and motion information, which cannot be satisfied by traditional regularization strategies. We propose a spatiotemporal dropout strategy to meet the need for regularization in spatial–temporal CNNs. We call it label guided spatial–temporal drop (LGST-Drop); it not only provides effectively structured dropout in the spatial dimension but also regularizes motion information in the temporal dimension. In addition, LGST-Drop’s mask is guided by the predicted categories of the model itself, which we called temporary labels. Extensive experiments on several standard datasets from action recognition domains show the usefulness of the proposed technique in comparison with the previous methods and theirstate-of-the-art variant algorithms.
Effect of Interaction and Alignment of Continuation Task in Chinese as a Second Language Writing

Continuation Task, which closely links language inputs and outputs, is considered to be an effective method in foreign language learning. This paper is an empirical research that investigates the effects of interaction and alignment of Continuation Task in Chinese as a Second Language (CSL) writing. The participants are a total of 30 Cambodian native speakers, who are arranged to one continuation group (Group A) and one control group (Group B), with every group consisting of 15 participants. Group A adopts the Continuation Task approach, and providing known texts, the participants are required to first read and then complete writing. The participants in Group B have to finish writing after listening to the known texts, without providing known texts. The results show that: (i) The Continuation Task can trigger Cambodian Chinese learners’ multi-level interaction and alignment effects between continuation compositions and known texts, which are mainly manifested on vocabulary, grammar, discourse cohesion, and situation mode. (ii) The Continuation Task can reduce language errors and improve the writing quality of Cambodian Chinese learners. Therefore, the Continuation Task plays a critical role in promoting learning, which further indicates its adaptability in Chinese learning.
The Southwestern Boundary of Cenozoic Qaidam Basin: Constraints from Heavy Mineral Analysis

The formation of the Qaidam Basin plays an important role in unraveling the growth history of the Tibetan plateau. An extraordinary thick Cenozoic sedimentary succession of the Qaidam Basin is a great contributor to the study of the basin’s evolution history. To date, there has been disagreement on the southwestern boundary of the Paleogene Qaidam Basin. In this study, the method of heavy mineral analysis was adopted to reconstruct the southwestern boundary of the Qaidam Basin. The stable heavy minerals which represent the maturity of detrital sediments can roughly reflect the distance between the source and the deposit area. Therefore, the isogram of the stable heavy mineral index (ZTR = 20) was compiled to infer the location of the source area of the southwestern Qaidam Basin. The isogram shows that the boundary of the southwestern Qaidam Basin stretched southwesterly to the present-day Qiman Tagh Eastern Kunlun Mountains during the Paleogene. Additionally, the isolines present a remarkable northward migration since the late Eocene, which indicates the boundary of the Cenozoic Qaidam Basin that withdrew northward since the late Eocene. The specific location of the southern source area of the Qaidam Basin can be deduced at the Adatan fault, the middle of the present-day Eastern Kunlun Mountains. This result also supports the idea that the Qaidam Basin was an independent basin during the early Cenozoic era, and the Eastern Kunlun Mountains have already been exhumed during that time, serving as a prominent source of clastic sediments in the southwestern Qaidam Basin.
See through them

In this paper, we investigate the potential of eye gaze modality in understanding the continuous cognitive states of the players when they are involved in an episode-based puzzle game and how this phenomenon can contribute insights to the game designers to achieve a better QoE of the game. We selected a puzzle game called "Machinarium" as the experimental interface to experiment. We collected the gaze data of the players and inferred their cognitive states in each intersection of decision-making from a game level. The inferred cognitive states were compared to the ground-truth experiences from the players via questionnaire and the official visual guidance extracted from the walkthrough of the game level. The results showed that the implemented framework could infer the cognitive states of the players in a guaranteed accuracy. Besides, the similarities and differences between the players' actual performance and the game level's visual guidance could be the feedback to impact the further optimization of the game design.
Exploring water-consuming personal care and hygiene practices in the bathroom environment and user intentions for improving effective water consumption

When influencing behaviours towards sustainable directions, the main focus is on understanding diverse factors that shape individuals’ behaviours, habits and routines of everyday practices, while recognizing the tendencies of individuals to alter these behaviours is often neglected. This paper presents an exploratory graduate research that investigates individuals’ diverse behaviours which are responsible for intensive water use during personal care and hygiene practices in the bathroom, and that also reveals users’ intentions to adopt responsible behaviours. With this purpose, a diary study and interview sessions were facilitated by incorporating various generative tools and techniques. The findings from the study provide guidance for designers and design researchers by revealing behaviour types along with their attitudinal and contextual determinants, user intentions, and design strategies that would inspire and support the exploration of diverse design solutions aiming to influence behaviours for the effective use of water.
Research on mountain environment factors and tang poetry's natural ecology using big data in the ecological urbanization

The process of urbanization in the world has improved the quality of people's lives, and has also intensified ecological and environmental problems. Mountain cities are an integral part of the urban ecosystem, and the development of cities is supported by various ecological services provided by the municipal ecosystem. Therefore, it is very important to evaluate the mountain environmental factors that affect the urban ecosystem. The mountain urban ecosystem has more and more complex regional environments and environmental characteristics. Therefore, this article analyzes the municipal ecosystem based on the application of big data, which can provide decision-making directions for city managers and facilitate the development of urbanization. Research on ecosystem services in mountainous cities is compatible with the promotion and improvement of mountainous city planning and management methods. At the same time, this article takes the traditional beauty poems described in Tang poems as examples, and uses the richly poetic landscape perspective to study the natural landscape environment more deeply. The study of natural ecology from a new perspective has further accelerated the pace of research on natural ecosystems, which is conducive to the development of natural landscape environments, provides support for the construction and depth of modern landscape environments, and has a positive impact on the natural ecological landscape of buildings. Through the research of mountain environment factors based on the application of big data, this paper applies it to the study of natural ecology in Tang poetry, thereby promoting the development of the ecological environment.
Gestural-Vocal Coordinated Interaction on Large Displays

On large displays, using keyboard and mouse input is challenging because small mouse movements do not scale well with the size of the display and individual elements on screen. We present "Large User Interface" (LUI), which coordinates gestural and vocal interaction to increase the range of dynamic surface area of interactions possible on large displays. The interface leverages real-time continuous feedback of free-handed gestures and voice to control a set of applications such as: photos, videos, 3D models, maps, and a gesture keyboard. Utilizing a single stereo camera and voice assistant, LUI does not require calibration or many sensors to operate, and it can be easily installed and deployed. We report results from user studies where participants found LUI efficient, learnable with minimal instruction, and preferred it to point-and-click interfaces.
Automated Labeling and Classification of Business Rules from Software Requirement Specifications

Business Rules (BRs) are a critical artifact in the requirements elicitation phase of the software development life cycle. Several taxonomies have been proposed for classification of BRs. In this paper, we utilize Ross's BR classification schema and present an approach to automatically label and classify BRs along this schema. Our approach uses Data Programming (DP) for generating labeled training data needed for training two deep learning-based models to classify the BRs. We obtained an average labeling accuracy of 0.73 for all the BR classes using DP. Upon evaluating the approach on industryspecific dataset, we obtained highest weighted F-score (0.69) with a Bi-LSTM with attention-based model.
Applications of Game Theory and Advanced Machine Learning Methods for Adaptive Cyberdefense Strategies in the Digital Music Industry

As the likelihood and impact of cyber-attacks continue to grow, organizations realize the need to invest in specialized methods to protect their digital data and the information they circulate or manage. Due to its broad use, game theory has evolved into a concept that can be applied practically while analyzing and modifying existing cyber protection methods to arrive at the best possible conclusions. This study presents an innovative hybrid model that combines game theory and advanced machine learning methods for adaptive cyber defense strategies. Specifically, a repetitive game methodology is implemented to analyze cyber-attacks and model behaviors and study how defenders and attackers make decisions in a competing field. Based on Bayesian inference, the proposed method can predict the next steps in the game to produce the appropriate countermeasures and implement the best cyber defense strategies that govern an organization. The suggested system introduced to the academic literature for the first time was successfully tested in a particular application scenario involving the digital music industry and coping with impending cyber-attacks.
Automatically Identifying Shared Root Causes of Test Breakages in SAP HANA

Continuous Integration (CI) of a largescale software system such as SAP HANA can produce a non-trivial number of test breakages. Each breakage that newly occurs from daily runs needs to be manually inspected, triaged, and eventually assigned to developers for debugging. However, not all new breakages are unique, as some test breakages would share the same root cause; in addition, human errors can produce duplicate bug tickets for the same root cause. An automated identification of breakages with shared root causes will be able to significantly reduce the cost of the (typically manual) post-breakage steps. This paper investigates multiple similarity functions between test breakages to assist and automate the identification of test breakages that are caused by the same root cause. We consider multiple information sources, such as static (i.e., the code itself), historical (i.e., whether the test results have changed in a similar way in the past), as well as dynamic (i.e., whether the coverage of test cases are similar to each other), for the purpose of such automation. We evaluate a total of 27 individual similarity functions, using realworld CI data of SAP HANA from a six-month period. Further, using these individual similarity functions as in-put features, we construct a classification model that can predict whether two test breakages share the same root cause or not. When trained using ground truth labels extracted from the issue tracker of SAP HANA, our model achieves an F1 score of 0.743 when evaluated using a set of unseen test breakages collected over three months. Our results show that a classification model based on test similarity functions can successfully support the bug triage stage of a CI pipeline.
Multi streams with dynamic balancing-based Conditional Generative Adversarial Network for paired image generation

Computer vision society experienced the birth of new CNN architecture known as Generative Adversarial Networks (GANs), which can generate fake images similar to real ones. The widespread use of GANs leads the image-to-image translation strategy dealing with more diverse tasks that were treated using traditional CNNs, such as medical analysis and semantic segmentation. In this paper, we propose a generic GAN referred to as Multi Streams with Dynamic Balancing-based Conditional Generative Adversarial Network (MSDB-CGAN). The MSDB-CGAN serves more challenging applications, that require multi input images such as binocular depth estimation, efficiently through its dedicated input streams and automatic skip connections. Moreover, the proposed GAN analyzes the inputs according to the target image, then assigns dynamic weights to the input streams. To validate the proposed MSDB-CGAN, we targeted four challenging tasks: binocular depth estimation, human-pose translation, middle frame interpolation, and future frame prediction. These applications present different inputs requirements and configurations. The reported quantitative and qualitative comparisons prove that the MSDB-CGAN significantly outperforms the existing GANs as well as traditional CNN-based architectures.