Reviewerly/exHarmony huggingface dataset:
Hydraulic Analysis of Floating Rubbish in Sungai Lumut Kiri with HEC-RAS

Plastic is one of the recognizable objects which has caused significant damage to the water environment. This floating debris does not only contribute to the environmental problem but as well as becoming a contributing factor to flooding events. The risk of flooding could occur significantly due to the sedimentation of sloid waste on the riverbed causing the flow to be stagnant and in return resulted in the water depth to exceeds the permissible limit. Therefore, continuous maintenance is required to ensure that there is no floating debris presence within the urban drainage area to allow the conveyance of the stormwater runoff to flow efficiently following a storm events as well as preserving the water quality of the river. Accumulation of floating rubbish in Sungai Lumut Kiri have experienced severe flooding during the high tides period as well as after heavy rain had occurred.
Accessing Diverse Web Knowledge with Natural Language Interface

Author(s): chen, wenhu | Advisor(s): Wang, William; Yan, Xifeng | Abstract: The Web is the primary source for humans to access information in our daily life. In the Information Age, Web knowledge has exploded and distributed in diverse forms like text, tables, charts, graphs, images. To help humans cope with such massive amount of Web information, building an intelligent Natural Language Interface has become a primary interest in artiﬁcial intelligence. The natural language interface needs to understand the human input and ground on Web knowledge to provide useful information. Technically, the interface needs two components: 1) natural language understanding: understanding the semantics of the natural language to navigate to supportive evidence. 2) natural language generation: grounding on the supportive evidence to generate natural language response.In the ﬁrst part, we will focus on the understanding component and use question answering as our evaluation task. Speciﬁcally, I will cover three diﬀerent grounding scenarios: 1) how to ground on the given hybrid data (structured + unstructured) to answer multi-hop questions, 2) how to search over the Web and then ground on retrieved hybrid data to answer multi-hop questions 3) how to ground on visual data to answer compositional questions.In the second part, we will focus on the generation component and use data-to-text generation as our evaluation task. Speciﬁcally, I will discuss two diﬀerent scenarios: 1) how to ground on structured tabular data to generate logically entailed claims, 2) how to leverage unlabeled Web data for pre-training to improve existing data-to-text models.In these scenarios, we designed novel algorithms to dramatically improve the existing models’ capability to generate more consistent and coherent text.Finally, we summarize the strengths, weaknesses, and implications of our work, and discuss the future research plan of pushing the direction forward.
Spatially Focused Attack against Spatiotemporal Graph Neural Networks

Spatiotemporal forecasting plays an essential role in various applications in intelligent transportation systems (ITS), such as route planning, navigation, and traffic control and management. Deep Spatiotemporal graph neural networks (GNNs), which capture both spatial and temporal patterns, have achieved great success in traffic forecasting applications. Understanding how GNNs-based forecasting work and the vulnerability and robustness of these models becomes critical to real-world applications. For example, if spatiotemporal GNNs are vulnerable in real-world traffic prediction applications, a hacker can easily manipulate the results and cause serious traffic congestion and even a city-scale breakdown. However, despite that recent studies have demonstrated that deep neural networks (DNNs) are vulnerable to carefully designed perturbations in multiple domains like objection classification and graph representation, current adversarial works cannot be directly applied to spatiotemporal forecasting due to the causal nature and spatiotemporal mechanisms in forecasting models. To fill this gap, in this paper we design Spatially Focused Attack (SFA) to break spatiotemporal GNNs by attacking a single vertex. To achieve this, we first propose the inverse estimation to address the causality issue; then, we apply genetic algorithms with a universal attack method as the evaluation function to locate the weakest vertex; finally, perturbations are generated by solving an inverse estimation-based optimization problem. We conduct experiments on real-world traffic data and our results show that perturbations in one vertex designed by SA can be diffused into a large part of the graph.
Features of Teaching Experimental Practice-Oriented Training Discipline ‘Activities of the Operative Employee of the Criminal Inspection During the Prime Proceedings in Criminal Cases’ for Preparations for Preparations

The article analyzes the experience of teaching an experimental practice-oriented academic discipline Activities of a criminal investigation officer in the course of pre-trial criminal proceedings.
CAST: Enhancing Code Summarization with Hierarchical Splitting and Reconstruction of Abstract Syntax Trees

Code summarization aims to generate concise natural language descriptions of source code, which can help improve program comprehension and maintenance. Recent studies show that syntactic and structural information extracted from abstract syntax trees (ASTs) is conducive to summary generation. However, existing approaches fail to fully capture the rich information in ASTs because of the large size/depth of ASTs. In this paper, we propose a novel model CAST that hierarchically splits and reconstructs ASTs. First, we hierarchically split a large AST into a set of subtrees and utilize a recursive neural network to encode the subtrees. Then, we aggregate the embeddings of subtrees by reconstructing the split ASTs to get the representation of the complete AST. Finally, AST representation, together with source code embedding obtained by a vanilla code token encoder, is used for code summarization. Extensive experiments, including the ablation study and the human evaluation, on benchmarks have demonstrated the power of CAST. To facilitate reproducibility, our code and data are available at https://anonymous.4open.science/r/CAST/.
Translation of Activities in a Global Virtual Teams Software Development

. This study explores Global Virtual Software Teams’ development practices and try to demystify some of the misconceptions about global software development practices based on findings from the global virtual software teams’ experiment that was carried out at DePaul University from 2011 – 2018. The moments of translation from the perspective of actor-network theory (ANT) was employed in the data analysis, to examine how development approach was selected by the global virtual teams. One of the key findings from our research is that the success of a global software development project does not have a strong dependency on the development approach. While we agree that it is one of the key influencing factors, there are other equally strong factors for global virtual software team’s success.
DARSI: An Ontology for Facilitating the Development of Data Sharing and Use Agreements

The advantages of data sharing across organizations and disciplines are indisputable; although, sensitive and restricted data cannot be easily shared due to policies and legal matters. The research presented in this paper takes a step toward systematizing the sharing of sensitive and restricted research data by developing an ontology to frame and guide DSUA (Data Sharing and Usage Agreement) development. The paper provides background context, describes the ontology creation process, and introduces the Data Sharing Agreements for Restricted and Sensitive Information (DARSI) ontology. DARSI contains four top level classes, 20 sub-classes, 33 sub-categories, and 17 simple properties for categories applicable at various levels. The discussion provides further insight into the work accomplished, and the conclusion identifies next steps.
Decentralized data encryption trading platform with enhanced privacy

In the modern era when the Internet is developed, information is no longer limited to the existence of paper, and digital information has become an extremely important asset. More and more information sharing and transactions appear on the Internet in digital form. There are many hidden dangers in many aspects such as data and transaction security, user privacy, etc. However, the existing data transaction platform is not completely trustworthy. Centralized storage has a negative impact on user privacy and cost. The current decentralized storage solutions do not have a good operating model and lack the participation of nodes, and the data provider does not have control over the right to data transactions. This research proposes a decentralized data encryption trading platform with enhanced privacy that uses a combination of multiple technologies such as blockchain and Inter Planetary File System (IPFS). The data part is protected by proxy re-encryption technology and stored in IPFS, combined with storage certificates for verification. Achieve safe and reliable storage, the transaction process combines ring signatures and stealth addresses to protect the identity and privacy of both parties in the transaction. In addition, there are appropriate rewards to incentivize the operation of the node. In the follow-up related research chapters, some current systems are also evaluated. In these technologies with the support of the trading platform, it provides an improved reference for the trading platform market.
Constructing Emotion Consensus and Utilizing Unpaired Data for Empathetic Dialogue Generation

Researches on dialogue empathy aim to endow an agent with the capacity of accurate understanding and proper responding for emotions. Existing models for empathetic dialogue generation focus on the emotion flow in one direction, that is, from the context to response. We argue that conducting an empathetic conversation is a bidirectional process, where empathy occurs when the emotions of two interlocutors could converge on the same point, i.e., reaching an emotion consensus. Besides, we also find that the empathetic dialogue corpus is extremely limited, which further restricts the model performance. To address the above issues, we propose a dual-generative model, Dual-Emp, to simultaneously construct the emotion consensus and utilize some external unpaired data. Specifically, our model integrates a forward dialogue model, a backward dialogue model, and a discrete latent variable representing the emotion consensus into a unified architecture. Then, to alleviate the constraint of paired data, we extract unpaired emotional data from open-domain conversations and employ Dual-Emp to produce pseudo paired empathetic samples, which is more efficient and low-cost than the human annotation. Automatic and human evaluations demonstrate that our method outperforms competitive baselines in producing coherent and empathetic responses.
토론 기반 영어 읽기 및 쓰기 학습 활동이 고등학교 학습자의 창의성, 영어 쓰기 및 창의력 자기평가에 미치는 영향

In the era of the 4th industrial revolution, creativity plays a pivotal role in the competitiveness of a country. The importance of creativity education therefore cannot be overemphasized. The purpose of this study is to examine the effects of discussion-based English reading and writing activities on Korean high school students' creativity, English writing, and self-assessed creative thinking. The participants were 45 high school students in Seoul, Korea. They were divided into two groups: the discussion-based English reading and writing group (n=24) and the traditional English instruction group (n=21). The experiment was administered during two semesters of the 2017 academic year. The major findings are as follows: First, the experimental group showed significant improvement on the creativity test, especially in the areas of fluency, flexibility, and original thinking. Second, the experimental group outperformed the control group on the writing test. Third, the experimental group showed positive changes in their self-assessed flexible thinking, convergent thinking, and intrinsic motivation. All of these indicate the beneficial effects of the discussion-based English reading and writing activities. Based on the results, some pedagogical suggestions were made for the effective integration of creativity education into the teaching of English as a foreign language.
Small-Vote Sample Selection for Label-Noise Learning

The small-loss criterion is widely used in recent label-noise learning methods. However, such a criterion only considers the loss of each training sample in a mini-batch but ignores the loss distribution in the whole training set. Moreover, the selection of clean samples depends on a heuristic clean data rate. As a result, some noisy-labeled samples are easily identified as clean ones, and vice versa. In this paper, we propose a novel yet simple sample selection method, which mainly consists of a Hierarchical Voting Scheme (HVS) and an Adaptive Clean data rate Estimation Strategy (ACES), to accurately identify clean samples and noisy-labeled samples for robust learning. Specifically, we propose HVS to effectively combine the global vote and the local vote, so that both epoch-level and batch-level information is exploited to assign a hierarchical vote for each mini-batch sample. Based on HVS, we further develop ACES to adaptively estimate the clean data rate by leveraging a 1D Gaussian Mixture Model (GMM). Experimental results show that our proposed method consistently outperforms several state-of-the-art label-noise learning methods on both synthetic and real-world noisy benchmark datasets.
Guiding Topic Flows in the Generative Chatbot by Enhancing the ConceptNet with the Conversation Corpora

Human conversations consist of reasonable and natural topic flows, which are observed as the shifts of the mentioned concepts across utterances. Previous chatbots that incorporate the external commonsense knowledge graph prove that modeling the concept shifts can effectively alleviate the dull and uninformative response dilemma. However, there still exists a gap between the concept relations in the natural conversation and those in the external commonsense knowledge graph, which is an issue to solve. Specifically, the concept relations in the external commonsense knowledge graph are not intuitively built from the conversational scenario but the world knowledge, which makes them insufficient for the chatbot construction. To bridge the above gap, we propose the method to supply more concept relations extracted from the conversational corpora and reconstruct an enhanced concept graph for the chatbot construction. In addition, we present a novel, powerful, and fast graph encoding architecture named the Edge-Transformer to replace the traditional GNN architecture. Experimental results on the Reddit conversation dataset indicate our proposed method significantly outperforms strong baseline systems and achieves new SOTA results. Further analysis individually proves the effectiveness of the enhanced concept graph and the Edge-Transformer architecture.
RVMDE: Radar Validated Monocular Depth Estimation for Robotics

Stereoscopy exposits a natural perception of distance in a scene, and its manifestation in 3D world understanding is an intuitive phenomenon. However, an innate rigid calibration of binocular vision sensors is crucial for accurate depth estimation. Alternatively, a monocular camera alleviates the limitation at the expense of accuracy in estimating depth, and the challenge exacerbates in harsh environmental conditions. Moreover, an optical sensor often fails to acquire vital signals in harsh environments, and radar is used instead, which gives coarse but more accurate signals. This work explores the utility of coarse signals from radar when fused with fine-grained data from a monocular camera for depth estimation in harsh environmental conditions. A variant of feature pyramid network (FPN) extensively operates on fine-grained image features at multiple scales with a fewer number of parameters. FPN feature maps are fused with sparse radar features extracted with a Convolutional neural network. The concatenated hierarchical features are used to predict the depth with ordinal regression. We performed experiments on the nuScenes dataset, and the proposed architecture stays on top in quantitative evaluations with reduced parameters and faster inference. The depth estimation results suggest that the proposed techniques can be used as an alternative to stereo depth estimation in critical applications in robotics and self-driving cars. The source code will be available in the following: \url{https://github.com/MI-Hussain/RVMDE}.
The Using Pre-Questioning to Improving the Students’ Reading Comprehension Skills to the Tenth Grade Students of SMA Negeri 2 Padangsidimpuan

This research is mainly conducted by the writer in order to know that there is a significant effect of using the pre-questioning to improving the students’ reading comprehension skills to the tenth grade students’ of SMA Negeri 2 Padangsidimpuan or not. The number of the sample is 52 students. In this research the writer applies the cluster sampling. And as the instrument for collecting the data the writer uses the questioners for variable X and the essay test for variable Y. For getting the needed data, the writer applies the library and field research. The method of research is experiment method, that is the writes find out the different of students’ score before-after using pre-questioning method. Based on the result of research can be known the students’ mean score in pre-test is 80.76. After treatment the students’ mean score increase into 89.04. After collecting the needed the data, the next step which is done by the writer is analyzing it by using statistical analysis. The statistical formulation which is used in this research is the formulation of Pearson’s Product Moment Correlation (rxy). After analyzing the collected data, it has been got that the value of correlation (rxy), that is 0.945. By consulting this value to the table interpretation given before, it is known that this value can be categorized into the very high correlation category. It means that there is a significant effect of using the pre-questioning to improving the students’ reading comprehension skills to the tenth grade students’ of SMA Negeri 2 Padangsidimpuan. The hypothesis of this research accepted.
Detecting and Classifying Toxic Language in Twitter using Machine Learning

Today international on-line content material has turned out to be a first-rate part due to growth in the use of net. Individuals of various societies and instructive foundation can speak through this platform. Therefore, for automatic detection of poisonous content, we need to distinguish between hate speech and offensive language. Here a way to robotically stumble on and classify tweets on Twitter into 3 commands: hateful, offensive and easy is proposed. We do not forget n-grams as functions and by way of passing their time period frequency-inverse document frequency (TFIDF) values to numerous system gaining knowledge of fashions using Twitter dataset, we perform comparative evaluation of the models. We work towards classification and comparison of different classifiers using the combination of best feature from each type of feature extraction and determining which model works best for the purpose of classification of tweets into hate-speech, offensive language or neither.
Ethics of architectural education in academic institutions

The research deals with the concept of ethics education as one of the most influential concepts in the process of teaching the foundations of architectural design.It defines it as "an integrated educational program organized according to the highest ethics values within an academic institution concerned with architecture.Its program consists of three main elements: Educational and assessment tools and assessment, and requires reliability and quality As two pillars for the maintenance of architecture as a profession to the level of comprehensive or partial reform, which requires professors to perpetuate the modernization of methods of ethics architectural education as well as the development, resulting in innovative architectural texts person and society", thus research problem is in the (development of a comprehensive theoretical perception about the ethics of education architecture).Therefore, the research aims at clarifying the strategy of the ethics education of architecture as well as its foundations and the methods of evaluating its quality.Accordingly, the research assumes that the indicators of the ethics of architectural education have a positive effect in the formulation of creative architectural productions.In order to address the research issue and achieve the objectives of the research and validation of the hypothesis is first built knowledge framework, and then a comprehensive theoretical framework of the ethics of architectural education derived from the architectural ideas and support to be embodied in final form in three main indicators, namely: the strategy of ethics education for architecture, and the foundations of ethics education of architecture, And then an exploratory study evaluating the quality of education and its ethics in the Department of Architecture / University of Technology.Finally, a set of conclusions were reached which show the control of the practice of architectural education according to all its indicators Creative abstraction .
Review of Performance Factors of Emotional Speaker Recognition System: Features, Feature Extraction Approaches and Databases

Emotion is a conscious mental reaction accompanied by physiological and behavior changes in human body.In speaker authentication system,emotional state of the speaker plays a vital role. Recently, the field of speaker recognition in emotional context attracts more and moreattention of many research focuses. However, to implement more realistic and intelligent emotional speaker recognition system it is interestingto study this system under real life conditions. Speech emotion recognition is a system in which speech signals are processed to classify the embedded emotions. In recent past, speaker emotion recognition has gained a lot of attention from different researchers as it has many applications.In this regards, study of prior works is useful for further research in the field of speaker verification in emotional context. So, performance andreliability of Emotional Speaker Recognition System depend on the proper selection of features to characterize different emotional states, featureextraction approaches and databases. In this paper we briefly discuss about different features, feature extraction approaches and emotionrecognition and speaker verification databases.DOI: https://doi.org/10.24412/1932-2321-2021-363-132-148