Reviewerly/exHarmony huggingface dataset:
Robot Motion Planning: can GPUs be a Game Changer?

This paper presents a parallel computing implementation of the Iterative Dynamic Programming (IDP) solution to the multipoint Markov-Dubins problem using GPUs. The multi-point Markov-Dubins problem requires the computation of the shortest path with bounded curvature that connects a sequence of planar points (waypoints). As well as being interesting in its own right, an efficient solution to this problem is key to finding optimal or suboptimal solutions of other problems such as the Dubins Travelling Salesman and the Dubins Orienteering problem. The constraint on the curvature makes the problem highly non-linear and complicates its solution. Classic methods are optimisation-based and cast the problem into the Nonlinear Programming (NLP) or Mixed Integer Nonlinear Programming (MINLP) frameworks, for which existing solutions cannot be significantly parallelised. On the contrary, the IDP solution proposed here is well suited for parallel execution. In the paper, we show that the parallel implementation of the IDP outperforms both the NLP/MINLP methods and the iterative version of the IDP methods in terms of accuracy, computation time and power consumption. Computation time and power consumption will be the main focus of the paper, because they are closely related to the implementation on an embedded platform.
Optimal Teaching Curricula with Compositional Simplicity Priors

Machine teaching under strong simplicity priors can teach any concept in universal languages. Remarkably, recent experiments suggest that the teaching sets are shorter than the concept description itself. This raises many important questions about the complexity of concepts and their teaching size, especially when concepts are taught incrementally. In this paper we put a bound to these surprising experimental findings and reconnect teaching size and concept complexity: complex concepts do require large teaching sets. Also, we analyse teaching curricula, and find a new interposition phenomenon: the teaching size of a concept can increase because examples are captured by simpler concepts built on previously acquired knowledge. We provide a procedure that not only avoids interposition but builds an optimal curriculum. These results indicate novel curriculum design strategies for humans and machines.
A Computer-Vision Based Engagement Evaluation System for More Effective Learning Design

Student engagement during online instruction has been a challenge. Particularly in an asynchronous learning environment, assessing and measuring student engagement is a difficult task. This paper proposes a solution to fill in the gap between the need to evaluate student engagement during online instruction and the lack of an effective method to do so. The proposed web-based learning environment fully incorporates the latest development in facial expression recognition to capture the facial expression changes during the course of instruction. The facial expression data can then be visualized for further analysis to help instructors pinpoint the exact locations where the instruction needs improvements. In addition, the proposed system can also be used in corporate training settings, particularly in scenarios where the learners' emotional responses are desired.
Towards an Extensible Architecture for an Empirical Software Engineering Computational Platform

One of the main objectives of Empirical Software Engineering is the improvement of methods, techniques, and tools for Software Engineering (SE) through the conduction and execution of experiments. Although research in empirical software engineering is well established in the literature, it is possible to verify that there are few supporting environments for conducting experiments, despite the increasing demand for this kind of study within the SE community. Moreover, existing approaches lack a number of important features, such as extensible mechanisms for the required phases of an experimentation in different domains of SE, integration with existing software development environments, ways to define metrics to be collected, automatic organization mechanisms to analyze collected data using a specific statistical analyzes method, and remote execution of experiments. This paper describes the SCCOPLA – an exten sible ar chitecture for empiri cal s oftware engineering com putational p latform. Our approach aims to address the aforementioned functionalities missing in available environments to conduct empirical SE studies. Preliminary results show its feasibility by applying an architecture-based tool to some of our previous software testing empirical studies.
Recognition of Teaching Activities from University Lecture Transcriptions

Research on language acquisition for academic purposes is not extensive. In this work, we propose to build a system for recognizing teaching activities from automatic transcriptions of classroom audio and video recordings centered on the professor's discourse. To this end, we identified the main teaching activities that cover the nature of the lecturer discourse when giving a course e.g. 'theoretical explanation', real-world practical example', interaction lecturer-student', 'course-related asides', etc. We labeled a dataset of lecture transcriptions from a repository with an approximate length of 50 h and we build a classifier by fine-tuning the XLM-RoBERTa model with a classification head on top of it. The results will show that our proposal is a promising step ahead towards recognition of discourse activities in academic contexts.
Learning Representation on Optimized High-Order Manifold for Visual Classification

Graph convolutional networks (GCNs) and graph neural networks (GNNs) have demonstrated convincing performance on many tasks by learning the intrinsic structure of the data. However, it is still valuable and challenging to consider the complex and complete correlations of objects, i.e., high-order manifold structures, for representation learning. In this paper, we present a novel representation learning method that utilizes the optimized high-order manifold of the data for classification tasks of nonstructural data and graph-structure data. In the method, we fully explore the complicated relationship of samples by highlighting the high-order manifold information in a hypergraph. Specifically, we incorporate high-order manifold information by graph <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula>-Laplacian into a hypergraph and propose <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula>-Laplacian-based hypergraph neural networks (pLapHGNN) to significantly learn hidden layer representations that encode both the high-order structure of data and the high-order manifold geometrical information. Confronting the difficulties of obtaining optimized high-order manifolds of the data, we propose an effective approximate approach by graph <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula>-Laplacian representing the relationship of hyperedges in the hypergraph. Furthermore, we study the weights of hyperedges in a hypergraph with high-order manifold information. Experiments on the ModelNet40 dataset and NTU dataset demonstrate that the proposed method is more effective than the other popular methods for 3D shape recognition. Extensive experiments on other visual classification tasks and citation networks also show the superiority of our proposed method for representation learning.
EdgeSaver: Edge-Assisted Energy-Aware Mobile Video Streaming for User Retention Enhancement

Video streaming service is one of the most important IoT applications/services at the mobile end. To provide better services and earn more customers, the mobile video service providers have paid considerable attention to enhance end users&#x2019; Quality of Experience (QoE) in video streaming. As an important aspect of the mobile device, however, the battery power and its impacts on the mobile services were seldom concerned. According to our survey over 2000&#x002B; mobile users, the low battery power of mobile phones could cause the user to give up watching videos. To quantify the relationship between the battery power and user&#x2019;s video abandoning probability (VAP), we first extract the VAP model from the collected survey data, leveraging a reversed accumulative histogram approach. Then, referring to the quantified VAP model, we present <i>EdgeSaver</i>, an edge-assisted video transmission framework, which aims at maintaining a sustainable overall user retention rate for the service providers by reducing the power consumption of video playback at the mobile ends. Particularly, as the core component of <i>EdgeSaver</i>, a low-power video scheduler is designed to strategically select user groups, such that the most profitable outcome can be achieved under the constraints of limited edge resources. With extensive experiments using a real-world data set, we demonstrate that <i>EdgeSaver</i> can help the mobile video service provider improve the user retention rate by up to 30&#x0025; and increase the average user viewing time by 20&#x0025;.
T-Bert: A Spam Review Detection Model Combining Group Intelligence and Personalized Sentiment Information

The content of online comments largely affects users' willingness to purchase goods or services. Driven by interests, spam reviews continue to emerge to induce users maliciously. Most of the existing related work is based on the easy-camouflaged feature information, and the deep learning model is rarely used. The BERT model is prominent in various tasks in the NLP field, and whether it can be successfully applied to the spam review identification task has not been verified. In this paper, we propose a new research strategy for this task: the multi-dimensional representation combining group intelligence and users' personalized sentiment information can more effectively detect spam reviews. Through fine-grained sentiment analysis of reviews based on product dimension and user dimension, we effectively acquire group intelligence and user personalized sentiment, respectively; Based on the ability of BERT to model the embedding of text context information, the semantic information is acquired. Finally, the three are combined based on Triple Network structure to detect spam reviews. We conduct a large number of experiments on three public datasets and the recall rate and F1 value both exceed the results of state-of-the-art works, which proves the feasibility and effectiveness of our proposed strategy, and verifies the modeling ability of the BERT in the task of detecting spam reviews.
Visual-Semantic Dual Channel Network for Visual Question Answering

Recently, the existing visual question answering (VQA) models based on the attention mechanism have achieved state-of-art results. However, attention-based networks only rely on question guidance to capture relevant image features, ignoring the high-level semantic information of the image. Therefore, the key challenge of the VQA task lies in obtaining effective semantic embedding and fine-grained visual understanding during the reasoning process. In this research, we propose a novel visual-semantic dual channel network to answer related questions from both visual and semantic perspectives. Specifically, the visual channel uses the relational reasoning method with an attention mechanism to capture visual objects and their relations, while the semantic channel can capture high-level semantic information from the global and local image by the semantic attention module. We confirmed the effectiveness of the proposed model and each module through extensive experiments on two versions of VQA datasets. Interpretability shows that the visual-semantic dual channel network can dynamically model to infer the most relevant answer to the question.
Target Detection in Rural River Image Based on Yolo V5 Model with the Cross-Stitch Unit

Due to the complexity of rural river environment, the performance of traditional Yolo V5 model for target detection is insufficient. Considering that the river environment includes river drifts and river slope accumulation, they have different characteristics but have correlation. Therefore, the target detection of river image includes two tasks: identifying the drifts on the river channel and identifying the accumulation on the river slope. In addition, there is no uniform standard to design the shared layer when using deep learning model for multi task learning. The cross-stitch network can decide the best sharing layer through end-to-end learning, and the cross-stitch unit can stitch two networks. Therefore, we introduce the cross-stitch unit into the backbone layer of Yolo V5 for the first time, which makes the YoloV5 network model a multi task learning model. At the same time, we use gamma correction method to preprocess the river drifts image set to improve the detection accuracy of the model. We took field photos of rural rivers and collected similar photos on the Internet to train and evaluate the model. The experimental results show that the average accuracy (map) of the proposed method is 78.6% and 78.0% respectively, which is 2.1 % and 2.3% higher than YoloV5 model, and better than the traditional target recognition model.
E-Voucher System Development for Social Assistance with Blockchain Technology

The system of providing social assistance in Indonesia is still far from perfect. One of the problems that will be discussed in this paper is the problem of distributing social assistance. In its implementation in the field, one of the alternative social assistance distribution systems is the electronic voucher system. However, this system still uses a centralized architecture which has several weaknesses, such as the existence of a single point of failure to transaction data that is still stored by one party, giving rise to potential fraud. Therefore, blockchain technology is used in an electronic voucher system for social assistance. By using blockchain technology, the electronic voucher system has data with integrity as a form of government accountability.
Learning Knowledge Graph Embedding in Semantic Space: A Novel Bi-linear Semantic Matching Method

Knowledge Graphs represent facts with triples containing head entity <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$h$</tex> , tail entity <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> and relation <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r$</tex> which facilitate applications of many scenarios, for example intelligent web search, community detection and question answering. Knowledge Graph Embedding (KGE) represents elements of triples in a low-dimensional continuous vector space. Though it has been widely studied by both academic and industry communities, most researches focus on learning embeddings of entities and relations separately rather than considering the interactive semantic information between them. However, neither relations nor entities exist lonely out of context. In this paper, we define an interactive semantic space to model the context of triples and propose a novel Bi-linear Semantic Matching Method using Convolutional networks (BiSC). Specifically, we use 1D convolutional neural networks to extract features of the interactive semantics and then compute the similarity scores in the bi-linear space. Compared to existing complex graph network methods, BiSC needs lower computational cost to reach competitive results on link prediction task. The consistent state-of-the-art performance through extensive experiments over two benchmarks demonstrates the advantages of the proposed BiSC model. Further analysis on convergence study and case study of interactive semantic space show the efficiency of our model.
Navigation with Uncertain Map Data for Automated Vehicles

Using external map information for automated driving is beneficial, as this follows an extension of the sensor range and global navigation to a priori specified goal. However, common systems use high definition maps, which are expensive to construct and hard to maintain. Therefore, the paper at hand proposes an sensor-independent approach for navigation based on uncertain map data. This work first builds an environment model and plans a global route based on publicly available OpenStreetMap-Data. Afterward, it plans a trajectory considering the uncertainty in the map. Experiments in simulation and on real-world data show the efficiency of the approach.
Classification of Multi-class Imbalanced Data: Data Difficulty Factors and Selected Methods for Improving Classifiers

The multiple class imbalanced problem is still less investigated than its binary counterpart. In particular, the sources of its difficulties have not been sufficiently studied so far. Therefore, in this paper we summarize the few literature works on the difficulty factors and present our own latest research results. The binary method for an identification of the types of minority examples is generalized for multiple imbalance classes. The second part of this paper presents three our recent methods for learning classifies from multi-class imbalanced data which exploit information on the aforementioned difficulty factors.
GSAPool: Gated Structure Aware Pooling for Graph Representation Learning

Graph Neural Networks (GNNs) are powerful tools for modeling graph-structured data to solve the tasks such as node classification, link prediction along with graph classification. For the graph classification task, properly defining the pooling strategies to vary the size and structure of the input graph, is of vital importance to generate a graph-level representation of the input graph. However, the existing GNN models usually fail to effectively capture the graph substructure information in pooling process. Besides, the importance of nodes(supernodes) within a graph has not been well-reflected. To remedy these limitations, we propose Gated Structure Aware Pooling (GSAPool), a sparse and differentiable pooling method, which focuses on retaining the graph substructure information during the process of pooling in an end-to-end fashion. Specifically, GSAPool utilizes dual gates along with a self-attention network to integrate the local structure to form clusters' embeddings. It also employs a novel formulation to capture the importance of each node/supernode in an input graph. Experiment results show that GSAPool achieves competitive graph classification performance over the state-of-the-art graph representation learning methods.
The Dilemmas and Reform of Translation Education in the Age of Artificial Intelligence

The wide application of artificial intelligence in the field of translation has changed the traditional mode of translation industry. Through market research and data analysis, it is found that translation education in the age of artificial intelligence also faces many challenges, such as the outdated hardware and software facilities, the traditional training mode, the shortage of translation technology teachers and the limited teaching material resources. To solve these problems, colleges should have the courage to make changes in laboratory construction, talent training mode, teachers' technology teaching ability and textbook compilation.
Improvement of Intent Classification Using Diacritic Restoration for Text Message in Chatbot

Today, chatbots or conversational agents are increasingly being used in various fields. In recent years, governments, organizations, and businesses have invested in dialogue systems to improve the engagement of their users. A smart chatbot is a chatbot that can understand the user’s intents. To achieve this goal, the developer always needs to improve the ability to classify the user’s intents. However, the user’s raw text usually contains spelling errors, abbreviations, or slang that the chatbot cannot understand. Therefore, processing the input text is also an urgent task of natural language understanding (NLU). In the Vietnamese dialogue system, there are always cases when users enter questions without signs. These questions greatly affect the understanding of the user’s intents. In this paper, an improvement in the intent classification for a Vietnamese chatbot is presented. We propose the Encoder-Decoder Bidirectional Long Short-Term Memory (BiLSTM) model for the diacritic restoration. On our evaluation dataset, this approach has an accuracy of 99.12% by using the pre-trained language model for word embedding. Then, the proposed model is applied as a custom component in the NLU pipeline. Using the proposed approach for constructing a model of intent classification is considered, comparison with basic models has shown the best results for the proposed model.
New Czechoslovak hyphenation patterns, word lists, and workflow

This thesis describes research leading to an improved word hyphenation algorithm for the TjrjX82 typesetting system.Hyphenation is viewed primarily as a data compression problem, where we are given a dictionary of words with allowable division points, and try to devise methods that take advantage of the large amount of redundancy present.The new hyphenation algorithm is based on the idea of hyphenating and inhibiting patterns.These are simply strings of letters that, when they match hi a word, give us information about hyphenation at some point in the pattern.For example, '-tion' and *c-c' are good hyphenating patterns.An important feature of this method is that a suitable set of patterns can be extracted automatical!/ from the dictionary.In order to represent the set of patterns in a compart form that is also reasonably efficient for searching, the author has developed a new data structure called a packed trie.This data structure allows the very fast search times characteristic of indexed tries, but in many cases it entirely eliminates the wasted space for null links usually present in such tries.We demonstrate the versatility and practical advantages of this data structure by using a variant of it as the critical component of the program that generates the patterns from the dictionary.The resulting hyphenation algorithm uses about 4500 patterns that compile into a packed trie occupying 25K bytes of storage.These patterns find 89% of the hyphens in a pocket dictionary word list, with essentially no error.By comparison, the uncompressed dictionary occupies over 500K bytes.
Comparative Analysis of Cloud Computing Security Frameworks for Financial Sector

Cloud computing has become a preferred operating model for financial organizations due to the flexibility it offers in business operations which made the platform readily available for consumption with advanced capabilities in security, technology, and cost-effective. This paper presents a comprehensive analysis of the salient features of four cloud security frameworks viz, Cloud security reference model, Novel open security framework, Temenos enterprise framework architecture (TEFA), and Sherwood applied business security architecture (SABSA) in regards to their suitability for a financial organization. The comparative analysis is primarily based on the deployment models, structure, service delivery model, security policy, and security standards and guidelines.