Reviewerly/exHarmony huggingface dataset:
A Hierarchical Path Tracking Method for High-speed Unmanned Tracked Vehicle

This paper proposes a hierarchical path tracking control framework divided into the upper controller and the lower controller for double motors independently driven unmanned high-speed tracked vehicle. The upper layer generates rolling speed command of the dual-side tracks using model predictive control. The Euler method, the second-order Runge-Kutta method and the revised fourth-order Runge-Kutta method are applied to compare the control performance in this layer. Meanwhile, to surmount control command execution delay, two compensatory methods based on control law in time domain are proposed. The lower controller translates the tracks speed command from the upper layer into motors torque according to the tracked vehicle dynamic model. Experiments show that the root-mean-square lateral error is 0.1176m and the root-mean-square heading error is 0.7552&#x00B0; when applying the second-order Runge-Kutta method that has the best tracking accuracy in straight off-road roads at 10m/s. Experiments also demonstrate that the control framework has good tracking performance in high-speed tracking straight path and low-speed tracking curved path. Results show the steady condition lateral error of 70km/h pavement tracking is less than 0.2m and 10km/h right-angled soft off-road road is less than 0.6m.
An Effective Method for Automated Railcar Number Detection and Recognition Based on Deep Learning

Railway transportation is of great value to the economy. Railcars usually travel at fast speeds in complex environments. Accurately detecting and recognizing the railcar numbers contributes significantly to logistic management. Therefore, this paper presents a deep-learning-based method for automated railcar number detection and recognition. It consists of two steps, i.e., railcar number detection and recognition, which are conducted by two separated deep learning models. In the detection process, both the whole region of railcar number and text instances are detected so our method is able to remove the detected noisy text regions, i.e., false positives. The cropped text areas in the railcar number regions are sent to the text recognizer. Crucially, the text is recognized on word level so there is no need to conduct an extra step to separate all characters. Experimental results on the railcar number dataset demonstrate that our proposed method can effectively detect and recognize railcar numbers.
Who you gonna call?

The security.txt proposed standard allows organizations to define how security researchers should disclose security issues. While it is still proceeding through the final stages of standardization, major online services have already adopted the standard (such as Google, Facebook, LinkedIn, and Github). In this work, we conduct an empirical investigation into how websites are deploying security.txt. We first monitor security.txt adoption over a 15-month period, identifying the level of deployment for top websites. We also characterize the information being provided through security.txt and issues present in the provided data. Ultimately, our analysis sheds light on how the security.txt mechanism manifests in practice and its implications for vulnerability reporting, particularly for large-scale automated notification campaigns.
On the application of intelligent speech aids in English teaching under multimedia environment

In order to improve the effect of English Teaching in multimedia environment, this paper proposes an English teaching method based on intelligent language AIDS in multimedia environment. Firstly, an intelligent speech teaching assistant system is designed in the multimedia environment. The software mainly includes four modules: new words and sentences, Chinese and English reading, voice evaluation, tools and settings. Learners can learn independently through the software, and teachers can also use the software to make various audio teaching resources in the classroom. In addition, the system can promote vocabulary teaching and support sentence pattern teaching and situational teaching in English teaching. The experimental results show that the average score of class 3A is increased from 86.35 to 86.79, with an increase of 0.64, which proves that the application of this method can effectively mobilize the enthusiasm of students in learning English and achieve good teaching effect.
Automatic Text Summarization of Konkani Folk Tales Using Supervised Machine Learning Algorithms and Language Independent Features

Automatic text summarization is an emerging field of research in Natural Language Processing. This work is a novel attempt to include a low-resource language to the domain of Automatic Text Summarization. We use supervised machine learning algorithms to perform single document extractive automatic text summarization on documents in a low-resource language, Konkani. In particular, we propose using language independent features to train supervised machine learning algorithms using a Konkani dataset, specifically devised for the experimentation using books on Konkani folktale literature. We approach the automatic text summarization task as a binary classification problem, and the algorithms, once trained, classify the sentences based on their relevance to generate a summary. Thereafter, the performance of popular linear and non-linear supervised machine learning algorithms is evaluated using K-fold cross-validation. The summary generated by the systems is compared with human-generated summaries to verify its effectiveness. The results show that the linear models exhibit better performance in comparison with the non-linear models; however, all the models could beat the baselines. The output produced by the proposed methodology generates promising summaries without the need for any language-specific domain knowledge.
On the constructions of MDS self-dual codes via cyclotomy

MDS self-dual codes over finite fields have attracted a lot of attention in recent years by their theoretical interests in coding theory and applications in cryptography and combinatorics. In this paper we present a series of MDS self-dual codes with new length by using generalized Reed-Solomon codes and extended generalized Reed-Solomon codes as the candidates of MDS codes and taking their evaluation sets as a union of cyclotomic classes. The conditions on such MDS codes being self-dual are expressed in terms of cyclotomic numbers.
Attribute-Level Interest Matching Network for Personalized Recommendation

Personalized recommendation refers to identifying items that satisfy users' interests from large-scale item databases according to users' habits and preferences. The task is very challenging due to the complexity of user interests. Previous works use a uniform representation to model user interests, neglecting the diversity of user preferences when they adopt items. However, users consider many different attributes when choosing an item. Introducing attribute-level matching information into the model can express user interests more accurately. To achieve this goal, we propose a novel Attribute-level Interest Matching Network (AIMN) for personalized recommendation. We first adopt a knowledge representation learning method to construct spaces of different attributes, then employ a knowledge graph to extend entities as side information for representing users. Finally, we project entities and candidate items into diverse attribute spaces, match and aggregate them to realize fine-grained attribute-level information matching. Empirical results demonstrate that the proposed AIMN achieves substantial gains on several benchmarks, beating many solid baselines and achieving state-of-art performance.
Investigating the Effect of Polyglot Programming on Developers

Polyglot programming is the practice of using multiple programming languages in the same project. While many developers use this practice of polyglot programming, its effect on developers remains largely unexplored in the literature. In this extended abstract, I present preliminary efforts to provide empirical evidence to explain these effects using eye tracking in a randomized controlled trial study. 31 participants completed tasks in three different language variants. The results show that for certain tasks, the language variants produced a significant difference in gaze transition rates between different line types. Future extensions to this study are proposed to better understand these effects in other tasks and domains.
Aspects of the Long-Term Preservation of Digitized Catalogue Data: Analysis of the Databases of Integrated Collection Management Systems

Abstract During this research, the catalogues of more than 200 libraries and museums of Hungary and its neighboring countries were examined. The authors calculated the amount and the size of the metadata and of the full content records in the databases of their collection management systems, as well as the size and the type of the full content data and the size of the databases. By analyzing the results, the goal was to answer the following three questions: (1) Can any significant difference be established between the results according to country, nationality, or type of institution?; (2) How large is a metadata record or a full content record?; (3) Is it possible to establish a methodology for selecting a representative sample of institutions to facilitate further research? For planning the costs of data management, the size of the databases, the number of metadata records, and the variability of metadata and media records shall all be considered. A distinction should be made between the indispensable “primary” data to be preserved for a long time, and the “secondary” data units which are derived from the primary data. It is investigated in this article how to establish the size of primary data in the databases of collection management systems.
Investigating Unrelated #COVID19 Twitter Expressions: Implications of Spam Content on Information Credibility

Twitter is considered one of the most popular social networking platforms. More than microblogging, it has evolved to be an integral source of information from daily news, current events, and other trending topics on social media. Since the emergence of COVID-19 in Asia in 2019, Twitter has become one of the platforms primarily utilized for information seeking and sharing by Internet users to keep updated with and disseminate relevant information about the virus and its spread. However, the health crisis was no exception in attracting the attention of spammers in exploiting the platform with scams and other malicious content. In this study, tweets containing trending hashtags related to COVID-19 were culled from Twitter to investigate the presence of spam tweets and to assess its possible impact on the information credibility of the platform. Though results suggest that the volume of unrelated Twitter expressions was minimal, this research argues that irrelevant tweets were substantial enough to pose implications detrimental to hashtag use and knowledge discovery.
Constrained Naïve Bayes with application to unbalanced data classification

Abstract The Naïve Bayes is a tractable and efficient approach for statistical classification. In general classification problems, the consequences of misclassifications may be rather different in different classes, making it crucial to control misclassification rates in the most critical and, in many realworld problems, minority cases, possibly at the expense of higher misclassification rates in less problematic classes. One traditional approach to address this problem consists of assigning misclassification costs to the different classes and applying the Bayes rule, by optimizing a loss function. However, fixing precise values for such misclassification costs may be problematic in realworld applications. In this paper we address the issue of misclassification for the Naïve Bayes classifier. Instead of requesting precise values of misclassification costs, threshold values are used for different performance measures. This is done by adding constraints to the optimization problem underlying the estimation process. Our findings show that, under a reasonable computational cost, indeed, the performance measures under consideration achieve the desired levels yielding a user-friendly constrained classification procedure.
User experience evaluation of academic progress information systems using retrospective think aloud and user experience questionnaire

Abstract SIsKA-NG is the result of advanced development of the thesis management information system at the Computer Science Study Program, Universitas Pendidikan Ganesha. Usability evaluation needs to validate development results based on user requirements. This research aimed to identify problems and user satisfaction when using SIsKA-NG with student access. This research used Retrospective Think Aloud in specifying difficulties or user suggestions and User Experience Questionnaires to obtain user satisfaction scores. An evaluation was carried out on 20 respondents who were selected using simple random sampling from 139 active SIsKA-NG users. Retrospective Think Aloud produced 4 recommendations for improvement of SIsKA-NG. User satisfaction analysis showed that all aspects of the user experience were in the excellent category. The average score for each aspect was as follows: attractiveness aspect was 2,23; perspicuity aspect was 2,30; efficiency aspect was 2,49; dependability aspect was 2,55; stimulation aspect was 2,30; and novelty aspect was 2,35. Future research will focus on the evaluation of SIsKA-NG in all other study programs at Postgraduate program in Universitas Pendidikan Ganesha.
Information Asymmetry and Insurance in Africa

In this study, we assess the relevance of decreasing information asymmetry on life and non-life insurance consumption, by using data from 48 African countries during the period 2004-2014. Reduced information asymmetry is proxied by information sharing offices, namely: public credit registries and private credit bureaus. The empirical evidence is based on the Generalised Method of Moments. The findings show that information sharing offices increase insurance consumption with a comparatively higher magnitude in life insurance penetration, relative to non-life insurance penetration. Practical and theoretical implications are discussed.
Accretive Computation of Global Transformations

Global transformations form a categorical framework adapting graph transformations to describe fully synchronous rule systems on a given data structure.In this work we focus on data structures that can be captured as presheaves and study the computational aspects of such synchronous rule systems.To obtain an online algorithm, a complete study of the sub-steps within each synchronous step is done at the semantic level.This leads to the definition of accretive rule systems and a local criterion to characterize these systems.Finally an online computation algorithm for theses systems is given.
A critical study on the recent deep learning based semi-supervised video anomaly detection methods

Video anomaly detection (VAD) is currently a trending research area within computer vision, given that anomalies form a key detection objective in surveillance systems, often requiring immediate responses. The primary challenges associated with video anomaly detection tasks stem from the scarcity of anomaly samples and the context-dependent nature of anomaly definitions. In light of the limited availability of labeled data for training (specifically, a shortage of labeled data for abnormalities), there has been a growing interest in semi-supervised anomaly detection methods. These techniques work by identifying anomalies through the detection of deviations from normal patterns. This paper provides a new perspective to researchers in the field, by categorizing semi-supervised VAD methods according to the proxy task type they employ to model normal data and consequently to detect anomalies. It also reviews recent deep learning based semi-supervised VAD methods, emphasizing their common tactic of slightly overfitting their models on normal data using a proxy task to detect anomalies. Our goal is to help researchers develop more effective video anomaly detection methods. As the selection of a right Deep Neural Network (DNN) plays an important role in several parts of this task, a quick comparative review on DNNs is also included. Unlike previous surveys, DNNs are reviewed from a spatiotemporal feature extraction viewpoint, customized for video anomaly detection. This part of the review can help researchers select suitable networks for different parts of their methods. The review provides a novel and deep look at existing methods and results in stating the shortcomings of these approaches, which can be a hint for future works.
AF-DNDF: Asynchronous Federated Learning of Deep Neural Decision Forests

In recent years, with more edge devices being put into use, the amount of data that is created, transmitted and stored is increasing exponentially. Moreover, due to the development of machine learning algorithms, modern software-intensive systems are able to take advantage of the data to further improve their service quality. However, it is expensive and inefficient to transmit large amounts of data to a central location for the purpose of training and deploying machine learning models. Data transfer from edge devices across the globe to central locations may also raise privacy and concerns related to local data regulations. As a distributed learning approach, Federated Learning has been introduced to tackle those challenges. Since Federated Learning simply exchanges locally trained machine learning models rather than the entire data set throughout the training process, the method not only protects user data privacy but also improves model training efficiency. In this paper, we have investigated an advanced machine learning algorithm, Deep Neural Decision Forests (DNDF), which unites classification trees with the representation learning functionality from deep convolutional neural networks. In this paper, we propose a novel algorithm, AF-DNDF which extends DNDF with an asynchronous federated aggregation protocol. Based on the local quality of each classification tree, our architecture can select and combine the optimal groups of decision trees from multiple local devices. The introduction of the asynchronous protocol enables the algorithm to be deployed in the industrial context with heterogeneous hardware settings. Our AF-DNDF architecture is validated in an automotive industrial use case focusing on road objects recognition and demonstrated by an empirical experiment with two different data sets. The experimental results show that our AF-DNDF algorithm significantly reduces the communication overhead and accelerates model training speed without sacrificing model classification performance. The algorithm can reach the same classification accuracy as the commonly used centralized machine learning methods but also greatly improve local edge model quality.
SAR: Spatial-Aware Regression for 3D Hand Pose and Mesh Reconstruction from a Monocular RGB Image

3D hand reconstruction is a popular research topic in recent years, which has great potential for VR/AR applications. However, due to the limited computational resource of VR/AR equipment, the reconstruction algorithm must balance accuracy and efficiency to make the users have a good experience. Nevertheless, current methods are not doing well in balancing accuracy and efficiency. Therefore, this paper proposes a novel framework that can achieve a fast and accurate 3D hand reconstruction. Our framework relies on three essential modules, including spatial-aware initial graph building (SAIGB), graph convolutional network (GCN) based belief maps regression (GBBMR), and pose-guided refinement (PGR). At first, given image feature maps extracted by convolutional neural networks, SAIGB builds a spatial-aware and compact initial feature graph. Each node in this graph represents a vertex of the mesh and has vertex-specific spatial information that is helpful for accurate and efficient regression. After that, GBBMR first utilizes adaptive-GCN to introduce interactions between vertices to capture short-range and long-range dependencies between vertices efficiently and flexibly. Then, it maps vertices’ features to belief maps that can model the uncertainty of predictions for more accurate predictions. Finally, we apply PGR to compress the redundant vertices’ belief maps to compact-joints’ belief maps with the pose guidance and use these joints’ belief maps to refine previous predictions better to obtain more accurate and robust reconstruction results. Our method achieves state-of-the-art performance on four public benchmarks, FreiHAND, HO-3D, RHD, and STB. Moreover, our method can run at a speed of two to three times that of previous state-of-the-art methods. Our code is available at https://github.com/zxz267/SAR.
Meta-learning Based Breast Abnormality Classification on Screening Mammograms

General breast cancer detection contains two steps, the breast abnormality classification, and the diagnostic classification. The determination of the abnormality contributes further to the following steps, and computational technologies can aid in the process. A lot of machine learning methods have been applied to automate the detection. However, most of them focus on the diagnostic classification and the breast abnormality classification only attracts little attention. The insufficient size of public mammogram datasets also limits the performance of many machine learning algorithms. Considering the importance of breast abnormality classification and the shortage of public large-scale medical datasets, we proposed a meta-learning-based breast abnormality classification method. Our model referred to the latest work of meta-learning-based image classifier and modified it. Specifically, we applied the idea of meta-learning to retrain a pretrained embedding neural network in order to adapt its feature extraction ability to the CBIS-DDSM dataset [1]. The dataset contains two types of abnormal breast mammograms, mass and calcification, and each type is made of two categories of medical images, full mammograms, and ROI [2]. The application of the data augmentation techniques and the idea of meta-learning helped to deal with the insufficient training sample problem and showed a final accuracy of 76%, which beat the 71% accuracy reached by a neural network baseline model.
Generating and disseminating intermediate-level knowledge on multiple levels of abstraction: An exploratory case in media architecture

The concept of intermediate-level knowledge is increasingly used in interaction design research and media architecture. Recently it has been proposed that intermediate-level knowledge is better actionable to designers when it is conveyed on multiple levels of abstraction. This paper shares an approach-in-the-making to generate and disseminate knowledge in line with this proposition. We describe an ongoing exploratory case in which the aim is to generate actionable insights with regards to promoting neighborhood resilience through media architecture. In our approach we explicate knowledge on three levels of abstraction: design examples (most concrete), a framework (most abstract) and design strategies (in between design examples and framework). We discuss how the roles of these levels of abstraction are distinct when either generating or disseminating knowledge. We conclude our paper by looking ahead. As our research team will start to engage with partners in social design, architecture and urban planning, we will explore how our multi-level approach to intermediate-level knowledge is fruitful in generating actionable insights for a more interdisciplinary audience.