Reviewerly/exHarmony huggingface dataset:
Digital Literacy Research: A Scientometric Mapping over the Past 22 Years

Digital literacy as a necessity for the industrial revolution 4.0 is a new challenge for teachers and students to prepare themselves to use digital resources to learn and digital benefits based on entrepreneurial spirit. It has been research yet the notion about digital literacy review which showed the big picture using data from all countries. This research aims to study the status of international digital literacy research that is indexed by Scopus using scientometric mapping. The study has carried out scientometric methods and analyzed research data using the analyze search results service from Scopus and the VOSviewer application. The research data of 2,749 documents published from 1997 to 2019 were obtained from the Scopus database. The results showed an increasing trend in the number of digital literacy publications at the international level each year. Most countries, affiliation, and authors in digital literacy research were the United States, Monash University, and Marsh, J. The most intensive subject areas and sources of publications in digital literacy research are computer science and the ACM international conference proceeding series. There were two collaborative researchers' patterns in digital literacy research. This research proposes a convergence axis classification consisting of digital literacy research to characterize the body of knowledge generated from two decades of research: Learning, Information communication, Technology, Human, and Education, abbreviated as LITHE themes.
Adversarial Skill Networks: Unsupervised Robot Skill Learning from Video

Key challenges for the deployment of reinforcement learning (RL) agents in the real world are the discovery, representation and reuse of skills in the absence of a reward function. To this end, we propose a novel approach to learn a task-agnostic skill embedding space from unlabeled multi-view videos. Our method learns a general skill embedding independently from the task context by using an adversarial loss. We combine a metric learning loss, which utilizes temporal video coherence to learn a state representation, with an entropy-regularized adversarial skill-transfer loss. The metric learning loss learns a disentangled representation by attracting simultaneous viewpoints of the same observations and repelling visually similar frames from temporal neighbors. The adversarial skill-transfer loss enhances re-usability of learned skill embeddings over multiple task domains. We show that the learned embedding enables training of continuous control policies to solve novel tasks that require the interpolation of previously seen skills. Our extensive evaluation with both simulation and real world data demonstrates the effectiveness of our method in learning transferable skills from unlabeled interaction videos and composing them for new tasks. Code, pretrained models and dataset are available at http://robotskills.cs.uni-freiburg.de.
On the Prevalence, Impact, and Evolution of SQL Code Smells in Data-Intensive Systems

Code smells indicate software design problems that harm software quality. Data-intensive systems that frequently access databases often suffer from SQL code smells besides the traditional smells. While there have been extensive studies on traditional code smells, recently, there has been a growing interest in SQL code smells. In this paper, we conduct an empirical study to investigate the prevalence and evolution of SQL code smells in open-source, data-intensive systems. We collected 150 projects and examined both traditional and SQL code smells in these projects. Our investigation delivers several important findings. First, SQL code smells are indeed prevalent in data-intensive software systems. Second, SQL code smells have a weak co-occurrence with traditional code smells. Third, SQL code smells have a weaker association with bugs than that of traditional code smells. Fourth, SQL code smells are more likely to be introduced at the beginning of the project lifetime and likely to be left in the code without a fix, compared to traditional code smells. Overall, our results show that SQL code smells are indeed prevalent and persistent in the studied data-intensive software systems. Developers should be aware of these smells and consider detecting and refactoring SQL code smells and traditional code smells separately, using dedicated tools.
Context-Controlled Topic-Aware Neural Response Generation for Open-Domain Dialog Systems

Incorporating topic information can help response generation models to produce informative responses for chat-bots. Previous work only considers the individual semantic of each topic, ignoring its specific dialog context, which may result in inaccurate topic representation and hurt response coherence. Besides, as an important feature of multi-turn conversation, dynamic topic transitions have not been well-studied. We propose a Context-Controlled Topic-Aware neural response generation model, i.e., CCTA, which makes dialog context interact with the process of topic representing and transiting to achieve balanced improvements on response informativeness and contextual coherence. CCTA focuses on capturing the semantical relations within topics as well as their corresponding contextual information in conversation, to produce context-dependent topic representations at the word-level and turn-level. Besides, CCTA introduces a context-controlled topic transition strategy, utilizing contextual topics to yield relevant transition words. Extensive experimental results on two benchmark multi-turn conversation datasets validate the superiority of our proposal on generating coherent and informative responses against the state-of-the-art baselines. We also find that topic transition modeling can work as an auxiliary learning task to boost the response generation.
Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset

We investigate privacy-preserving, video-based action recognition in deep learning, a problem with growing importance in smart camera applications. A novel adversarial training framework is formulated to learn an anonymization transform for input videos such that the trade-off between target utility task performance and the associated privacy budgets is explicitly optimized on the anonymized videos. Notably, the privacy budget, often defined and measured in task-driven contexts, cannot be reliably indicated using any single model performance because strong protection of privacy should sustain against any malicious model that tries to steal private information. To tackle this problem, we propose two new optimization strategies of model restarting and model ensemble to achieve stronger universal privacy protection against any attacker models. Extensive experiments have been carried out and analyzed. On the other hand, given few public datasets available with both utility and privacy labels, the data-driven (supervised) learning cannot exert its full power on this task. We first discuss an innovative heuristic of cross-dataset training and evaluation, enabling the use of multiple single-task datasets (one with target task labels and the other with privacy labels) in our problem. To further address this dataset challenge, we have constructed a new dataset, termed PA-HMDB51, with both target task labels (action) and selected privacy attributes (skin color, face, gender, nudity, and relationship) annotated on a per-frame basis. This first-of-its-kind video dataset and evaluation protocol can greatly facilitate visual privacy research and open up other opportunities. Our codes, models, and the PA-HMDB51 dataset are available at: https://github.com/VITA-Group/PA-HMDB51.
One size does not fit all

A wide range of tools exist to assist developers in creating secure software. Many of these tools, such as static analysis engines or security checkers included in compilers, use warnings to communicate security issues to developers. The effectiveness of these tools relies on developers heeding these warnings, and there are many ways in which these warnings could be displayed. Johnson et al. [46] conducted qualitative research and found that warning presentation and integration are main issues. We built on Johnson et al.'s work and examined what developers want from security warnings, including what form they should take and how they should integrate into their workflow and work context. To this end, we conducted a Grounded Theory study with 14 professional software developers and 12 computer science students as well as a focus group with 7 academic researchers to gather qualitative insights. To back up the theory developed from the qualitative research, we ran a quantitative survey with 50 professional software developers. Our results show that there is significant heterogeneity amongst developers and that no one warning type is preferred over all others. The context in which the warnings are shown is also highly relevant, indicating that it is likely to be beneficial if IDEs and other development tools become more flexible in their warning interactions with developers. Based on our findings, we provide concrete recommendations for both future research as well as how IDEs and other security tools can improve their interaction with developers.
Multi-Robot Path Deconfliction through Prioritization by Path Prospects

This work deals with the problem of planning conflict-free paths for mobile robots in cluttered environments. Since centralized, coupled planning algorithms are computationally intractable for large numbers of robots, we consider decoupled planning, in which robots plan their paths sequentially in order of priority. Choosing how to prioritize the robots is a key consideration. State-of-the-art prioritization heuristics, however, do not model the coupling between a robot's mobility and its environment. This is particularly relevant when prioritizing between robots with different degrees of mobility. In this paper, we propose a prioritization rule that can be computed online by each robot independently, and that provides consistent, conflict-free path plans. Our innovation is to formalize a robot's path prospects to reach its goal from its current location. To this end, we consider the number of homology classes of trajectories, which capture distinct prospects of paths for each robot. This measure is used as a prioritization rule, whenever any robots enter negotiation to deconflict path plans. We perform simulations with heterogeneous robot teams and compare our method to five benchmarks. Our method achieves the highest success rate, and strikes a good balance between makespan and flowtime objectives.
DMNet: Difference Minimization Network for Semi-supervised Segmentation in Medical Images

Semantic segmentation is an important task in medical image analysis. In general, training models with high performance needs a large amount of labeled data. However, collecting labeled data is typically difficult, especially for medical images. Several semi-supervised methods have been proposed to use unlabeled data to facilitate learning. Most of these methods use a self-training framework, in which the model cannot be well trained if the pseudo masks predicted by the model itself are of low quality. Co-training is another widely used semi-supervised method in medical image segmentation. It uses two models and makes them learn from each other. All these methods are not end-to-end. In this paper, we propose a novel end-to-end approach, called difference minimization network (DMNet), for semi-supervised semantic segmentation. To use unlabeled data, DMNet adopts two decoder branches and minimizes the difference between soft masks generated by the two decoders. In this manner, each decoder can learn under the supervision of the other decoder, thus they can be improved at the same time. Also, to make the model generalize better, we force the model to generate low-entropy masks on unlabeled data so the decision boundary of model lies in low-density regions. Meanwhile, adversarial training strategy is adopted to learn a discriminator which can encourage the model to generate more accurate masks. Experiments on a kidney tumor dataset and a brain tumor dataset show that our method can outperform the baselines, including both supervised and semi-supervised ones, to achieve the best performance.
A Secure Blockchain-based Communication Approach for UAV Networks

Unmanned aerial vehicle (UAV) networks offer enormous potential in civil, commercial, and military applications. As network sizes become large, the communication of UAV networks poses serious cybersecurity challenges. One solution for providing a secure, scalable communication mechanism is to integrate blockchain in the peer-to-peer UAV networks. Blockchain provides a way for multiple entities to communicate securely in a decentralized and cooperative manner. Because blockchain is primarily used for preventing double-spending in cryptocurrency, it lacks methods to secure communications in a network. Specifically, this paper proposes a novel consensus-building mechanism for securing communications in a UAV network, integrating blockchain with public key cryptography method Elliptic Curve Diffie-Hellman and one-time pad encryption method.
Local structured feature learning with dynamic maximum entropy graph

In recent years, Linear Discriminant Analysis (LDA) has seen huge adoption in data mining applications. Due to its globality, it is incompetent to handle multimodal data. Besides, most of LDA’s variants learn the projection matrix based on the pre-defined similarity matrix, which is easily affected by noisy and irrelevant features. To address above two issues, a novel local structured feature learning with Dynamic Maximum Entropy Graph (DMEG) method is developed which firstly develops a more discriminative LDA with whitening constraint that can minimize the within-class scatter while keeping the total samples scatter unchanged simultaneously. Second, for exploring the local structure of data, the ℓ0-norm constraint is imposed on similarity matrix to ensure the k connectivity on graph. More importantly, proposed model learns the similarity and projection matrix simultaneously to ensure that the neighborships can be found in the optimal subspace where the noise have been removed already. Moreover, a maximum entropy regularization is employed to reinforce the discriminability of graph and avoid the trivial solution. Last but not least, an efficient iterative optimization algorithm is provided to optimize proposed model with a NP-hard constraint. Extensive experiments conducted on synthetic and several real-world data sets demonstrate the efficiency in classification task and robustness to noise of proposed method.
Forget Me Not: Reducing Catastrophic Forgetting for Domain Adaptation in Reading Comprehension

The creation of large-scale open domain reading comprehension data sets in recent years has enabled the development of end-to-end neural comprehension models with promising results. To use these models for domains with limited training data, one of the most effective approach is to first pre-train them on large out-of-domain source data and then fine-tune them with the limited target data. The caveat of this is that after fine-tuning the comprehension models tend to perform poorly in the source domain, a phenomenon known as catastrophic forgetting. In this paper, we explore methods that reduce catastrophic forgetting during fine-tuning without assuming access to data from the source domain. We introduce new auxiliary penalty terms and observe the best performance when a combination of auxiliary penalty terms is used to regularise the fine-tuning process for adapting comprehension models. To test our methods, we develop and release 6 narrow domain data sets that can potentially be used as reading comprehension benchmarks.
Korean clinical entity recognition from diagnosis text using BERT

Abstract Background While clinical entity recognition mostly aims at electronic health records (EHRs), there are also the demands of dealing with the other type of text data. Automatic medical diagnosis is an example of new applications using a different data source. In this work, we are interested in extracting Korean clinical entities from a new medical dataset, which is completely different from EHRs. The dataset is collected from an online QA site for medical diagnosis. Bidirectional Encoder Representations from Transformers (BERT), which is one of the best language representation models, is used to extract the entities. Results A slightly modified version of BERT labeling strategy replaces the original labeling to enhance the separation of postpositions in Korean. A new clinical entity recognition dataset that we construct, as well as a standard NER dataset, have been used for the experiments. A pre-trained multilingual BERT model is used for the initialization of the entity recognition model. BERT significantly outperforms a character-level bidirectional LSTM-CRF, a benchmark model, in terms of all metrics. The micro-averaged precision, recall, and f1 of BERT are 0.83, 0.85 and 0.84, whereas that of bi-LSTM-CRF are 0.82, 0.79 and 0.81 respectively. The recall values of BERT are especially better than that of the other model. It can be interpreted that the trained BERT model could detect out of vocabulary (OOV) words better than bi-LSTM-CRF. Conclusions The recently developed BERT and its WordPiece tokenization are effective for the Korean clinical entity recognition. The experiments using a new dataset constructed for the purpose and a standard NER dataset show the superiority of BERT compared to a state-of-the-art method. To the best of our knowledge, this work is one of the first studies dealing with clinical entity extraction from non-EHR data.
A Large-Scale Comparative Evaluation of IR-Based Tools for Bug Localization

This paper reports on a large-scale comparative evaluation of IR-based tools for automatic bug localization. We have divided the tools in our evaluation into the following three generations: (1) The first-generation tools, now over a decade old, that are based purely on the Bag-of-Words (BoW) modeling of software libraries. (2) The somewhat more recent second-generation tools that augment BoW-based modeling with two additional pieces of information: historical data, such as change history, and structured information such as class names, method names, etc. And, finally, (3) The third-generation tools that are currently the focus of much research and that also exploit proximity, order, and semantic relationships between the terms. It is important to realize that the original authors of all these three generations of tools have mostly tested them on relatively small-sized datasets that typically consisted no more than a few thousand bug reports. Additionally, those evaluations only involved Java code libraries. The goal of the present paper is to present a comprehensive large-scale evaluation of all three generations of bug-localization tools with code libraries in multiple languages. Our study involves over 20,000 bug reports drawn from a diverse collection of Java, C/C++, and Python projects. Our results show that the third-generation tools are significantly superior to the older tools. We also show that the word embeddings generated using code files written in one language are effective for retrieval from code libraries in other languages.
Real-Time Semantic Background Subtraction

Semantic background subtraction (SBS) has been shown to improve the performance of most background subtraction algorithms by combining them with semantic information, derived from a semantic segmentation network. However, SBS requires high-quality semantic segmentation masks for all frames, which are slow to compute. In addition, most state-of-the-art background subtraction algorithms are not real-time, which makes them unsuitable for real-world applications. In this paper, we present a novel background subtraction algorithm called Real-Time Semantic Background Subtraction (denoted RT-SBS) which extends SBS for real-time constrained applications while keeping similar performances. RT-SBS effectively combines a real-time background subtraction algorithm with high-quality semantic information which can be provided at a slower pace, independently for each pixel. We show that RT-SBS coupled with ViBe sets a new state of the art for real-time background subtraction algorithms and even competes with the non real-time state-of-the-art ones. Note that we provide python CPU and GPU implementations of RT-SBS at https://github.com/cioppaanthony/rt-sbs.
Fusion-Catalyzed Pruning for Optimizing Deep Learning on Intelligent Edge Devices

The increasing computational cost of deep neural network models limits the applicability of intelligent applications on resource-constrained edge devices. While a number of neural network pruning methods have been proposed to compress the models, prevailing approaches focus only on parametric operators (e.g., convolution), which may miss optimization opportunities. In this article, we present a novel fusion-catalyzed pruning approach, called FuPruner, which simultaneously optimizes the parametric and nonparametric operators for accelerating neural networks. We introduce an aggressive fusion method to equivalently transform a model, which extends the optimization space of pruning and enables nonparametric operators to be pruned in a similar manner as parametric operators, and a dynamic filter pruning method is applied to decrease the computational cost of models while retaining the accuracy requirement. Moreover, FuPruner provides configurable optimization options for controlling fusion and pruning, allowing much more flexible performance-accuracy tradeoffs to be made. Evaluation with state-of-the-art residual neural networks on five representative intelligent edge platforms, Jetson TX2, Jetson Nano, Edge tensor processing unit, neural compute stick, and neural compute stick 2, demonstrates the effectiveness of our approach, which can accelerate the inference of models on CIFAR-10 and ImageNet datasets.
On Security and Data Integrity Framework for Cloud Computing Using Tamper-Proofing

The cloud computing has become an emerging issue in IT industry as well as in academics due to its variant advantages. Cloud computing consist of many computing possessions like memory, storage and processor which are not physically available at the user’s site. These devices are stored outside and controlled by the cloud service providers, there is always a risk of losing data or the data may be altered by internal as well as external attackers. However, it may not be fully trustworthy because the user does not have copy of all stored data which refer to the fact that while users can put their files into the cloud server, nobody knows that where exactly they should be. Further, it poses various security challenges as generating secure and reliable data storage over unreliable cloud service provider. The data integrity is one of the prime concern in cloud computing. This paper presents a framework for data integrity in cloud computing using tamper-proofing algorithms (TPA) such as tamper-proof checking code (TPC) and tamper- resistance (TPR).