Reviewerly/exHarmony huggingface dataset:
Improving Maintenance-Consistency Prediction During Code Clone Creation

Developers frequently introduce code clones into software through the copy-and-paste operations during the software development phase in order to shorten development time. Not all such clone creations are beneficial to software maintenance, as they may introduce extra effort at the software maintenance phase, where additional care is needed to ensure consistent change among these clones; i.e., changes made to a piece of code may need to be propagated to other clones. Failure in doing so may risk introducing bugs into software, which are usually called consistent defect. In response to the rampant maintenance cost caused by the introduction of new clones, some researchers have advocated the use of machine-learning approach to predict the likelihood of consistent change requirement when clones are freshly introduced. Leading in this approach is the work by Wang et al., which uses Bayesian Network to model maintenance-consistency of newly introduced clones. In this work, we leverage the success of the above-mentioned work by providing a revised set of attributes that has been shown to strengthen the predictive power of the Bayesian network model, as determined more quantitatively by the precision and recall levels. We firstly provide the definition of clone consistency-maintenance requirement, which can help transfer this problem to a classification problem. Then, based on collecting all clone creation operations through traversing clone genealogies, we redesign the attribute sets for representing clone creation with more information in code and context perspective. We evaluate the effectiveness of our approach on four open source projects with more quantitative analysis, and the experimental results show that our approach possesses a powerful ability in predicting clone consistency. To transfer this work into practice, we develop an Eclipse plug-in tool of this prediction to aid developers in software development and maintenance.
English Informal Language Learning through Social Networking Sites Among Malaysian University Students

The use of ICT in education has benefitted both students and educators in the education field. Currently, social networking sites are being used widely by university students in their daily lives for various purposes such as entertainment, networking, as well as educational purposes. There are many studies conducted to figure out whether the social networking sites benefit the students in formal education but there are not many studies done to investigate whether they are beneficial to students’ informal learning, especially in English language. Therefore, this study aims to investigate the contribution of social networking sites to the university students' English informal language learning. This quantitative study was conducted on Malaysian public university students in Selangor and Negeri Sembilan. A descriptive quantitative survey research was conducted in order to answer the research questions of this study. The results of this study revealed that most of the respondents learned English informally by saving interesting English quotes or images posted on social networking sites as well as by paying attention to the sentence patterns and accent while watching videos on those sites. The respondents of this study also perceived that they informally learned listening skills and vocabulary the most from the social networking sites. To conclude, social networking sites are not only useful for entertaining purposes, but they could also be used to informally learn and improve one’s English language skills.&#x0D; &#x0D; KEYWORDS: Language Learning, English Informal Learning, Social Networking Sites, Millennials, English In Social Networking Sites, ICT In Education
i-Sign: Sign Language Learning Application Via Gamification

Hearing loss is a type of disability where people who suffer from it could not hear a sound or can only listen to a sound with certain levels. People with hearing loss use sign language to communicate with others and to help them in their learning process. For this study, an interview and survey were conducted with teachers and students from SK Pendidikan Khas Melaka and few problems have been identified in the learning spectrum of children with hearing disability. They face difficulties in learning due to the lack of exposure about the correct sign language since young, difficulty in interpreting sign language in the conventional teaching material due to the nature of its static image contents, and hearing loss children are also usually less focused in the learning process and are easily distracted by their surroundings when the class is in session. To solve these problems, i-Sign is developed as a learning application that incorporates simple gamification in its development that could help hearing loss children to learn the alphabet and basic words of sign language. The use of gamification and multimedia elements provide a more interactive and fun learning environment for the students instead of using static contents, and are also used to direct students’ attention to the learning activity making them more focused in the learning process. i-Sign was developed using the ADDIE model which includes the Analysis, Design, Development, Implementation, and Evaluation phases. In the evaluation phase, a usability test was conducted on 8 children with hearing loss to test the acceptance of the user towards this application and 91.89% of the respondents found that the application meets their expectation of the application capability in terms of its effectiveness, efficiency and user satisfaction. This shows that gamification can be used as an alternative learning material for hearing loss children and help expose the correct sign language to them. The application can also be used by the public that wants to learn about sign language.
Tracking Holistic Object Representations

Recent advances in visual tracking are based on siamese feature extractors and template matching. For this category of trackers, latest research focuses on better feature embeddings and similarity measures. In this work, we focus on building holistic object representations for tracking. We propose a framework that is designed to be used on top of previous trackers without any need for further training of the siamese network. The framework leverages the idea of obtaining additional object templates during the tracking process. Since the number of stored templates is limited, our method only keeps the most diverse ones. We achieve this by providing a new diversity measure in the space of siamese features. The obtained representation contains information beyond the ground truth object location provided to the system. It is then useful for tracking itself but also for further tasks which require a visual understanding of objects. Strong empirical results on tracking benchmarks indicate that our method can improve the performance and robustness of the underlying trackers while barely reducing their speed. In addition, our method is able to match current state-of-the-art results, while using a simpler and older network architecture and running three times faster.
SegaBERT: Pre-training of Segment-aware BERT for Language Understanding

Pre-trained language models have achieved state-of-the-art results in various natural language processing tasks. Most of them are based on the Transformer architecture, which distinguishes tokens with the token position index of the input sequence. However, sentence index and paragraph index are also important to indicate the token position in a document. We hypothesize that better contextual representations can be generated from the text encoder with richer positional information. To verify this, we propose a segment-aware BERT, by replacing the token position embedding of Transformer with a combination of paragraph index, sentence index, and token index embeddings. We pre-trained the SegaBERT on the masked language modeling task in BERT but without any affiliated tasks. Experimental results show that our pre-trained model can outperform the original BERT model on various NLP tasks.
Image Morphing With Perceptual Constraints and STN Alignment

In image morphing, a sequence of plausible frames are synthesized and composited together to form a smooth transformation between given instances. Intermediates must remain faithful to the input, stand on their own as members of the set, and maintain a well-paced visual transition from one to the next. In this paper, we propose a conditional GAN morphing framework operating on a pair of input images. The network is trained to synthesize frames corresponding to temporal samples along the transformation, and learns a proper shape prior that enhances the plausibility of intermediate frames. While individual frame plausibility is boosted by the adversarial setup, a special training protocol producing sequences of frames, combined with a perceptual similarity loss, promote smooth transformation over time. Explicit stating of correspondences is replaced with a grid-based freeform deformation spatial transformer that predicts the geometric warp between the inputs, instituting the smooth geometric effect by bringing the shapes into an initial alignment. We provide comparisons to classic as well as latent space morphing techniques, and demonstrate that, given a set of images for self-supervision, our network learns to generate visually pleasing morphing effects featuring believable in-betweens, with robustness to changes in shape and texture, requiring no correspondence annotation.
Cosine K-Nearest Neighbor in Milkfish Eye Classification

K-Nearest Neighbors (K-NN) classification method gains refined version proposed by the researcher.The refinement aims to solve noise sensitive when using small K, and irrelevant class as classification result when using large K.The problem in the previous version of method was that the weights were calculated individually, so the result was not optimal.We propose recent weighting scheme where the weights were no longer gained from the nearest neighbor individually, but by involving all pair of the nearest neighbor, called Cosine K-NN (CosKNN).We also introduce a trigonometric map to describe the Cosine weight.CosKNN is soft value to represent ownership of each class to the testing data.Empirically, CosKNN is tested and compared with other K-NN refinement using milkfish eye, UCI, and KEEL dataset.The result shows that CosKNN hold superior performance compared to the other methods although K number is higher of which accuracy is 96.79%.
Distributed Information-Theoretic Semisupervised Learning for Multilabel Classification

Multilabel classification (MLC) has received much attention recently. The existing MLC algorithms usually learn multiple classifiers simultaneously by exploiting the correlations among different labels. However, it is difficult and/or expensive to collect a large amount of multilabeled data in practice. The lack of labeled data significantly deteriorates the performance of classification. Moreover, the existing algorithms belong to centralized learning, that is, all the data with their labels must be transmitted to a fusion center for processing. But in many real applications, data may be dispersedly collected/stored in distributed nodes of networks. Due to the concerns of communication cost, processing ability, and data privacy, it is impossible to transmit and/or process the data centrally at one node. Considering this, the problem of distributed MLC over networks is studied, and two distributed information-theoretic semisupervised multilabel learning (dITS2ML2) algorithms are proposed, which are, respectively, used for solving linear and nonlinear MLC problems. In the proposed algorithms, a cost-sensitive objective function is designed, in which a new label correlation term defined on some anchor data is suggested. Besides, to decentralize the global objective function, a distributed matrix completion algorithm is developed to distributively complete the label matrix of the anchor data. Then, by exchanging and combining a few intermediate quantities instead of the original data for both linear and nonlinear cases, the model parameters can be adaptively estimated. The convergence of the proposed dITS2ML2 algorithms is analyzed, and their effectiveness in MLC is verified by simulations on various real datasets.
Marathi Document: Similarity Measurement using Semantics-based Dimension Reduction Technique

Textual data is increasing exponentially and to extract the required information from the text, different techniques are being researched.Some of these techniques require the data to be presented in the tabular or matrix format.The proposed approach designs the Document Term Matrix for Marathi (DTMM) corpus and converts unstructured data into a tabular format.This approach has been called DTMM in this paper and it fails to consider the semantics of the terms.We propose another approach that forms synsets and in turn reduces dimensions to formulate a Document Synset Matrix for Marathi (DSMM) corpus.This also helps in better capturing the semantics and hence is context-based.We abbreviate and call this approach as DSMM and carry out experiments for documentsimilarity measurement on a corpus consisting of more than 1200 documents, consisting of both verses as well as proses, of Marathi language of India.Marathi text processing has been largely an untouched area.The precision, recall, accuracy, F1-score and error rate are used to prove the betterment of the proposed technique.
Real-time video stabilization via camera path correction and its applications to augmented reality on edge devices

With the rapid development of edge devices such as mobile phones, in the past decade, many videos have appeared on the professional video website and they are easy to retrieve. However, most of the videos captured by mobile cameras are jittery, and even motion blurred. These low-quality videos may affect the experience of users. Thus, the problem that how to eliminate the jittery issues, and make unstable video became stable one is very urgent. To enhance the stability of low-quality video, in this paper, we propose a novel method for video stabilization. The proposed method is called SimpleStab, and consists of motion estimation, trajectory smoothing, and compositing image. The SimpleStab is not only able to process offline videos but also can deal with live video streaming due to the novel architecture. We conduct a comprehensive experiment on the benchmarking dataset and make comparison with the state-of-the-art approaches. Experimental results show that the performance of SimpleStab is superior to the state-of-the-art methods.
Exploring the Vulnerability of Single Shot Module in Object Detectors via Imperceptible Background Patches.

Recent works succeeded to generate adversarial perturbations on the entire image or the object of interests to corrupt CNN based object detectors. In this paper, we focus on exploring the vulnerability of the Single Shot Module (SSM) commonly used in recent object detectors, by adding small perturbations to patches in the background outside the object. The SSM is referred to the Region Proposal Network used in a two-stage object detector or the single-stage object detector itself. The SSM is typically a fully convolutional neural network which generates output in a single forward pass. Due to the excessive convolutions used in SSM, the actual receptive field is larger than the object itself. As such, we propose a novel method to corrupt object detectors by generating imperceptible patches only in the background. Our method can find a few background patches for perturbation, which can effectively decrease true positives and dramatically increase false positives. Efficacy is demonstrated on 5 two-stage object detectors and 8 single-stage object detectors on the MS COCO 2014 dataset. Results indicate that perturbations with small distortions outside the bounding box of object region can still severely damage the detection performance.
3D Hand Gesture Representation and Recognition through Deep Joint Distance Measurements

Hand gestures with finger relationships are among the toughest features to extract for machine recognition.In this paper, this particular research challenge is addressed with 3D hand joint features extracted from distance measurements which are then colour mapped as spatio temporal features.Further patterns are learned using an 8-layer convolutional neural network (CNN) to estimate the hand gesture.The results showed a higher degree of recognition accuracy when compared to similar 3D hand gesture methods.The recognition accuracy for our dataset KL 3DHG with 220 classes was around 94.32%.Robustness of the proposed method was validated with only available benchmark 3D skeletal hand gesture dataset DGH 14/28.
Syntax-Aware Data Augmentation for Neural Machine Translation

Data augmentation is an effective method for the performance enhancement of neural machine translation (NMT) by generating additional bilingual data. In this paper, we propose a novel data augmentation strategy for neural machine translation. Unlike existing data augmentation methods that simply modify words with the same probability across different sentences, we introduce a sentence-specific probability approach for word selection based on the syntactic roles of words in the sentence. Our motivation is to consider a linguistics-motivated method to obtain more ingenious language generation rather than relying on computation-motivated approaches only. We argue that high-quality aligned bilingual data is crucial for NMT, and only computation-motivated data augmentation is insufficient to provide good enough extra enhancement data. Our approach leverages dependency parse trees of input sentences to determine the selection probability of each word in the sentence using three different functions to calculate probabilities for words with different depths. Besides, our method also revises the probability for words considering the sentence length. We evaluate our methods on multiple translation tasks. The experimental results demonstrate that our proposed data augmentation method does effectively boost existing sentence-independent methods for significant improvement of performance on translation tasks. Furthermore, an ablation study shows that our method does select fewer essential words and preserves the syntactic structure.
Power-Constrained Quality Optimization for Mobile Video Chatting With Coding-Transmission Adaptation

Mobile video chatting has emerged as an important Internet multimedia application that greatly enriches interpersonal communications. Mobile power efficiency is crucial to the service quality and time of video chatting on battery-limited smartphones. However, the power characteristics of the video coding and data communication are highly complex due to the time-varying network conditions and dynamic mobile energy features. This incurs crucial challenges to maintaining the low power dissipation of mobile chatting application while streaming satisfactory-quality videos. To address these challenges, this paper presents a joinT cOding-tranSmission Optimization (TOSO) protocol at application layer that performs machine learning based adaptation of the video bit rate and FEC (Forward Error Correction) coding parameters. By taking advantage of analytical and empirical models characterizing the quality-power relationship, TOSO is able to maximize video quality subject to a specified upper bound of power consumption in mobile chat application. This distinguishing feature prevents the video chat from draining battery too quickly. Moreover, it allows the smartphone operating system or the mobile user to define a desired video chat duration given the remaining battery, avoiding unpleasant conversation disruption due to battery depletion. Extensive experiments based on the Linphone platform and Exata network emulator show that TOSO outperforms baseline approaches by 29.3 percent in power conservation while achieving the same video quality level.
Graph Stories in Small Area

We study the problem of drawing a dynamic graph, where each vertex appears in the graph at a certain time and remains in the graph for a fixed amount of time, called the window size.This defines a graph story, i.e., a sequence of subgraphs, each induced by the vertices that are in the graph at the same time.The drawing of a graph story is a sequence of drawings of such subgraphs.To support readability, we require that each drawing is straight-line and planar and that each vertex maintains its placement in all the drawings.Ideally, the area of the drawing of each subgraph should be a function of the window size, rather than a function of the size of the entire graph, which could be too large.We show that the graph stories of paths and trees can be drawn on a 2W × 2W and on an (8W + 1) × (8W + 1) grid, respectively, where W is the window size.These results are constructive and yield linear-time algorithms.Further, we show that there exist graph stories of planar graphs whose subgraphs cannot be drawn within an area that is only a function of W .
Exploring the Differences of Visual Discomfort Caused by Long-term Immersion between Virtual Environments and Physical Environments

To investigate the effects of visual discomfort caused by long-term immersing in virtual environments (VEs), we conducted a comparative study to evaluate users’ visual discomfort in an eight-hour working rhythm and compared the differences between the VEs and the physical environments. Twenty-seven participants performed four different visual tasks with a head-mounted display (HMD) for the VE condition and with a monitor for the physical condition. Their subjective visual discomfort and objective oculomotor indicators were measured to evaluate their visual performances. The results show that the subjective visual fatigue symptoms, the objective pupil size, and the relative accommodation response vary across time for the two conditions, in which VEs affects visual fatigue the most compared to the physical environments. The results also show that pupil size is negatively related to subjective visual fatigue, and the long-term work based on displays only influences the maximum accommodation response of participants. This work is a supplement to the necessary but insufficient-researched field of visual fatigue in long-term immersing in VEs, which should be valuable to researchers involved in the evaluation of visual fatigue using HMDs.
The impact of corporate political activity on innovation in Sub-Saharan African countries: A double-edged sword

This study examines the impact of firms' resources and corporate political activity on firm innovation. Using a dataset of 7461 firms in 12 sub-Saharan African countries, we find that firms' financial capacity and knowledge contribute significantly to innovation. Moreover, although the corporate political activity positively affects firm innovation, it adversely influences the efficiency of firms’ internal knowledge utilization for innovation. As corporate political activity is a double-edged sword for innovation, this study provides insightful implications for policymakers to boost innovation in sub-Saharan African countries.