Reviewerly/exHarmony huggingface dataset:
Onboarding to Open Source Projects with Good First Issues: A Preliminary Analysis

While open source software (OSS) development projects always seek contributions from newcomers to make the projects sustainable, newcomers often face with a challenge of onboarding. For developers with little experience, it is difficult to decide which of the many OSS projects to contribute to and which issues to tackle. To mitigate the barrier against onboarding, some OSS projects start using a label so called good first issue (GFI) which indicates the issue is easy to resolve and suitable for newcomers to tackle. The final goal of this study is to construct a model for recommending good first issues to help reduce maintainers' manual effort of selecting and labeling issues as GFIs and at the same time to help find GFIs suitable for each newcomer. Toward the final goal, we analyze GFIs in GitHub to deeply understand the current state of the GFI mechanism and its impact on new members' onboarding. This paper reports a result of our preliminary analysis of GFIs, based on 9,475 GFIs collected from 11 famous OSS projects in GitHub. We find that (1) developers who have resolved GFIs have 92 fewer median PR (pull request) posts than developers who have resolved regular issues (i.e., developers tackling GFIs have less experience, compared to other developers), (2) on average, GFIs are resolved 32.5% more than regular issues (i.e., GFIs are easier to resolve. The dataset of GFIs might be able to be used as a training set for recommending GFIs to developers with less experience), and however (3) the percentage of developers who keep contributing to the same project even after resolving GFIs varies greatly from project to project (24.9% to 83.9%) (i.e, GFIs guide newcomers' onboarding only in particular projects). Based on the analysis result, we discuss the contents of GFIs which tend to keep newcomers on OSS projects.
Relaxed Wasserstein with Applications to GANs

Wasserstein Generative Adversarial Networks (WGANs) provide a versatile class of models, which have attracted great attention in various applications. However, this framework has two main drawbacks: (i) Wasserstein-1 (or Earth-Mover) distance is restrictive such that WGANs cannot always fit data geometry well; (ii) It is difficult to achieve fast training of WGANs. In this paper, we propose a new class of Relaxed Wasserstein (RW) distances by generalizing Wasserstein-1 distance with Bregman cost functions. We show that RW distances achieve nice statistical properties while not sacrificing the computational tractability. Combined with the GANs framework, we develop Relaxed WGANs (RWGANs) which are not only statistically flexible but can be approximated efficiently using heuristic approaches. Experiments on real images demonstrate that the RWGAN with Kullback-Leibler (KL) cost function outperforms other competing approaches, e.g., WGANs, even with gradient penalty.
Indonesian Abstractive Summarization using Pre-trained Model

Automatic text summarization systems are increasingly needed to encounter the information explosion caused by internet growth. Since Indonesian is still considered an under-resourced language, we take advantage of pre-trained language models to perform abstractive summarization. This paper investigates the BERT performance given the Indonesian article by comparing several BERT pre-trained models and evaluated the results based on the ROUGE values. Our experiment shows that an English pre-trained model can produce a good summary given Indonesian text, but it is more effective for using the Indonesian pre-trained model. The default training model only with the abstractive objective is better than using two-stage fine-tuning, where the extractive model must be trained in advance. We also found a lot of meaningless words in the summary words construction. This finding is the result of a preliminary study to improve the Indonesian abstractive summarization model.
Research and Application of Facial Expression Recognition Based on Attention Mechanism

The scale of the existing convolutional neural network is getting larger and larger, resulting in too large amounts of parameters, and the structure is not light enough. Moreover, existing convolutional neural networks are difficult to recognize the subtle changes of facial expressions and cannot extract facial expression features accurately. Therefore, the performance of facial expression recognition needs to be improved. To solve the above problems, a new deep learning network model is proposed for facial expression recognition. Based on the deep residual network, the attention mechanism module (Convolutional Block Attention Module, CBAM) is added to the last layer of convolution and the first layer of convolution of the network. The spatial attention mechanism and channel attention mechanism are used to suppress the unimportant feature information and focus on the effective feature information. In the bottom layer, the influence of other factors is eliminated as much as possible, and more attention is paid to the extraction of facial expression features, which enriches the learning of facial expression features and improves the accuracy of facial expression recognition. The method proposed in this paper has been tested and verified on two public data sets FER2013 and CK+, and the results prove that the method has a high accuracy rate.
An Integrated Mission Planning Framework for Sensor Allocation and Path Planning of Heterogeneous Multi-UAV Systems

Mission planning is the guidance for a UAV team to perform missions, which plays the most critical role in military and civil applications. For complex tasks, it requires heterogeneous cooperative multi-UAVs to satisfy several mission requirements. Meanwhile, airborne sensor allocation and path planning are the critical components of heterogeneous multi-UAVs system mission planning problems, which affect the mission profit to a large extent. This paper establishes the mathematical model for the integrated sensor allocation and path planning problem to maximize the total task profit and minimize travel costs, simultaneously. We present an integrated mission planning framework based on a two-level adaptive variable neighborhood search algorithm to address the coupled problem. The first-level is devoted to planning a reasonable airborne sensor allocation plan, and the second-level aims to optimize the path of the heterogeneous multi-UAVs system. To improve the mission planning framework’s efficiency, an adaptive mechanism is presented to guide the search direction intelligently during the iterative process. Simulation results show that the effectiveness of the proposed framework. Compared to the conventional methods, the better performance of planning results is achieved.
Encoder-Decoder Model for Automatic Video Captioning Using Yolo Algorithm

Humans can use informed visual perception to generate sentences by bridging the gap between the recognition of visual features (images) and linguistic expression (words) describing these images. Videos are an example of visual perception; humans can describe the content of the video in meaningful sentences based on understanding their contents as a caption for the video. However, automating the video caption process is a challenging task as it confronts the model with two problems are: object detection and generating a sentence. This research aims to develop a model that automates video captioning based on Encoder-Decoder using a deep learning algorithm following these two steps. Firstly, using the KATNA model to select the most significant frames from the video and remove redundant ones. Secondly, combining the two deep learning algorithms YOLO and LSTM. The You Only Look Once (YOLO) algorithm recognizes objects in the video frames and the Long Short-Term Memory (LSTM) algorithm generates the video caption. The proposed model describes the video's content in a meaningful sentence and it shows good accuracy and efficiency, it applies YOLO on the MSVD dataset unlike other video captions using other deep learning techniques.
An Underwater Image Enhancement Method Based on Local White Balance

Underwater image enhancement is an important low-level task for marine industry. This paper proposed an underwater image enhancement method based on colour constancy theory. Compared with many existing researches, time and space complexity of the method proposed in this paper is low and our method achieves excellent performance. Firstly, we analyzed the underwater imaging model and distortion of underwater images. Then, by compensating the specific channel and local white balance, the linear transformation is performed on the images to restore distortion in the images. Finally, the results are obtained by stretching the histogram on RGB channel and intensity channel of images. Experiments show that our method has achieved excellent performance.
Enterprise-Driven Open Source Software: A Case Study on Security Automation

Agile and DevOps are widely adopted by the industry. Hence, integrating security activities with industrial practices, such as continuous integration (CI) pipelines, is necessary to detect security flaws and adhere to regulators' demands early. In this paper, we analyze automated security activities in CI pipelines of enterprise-driven open source software (OSS). This shall allow us, in the long-run, to better understand the extent to which security activities are (or should be) part of automated pipelines. In particular, we mine publicly available OSS repositories and survey a sample of project maintainers to better understand the role that security activities and their related tools play in their CI pipelines. To increase transparency and allow other researchers to replicate our study (and to take different perspectives), we further disclose our research artefacts. Our results indicate that security activities in enterprise-driven OSS projects are scarce and protection coverage is rather low. Only 6.83% of the analyzed 8,243 projects apply security automation in their CI pipelines, even though maintainers consider security to be rather important. This alerts industry to keep the focus on vulnerabilities of 3rd Party software and it opens space for other improvements of practice which we outline in this manuscript.
An Evaluation of DNN Architectures for Page Segmentation of Historical Newspapers

One important and particularly challenging step in the optical character recognition of historical documents with complex layouts, such as newspapers, is the separation of text from non-text content (e.g. page borders or illustrations). This step is commonly referred to as page segmentation. While various rule-based algorithms have been proposed, the applicability of Deep Neural Networks for this task recently has gained a lot of attention. In this paper, we perform a systematic evaluation of 11 different published backbone architectures and 9 different tiling and scaling configurations for separating text, tables or table column lines. We also show the influence of the number of labels and the number of training pages on the segmentation quality, which we measure using the Matthews Correlation Coefficient. Our results show that (depending on the task) Inception-ResNet-v2 and EfficientNet backbones work best, vertical tiling is generally preferable to other tiling approaches, and training data that comprises 30 to 40 pages will be sufficient most of the time.
Speculating on Biodesign in the Future Home

The home is a place of shelter, a place for family, and for separation from other parts of life, such as work. Global challenges, the most pressing of which are currently the COVID-19 pandemic and climate change has forced extra roles into many homes and will continue to do so in the future. Biodesign integrates living organisms into designed solutions and can offer opportunities for new kinds of technologies to facilitate a transition to the home of the future. Many families have had to learn to work alongside each other, and technology has mediated a transition from standard models of operation for industries. These are the challenges of the 21st century that mandate careful thinking around interactive systems and innovations that support new ways of living and working at home. In this workshop, we will explore opportunities for biodesign interactive systems in the future home. We will bring together a broad group of researchers in HCI, design, and biosciences to build the biodesign community and discuss speculative design futures. The outcome will generate an understanding of the role of interactive biodesign systems at home, as a place with extended functionalities.
Physical Design Strategies for Mitigating Fine-Grained Electromagnetic Side-Channel Attacks

We present physical design strategies viz. (i) power grid shielding, (ii) power grid twisting, (iii) increased local decoupling capacitors with VSS shields, and (iv) isolated S-Box module placement to improve the resilience of the Advanced Encryption Standard (AES-128) cryptographic core against fine-grained electromagnetic (EM) side-channel analysis (SCA). Localized EM field measurements are performed using a 0.5 mm radius H-field probe on 3 different, 40nm CMOS test-chips implementing 9 physical design configurations of the AES core. These physical design strategies show 2.45x, 1.51x, 2.61x, and 2.71x higher measurements to disclosure (MTD) respectively compared to the baseline design without incurring any power overhead. These strategies can be applied independently or optimally combined further improving fine-grained EM SCA resilience.
Construction of a Large-Scale Japanese ASR Corpus on TV Recordings

This paper presents a new large-scale Japanese speech corpus for training automatic speech recognition (ASR) systems. This corpus contains over 2,000 hours of speech with transcripts built on Japanese TV recordings and their subtitles. We develop herein an iterative workflow to extract matching audio and subtitle segments from TV recordings based on a conventional method for lightly-supervised audio-to-text alignment. We evaluate a model trained with our corpus using an evaluation dataset built on Japanese TEDx presentation videos and confirm that the performance is better than that trained with the Corpus of Spontaneous Japanese (CSJ). The experiment results show the usefulness of our corpus for training ASR systems. This corpus is made public for the research community along with Kaldi scripts for training the models reported in this paper.
Camera Space Synthesis of Motion Effects Emphasizing a Moving Object in 4D films

Four-dimensional (4D) films, which provide special physical effects to the audience with audiovisual stimuli, are gaining more popularity and acceptance. One of the most frequent 4D effects is the object-based motion effect, which refers to the vestibular stimulus generated by a motion chair to emphasize a moving object of interest, e.g., the flying iron man, displayed on the screen. In this paper, we present an algorithm for synthesizing convincing object-based motion effects automatically from a given object motion trajectory. While previous approaches use the 2D object position on the screen as input, our method takes the 3D position and orientation of the object in the camera space and computes its motion proxy that reflects both the object translation and rotation, as well as its size to the viewers' eyes. The proxy is determined based on the results of a perceptual experiment that presents an optimal additive rule of the translation and rotation information scaled by the object's visual size. The motion proxy is fed to a motion cueing algorithm (MCA) that computes the command using a washout filter or model predictive control. The most appropriate MCA for our purpose is selected from six candidates by a user study. We also consider the effects of visual perception by incorporating two types of motion field equations into the computation of the visually perceived velocity. The results of a user study indicate that our algorithm can generate compelling object-based motion effects that better enhance the 4D film viewing experience than the previous methods.
Statistical inference for distributions with one Poisson conditional

It will be recalled that the classical bivariate normal distributions have normal marginals and normal conditionals. It is natural to ask whether a similar phenomenon can be encountered involving Poisson marginals and conditionals. However, it is known, from research on conditionally specified models, that Poisson marginals will be encountered, together with both conditionals being of the Poisson form, only in the case in which the variables are independent. In order to have a flexible dependent bivariate model with some Poisson components, in the present article, we will be focusing on bivariate distributions with one marginal and the other family of conditionals being of the Poisson form. Such distributions are called Pseudo-Poisson distributions. We discuss distributional features of such models, explore inferential aspects and include an example of applications of the Pseudo-Poisson model to sets of over-dispersed data.
Mid-Air Finger Sketching for Tree Modeling

2D sketch-based tree modeling cannot guarantee to generate plausible depth values and full 3D tree shapes. With the advent of virtual reality (VR) technologies, 3D sketching enables a new form for 3D tree modeling. However, it is labor-intensive and difficult to create realistically-looking 3D trees with complicated geometry and lots of detailed twigs with a reasonable amount of effort. In this paper, we explore the use of mid-air finger 3D sketching in VR for tree modeling. We present a hybrid approach that integrates freehand 3D sketches with an automatic population of branch geometries. The user only needs to draw a few 3D strokes in mid-air to define the envelope of the foliage (denoted as lobes) and main branches. Our algorithm then automatically generates a full 3D tree model based on these stroke inputs. Additionally, the shape of the 3D tree model can be modified by freely dragging, squeezing, or moving lobes in mid-air. We demonstrate the ease-of-use, efficiency, and flexibility in tree modeling and overall shape control. We perform user studies and show a variety of realistic tree models generated instantaneously from 3D finger sketching.
Investigation of Adequate Calibration Methods for X-ray Fluorescence Core Scanning Element Count Data: A Case Study of a Marine Sediment Piston Core from the Gulf of Alaska

X-ray fluorescence (XRF) core scanner elemental count data are useful for high-resolution paleoceanographic studies. However, because several factors, such as changes in physical core properties, significantly affect element count intensities, the appropriate calibration of the count data is required. Besides, the existing approaches for calibration were not widely employed and require rigorous testing based on sediment variety. In this study, we analyzed high-resolution element intensity (cps) using a wet muddy marine sediment piston core that was collected from the northeast Gulf of Alaska and tested several approaches with ratio and log-ratio methods, and the reliability was evaluated by comparison with the concentrations that were measured by WD-XRF and an elemental analyzer. The results show that the lighter elements (Ti and K) exhibited a significantly weak relationship between raw counts measured by ITRAX and concentrations that were measured by the WD-XRF, indicating that some factors artificially influence ITRAX intensity data. The Cl intensity that is expressed as the water content in marine sediment increased significantly in the upper 202 cm by 42% and the top 25 cm by 73% as compared to the down-core (below 202 cm), which deviates the X-ray scattering and element-counts. The calibration of raw data through coherent/incoherent X-ray scattering ratio (CIR) and additive- and centered-log ratio reduces the offsets. The calibration by CIR performed best for Sr, Fe, Mn, Ti, Ca, K, and Br (0.56 &lt; R2 &lt; 0.91), and the correlation with concentration significantly increased for Ti and K of 100% and 56%, respectively. Therefore, the study suggests that the correction of raw counts through CIR is an effective approach for wet marine sediment when core physical properties have greater variability.
Hybrid Decomposition Convolution Neural Network and Vocabulary Forest for Image Retrieval

This paper introduces a highly efficient image retrieval technique called DCNN-vForest (Decomposition Convolution Neural Network and vocabulary Forest), which aims to retrieve the relevant images to the given image query by studying the correlation between images in the image database based on decomposition. The regional and global features of the image database are first extracted using the convolution neural network, and then divided into clusters of similar images using the Kmeans algorithm. We propose a new structure called vForest (vocabulary Forest), by calculating the vocabulary tree on each cluster of images. The retrieval process benefits from the knowledge provided by the vForest, and instead of considering the whole image database, only the most similar cluster to the image query is explored. To demonstrate the usefulness of our approach, intensive experiments have been carried out on ground-truth image databases, the results reveal the superiority of DCNN- vForest against the baseline image retrieval solutions, in terms of runtime and accuracy.
Using the project method to motivate students studying physics

Abstract Modern graduates of physical specialties are required not only to possess a certain amount of knowledge, but also to be able to apply this knowledge to solve practical problems, as well as independently acquire the knowledge required for their professional activities. During the implementation of projects, students have the opportunity to solve practical professional problems. The use of the project method in teaching students of physical specialties, in our opinion, allows to increase the degree of interest of students in acquiring new knowledge. In the process of implementing projects, students gain skills of teamwork on the project, they develop systematic and analytical thinking. In addition, the implementation of the project allows students to acquire the skills to independently formulate a project implementation plan and search for information necessary to solve the problem.
Graph Enhanced Query Rewriting for Spoken Language Understanding System

Query rewriting (QR) is an increasingly important component in voice assistant systems to reduce customer friction caused by errors in a spoken language understanding pipeline. These errors originate from various sources such as Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU) modules. In this work, we construct a user interaction graph from their queries using data mined from a Markov Chain Model [1], and introduce a self-supervised pre-training process for learning query embeddings by leveraging the recent developments in Graph Representation Learning (GRL). We then fine-tune these embeddings with weak supervised data for the query rewriting task, and observe improvement over the neural retrieval baseline system, demonstrating the effectiveness of the proposed method.