Reviewerly/exHarmony huggingface dataset:
A hybrid code representation learning approach for predicting method names

Program semantic properties such as class names, method names, and variable names and types play an important role in software development and maintenance. Method names are of particular importance because they provide the cornerstone of abstraction for developers to communicate with each other for various purposes (e.g., code review and program comprehension). Existing method name prediction approaches often represent code as lexical tokens or syntactical AST (abstract syntax tree) paths, making them difficult to learn code semantics and hindering their effectiveness in predicting method names. Initial attempts have been made to represent code as execution traces to capture code semantics, but suffer scalability in collecting execution traces. In this paper, we propose a hybrid code representation learning approach, named Meth2Seq, to encode a method as a sequence of distributed vectors. Meth2Seq represents a method as (1) a bag of paths on the program dependence graph, (2) a sequence of typed intermediate representation statements and (3) a sentence of natural language comment, to scalably capture code semantics. The learned sequence of vectors of a method is fed to a decoder model to predict method names. Our evaluation with a dataset of 280.5K methods in 67 Java projects has demonstrated that Meth2Seq outperforms the two state-of-the-art code representation learning approaches in F1-score by 92.6% and 36.6%, while also outperforming two state-of-the-art method name prediction approaches in F1-score by 85.6% and 178.1%.
Sound probabilistic inference via guide types

Probabilistic programming languages aim to describe and automate Bayesian modeling and inference. Modern languages support programmable inference, which allows users to customize inference algorithms by incorporating guide programs to improve inference performance. For Bayesian inference to be sound, guide programs must be compatible with model programs. One pervasive but challenging condition for model-guide compatibility is absolute continuity, which requires that the model and guide programs define probability distributions with the same support.
A Comprehensive Study on Human Activity Recognition

Recognizing the activity of a person and his motive from video sequences and sensor data is one of the major challenges in human-computer interaction and computer vision. Identifying the activities, processing them for classification and making decision on whether it is walk, sit, stand or fall is the prime functionality of Human Activity Recognition (HAR). Elderly people on been alone at home, face many problems including collapsing, vomiting, chest pain, stomach ache etc. Therefore, elderly people or patients could be definitely supported by HAR which would help to monitor their activity and any changes in their behavior or any occurring critical activity. This review provides a comprehensive study on the state-of-the-art HAR approaches along with the datasets used. The performance metrics used for the experimental evaluations are also analyzed and highlighted. The motivation of the research on HAR and the directions for future research are also explored.
Robust Single Image Deblurring Using Gyroscope Sensor

Motion blur in an image is caused by the movement of the camera during exposure time; thus, awareness of the camera motion is a key factor in image deblurring algorithms. Among the various sensors that can be utilized while taking a picture in handheld devices, a gyroscope sensor, which measures the angular velocity, can help in estimating the camera motion. To achieve accurate and efficient single-image deblurring with a gyroscope sensor, we present a novel deep network with a flexible receptive field that is appropriate for training features related to the nature of the blur. Two specialized modules are sequentially placed in the proposed network to adaptively convert the shapes of the convolutional kernels. The first module directly transforms the kernel shape into the direction of the camera motion indicated by the gyroscope measurements. In the middle of the network, where the feature abstraction is sufficiently proceeded, the second module integrates features from the blurry image along with the information from the gyroscope to convert the kernel shape effectively, even when the gyroscope sensor is unreliable. Using a new gyro-image paired dataset, extensive experiments were conducted to show the effects of the reliability of the gyroscope measurements on the deblurring performance and to prove the effectiveness of our strategy.
Deep Gaussian Denoiser Epistemic Uncertainty and Decoupled Dual-Attention Fusion

Following the performance breakthrough of denoising networks, improvements have come chiefly through novel architecture designs and increased depth. While novel denoising networks were designed for real images coming from different distributions, or for specific applications, comparatively small improvement was achieved on Gaussian denoising. The denoising solutions suffer from epistemic uncertainty that can limit further advancements. This uncertainty is traditionally mitigated through different ensemble approaches. However, such ensembles are prohibitively costly with deep networks, which are already large in size.Our work focuses on pushing the performance limits of state-of-the-art methods on Gaussian denoising. We propose a model-agnostic approach for reducing epistemic uncertainty while using only a single pretrained network. We achieve this by tapping into the epistemic uncertainty through augmented and frequency-manipulated images to obtain denoised images with varying error. We propose an ensemble method with two decoupled attention paths, over the pixel domain and over that of our different manipulations, to learn the final fusion. Our results significantly improve over the state-of-the-art baselines and across varying noise levels.
POCASUM: policy categorizer and summarizer based on text mining and machine learning

Having control over your data is a right and a duty that every citizen has in our digital society. It is often that users skip entire policies of applications or websites to save time and energy without realizing the potential sticky points in these policies. Due to obscure language and verbose explanations majority of users of hypermedia do not bother to read them. Further, sometimes digital media companies do not spend enough effort in stating their policies clearly which often time can also be incomplete. A summarized version of these privacy policies that can be categorized into the useful information can help the users. To solve this problem, in this work we propose to use machine learning based models for policy categorizer that classifies the policy paragraphs under the attributes proposed like security, contact etc. By benchmarking different machine learning based classifier models, we show that artificial neural network model performs with higher accuracy on a challenging dataset of textual privacy policies. We thus show that machine learning can help summarize the relevant paragraphs under the various attributes so that the user can get the gist of that topic within a few lines.
Prediction and Description of Near-Future Activities in Video

Most of the existing works on human activity analysis focus on recognition or early recognition of the activity labels from complete or partial observations. Similarly, almost all of the existing video captioning approaches focus on the observed events in videos. Predicting the labels and the captions of future activities where no frames of the predicted activities have been observed is a challenging problem, with important applications that require anticipatory response. In this work, we propose a system that can infer the labels and the captions of a sequence of future activities. Our proposed network for label prediction of a future activity sequence has three branches where the first branch takes visual features from the objects present in the scene, the second branch takes observed sequential activity features, and the third branch captures the last observed activity features. The predicted labels and the observed scene context are then mapped to meaningful captions using a sequence-to-sequence learning-based method. Experiments on four challenging activity analysis datasets and a video description dataset demonstrate that our label prediction approach achieves comparable performance with the state-of-the-arts and our captioning framework outperform the state-of-the-arts.
Spatial Super Resolution of Real-World Aerial Images for Image-Based Plant Phenotyping

Unmanned aerial vehicle (UAV) imaging is a promising data acquisition technique for image-based plant phenotyping. However, UAV images have a lower spatial resolution than similarly equipped in field ground-based vehicle systems, such as carts, because of their distance from the crop canopy, which can be particularly problematic for measuring small-sized plant features. In this study, the performance of three deep learning-based super resolution models, employed as a pre-processing tool to enhance the spatial resolution of low resolution images of three different kinds of crops were evaluated. To train a super resolution model, aerial images employing two separate sensors co-mounted on a UAV flown over lentil, wheat and canola breeding trials were collected. A software workflow to pre-process and align real-world low resolution and high-resolution images and use them as inputs and targets for training super resolution models was created. To demonstrate the effectiveness of real-world images, three different experiments employing synthetic images, manually downsampled high resolution images, or real-world low resolution images as input to the models were conducted. The performance of the super resolution models demonstrates that the models trained with synthetic images cannot generalize to real-world images and fail to reproduce comparable images with the targets. However, the same models trained with real-world datasets can reconstruct higher-fidelity outputs, which are better suited for measuring plant phenotypes.
Classifying Math Knowledge Components via Task-Adaptive Pre-Trained BERT

Educational content labeled with proper knowledge components (KCs) are particularly useful to teachers or content organizers. However, manually labeling educational content is labor intensive and error-prone. To address this challenge, prior research proposed machine learning based solutions to auto-label educational content with limited success. In this work, we significantly improve prior research by (1) expanding the input types to include KC descriptions, instructional video titles, and problem descriptions (i.e., three types of prediction task), (2) doubling the granularity of the prediction from 198 to 385 KC labels (i.e., more practical setting but much harder multinomial classification problem), (3) improving the prediction accuracies by 0.5–2.3% using Task-adaptive Pre-trained BERT, outperforming six baselines, and (4) proposing a simple evaluation measure by which we can recover 56–73% of mispredicted KC labels. All codes and data sets in the experiments are available at: https://github.com/tbs17/TAPT-BERT
Bootstrapping Recommendations at Chrome Web Store

Google Chrome, one of the world's most popular web browsers, features an extension framework allowing third-party developers to enhance Chrome's functionality. Chrome extensions are distributed through the Chrome Web Store (CWS), a Google-operated online marketplace. In this paper, we describe how we developed and deployed three recommender systems for discovering relevant extensions in CWS, namely non-personalized recommendations, related extension recommendations, and personalized recommendations. Unlike most existing papers that focus on novel algorithms, this paper focuses on sharing practical experiences when building large-scale recommender systems under various real-world constraints, such as privacy constraints, data sparsity and skewness issues, and product design choices (e.g., user interface). We show how these constraints make standard approaches difficult to succeed in practice. We share success stories that turn negative live metrics to positive ones, including: 1) how we use interpretable neural models to bootstrap the systems, help identifying pipeline issues, and pave the way for more advanced models; 2) a new item-item based algorithm for related recommendations that works under highly skewed data distributions; and 3) how the previous two techniques can help bootstrapping the personalized recommendations, which significantly reduces development cycles and bypasses various real-world difficulties. All the explorations in this work are verified in live traffic on millions of users. We believe that the findings in this paper can help practitioners to build better large-scale recommender systems.
An Efficient Face Recognition Method Using CNN

Convolutional Neural Networks (CNNs) have shown a great success within the field of face recognition. In this paper, we propose a robust face recognition method, which is based on Principal Component Analysis (PCA) and CNN. In our method, PCA is employed to reduce the size of data. Afterwards, we use a CNN as a classifier for face recognition. We relatively decrease the number of layers used in the CNN architecture, utilizing the dropout regulation technique. Most importantly, we implement the classification step on the GPU component. Several experiments have been implemented using available well-known databases. The experimental results verify the effectiveness of our approach, which keeps good recognition accuracy. Thus, it expands an important acceleration of the classification face compared to the standard CNN implementation without data reduction. It achieves also lower memory consumption due to the smaller amounts of data processing used through the PCA method. Moreover, our model is intentionally designed such that both its running time and memory requirements are decreased.
Tweet-aware News Summarization with Dual-Attention Mechanism

Neural models have been applied to many text summarization tasks recently. In general, a large number of high quality reference summaries are required to train well-performing neural models. The reference summaries, i.e., ground truth, are usually written by human, and are costly to obtain. Thus, in this paper, we focus on unsupervised summarization problem by exploring news and readers' comments in linking tweets, i.e., tweets with URLs linking to the news. Our data analysis shows that the linking tweets, collectively highlight important information in news but may not fully cover all content in news. This inspires us to propose the dual-attention based model, named DAS, to address the observed issues above. The dual-attention mechanism extracts both important information highlighted by linking tweets and the salient content in news. Specifically, it consists of two similar structures of Transformer with multi-head attention. We propose position-dependent word salience, which reflects the effect of local context. The word salience is computed from dual-attention mechanism. Sentence salience is then estimated from the word salience. Experimental results on a benchmark dataset show that DAS outperforms state-of-the-art unsupervised models and achieves comparable results with state-of-the-art supervised models.
Word-Sense disambiguation system for text readability

People with cognitive, language and learning disabilities face accessibility barriers when reading texts with complex or specialized words. In order for these needs to be addressed, and in accordance with accessibility guidelines, it is beneficial to provide the definitions of complex words to the user. In this sense, human language can, at times, be ambiguous, and many words may be interpreted in multiple ways depending on the context. To offer a correct definition, it is often necessary to carry out Word Sense Disambiguation. In this paper, a system that is based on Natural Language Processing and Language Resources in the field of easy reading to provide a contextualized definition for a complex word is presented. An expert linguistic, specialized in easy reading and plain language, has evaluated the Word Sense Disambiguation system. This research work is part of the EASIER project that offers an accessible platform to provide users with the easiest possible Spanish text to understand and read.
Current Situation of COVID-19 and Its Impact on Phubbing and Student Performance

The use of smartphones and the Internet increasingly affects people's lives, thereby establishing new forms of communication. Digital tools are necessary for performance and social interaction. This research study sought to identify the impact of the COVID-19 pandemic on the performance of the first year of computational systems engineering at the Universidad Privada del Norte – Cajamarca, specifically with regard to phubbing. The study was nonexperimental in nature, followed a correlate descriptive approach, and was of longitudinal design with two observations (cycles 2019-2 and 2020-1). The Chotpitayasunondh and Douglas measurement scale was used for the assessment of phubbing, which was validated in pilot testing (α = 0.8512). Academic performance was obtained from the final averages of the Programming subject, where the phubbing survey was applied with the voluntary participation of students. Correlation results determined no positive relationship between phubbing level and performance level; hence, the variable situation of the COVID-19 pandemic, which modified the academic space, was analyzed in accordance with the needs associated with this environment and the life of students. The level of phubbing in students decreased from 40.33% to 35.51%, while their performance level increased from 12.98 and 13 to 14.48 and 15 in terms of average and median, respectively. The aspects of phubbing were evaluated with a coherent perception, reaffirming the validity of the instrument. Educational policies that promote positive smartphone use are required to improve academic performance.
How much data is sufficient to learn high-performing algorithms? generalization guarantees for data-driven algorithm design

Algorithms often have tunable parameters that impact performance metrics such as runtime and solution quality. For many algorithms used in practice, no parameter settings admit meaningful worst-case bounds, so the parameters are made available for the user to tune. Alternatively, parameters may be tuned implicitly within the proof of a worst-case guarantee. Worst-case instances, however, may be rare or nonexistent in practice. A growing body of research has demonstrated that data-driven algorithm design can lead to significant improvements in performance. This approach uses a training set of problem instances sampled from an unknown, application-specific distribution and returns a parameter setting with strong average performance on the training set.
Self-Training for Unsupervised Neural Machine Translation in Unbalanced Training Data Scenarios

Unsupervised neural machine translation (UNMT) that relies solely on massive monolingual corpora has achieved remarkable results in several translation tasks.However, in real-world scenarios, massive monolingual corpora do not exist for some extremely low-resource languages such as Estonian, and UNMT systems usually perform poorly when there is not adequate training corpus for one language.In this paper, we first define and analyze the unbalanced training data scenario for UNMT.Based on this scenario, we propose UNMT self-training mechanisms to train a robust UNMT system and improve its performance in this case.Experimental results on several language pairs show that the proposed methods substantially outperform conventional UNMT systems.
Preprocessing of automobile engine connecting rod based on shadow removal and image enhancement

As the main transmission parts, the quality of automobile engine connecting rod directly affects the engine performance, and ultimately affects the safety of the vehicle. Image preprocessing is the key basic technology that affects the precision of multi-parameters detection based on machine vision for the quality of automobile engine connecting rod. The image preprocessing based on multi threshold analysis and homomorphic filter is proposed to remove the shadow and enhance the contrast of the engine connecting rod image. The multi-parameters inspection vision system of automobile engine connecting rod quality is constructed to realize the simultaneous detection of geometric parameters, bending and twisting for connecting rod. The problem of shadow elimination method in single color space is analyzed for engine connecting rod image. The shadow elimination method of color image is studied based on multi threshold analysis. A Gaussian homomorphic filter is designed to enhance image contrast. An application example of image preprocessing for the connecting rod of a vehicle engine shows the effectiveness of the proposed method.
Optimization of Tilt Angles for Solar Devices to Gain Maximum Solar Energy in Indian Climate

Availability of maximum solar radiation can be ensured by optimizing the tilt angles for a given location. Most of the optimization techniques are based on the available theoretical models. Keeping this in view, tilt angles were optimized for composite climate (Nagpur and Delhi) and hot and dry climate (Jodhpur), India, using actual solar radiation data of India Meteorological Department (IMD). The optimization of tilt angle is done by establishing a polynomial relation between tilt angle and solar radiation data for annual, bi-annual, seasonal, bi-monthly, and monthly tilts. The optimum tilt angles for New Delhi and Nagpur were found as Φ − 5° and Φ + 4°, respectively, while for Jodhpur it was Φ + 4° for south facing. The highest solar radiation was predicted for monthly tilt. However, total solar radiation for bi-annual tilt was also found very close to that of monthly optimum. According to the analysis carried out, it is recommended to have bi-annual tilt (zero tilt from April to September and 42°–49° degree tilt from October to March).
Toward Secure Data Computation and Outsource for Multi-User Cloud-Based IoT

Cloud computing has promoted the success of Internet of Things (IoT) with offering abundant storage and computation resources where the data from IoT sensors can be remotely outsourced to the cloud servers, whereas storing, exchanging and processing data collected through IoT sensors via centralised or decentralised cloud servers make cloud-based IoT systems prone to internal or external attacks. To protect IoT data against potential malicious users and adversaries, some cryptographic schemes have been applied to ensure confidentiality and integrity of IoT data. It is however a challenging task to perform any arithmetical computations once data items are encrypted. Fully-homomorphic encryption which is based on lattices can, in principle, provide a solution, but it is unfortunately inefficient in computation and hence cannot be applied to IoT. Fully-homomorphic encryption is feasible when we allow the involvement of a semi-trusted server. However, it is challenging to provide such a system in the situation of distributed environments for shared IoT data. We solve this problem and provide a fully-homomorphic encryption scheme for cloud-based IoT applications. We introduce a new method with the aid of a semi-trusted server that can help compute the homomorphic multiplications without gaining any useful information of the encrypted data. We show how our scheme is applied to multi-user IoT security and prove its semantic security. We also conduct experiments to justify its efficiency and applicability to multi-user cloud-based IoT systems.