Reviewerly/exHarmony huggingface dataset:
HTKG

Keyphrases can concisely describe the high-level topics discussed in a document that usually possesses hierarchical topic structures. Thus, it is crucial to understand the hierarchical topic structures and employ it to guide the keyphrase identification. However, integrating the hierarchical topic information into a deep keyphrase generation model is unexplored. In this paper, we focus on how to effectively exploit the hierarchical topic to improve the keyphrase generation performance (HTKG). Specifically, we propose a novel hierarchical topic-guided variational neural sequence generation method for keyphrase generation, which consists of two major modules: a neural hierarchical topic model that learns the latent topic tree across the whole corpus of documents, and a variational neural keyphrase generation model to generate keyphrases under hierarchical topic guidance. Finally, these two modules are jointly trained to help them learn complementary information from each other. To the best of our knowledge, this is the first attempt to leverage the neural hierarchical topic to guide keyphrase generation. The experimental results demonstrate that our method significantly outperforms the existing state-of-the-art methods across five benchmark datasets.
Practitioners' expectations on automated code comment generation

Good comments are invaluable assets to software projects, as they help developers understand and maintain projects. However, due to some poor commenting practices, comments are often missing or inconsistent with the source code. Software engineering practitioners often spend a significant amount of time and effort reading and understanding programs without or with poor comments. To counter this, researchers have proposed various techniques to automatically generate code comments in recent years, which can not only save developers time writing comments but also help them better understand existing software projects. However, it is unclear whether these techniques can alleviate comment issues and whether practitioners appreciate this line of research. To fill this gap, we performed an empirical study by interviewing and surveying practitioners about their expectations of research in code comment generation. We then compared what practitioners need and the current state-of-the-art research by performing a literature review of papers on code comment generation techniques published in the premier publication venues from 2010 to 2020. From this comparison, we highlighted the directions where researchers need to put effort to develop comment generation techniques that matter to practitioners.
EFLEC: Efficient Feature-LEakage Correction in GNN based Recommendation Systems

Graph Convolutional Neural Networks (GNN) based recommender systems are state-of-the-art since they can capture the high order collaborative signals between users and items. However, they suffer from the feature leakage problem since label information determined by edges can be leaked into node embeddings through the GNN aggregation procedure guided by the same set of edges, leading to poor generalization. We propose the accurate removal algorithm to generate the final embedding. For each edge, the embeddings of the two end nodes are evaluated on a graph with that edge removed. We devise an algebraic trick to efficiently compute this procedure without explicitly constructing separate graphs for the LightGCN model. Experiments on four datasets demonstrate that our algorithm can perform better on datasets with sparse interactions, while the training time is significantly reduced.
Diversity-driven automated formal verification

Formally verified correctness is one of the most desirable properties of software systems. But despite great progress made via interactive theorem provers, such as Coq, writing proof scripts for verification remains one of the most effort-intensive (and often prohibitively difficult) software development activities. Recent work has created tools that automatically synthesize proofs or proof scripts. For example, CoqHammer can prove 26.6% of theorems completely automatically by reasoning using precomputed facts, while TacTok and ASTactic, which use machine learning to model proof scripts and then perform biased search through the proof-script space, can prove 12.9% and 12.3% of the theorems, respectively. Further, these three tools are highly complementary; together, they can prove 30.4% of the theorems fully automatically. Our key insight is that control over the learning process can produce a diverse set of models, and that, due to the unique nature of proof synthesis (the existence of the theorem prover, an oracle that infallibly judges a proof's correctness), this diversity can significantly improve these tools' proving power. Accordingly, we develop Diva, which uses a diverse set of models with TacTok's and ASTactic's search mechanism to prove 21.7% of the theorems. That is, Diva proves 68% more theorems than TacTok and 77% more than ASTactic. Complementary to CoqHammer, Diva proves 781 theorems (27% added value) that CoqHammer does not, and 364 theorems no existing tool has proved automatically. Together with CoqHammer, Diva proves 33.8% of the theorems, the largest fraction to date. We explore nine dimensions for learning diverse models, and identify which dimensions lead to the most useful diversity. Further, we develop an optimization to speed up Diva's execution by 40X. Our study introduces a completely new idea for using diversity in machine learning to improve the power of state-of-the-art proof-script synthesis techniques, and empirically demonstrates that the improvement is significant on a dataset of 68K theorems from 122 open-source software projects.
Modeling review history for reviewer recommendation

Modern code review is a critical and indispensable practice in a pull-request development paradigm that prevails in Open Source Software (OSS) development. Finding a suitable reviewer in projects with massive participants thus becomes an increasingly challenging task. Many reviewer recommendation approaches (recommenders) have been developed to support this task which apply a similar strategy, i.e. modeling the review history first then followed by predicting/recommending a reviewer based on the model. Apparently, the better the model reflects the reality in review history, the higher recommender's performance we may expect. However, one typical scenario in a pull-request development paradigm, i.e. one Pull-Request (PR) (such as a revision or addition submitted by a contributor) may have multiple reviewers and they may impact each other through publicly posted comments, has not been modeled well in existing recommenders. We adopted the hypergraph technique to model this high-order relationship (i.e. one PR with multiple reviewers herein) and developed a new recommender, namely HGRec, which is evaluated by 12 OSS projects with more than 87K PRs, 680K comments in terms of accuracy and recommendation distribution. The results indicate that HGRec outperforms the state-of-the-art recommenders on recommendation accuracy. Besides, among the top three accurate recommenders, HGRec is more likely to recommend a diversity of reviewers, which can help to relieve the core reviewers' workload congestion issue. Moreover, since HGRec is based on hypergraph, which is a natural and interpretable representation to model review history, it is easy to accommodate more types of entities and realistic relationships in modern code review scenarios. As the first attempt, this study reveals the potentials of hypergraph on advancing the pragmatic solutions for code reviewer recommendation.
Universal Domain Adaptive Object Detector

Universal domain adaptive object detection (UniDAOD) is more challenging than domain adaptive object detection (DAOD) since the label space of the source domain may not be the same as that of the target and the scale of objects in the universal scenarios can vary dramatically (i.e, category shift and scale shift). To this end, we propose US-DAF, namely Universal Scale-Aware Domain Adaptive Faster RCNN with Multi-Label Learning, to reduce the negative transfer effect during training while maximizing transferability as well as discriminability in both domains under a variety of scales. Specifically, our method is implemented by two modules: 1) We facilitate the feature alignment of common classes and suppress the interference of private classes by designing a Filter Mechanism module to overcome the negative transfer caused by category shift. 2) We fill the blank of scale-aware adaptation in object detection by introducing a new Multi-Label Scale-Aware Adapter to perform individual alignment between corresponding scale for two domains. Experiments show that US-DAF achieves state-of-the-art results on three scenarios (\emphi.e, Open-Set, Partial-Set, and Closed-Set) and yields 7.1% and 5.9% relative improvement on benchmark datasets Clipart1k and Watercolor in particular.
CLEAR

Automatic API recommendation has been studied for years. There are two orthogonal lines of approaches for this task, i.e., information-retrieval-based (IR-based) and neural-based methods. Although these approaches were reported having remarkable performance, our observation shows that existing approaches can fail due to the following two reasons: 1) most IR-based approaches treat task queries as bag-of-words and use word embedding to represent queries, which cannot capture the sequential semantic information. 2) both the IR-based and the neural-based approaches are weak at distinguishing the semantic difference among lexically similar queries.
A Novel Machine Learning Technique for Selecting Suitable Image Encryption Algorithms for IoT Applications

The Internet of Things connects billions of intelligent devices that can interact with one another without human intervention, and during communication, a large amount of data is exchanged between the devices. As a result, it is critical to secure digital data using an encryption technique that provides a suitable degree of security. Numerous existing encryption techniques do not offer sufficient security. Therefore, it is critical to figure out which encryption technique is most appropriate for a particular kind of data. When it comes to manually deciding which encryption technique to use, the process might take a long time. In this research, we present a novel technique for selecting Encryption Algorithms (EAs) based on a particular application using pattern recognition and machine learning techniques. To accomplish this goal, we also prepare a dataset. Several machine learning techniques, such as Support Vector Machines (SVMs), Linear Regression (LR), <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>K</mi> </math> -Nearest Neighbour (KNN), Naïve Bayes (NB), Decision Trees (DT), and Random Forests (RF), are evaluated. Based on the evaluation, the SVM has been chosen as the best option for the intended technique because its classification accuracy is 98.7%. The experimental results, including accuracy, precision, recall, and F1-score, are used to gauge the performance of the suggested technique. The proposed technique is also compared with the existing techniques to demonstrate its effectiveness.
Introducing Problem Schema with Hierarchical Exercise Graph for Knowledge Tracing

Knowledge tracing (KT) which aims at predicting learner's knowledge mastery plays an important role in the computer-aided educational system. The goal of KT is to provide personalized learning paths for learners by diagnosing the mastery of each knowledge, thus improving the learning efficiency. In recent years, many deep learning models have been applied to tackle the KT task, which has shown promising results. However, most existing methods simplify the exercising records as knowledge sequences, which fail to explore the rich information that existed in exercises. Besides, the existing diagnosis results of knowledge tracing are not convincing enough since they neglect hierarchical relations between exercises. To solve the above problems, we propose a hierarchical graph knowledge tracing model called HGKT to explore the latent complex relations between exercises. Specifically, we introduce the concept of problem schema to construct a hierarchical exercise graph that could model the exercise learning dependencies. Moreover, we employ two attention mechanisms to highlight important historical states of learners. In the testing stage, we present a knowledge&schema diagnosis matrix that could trace the transition of mastery of knowledge and problem schema, which can be more easily applied to different applications. Extensive experiments show the effectiveness and interpretability of our proposed model.
Quantum Oblivious Transfer: A Short Review

Quantum cryptography is the field of cryptography that explores the quantum properties of matter. Its aim is to develop primitives beyond the reach of classical cryptography or to improve on existing classical implementations. Although much of the work in this field is dedicated to quantum key distribution (QKD), some important steps were made towards the study and development of quantum oblivious transfer (QOT). It is possible to draw a comparison between the application structure of both QKD and QOT primitives. Just as QKD protocols allow quantum-safe communication, QOT protocols allow quantum-safe computation. However, the conditions under which QOT is actually quantum-safe have been subject to a great amount of scrutiny and study. In this review article, we survey the work developed around the concept of oblivious transfer in the area of theoretical quantum cryptography, with an emphasis on some proposed protocols and their security requirements. We review the impossibility results that daunt this primitive and discuss several quantum security models under which it is possible to prove QOT security.
A Collaborative Learning Tracking Network for Remote Sensing Videos

With the increasing accessibility of remote sensing videos, remote sensing tracking is gradually becoming a hot issue. However, accurately detecting and tracking in complex remote sensing scenes is still a challenge. In this article, we propose a collaborative learning tracking network for remote sensing videos, including a consistent receptive field parallel fusion module (CRFPF), dual-branch spatial-channel co-attention (DSCA) module, and geometric constraint retrack strategy (GCRT). Considering the small-size objects of remote sensing scenes are difficult for general forward networks to extract effective features, we propose a CRFPF-module to establish parallel branches with consistent receptive fields to separately extract from shallow to deep features and then fuse hierarchical features adaptively. Since the objects and their background are difficult to distinguish, the proposed DSCA-module uses the spatial-channel co-attention mechanism to collaboratively learn the relevant information, which enhances the saliency of the objects and regresses to precise bounding boxes. Considering the interference of similar objects, we designed a GCRT-strategy to judge whether there is a false detection through the estimated motion trajectory and then recover the correct object by weakening the feature response of interference. The experimental results and theoretical analysis on multiple datasets demonstrate our proposed method's feasibility and effectiveness. Code and net are available at https://github.com/Dawn5786/CoCRF-TrackNet.
Informal STEM Learning for Young Children: A Systematic Literature Review

Studies show that children spend considerable time engaged in informal learning outside of educational settings. Informal educational settings such as museums can provide a variety of opportunities to engage children in STEM learning and scientific discovery, which can increase school readiness. Research has also determined an achievement gap in students from low socio-economic backgrounds and in students who are dual language learners. The literature shows that this gap begins even before children enter formal schooling. This systematic review serves two purposes: to explore the impact of informal STEM learning (ISL) on school readiness and to examine the relationship between ISL and children’s social-emotional development, particularly in children who are dual language learners. Using PRISMA procedures, we identified 36 eligible studies in this systematic review. The findings illuminate the important role of parents and/or caregivers and the quality of design and interventions used at ISL sites, such as museums and zoos, on how ISL can impact school readiness for preschoolers. No research was found to specifically address the impact of ISL on school readiness for children who are dual language learners. The implications from the findings suggest that further research is needed on ISL for young children, particularly considering the dearth in research on young dual language learners. The implications further suggest that parents, ISL site designers, facilitators, and educators can benefit from learning about the impact of ISL on school readiness.
A Close Look into Human Activity Recognition Models using Deep Learning

Human activity recognition using deep learning techniques has become increasing popular because of its high effectivity with recognizing complex tasks, as well as being relatively low in costs compared to more traditional machine learning techniques. This paper surveys some state-of-the-art human activity recognition models that are based on deep learning architecture and has layers containing Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), or a mix of more than one type for a hybrid system. The analysis outlines how the models are implemented to maximize its effectivity and some of the potential limitations it faces. Keywords: Human Activity Recognition, Deep Learning
A Novel CNN-Inception-V4-Based Hybrid Approach for Classification of Breast Cancer in Mammogram Images

Breast cancer is the most frequent disease in women, with one in every 19 women at risk. Breast cancer is the fifth leading cause of cancer death in women around the world. The most effective and efficient technique of controlling cancer development is early identification. Mammography helps in the early detection of cancer, which saves lives. Many studies conducted various tests to categorize the tumor and obtained positive findings. However, there are certain limits. Mass categorization in mammography is still a problem, although it is critical in aiding radiologists in establishing correct diagnoses. The purpose of this study is to develop a unique hybrid technique to identify breast cancer mass pictures as benign or malignant. The combination of two networks helps accelerate the categorization process. This study proposes a novel-based hybrid approach, CNN-Inception-V4, based on the fusing of these two networks. Mass images are used in this research from the CBIS-DDSM dataset. 450 images are taken for benign, and 450 images are used for malignant. The images are first cleaned by removing pectoral muscles, labels, and white borders. Then, CLAHE is used to these images to improve their quality in order to produce promising classification results. Following preprocessing, our model classifies cancer in mammography pictures as benign or malignant abnormalities. Our proposed model’s accuracy is 99.2%, with sensitivity of 99.8%, specificity of 96.3%, and F1-score of 97%. We also compared our proposed model to CNN, Inception-V4, and ResNet-50. Our proposed model outperforms existing classification models, according to the results.
Towards automated coronary artery segmentation: A systematic review

Vessel segmentation is the first processing stage of 3D medical images for both clinical and research use. Current segmentation methods are tedious and time consuming, requiring significant manual correction and hence are infeasible to use in large data sets.Here, we review and analyse available coronary artery segmentation methods, focusing on fully automated methods capable of handling the rapidly growing medical images available. All manuscripts published since 2010 are systematically reviewed, categorised into different groups based on the approach taken, and characteristics of the different approaches as well as trends over the past decade are explored.The manuscripts were divided intro three broad categories, consisting of region growing, voxelwise prediction and partitioning approaches. The most common approach overall was region growing, particularly using active contour models, however these have had a sharp fall in popularity in recent years with convolutional neural networks becoming significantly more popular.The systematic review of current coronary artery segmentation methods shows interesting trends, with rising popularity of machine learning methods, a focus on efficient methods, and falling popularity of computationally expensive processing steps such as vesselness and multiplanar reformation.
Understanding Privacy Risks and Perceived Benefits in Open Dataset Collection for Mobile Affective Computing

Collecting large-scale mobile and wearable sensor datasets from daily contexts is essential in developing machine learning models for enabling everyday affective computing applications. However, there is a lack of knowledge on data contributors' perceived benefits and risks in participating in open dataset collection projects. To bridge this gap, we conducted an in-situ study on building an open dataset with mobile and wearable devices for affective computing research (N = 100, 4 weeks). Our study results showed that a mixture of financial and altruistic benefits was important in eliciting data contribution. Sensor-specific risks were largely associated with the revelation of personal traits and social behaviors. However, most of the participants were less concerned with open dataset collection and their perceived sensitivity of each sensor data did not change over time. We further discuss alternative approaches to promote data contributors' motivations and suggest design guidelines to alleviate potential privacy concerns in mobile open dataset collection.
Towards Understanding the Skill Gap in Cybersecurity

Given the ongoing "arms race" in cybersecurity, the shortage of skilled professionals in this field is one of the strongest in computer science. The currently unmet staffing demand in cybersecurity is estimated at over 3 million jobs worldwide. Furthermore, the qualifications of the existing workforce are largely believed to be insufficient. We attempt to gain deeper insights into the nature of the current skill gap in cybersecurity. To this end, we correlate data from job ads and academic curricula using two kinds of skill characterizations: manual definitions from established skill frameworks as well as "skill topics" automatically derived by text mining tools. Our analysis shows a strong agreement between these two analysis techniques and reveals a substantial undersupply in several crucial skill categories, e.g., software and application security, security management, requirements engineering, compliance and certification. Based on the results of our analysis, we provide recommendations for future curricula development in cybersecurity so as to decrease the identified skill gaps.