Reviewerly/exHarmony huggingface dataset:
A Survey of Automatic Generation of Source Code Comments: Algorithms and Techniques

As an integral part of source code files, code comments help improve program readability and comprehension. However, developers sometimes do not comment their program code adequately due to the incurred extra efforts, lack of relevant knowledge, unawareness of the importance of code commenting or some other factors. As a result, code comments can be inadequate, absent or even mismatched with source code, which affects the understanding, reusing and the maintenance of software. To solve these problems of code comments, researchers have been concerned with generating code comments automatically. In this work, we aim at conducting a survey of automatic code commenting researches. First, we generally analyze the challenges and research framework of automatic generation of program comments. Second, we present the classification of representative algorithms, the design principles, strengths and weaknesses of each category of algorithms. Meanwhile, we also provide an overview of the quality assessment of the generated comments. Finally, we summarize some future directions for advancing the techniques of automatic generation of code comments and the quality assessment of comments.
Adversarial Framing for Image and Video Classification

Neural networks are prone to adversarial attacks. In general, such attacks deteriorate the quality of the input by either slightly modifying most of its pixels, or by occluding it with a patch. In this paper, we propose a method that keeps the image unchanged and only adds an adversarial framing on the border of the image. We show empirically that our method is able to successfully attack state-of-theart methods on both image and video classification problems. Notably, the proposed method results in a universal attack which is very fast at test time. Source code can be found at github.com/zajaczajac/adv_framing.
Mobile App Recommendation: An Involvement-Enhanced Approach

Given the ubiquitous and critical role of mobile apps in people’s lives as well as the sheer size of the market, developing effective mobile app recommendation methods that can help users locate the apps they desire is critical for both users and platform
Quantum Walks of a Phonon in Trapped Ions

Propagation and interference of quantum-mechanical particles comprise an important part of elementary processes in quantum physics, and their essence can be modeled using a quantum walk, a mathematical concept that describes the motion of a quantum-mechanical particle among discretized spatial regions. Here we report the observation of the quantum walks of a phonon, a vibrational quantum, in a trapped-ion crystal. By employing the capability of preparing and observing a localized wave packet of a phonon, the propagation of a single radial local phonon in a four-ion linear crystal is observed with single-site resolution. The results show an agreement with numerical calculations, indicating the predictability and reproducibility of the phonon system. These characteristics may contribute advantageously in advanced experimental studies of quantum walks with large numbers of nodes, as well as realization of boson sampling and quantum simulation using phonons as computational resources.
Zero-Shot Anticipation for Instructional Activities

How can we teach a robot to predict what will happen next for an activity it has never seen before? We address the problem of zero-shot anticipation by presenting a hierarchical model that generalizes instructional knowledge from large-scale text-corpora and transfers the knowledge to the visual domain. Given a portion of an instructional video, our model predicts coherent and plausible actions multiple steps into the future, all in rich natural language. To demonstrate the anticipation capabilities of our model, we introduce the Tasty Videos dataset, a collection of 2511 recipes for zero-shot learning, recognition and anticipation.
Harnessing Pre-Trained Neural Networks with Rules for Formality Style Transfer

Formality text style transfer plays an important role in various NLP applications, such as non-native speaker assistants and child education.Early studies normalize informal sentences with rules, before statistical and neural models become a prevailing method in the field.While a rule-based system is still a common preprocessing step for formality style transfer in the neural era, it could introduce noise if we use the rules in a naïve way such as data preprocessing.To mitigate this problem, we study how to harness rules into a stateof-the-art neural network that is typically pretrained on massive corpora.We propose three fine-tuning methods in this paper and achieve a new state-of-the-art on benchmark datasets.
Spatio-Temporal Unity Networking for Video Anomaly Detection

Anomaly detection in video surveillance is challenging due to the variety of anomaly types and definitions, which limit the use of supervised techniques. As such, auto-encoder structures, a type of classical unsupervised method, have recently been utilized in this field. These structures consist of an encoder followed by a decoder and are typically adopted to restructure a current input frame or predict a future frame. However, regardless of whether a 2D or 3D autoencoder structure is adopted, only single-scale information from the previous layer is typically used in the decoding process. This can result in a loss of detail that could potentially be used to predict or reconstruct video frames. As such, this study proposes a novel spatio–temporal U-Net for frame prediction using normal events and abnormality detection using prediction error. This framework combines the benefits of U-Nets in representing spatial information with the capabilities of ConvLSTM for modeling temporal motion data. In addition, we propose a new regular score function, consisting of a prediction error for not only the current frame but also future frames, to further improve the accuracy of anomaly detection. Extensive experiments on common anomaly datasets, including UCSD (98 video clips in total) and CUHK Avenue (30 video clips in total), validated the performance of the proposed technique and we achieved 96.5% AUC for the Ped2 dataset, which is much better than existing autoencoder-based and U-Net-based methods.
Using Technology in ESL Classroom: Highlights and Challenges

Technology is evolving rapidly in the past two decades.Many are familiar with technology and gradually becoming digital natives.In this technological era, the application of technology has eased our ways in various fields of work particularly in the education field.Technology has also proven its effectiveness in language teaching which includes as a source of motivation and provides room for authentic learning.However, there are still disadvantages in using technology to teach.Students may get easily distracted and might misuse the technology.Other than that, frequent use of technology can limit students thinking potential.Hence, this paper reviews the highlights and challenges of using technology in language teaching.This paper could be a source of reference by teachers who are planning to integrate technology in ESL classroom.Future research can look into how technology affects students' attitude.
A weakly supervised framework for abnormal behavior detection and localization in crowded scenes

In this paper, a weakly supervised framework is proposed for Abnormal Behavior Detection and Localization (ABDL) in the scenes. First, the objects in the scene such as pedestrians, vehicles, etc. are detected using the Faster Regional Conventional Neural Network (Faster R-CNN); then, the object behavior is described by a Histogram of Large Scale Optical Flow (HLSOF) descriptor; finally, the Multiple Instance Support Vector Machine (MISVM) is trained and then used to identify the testing behaviors as normal or abnormal. Summarily, the proposed approach has three main advantages: (1) Benefit from the Faster R-CNN, our approach can analyze the behavior at object-wise, which makes our approach has good generality and high computational efficiency; (2) The HLSOF descriptor can characterize the object behavior efficiently, and is insensitive to the variations of the size of objects; (3) As a weakly supervised learning framework, the MISVM only requires the labels at the bag level rather than instance level, which makes our approach has high accuracy as the supervised approaches but not requires completely labeled training samples, only the frame-level label is required. Experimental results analysis on different datasets validates the effectiveness of our approach.
Software Framework for Customized Augmented Reality Headsets in Medicine

The growing availability of self-contained and affordable augmented reality headsets such as the Microsoft HoloLens is encouraging the adoption of these devices also in the healthcare sector.However, technological and human-factor limitations still hinder their routine use in clinical practice.Among them, the major drawbacks are due to their general-purpose nature and to the lack of a standardized framework suited for medical applications and devoid of platform-dependent tracking techniques and/or complex calibration procedures.To overcome such limitations, in this paper we present a software framework that is designed to support the development of augmented reality applications for custom-made headmounted displays designed to aid high-precision manual tasks.The software platform is highly configurable, computationally efficient, and it allows the deployment of augmented reality applications capable to support in situ visualization of medical imaging data.The framework can provide both optical and video see-throughbased augmentations and it features a robust optical tracking algorithm.An experimental study was designed to assess the efficacy of the platform in guiding a simulated task of surgical incision.In the experiments, the user was asked to perform a digital incision task, with and without the aid of the augmented reality headset.The task accuracy was evaluated by measuring the similarity between the traced curve and the planned one.The average error in the augmented reality tests was < 1 mm.The results confirm that the proposed framework coupled with the new-concept headset may boost the integration of augmented reality headsets into routine clinical practice.
Role of virtual avatars in digitalized hotel service

Purpose This study aim to attempt to conceptualize agency in a hospitality setting and examine the psychological effects of agency-related visual cues on user perception and intention to use to understand the role of agency in the digitalization of hotel services. Design/methodology/approach After developing demo videos of an express check-out application, two experiments were conducted to examine the effects of using an avatar and explain the psychological mechanism of how attributes of an avatar increase intention to use. Findings Study 1 found that the presence of an avatar had a positive influence on intention to use. Study 2 retested the findings of Study 1 and illustrated the psychological mechanism of how two attributes of an avatar (social position and gender) influenced perceived expertise and intention to use. A significant interaction effect between social position and gender was found on perceived expertise. Perceived expertise also mediated the effect of an avatar on intention to use in the male avatar conditions. Originality/value As the first attempt to investigate the role of avatars in human–computer interaction in a hotel setting, this study will serve as an example in testing the effects of agency-related technical features on user experience and behavioral intention, possibly broadening the current research scope of hospitality and tourism. This study also provides a useful guideline to develop and design a successful interface of digitalized hotel services.
A photovoltaic power forecasting model based on dendritic neuron networks with the aid of wavelet transform

The ever increasing proportion of photovoltaic (PV), which is, in effect, a random and intermittent energy source, makes PV power forecasting increasingly important for power grid stability. Artificial neural networks (ANN) have become one of the commonly utilized methods in PV power prediction. Since there is no ideal theoretical guidance as yet on the determination of the number of hidden layers and hidden units, there are always abundant neurons in traditional neural networks in order to learn as many data characteristics as possible, which often results in overfitting and high computational costs. The dendritic model proposed in recent years has the characteristics of simple structure, fast convergence and better fitting ability. This paper proposes a PV power forecasting model based on the dendritic neuron networks, which seeks to improve the computational efficiency and prediction accuracy. In order to better extract characteristics of different frequencies of the input data, the approach introduces a wavelet transform. Firstly, the data is decomposed into high-frequency and low-frequency components via a wavelet transform. Thereafter, the input data of different frequencies obtained by the decomposition are transmitted respectively to different sub-models. Finally, the results of sub-models are reconstructed to obtain the final output. The proposed PV power forecasting model was tested upon actual photovoltaic datasets. Results obtained through simulation demonstrate significant improvement in terms of accuracy and efficiency.
Information and communication technology utilization effectiveness in distance education systems

Learning by using online application facilities through the Internet is a new service for all users. However, there are many problems and obstacles faced by users, both by students and by lecturers in utilizing online application services via the Internet. The development of a distance education service management software application model provides facilities for academics to facilitate interaction between students and lecturers with using online-based information technology communication services through the Internet. In addition, the management of distance education services is able to provide reports desired by students and lecturers using online-based information technology communication services through the Internet. This study used a descriptive–analytic method by presenting a summary of interviews and survey results in the form of a questionnaire to the faculty member. The method used is a qualitative method because it provides an explanation using analysis. This research uses Moodle application as a Distance Education System. The distance learning model used in the Trilogi University is considered sufficient, in terms of learning goals, learning materials, interactivity, and rules. The results also show that both lecturers and students argue that learning with a distance education system can simplify work, speed up work, accurate work, and be more efficient because it is interactive and user-friendly. This research can be applied to other school units or universities so that distance education services can help academics interact easily, quickly, and accurately. For its development, it can be applied using cellular-based applications.
Entanglement classification via neural network quantum states

The task of classifying the entanglement properties of a multipartite quantum state poses a remarkable challenge due to the exponentially increasing number of ways in which quantum systems can share quantum correlations. Tackling such challenge requires a combination of sophisticated theoretical and computational techniques. In this paper we combine machine-learning tools and the theory of quantum entanglement to perform entanglement classification for multipartite qubit systems in pure states. We use a parameterisation of quantum systems using artificial neural networks in a restricted Boltzmann machine (RBM) architecture, known as Neural Network Quantum States (NNS), whose entanglement properties can be deduced via a constrained, reinforcement learning procedure. In this way, Separable Neural Network States (SNNS) can be used to build entanglement witnesses for any target state.
Personalized predictions of patient outcomes during and after hospitalization using artificial intelligence

Hospital systems, payers, and regulators have focused on reducing length of stay (LOS) and early readmission, with uncertain benefit. Interpretable machine learning (ML) may assist in transparently identifying the risk of important outcomes. We conducted a retrospective cohort study of hospitalizations at a tertiary academic medical center and its branches from January 2011 to May 2018. A consecutive sample of all hospitalizations in the study period were included. Algorithms were trained on medical, sociodemographic, and institutional variables to predict readmission, length of stay (LOS), and death within 48-72 h. Prediction performance was measured by area under the receiver operator characteristic curve (AUC), Brier score loss (BSL), which measures how well predicted probability matches observed probability, and other metrics. Interpretations were generated using multiple feature extraction algorithms. The study cohort included 1,485,880 hospitalizations for 708,089 unique patients (median age of 59 years, first and third quartiles (QI) [39, 73]; 55.6% female; 71% white). There were 211,022 30-day readmissions for an overall readmission rate of 14% (for patients ≥65 years: 16%). Median LOS, including observation and labor and delivery patients, was 2.94 days (QI [1.67, 5.34]), or, if these patients are excluded, 3.71 days (QI [2.15, 6.51]). Predictive performance was as follows: 30-day readmission (AUC 0.76/BSL 0.11); LOS > 5 days (AUC 0.84/BSL 0.15); death within 48-72 h (AUC 0.91/BSL 0.001). Explanatory diagrams showed factors that impacted each prediction.
7.3 STATICA: A 512-Spin 0.25M-Weight Full-Digital Annealing Processor with a Near-Memory All-Spin-Updates-at-Once Architecture for Combinatorial Optimization with Complete Spin-Spin Interactions

Combinatorial optimization problems ubiquitously appear in AI applications, e.g. machine learning, operational planning, drug discovery, etc. Yet, their NP-hardness makes them notoriously difficult to solve on present computers. To address this problem, new hardware architectures [1, 4], called annealers, have emerged in recent years. Annealers exploit the fact that combinatorial optimization problems can be mapped to the ground state search of an Ising model, where the combination of N spins (σ- ∈ f-1}. x=1, ...,N) with lowest Ising energy represents the optimal solution to the problem (Fig. 7.3.1).
An Attention-Based Unsupervised Adversarial Model for Movie Review Spam Detection

With the prevalence of the Internet, online reviews have become a valuable information resource for people.However, the authenticity of online reviews remains a concern, and deceptive reviews have become one of the most urgent network security problems to be solved.Review spams will mislead users into making suboptimal choices and inflict their trust in online reviews.Most existing research manually extracted features and labeled training samples, which are usually complicated and timeconsuming.This paper focuses primarily on a neglected emerging domain -movie review, and develops a novel unsupervised spam detection model with an attention mechanism.By extracting the statistical features of reviews, it is revealed that users will express their sentiments on different aspects of movies in reviews.An attention mechanism is introduced in the review embedding, and the conditional generative adversarial network is exploited to learn users' review style for different genres of movies.The proposed model is evaluated on movie reviews crawled from Douban, a Chinese online community where people could express their feelings about movies.The experimental results demonstrate the superior performance of the proposed approach.
Augmenting Static Visualizations with PapARVis Designer

This paper presents an authoring environment for augmenting static visualizations with virtual content in augmented reality.Augmenting static visualizations can leverage the best of both physical and digital worlds, but its creation currently involves different tools and devices, without any means to explicitly design and debug both static and virtual content simultaneously. To address these issues, we design an environment that seamlessly integrates all steps of a design and deployment workflow through its main features: i) an extension to Vega, ii) a preview, and iii) debug hints that facilitate valid combinations of static and augmented content. We inform our design through a design space with four ways to augment static visualizations. We demonstrate the expressiveness of our tool through examples, including books, posters, projections, wall-sized visualizations. A user study shows high user satisfaction of our environment and confirms that participants can create augmented visualizations in an average of 4.63 minutes.
Radio Identity Verification-Based IoT Security Using RF-DNA Fingerprints and SVM

It is estimated that the number of Internet-of-Things (IoT) devices will reach 75 billion in the next five years. Most of those currently and soon-to-be deployed devices lack sufficient security to protect themselves and their networks from attacks by malicious IoT devices masquerading as authorized devices in order to circumvent digital authentication approaches. This work presents a physical (PHY) layer IoT authentication approach capable of addressing this critical security need through the use of feature-reduced, radio frequency-distinct native attributes (RF-DNA) fingerprints and support vector machines (SVM). This work successfully demonstrates: 1) authorized identity (ID) verification across three trials of six randomly chosen radios at signal-to-noise ratios greater than or equal to 6 dB and 2) rejection of all rogue radio ID spoofing attacks at signal-to-noise ratios greater than or equal to 3 dB using RF-DNA fingerprints whose features are selected using the Relief-F algorithm.
Continuous authentication by free-text keystroke based on CNN and RNN

Personal keystroke modes are difficult to imitate and can therefore be used for identity authentication. The keystroke habits of a person can be learned according to the keystroke data generated when the person inputs free text. Detecting a user's keystroke habits as the user enters text can continuously verify the user's identity without affecting user input. The method proposed in this paper authenticates users via their keystrokes when they type free text. The user keystroke data is divided into a fixed-length keystroke sequence, which is then converted into a keystroke vector sequence according to the time feature of the keystroke. A model that combines a convolutional neural network and a recursive neural network is used to learn a sequence of individual keystroke vectors to obtain individual keystroke features for identity authentication. The model is tested using two open datasets, and the best false rejection rate (FRR) is found to be (2.07%,6.61%), the best false acceptance rate (FAR) is found to be (3.26%, 5.31%), and the best equal error rate (EER) is found to be (2.67%, 5.97%).