Reviewerly/exHarmony huggingface dataset:
Machine Learning Based Recommendation of Method Names: How Far are We

High quality method names are critical for the readability and maintainability of programs. However, constructing concise and consistent method names is often challenging, especially for inexperienced developers. To this end, advanced machine learning techniques have been recently leveraged to recommend method names automatically for given method bodies/implementation. Recent large-scale evaluations also suggest that such approaches are accurate. However, little is known about where and why such approaches work or don't work. To figure out the state of the art as well as the rationale for the success/failure, in this paper we conduct an empirical study on the state-of-the-art approach code2vec. We assess code2vec on a new dataset with more realistic settings. Our evaluation results suggest that although switching to new dataset does not significantly influence the performance, more realistic settings do significantly reduce the performance of code2vec. Further analysis on the successfully recommended method names also reveals the following findings: 1) around half (48.3%) of the accepted recommendations are made on getter/setter methods; 2) a large portion (19.2%) of the successfully recommended method names could be copied from the given bodies. To further validate its usefulness, we ask developers to manually score the difficulty in naming methods they developed. Code2vec is then applied to such manually scored methods to evaluate how often it works in need. Our evaluation results suggest that code2vec rarely works when it is really needed. Finally, to intuitively reveal the state of the art and to investigate the possibility of designing simple and straightforward alternative approaches, we propose a heuristics based approach to recommending method names. Evaluation results on large-scale dataset suggest that this simple heuristics-based approach significantly outperforms the state-of-the-art machine learning based approach, improving precision and recall by 65.25% and 22.45%, respectively. The comparison suggests that machine learning based recommendation of method names may still have a long way to go.
Asynchronous dual-pipeline deep learning framework for online data stream classification

Data streaming classification has become an essential task in many fields where real-time decisions have to be made based on incoming information. Neural networks are a particularly suitable technique for the streaming scenario due to their incremental learning nature. However, the high computation cost of deep architectures limits their applicability to high-velocity streams, hence they have not yet been fully explored in the literature. Therefore, in this work, we aim to evaluate the effectiveness of complex deep neural networks for supervised classification in the streaming context. We propose an asynchronous deep learning framework in which training and testing are performed simultaneously in two different processes. The data stream entering the system is dual fed into both layers in order to concurrently provide quick predictions and update the deep learning model. This separation reduces processing time while obtaining high accuracy on classification. Several time-series datasets from the UCR repository have been simulated as streams to evaluate our proposal, which has been compared to other methods such as Hoeffding trees, drift detectors, and ensemble models. The statistical analysis carried out verifies the improvement in performance achieved with our dual-pipeline deep learning framework, that is also competitive in terms of computation time.
A semantic-based classification approach for an enhanced spam detection

In this paper, we explore the use of a text semantic analysis to improve the accuracy of spam detection. We propose a method based on two semantic level analysis. In the first level, we categorize emails by specific domains (e.g., Health, Education, Finance, etc.) to enable a separate conceptual view for spams in each domain. In the second level, we combine a set of manually-specified and automatically-extracted semantic features for spam detection in each domain. These features are meant to summarize the email content into compact topics discriminating spam from non-spam emails in an efficient way. We show that the proposed method enables a better spam detection compared to existing methods based on bag-of-words (BoW) and semantic content, and leads to more interpretable results.
Robust neighborhood embedding for unsupervised feature selection

Unsupervised feature selection is an efficient approach of dimensionality reduction for alleviating the curse of dimensionality in the countless unlabeled high-dimensional data. In view of the sparseness of the high-dimensional data, we propose a robust neighborhood embedding (RNE) method for unsupervised feature selection. First, with the fact that each data point and its neighbors are close to a locally linear patch of some underlying manifold, we obtain the feature weight matrix through the locally linear embedding (LLE) algorithm. Second, we use ℓ1-norm to describe reconstruction error minimization, i.e., loss function to suppress the impact of outlier and noises in the dataset. As the RNE model is convex but non-smooth, we exploit alternation direction method of multipliers (ADMM) to solve it. Finally, extensive experimental results on benchmark datasets validate that the RNE method is effective and superior to the state-of-the-art unsupervised feature selection algorithms in terms of clustering performance.
Error Tolerance Bound in QKD-Based Quantum Private Query

Most existing quantum private query (QPQ) protocols can hardly work in the presence of noise. The user Alice may obtain a false database item in noisy environments and both participants may cheat under the disguise of noise, so dealing with the noise needs an overall consideration of error correction, user privacy and database security. However, the only two existing protocols aiming to correct errors in QPQ lack such an overall consideration (at least one party's privacy can be revealed), and they did not estimate what extent of errors can be tolerated (actually, noise is seldom discussed in quantum two-party secure computations, and to the best of our knowledge, relevant bounds on tolerable errors remain unattainable so far). To solve this problem, we first exemplify how one participant reveals the other party's privacy in the existing QPQ protocols aiming to correct errors. Then we propose a practical protocol which can really work via noisy channel, that is, the error rate of the retrieved database item is reduced significantly and both parties' privacy are well protected. Besides, we deduce that the final error rate, user privacy and database security are pairwise in a “trade-off” relationship. By balancing them according to the required level of security and reliability, we obtain an upper bound on tolerable errors.
Outline Pursuits: Gaze-assisted Selection of Occluded Objects in Virtual Reality

In 3D environments, objects can be difficult to select when they overlap, as this affects available target area and increases selection ambiguity. We introduce Outline Pursuits which extends a primary pointing modality for gaze-assisted selection of occluded objects. Candidate targets within a pointing cone are presented with an outline that is traversed by a moving stimulus. This affords completion of the selection by gaze attention to the intended target's outline motion, detected by matching the user's smooth pursuit eye movement. We demonstrate two techniques implemented based on the concept, one with a controller as the primary pointer, and one in which Outline Pursuits are combined with head pointing for hands-free selection. Compared with conventional raycasting, the techniques require less movement for selection as users do not need to reposition themselves for a better line of sight, and selection time and accuracy are less affected when targets become highly occluded.
Adaptive Bitrate Streaming in Wireless Networks With Transcoding at Network Edge Using Deep Reinforcement Learning

Adaptive bitrate (ABR) streaming has been used in wireless networks to deal with the time-varying wireless channels. Traditionally, wireless video is fetched from remote Internet server. However, wireless video streaming from Internet server faces challenges such as congestion and long latency. ABR streaming and video transcoding at the radio access network (RAN) edge have shown the potential to overcome such problem and provided better video streaming service. In the paper, we consider joint computation and communication for ABR streaming based on mobile edge computing (MEC) under time-varying wireless channels. We propose a joint video transcoding and quality adaptation framework for ABR streaming by enabling RAN with computing capability. By modeling the wireless channel as a finite state Markov channel, we formulate the optimization problem as a stochastic optimization problem of joint computational resource assignment and video quality adaptation for maximizing the average reward, which is defined as the tradeoff between user perceived quality of experience and the cost of performing transcoding at edge server. By using deep reinforcement learning (DRL) algorithm, we develop an automatic algorithm to perform the computational resource assignment and video quality adaptation without any prior knowledge of channel statistics. Simulation results using Tensorflow show the effectiveness of the designed MEC-enabled ABR streaming system and DRL algorithm.
Quantum-memory-assisted entropic uncertainty, teleportation, and quantum discord under decohering environments

The one-qubit teleportation protocol is one of the most well-known topics of quantum information theory. Indeed, the goal of this quantum protocol is to teleport an arbitrary quantum state. Herein, we investigate the entropic uncertainty relation in the presence of quantum memory (EUR-QM), quantum discord (QD), and fidelity of teleportation under dephasing, dissipative, and noisy environments. We relate the EUR-QM to QD and quantum teleportation and show analytically that there is a fully anti-correlated relation between QD and entropic uncertainty in a dephasing environment. In the process of our analysis, we also find an explicit relationship between the average fidelity of teleportation and the entropic uncertainty of the channel state. Significantly, the results report that the fidelity of teleportation and QD of the channel state are more stable and most efficiently under the influence of a dephasing environment than those under the influence of a dissipative or noisy environment. It turns out that under a dephasing environment, these results can be vital in practical goals where the maximum fidelity of teleportation and minimum uncertainty are required such as in quantum teleportation protocols and quantum computation.
Semi-quantum cryptography

Semi-quantum communication, a model introduced by Boyer et al. (Phys Rev Lett 99:140501, 2007), involves the use of fully quantum users and semi-quantum, or “classical” users. These restricted users are only allowed to interact with the quantum channel in a limited manner. Originally introduced to study the key-distribution problem, semi-quantum research has since expanded, and continues to grow, with new protocols, security proof methods, experimental implementations, and new cryptographic applications beyond key distribution. Research in the field of semi-quantum cryptography requires new insights into working with restricted protocols and, so, the tools and techniques derived in this field can translate to results in broader quantum information science. Furthermore, other questions such as the connection between quantum and classical processing, including how classical information processing can be used to counteract a quantum deficiency in a protocol, can shed light on important theoretical questions. This work surveys the history and current state of the art in semi-quantum research. We discuss the model and several protocols offering the reader insight into how protocols are constructed in this realm. We discuss security proof methods and how classical post-processing can be used to counteract users’ inability to perform certain quantum operations. Moving beyond key distribution, we survey current work in other semi-quantum cryptographic protocols and current trends. We also survey recent work done in attempting to construct practical semi-quantum systems including recent experimental results in this field. Finally, as this is still a growing field, we highlight, throughout this survey, several open problems that we feel are important to investigate in the hopes that this will spur even more research in this topic.
Image Inpainting Based on Generative Adversarial Networks

Image inpainting aims to fill missing regions of a damaged image with plausibly synthesized content. Existing methods for image inpainting either fill the missing regions by borrowing information from surrounding areas or generating semantically coherent content from region context. They often produce ambiguous or semantically incoherent content when the missing region is large or with complex structures. In this paper, we present an approach for image inpainting. The completion model based on our proposed algorithm contains one generator, one global discriminator, and one local discriminator. The generator is responsible for inpainting the missing area, the global discriminator aims evaluating whether the repair result has global consistency, and the local discriminator is responsible for identifying whether the repair area is correct. The architecture of the generator is an auto-encoder. We use the skip-connection in the generator to improve the prediction power of the model. Also, we use Wasserstein GAN loss to ensure the stability of training. Experiments on CelebA dataset and LFW dataset demonstrate that our proposed model can deal with large-scale missing pixels and generate realistic completion results.
Multi-View Photometric Stereo: A Robust Solution and Benchmark Dataset for Spatially Varying Isotropic Materials

We present a method to capture both 3D shape and spatially varying reflectance with a multi-view photometric stereo (MVPS) technique that works for general isotropic materials. Our algorithm is suitable for perspective cameras and nearby point light sources. Our data capture setup is simple, which consists of only a digital camera, some LED lights, and an optional automatic turntable. From a single viewpoint, we use a set of photometric stereo images to identify surface points with the same distance to the camera. We collect this information from multiple viewpoints and combine it with structure-from-motion to obtain a precise reconstruction of the complete 3D shape. The spatially varying isotropic bidirectional reflectance distribution function (BRDF) is captured by simultaneously inferring a set of basis BRDFs and their mixing weights at each surface point. In experiments, we demonstrate our algorithm with two different setups: a studio setup for highest precision and a desktop setup for best usability. According to our experiments, under the studio setting, the captured shapes are accurate to 0.5 millimeters and the captured reflectance has a relative root-mean-square error (RMSE) of 9%. We also quantitatively evaluate state-of-the-art MVPS on a newly collected benchmark dataset, which is publicly available for inspiring future research.
The consistency analysis of failure mode and effect analysis (FMEA) in information technology risk assessment

FMEA is as a method for assessing IT risks. This research aimed to examine the consistency of both traditional FMEA and improved FMEA in IT risk assessment. Improved FMEA is the result of a synthesis framework to minimize consistency in traditional FMEA. Two sets of action research cycles (plan, act, observe, reflect) were applied in this research. Action Research 1 was used to examine and prove the consistency of traditional FMEA. On the other hand, Action Research 2 was applied to examine the consistency of improved FMEA. Tests were carried out by two different teams in the same case study. The consistency was observed in the gap of the RPN results in both teams, and the differences result in both action research cycles. Action Research 1 proved that traditional FMEA was not consistent. The gap in the amount of risk at a very high level was four risks. However, Action research 2 had the same amount of risk at a very high level. Based on the correlation test, the consistency of action research 1 was 0.848 (very large correlation), and the action research 2 was 0.937 (near-perfect correlation). The consistency of improved FMEA proved to be more consistent than traditional FMEA. The limitation of this study was memory issues because both action research cycles were carried out by the same team and with similar case studies. Further research is expected to compare traditional FMEA and improved FMEA in different case studies. The theoretical contribution was the improved FMEA synthesis based on limitations of traditional FMEA. The FMEA team may use Improved FMEA Framework.
A YOLO-Based Table Detection Method

Due to various table layouts and styles, table detection is always a difficult task in the field of document analysis. Inspired by the great progress of deep learning based methods on object detection, in this paper, we present a YOLO-based method for this task. Considering the large difference between document objects and natural objects, we introduce some adaptive adjustments to YOLOv3, including an anchor optimization strategy and two post processing methods. For anchor optimization, we use k-means clustering to find anchors which are more suitable for tables rather than natural objects and make it easier for our model to find exact positions of tables. In post-processing process, the extra whitespaces and noisy page objects (e.g. page headers, page footers) are removed from the predicted results, so that our model can get more accurate table margins and higher IoU scores. The proposed method is evaluated on two datasets from ICDAR 2013 Table Competition and ICDAR 2017 Page Object Detection (POD) Competition and achieves state-of-the-art performance.
Chlorite and Epidote Mineral Chemistry in Porphyry Ore Systems: A Case Study of the Northparkes District, New South Wales, Australia

Abstract Propylitic alteration, characterized by the occurrence of chlorite and epidote, is typically the most extensive and peripheral alteration facies developed around porphyry ore deposits. However, exploration within this alteration domain is particularly challenging, commonly owing to weak or nonexistent whole-rock geochemical gradients and the fact that similar assemblages can be developed in other geologic settings, particularly during low-grade metamorphism. We document and interpret systematic spatial trends in the chemistry of chlorite and epidote from propylitic alteration around the E48 and E26 porphyry Cu-Au deposits of the Northparkes district, New South Wales, Australia. These trends vary as a function of both distance from hydrothermal centers and alteration paragenesis. The spatial trends identified in porphyry-related chlorite and epidote at Northparkes include (1) a deposit-proximal increase in Ti, As, Sb, and V in epidote and Ti in chlorite, (2) a deposit-distal increase in Co and Li in chlorite and Ba in epidote, and (3) a pronounced halo around deposits in which Mn and Zn in chlorite, as well as Mn, Zn, Pb, and Mg in epidote, are elevated. Chlorite Al/Si ratios and epidote Al/Fe ratios may show behavior similar to that of Mn-Zn or may simply decrease outward, and V and Ni concentrations in chlorite are lowest in the peak Mn-Zn zone. In comparison to porphyry-related samples, chlorite from the regional metamorphic assemblage in the district contains far higher concentrations of Li, Ca, Ba, Pb, and Cu but much less Ti. Similarly, metamorphic epidote contains higher concentrations of Sr, Pb, As, and Sb but less Bi and Ti. These chlorite and epidote compositional trends are the net result of fluid-mineral partitioning under variable physicochemical conditions within a porphyry magmatic-hydrothermal system. They are most easily explained by the contribution of spent magmatic-derived ore fluid(s) into the propylitic domain. It is envisaged that such fluids experience progressive cooling and reduction in fs2 during outward infiltration into surrounding country rocks, with their pH controlled by the extent of rock-buffering experienced along the fluid pathway.
Deep Learning-Enhanced Framework for Performance Evaluation of a Recommending Interface with Varied Recommendation Position and Intensity Based on Eye-Tracking Equipment Data Processing

The increasing amount of marketing content in e-commerce websites results in the limited attention of users. For recommender systems, the way recommended items are presented becomes as important as the underlying algorithms for product selection. In order to improve the effectiveness of content presentation, marketing experts experiment with the layout and other visual aspects of website elements to find the most suitable solution. This study investigates those aspects for a recommending interface. We propose a framework for performance evaluation of a recommending interface, which takes into consideration individual user characteristics and goals. At the heart of the proposed solution is a deep neutral network trained to predict the efficiency a particular recommendation presented in a selected position and with a chosen degree of intensity. The proposed Performance Evaluation of a Recommending Interface (PERI) framework can be used to automate an optimal recommending interface adjustment according to the characteristics of the user and their goals. The experimental results from the study are based on research-grade measurement electronics equipment Gazepoint GP3 eye-tracker data, together with synthetic data that were used to perform pre-assessment training of the neural network.
GANILLA: Generative adversarial networks for image to illustration translation

In this paper, we explore illustrations in children's books as a new domain in unpaired image-to-image translation. We show that although the current state-of-the-art image-to-image translation models successfully transfer either the style or the content, they fail to transfer both at the same time. We propose a new generator network to address this issue and show that the resulting network strikes a better balance between style and content. There are no well-defined or agreed-upon evaluation metrics for unpaired image-to-image translation. So far, the success of image translation models has been based on subjective, qualitative visual comparison on a limited number of images. To address this problem, we propose a new framework for the quantitative evaluation of image-to-illustration models, where both content and style are taken into account using separate classifiers. In this new evaluation framework, our proposed model performs better than the current state-of-the-art models on the illustrations dataset. Our code and pretrained models can be found at https://github.com/giddyyupp/ganilla.