Reviewerly/exHarmony huggingface dataset:
Improving high-impact bug report prediction with combination of interactive machine learning and active learning

Bug reports record issues found during software development and maintenance. A high-impact bug report (HBR) describes an issue that can cause severe damage once occurred after deployment. Identifying HBRs from the bug repository as early as possible is crucial for guaranteeing software quality. In recent years, many machine learning-based approaches have been proposed for HBR prediction, and most of them are based on supervised machine learning. However, the assumption of supervised machine learning is that it needs a large number of labeled data, which is often difficult to gather in practice. In this paper, we propose hbrPredictor, which combines interactive machine learning and active learning to HBR prediction. On the one hand, it can dramatically reduce the number of bug reports required for prediction model training; on the other hand, it improves the diversity and generalization ability of training samples via uncertainty sampling. We take security bug report (SBR) prediction as an example of HBR prediction and perform a large-scale experimental evaluation on datasets from different open-source projects. The results show: (1) hbrPredictor substantially outperforms the two baselines and obtains the maximum values of F1-score (0.7939) and AUC (0.8789); (2) with the dynamic stop criteria, hbrPredictor could reach its best performance with only 45% and 13% of the total bug reports for small-sized datasets and large-sized datasets, respectively. By reducing the number of required training samples, hbrPredictor could substantially save the data labeling effort without decreasing the effectiveness of the model.
Non-Fungible Tokens: Blockchains, Scarcity, and Value

Non-Fungible Tokens (NFTs) have garnered remarkable investor attention recently, with some NFTs securing selling prices that may have seemed unthinkable for a non-fungible virtual asset. This raises fascinating questions about “value” and “scarcity” with respect to blockchain technology, through a prism of non-fungibility of a digital asset, and this paper aims to draw attention to these questions insofar as they may shape an alternative space of blockchain development and exchange going forward.
Efficiency in the markets of crypto-currencies

We show that the level of market-efficiency in the five largest cryptocurrencies is highly time-varying. Specifically, before 2017, cryptocurrency-markets are mostly inefficient. This corroborates recent results on the matter. However, the cryptocurrency-markets become more efficient over time in the period 2017–2019. This contradicts other, more recent, results on the matter. One reason is that we apply a longer sample than previous studies. Another important reason is that we apply a robust measure of efficiency, being directly able to determine if the efficiency is significant or not. On average, Litecoin is the most efficient cryptocurrency, and Ripple being the least efficient cryptocurrency.
An improved index for clustering validation based on Silhouette index and Calinski-Harabasz index

Abstract The evaluation of clustering effects has been an important issue for a long time. How to effectively evaluate the clustering results of clustering algorithms is the key to the problem. The clustering effect evaluation is generally divided into internal clustering effect evaluation and external clustering effect evaluation. This paper focuses on the internal clustering effect evaluation, and proposes an improved index based on the Silhouette index and the Calinski-Harabasz index: Peak Weight Index (PWI). PWI combines the characteristics of Silhouette index and Calinski-Harabasz index, and takes the peak value of the two indexes as the impact point and gives appropriate weight within a certain range. Silhouette index and Calinski-Harabasz index will help improve the fluctuation of clustering results in the data set. Through the simulation experiments on four self-built influence data sets and two real data sets, it will prove that the PWI has excellent evaluation of clustering results.
The shape of personal space

The notion of a personal space surrounding one's ego-center is time-honored. However, few attempts have been made to measure the shape of this space. With increasing use of virtual environments, the question has arisen if real-world aspects, such as gender-effects or the shape of personal space, translate to virtual setups. We conducted two experiments, one with real people matched according to body height and level of acquaintance in a large laboratory setting, and one where subjects faced a virtual character, likewise matched to their body height. The first experiment also used a mannequin in place of the second human observer. The second experiment additionally manipulated the perspective of the subject to compare estimates of interpersonal distance between an egocentric and an allocentric perspective (in third-person view). Subjects approached (or were approached) from different angles until a comfortable distance for conversation with a stranger was reached (stop-distance task). Personal space turned out to be rather circular with a radius of about 1 m. Male pairs kept larger distances from one another than female or mixed-gender pairs. All subjects assumed larger distances to the mannequin compared to the real observer. Very comparable distances were preferred to the avatar in the virtual environment. Also, it did not matter whether the subject was engaged in active approach, was approached, or merely adjusted the distance between two avatars. Implications for theories of personal space are discussed.
The Information Bottleneck Problem and its Applications in Machine Learning

Inference capabilities of machine learning (ML) systems skyrocketed in recent years, now playing a pivotal role in various aspect of society. The goal in statistical learning is to use data to obtain simple algorithms for predicting a random variable Y from a correlated observation X. Since the dimension of X is typically huge, computationally feasible solutions should summarize it into a lower-dimensional feature vector T, from which Y is predicted. The algorithm will successfully make the prediction if T is a good proxy of Y, despite the said dimensionality-reduction. A myriad of ML algorithms (mostly employing deep learning (DL)) for finding such representations T based on real-world data are now available. While these methods are effective in practice, their success is hindered by the lack of a comprehensive theory to explain it. The information bottleneck (IB) theory recently emerged as a bold information-theoretic paradigm for analyzing DL systems. Adopting mutual information as the figure of merit, it suggests that the best representation T should be maximally informative about Y while minimizing the mutual information with X. In this tutorial we survey the information-theoretic origins of this abstract principle, and its recent impact on DL. For the latter, we cover implications of the IB problem on DL theory, as well as practical algorithms inspired by it. Our goal is to provide a unified and cohesive description. A clear view of current knowledge is important for further leveraging IB and other information-theoretic ideas to study DL models.
Content-Consistent Matching for Domain Adaptive Semantic Segmentation

This paper considers the adaptation of semantic segmentation from the synthetic source domain to the real target domain. Different from most previous explorations that often aim at developing adversarial-based domain alignment solutions, we tackle this challenging task from a new perspective, i.e., content-consistent matching (CCM). The target of CCM is to acquire those synthetic images that share similar distribution with the real ones in the target domain, so that the domain gap can be naturally alleviated by employing the content-consistent synthetic images for training. To be specific, we facilitate the CCM from two aspects, i.e., semantic layout matching and pixel-wise similarity matching. First, we use all the synthetic images from the source domain to train an initial segmentation model, which is then employed to produce coarse pixel-level labels for the unlabeled images in the target domain. With the coarse/accurate label maps for real/synthetic images, we construct their semantic layout matrixes from both horizontal and vertical directions and perform the matrixes matching to find out the synthetic images with similar semantic layout to real images. Second, we choose those predicted labels with high confidence to generate feature embeddings for all classes in the target domain, and further perform the pixel-wise matching on the mined layout-consistent synthetic images to harvest the appearance-consistent pixels. With the proposed CCM, only those content-consistent synthetic images are taken into account for learning the segmentation model, which can effectively alleviate the domain bias caused by those content-irrelevant synthetic images. Extensive experiments are conducted on two popular domain adaptation tasks, i.e., GTA5 $$\xrightarrow {}$$ Cityscapes and SYNTHIA $$\xrightarrow {}$$ Cityscapes. Our CCM yields consistent improvements over the baselines and performs favorably against previous state-of-the-arts.
Service robots and COVID-19: exploring perceptions of prevention efficacy at hotels in generation Z

Purpose COVID-19 is expected to enhance hospitality robotization because frontline robots facilitate social distancing, lowering contagion risk. Investing in frontline robots emerges as a solution to recover customer trust and encourage demand. However, we ignore how customers perceive these initiatives and, therefore, their efficacy. Focusing on robot employment at hotels and on Generation Z customers, this study aims to analyze guests’ perceptions about robots’ COVID-19 prevention efficacy and their impact on booking intentions. Design/methodology/approach This study tests its hypotheses combining an experimental design methodology with partial least squares. Survey data from 711 Generation Z individuals in Spain were collected in 2 periods of time. Findings Generation Z customers consider that robots reduce contagion risk at hotels. Robot anthropomorphism increases perceived COVID-19 prevention efficacy, regardless of the context where the robots are used. Robots’ COVID-19 prevention efficacy provokes better attitudes and higher booking intentions. Research limitations/implications The sampling method used in this research impedes this study’s results generalization. Further research could replicate this study using random sampling methods to ensure representativeness, even for other generational cohorts. Practical implications Employing robots as a COVID-19 prevention measure can enhance demand, especially if robots are human-like. Hoteliers need to communicate that robots can reduce contagion risk, particularly in markets more affected by COVID-19. Robots must be employed in low social presence contexts. Governments could encourage robotization by financially supporting hotels and publicly acknowledging its benefits regarding COVID-19 prevention. Originality/value This study combines preventive health, robotics and hospitality literature to study robot implementation during the COVID-19 pandemic, focusing on Generation Z guests – potential facilitators of robot diffusion.
Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation

Vision Transformers (ViTs) have emerged with superior performance on computer vision tasks compared to the convolutional neural network (CNN)-based models. However, ViTs mainly designed for image classification will generate single-scale low-resolution representations, which makes dense prediction tasks such as semantic segmentation challenging for ViTs. Therefore, we propose HRViT, which enhances ViTs to learn semantically-rich and spatially-precise multi-scale representations by integrating high-resolution multi-branch architectures with ViTs. We balance the model performance and efficiency of HRViT by various branch-block co-optimization techniques. Specifically, we explore heterogeneous branch designs, reduce the redundancy in linear layers, and augment the attention block with enhanced expressiveness. Those approaches enabled HRViT to push the Pareto frontier of performance and efficiency on semantic segmentation to a new level, as our evaluation results on ADE20K and Cityscapes show. HRViT achieves 50.20% mIoU on ADE20K and 83.16% mIoU on Cityscapes, surpassing state-of-the-art MiT and CSWin backbones with an average of +1.78 mIoU improvement, 28% parameter saving, and 21% FLOPs reduction, demonstrating the potential of HRViT as a strong vision backbone for semantic segmentation. Our code is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/facebookresearch/HRViT.
A PatchMatch-Based Dense-Field Algorithm for Video Copy–Move Detection and Localization

We propose a new algorithm for the reliable detection and localization of video copy–move forgeries. Discovering well-crafted video copy–moves may be very difficult, especially when some uniform background is copied to occlude foreground objects. To reliably detect both additive and occlusive copy–moves, we use a dense-field approach, with invariant features that guarantee robustness to several postprocessing operations. To limit complexity, a suitable video-oriented version of PatchMatch is used, with a multiresolution search strategy, and a focus on volumes of interest. Performance assessment relies on a new dataset, designed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> , with realistic copy–moves and a wide variety of challenging situations. Experimental results show the proposed method to detect and localize video copy–moves with good accuracy even in adverse conditions.
Supporting private data on Hyperledger Fabric with secure multiparty computation

Hyperledger Fabric is a “permissioned” blockchain architecture, providing a consistent distributed ledger, shared by a set of “peers” that must all have the same view of its state. For many applications, it is desirable to keep private data on the ledger, but the same-view principle makes it challenging to implement. In this paper, we explore supporting private data on Fabric using secure multiparty computation (MPC). In our solution, peers encrypt their private data before storing it on the chain and use secure MPC whenever such private data are needed in a transaction. We created a demo of our solution, implementing a bidding system where sellers list assets on the ledger with a secret reserve price, and bidders publish their bids on the ledger but keep secret the bidding price. We implemented a smart contract that runs the auction on this secret data, using a simple secure-MPC protocol that was built using the EMP-toolkit library. We identified two basic services that should be added to Hyperledger Fabric to support our solution, inspiring follow-up work to implement and add these services to the Hyperledger Fabric architecture.
Fringe projection profilometry by conducting deep learning from its digital twin

High-accuracy and high-speed three-dimensional (3D) fringe projection profilometry (FPP) has been widely applied in many fields. Recently, researchers discovered that deep learning can significantly improve fringe analysis. However, deep learning requires numerous objects to be scanned for training data. In this paper, we propose to build the digital twin of an FPP system and perform virtual scanning using computer graphics, which can significantly save cost and labor. The proposed method extracts 3D geometry directly from a single-shot fringe image, and real-world experiments have demonstrated the success of the virtually trained model. Our virtual scanning method can automatically generate 7,200 fringe images and 800 corresponding 3D scenes within 1.5 hours.
Challenges of Multi-Factor Authentication for Securing Advanced IoT Applications

The unprecedented proliferation of smart devices together with novel communication, computing, and control technologies have paved the way for A-IoT. This development involves new categories of capable devices, such as high-end wearables, smart vehicles, and consumer drones aiming to enable efficient and collaborative utilization within the smart city paradigm. While massive deployments of these objects may enrich people's lives, unauthorized access to said equipment is potentially dangerous. Hence, highly secure human authentication mechanisms have to be designed. At the same time, human beings desire comfortable interaction with the devices they own on a daily basis, thus demanding authentication procedures to be seamless and user-friendly, mindful of contemporary urban dynamics. In response to these unique challenges, this work advocates for the adoption of multi-factor authentication for A-IoT, such that multiple heterogeneous methods - both well established and emerging - are combined intelligently to grant or deny access reliably. We thus discuss the pros and cons of various solutions as well as introduce tools to combine the authentication factors, with an emphasis on challenging smart city environments. We finally outline the open questions to shape future research efforts in this emerging field.
Parameters for the formation of orogenic gold deposits

Orogenic gold deposits are the main source of gold for humanity. These deposits formed from the Archaean to Phanerozoic at crustal depths of more than 4 km. The key parameters accounting for their formation remain under discussion. I review these parameters in light of recent scientific advances using a systemic approach, where the relative pertinence of each component of a complex system in time and space is addressed. From the base to the top of the mineralising system, the following components and processes are reviewed: (1) the fluid sources; (2) the appropriate ligands and their sources; (3) the solubility of gold and its potential sources; (4) the function of faults as conduits; and (5) the mechanisms for precipitating gold. This review provides further support for the role of carbon-rich and pyritic sedimentary rocks in the formation of orogenic gold deposits throughout geological time.
FIVE COMMON ASSUMPTIONS THAT PREVENT DIGITAL TRANSFORMATION AT HIGHER EDUCATION INSTITUTIONS

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 1448-1457ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.0445Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
Abstractive Summarization of

Byeongchang Kim, Hyunwoo Kim, Gunhee Kim. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Time-varying hierarchical chains of salps with random weight networks for feature selection

Feature selection (FS) is considered as one of the most common and challenging tasks in Machine Learning. FS can be considered as an optimization problem that requires an efficient optimization algorithm to find its optimal set of features. This paper proposes a wrapper FS method that combines a time-varying number of leaders and followers binary Salp Swarm Algorithm (called TVBSSA) with Random Weight Network (RWN). In this approach, the TVBSSA is used as a search strategy, while RWN is utilized as an induction algorithm. The objective function is formulated in a manner to aggregate three objectives: maximizing the classification accuracy, maximizing the reduction rate of the selected features, and minimizing the complexity of generated RWN models. To assess the performance of the proposed approach, 20 well-known UCI datasets and a number of existing FS methods are employed. The comparative results show the ability of the proposed approach in outperforming similar algorithms in the literature and its merits to be used in systems that require FS.
On Mutual Information Maximization for Representation Learning

Many recent methods for unsupervised or self-supervised representation learning train feature extractors by maximizing an estimate of the mutual information (MI) between different views of the data. This comes with several immediate problems: For example, MI is notoriously hard to estimate, and using it as an objective for representation learning may lead to highly entangled representations due to its invariance under arbitrary invertible transformations. Nevertheless, these methods have been repeatedly shown to excel in practice. In this paper we argue, and provide empirical evidence, that the success of these methods cannot be attributed to the properties of MI alone, and that they strongly depend on the inductive bias in both the choice of feature extractor architectures and the parametrization of the employed MI estimators. Finally, we establish a connection to deep metric learning and argue that this interpretation may be a plausible explanation for the success of the recently introduced methods.
Learning Light Field Angular Super-Resolution via a Geometry-Aware Network

The acquisition of light field images with high angular resolution is costly. Although many methods have been proposed to improve the angular resolution of a sparsely-sampled light field, they always focus on the light field with a small baseline, which is captured by a consumer light field camera. By making full use of the intrinsic geometry information of light fields, in this paper we propose an end-to-end learning-based approach aiming at angularly super-resolving a sparsely-sampled light field with a large baseline. Our model consists of two learnable modules and a physically-based module. Specifically, it includes a depth estimation module for explicitly modeling the scene geometry, a physically-based warping for novel views synthesis, and a light field blending module specifically designed for light field reconstruction. Moreover, we introduce a novel loss function to promote the preservation of the light field parallax structure. Experimental results over various light field datasets including large baseline light field images demonstrate the significant superiority of our method when compared with state-of-the-art ones, i.e., our method improves the PSNR of the second best method up to 2 dB in average, while saves the execution time 48×. In addition, our method preserves the light field parallax structure better.
A Category-Aware Deep Model for Successive POI Recommendation on Sparse Check-in Data

As considerable amounts of POI check-in data have been accumulated, successive point-of-interest (POI) recommendation is increasingly popular. Existing successive POI recommendation methods only predict where user will go next, ignoring when this behavior will occur. In this work, we focus on predicting POIs that will be visited by users in the next 24 hours. As check-in data is very sparse, it is challenging to accurately capture user preferences in temporal patterns. To this end, we propose a category-aware deep model CatDM that incorporates POI category and geographical influence to reduce search space to overcome data sparsity. We design two deep encoders based on LSTM to model the time series data. The first encoder captures user preferences in POI categories, whereas the second exploits user preferences in POIs. Considering clock influence in the second encoder, we divide each user’s check-in history into several different time windows and develop a personalized attention mechanism for each window to facilitate CatDM to exploit temporal patterns. Moreover, to sort the candidate set, we consider four specific dependencies: user-POI, user-category, POI-time and POI-user current preferences. Extensive experiments are conducted on two large real datasets. The experimental results demonstrate that our CatDM outperforms the state-of-the-art models for successive POI recommendation on sparse check-in data.