Reviewerly/exHarmony huggingface dataset:
Effort-Aware Just-in-Time Bug Prediction for Mobile Apps Via Cross-Triplet Deep Feature Embedding

Just-in-time (JIT) bug prediction is an effective quality assurance activity that identifies whether a code commit will introduce bugs into the mobile app, aiming to provide prompt feedback to practitioners for priority review. Since collecting sufficient labeled bug data is not always feasible for some mobile apps, one possible approach is to leverage cross-app models. In this work, we propose a new cross-triplet deep feature embedding method, called CDFE, for cross-app JIT bug prediction task. The CDFE method incorporates a state-of-the-art cross-triplet loss function into a deep neural network to learn high-level feature representation for the cross-app data. This loss function adapts to the cross-app feature learning task and aims to learn a new feature space to shorten the distance of commit instances with the same label and enlarge the distance of commit instances with different labels. In addition, this loss function assigns higher weights to losses caused by cross-app instance pairs than that by intra-app instance pairs, aiming to narrow the discrepancy of cross-app bug data. We evaluate our CDFE method on a benchmark bug dataset from 19 mobile apps with two effort-aware indicators. The experimental results on 342 cross-app pairs show that our proposed CDFE method performs better than 14 baseline methods.
Non-Profiled Deep Learning-Based Side-Channel Preprocessing With Autoencoders

In recent years, deep learning-based side-channel attacks have established their position as mainstream.However, most deep learning techniques for cryptanalysis mainly focused on classifying sidechannel information in a profiled scenario where attackers can obtain a label of training data.In this paper, we introduce a novel approach with deep learning for improving side-channel attacks, especially in a nonprofiling scenario.We also propose a new principle of training that trains an autoencoder through the noise from real data using noise-reduced labels.It notably diminishes the noise in measurements by modifying the autoencoder framework to the signal preprocessing.We present convincing comparisons on our custom dataset, captured from ChipWhisperer-Lite board, that demonstrate our approach outperforms conventional preprocessing methods such as principal component analysis and linear discriminant analysis.Furthermore, we apply the proposed methodology to realign de-synchronized traces that applied hiding countermeasures, and we experimentally validate the performance of the proposal.Finally, we experimentally show that we can improve the performance of higher-order side-channel attacks by using the proposed technique with domain knowledge for masking countermeasures.
Implementation of a Character Recognition System Based on Finger-Joint Tracking Using a Depth Camera

The joint tracking-based writing system refers to writing characters by changing the position of the finger joint. It is a new research field in interaction-based input systems. However, joint tracking is a very challenging task. In this article, we present a new method for a finger-joint tracking-based character recognition system using a 3-D camera. The proposed method tracks the finger-joint from 3-D information to identify a numerical digit, alphabet, character, special key, or symbol using the distance between the thumb tip and another finger-joint. The recognition is based on Euclidean distance thresholding and geometric slope techniques. Joint data are stored in a 3-D matrix to assign the 3-D coordinate values. The exact character is identified according to the specified definitions. First, a single hand-based digit recognition method is introduced, in which the left or right hand is used. Second, a double hand-based writing system is presented in which both hands are used simultaneously; this system features a full keyboard with 124 different characters. Our results show an overall accuracy of 91.95% and 91.85% for single-hand and double-hand recognition, respectively; with a recognition time of less than 60 ms for each character. An important contribution of this article is that the proposed system can work in both light and dark environments, requires only a small computation area and has a large number of character sets (124 characters). In addition, a region-based user study has been conducted to verify the approach.
Judicious Judgment Meets Unsettling Updating: Dilation, Sure Loss and Simpson’s Paradox

Imprecise probabilities alleviate the need for high-resolution and unwarranted assumptions in statistical modeling. They present an alternative strategy to reduce irreplicable findings. However, updating imprecise models requires the user to choose among alternative updating rules. Competing rules can result in incompatible inferences, and exhibit dilation, contraction and sure loss, unsettling phenomena that cannot occur with precise probabilities and the regular Bayes rule. We revisit some famous statistical paradoxes and show that the logical fallacy stems from a set of marginally plausible yet jointly incommensurable model assumptions akin to the trio of phenomena above. Discrepancies between the generalized Bayes (B) rule, Dempster’s (D) rule and the Geometric (G) rule as competing updating rules for Choquet capacities of order 2 are discussed. We note that (1) B-rule cannot contract nor induce sure loss, but is the most prone to dilation due to “overfitting” in a certain sense; (2) in absence of prior information, both B- and G-rules are incapable to learn from data however informative they may be; (3) D- and G-rules can mathematically contradict each other by contracting while the other dilating. These findings highlight the invaluable role of judicious judgment in handling low-resolution information, and the care that needs to be take when applying updating rules to imprecise probability models.
ColChain: Collaborative Linked Data Networks

One of the major obstacles that currently prevents the Semantic Web from exploiting its full potential is that the data it provides access to is sometimes not available or outdated. The reason is rooted deep within its architecture that relies on data providers to keep the data available, queryable, and up-to-date at all times – an expectation that many data providers in reality cannot live up to for an extended (or infinite) period of time. Hence, decentralized architectures have recently been proposed that use replication to keep the data available in case the data provider fails. Although this increases availability, it does not help keeping the data up-to-date or allow users to query and access previous versions of a dataset. In this paper, we therefore propose ColChain (COLlaborative knowledge CHAINs), a novel decentralized architecture based on blockchains that not only lowers the burden for the data providers but at the same time also allows users to propose updates to faulty or outdated data, trace updates back to their origin, and query older versions of the data. Our extensive experiments show that ColChain reaches these goals while achieving query processing performance comparable to the state of the art.
Bayesian Graph Convolutional Neural Networks via Tempered MCMC

Deep learning models, such as convolutional neural networks, have long been applied to image and multi-media tasks, particularly those with structured data. More recently, there has been more attention to unstructured data that can be represented via graphs. These types of data are often found in health and medicine, social networks, and research data repositories. Graph convolutional neural networks have recently gained attention in the field of deep learning that takes advantage of graph-based data representation with automatic feature extraction via convolutions. Given the popularity of these methods in a wide range of applications, robust uncertainty quantification is vital. This remains a challenge for large models and unstructured datasets. Bayesian inference provides a principled approach to uncertainty quantification of model parameters for deep learning models. Although Bayesian inference has been used extensively elsewhere, its application to deep learning remains limited due to the computational requirements of the Markov Chain Monte Carlo (MCMC) methods. Recent advances in parallel computing and advanced proposal schemes in MCMC sampling methods has opened the path for Bayesian deep learning. In this paper, we present Bayesian graph convolutional neural networks that employ tempered MCMC sampling with Langevin-gradient proposal distribution implemented via parallel computing. Our results show that the proposed method can provide accuracy similar to advanced optimisers while providing uncertainty quantification for key benchmark problems.
Graph-based Embedding Smoothing for Sequential Recommendation

In real-world scenarios, a user's interactions with items could be formalized as a behavior sequence, indicating his/her dynamic and evolutionary preferences. To this end, a series of recent efforts in recommender systems aim at improving recommendation performance by considering the sequential information. However, impacts of sequential behavior on future interactions may vary greatly in different scenarios. Additionally, semantic item relations underlying item attributes have not been well exploited in sequential recommendation models, which could be crucial for measuring item similarities in recommendation. To deal with the above problems, this paper provides a general embedding smoothing framework for sequential recommendation models. Specifically, we first construct a hybrid item graph by fusing sequential item relations derived from user-item interactions with semantic item relations built upon item attributes. Second, we perform graph convolutions on the hybrid item graph to generate smoothed item embedding. Finally, we equip sequential recommendation models with the smoothed item representations to enhance their performances. Experimental results demonstrate that with our embedding smoothing framework, the state-of-the-art sequential recommendation model, SASRec, achieves superior performance to most baseline methods on three real-world datasets. Moreover, the results show that most mainstream sequential recommendation models could benefit from our framework.
Assessment of off-the-shelf SE-specific sentiment analysis tools: An extended replication study

Sentiment analysis methods have become popular for investigating human communication, including discussions related to software projects. Since general-purpose sentiment analysis tools do not fit well with the information exchanged by software developers, new tools, specific for software engineering (SE), have been developed. We investigate to what extent SE-specific tools for sentiment analysis mitigate the threats to conclusion validity of empirical studies in software engineering, highlighted by previous research. First, we replicate two studies addressing the role of sentiment in security discussions on GitHub and in question-writing on Stack Overflow. Then, we extend the previous studies by assessing to what extent the tools agree with each other and with the manual annotation on a gold standard of 600 documents. We find that different SE-specific sentiment analysis tools might lead to contradictory results at a fine-grain level, when used 'off-the-shelf'. Conversely, platform-specific tuning or retraining might be needed to take into account differences in platform conventions, jargon, or document lengths.
Sampling-based Motion Planning with Temporal Logic Missions and Spatial Preferences

While motion planning under temporal logic specifications has been addressed in several state-of-the-art works, spatial aspects have been so far largely neglected. In this work, we enrich the semantics of robot motion specifications by including preferences on spatial relations between its trajectory and various elements in its environment. The spatial preferences are given in a fragment of Signal Temporal Logic (STL) on top of complex missions in syntactically co-safe Linear Temporal Logic (scLTL). We propose a cost function with user-specified parameters, which determines the compromise between efficiency and spatial robustness of a trajectory. The proposed modification of the incremental sampling-based RRT* driven by this cost function guarantees that the motion plan (if found) simultaneously satisfies the mission and asymptotically minimize the cost. The paper includes several case studies showcasing the effects of the user-adjustable parameters on the resulting trajectories.
Attention in Attention Networks for Person Retrieval

This paper generalizes the Attention in Attention (AiA) mechanism, in P. Fang et al., 2019 by employing explicit mapping in reproducing kernel Hilbert spaces to generate attention values of the input feature map. The AiA mechanism models the capacity of building inter-dependencies among the local and global features by the interaction of inner and outer attention modules. Besides a vanilla AiA module, termed linear attention with AiA, two non-linear counterparts, namely, second-order polynomial attention and Gaussian attention, are also proposed to utilize the non-linear properties of the input features explicitly, via the second-order polynomial kernel and Gaussian kernel approximation. The deep convolutional neural network, equipped with the proposed AiA blocks, is referred to as Attention in Attention Network (AiA-Net). The AiA-Net learns to extract a discriminative pedestrian representation, which combines complementary person appearance and corresponding part features. Extensive ablation studies verify the effectiveness of the AiA mechanism and the use of non-linear features hidden in the feature map for attention design. Furthermore, our approach outperforms current state-of-the-art by a considerable margin across a number of benchmarks. In addition, state-of-the-art performance is also achieved in the video person retrieval task with the assistance of the proposed AiA blocks.
Wizard of Search Engine

Conversational information seeking (CIS) is playing an increasingly important role in connecting people to information. Due to a lackof suitable resources, previous studies on CIS are limited to thestudy of conceptual frameworks, laboratory-based user studies, or a particular aspect of CIS (e.g., asking clarifying questions). In this work, we make three main contributions to facilitate research into CIS: (1) We formulate a pipeline for CIS with six subtasks: intent detection, keyphrase extraction, action prediction, query selection, passage selection, and response generation. (2) We release a benchmark dataset, called wizard of search engine (WISE), which allows for comprehensive and in-depth research on all aspects of CIS. (3) We design a neural architecture capable of training and evaluating both jointly and separately on the six sub-tasks, and devise a pre-train/fine-tune learning scheme, that can reduce the requirements of WISE in scale by making full use of available data. We report useful characteristics of the CIS task based on statistics of the WISE dataset. We also show that our best performing model variant is able to achieve effective CIS. We release the dataset, code as well as evaluation scripts to facilitate future research by measuring further improvements in this important research direction.
Design framework for metasurface optics-based convolutional neural networks

Deep learning using convolutional neural networks (CNNs) has been shown to significantly outperform many conventional vision algorithms. Despite efforts to increase the CNN efficiency both algorithmically and with specialized hardware, deep learning remains difficult to deploy in resource-constrained environments. In this paper, we propose an end-to-end framework to explore how to optically compute the CNNs in free-space, much like a computational camera. Compared to existing free-space optics-based approaches that are limited to processing single-channel (i.e., gray scale) inputs, we propose the first general approach, based on nanoscale metasurface optics, that can process RGB input data. Our system achieves up to an order of magnitude energy savings and simplifies the sensor design, all the while sacrificing little network accuracy.
Perceptual quality-preserving black-box attack against deep learning image classifiers

Deep neural networks provide unprecedented performance in all image classification problems, including biometric recognition systems, key elements in all smart city environments. Recent studies, however, have shown their vulnerability to adversarial attacks, spawning intense research in this field. To improve system security, new countermeasures and stronger attacks are proposed by the day. On the attacker’s side, there is growing interest for the realistic black-box scenario, in which the user has no access to the network parameters. The problem is to design efficient attacks which mislead the neural network without compromising image quality. In this work, we propose to perform the black-box attack along a high-saliency and low-distortion path, so as to improve both attack efficiency and image perceptual quality. Experiments on real-world systems prove the effectiveness of the proposed approach both on benchmark tasks and actual biometric applications.
An empirical assessment of the universality of ANNs to predict oscillatory time series

Artificial neural networks (ANNs) are universal function approximators, therefore suitable to be trained as predictors of oscillatory time series. Though several ANN architectures have been tested to predict both synthetic and real-world time series, the universality of their predictive power remained unexplored. Here we empirically test this universality across five well-known chaotic oscillators, limiting the analysis to the simplest architecture, namely multi-layer feed-forward ANN trained to predict one sampling step ahead. To compare different predictors, data are sampled according to their frequency content and the ANN structure scales with the characteristic dimensions of the oscillator. Moreover, the quality of recursive multi-step-ahead predictions are compared in terms of the system’s (largest) Lyapunov exponent (LLE), i.e., the predictive power is measured in terms of the number of Lyapunov times (LT, the LLE inverse) predicted within a prescribed (relative) error. The results confirm the rather uniform predictive power of the proposed ANN architecture.
Demon in the Variant: Statistical Analysis of {DNNs} for Robust Backdoor Contamination Detection

A security threat to deep neural networks (DNN) is backdoor contamination, in which an adversary poisons the training data of a target model to inject a Trojan so that images carrying a specific trigger will always be classified into a specific label. Prior research on this problem assumes the dominance of the trigger in an image's representation, which causes any image with the trigger to be recognized as a member in the target class. Such a trigger also exhibits unique features in the representation space and can therefore be easily separated from legitimate images. Our research, however, shows that simple target contamination can cause the representation of an attack image to be less distinguishable from that of legitimate ones, thereby evading existing defenses against the backdoor infection. In our research, we show that such a contamination attack actually subtly changes the representation distribution for the target class, which can be captured by a statistic analysis. More specifically, we leverage an EM algorithm to decompose an image into its identity part (e.g., person, traffic sign) and variation part within a class (e.g., lighting, poses). Then we analyze the distribution in each class, identifying those more likely to be characterized by a mixture model resulted from adding attack samples to the legitimate image pool. Our research shows that this new technique effectively detects data contamination attacks, including the new one we propose, and is also robust against the evasion attempts made by a knowledgeable adversary.
Secure Outsourced Attribute-Based Sharing Framework for Lightweight Devices in Smart Health Systems

The rapid evolution of the Internet of Things has led to the development of smart health. As a form of medical care that uses advanced Internet technology to realize better diagnosis and treatment of patients, smart health transitions medical services move toward real intelligence and greatly helps users. And in smart health, the secure sharing of personal health records (PHRs) is one of the main concerns of patients and medical personnel. Many attribute-based sharing models have been proposed to secure the sharing of PHRs, but there are still two problems to resolve. One is the potential disclosure of the patient data. The attribute-based model achieves flexible access control, but the access policies contain sensitive information of patients. The disclosure of the policy will lead to the leakage of data of the users. The other is the high computational and storage overhead, particularly in smart health systems with limited computing power. In this article, we present a Smart Health-Lightweight Fine-Grained Sharing (SH-LFGS) framework based on attribute-based encryption (ABE). It achieves a fully hidden access policy by adopting Viéte's formula. SH-LFGS introduces an online/offline mechanism in the PHR encryption phase and the outsourced verifiable decryption mechanism. Because the decrypting test requires only one bilinear pair operation, the SH-LFGS can achieve the task of lightweight computation. Analysis of the performance and security of the proposed model confirm its efficiency and security.
CIAO⁎: MPC-based Safe Motion Planning in Predictable Dynamic Environments

Robots have been operating in dynamic environments and shared workspaces for decades. Most optimization based motion planning methods, however, do not consider the movement of other agents, e.g. humans or other robots, and therefore do not guarantee collision avoidance in such scenarios. This paper builds upon the Convex Inner ApprOximation (CIAO) method and proposes a motion planning algorithm that guarantees collision avoidance in predictable dynamic environments. Furthermore, it generalizes CIAO’s free region concept to arbitrary norms and proposes a cost function to approximate time optimal motion planning. The proposed method, CIAO*, finds kinodynamically feasible and collision free trajectories for constrained single body robots using model predictive control (MPC). It optimizes the motion of one agent and accounts for the predicted movement of surrounding agents and obstacles. The experimental evaluation shows that CIAO* reaches close to time optimal behavior.