Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Automatically Localizing Function Errors in Mobile Apps With User Reviews

Removing all function errors is critical for making successful mobile apps. Since app testing may miss some function errors given limited time and resource, the user reviews of mobile apps are very important to developers for learning the uncaught errors. Unfortunately, manually handling each review is time-consuming and even error-prone. Existing studies on mobile apps’ reviews could not help developers effectively locate the problematic code according to the reviews, because the majority of such research focus on review classification, requirements engineering, sentiment analysis, and summarization [1]. They do not localize the function errors described in user reviews in apps’ code. Moreover, recent studies on mapping reviews to problematic source files look for the matching between the words in reviews and that in source code, bug reports, commit messages, and stack traces, thus may result in false positives and false negatives since they do not consider the semantic meaning and part of speech tag of each word. In this paper, we propose a novel approach to localize function errors in mobile apps by exploiting the context information in user reviews and correlating the reviews and bytecode through their semantic meanings. We realize our new approach as a tool named ReviewSolver, and carefully evaluate it with reviews of real apps. The experimental result shows that ReviewSolver has much better performance than the state-of-the-art tools (i.e., ChangeAdvisor and Where2Change).
Enhancing the Energy Efficiency of Journaling File System via Exploiting Multi-Write Modes on MLC NVRAM

Non-volatile random-access memory (NVRAM) is regarded as a great alternative storage medium owing to its attractive features, including low idle energy consumption, byte addressability, and short read/write latency. In addition, multi-level-cell (MLC) NVRAM has also been proposed to provide higher bit density. However, MLC NVRAM has lower energy efficiency and longer write latency when compared with single-level-cell (SLC) NVRAM. These drawbacks could lead to higher energy consumption of MLC NVRAM-based storage systems. The energy consumption is magnified by existing journaling file systems (JFS) on MLC NVRAM-based storage devices due to the JFS's fail-safe policy of writing the same data twice. Such observations motivate us to propose a multi-write-mode journaling file systems (mwJFS) to alleviate the drawbacks of MLC NVRAM and lower the energy consumption of MLC NVRAM-based JFS. The proposed mwJFS differentiates the data retention requirement of journaled data and applies different write modes to enhance the energy efficiency with better access performance. A series of experiments was conducted to demonstrate the capability of mwJFS on a MLC NVRAM-based storage system.
Towards Model-Driven Engineering of Smart Contracts for Cyber-Physical Systems

Applications of Distributed Ledger Technologies (DLTs) in IoT and Cyber-Physical Systems (CPS) are rapidly emerging. However, developing correct and resilient smart contracts for these use cases is even less understood than it is for cryptocurrency-based contracts. This paper presents an initial approach for generating smart contracts for coordinating the usage of cyber-physical system elements from UML statecharts. While the current target platform is Ethereum, our approach can easily be extended to other blockchain platforms.
Finding Similar Exercises in Online Education Systems

In online education systems, finding similar exercises is a fundamental task of many applications, such as exercise retrieval and student modeling. Several approaches have been proposed for this task by simply using the specific textual content (e.g. the same knowledge concepts or the similar words) in exercises. However, the problem of how to systematically exploit the rich semantic information embedded in multiple heterogenous data (e.g. texts and images) to precisely retrieve similar exercises remains pretty much open. To this end, in this paper, we develop a novel Multimodal Attention-based Neural Network (MANN) framework for finding similar exercises in large-scale online education systems by learning a unified semantic representation from the heterogenous data. In MANN, given exercises with texts, images and knowledge concepts, we first apply a convolutional neural network to extract image representations and use an embedding layer for representing concepts. Then, we design an attention-based long short-term memory network to learn a unified semantic representation of each exercise in a multimodal way. Here, two attention strategies are proposed to capture the associations of texts and images, texts and knowledge concepts, respectively. Moreover, with a Similarity Attention, the similar parts in each exercise pair are also measured. Finally, we develop a pairwise training strategy for returning similar exercises. Extensive experimental results on real-world data clearly validate the effectiveness and the interpretation power of MANN.
Traffic-Aware Backscatter Communications in Wireless-Powered Heterogeneous Networks

With the emerging Internet-of-Things (IoT) services, massive machine-to-machine (M2M) communication will be deployed on top of human-to-human (H2H) communication in the near future. Due to the coexistence of M2M and H2H communications, the performance of M2M (i.e., secondary) network depends largely on the H2H (i.e., primary) network. In this paper, we propose ambient backscatter communication for the M2M network which exploits the resources of the H2H network, referring to traffic applications and popularity. In order to maximize the harvesting and transmission opportunities offered by varying traffic resources of the H2H network, we adopt a Bayesian nonparametric (BNP) learning algorithm to classify traffic applications (patterns) for secondary user (SU). We then analyze the performance of SU using the stochastic geometrical approach, based on a criterion for optimum traffic pattern selection. Results are presented to validate the performance of the proposed BNP classification algorithm and the criterion.
Comparison of Voxel-Wise Tumor Perfusion Changes Measured With Dynamic Contrast-Enhanced (DCE) MRI and Volumetric DCE CT in Patients With Metastatic Brain Cancer Treated with Radiosurgery

Dynamic contrast-enhanced (DCE)-MRI metrics are evaluated against volumetric DCE-CT quantitative parameters as a standard for tracer-kinetic validation using a common 4-dimensional temporal dynamic analysis platform in tumor perfusion measurements following stereotactic radiosurgery (SRS) for brain metastases. Patients treated with SRS as part of Research Ethics Board-approved clinical trials underwent volumetric DCE-CT and DCE-MRI at baseline, then at 7 and 21 days after SRS. Temporal dynamic analysis was used to create 3-dimensional pharmacokinetic parameter maps for both modalities. Individual vascular input functions were selected for DCE-CT and a population function was used for DCE-MRI. Semiquantitative and pharmacokinetic DCE parameters were assessed using a modified Tofts model within each tumor at every time point for both modalities for characterization of perfusion and capillary permeability, as well as their dependency on precontrast relaxation times (TRs), T10, and input function. Direct voxel-to-voxel Pearson analysis showed statistically significant correlations between CT and magnetic resonance which peaked at day 7 for Ktrans (R = 0.74, P ≤ .0001). The strongest correlation to DCE-CT measurements was found with DCE-MRI analysis using voxel-wise T10 maps (R = 0.575, P < .001) instead of assigning a fixed T10 value. Comparison of histogram features showed statistically significant correlations between modalities over all tumors for median Ktrans (R = 0.42, P = .01), median area under the enhancement curve (iAUC90) (R = 0.55, P < .01), and median iAUC90 skewness (R = 0.34, P = .03). Statistically significant, strong correlations were found for voxel-wise Ktrans, iAUC90, and ve values between DCE-CT and DCE-MRI. For DCE-MRI, the implementation of voxel-wise T10 maps plays a key role in ensuring the accuracy of heterogeneous pharmacokinetic maps.
A Dual Markov Chain Topic Model for Dynamic Environments

The abundance of digital text has led to extensive research on topic models that reason about documents using latent representations. Since for many online or streaming textual sources such as news outlets, the number, and nature of topics change over time, there have been several efforts that attempt to address such situations using dynamic versions of topic models. Unfortunately, existing approaches encounter more complex inferencing when their model parameters are varied over time, resulting in high computation complexity and performance degradation. This paper introduces the DM-DTM, a dual Markov chain dynamic topic model, for characterizing a corpus that evolves over time. This model uses a gamma Markov chain and a Dirichlet Markov chain to allow the topic popularities and word-topic assignments, respectively, to vary smoothly over time. Novel applications of the Negative-Binomial augmentation trick result in simple, efficient, closed-form updates of all the required conditional posteriors, resulting in far lower computational requirements as well as less sensitivity to initial conditions, as compared to existing approaches. Moreover, via a gamma process prior, the number of desired topics is inferred directly from the data rather than being pre-specified and can vary as the data changes. Empirical comparisons using multiple real-world corpora demonstrate a clear superiority of DM-DTM over strong baselines for both static and dynamic topic models.
Decoding Neural Correlates of Cognitive States to Enhance Driving Experience

Modern cars can support their drivers by assessing and autonomously performing different driving maneuvers based on information gathered by in-car sensors. We propose that brain–machine interfaces (BMIs) can provide complementary information that can ease the interaction with intelligent cars in order to enhance the driving experience. In our approach, the human remains in control, while a BMI is used to monitor the driver's cognitive state and use that information to modulate the assistance provided by the intelligent car. In this paper, we gather our proof-of-concept studies demonstrating the feasibility of decoding electroencephalography correlates of upcoming actions and those reflecting whether the decisions of driving assistant systems are in-line with the drivers' intentions. Experimental results while driving both simulated and real cars consistently showed neural signatures of anticipation, movement preparation, and error processing. Remarkably, despite the increased noise inherent to real scenarios, these signals can be decoded on a single-trial basis, reflecting some of the cognitive processes that take place while driving. However, moderate decoding performance compared to the controlled experimental BMI paradigms indicate there exists room for improvement of the machine learning methods typically used in the state-of-the-art BMIs. We foresee that neural fusion correlates with information extracted from other physiological measures, e.g., eye movements or electromyography as well as contextual information gathered by in-car sensors will allow intelligent cars to provide timely and tailored assistance only if it is required; thus, keeping the user in the loop and allowing him to fully enjoy the driving experience.
Classification of APIs by Hierarchical Clustering

APIs can be classified according to the programming domains (e.g., GUIs, databases, collections, or security) that they address. Such classification is vital in searching repositories (e.g., the Maven Central Repository for Java) and for understanding the technology stack used in software projects. We apply hierarchical clustering to a curated suite of Java APIs to compare the computed API clusters with preexisting API classifications. Clustering entails various parameters (e.g., the choice of IDF versus LSI versus LDA). We describe the corresponding variability in terms of a feature model. We exercise all possible con gurations to determine the maximum correlation with respect to two baselines: i) a smaller suite of APIs manually classified in previous research; ii) a larger suite of APIs from the Maven Central Repository, thereby taking advantage of crowd-sourced classification while relying on a threshold-based approach for identifying important APIs and versions thereof, subject to an API dependency analysis on GitHub. We discuss the configurations found in this way and we examine the influence of particular features on the correlation between computed clusters and baselines. To this end, we also leverage interactive exploration of the parameter space and the resulting dendrograms. In this manner, we can also identify issues with the use of classifiers (e.g., missing classifiers) in the baselines and limitations of the clustering approach.
Liquid Mail - A Client Mail Based on CUBE Model

Nowadays we live with a large number of different connected devices with various operating systems. This is leading us to a lack of technological solutions to deal with this multiple connected devices owned by the same user. Thus, a new way to create applications is required in order to enhance the user experience regardless the operating systems of devices. In this work, we present a client mail, based on CUBE model principals, allowing the user changing device while using a mail service without losing session/data or connection. Preliminary results demonstrate its feasibility to move from a Windows system to an Android one while keeping the mail service running.
Visual Navigation for Biped Humanoid Robots Using Deep Reinforcement Learning

In this letter, we propose a map-less visual navigation system for biped humanoid robots, which extracts information from color images to derive motion commands using deep reinforcement learning (DRL). The map-less visual navigation policy is trained using the Deep Deterministic Policy Gradients (DDPG) algorithm, which corresponds to an actor-critic DRL algorithm. The algorithm is implemented using two separate networks, one for the actor and one for the critic, but with similar structures. In addition to convolutional and fully connected layers, Long Short-Term Memory (LSTM) layers are included to address the limited observability present in the problem. As a proof of concept, we consider the case of robotic soccer using humanoid NAO V5 robots, which have reduced computational capabilities, and low-cost Red - Green - Blue (RGB) cameras as main sensors. The use of DRL allowed to obtain a complex and high performant policy from scratch, without any prior knowledge of the domain, or the dynamics involved. The visual navigation policy is trained in a robotic simulator and then successfully transferred to a physical robot, where it is able to run in 20 ms, allowing its use in real-time applications.
Efficient and Accurate Traffic Flow Prediction via Incremental Tensor Completion

Timely and accurate prediction of traffic flow plays an important role in improving living quality of the public, which greatly influences the policies and regulations to be enforced and abided by. In this paper, we propose to model urban highway traffic data with an incremental tensor structure to exploit all available feature aspects. It is conceived on the solid basis of dynamic tensor model for traffic prediction, and a fast low-rank tensor completion algorithm, equipped with gravitational search algorithm, is harnessed to optimize the parameters. The proposed method excavates the inner law of traffic flow data by taking account of multi-mode features, such as daily and weekly periodicity, spatial information, and temporal variations, and so on. Empirically, multi-view experiments demonstrate the superiority of Trapit, and indicate that the proposed method is potentially applicable in large and dynamic highway networks.
Self-Synchronization and Self-Stabilization of Walking Gaits Modeled by the Three-Dimensional LIP Model

The linear inverted pendulum model is often used to study walking gaits, but the transition from one step to the following step is often neglected, whereas it is really important for the walking stability. This letter studies different landing positions of the swing foot, and different conditions to switch the stance leg, based on time or on the configuration of the robot. It is shown that self-synchronization of the motion in sagittal and frontal planes is dependent on different switching conditions. Neither self-synchronization nor self-stabilization is observed when the stance leg switching is based on time or when both the step length and width are fixed. On the other hand, self-synchronization can be obtained when the switching condition of the stance leg is based on a linear combination of the positions of the center of mass (CoM) along the sagittal and frontal axes. Moreover, self-stabilization can be obtained when the velocity of the CoM in the sagittal plane is taken into account.
Demand-Aware Charger Planning for Electric Vehicle Sharing

Cars of the future have been predicted as shared and electric. There has been a rapid growth in electric vehicle (EV) sharing services worldwide in recent years. For EV-sharing platforms to excel, it is essential for them to offer private charging infrastructure for exclusive use that meets the charging demand of their clients. Particularly, they need to plan not only the places to build charging stations, but also the amounts of chargers per station, to maximally satisfy the requirements on global charging coverage and local charging demand. Existing research efforts are either inapplicable for their different problem formulations or are at a coarse granularity. In this paper, we formulate the \underlineE lectric \underlineV ehicle \underlineC harger \underlineP lanning (EVCP) problem especially for EV-sharing. We prove that the \shortpro problem is NP-hard, and design an approximation algorithm to solve the problem with a theoretical bound of $1-\frac1 e $. We also devise some optimization techniques to speed up the solution. Extensive experiments on real-world datasets validate the effectiveness and the efficiency of our proposed solutions.
Pre-Processing and Feature Extraction Technique for Hand-drawn Finite Automata Recognition

Recognition of hand-drawn images is an easy task for human beings, but a challenging and cumbersome task for the computer to do automatically due to many factors including handwriting variations. This paper focuses on image pre-processing and feature extraction and proposes an effective offline technique to process handdrawn finite automata (FAs). Pre-processing techniques are the first approach in a recognition system. We applied a number of pre-processing techniques to 20 handdrawn FA images to see how feasible it is to extract the primitives of the input FA images. The output of the pre-processing stage was used as an input for the feature extraction stage. The histogram of oriented gradients (HOG) technique was used to extract the features in an FA diagram. A HOG descriptor takes the visual components of an input FA and describes them by the edge directions. The result of our experiment shows that using a HOG algorithm reduces the dimensionality of the feature space without disturbing the performance of the classifier, thus maintaining high efficiency with little computational complexity. Also, our experiment result shows that our system produced good results that can be used in classification.
Disturbance Observer Based Finite Time Trajectory Tracking Control for a 3 DOF Hydraulic Manipulator Including Actuator Dynamics

This paper gives the kinematic description and mathematical dynamic model of a three-degrees-of-freedom manipulator, including hydraulic actuator dynamics, and then proposes a disturbance observer-based robust control scheme for the position and torque tracking control subjected to the external disturbances and parameter uncertainties. First of all, the kinematic of the manipulator system is built according to the Denavit–Hartenberg notation. The joint space, actuator space, and the mathematical model of the manipulator, including hydraulic actuator dynamics, are then presented. Next, a robust control technique is designed for the fast and finite-time tracking capability of the torque signals along the desired torque commands, and a fast nonsingular terminal sliding mode control algorithm is developed to guarantee the fast convergence of the joint positions to their desired values. Moreover, two disturbance observer schemes are proposed to estimate and compensate the external disturbances and modeling errors in the manipulator system and hydraulic actuator system. Stability analysis of the cascade hydraulic manipulator system is analyzed and proved using the backstepping technique and Lyapunov theory. Finally, numerical simulations are obtained to validate the effectiveness of the designed control algorithm.
Bridging Big Data and Policy Making: A case study of failure

This paper investigates into a question through a failure case. The research question is; why we often fail to use the existing information and knowledge, including big data to design and/or implement public policies? The case is; the ticketing data, which was collected during the London Olympic Games and its so far under usage to design public policy related to health, well-being, and physical activities of the citizens. The research adopts qualitative analysis, including analysis of primary documents and semi-directive interviews. There is a limitation of single case study: however the case well represents the research question to provide preliminary investigation and to generate hypotheses for further studies.
Ion-Exchange Chromatography Coupled With Dynamic Coating Capillary Electrophoresis for Simultaneous Determination of Tropomyosin and Arginine Kinase in Shellfish

Tropomyosin (TM) and arginine kinase (AK) are known as two major allergens in seafood. For the first time, we demonstrate a newly developed ion-exchange chromatography coupled with dynamic coating capillary electrophoresis (IEC-DCCE) method to simultaneously analyze the TM and AK in shellfish. First, we have optimized the procedure of IEC for simple enrichment of TM and AK crude extract. By using 30 mM borate-borax at pH 9.0 with 0.3% (v/v) Tween-20 as a dynamic coating modifier for capillary electrophoresis (CE) separation, the migration time, separation efficiency and electrophoretic resolution greatly improved. The limits of detection (LOD) were 1.2 μg mL−1 for AK and 1.1 μg mL−1 for TM (S/N = 3), and the limits of quantification (LOQ) were 4.0 μg mL−1 for AK and 3.7 μg mL−1 for TM (S/N = 10). The recovery of AK ranged from 91.5 to 106.1%, while that of TM ranged from 94.0 to 109.5%. We also found that only when the concentrations of AK and TM were above LOD reported here, these proteins can stimulate human mast cell (LAD2) degranulation. Finally, the use of IEC-DCCE to analyze fresh shellfish samples highlights the applicability of this method for the simultaneous detection of these allergens in complex food systems.