Reviewerly/exHarmony huggingface dataset:
Robot Co-design: Beyond the Monotone Case

Recent advances in 3D printing and manufacturing of miniaturized robotic hardware and computing are paving the way to build inexpensive and disposable robots. This will have a large impact on several applications including scientific discovery (e.g., hurricane monitoring), search-and-rescue (e.g., operation in confined spaces), and entertainment (e.g., nano drones). The need for inexpensive and task-specific robots clashes with the current practice, where human experts are in charge of designing hardware and software aspects of the robotic platform. This makes the robot design process expensive and time consuming, and ultimately unsuitable for small-volumes low-cost applications. This paper considers the computational robot co-design problem, which aims to create an automatic algorithm that selects the best robotic modules (sensing, actuation, computing) in order to maximize the performance on a task, while satisfying given specifications (e.g., maximum cost of the resulting design). We propose a binary optimization formulation of the co-design problem and show that such formulation generalizes previous work based on strong modeling assumptions. We show that the proposed formulation can solve relatively large co-design problems in seconds and with minimal human intervention. We demonstrate the proposed approach in two applications: the co-design of an autonomous drone racing platform and the co-design of a multi-robot system.
Towards Unknown Traffic Identification via Embeddings and Deep Autoencoders

Traffic classification, as a fundamental tool for network management and security, is suffering from a critical problem, namely “unknown traffic”. The unknown traffic is defined as network traffic generated by previously unknown applications (i.e., zero-day applications) in a traffic classification system. The ability to divide the mixed unknown traffic into clusters, each of which contains only one application traffic as far as possible, is the key to solve this problem. This paper reports our recent exploration of the n-gram embeddings strategy, deep neural networks and clustering algorithms for constructing an unsupervised scheme for unknown network traffic identification. Experimental results on real-world traces demonstrate that our method gains average clustering purity rate about 97.35% when we use DNS, DHCP, BitTorrent, SSH, HTTP, IMAP, MySQL, and Github to simulate unknown traffic.
Moiré Pattern Removal with Multi-scale Feature Enhancing Network

Taking high-quality photos of digital screens is difficult, as such photos are usually contaminated with moire patterns. Considering the nature of wide-range frequencies of moire' patterns, existing works adopt the multi-scale framework to address this challenge. However, the relationship among feature maps at different scales is significantly ignored, resulting in the degraded performance due to the missing of the semantic information. In this paper, we propose a novel Multi-Scale Feature Enhancing network, named MSFE. By virtue of the multi-scale architecture for extracting moire-irrelevant contexts from multiple resolutions. Furthermore, we design a Feature Enhancing Branch (FEB) to combine high-level features with low-level ones for modeling the correlations of multiple scales. In this way, features with richer semantic information can be learned at each scale. Consequently, moire' patterns at different levels can be tackled properly. Experiments on the publicly moire pattern dataset demonstrate that the proposed method outperforms the state-of-the-arts.
Incremental-Learning-Based Unsupervised Anomaly Detection Algorithm for Terminal Airspace Operations

The terminal airspace system is one of the most complex human-made dynamical systems with a continuously evolving structure. Modern aviation technologies in the terminal airspace record detailed flight and airport information, including the states of aircraft and the operating conditions of airports as time series datasets, through on-board and ground-based systems. Detecting anomalies in such datasets is emerging as a key problem to understand air transportation system complexity and behavior. This paper proposes an incremental-learning-based anomaly detection algorithm that generates anomaly detection models from surveillance data. The algorithm is developed in a recursive fashion, such that it uses the overnight recorded data to improve the temporal-logic-based anomaly detection models daily—thus it is termed as an overnight update algorithm. The proposed algorithm is demonstrated with real air traffic surveillance data comprising of arrival flights to LaGuardia, John F. Kennedy, and Newark airports, thereby detecting anomalies for arrivals in the terminal airspace of the New York metroplex. This is followed by some analysis and insights from the results of these extensive tests.
Robust Weighted Fusion Kalman Estimators for Networked Multisensor Mixed Uncertain Systems With Random One-Step Sensor Delays, Uncertain-Variance Multiplicative, and Additive White Noises

For networked multisensor systems with mixed uncertainties, including random one-step sensor delays, multiplicative noises and uncertain noise variances, a new augmented state approach with the fictitious noises is presented, by which the original system model is transformed into one without sensor delays and only with white noises. An extended Lyapunov equation approach with two Lyapunov equations is presented, which is applied to prove the robustness of the estimators. According to the minimax robust estimation principle, based on the worst-case system with conservative upper bounds of uncertain noise variances, the four weighted fusion robust time-varying and steady-state Kalman estimators (predictor, filter and smoother) are presented in the sense that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. Their robustness and accuracy relations are proved, and their convergence in a realization is proved by the dynamic error system analysis (DESA) method. Specially, the presented modified robust covariance intersection (CI) fuser has higher robust accuracy than the original one. A simulation example applied to the uninterruptible power system (UPS) shows the effectiveness of the proposed approaches and results.
Migrating the Android Apo-Games into an Annotation-Based Software Product Line

Most organizations start to reuse software by cloning complete systems and adapting them to new customer requirements. However, with an increasing number of cloned systems, the problems of this approach become severe, due to synchronization efforts. In such cases, organizations often decide to extract a software product line, which promises to reduce development and maintenance costs. While this scenario is common in practice, the research community is still missing knowledge about best practices and needs datasets to evaluate supportive techniques. In this paper, we report our experiences with extracting a preprocessor-based software product line from five cloned Android games of the Apo-Games challenge. Besides the process we employed, we also discuss lessons learned and contribute corresponding artifacts, namely a feature model and source code. The insights into the processes help researchers and practitioners to improve their understanding of extractive software-product-line adoption. Our artifacts can serve as a valuable dataset for evaluations and can be extended in the future to support researchers as a real-world baseline.
A framework for writing trigger-action todo comments in executable format

Natural language elements, e.g., todo comments, are frequently used to communicate among developers and to describe tasks that need to be performed (actions) when specific conditions hold on artifacts related to the code repository (triggers), e.g., from the Apache Struts project: “remove expectedJDK15 and if() after switching to Java 1.6”. As projects evolve, development processes change, and development teams reorganize, these comments, because of their informal nature, frequently become irrelevant or forgotten. We present the first framework, dubbed TrigIt, to specify trigger-action todo comments in executable format. Thus, actions are executed automatically when triggers evaluate to true. TrigIt specifications are written in the host language (e.g., Java) and are evaluated as part of the build process. The triggers are specified as query statements over abstract syntax trees, abstract representation of build configuration scripts, issue tracking systems, and system clock time. The actions are either notifications to developers or code transformation steps. We implemented TrigIt for the Java programming language and migrated 44 existing trigger-action comments from several popular open-source projects. Evaluation of TrigIt, via a user study, showed that users find TrigIt easy to learn and use. TrigIt has the potential to enforce more discipline in writing and maintaining comments in large code repositories.
Dense 3D Scene Reconstruction from Multiple Spherical Images for 3-DoF+ VR Applications

We propose a novel method for estimating the 3D geometry of indoor scenes based on multiple spherical images. Our technique produces a dense depth map registered to a reference view so that depth-image-based-rendering (DIBR) techniques can be explored for providing three-degrees-of-freedom plus immersive experiences to virtual reality users. The core of our method is to explore large displacement optical flow algorithms to obtain point correspondences, and use cross-checking and geometric constraints to detect and remove bad matches. We show that selecting a subset of the best dense matches leads to better pose estimates than traditional approaches based on sparse feature matching, and explore a weighting scheme to obtain the depth maps. Finally, we adapt a fast image-guided filter to the spherical domain for enforcing local spatial consistency, improving the 3D estimates. Experimental results indicate that our method quantitatively outperforms competitive approaches on computer-generated images and synthetic data under noisy correspondences and camera poses. Also, we show that the estimated depth maps obtained from only a few real spherical captures of the scene are capable of producing coherent synthesized binocular stereoscopic views by using traditional DIBR methods.
A Comparison of Desktop and Augmented Reality Scenario Based Training Authoring Tools

This work presents a comparison of two applications (Augmented Reality (AR) and desktop) to author Scenario-Based Training (SBT) simulations. Through an iterative design process two interface conditions are developed and then evaluated qualitatively and quantitatively. A graph based authoring visualization help designers understand the scenario learning artifacts and relationships. No significant difference was found on time taken to complete tasks nor on the perceived usability of the systems. However, Desktop was perceived as more efficient, corroborated by the significantly higher number of mistakes made in AR. Findings are presented towards building better AR immersive authoring tools.
PRESISTANT: Learning based assistant for data pre-processing

Data pre-processing is one of the most time consuming and relevant steps in a data analysis process (e.g., classification task). A given data pre-processing operator can have positive, negative, or zero impact on the final result of the analysis. Expert users have the required knowledge to find the right pre-processing operators. However, when it comes to non-experts, they are overwhelmed by the amount of pre-processing operators and it is challenging for them to find operators that would positively impact their analysis (e.g., increase the predictive accuracy of a classifier). Existing solutions either assume that users have expert knowledge, or they recommend pre-processing operators that are only “syntactically” applicable to a dataset, without taking into account their impact on the final analysis. In this work, we aim at providing assistance to non-expert users by recommending data pre-processing operators that are ranked according to their impact on the final analysis. We developed a tool, PRESISTANT, that uses Random Forests to learn the impact of pre-processing operators on the performance (e.g., predictive accuracy) of 5 different classification algorithms, such as Decision Tree (J48), Naive Bayes, PART, Logistic Regression, and Nearest Neighbor (IBk). Extensive evaluations on the recommendations provided by our tool, show that PRESISTANT can effectively help non-experts in order to achieve improved results in their analytic tasks.
Multispectral Transmission Map Fusion Method and Architecture for Image Dehazing

Image dehazing is an essential preprocessing stage for several applications such as surveillance, long-range imaging, automatic driver-assistance system (ADAS), and remote sensing. Haze particles degrade visible-band images more severely than the infrared images due to scattering phenomena. Therefore, infrared data can be effectively utilized to enhance the visibility of color images captured in a hazy environment. In this brief, for the first time, a multispectral transmission map fusion approach is presented, wherein a haze-aware weight generation scheme is used to improve transmission map in specific regions. This achieves significant quantitative improvement as compared with the existing methods. In addition, the method is extremely suitable for hardware implementation. Therefore, a low-cost very large-scale integration (VLSI) architecture is also presented. The application-specified integrated circuit (ASIC) and field-programmable gate array (FPGA) implementations dissipate 3.45- and 57-mW power at 150 MHz, respectively. Both ASIC and FPGA achieve a high throughput of 450 and 308 Mpixels/s, respectively. The design does not require any external memory such as dynamic random access memory, thus making it suitable for on-chip processing that can be closely integrated with an image sensor.
AnchorViz

When building a classifier in interactive machine learning (iML), human knowledge about the target class can be a powerful reference to make the classifier robust to unseen items. The main challenge lies in finding unlabeled items that can either help discover or refine concepts for which the current classifier has no corresponding features (i.e., it has feature blindness ). Yet it is unrealistic to ask humans to come up with an exhaustive list of items, especially for rare concepts that are hard to recall. This article presents AnchorViz , an interactive visualization that facilitates the discovery of prediction errors and previously unseen concepts through human-driven semantic data exploration. By creating example-based or dictionary-based anchors representing concepts, users create a topology that (a) spreads data based on their similarity to the concepts and (b) surfaces the prediction and label inconsistencies between data points that are semantically related. Once such inconsistencies and errors are discovered, users can encode the new information as labels or features and interact with the retrained classifier to validate their actions in an iterative loop. We evaluated AnchorViz through two user studies. Our results show that AnchorViz helps users discover more prediction errors than stratified random and uncertainty sampling methods. Furthermore, during the beginning stages of a training task, an iML tool with AnchorViz can help users build classifiers comparable to the ones built with the same tool with uncertainty sampling and keyword search, but with fewer labels and more generalizable features. We discuss exploration strategies observed during the two studies and how AnchorViz supports discovering, labeling, and refining of concepts through a sensemaking loop.
CloneCognition: machine learning based code clone validation tool

A code clone is a pair of similar code fragments, within or between software systems. To detect each possible clone pair from a software system while handling the complex code structures, the clone detection tools undergo a lot of generalization of the original source codes. The generalization often results in returning code fragments that are only coincidentally similar and not considered clones by users, and hence requires manual validation of the reported possible clones by users which is often both time-consuming and challenging. In this paper, we propose a machine learning based tool 'CloneCognition' (Open Source Codes: https://github.com/pseudoPixels/CloneCognition ; Video Demonstration: https://www.youtube.com/watch?v=KYQjmdr8rsw) to automate the laborious manual validation process. The tool runs on top of any code clone detection tools to facilitate the clone validation process. The tool shows promising clone classification performance with an accuracy of up to 87.4%. The tool also exhibits significant improvement in the results when compared with state-of-the-art techniques for code clone validation.
Adaptively Secure and Succinct Functional Encryption: Improving Security and Efficiency, Simultaneously

Functional encryption (FE) is advanced encryption that enables us to issue functional decryption keys where functions are hardwired. When we decrypt a ciphertext of a message m by a functional decryption key where a function f is hardwired, we can obtain f(m) and nothing else. We say FE is selectively or adaptively secure when target messages are chosen at the beginning or after function queries are sent, respectively. In the weakly-selective setting, function queries are also chosen at the beginning. We say FE is single-key/collusion-resistant when it is secure against adversaries that are given only-one/polynomially-many functional decryption keys, respectively. We say FE is sublinearly-succinct/succinct when the running time of an encryption algorithm is sublinear/poly-logarithmic in the function description size, respectively. In this study, we propose a generic transformation from weakly-selectively secure, single-key, and sublinearly-succinct (we call "building block") PKFE for circuits into adaptively secure, collusion-resistant, and succinct (we call "fully-equipped") one for circuits. Our transformation relies on neither concrete assumptions such as learning with errors nor indistinguishability obfuscation (IO). This is the first generic construction of fully-equipped PKFE that does not rely on IO. As side-benefits of our results, we obtain the following primitives from the building block PKFE for circuits: (1) laconic oblivious transfer (2) succinct garbling scheme for Turing machines (3) selectively secure, collusion-resistant, and succinct PKFE for Turing machines (4) low-overhead adaptively secure traitor tracing (5) key-dependent message secure and leakage-resilient public-key encryption. We also obtain a generic transformation from simulation-based adaptively secure garbling schemes that satisfy a natural decomposability property into adaptively indistinguishable garbling schemes whose online complexity does not depend on the output length.
Mining Sequential Patterns of Historical Purchases for E-commerce Recommendation

In E-commerce Recommendation system, accuracy will be improved if more complex sequential patterns of user purchase behavior are learned and included in its user-item matrix input, to make it more informative before collaborative filtering. Existing recommendation systems that attempt to use mining and some sequences are those referred to as LiuRec09, ChoiRec12, SuChenRec15, and HPCRec18. These systems use mining based techniques of clustering, frequent pattern mining with similarity measures of purchases and clicks to predict the probability of purchases by users as their ratings before running collaborative filtering algorithm. HPCRec18 improved on the user-item matrix both quantitatively (finding values where there were 0 ratings) and qualitatively (finding specific interest values where there were 1 ratings). None of these algorithms explored enriching the user-item matrix with sequential pattern of customer clicks and purchases to capture better customer behavior. This paper proposes an algorithm called HSPRec (Historical Sequential Pattern Recommendation System), which mines frequent sequential click and purchase patterns for enriching the (i) user-item matrix quantitatively, and (ii) qualitatively. Then, finally, the improved matrix is used for collaborative filtering for better recommendations. Experimental results with mean absolute error, precision and recall show that the proposed sequential pattern mining based recommendation system, HSPRec provides more accurate recommendations than the tested existing systems.
A Discrete Wavelet Based Watermarking Scheme for Authentication of Medical Images

The growth and emergence of telemedicine has resulted into considerable increase in the transfer of medical images between various health centers over computer networks for various purposes, such as diagnosis, clinical interpretation, and archives. The security of these images is a serious concern since these images can be manipulated during their transmission over the network. Moreover, as the authentication of the medical images is required for various legal purposes; the information security requirement specifications specified by various legislative rules must be adhered by the concerned stakeholders. In this research, a discrete wavelet-based watermarking scheme has been proposed for the authentication of medical images. The experimental results obtained have shown that the proposed scheme is a secure one and robust against various common image processing attacks.
A Practical Attestation Protocol for Autonomous Embedded Systems

With the recent advent of the Internet of Things (IoT), embedded devices increasingly operate collaboratively in autonomous networks. A key technique to guard the secure and safe operation of connected embedded devices is remote attestation. It allows a third party, the verifier, to ensure the integrity of a remote device, the prover. Unfortunately, existing attestation protocols are impractical when applied in autonomous networks of embedded systems due to their limited scalability, performance, robustness, and security guarantees. In this work, we propose PASTA, a novel attestation protocol that is particularly suited for autonomous embedded systems. PASTA is the first that (i) enables many low-end prover devices to attest their integrity towards many potentially untrustworthy low-end verifier devices, (ii) is fully decentralized, thus, able to withstand network disruptions and arbitrary device outages, and (iii) is in addition to software attacks capable of detecting physical attacks in a much more robust way than any existing protocol. We implemented our protocol, conducted measurements, and simulated large networks. The results show that PASTA is practical on low-end embedded devices, scales to large networks with millions of devices, and improves robustness by multiple orders of magnitude compared with the best existing protocols.
Hybrid of PSO and CMA-ES for Global Optimization

Both Particle Swarm Optimization (PSO) and Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) exhibit good performance when solving global optimization problems. However, PSO could be misled by historical information and falls into a local optimum. Further, CMA-ES cannot fully utilize global information. Therefore, in this paper, we first propose a time-window PSO (TW-PSO) as an improvement of PSO, which could enhance the exploration ability of the algorithm. Second, we design a hybrid algorithm of TW-PSO, PSO and CMA-ES, i.e., HTPC, which combines the advantages of TW-PSO, PSO, and CMA-ES. We test HTPC on single-objective optimization problems from the CEC-2019 100-Digit Challenge, and the experimental results show that the performance of HTPC is competitive.