Reviewerly/exHarmony huggingface dataset:
Unscented-Transformation-Based Distributed Nonlinear State Estimation: Algorithm, Analysis, and Experiments

The problem of fully distributed state estimation using networked local sensors is studied in this paper. Specifically, the scenario with general nonlinear process model and local sensing models is considered by extending the distributed hybrid information fusion (DHIF) algorithm proposed by Wang and Ren. Different from the extended Kalman filter-based approaches which require the computation of the Jacobian matrix at every time instant, the unscented transformation (UT) approach is adopted for such an extension to better characterize the statistics after nonlinear transformations. The extended algorithm inherits the advantages of the original DHIF algorithm for requiring only one communication iteration between every two consecutive time instants and for requiring no global information. As well recognized that the stability analysis in the distributed UT-based framework is challenging, in the special case where the sensing models are linear, it is also analytically shown that the local estimate errors are bounded in the mean square sense. Simulations are extensively studied to show the performance of the extended algorithm. More importantly, the effectiveness of the algorithm is also verified using real data collected in a robot tracking task with networked Vicon cameras.
Beyond Pixels: Image Provenance Analysis Leveraging Metadata

Creative works, whether paintings or memes, follow unique journeys that result in their final form. Understanding these journeys, a process known as "provenance analysis," provides rich insights into the use, motivation, and authenticity underlying any given work. The application of this type of study to the expanse of unregulated content on the Internet is what we consider in this paper. Provenance analysis provides a snapshot of the chronology and validity of content as it is uploaded, re-uploaded, and modified over time. Although still in its infancy, automated provenance analysis for online multimedia is already being applied to different types of content. Most current works seek to build provenance graphs based on the shared content between images or videos. This can be a computationally expensive task, especially when considering the vast influx of content that the Internet sees every day. Utilizing non-content-based information, such as timestamps, geotags, and camera IDs can help provide important insights into the path a particular image or video has traveled during its time on the Internet without large computational overhead. This paper tests the scope and applicability of metadata-based inferences for provenance graph construction in two different scenarios: digital image forensics and cultural analytics.
High-order Intuitionistic Fuzzy Cognitive Map Based on Evidential Reasoning Theory

An intuitionistic fuzzy cognitive map (IFCM) is an extension of a fuzzy cognitive map (FCM) that forms a graph-oriented fuzzy map describing both causal relationships between pairs of concepts and the states of concepts via intuitionistic fuzzy sets (IFSs). In contrast with an FCM, an IFCM provides much more flexibility in system modeling. However, IFCMs may lead to confusing or unreasonable results in system modeling since they do not fully consider the negative influence from conventional operations on IFSs, the activation process of concepts, and the problem of aggregating knowledge with different importance levels. To solve the challenges of IFCMs, we propose a high-order IFCM based on evidential reasoning (ER) (IFCMR) theory in this study. First, we introduce an evidential intuitionistic fuzzy aggregation (EIFA) operator and a multiplication operation on IFSs using an ER theory. Second, we establish the theory of IFCMR based on the EIFA operator and the newly introduced multiplication operation on IFSs. Third, we propose a scheme of aggregating IFCMRs with different importance levels using the EIFA operator, which can also be utilized to aggregate conflict knowledge and to determine objective connections in terms of an evidential cognitive map (ECM). Finally, several numerical and practical examples are employed to test and verify the feasibility and validity of IFCMRs in comparison with both IFCMs and ECMs.
XMAS: An Efficient Mobile Adaptive Streaming Scheme Based on Traffic Shaping

Adaptive video streaming can provide adequate Quality of Experience by dynamically adjusting video rates in responding to fluctuating service conditions. However, adaptive video streaming shows dismal performances in wireless networks where several players share a wireless bottleneck link. This paper proposes a novel video rate selection scheme called XMAS for efficient video streaming in wireless networks. XMAS consists of two components: an available bandwidth estimation part and a video rate selection part. To redress the problem of inaccurate bandwidth estimation due to peculiar on-off transmission patterns of mobile video streaming, we devised a novel client-based traffic shaping scheme that effectively throttles server's packet transmission. Equipped with accurate bandwidth estimates, XMAS determines target transmission rates considering playback buffer levels. We implemented XMAS on Linux and performed rigorous experiments to analyze its behavior and performance. Our performance results showed that XMAS achieves up to 20% increase in average video rates while reducing rebuffer rates significantly.
Particle Swarm Optimization with Moving Particles on Scale-Free Networks

PSO is a nature-inspired optimization algorithm widely applied in many fields. In this paper, we present a variant named MP-PSO, in which some particles are allowed to move on a scale-free network and change the interaction pattern during the search course. In contrast to traditional PSOs with fixed interaction sources, MP-PSO shows better flexibility and diversity, where the structure of the particle swarm could change adaptively and balance exploration and exploitation to a large extent. Experiments on benchmark functions show that MP-PSO outperforms other PSO variants on solution quality and success rate, especially for multimodal functions. We further investigate effects of the moving strategy from a microscopic view, finding that the cooperation mechanism of particles located on hub and non-hub nodes plays a crucial role during the optimization process. In particular, owing to the movement of particles on non-hub nodes, the exploration can be guaranteed to some extent even in the final stage, which may be benefit for optimization. We demonstrate the applicability of MP-PSO by using it to solve an important optimization problem, arrival sequencing and scheduling, in the field of air traffic control.
Kid on the phone! Toward automatic detection of children on mobile devices

Studies have shown that children can be exposed to smart devices at a very early age. This has important implications on research in children–computer interaction, children online safety and early education. Many systems have been built based on such research. In this work, we present multiple techniques to automatically detect the presence of a child on a smart device, which could be used as the first step on such systems. Our methods distinguish children from adults based on behavioral differences while operating a touch-enabled modern computing device. Behavioral differences are extracted from data recorded by the touchscreen and built-in sensors. To evaluate the effectiveness of the proposed methods, a new data set has been created from 50 children and adults who interacted with off-the-shelf applications on smart phones. Results show that it is possible to achieve 99% accuracy and less than 0.5% error rate after 8 consecutive touch gestures using only touch information or 5 s of sensor reading. If information is used from multiple sensors, then only after 3 gestures, similar performance could be achieved.
Cache-Attacks on the ARM TrustZone Implementations of AES-256 and AES-256-GCM via GPU-Based Analysis

The ARM TrustZone is a security extension which is used in recent Samsung flagship smartphones to create a Trusted Execution Environment (TEE) called a Secure World, which runs secure processes (Trustlets). The Samsung TEE includes cryptographic key storage and functions inside the Keymaster trustlet. The secret key used by the Keymaster trustlet is derived by a hardware device and is inaccessible to the Android OS. However, the ARM32 AES implementation used by the Keymaster is vulnerable to side channel cache-attacks. The Keymaster trustlet uses AES-256 in GCM mode, which makes mounting a cache attack against this target much harder. In this paper we show that it is possible to perform a successful cache attack against this AES implementation, in AES-256/GCM mode, using widely available hardware. Using a laptop's GPU to parallelize the analysis, we are able to extract a raw AES-256 key with 7 min of measurements and under a minute of analysis time and an AES-256/GCM key with 40 min of measurements and 30 min of analysis.
Self-paced multi-task clustering

Multi-task clustering (MTC) has attracted a lot of research attentions in machine learning due to its ability in utilizing the relationship among different tasks. Despite the success of traditional MTC models, they are either easy to stuck into local optima, or sensitive to outliers and noisy data. To alleviate these problems, we propose a novel self-paced multi-task clustering (SPMTC) paradigm. In detail, SPMTC progressively selects data examples to train a series of MTC models with increasing complexity, thus highly decreases the risk of trapping into poor local optima. Furthermore, to reduce the negative influence of outliers and noisy data, we design a soft version of SPMTC to further improve the clustering performance. The corresponding SPMTC framework can be easily solved by an alternating optimization method. The proposed model is guaranteed to converge and experiments on real data sets have demonstrated its promising results compared with state-of-the-art multi-task clustering methods.
A High Throughput and Secure Authentication-Encryption AES-CCM Algorithm on Asynchronous Multicore Processor

We propose an authentication-based matrix-transformation cum parallel-encryption implemented on an asynchronous multicore processor (AMP-MP) to achieve a high throughput and yet secure advanced encryption standard based on counter with chaining mode (AES-CCM). There are four main features in our proposed AMP-MP. First, we employ the matrix multiplication in GF(28) computation to transform the 16 plaintexts into one plaintext, hence improving the authentication speed by 32× collectively at the transmitter and receiver. Second, we reschedule the operations of three AES encryptions in three different cores such that their physical leakages are compensated and equalized, thus reducing the correlation of physical leakage with the processed data by >3×. Third, the intermediate values of AES-CCM are propagated asynchronously between different cores to randomize the physical leakages with the processed data, and therefore further enhance the security of AES-CCM against the SCA by another 3×. Fourth, we propose a key adjusting technique based on S-Box byte-key transformation to protect the key against pattern-based attack. Our proposed AMP-MP is realized on an 8-bit asynchronous 9-core processor fabricated based on the 65 nm CMOS process. The experimental results show that the throughput of the authentication is 13.54 Gbps while the throughput for both authentication and encryption collectively is 8.32 Gbps, which are 17× and 70× faster than the reported counterparty, respectively. Based on power dissipation and EM SCA on our proposed AMP-MP, the secret key is unrevealed at 5 × 105 traces, which is ~17× more secured than the standard ASIC AES-CCM implementation.
Backtracking search optimization algorithm based on knowledge learning

As a new evolutionary computation method, the structure of backtracking search optimization algorithm (BSA) is simple and the exploration capability of it is strong. However, the global performance of the BSA is significantly affected by mutation strategies and control parameters. Designing appropriate mutation strategies and control parameters is important to improve the global performance of the BSA. In this paper, an adaptive BSA with knowledge learning (KLBSA) is developed to improve the global performance of the BSA. In the method, an adaptive control parameter based on the global and local information of the swarms in the current iteration is designed to adjust the search step length of individuals, which helps to balance the exploration and exploitation abilities of the algorithm. Moreover, a new mutation strategy based on the guidance of different information is designed to improve the optimization ability of the algorithm. In addition, a multi-population strategy is implemented to thoroughly improve the searching ability of the algorithm for different searching areas. To this end, experiments on three groups of benchmark functions and three real-world problems are implemented to verify the performance of the proposed KLBSA algorithm. The results indicate that the proposed algorithm performs competitively and effectively when compared to some other evolutionary algorithms.
Energy-Optimal Collision-Free Motion Planning for Multiaxis Motion Systems: An Alternating Quadratic Programming Approach

This work investigates energy-optimal motion planning for a class of multiaxis motion systems where the system dynamics are linear time-invariant and decoupled in each axis. Solving the problem in a reliable and efficient manner remains challenging owing to the presence of various constraints on control and states, nonconvexity in its cost function, and obstacles. This paper shows how the cost function can be convexified by considering the system dynamics, while decomposing decision variables to obtain a convex representation of collision avoidance constraints. With the convexified cost function and constraints, the original problem is decomposed into two quadratic programming (QP) problems. An alternating quadratic programming (AQP) algorithm is proposed to solve both the QP problems alternatingly and iteratively till convergence. Requiring an initial feasible trajectory as a guess, AQP necessarily converges to an energy-efficient solution that is homotopic to the initial guess. Under certain circumstances, AQP is guaranteed to produce a local optimum. Simulation demonstrates that AQP is computationally efficient and reliable while claiming comparable energy saving as the mixed-integer QP approach.
Does Reviewer Recommendation Help Developers?

Selecting reviewers for code changes is a critical step for an efficient code review process. Recent studies propose automated reviewer recommendation algorithms to support developers in this task. However, the evaluation of recommendation algorithms, when done apart from their target systems and users (i.e., code review tools and change authors), leaves out important aspects: perception of recommendations, influence of recommendations on human choices, and their effect on user experience. This study is the first to evaluate a reviewer recommender in vivo. We compare historical reviewers and recommendations for over 21,000 code reviews performed with a deployed recommender in a company environment and set out to measure the influence of recommendations on users' choices, along with other performance metrics. Having found no evidence of influence, we turn to the users of the recommender. Through interviews and a survey we find that, though perceived as relevant, reviewer recommendations rarely provide additional value for the respondents. We confirm this finding with a larger study at another company. The confirmation of this finding brings up a case for more user-centric approaches to designing and evaluating the recommenders. Finally, we investigate information needs of developers during reviewer selection and discuss promising directions for the next generation of reviewer recommendation tools. Preprint: https://doi.org/10.5281/zenodo.1404814.
Chain based sampling for monotonic imbalanced classification

Classification with monotonic constraints arises from some ordinal real-life problems. In these real-life problems, it is common to find a big difference in the number of instances representing middle-ranked classes and the top classes, because the former usually represents the average or the normality, while the latter are the exceptional and uncommon. This is known as class imbalance problem, and it deteriorates the learning of those under-represented classes. However, the traditional solutions cannot be applied to applications that require monotonic restrictions to be asserted. Since these were not designed to consider monotonic constraints, they compromise the monotonicity of the data-sets and the performance of the monotonic classifiers. In this paper, we propose a set of new sampling techniques to mitigate the imbalanced class distribution and, at the same time, maintain the monotonicity of the data-sets. These methods perform the sampling inside monotonic chains, sets of comparable instances, in order to preserve them and, as a result, the monotonicity. Five different approaches are redesigned based on famous under- and over-sampling techniques and their standard and ordinal versions are compared with outstanding results.
Supporting user-perceived usability benchmarking through a developed quantitative metric

Most user-centered assessment activities for ensuring usability are principally focused on performing formative evaluations, enrolling users to complete different tasks and thus obtaining indicators such as effectiveness and efficiency. However, when considering broader scenarios, such as in User Experience (UX) assessments, user perceived satisfaction (or perceived usability) is even much more relevant. There are different methods for measuring user perception, however most of them are mainly qualitative and based on individual assessments, providing little specific support to carry out comparisons–i.e., benchmarking on user-perceived usability. In this paper, we propose a quantitative metric to achieve comparative evaluations of usability perception based on Reaction Cards, a popular method for obtaining the user's subjective satisfaction in UX assessments. The metric was developed through an empirical study. Additionally, it has been validated with usability experts. Besides, we provide a supporting tool based on the developed metric, featuring a framework to store historical evaluations in order to obtain charts and benchmark levels for comparing perceived usability from different artifacts such as software products, applications categories, services, mockups, prototypes and so on. Furthermore, an evaluation involving usability professionals was achieved, providing satisfactory results to answer research questions, thus demonstrating the suitability of the approach proposed.
Neural self-compressor: Collective interpretation by compressing multi-layered neural networks into non-layered networks

The present paper proposes a new method called "neural self-compressors" to compress multi-layered neural networks into the simplest possible ones (i.e., without hidden layers) to aid in the interpretation of relations between inputs and outputs. Though neural networks have shown great success in improving generalization, the interpretation of internal representations becomes a serious problem as the number of hidden layers and their corresponding connection weights becomes larger and larger. To overcome this interpretation problem, we introduce a method that compresses multi-layered neural networks into ones without hidden layers. In addition, this method simplifies entangled weights as much as possible by maximizing mutual information between inputs and outputs. In this way, final connection weights can be interpreted as easily as by the logistic regression analysis. The method was applied to four data sets: a symmetric data set, ovarian cancer data set, restaurant data set, and credit card holders' default data set. In the first set, the symmetric data set, we tried to explain how the present method could produce interpretable outputs intuitively. In all the other cases, we succeeded in compressing multi-layered neural networks into their simplest forms with the help of mutual information maximization. In addition, by de-correlating outputs, we were able to transform connection weights from those close to the regression coefficients to ones with more explicit features.
An Unsupervised Multicode Hashing Method for Accurate and Scalable Remote Sensing Image Retrieval

Hashing methods have recently attracted great attention for approximate nearest neighbor search in massive remote sensing (RS) image archives due to their computational and storage effectiveness. The existing hashing methods in RS represent each image with a single-hash code that is usually obtained by applying hash functions to global image representations. Such an approach may not optimally represent the complex information content of RS images. To overcome this problem, in this letter, we present a simple yet effective unsupervised method that represents each image with primitive-cluster sensitive multi-hash codes (each of which corresponds to a primitive present in the image). To this end, the proposed method consists of two main steps: 1) characterization of images by descriptors of primitive-sensitive clusters and 2) definition of multi-hash codes from the descriptors of the primitive-sensitive clusters. After obtaining multi-hash codes for each image, retrieval of images is achieved based on a multi-hash-code-matching scheme. Any hashing method that provides single-hash code can be embedded within the proposed method to provide primitive-sensitive multi-hash codes. Compared with state-of-the-art single-code hashing methods in RS, the proposed method achieves higher retrieval accuracy under the same retrieval time, and thus it is more efficient for operational applications.
A Compound Sinusoidal Differential Evolution algorithm for continuous optimization

We propose a new variant of the Sinusoidal Differential Evolution (SinDE) algorithm, we call it the OCSinDE for Opposition-based Compound SinDE. It is based on the use of a compound sinusoidal formula for adjusting the scaling factor and crossover rate values of the Differential Evolution (DE) algorithm. In addition, Opposition-Based Learning (OBL) and a restart mechanism are adopted to boost the algorithm's exploration ability and avoid stagnation. The proposed approach has been tested on the reference black-box optimization benchmarking framework, BBOB, and compared to the standard DE algorithm, six variants of the original SinDE, and seven state-of-the-art differential evolution algorithms. Further comparisons to other state-of-the-art algorithms, including the famous multi-start CMAES, have been conducted. The obtained results have proven that the proposed OCSinDE does not only eliminate the effort dedicated to set the F and CR parameters, but is also very effective in terms of search performance; it outperformed the SinDE variants, many DE algorithms, the CMAES, and other metaheuristics.