Reviewerly/exHarmony huggingface dataset:
A distance-based weighting framework for boosting the performance of dynamic ensemble selection

Dynamic Ensemble Selection (DES) strategy is one of the most common and effective techniques in machine learning to deal with classification problems. DES systems aim to construct an ensemble consisting of the most appropriate classifiers selected from the candidate classifier pool according to the competence level of the individual classifier. Since several classifiers are selected, their combination becomes crucial. However, most of current DES approaches focus on the combination of the selected classifiers while ignoring the local information surrounding the query sample needed to be classified. In order to boost the performance of DES-based classification systems, we in this paper propose a dynamic weighting framework for the classifier fusion during obtaining the final output of an DES system. In particular, the proposed method first employs a DES approach to obtain a group of classifiers for a query sample. Then, the hypothesis vector of the selected ensemble is obtained based on the analysis of consensus. Finally, a distance-based weighting scheme is developed to adjust the hypothesis vector depending on the closeness of the query sample to each class. The proposed method is tested on 30 real-world datasets with six well-known DES approaches based on both homogeneous and heterogeneous ensemble. The obtained results, supported by proper statistical tests, show that our method outperforms, both in terms of accuracy and kappa measures, the original DES framework.
Bootstrapping cookbooks for APIs from crowd knowledge on Stack Overflow

Well established libraries typically have API documentation. However, they frequently lack examples and explanations, possibly making difficult their effective reuse. Stack Overflow is a question-and-answer website oriented to issues related to software development. Despite the increasing adoption of Stack Overflow, the information related to a particular topic (e.g., an API) is spread across the website. Thus, Stack Overflow still lacks organization of the crowd knowledge available on it. Our target goal is to address the problem of the poor quality documentation for APIs by providing an alternative artifact to document them based on the crowd knowledge available on Stack Overflow, called crowd cookbook. A cookbook is a recipe-oriented book, and we refer to our cookbook as crowd cookbook since it contains content generated by a crowd. The cookbooks are meant to be used through an exploration process, i.e. browsing. In this paper, we present a semi-automatic approach that organizes the crowd knowledge available on Stack Overflow to build cookbooks for APIs. We have generated cookbooks for three APIs widely used by the software development community: SWT, LINQ and QT. We have also defined desired properties that crowd cookbooks must meet, and we conducted an evaluation of the cookbooks against these properties with human subjects. The results showed that the cookbooks built using our approach, in general, meet those properties. As a highlight, most of the recipes were considered appropriate to be in the cookbooks and have self-contained information. We concluded that our approach is capable to produce adequate cookbooks automatically, which can be as useful as manually produced cookbooks. This opens an opportunity for API designers to enrich existent cookbooks with the different points of view from the crowd, or even to generate initial versions of new cookbooks.
You Are Not Acting Like Yourself: A Study on Soft Biometric Classification, Person Identification, and Mobile Device Use

In this paper, we explore the soft biometric classification of 13 demographic and behavioral attributes using phone data collected from 46 subjects. We utilize the results of this analysis to further evaluate reduced search spaces for the primary identification task by implementing a ranking formula which quantifies the performance of each attribute considering the predictability of some attributes compared to others. Results show that soft biometric classification is feasible with up to 90% accuracy; however, because people exhibit high intra-class variance, templates and queries are significantly affected in terms of how well they match, even with reduced search spaces. We analyze these findings using a combination of approaches, including an evaluation of the biometric menagerie, visualizing the distribution of the data, and observing how subjects vary in their soft biometric class across different times of the day.
Hierarchical Normalized Completely Random Measures to Cluster Grouped Data

In this paper we propose a Bayesian nonparametric model for clustering grouped data.We adopt a hierarchical approach: at the highest level, each group of data is modeled according to a mixture, where the mixing distributions are conditionally independent normalized completely random measures (NormCRMs) centered on the same base measure, which is itself a NormCRM.The discreteness of the shared base measure implies that the processes at the data level share the same atoms.This desired feature allows to cluster together observations of different groups.We obtain a representation of the hierarchical clustering model by marginalizing with respect to the infinite dimensional NormCRMs.We investigate the properties of the clustering structure induced by the proposed model and provide theoretical results concerning the distribution of the number of clusters, within and between groups.Furthermore, we offer an interpretation in terms of generalized Chinese restaurant franchise process, which allows for posterior inference under both conjugate and non-conjugate models.We develop algorithms for fully Bayesian inference and assess performances by means of a simulation study and a real-data illustration.Supplementary Materials for this work is available online.
Robust quantum control against clock noises in multiqubit systems

High-precision manipulation of multi-qubit quantum systems requires strictly clocked and synchronized multi-channel control signals. However, practical Arbitrary Waveform Generators (AWGs) always suffer from random signal jitters and channel latencies that induces non-ignorable state or gate operation errors. In this paper, we analyze the average gate error caused by clock noises, from which an estimation formula is derived for quantifying the control robustness against clock noises. This measure is then employed for finding robust controls via a homotopic optimization algorithm. We also introduce our recently proposed stochastic optimization algorithm, b-GRAPE, for training robust controls via randomly generated clock noise samples. Numerical simulations on a two-qubit example demonstrate that both algorithms can greatly improve the control robustness against clock noises. The homotopic algorithm converges much faster than the b-GRAPE algorithm, but the latter can achieve more robust controls against clock noises.
Multi-Sensor Space Debris Tracking for Space Situational Awareness With Labeled Random Finite Sets

As a result of the dependence worldwide on satellite technology, it is now necessary to use advanced multi-target tracking algorithms for space debris tracking systems to maintain custody of space objects around the earth.One principal challenge is the correct association of observations with objects.This paper presents a multi-sensor, space-debris tracking algorithm using δ-generalized labeled multi-Bernoulli (δ-GLMB) filtering.The algorithm provides a solution to the key challenges (e.g., detection uncertainty, data association uncertainty, and clutter) in multiple object tracking.An efficient implementation of the multi-sensor δ-GLMB filter is proposed.In order to avoid exhaustively computing all the terms, we propose to use the ranked assignment algorithm with an extended assignment matrix for multiple sensors to determine the most significant terms.A measurement-based birth model is used to identify the previously unknown space objects.Sensors can have the same or different observation volumes.The expectation-maximization (EM) algorithm is used to approximate densities across observation volumes.The performance is demonstrated using the simulation results.
Nurse (Re)scheduling via answer set programming1

The goal of the Nurse Scheduling Problem is to find an assignment of nurses to shifts according to specific requirements. Frequently, a computed schedule may become not usable because of sudden absences of some nurses. In this cases, Nurse Rescheduling amounts to the computation of a new schedule, which has to satisfy the original requirements and the new absences. Additionally, a good solution to the Nurse Rescheduling Problem must be as similar as possible to the original schedule, which practically means that the number of changes has to be minimized. This paper focuses on the requirements specified by an Italian hospital, and recently addressed by an approach based on Answer Set Programming (ASP). Even if promising results have been obtained with ASP, the original encoding presents some intrinsic weaknesses, which are identified and eventually circumvented in this paper. The new encoding is designed by taking into account both intrinsic properties of Nurse Scheduling and internal details of ASP solvers, such as cardinality and weight constraint propagators. The performance gain of clingo and wasp is empirically verified on instances from ASP literature. As an additional contribution, the performance of clingo and wasp is compared to other declarative frameworks, namely SAT and ILP; the best performance is obtained by clingo running the new ASP encoding. The advanced ASP encodings are then extended to solve Nurse Rescheduling, and an empirical evaluation is conducted with clingo and wasp.
Exploiting Mid-Level Semantics for Large-Scale Complex Video Classification

As the amount of available video data has grown substantially, automatic video classification has become an urgent yet challenging task. Most video classification methods focus on acquiring discriminative spacial visual features and motion patterns for video representation, especially deep learning methods, which have achieved very good results on action recognition problems. However, the performance of most of these methods drastically degenerates for more generic video classification tasks where the video contents are much more complex. Thus, in this paper, the mid-level semantics of videos are exploited to bridge the semantic gap between low-level features and high-level video semantics. Inspired by the term ``frequency-inverse document frequency'', a word weighting method for the problem of text classification is introduced to the video domain. The visual objects in videos are regarded as the words in texts, and two new weighting methods are proposed to encode videos by weighting visual objects according to the characteristics of videos. In addition, the semantic similarities between video categories and visual objects are introduced from the text domain as privileged information to facilitate classifier training on the obtained semantic representations of videos. The proposed semantic encoding method (semantic stream) is then fused with the popular two-stream CNN model for the final classification results. Experiments are conducted on two large-scale complex video datasets, CCV and ActivityNet. The experimental results validate the effectiveness of the proposed methods.
3D Strain Mapping of Opaque Materials Using an Improved Digital Volumetric Speckle Photography Technique with X-Ray Microtomography

Digital volumetric speckle photography (DVSP) method has been used to strain investigation in opaque materials. In this paper, an improved DVSP algorithm is introduced, in which a multi-scale coarse–fine subset calculation process and a subvoxel shifting technique are applied to increase accuracy. We refer to the new algorithm as Multi-scale and Subvoxel shifting Digital Volumetric Speckle Photography (MS-DVSP). The displacement and strain fields of a red sandstone cylinder exposed to uniaxial compression and a woven composite beam under three-point bending are mapped in detail. The characteristics of the interior deformation of the specimens are clearly depicted, thus elucidating the failure mechanism of the materials.
Automatic Modulation Recognition of Radar Signals Based on Manhattan Distance-Based Features

Feature-based (FB) algorithms for automatic modulation recognition of radar signals have received much attention since they are usually simple to realize. However, existing FB approaches usually focus on several specific modulations and fail when applied to various modulations. To overcome this issue, we propose a simple and effective FB algorithm based on Manhattan distance-based features (MDBFs) in this paper. MDBFs are new features for radar signals that can be applied for recognition of different modulations. The main contributions of this paper are as follows. First, radar signals are represented as wavelet ridges, which includes important information that can distinguish different modulations, and the piecewise aggregate approximation algorithm is introduced to reduce signal dimensions. Then, the dynamic time warping averaging is employed instead of the traditional k-means algorithm to extract realistic centroids for each class. Finally, the Manhattan distances between each data sample and each centroid are used to construct MDBFs, and decisions are made using the k-nearest neighbor. In addition, we prove that MDBFs have better class separability power than the Euclidean-based features. MDBFs contain information about the correlations between different classes, which means that these features suitable for discriminating various modulations when their class distributions do not overlap badly in representation space. The extensive experiments on a synthetic dataset demonstrate the outstanding performance of our proposed method and are hardly affected by the pulse width of the signal. Thus, the proposed method with the effectiveness and robustness could be a promising modulation recognition method of the radar signal.
Automatic Tracking of Muscle Cross‐Sectional Area Using Convolutional Neural Networks with Ultrasound

The purpose of this study was to develop an automatic tracking method for the muscle cross-sectional area (CSA) on ultrasound (US) images using a convolutional neural network (CNN). The performance of the proposed method was evaluated and compared with that of the state-of-the art muscle segmentation method.A real-time US image sequence was obtained from the rectus femoris muscle during voluntary contraction. A CNN was built to segment the rectus femoris muscle and calculate the CSA in each US frame. This network consisted of 2 stages: feature extraction and score map reconstruction. The training of the network was divided into 3 steps with output score map resolutions of one-fourth, one-half, and all of the original image. We evaluated the segmentation performance of our method with 5-fold cross-validation. The mean precision, recall, and dice similarity score were calculated.The mean precision, recall, and Dice's coefficient (DSC) ± SD were 0.936 ± 0.029, 0.882 ± 0.045, and 0.907 ± 0.023, respectively. Compared with the state-of-the-art muscle segmentation method (constrained mutual-information-based free-form deformation), the proposed method using CNN showed high performance.The automated method proposed in this study provides an accurate and efficient approach to the estimation of the muscle CSA during muscle contraction.
Dual image reversible data hiding using trinary assignment and centre folding strategy with low distortion

Dual image Reversible Data Hiding (RDH) algorithms generate two stego images instead of one. These algorithms are considered more secure because the secret data can be recovered only if both stego images are available. This paper proposes a dual image RDH algorithm based on the Centre Folding Strategy (CFS) and the Shiftable Pixel Coordinate Selection Strategy. The proposed algorithm improves the visual quality of the two stego images while maintaining a high embedding rate. It uses a look-up table containing the shifts that the cover pixel will undergo after the data has been embedded. Some overhead information is required for accurate extraction of the hidden data and it is delivered to the users as a password after embedding. The results show that the proposed algorithm outperforms other methods in terms of visual quality with an average Peak-Signal-to-Noise-Ratio (PSNR) of 50.66 dB with an embedding rate of 1.56 bits per pixel.
Exact Synthesis of Nearest Neighbor Compliant Quantum Circuits in 2-D Architecture and Its Application to Large-Scale Circuits

In this paper, we propose an exact method of directly synthesizing a nearest neighbor compliant (NNC) quantum circuit with the smallest depth in 2-D architecture, given a reversible function. Our method maps the synthesis problem to a Boolean satisfiability (SAT) problem and uses a satisfiability modulo theories (SMT) solver to find an assignment of a network of allowed quantum gates. Since an SMT solver performs an exhaustive search, it can be ensured that on a specific qubit placement, the NNC quantum circuit synthesized by our method has the smallest number of quantum gates. However, the exhaustive search also makes our exact method not scale well. For that reason, we propose another method of applying our exact method in the local synthesis of large-scale circuits, to in parallel synthesize sub-NNC quantum circuits. From the experimental results, the quantum costs of the NNC quantum circuits synthesized by our methods are reduced by an average of 23.89%, when compared to an optimal heuristic method which determines the smallest number of SWAP gates in 2-D architecture.
Aligned Dynamic-Preserving Embedding for Zero-Shot Action Recognition

Zero-shot learning (ZSL) typically explores a shared semantic space in order to recognize novel categories in the absence of any labeled training data. However, the traditional ZSL methods always suffer from serious domain shift problem in human action recognition. This is because: 1) existing ZSL methods are specifically designed for object recognition from static images, which do not capture the temporal dynamics of video sequences, and poor performances are always generated if those methods are directly applied to zero-shot action recognition; 2) these methods always blindly project the target data into a shared space using a semantic mapping obtained by the source data without any adaptation, in which the underlying structures of target data are ignored; and 3) severe inter-class variations exist in various action categories. The traditional ZSL methods do not take relationships across different categories into consideration. In this paper, we propose a novel aligned dynamic-preserving embedding (ADPE) model for zero-shot action recognition in a transductive setting. In our model, an adaptive embedding of target videos is learned, exploring the distributions of both the source and target data. An aligned regularization is further proposed to couple the centers of target semantic representations with their corresponding label prototypes in order to preserve the relationships across different categories. Most significantly, during our embedding, the temporal dynamics of video sequences are simultaneously preserved via exploiting the temporal consistency of video sequences and capturing the temporal evolution of successive segments of actions. Our model can effectively overcome the domain shift problem in zero-shot action recognition. The experiments on Olympic sports, HMDB51, and UCF101 datasets demonstrate the effectiveness of our model.
Decoding the Binary: Reconsidering the Hacker Subculture through a Gendered Lens

Research has demonstrated that hacker subculture – like many other tech-oriented communities – is disproportionately composed of men. While prior attempts have been made to explain this disparity, few, if any, explore the role of subculture in this gendered divide. Drawing from feminist theories, subcultural theory, and cultural criminology, this theoretical analysis examines the intersection of gender, social structure, hegemony, situated action, and subculture to argue that hacker subculture is (1) male-dominated and androcentric, (2) mired in language, like meritocratic rhetoric, which masks inequity, and (3) conducive to forms of sexual harassment and gendered exclusion. Implications for hacking research and subcultural theory are discussed throughout.
A Resource-Efficient Multiplierless Systolic Array Architecture for Convolutions in Deep Networks

This brief presents a resource-efficient VLSI architecture for convolution operations in deep networks. Taking advantage of a feature of the max pooling layer in classical convolutional neural networks (CNNs), the image pixels are scaled such that the weight values are constrained to lie in [-1, +1] range. Under this constraint, the weight parameters are chosen as trigonometric functions, enabling realization of convolution without multipliers. In particular, the convolution is realized using the CORDIC algorithm. We also propose a dataflow model based on a reconfigurable systolic ring array to achieve performance comparable to contemporary CNN architectures but with substantially less hardware, high resource utilization efficiency, and reduced power consumption. FPGA implementation of the proposed architecture on Xilinx Virtex-5 XC5VLX5OT achieves roughly 55% higher resource efficiency, with approximately 53% reduction in the slice-delay product and 55% reduction in power consumption compared to recent architectures.
Blockchain and IoT Based Disruption in Logistics

Blockchain technology along with Internet of Things (IoT) and Cloud Computing is expected to cause disruption in the existing business processes at unprecedented scale. Smart Contracts which represent business logic are consolidated by blockchain architecture. A smart contract is a self-executing program that may be triggered on events generated by IoT sensors, actuators or tags. Blockchain and smart contracts which find their optimum suitability in multiparty arrangements are anticipated to bring a paradigm shift in the domains of Supply-chain and logistics. This paper presents a comprehensive analysis of the disruptive innovation happening in the logistics processes due to blockchain, smart contracts and Internet of Things. We highlight the some of the most important Key Performance Indicators (KPIs) of the logistics domain being positively affected due to this digital disruption and the required characteristics in the blockchain technologies for this paradigm shift to take place.
Flow Adaptive Video Object Segmentation

We tackle the task of semi-supervised video object segmentation, i.e. pixel-level object classification of the images in video sequences using very limited ground truth training data of its corresponding video. We present FLow Adaptive Video Object Segmentation, an efficient pipeline based on a novel online adaptation algorithm that utilizes optical flow, capable of tracking objects effectively throughout videos. Comparing with most of the recent deep learning based approaches that trade efficiency for accuracy, we provide extensive complexity analysis and additionally demonstrate that FLAVOS is natural for real world applications by introducing an interactive pipeline that enables the user to provide feedback for online training. Our method achieves state-of-the-art accuracy on three challenging benchmark datasets and nearly ground-truth level segmentation results with interactive user feedback.