Reviewerly/exHarmony huggingface dataset:
A novel approach for facial expression recognition

In this paper, we propose a new facial expression recognition approach based on the use of Gabor filter features. These features are extracted only from regions of interest (ROI). These regions are the components of the face most involved in the expression of emotions. Then we select the best features with PCA. Finally, the SVM classifier is used for the classification of basic facial expressions. The experiment results show the superiority of the method and researched recognition rates of 95.11% and 92.19% for JAFFE and CK+ datasets respectively.
Edge Exclusion Tests for Graphical Model Selection: Complex Gaussian Vectors and Time Series

We consider the problem of inferring the conditional independence graph (CIG) of a stationary multivariate Gaussian time series. A p-variate Gaussian time series graphical model associated with an undirected graph with p vertices is defined as the family of time series that obey the conditional independence restrictions implied by the edge set of the graph. In some existing methods, partial coherence has been used as a test statistic for graphical model selection. To test inclusion/exclusion of a given edge in the graph, the test is applied at distinct frequencies, requiring multiple tests and leading to a loss in test power. The nonparametric method of Matsuda uses the Kullback-Leibler divergence measure to define a test statistic which does not need multiple testing to test between two competing models. In this paper we propose a generalized likelihood ratio test (GLRT) based edge exclusion test statistic that also does not need multiple testing. It is computationally significantly faster than the method of Matsuda, and simulations show that we achieve comparable power levels. Our proposed approach is based on a novel formulation of a GLRT based edge exclusion test for pvariate complex Gaussian graphical models (CGGMs); this result is also of independent interest. The computational complexity of the proposed statistic is O(p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) compared to O(p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> ) for the existing result. We also apply our time series graphical model selection method to a foreign exchange data set consisting of monthly trends of foreign exchange rates of 10 countries.
The Linear Optical Unambiguous Discrimination of Hyperentangled Bell States Assisted by Time Bin

Abstract A linear optical unambiguous discrimination of hyperentangled Bell states is proposed for two‐photon systems entangled in both the polarization and momentum degrees of freedom (DOFs) assisted by time bin. This unambiguous discrimination scheme can completely identify 16 orthogonal hyperentangled Bell states using only linear optical elements, where the function of the auxiliary entangled Bell state is replaced by time bin. Moreover, the possibility of extending this scheme for distinguishing hyperentangled Bell states in n DOFs is discussed, and it shows that hyperentangled Bell states in n () DOFs can be distinguished with k () auxiliary entangled states of additional DOFs by introducing a time delay, which decreases the auxiliary entanglement resource required for unambiguous discrimination of hyperentangled Bell state. Therefore, this scheme provides a new way for distinguishing hyperentangled states with current technology, which will extend the application of discrimination of hyperentangled states via linear optics to other quantum information protocols besides hyperdense coding schemes in the future.
The role of airborne geophysics in the investigation of gold occurrences in the Itapetim Region, Borborema Province, Northeast Brazil

We present a combined analysis of airborne geophysical data (magnetics and gamma-ray spectrometry) and field aspects of Itapetim lode gold district region, aiming to identify structurally deformed and hydrothermally altered zones, which are suggestive of gold mineralized sectors. The main geophysical lineaments are oriented in the NE-SW and E-W directions and display a clear correlation with the major gold-bearing structures in the region. In the magnetic maps, the key pathfinder for gold occurrences is their association with strongly magnetized areas positioned along the Itapetim Shear Zone and outer contour of Teixeira Batholith. In the field, such structures are materialized in protomylonitic and mylonitic rocks that present evidence of ductile and brittle deformation, whereas kinematic criteria are suggestive of a transpressional tectonic regime. In addition, the main radiometric pattern is characterized by K enrichment, which is correlated with a hydrothermal mineral assemblage dominated by quartz, potassium feldspar, and tourmaline. Based on the obtained data integration, we produced an integrative map and located ten new target areas of possible gold mineralization, which is correlative to other well-known lode deposits in Northeast Brazil and Africa.
Learning Image Profile Enhancement and Denoising Statistics Priors for Single-Image Super-Resolution

Single-image super-resolution (SR) has been widely used in computer vision applications. The reconstruction-based SR methods are mainly based on certain prior terms to regularize the SR problem. However, it is very challenging to further improve the SR performance by the conventional design of explicit prior terms. Because of the powerful learning ability, deep convolutional neural networks (CNNs) have been widely used in single-image SR task. However, it is difficult to achieve further improvement by only designing the network architecture. In addition, most existing deep CNN-based SR methods learn a nonlinear mapping function to directly map low-resolution (LR) images to desirable high-resolution (HR) images, ignoring the observation models of input images. Inspired by the split Bregman iteration (SBI) algorithm, which is a powerful technique for solving the constrained optimization problems, the original SR problem is divided into two subproblems: 1) inversion subproblem and 2) denoising subproblem. Since the inversion subproblem can be regarded as an inversion step to reconstruct an intermediate HR image with sharper edges and finer structures, we propose to use deep CNN to capture low-level explicit image profile enhancement prior (PEP). Since the denoising subproblem aims to remove the noise in the intermediate image, we adopt a simple and effective denoising network to learn implicit image denoising statistics prior (DSP). Furthermore, the penalty parameter in SBI is adaptively tuned during the iterations for better performance. Finally, we also prove the convergence of our method. Thus, the deep CNNs are exploited to capture both implicit and explicit image statistics priors. Due to SBI, the SR observation model is also leveraged. Consequently, it bridges between two popular SR approaches: 1) learning-based method and 2) reconstruction-based method. Experimental results show that the proposed method achieves the state-of-the-art SR results.
$K$-Graphs: An Algorithm for Graph Signal Clustering and Multiple Graph Learning

In graph signal processing (GSP), graph learning is concerned with the inference of an underlying graph best capable of modeling a dataset of graph signals. However, more complex datasets are derived from multiple underlying graphs. In such instances, it is necessary to learn multiple graph structures, each corresponding to the graph signals residing on the same structure. In other words, the graph signals need to be partitioned into a set of clusters, with a designated topology for each cluster. In this letter, inspired from classical K-means, a new algorithm for multiple graph learning, called K-graphs, is proposed. Numerical experiments demonstrate the high performance of this algorithm, in both graph learning and data clustering.
Genetic Algorithm Based Selection of Appropriate Biomarkers for Improved Breast Cancer Prediction

One of the most common types of cancer among women is Breast Cancer which amounts to a staggeringly high number of deaths every year. According to the World Health Organization (WHO), the projected number of Breast Cancer cases in 2018 is 2.09 million. However, early diagnosis of Breast Cancer does lead to a much higher five year survival rate. In this study, Genetic Algorithm (GA) is used to identify appropriate biomarkers for predicting Breast Cancer, from routine consultation and blood analysis data, which can subsequently help in the process of early diagnosis of the disease. It was observed during experimental study, that Resistin, Leptin, Glucose, BMI and Age of the patients, can be used for the purpose of automatic breast cancer prediction. In this paper, Breast Cancer Coimbra Dataset (BCCD) has been considered for performing experimentations. The GA-selected patient-attributes were subsequently utilized, by a number of Machine Learning (ML) classifiers, to learn a prediction model for the disease. It has been observed that, GA combined with Gradient Boosting Classifier performed better than all other classifiers under consideration, in terms of prediction accuracy, AUC and F-measure values, suggesting the potential applicability of this model in practical scenarios.
A Procedure and Guidelines for Analyzing Groups of Software Engineering Replications

Context: Researchers from different groups and institutions are collaborating on building groups of experiments by means of replication (i.e., conducting groups of replications). Disparate aggregation techniques are being applied to analyze groups of replications. The application of unsuitable techniques to aggregate replication results may undermine the potential of groups of replications to provide in-depth insights from experiment results. Objectives: Provide an analysis procedure with a set of embedded guidelines to aggregate software engineering (SE) replication results. Method: We compare the characteristics of groups of replications for SE and other mature experimental disciplines such as medicine and pharmacology. In view of their differences, the limitations with regard to the joint data analysis of groups of SE replications and the guidelines provided in mature experimental disciplines to analyze groups of replications, we build an analysis procedure with a set of embedded guidelines specifically tailored to the analysis of groups of SE replications. We apply the proposed analysis procedure to a representative group of SE replications to illustrate its use. Results: All the information contained within the raw data should be leveraged during the aggregation of replication results. The analysis procedure that we propose encourages the use of stratified individual participant data and aggregated data in tandem to analyze groups of SE replications. Conclusion: The aggregation techniques used to analyze groups of replications should be justified in research articles. This will increase the reliability and transparency of joint results. The proposed guidelines should ease this endeavor.
Face Segmentor-Enhanced Deep Feature Learning for Face Recognition

In this paper, we propose a Face Segmentor-Enhanced Network (FSENet) for face recognition to exploit facial localized property. Most existing methods emphasize the holistic characteristics on entire face images, which have limit discriminative ability due to large intra-class variations and inter-class fine-grain. To address this, we present a face segmentor to parse the face into local components and explore their internal correlations, which strengthens the discriminability to discern identities. Specifically, we introduce a semantic parsing module to assign each pixel with a semantic part label. This module generates a set of parsing maps, where each of them represents the pixel-wise occurrence probability of a certain facial component. We then segment facial regions masked by the parsing maps to achieve local features. We further build the structure correlation of facial part features to boost personalized attribute. We finally incorporate holistic and local information to improve the discriminative power of the face descriptor. Extensive experiments on popular public-domain datasets including Labeled Face in the Wild (LFW), Youtube Faces (YTF), IARPA IJB-A, IJB-B and IJB-C, and the MegaFace Challenge show that our method achieves promising performance.
A Deep Learning Approach for Dog Face Verification and Recognition

Recently, deep learning methods for biometrics identification have mainly focused on human face identification and have proven their efficiency. However, little research have been performed on animal biometrics identification. In this paper, a deep learning approach for dog face verification and recognition is proposed and evaluated. Due to the lack of available datasets and the complexity of dog face shapes this problem is harder than human identification. The first publicly available dataset is thus composed, and a deep convolutional neural network coupled with the triplet loss is trained on this dataset. The model is then evaluated on a verification problem, on a recognition problem and on clustering dog faces. For an open-set of 48 different dogs, it reaches an accuracy of 92% on a verification task and a rank-5 accuracy of 88% on a one-shot recognition task. The model can additionally cluster pictures of these unknown dogs. This work could push zoologists to further investigate these new kinds of techniques for animal identification or could help pet owners to find their lost animal. The code and the dataset of this project are publicly available ( https://github.com/GuillaumeMougeot/DogFaceNet ).
One stage lesion detection based on 3D context convolutional neural networks

Lesion detection from Computed Tomography (CT) scans is a challenge because non-lesions and true lesions always have similar appearances. Therefore, the performance of mainstream 2D image-based object detection algorithms is not promising since the texture and shape of inner-classes are always different. To detect lesions, we propose a novel deep convolutional feature fusion scheme, 3D Context Feature Fusion (3DCFF). Motivated by state-of-the-art object detection algorithms, we use a one-stage framework, rather than a Region Proposal Network, to extract lesions. In addition, because 3D context provides texture, contour, and shape information that are helpful for generating distinguishable lesion features, 3D context is used as the input for the proposed network. Furthermore, the network adopts a multi-resolution fusion scheme among different scales of feature maps. Results of experiments, conducted with the Deeplesion database, show that the proposed 3DCFF performs better and faster than state-of-the-art algorithms, such as Faster R-CNN, RetinaNet, and 3DCE.
Comparative Analysis of Requirement Change Management Challenges Between in-House and Global Software Development: Findings of Literature and Industry Survey

Requirement changes are inevitable, and Requirement Change Management (RCM) is a complex process in software development.In-house software development and Global Software Development (GSD) are two widely used development approaches and there is a need to explore the RCM commonalities and differences in the two development approaches.The primary objective of this study is to identify the challenges that influence RCM in both approaches.First, we have implemented Systematic Literature Review (SLR) and identified 9 challenges that impact the general RCM process and 3 more challenges related to RCM with GSD.Second, we have conducted a questionnaire survey based on SLR results and collected feedback from 69 industry practitioners.The survey result indicates that there are four out of nine challenges, namely impact analysis, requirement traceability, requirement dependency, and system instability having the same impact in both in-house and GSD approaches.On the other hand, cost/time estimation, artifacts documents management, user involvement, requirement consistency, and requirement prioritization need more attention while implemented in GSD paradigm.Furthermore, regarding two important project management structures in GSD, centralized project structure and distributed project structure, the survey results reveal that all challenges have same impact except user involvement and change control board management, which are more important in centralized project structure.Lastly, the result from t-test indicates that both data sets retrieved from SLR and survey are close to each other.This study distinguishes RCM challenges in in-house and GSD approaches and in the context of two prominent project management structures followed in GSD projects.It would assist researchers by providing potential research directions and industry professionals to understand and implement RCM in different context more efficiently.
What to consider about events: A survey on the ontology of occurrents

This work presents a review of the ideas that are currently in use on the ontology-based conceptual modeling of occurrents (sometimes referred to as "events", "perdurants", or "processes"). It collects such ideas from a set of 11 ontologies, which includes some of the most important and widely used upper ontologies (i.e., BFO, UFO, DOLCE, YAMATO, SUMO, GFO). We analyze the ontologies with respect to the definition of occurrent they present and their understanding about participation, mereology, and causation. The commitments regarding these four facets of occurrents are gathered in three categories (pervasive aspects, complementary aspects, and conflicting aspects). Additionally, we identify the main occurrent classification criteria used to branch the taxonomy of the ontologies. These findings are summarized in two tables at the end of the paper, which may be used by modeling practitioners as reference. The review shows that the considered ontologies agree in a significant set of common aspects as well as present some relevant divergences. However, there is a considerable set of non-conflicting, complementary aspects scattered among the diverse ontologies. It suggests an opportunity for efforts aiming to harmonize those views in a single approach that may enrich the analysis and representation of occurrents.
Tell Me More Than Just Assembly! Reversing Cyber-Physical Execution Semantics of Embedded IoT Controller Software Binaries

The safety of critical cyber-physical IoT devices hinges on the security of their embedded software that implements control algorithms for monitoring and control of the associated physical processes, e.g., robotics and drones. Reverse engineering of the corresponding embedded controller software binaries enables their security analysis by extracting high-level, domain-specific, and cyber-physical execution semantic information from executables. We present MISMO, a domain-specific reverse engineering framework for embedded binary code in emerging cyber-physical IoT control application domains. The reverse engineering outcomes can be used for firmware vulnerability assessment, memory forensics analysis, targeted memory data attacks, or binary patching for dynamic selective memory protection (e.g., important control algorithm parameters). MISMO performs semantic-matching at an algorithmic level that can help with the understanding of any possible cyber-physical security flaws. MISMO compares low-level binary symbolic values and high-level algorithmic expressions to extract domain-specific semantic information for the binary's code and data. MISMO enables a finer-grained understanding of the controller by identifying the specific control and state estimation algorithms used. We evaluated MISMO on 2,263 popular firmware binaries by 30 commercial vendors from 6 application domains including drones, self-driving cars, smart homes, robotics, 3D printers, and the Linux kernel controllers. The results show that MISMO can accurately extract the algorithm-level semantics of the embedded binary code and data regions. We discovered a zero-day vulnerability in the Linux kernel controllers versions 3.13 and above.
Understanding speculative investment behavior in the Bitcoin context from a dual-systems perspective

Purpose The purpose of this paper is to examine users’ decision-making mechanism of speculative investment behavior and its sequential consequences in the Bitcoin context from a dual-systems perspective. Design/methodology/approach Original data were collected via a survey of 334 participants with experience in Bitcoin speculative investment. The partial least squares method was used to test the proposed model. Findings Speculative investment behavior in the Bitcoin context is driven by strong impulse and weak self-control, leading to negative consequences. The extent of the imbalance between the two cognitive systems is greater with the subjective norm than without it, thus facilitating speculative investment behavior. Noteworthy differences in the impulse and self-control effects on Bitcoin speculative investment are found with differences in Bitcoin objective and subjective knowledge. Originality/value This study is the first attempt to empirically investigate users’ decision-making mechanism used when speculating in Bitcoin.
Robust exploration in linear quadratic reinforcement learning

Learning to make decisions in an uncertain and dynamic environment is a task of fundamental performance in a number of domains. This paper concerns the problem of learning control policies for an unknown linear dynamical system so as to minimize a quadratic cost function. We present a method, based on convex optimization, that accomplishes this task ‘robustly’, i.e., the worst-case cost, accounting for system uncertainty given the observed data, is minimized. The method balances exploitation and exploration, exciting the system in such a way so as to reduce uncertainty in the model parameters to which the worst-case cost is most sensitive. Numerical simulations and application to a hardware-in-the-loop servo-mechanism are used to demonstrate the approach, with appreciable performance and robustness gains over alternative methods observed in both.
Investigating User Requirements for Communication Between Automated Vehicles and Vulnerable Road Users

As automated vehicles increase on public roads, research on communication with vulnerable road users (VRUs) becomes increasingly important. Recently, numerous solutions to tackle this problem have been presented. However, it is not clear whether such concepts fit the needs and requirements of future users. The aim of this work is to identify the requirements and expectations of users after real-life exposure with an automated vehicle. We conducted a field study (N=32) in a small town where an automated vehicle is in regular operation and collected both subjective (surveys, semi-structured interviews) and objective (video analysis) data. Results suggest that VRUs prefer rather simple forms of communication derived from well-established concepts like traffic signs, horns or indicators. Furthermore, we could identify important scenarios that have yet not been addressed, and our findings are in particular useful for the provision of automated vehicles in shared spaces.