Reviewerly/exHarmony huggingface dataset:
PSO+: A new particle swarm optimization algorithm for constrained problems

The Particle Swarm Optimization algorithm is a metaheuristic based on populations of individuals in which solution candidates evolve through simulation of a simplified model of social adaptation. By aggregating robustness, efficiency and simplicity, PSO has gained great popularity. Modified PSO algorithms have been proposed to solve optimization problems with domain, linear and nonlinear constraints. Other algorithms that use multi-objective optimization to deal with constrained problems face the problem of not being able to guarantee finding feasible solutions. Current PSO algorithms that deal with constraints only treat domain constraints by controlling the velocity of particle displacement in the swarm, or do so inefficiently by randomly resetting each infeasible particle. This approach may make it infeasible to optimize certain problems, especially real ones. This work presents a new particle swarm optimization algorithm, called PSO+, capable of solving problems with linear and nonlinear constraints in order to solve these deficiencies. The proposed algorithm uses a feasibility repair operator and two swarms to ensure there will always be a swarm whose particles fully respect every constraint. A new particle update method is also proposed to insert diversity into the swarm and improve search-space coverage, allowing the search-space border to be exploited as well, which is particularly convenient when the optimization involves active constraints in global optimum. Two heuristics are proposed to initialize a feasible swarm with the purpose of speeding up the initialization mechanism and ensuring diversity at the starting point of the optimization process. Furthermore, a neighborhood topology is proposed to minimize premature convergence. The proposed algorithm was tested for twenty-four benchmark functions, as well as in a real reservoir drainage plan optimization problem. Results attest that the new algorithm is competitive, since it increases the efficiency of the PSO and the speed of convergence.
Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method

In this work, we propose a novel crowd counting network that progressively generates crowd density maps via residual error estimation. The proposed method uses VGG16 as the backbone network and employs density map generated by the final layer as a coarse prediction to refine and generate finer density maps in a progressive fashion using residual learning. Additionally, the residual learning is guided by an uncertainty-based confidence weighting mechanism that permits the flow of only high-confidence residuals in the refinement path. The proposed Confidence Guided Deep Residual Counting Network (CG-DRCN) is evaluated on recent complex datasets, and it achieves significant improvements in errors. Furthermore, we introduce a new large scale unconstrained crowd counting dataset (JHU-CROWD) that is ~2.8 larger than the most recent crowd counting datasets in terms of the number of images. It contains 4,250 images with 1.11 million annotations. In comparison to existing datasets, the proposed dataset is collected under a variety of diverse scenarios and environmental conditions. Specifically, the dataset includes several images with weather-based degradations and illumination variations in addition to many distractor images, making it a very challenging dataset. Additionally, the dataset consists of rich annotations at both image-level and head-level. Several recent methods are evaluated and compared on this dataset.
Frugal following

Accurate tracking of objects in the real world is highly desirable in Augmented Reality (AR) to aid proper placement of virtual objects in a user's view. Deep neural networks (DNNs) yield high precision in detecting and tracking objects, but they are energy-heavy and can thus be prohibitive for deployment on mobile devices. Towards reducing energy drain while maintaining good object tracking precision, we develop a novel software framework called MARLIN. MARLIN only uses a DNN as needed, to detect new objects or recapture objects that significantly change in appearance. It employs lightweight methods in between DNN executions to track the detected objects with high fidelity. We experiment with several baseline DNN models optimized for mobile devices, and via both offline and live object tracking experiments on two different Android phones (one utilizing a mobile GPU), we show that MARLIN compares favorably in terms of accuracy while saving energy significantly. Specifically, we show that MARLIN reduces the energy consumption by up to 73.3% (compared to an approach that executes the best baseline DNN continuously), and improves accuracy by up to 19× (compared to an approach that infrequently executes the same best baseline DNN). Moreover, while in 75% or more cases, MARLIN incurs at most a 7.36% reduction in location accuracy (using the common IOU metric), in more than 46% of the cases, MARLIN even improves the IOU compared to the continuous, best DNN approach.
Real-Time Sensor Anomaly Detection and Recovery in Connected Automated Vehicle Sensors

In this paper we propose a novel observer-based method to improve the safety and security of connected and automated vehicle (CAV) transportation.The proposed method combines model-based signal filtering and anomaly detection methods.Specifically, we use adaptive extended Kalman filter (AEKF) to smooth sensor readings of a CAV based on a nonlinear car-following motion model.Under the assumption of a carfollowing model, the subject vehicle utilizes its leading vehicle's information to detect sensor anomalies by employing previouslytrained One Class Support Vector Machine (OCSVM) models.This approach allows the AEKF to estimate the state of a vehicle not only based on the vehicle's location and speed, but also by taking into account the state of the surrounding traffic.A communication time delay factor is considered in the carfollowing model to make it more suitable for real-world applications.Our experiments show that compared with the AEKF with a traditional χ 2 -detector, our proposed method achieves a better anomaly detection performance.We also demonstrate that a larger time delay factor has a negative impact on the overall detection performance.
Action Recognition With Spatial-Temporal Discriminative Filter Banks

Action recognition has seen a dramatic performance improvement in the last few years. Most of the current state-of-the-art literature either aims at improving performance through changes to the backbone CNN network, or exploring different trade-offs between computational efficiency and performance, again through altering the backbone network. However, almost all of these works maintain the same last layers of the network, which simply consist of a global average pooling followed by a fully connected layer. In this work we focus on how to improve the representation capacity of the network, but rather than altering the backbone, we focus on improving the last layers of the network, where changes have low impact in terms of computational cost. In particular, we hypothesize that current architectures have poor sensitivity to finer details and we exploit recent advances in the fine-grained recognition literature to improve our model in this aspect. With the proposed approach, we obtain state-of-the-art performance on Kinetics-400 and Something-Something-V1, the two major large-scale action recognition benchmarks.
Attribute Manipulation Generative Adversarial Networks for Fashion Images

Recent advances in Generative Adversarial Networks (GANs) have made it possible to conduct multi-domain image-to-image translation using a single generative network. While recent methods such as Ganimation and SaGAN are able to conduct translations on attribute-relevant regions using attention, they do not perform well when the number of attributes increases as the training of attention masks mostly rely on classification losses. To address this and other limitations, we introduce Attribute Manipulation Generative Adversarial Networks (AMGAN) for fashion images. While AMGAN's generator network uses class activation maps (CAMs) to empower its attention mechanism, it also exploits perceptual losses by assigning reference (target) images based on attribute similarities. AMGAN incorporates an additional discriminator network that focuses on attribute-relevant regions to detect unrealistic translations. Additionally, AMGAN can be controlled to perform attribute manipulations on specific regions such as the sleeve or torso regions. Experiments show that AMGAN outperforms state-of-the-art methods using traditional evaluation metrics as well as an alternative one that is based on image retrieval.
MVSCRF: Learning Multi-View Stereo With Conditional Random Fields

We present a deep-learning architecture for multi-view stereo with conditional random fields (MVSCRF). Given an arbitrary number of input images, we first use a U-shape neural network to extract deep features incorporating both global and local information, and then build a 3D cost volume for the reference camera. Unlike previous learning based methods, we explicitly constraint the smoothness of depth maps by using conditional random fields (CRFs) after the stage of cost volume regularization. The CRFs module is implemented as recurrent neural networks so that the whole pipeline can be trained end-to-end. Our results show that the proposed pipeline outperforms previous state-of-the-arts on large-scale DTU dataset. We also achieve comparable results with state-of-the-art learning based methods on outdoor Tanks and Temples dataset without fine-tuning, which demonstrates our method's generalization ability.
Hierarchical Shot Detector

Single shot detector simultaneously predicts object categories and regression offsets of the default boxes. Despite of high efficiency, this structure has some inappropriate designs: (1) The classification result of the default box is improperly assigned to that of the regressed box during inference, (2) Only regression once is not good enough for accurate object detection. To solve the first problem, a novel reg-offset-cls (ROC) module is proposed. It contains three hierarchical steps: box regression, the feature sampling location predication, and the regressed box classification with the features of offset locations. To further solve the second problem, a hierarchical shot detector (HSD) is proposed, which stacks two ROC modules and one feature enhanced module. The second ROC treats the regressed boxes and the feature sampling locations of features in the first ROC as the inputs. Meanwhile, the feature enhanced module injected between two ROCs aims to extract the local and non-local context. Experiments on the MS COCO and PASCAL VOC datasets demonstrate the superiority of proposed HSD. Without the bells or whistles, HSD outperforms all one-stage methods at real-time speed.
Cross-Domain Recommendation via Preference Propagation GraphNet

Recommendation can be framed as a graph link prediction task naturally. The user-item interaction graph built within a single domain often suffers from high sparsity. Thus, there has been a surge of approaches to alleviate the sparsity issue via cross-domain mutual augmentation. The SOTA cross-domain recommendation algorithms all try to bridge the gap via knowledge transfer in the latent space. We find there are mainly three problems in their formulations: 1) their knowledge transfer is unaware of the cross-domain graph structure. 2) their framework cannot capture high-order information propagation on the graph. 3) their cross-domain transfer formulations are generally more complicated to be optimized than the unified methods. In this paper, we propose the Preference Propagation GraphNet (PPGN) to address the above problems. Specifically, we construct a Cross-Domain Preference Matrix (CDPM) to model the interactions of different domains as a whole. Through the propagation layer of PPGN, we try to capture how user preferences propagate in the graph. Consequently, a joint objective for different domains is defined, and we simplify the cross-domain recommendation into a unified multi-task model. Extensive experiments on two pairs of real-world datasets show PPGN outperforms the SOTA algorithms significantly.
The digital divide in light of sustainable development: An approach through advanced machine learning techniques

The growing use of ICT in the workplace has meant that the labor market demands new skills and digital capabilities, either in the production of goods and services or in the need for workers with complementary skills. Concerns about unequal access to new technologies, integrated within the concept of digital divide, has been a topic of study since the use of the Internet began to be generalized among the population. Traditionally, research into the inequalities arising from the emergence of digital technologies has incorporated socioeconomic variables, especially gender, age, educational level, income, and habitat. This study uses advanced data analysis techniques to analyze the main socioeconomic digital skills drivers of the Spanish population. This information allows us to identify whether the population has training needs in relation to their digital competences, which will have a positive influence on improving the level of sustainable development of the country.
How developers engage with static analysis tools in different contexts

Automatic static analysis tools (ASATs) are instruments that support code quality assessment by automatically detecting defects and design issues. Despite their popularity, they are characterized by (i) a high false positive rate and (ii) the low comprehensibility of the generated warnings. However, no prior studies have investigated the usage of ASATs in different development contexts (e.g., code reviews, regular development), nor how open source projects integrate ASATs into their workflows. These perspectives are paramount to improve the prioritization of the identified warnings. To shed light on the actual ASATs usage practices, in this paper we first survey 56 developers (66% from industry and 34% from open source projects) and interview 11 industrial experts leveraging ASATs in their workflow with the aim of understanding how they use ASATs in different contexts. Furthermore, to investigate how ASATs are being used in the workflows of open source projects, we manually inspect the contribution guidelines of 176 open-source systems and extract the ASATs’ configuration and build files from their corresponding GitHub repositories. Our study highlights that (i) 71% of developers do pay attention to different warning categories depending on the development context; (ii) 63% of our respondents rely on specific factors (e.g., team policies and composition) when prioritizing warnings to fix during their programming; and (iii) 66% of the projects define how to use specific ASATs, but only 37% enforce their usage for new contributions. The perceived relevance of ASATs varies between different projects and domains, which is a sign that ASATs use is still not a common practice. In conclusion, this study confirms previous findings on the unwillingness of developers to configure ASATs and it emphasizes the necessity to improve existing strategies for the selection and prioritization of ASATs warnings that are shown to developers.
Control of Automated Guided Vehicles Without Collision by Quantum Annealer and Digital Devices

Recent advance on quantum devices realizes an artificial quantum spin system known as the D-Wave 2000Q, which implements the Ising model with tunable transverse field. In this system, we perform a specific protocol of quantum annealing to attain the ground state, the minimizer of the energy. Therefore the device is often called the quantum annealer. However the resulting spin configurations are not always in the ground state. It can rather quickly generate many spin configurations following the Gibbs-Boltzmann distribution. In the present study, we formulate an Ising model to control a large number of automated guided vehicles in a factory without collision. We deal with an actual factory in Japan, in which vehicles run, and assess efficiency of our formulation. Compared to the conventional powerful techniques performed in digital computer, still the quantum annealer does not show outstanding advantage in the practical problem. Our study demonstrates a possibility of the quantum annealer to contribute solving industrial problems.
Aspect-based sentiment analysis using smart government review data

Digital resources such as smart applications reviews and online feedback information are important sources to seek customers’ feedback and input. This paper aims to help government entities gain insights on the needs and expectations of their customers. Towards this end, we propose an aspect-based sentiment analysis hybrid approach that integrates domain lexicons and rules to analyse the entities smart apps reviews. The proposed model aims to extract the important aspects from the reviews and classify the corresponding sentiments. This approach adopts language processing techniques, rules, and lexicons to address several sentiment analysis challenges, and produce summarized results. According to the reported results, the aspect extraction accuracy improves significantly when the implicit aspects are considered. Also, the integrated classification model outperforms the lexicon-based baseline and the other rules combinations by 5% in terms of Accuracy on average. Also, when using the same dataset, the proposed approach outperforms machine learning approaches that uses support vector machine (SVM). However, using these lexicons and rules as input features to the SVM model has achieved higher accuracy than other SVM models.
Ground-to-Aerial Image Geo-Localization With a Hard Exemplar Reweighting Triplet Loss

The task of ground-to-aerial image geo-localization can be achieved by matching a ground view query image to a reference database of aerial/satellite images. It is highly challenging due to the dramatic viewpoint changes and unknown orientations. In this paper, we propose a novel in-batch reweighting triplet loss to emphasize the positive effect of hard exemplars during end-to-end training. We also integrate an attention mechanism into our model using feature-level contextual information. To analyze the difficulty level of each triplet, we first enforce a modified logistic regression to triplets with a distance rectifying factor. Then, the reference negative distances for corresponding anchors are set, and the relative weights of triplets are computed by comparing their difficulty to the corresponding references. To reduce the influence of extreme hard data and less useful simple exemplars, the final weights are pruned using upper and lower bound constraints. Experiments on two benchmark datasets show that the proposed approach significantly outperforms the state-of-the-art methods.
Non-Functional Requirements for Machine Learning: Challenges and New Directions

Machine Learning (ML) provides approaches which use big data to enable algorithms to "learn", producing outputs which would be difficult to obtain otherwise. Despite the advances allowed by ML, much recent attention has been paid to certain qualities of ML solutions, particularly fairness and transparency, but also qualities such as privacy, security, and testability. From a requirements engineering (RE) perspective, such qualities are also known as non-functional requirements (NFRs). In RE, the meaning of certain NFRs, how to refine those NFRs, and how to use NFRs for design and runtime decision making over traditional software is relatively well established and understood. However, in a context where the solution involves ML, much of our knowledge about NFRs no longer applies. First, the types of NFRs we are concerned with undergo a shift: NFRs like fairness and transparency become prominent, whereas other NFRs such as modularity may become less relevant. The meanings and interpretations of NFRs in an ML context (e.g., maintainability, interoperability, and usability) must be rethought, including how these qualities are decomposed into sub-qualities. Trade-offs between NFRs in an ML context must be re-examined. Beyond the changing landscape of NFRs, we can ask if our known approaches to understanding, formalizing, modeling, and reasoning over NFRs at design and runtime must also be adjusted, or can be applied as-is to this new area? Given these questions, this work outlines challenges and a proposed research agenda for the exploration of NFRs for ML-based solutions.
Recognizing Spontaneous Micro-Expression Using a Three-Stream Convolutional Neural Network

Micro-expression recognition (MER) has attracted much attention with various practical applications, particularly in clinical diagnosis and interrogations. In this paper, we propose a three-stream convolutional neural network (TSCNN) to recognize MEs by learning ME-discriminative features in three key frames of ME videos. We design a dynamic-temporal stream, static-spatial stream, and local-spatial stream module for the TSCNN that respectively attempt to learn and integrate temporal, entire facial region, and facial local region cues in ME videos with the goal of recognizing MEs. In addition, to allow the TSCNN to recognize MEs without using the index values of apex frames, we design a reliable apex frame detection algorithm. Extensive experiments are conducted with five public ME databases: CASME II, SMIC-HS, SAMM, CAS(ME) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and CASME. Our proposed TSCNN is shown to achieve more promising recognition results when compared with many other methods.
Vehicle Position Correction: A Vehicular Blockchain Networks-Based GPS Error Sharing Framework

The positioning accuracy of the existing vehicular Global Positioning System (GPS) is far from sufficient to support autonomous driving and ITS applications.To remedy that, leading methods such as ranging and cooperation have improved the positioning accuracy to varying degrees, but they are still full of challenges in practical applications.Especially for cooperative positioning, in addition to the performance of methods, cooperators may provide false data due to attacks or selfishness, which can seriously affect the positioning accuracy.By fully exploiting the characteristics of blockchain and edge computing, this paper proposes a vehicular blockchain-based secure and efficient GPS positioning error evolution sharing framework, which improves vehicle positioning accuracy from ensuring security and credibility of cooperators and data.First, by analyzing the GPS error, a bridge can be established between the sensor-rich vehicles and the common vehicles to achieve cooperation by sharing the positioning error evolution at a specific time and location.Particularly, the positioning error evolution is obtained by a deep neural network (DNN)-based prediction algorithm running on the edge server.We further propose to use blockchain technology for storage and sharing the evolution of positioning errors, mainly to guarantee the security of cooperative vehicles and mobile edge computing nodes (MECNs).In addition, the corresponding smart contracts are designed to automate and efficiently perform storage and sharing tasks as well as solve inconsistencies in time scales.Extensive simulations based on actual data indicate the accuracy and security of our proposal in terms of positioning error correction and data sharing.