Reviewerly/exHarmony huggingface dataset:
Structural Analysis of Attributes for Vehicle Re-Identification and Retrieval

Vehicle re-identification plays an important role in video surveillance applications. Despite the efforts made on this problem in the past few years, it remains a challenging task due to various factors such as pose variation, illumination changes, and subtle inter-class difference. We believe that the key information for identification has not been well explored in the literature. In this paper, we first collect a vehicle dataset `VAC21' which contains 7129 images of five types of vehicles. Then, we carefully label the 21 classes of structural attributes hierarchically with bounding boxes. To our knowledge, this is the first dataset with several detailed attributes labeled. Based on this dataset, we use the state-of-the-art one-stage detection method, Single-shot Detection, as a baseline model for detecting attributes. Subsequently, we make a few important modifications tailored for this application to improve accuracy: 1) adding more proposals from low-level layers to improve the accuracy of detecting small objects and 2) employing the focal loss to improve the mean average precision. Furthermore, the results of the attribute detection can be applied to a series of vision tasks that focus on analyzing the images of vehicles. Finally, we propose a novel region of interests (ROIs)-based vehicle re-identification and retrieval method in which the ROIs' deep features are used as discriminative identifiers, encoding the structure information of a vehicle. These deep features are input to a boosting model to improve the accuracy. A set of experiments are conducted on the dataset VehicleID and the experimental results show that our method outperforms the state-of-the-art methods.
M3Net: A multi-model, multi-size, and multi-view deep neural network for brain magnetic resonance image segmentation

Segmentation of the brain into gray matter, white matter, and cerebrospinal fluid (CSF) using magnetic resonance (MR) imaging plays a fundamental role in neuroimaging research and clinical settings. Due to the complexity of brain anatomy, low image quality, and insufficient training data, both traditional and deep learning segmentation methods have a limited performance. In this paper, we propose a multi-model, multi-size and multi-view deep neural network (M3Net) for brain MR image segmentation, which uses three identical modules to segment transaxial, coronal, and sagittal MR slices, respectively. Each module consists of multi-size U-Nets and multi-size back propagation neural networks. It also uses a probabilistic atlas to explore brain anatomy and a convolutional auto-encoder (CAE) to restore MR images. The proposed M3Net model was evaluated against widely used segmentation methods on both synthetic and clinical studies. Our results suggest that the proposed model is able to segment Brain MR Images more accurately.
A multistage risk decision making method for normal cloud model considering behavior characteristics

It is difficult to make scientific decisions for multistage risk decision-making problems, which are mainly influenced by these challenges, such as uncertainty of information, the passage of time and complexity of the problems The multistage risk decision-making problem is analyzed for normal cloud model considering behavior characteristics in this paper. Firstly, considering the fuzziness and randomness of multi-stage risk decision-making information, the relative entropy and modified relative entropy are defined as the distance measures for the normal cloud model from the perspective of full utilization of information. Secondly, the prospect theory is extended to normal cloud model environment. Considering the behavior characteristics of reference dependence, two reference points are set up considering the short-term development and long-term development. Thirdly, the optimization model is built to obtain the attribute weights and stage weights based on the principle of maximizing the difference between ranking values. The ranking of alternatives is determined by the final ranking values. The case analysis and the method comparison illustrate the feasibility and rationality of the proposed method.
Automatic Classification Method for Software Vulnerability Based on Deep Neural Network

Software vulnerabilities are the root causes of various security risks. Once a vulnerability is exploited by malicious attacks, it will greatly compromise the safety of the system, and may even cause catastrophic losses. Hence automatic classification methods are desirable to effectively manage the vulnerability in software, improve the security performance of the system, and reduce the risk of the system being attacked and damaged. In this paper, a new automatic vulnerability classification model (TFI-DNN) has been proposed. The model is built upon term frequency-inverse document frequency (TF-IDF), information gain (IG), and deep neural network (DNN): the TF-IDF is used to calculate the frequency and weight of each word from vulnerability description; the IG is used for feature selection to obtain an optimal set of feature word, and; the DNN neural network model is used to construct an automatic vulnerability classifier to achieve effective vulnerability classification. The National Vulnerability Database of the United States has been used to validate the effectiveness of the proposed model. Compared to SVM, Naive Bayes, and KNN, the TFI-DNN model has achieved better performance in multi-dimensional evaluation indexes including accuracy, recall rate, precision, and F1-score.
VisNet: Deep Convolutional Neural Networks for Forecasting Atmospheric Visibility

Visibility is a complex phenomenon inspired by emissions and air pollutants or by factors, including sunlight, humidity, temperature, and time, which decrease the clarity of what is visible through the atmosphere. This paper provides a detailed overview of the state-of-the-art contributions in relation to visibility estimation under various foggy weather conditions. We propose VisNet, which is a new approach based on deep integrated convolutional neural networks for the estimation of visibility distances from camera imagery. The implemented network uses three streams of deep integrated convolutional neural networks, which are connected in parallel. In addition, we have collected the largest dataset with three million outdoor images and exact visibility values for this study. To evaluate the model's performance fairly and objectively, the model is trained on three image datasets with different visibility ranges, each with a different number of classes. Moreover, our proposed model, VisNet, evaluated under dissimilar fog density scenarios, uses a diverse set of images. Prior to feeding the network, each input image is filtered in the frequency domain to remove low-level features, and a spectral filter is applied to each input for the extraction of low-contrast regions. Compared to the previous methods, our approach achieves the highest performance in terms of classification based on three different datasets. Furthermore, our VisNet considerably outperforms not only the classical methods, but also state-of-the-art models of visibility estimation.
A novel density peaks clustering algorithm based on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e2981" altimg="si249.gif"><mml:mi>k</mml:mi></mml:math> nearest neighbors for improving assignment process

Density Peaks Clustering (DPC) algorithm is a kind of density-based clustering approach, which can quickly search and find density peaks. However, DPC has deficiency in assignment process, which is likely to trigger domino effect. Especially, it cannot process some non-spherical data sets such as Spiral. The research results indicate that assignment process appears to be the most significant step in deciding the success of the clustering performance. Therefore, we propose a density peaks clustering based on k nearest neighbors (DPC-KNN) which aims to overcome the weakness of DPC. The proposed DPC-KNN integrates the idea of k nearest neighbors into the distance computation and assignment process, which is more reasonable. It can be seen from experimental results that the DPC-KNN algorithm is more feasible and effective, compared with K-means, DBSCAN and DPC.
Multi-View Scaling Support Vector Machines for Classification and Feature Selection

With the explosive growth of data, the multi-view data is widely used in many fields, such as data mining, machine learning, computer vision, and so on. Because such data always has a complex structure, i.e., many categories, many perspectives of description and high dimension, how to formulate an accurate and reliable framework for the multi-view classification is a very challenging task. In this paper, we propose a novel multi-view classification method by using multiple multi-class Support Vector Machines (SVMs) with a novel collaborative strategy. Here, each multi-class SVM embeds the scaling factor to renewedly adjust the weight allocation of all features, which is beneficial to highlight more important and discriminative features. Furthermore, we adopt the decision function values to integrate multiple multi-class learners and introduce the confidence score across multiple classes to determine the final classification result. In addition, through a series of the mathematical deduction, we bridge the proposed model with the solvable problem and solve it through an alternating iteration optimization method. We evaluate the proposed method on several image and face datasets, and the experimental results demonstrate that our proposed method performs better than other state-of-the-art learning algorithms.
Guiding High-Performance SAT Solvers with Unsat-Core Predictions

The NeuroSAT neural network architecture was introduced in [37] for predicting properties of propositional formulae. When trained to predict the satisfiability of toy problems, it was shown to find solutions and unsatisfiable cores on its own. However, the authors saw "no obvious path" to using the architecture to improve the state-of-the-art. In this work, we train a simplified NeuroSAT architecture to directly predict the unsatisfiable cores of real problems. We modify several state-of-the-art SAT solvers to periodically replace their variable activity scores with NeuroSAT's prediction of how likely the variables are to appear in an unsatisfiable core. The modified MiniSat solves 10% more problems on SATCOMP-2018 within the standard 5,000 second timeout than the original does. The modified Glucose solves 11% more problems than the original, while the modified Z3 solves 6% more. The gains are even greater when the training is specialized for a specific distribution of problems; on a benchmark of hard problems from a scheduling domain, the modified Glucose solves 20% more problems than the original does within a one-hour timeout. Our results demonstrate that NeuroSAT can provide effective guidance to high-performance SAT solvers on real problems.
Model-Based Synthetic Sampling for Imbalanced Data

Imbalanced data is characterized by the severe difference in observation frequency between classes and has received a lot of attention in data mining research. The prediction performances usually deteriorate as classifiers learn from imbalanced data, as most classifiers assume the class distribution is balanced or the costs for different types of classification errors are equal. Although several methods have been devised to deal with imbalance problems, it is still difficult to generalize those methods to achieve stable improvement in most cases. In this study, we propose a novel framework called model-based synthetic sampling (MBS) to cope with imbalance problems, in which we integrate modeling and sampling techniques to generate synthetic data. The key idea behind the proposed method is to use regression models to capture the relationship between features and to consider data diversity in the process of data generation. We conduct experiments on 13 datasets and compare the proposed method with 10 methods. The experimental results indicate that the proposed method is not only comparative but also stable. We also provide detailed investigations and visualizations of the proposed method to empirically demonstrate why it could generate good data samples.
Visualization of indoor thermal environment on mobile devices based on augmented reality and computational fluid dynamics

Augmented reality (AR) based visualization of computational fluid dynamics (CFD) simulation on mobile devices is important for the understanding and discussion of indoor thermal environments in the design process. However, utilizing AR-based mobile device for indoor thermal environment understanding still encounters problems due to limited computational power and lack of efficient interaction methods. To improve the performance of AR-based CFD visualization on a mobile device and provide the users with intuitive interaction with indoor environment, an integrated approach based on client-server framework is established. Within the approach, a new mobile friendly data format (cfd4a) with low computational complexity is proposed for CFD simulation results representation. Server-side data pre-processing method is also introduced to reduce the computational power and time needed on the client side. Furthermore, interactive section view selection and time-step animation methods are proposed for intuitive interaction with the AR environment. Then, a prototype system is developed with Unity3D engine and Tango Tablet. Demonstration in some typical scenarios shows that the proposed method and prototype system provide an intuitive and fluent AR-based environment for interactive indoor thermal environment visualization. Comparing to the widely used vtk format, a data compression ratio of 63.4% and a loading time saving ratio of 89.3% are achieved in the performance test with the proposed method. The proposed approach also shows good flexibility and extensibility in supporting different AR devices through exposing standard web services and utilizing the widely used Unity3D engine. Source code of the developed prototype and relevant testing data are also shared through github, enabling other researchers to compare our work with theirs. It is also suggested that stability of AR-based mobile devices, new interaction methods and integration with cloud computing still need further investigation and improvement.
Image caption generation with high-level image features

Recently, caption generation has raised a huge interests in images and videos. However, it is challenging for the models to select proper subjects in a complex background and generate desired captions in high-level vision tasks. Inspired by recent works, we propose a novel image captioning model based on high-level image features. We combine low-level information, such as image quality, with high-level features, such as motion classification and face recognition to detect attention regions of an image. We demonstrate that our attention model produces good performance in experiments on MSCOCO, Flickr 30K, PASCL and SBU datasets.
Consider the Human Work Experience When Integrating Robotics in the Workplace

Worldwide, manufacturers are reimagining the future of their workforce and its connection to technology.Rather than replacing humans, Industry 5.0 explores how humans and robots can best complement one another's unique strengths.However, realizing this vision requires an in-depth understanding of how workers view the positive and negative attributes of their jobs, and the place of robots within it.In this paper, we explore the relationship between work attributes and automation goals by engaging in field research at a manufacturing plant.We conducted 50 face-to-face interviews with assembly-line workers (n = 50), which we analyzed using discourse analysis and social constructivist methods.We found that the work attributes deemed most positive by participants include social interaction, movement and exercise, (human) autonomy, problem solving, task variety, and building with their hands.The main negative work attributes included health and safety issues, feeling rushed, and repetitive work.We identified several ways robots could help reduce negative work attributes and enhance positive ones, such as reducing work interruptions and cultivating physical and psychological wellbeing.Based on our findings, we created a set of integration considerations for organizations planning to deploy robotics technology, and discuss how the manufacturing and HRI communities can explore these ideas in the future.
Architectural Smells Detected by Tools: a Catalogue Proposal

Architectural smells can negatively impact on different software qualities and can represent a relevant source of architectural debt. Several architectural smells have been defined by different researchers. Moreover, both academia and industry proposed several tools for software quality analysis, but it is not always clear to understand which tools provide also support for architectural smells detection and if the tools developed for this specific purpose are effectively available or not. In this paper we propose a catalogue of architectural smells for which, at least one tool able to detect the smell exists. We outline the main differences in the detection techniques exploited by the tools and we propose a classification of these architectural smells according to the violation of three design principles.
Beyond Credential Stuffing: Password Similarity Models Using Neural Networks

Attackers increasingly use passwords leaked from one website to compromise associated accounts on other websites. Such targeted attacks work because users reuse, or pick similar, passwords for different websites. We recast one of the core technical challenges underlying targeted attacks as the task of modeling similarity of human-chosen passwords. We show how to learn good password similarity models using a compilation of 1.4 billion leaked email, password pairs. Using our trained models of password similarity, we exhibit the most damaging targeted attack to date. Simulations indicate that our attack compromises more than 16% of user accounts in less than a thousand guesses, should one of their other passwords be known to the attacker and despite the use of state-of-the art countermeasures. We show via a case study involving a large university authentication service that the attacks are also effective in practice. We go on to propose the first-ever defense against such targeted attacks, by way of personalized password strength meters (PPSMs). These are password strength meters that can warn users when they are picking passwords that are vulnerable to attacks, including targeted ones that take advantage of the user's previously compromised passwords. We design and build a PPSM that can be compressed to less than 3 MB, making it easy to deploy in order to accurately estimate the strength of a password against all known guessing attacks.
Enhancing cultural recommendations through social and linked open data

In this article, we describe a hybrid recommender system (RS) in the artistic and cultural heritage area, which takes into account the activities on social media performed by the target user and her friends, and takes advantage of linked open data (LOD) sources. Concretely, the proposed RS (1) extracts information from Facebook by analyzing content generated by users and their friends; (2) performs disambiguation tasks through LOD tools; (3) profiles the active user as a social graph; (4) provides her with personalized suggestions of artistic and cultural resources in the surroundings of the user’s current location. The last point is performed by integrating collaborative filtering algorithms with semantic technologies in order to leverage LOD sources such as DBpedia and Europeana. Based on the recommended points of cultural interest, the proposed system is also able to suggest to the active user itineraries among them, which meet her preferences and needs and are sensitive to her physical and social contexts as well. Experimental results on real users showed the effectiveness of the different modules of the proposed recommender.
ST-CNN: Spatial-Temporal Convolutional Neural Network for crowd counting in videos

The task of crowd counting and density maps estimating from videos is challenging due to severe occlusions, scene perspective distortions and diverse crowd distributions. Conventional crowd counting methods via deep learning technique process each video frame independently with no consideration of the intrinsic temporal correlation among neighboring frames, thus making the performance lower than the required level of real-world applications. To overcome this shortcoming, a new end-to-end deep architecture named Spatial-Temporal Convolutional Neural Network (ST-CNN) is proposed, which unifies 2D convolutional neural network (C2D) and 3D convolutional neural network (C3D) to learn spatial-temporal features in the same framework. On top of that, a merging scheme is performed on the resulting density maps, taking advantages of the spatial-temporal information simultaneously for the crowd counting task. Experimental results on two benchmark data sets â Mall dataset and WorldExpo′10 dataset show that our ST-CNN outperforms the state-of-the-art models in terms of mean absolutely error (MAE) and mean squared error (MSE).
Combining Unmanned Aerial Vehicles With Artificial-Intelligence Technology for Traffic-Congestion Recognition: Electronic Eyes in the Skies to Spot Clogged Roads

Unmanned aerial vehicles (UAVs) are gradually becoming useful and common. In the consumer electronics (CE) category, unmanned systems have changed the way monitoring by air is conducted in such fields as transportation, the environment, and emergency rescue. This article examines how UAVs combined with artificial-intelligence (AI) technology are used for recognizing traffic congestion. Congested roadways cause traffic delays, which wastes fuel, adds to pollution, and invites road rage. Society can benefit from technologies that help recognize and relieve traffic congestion. We present a practical framework for using UAVs to recognize traffic problems. According to this framework, images of traffic scenes are first captured by a UAV system based on route-planning technology. Then the aerial images are further processed by using convolutional neural networks (CNNs). Finally, the output is transferred to a traffic management center.
An Energy-Efficient Dual Prediction Scheme Using LMS Filter and LSTM in Wireless Sensor Networks for Environment Monitoring

Environmental monitoring is a practical application where a wireless sensor network (WSN) may be utilized effectively. However, the energy consumption issues have become a major concern in using a WSN, particularly in remote locations without readily accessible electrical power supply. In general, the activities of data transmission among sensor nodes and the gateway (GW) can be a significant fraction of the total energy consumption within a WSN. Hence, reducing the number and the duration of transmissions as much as possible while maintaining a high level of data accuracy can be an effective strategy for saving energy. To achieve this objective, a least mean square (LMS) filter is used for a dual prediction scheme (DPS), in this paper. The DPS is data quality-based, allowing both the sensor nodes and the GW to predict the data simultaneously. Only when the error between the predicted data and the real sensed data exceeds a predefined threshold, the sensor nodes will send the sensed data to the GW/another node and consequently will update the coefficients of the filter. It is observed that, with this scheme, the total number of transmissions and their overall duration can be effectively reduced, and therefore, further energy savings can be realized. With the developed methodologies, at least 62.3% of the total energy for data transmission could be saved while achieving a 93.1% prediction accuracy.