Reviewerly/exHarmony huggingface dataset:
An Unsupervised Remote Sensing Single-Image Super-Resolution Method Based on Generative Adversarial Network

Image super-resolution (SR) technique can improve the spatial resolution of images without upgrading the imaging system.As a result, SR promotes the development of high resolution (HR) remote sensing image applications.Many remote sensing image SR algorithms based on deep learning have been proposed recently, which can effectively improve the spatial resolution under the constraints of HR images.However, images acquired by remote sensing imaging devices typically have lower resolution.Hence, an insufficient number of HR remote sensing images are available for training deep neural networks.In view of this problem, we propose an unsupervised SR method that does not require HR remote sensing images.The proposed method introduces a generative adversarial network (GAN) that obtains SR images through the generator; then, the SR images are downsampled to train the discriminator with low resolution (LR) images.Our method outperformed several methods in terms of the quality of the obtained SR images as measured by 6 evaluation metrics, which proves the satisfactory performance of the proposed unsupervised method for improving the spatial resolution of remote sensing images.
Joint Probabilistic People Detection in Overlapping Depth Images

Privacy-preserving high-quality people detection is a vital computer vision task for various indoor scenarios, e.g. people counting, customer behavior analysis, ambient assisted living or smart homes. In this work a novel approach for people detection in multiple overlapping depth images is proposed. We present a probabilistic framework utilizing a generative scene model to jointly exploit the multi-view image evidence, allowing us to detect people from arbitrary viewpoints. Our approach makes use of mean-field variational inference to not only estimate the maximum a posteriori (MAP) state but to also approximate the posterior probability distribution of people present in the scene. Evaluation shows state-of-the-art results on a novel data set for indoor people detection and tracking in depth images from the top-view with high perspective distortions. Furthermore it can be demonstrated that our approach (compared to the the mono-view setup) successfully exploits the multi-view image evidence and robustly converges in only a few iterations.
Risk Assessment for Cyber Security of Manufacturing Systems: A Game Theory Approach

This paper presents a novel approach using game theory to assess the risk likelihood in manufacturing systems quantifiably. Cybersecurity is a pressing issue in the manufacturing sector. Nevertheless, managing the risk in cybersecurity has become a critical challenge for modern manufacturing enterprises. In risk management thinking, the first step is to identify the risk, then validate it, and lastly, consider responses to the risk. If the risk is below the security risk appetite of the manufacturing system, it could be accepted. However, if it is above the risk appetite, the system should appropriately respond by either avoiding, transferring, or mitigating the risk. The validation of the risk in terms of severity and likelihood of the threat, however, is challenging because the later component is hard to quantify. In this paper, Failure Modes and Effects Analysis (FMEA) method is modified by employing game theory to quantitatively assess the likelihood of cyber-physical security risks. This method utilizes the game theory approach by modeling the rivalry between the attacker and the system as a game and then try to analyze it to find the likelihood of the attacker's action. We first define players of the game, action sets, and the utility function. Major concerns of cyber security issues in the manufacturing area are carefully considered in defining the cost function composed of defense policy, loss in production, and recovery. A linear optimization model is utilized to find a mixed-strategy Nash Equilibrium, which is the probability of choosing any action by the attacker also known as the likelihood of an attack. Numerical experiments are presented to further illustrate the method. Forecasting the attacker's behavior enables us to assess the cybersecurity risk in a manufacturing system and thereby be more prepared with plans of proper responses.
Optimization of coverage mission for lightweight unmanned aerial vehicles applied in crop data acquisition

The crop data acquisition with unmanned aerial vehicles is a popularized alternative to manage the agricultural processes, due to data emerging from portable sensors for image-gathering. Nevertheless, most unmanned aerial vehicles for data acquisition excess the cost of hundreds of dollars, making them inappropriate for small agricultural producers. In this paper, we proposed to achieve crop data acquisition using a Lightweight Unmanned Aerial Vehicle (LUAV), available at a reasonable cost. However, a LUAV has less flight time and robustness than the professional vehicles. To overcome the limitations, we designed a LUAV agent with the goal of optimizing coverage paths using a heuristic strategy in known areas. The path to follow can be selected from three algorithms, Wavefront, Dijkstra or Spiral, which are compared to define an option for the crop under study. A second goal is to improve the LUAV robustness, which was resolved from planning by selecting the start of the coverage mission in order to the flight lines cross the direction of the wind. We complemented the robustness of outdoors positioning using a Kalman Filter extension to specify movements during missions. Finally, using an AR Drone 2.0 quadcopter, we developed a prototype of the LUAV agent to obtain the mosaic of a grass crop. The results respect to optimized coverage mission showed that the Spiral algorithm with a Backtracking technique and avoiding areas of little interest, got the balanced score between revisits, turns, coverage percent and traveled distance. About the LUAV robustness in the presence of wind, the results stated an error of less than 2 m, considered acceptable for image-acquisition purposes. The developed work is simple but effective, and makes evident the viability for that any LUAV type can support the precision agriculture processes in favorable costs.
Supervised Learning of Natural-Terrain Traversability with Synthetic 3D Laser Scans

Autonomous navigation of ground vehicles on natural environments requires looking for traversable terrain continuously. This paper develops traversability classifiers for the three-dimensional (3D) point clouds acquired by the mobile robot Andabata on non-slippery solid ground. To this end, different supervised learning techniques from the Python library Scikit-learn are employed. Training and validation are performed with synthetic 3D laser scans that were labelled point by point automatically with the robotic simulator Gazebo. Good prediction results are obtained for most of the developed classifiers, which have also been tested successfully on real 3D laser scans acquired by Andabata in motion.
Modeling programs hierarchically with stack-augmented LSTM

Programming language modeling has attracted extensive attention in recent years, and it plays an essential role in program processing fields. Statistical language models, which are initially designed for natural languages, have been generally used for modeling programming languages. However, different from natural languages, programming languages contain explicit and hierarchical structure that is hard to learn by traditional statistical language models. To address this challenge, we propose a novel Stack-Augmented LSTM neural network for programming language modeling. Adding a stack memory component into the LSTM network enables our model to capture the hierarchical information of programs through the PUSH and POP operations, which further allows our model capturing the long-term dependency in the programs. We evaluate the proposed model on three program analysis tasks, i.e., code completion, program classification, and code summarization. Evaluation results show that our proposed model outperforms baseline models in all the three tasks, indicating that by capturing the structural information of programs with a stack, our proposed model can represent programs more precisely.
Detection and Localization of Multiple Objects Using VGGNet and Single Shot Detection

While profound convolutional neural systems (CNNs) have demonstrated an extraordinary accomplishment in single-mark picture characterization, take note of that true pictures for the most part contain numerous names, which could relate to various items, scenes, activities, and qualities in a picture. Conventional ways to deal with multi-name picture grouping learn free classifiers for every classification and utilize positioning or thresholding on the characterization results. These systems, albeit functioning admirably, neglect to expressly abuse the mark conditions in a picture. In this paper, we will utilize SmallerVGGNet, the Keras neural system design, which we will actualize and utilize for multi-mark classification. The VGG organize engineering was presented by Simonyan and Zisserman in their 2014 paper, Very Deep Convolutional Networks for Large Scale Image Recognition. This system is described by its straightforwardness, utilizing just 3 × 3 convolutional layers stacked over one another in expanding profundity. Diminishing volume gauge is managed by max pooling. Two totally related layers, each with 4096 center points are then trailed by a softmaxClassifier.
Breaking the Chains of Open Innovation: Post-Blockchain and the Case of Sensorica

Open innovation is a concept in flux; from the practice of large-scale, internet-mediated collaboration, to a strategic option and business model for firms. However, the scope and breadth of its transformative dynamic is arguably restrained. Despite the theoretical and empirical benefits of openness, established firms face significant challenges deploying the coordination patterns of open innovation communities, further reducing the potential of spill-overs in the supply chain. Viewed differently, open innovation presents more user-centric and responsible innovation paths. These are manifested in the processes and outputs of open innovation by empowering participation and by successfully employing the capacities of user communities. To reap the benefits of open innovation, a rapid reconfiguration of the production and exchange structures is needed in intrafirm and interfirm relations. Sensorica is an open enterprise that achieves such forms of organization and a unique techno-social infrastructure supporting them. It illustrates a potential path that can realize the full potential of open innovation, for users, firms, and the economic system as a whole.
Origraph: Interactive Network Wrangling

Networks are a natural way of thinking about many datasets. The data on which a network is based, however, is rarely collected in a form that suits the analysis process, making it necessary to create and reshape networks. Data wrangling is widely acknowledged to be a critical part of the data analysis pipeline, yet interactive network wrangling has received little attention in the visualization research community. In this paper, we discuss a set of operations that are important for wrangling network datasets and introduce a visual data wrangling tool, Origraph, that enables analysts to apply these operations to their datasets. Key operations include creating a network from source data such as tables, reshaping a network by introducing new node or edge classes, filtering nodes or edges, and deriving new node or edge attributes. Our tool, Origraph, enables analysts to execute these operations with little to no programming, and to immediately visualize the results. Origraph provides views to investigate the network model, a sample of the network, and node and edge attributes. In addition, we introduce interfaces designed to aid analysts in specifying arguments for sensible network wrangling operations. We demonstrate the usefulness of Origraph in two Use Cases: first, we investigate gender bias in the film industry, and then the influence of money on the political support for the war in Yemen.
A Safety Analysis Method for Perceptual Components in Automated Driving

The use of machine learning (ML) is increasing in many sectors of safety-critical software development and in particular, for the perceptual components of automated driving (AD) functionality. Although some traditional safety engineering techniques such as FTA and FMEA are applicable to ML components, the unique characteristics of ML create challenges. In this paper, we propose a novel safety analysis method called Classification Failure Mode Effects Analysis (CFMEA) which is specialized to assess classification-based perception in AD. Specifically, it defines a systematic way to assess the risk due to classification failure under adversarial attacks or varying degrees of classification uncertainty across the perception-control linkage. We first present the theoretical and methodological foundations for CFMEA, and then demonstrate it by applying it to an AD case study using semantic segmentation perception trained with the Cityscapes driving dataset. Finally, we discuss how CFMEA results could be used to improve an ML-model.
Facial feedback for reinforcement learning: a case study and offline analysis using the TAMER framework

Abstract Interactive reinforcement learning provides a way for agents to learn to solve tasks from evaluative feedback provided by a human user. Previous research showed that humans give copious feedback early in training but very sparsely thereafter. In this article, we investigate the potential of agent learning from trainers’ facial expressions via interpreting them as evaluative feedback. To do so, we implemented TAMER which is a popular interactive reinforcement learning method in a reinforcement-learning benchmark problem—Infinite Mario, and conducted the first large-scale study of TAMER involving 561 participants. With designed CNN–RNN model, our analysis shows that telling trainers to use facial expressions and competition can improve the accuracies for estimating positive and negative feedback using facial expressions. In addition, our results with a simulation experiment show that learning solely from predicted feedback based on facial expressions is possible and using strong/effective prediction models or a regression method, facial responses would significantly improve the performance of agents. Furthermore, our experiment supports previous studies demonstrating the importance of bi-directional feedback and competitive elements in the training interface.
A Machine Vision-based Realtime Anomaly Detection Method for Industrial Products Using Deep Learning

In the process of industrial production, anomaly detection is the key link to ensure the high quality of the product. This paper deeply studies the method of anomaly detection for industrial products based on deep learning. For the balanced image data set of industrial production products, this paper proposes a supervised anomaly detection model based on YOLOv3. This model constructs the ROI classifier to detect anomaly types. For the unbalanced image data set (only few anomaly images) of industrial production products, this paper proposes a semi-supervised anomaly detection model based on Fast-AnoGAN. This model is built from normal samples only. It uses the trained WGAN-GP model to generate images, and achieves anomaly detection by monitoring the anomaly score which is obtained by calculating the difference between the generated image and the test image. The two proposed anomaly detection models evaluated on both balanced and unbalanced data sets in the real industrial production scenarios. The performance evaluation results demonstrate that the two proposed anomaly detection models based on deep learning can well meet the dual requirements of real-time and accuracy for anomaly detection in the high-speed industrial production scenarios.
Blockchain: A distributed solution to UAV‐enabled mobile edge computing

Mobile edge computing (MEC) is to process, analyse, store and calculate the network data at the edge of the network. When the ground infrastructure is damaged in an emergency, the unmanned aerial vehicle (UAV) formation can be rapidly deployed to undertake the task of MEC. However, there are some potential problems to be considered in UAV-enabled MEC, such as the trust among UAVs from different sources and the stability of UAV formation network. In view of the problems existing, this study proposes a blockchain-based architecture to build a system of mutual trust, fairness, openness, and stability in this scenario. Through the implementation of blockchain technology, key data such as device computing capacity, task allocation, and task execution process are recorded openly, transparently, and irrevocably. As multi-party trust is built to reduce the occurrence of fraud, system participants can get a reasonable reward. On this basis, the smart contract is used to ensure that algorithms are accessible to the public, and the sub-blockchain technology improves the stability of the system. In the case study, the simulation results show that the resource consumption and time cost of the proposed scheme is reasonable and feasible.
Supervised Representation Learning Approach for Cross-Project Aging-Related Bug Prediction

Software aging, which is caused by Aging-Related Bugs (ARBs), tends to occur in long-running systems and may lead to performance degradation and increasing failure rate during software execution. ARB prediction can help developers discover and remove ARBs, thus alleviating the impact of software aging. However, ARB-prone files occupy a small percentage of all the analyzed files. It is usually difficult to gather sufficient ARB data within a project. To overcome the limited availability of training data, several researchers have recently developed cross-project models for ARB prediction. A key point for cross-project models is to learn a good representation for instances in different projects. Nevertheless, most of the previous approaches neither consider the reconstruction property of new representation nor encode source samples' label information in learning representation. To address these shortcomings, we propose a Supervised Representation Learning Approach (SRLA), which is based on double encoding-layer autoencoder, to perform cross-project ARB prediction. Moreover, we present a transfer cross-validation framework to select the hyper-parameters of cross-project models. Experiments on three large open-source projects demonstrate the effectiveness and superiority of our approach compared with the state-of-the-art approach TLAP.
Open-set face identification with index-of-max hashing by learning

Large-scale face identification or 1-to-N matching where N is huge, plays a vital role in biometrics and surveillance. The system demands accurate and speedy matching where compact facial feature representation and a simple matcher are favored. On the other hand, most research considers closed-set identification that assumes that all identities of probe samples are enclosed in the gallery. On the contrary, open-set identification expects that some probe identities are not known to the system. This setup poses an additional challenge, where the system should be able to reject those probes that correspond to unknown identities. In this paper, we address the large-scale open-set face identification problem with a compact facial representation that is based on the index-of-maximum (IoM) hashing, which was designed for biometric template protection. To be specific, the existing random IoM hashing is advanced to a data-driven based hashing technique, where the hashed face code can be made compact and matching can be easily performed by the Hamming distance, which can offer highly efficient matching. Furthermore, since IoM hashing transforms the original facial features non-invertibly, the privacy of users can also be preserved. Along with IoM hashed face code, we explore several fusion strategies to address the open-set face identification problem. The comprehensive evaluations are carried out with three large-scale unconstrained face datasets, namely LFW, VGG2 and IJB-C.
Effects of environment and posture on the concentration and achievement of students in mobile learning

Mobile learning (M-learning) is currently popular. Although M-learning has a positive effect on the interest, attitude, initiative, and learning of learners, it comes with the negative effect of distraction. Exploring the factors that influence M-learning concentration have become a popular research subject. Environment (quiet and noisy) and posture (sitting, standing, and moving) are two typical factors that directly influence the concentration of mobile learners. This study focuses on the influence of environment and posture on concentration in M-learning. A total of 120 college students were randomly divided into two environment groups, namely, the quiet and noisy environments. The two groups were subjected to M-learning experiments by adopting sitting, standing, and moving postures. This study shows that (1) interaction effects on M-learning concentration existed between environment and posture and a quiet environment and a sitting posture enable learners to concentrate better; and (2) no interaction effects on M-learning achievement existed between environment and posture, while environment and posture alone had a significant effect on learning achievement. This study presents several implications of the M-learning practice, deficiencies of the research, and the direction of future studies in this field.
Open research data, an archival challenge?

Abstract The Swedish government has decided that all research results in the form of research data and scientific publications financed with public funds should be openly accessible as far as possible. The question is whether the responsible actors and if the universities are ready to implement the change. The significance of open access has amplified in Sweden. Earlier research has brought to light that the collection and preservation of research data are often surrounded by ambiguous rules and lack a comprehensive structure. For example, archiving is not given enough consideration in connection to research projects and researchers often tend to save their material on platforms that are not persistent over time. This article is based upon a qualitative research approach where 15 semi-structured interviews have been used as primarily data sources to investigate the implementation of open access of research data and scientific publications. The article investigated how Swedish universities and public authorities were working with archiving and implementation of open research data and their opinions on open access. The results displayed a lack of coordination, resources and infrastructure but also that common agreed nomenclature were missing. The management of research data was not part of an overall recordkeeping strategy. One explanation could be differences in the information culture among researchers and archivists. Social sciences theory has been combined with archival theory in order to explain the reasons to this. These have been put in relation to the principles of the open data directive.