Reviewerly/exHarmony huggingface dataset:
Achieving flatness: Graph labeling can generate graphical honeywords

In this paper, we propose a method to protect the hashed passwords by using topological graphic sequences. This method works effectively even if the password file is leaked. First, the user password is divided based on its length. Then the processed string and the topological graphic sequence are operated for obtaining the real decoy honeywords. In this way, a flatness honeywords generation method is generated. Since every password seems unrealistic, the hacker who steals the hashed password file cannot distinguish between the real passwords and the honeywords. If he uses the honeywords for login, the system will know that it is the intruder’s attack, and then the service provider (SP) can take security measures. Finally, several typical attack methods are analyzed to verify the effectiveness of our scheme. We use the topological graph to generate the honeywords, which is the first application of graphic labeling in the honeywords generation.
E-Agriculture for Direct Marketing of Food Crops Using Chatbots

The main objective of this system is to warrant fair price to the agricultural community by developing chatbots between farmers and consumers. We aim to make this chatbot as an interactive system so that farmers can use it to sell their produce directly to consumers who may need it. This is an advantage for both sellers (farmers) and consumers to sell and buy the farm products that are required without the participation of middlemen at a reasonable and profitable price. The consumer is provided access to the chatbot in any social network (Eg: facebook messenger) for a farmer who is ready to cultivate a particular crop at a price. With the help of an intelligent system, the consumer hires the farmer and his land and supplies him with all the inputs for water, seeds, etc. The farmer farms and delivers the organic and fresh crop to the customer and gets his pay for this job. In this way, all the available farm products can be easily accessed by consumers. Hence it provides direct marketing and also ensures the farmers with a good pay and least investment.
Real-Time Implementation of Traffic Signs Detection and Identification Application on Graphics Processing Units

Traffic signs detection has become an important feature of Advanced driving assisting systems and even self-driving cars. In this paper, we present an implementation of a traffic signs detection method on Graphics Processing Units (GPU) under real-time conditions. The proposed model is based on deep convolutional neural networks, a deep learning model used in computer vision applications. The deep convolutional neural networks have recently been used to solve many computer vision tasks successfully. Unlike old techniques, the model is used to detect and identify the traffic signs at the same time without the need for any external modules. To achieve real-time inference, we implement the proposed model on the GPU as a natural choice for the implementation of deep learning-based models. Also, we build large traffic signs detection dataset. The dataset contains 10[Formula: see text]000 images captured from the Chinese roads under real-world factors like lightning, occlusion, complex background, etc. 73 traffic sign classes were considered in this dataset. The evaluation of the proposed model on the proposed dataset shows robust performance in terms of speed and accuracy.
Frequency correlated photon generation at telecom band using silicon nitride ring cavities

Frequency entangled photon sources are in high demand in a variety of optical quantum technologies, including quantum key distribution, cluster state quantum computation and quantum metrology. In the recent decade, chip-scale entangled photon sources have been developed using silicon platforms, offering robustness, large scalability and CMOS technology compatibility. Here, we report the generation of frequency correlated photon pairs using a 150-GHz silicon nitride ring cavity. First, the device is characterized for studying the phase matching condition during spontaneous four-wave mixing. Next, we evaluate the joint spectrum intensity of the generated photons and confirm the photon pair generation in a total of 42 correlated frequency mode pairs, corresponding to a bandwidth of 51.25 nm. Finally, the experimental results are analyzed and the joint spectral intensity is quantified in terms of the phase matching condition.
Between Journalist Authorship and User Agency: Exploring the Concept of Objectivity in VR Journalism

Virtual Reality (VR) journalism has brought many changes to journalistic practice, calling facticity, accuracy, objectivity and autonomy into question. This study examines factors influencing objectivity and its significance in VR journalism from the perspective of VR content creators. The researcher conducted 27 semi-structured interviews with fact-based VR content creators. The findings showed the diverse understandings of objectivity among VR content creators and the growing acceptance of VR as a subjective experience. The 360° spherical view reduces the ability to frame the content. The different story-paths in interactive VR storytelling may add perspectives to the story. However, the VR user retains the ability to actively explore the virtual environment, deciding what to experience and what to dismiss. VR emphasises user-induced subjectivity in addition to the already existing journalist-induced subjectivity, in which the journalist chooses camera placement and provides a guided experience. Subjectivity is inescapable in VR and its impact is magnified, suggesting extra caution from VR content creators to protect users and affirming the need for the notion of pragmatic objectivity.
A Goal-Biased RRT Path Planning Approach for Autonomous Ground Vehicle

For the application of autonomous ground vehicle (AGV) operating in unstructured environment, a path planning method based on an improved goal-biased Rapidly-exploring Random Trees (bias-RRT) is proposed. The algorithm combines random sampling with numerical optimization to achieve fast convergence speed and satisfy constraints. KD-Tree and potential field of the environment are implemented to increase the sampling efficiency, and cubic B-splines are used to smooth the path for better tracking performance. The algorithm improves the efficiency of searching while guarantee safety and quality of the planned path. Simulation results verify the effectiveness of the proposed method.
A Novel Video Coding Strategy in HEVC for Object Detection

Occupying the most significant portion of global data traffic, video is being generated in almost every aspect of our life. Because of its huge volume, we are depending much more heavily on machine intelligence based analysis. In the meantime, video coding technology has been continuously improved for better compression efficiency. However, the state-of-the-art video coding standards, such as H.265/HEVC and versatile video coding (VVC), are still designed assuming that the compressed video will be watched by a human later. Such a design is not optimal when the compressed video will be used by computer vision applications. While the human visual system (HVS) is consistently sensitive to the content with high contrast, the impact of pixels on computer vision algorithms is task driven. For example, because of the different categories of objects used to train detection algorithms, the influence of the same image content on those detectors also varies. Therefore, human oriented video coding strategies may not be optimal when the compressed signal is further processed by algorithms, as the encoder is unaware of the task specific information. In this article, taking object detection as an example, we propose a novel video coding strategy for computer vision. By protecting the information according to its importance for an object detector rather than for the human visual system, our proposed method has the potential to achieve a better object detection performance with the same bandwidth. The main contributions of our paper are: 1) the modeling of the relationship between object detection accuracy and bit rate; 2) a back propagation based method to analyze the influence of each pixel on the detection of target objects; 3) an object detection oriented bit allocation and codec control parameter determination scheme; 4) an evaluation metric to compare the impact of video coding strategies on a given object detector over a predefined range of bit rate. Experimental results demonstrate that our proposed algorithm can better preserve the video content vital for object detection than state-of-the-art video coding schemes.
Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals

Counterfactual examples for an input—perturbations that change specific features but not others—have been shown to be useful for evaluating bias of machine learning models, e.g., against specific demographic groups. However, generating counterfactual examples for images is nontrivial due to the underlying causal structure on the various features of an image. To be meaningful, generated perturbations need to satisfy constraints implied by the causal model. We present a method for generating counterfactuals by incorporating a structural causal model (SCM) in an improved variant of Adversarially Learned Inference (ALI), that generates counterfactuals in accordance with the causal relationships between attributes of an image. Based on the generated counterfactuals, we show how to explain a pre-trained machine learning classifier, evaluate its bias, and mitigate the bias using a counterfactual regularizer. On the Morpho-MNIST dataset, our method generates counterfactuals comparable in quality to prior work on SCM-based counterfactuals (DeepSCM), while on the more complex CelebA dataset our method outperforms DeepSCM in generating high-quality valid counterfactuals. Moreover, generated counterfactuals are indistinguishable from reconstructed images in a human evaluation experiment and we subsequently use them to evaluate the fairness of a standard classifier trained on CelebA data. We show that the classifier is biased w.r.t. skin and hair color, and how counterfactual regularization can remove those biases.
AIR-Act2Act: Human–human interaction dataset for teaching non-verbal social behaviors to robots

To better interact with users, a social robot should understand the users’ behavior, infer the intention, and respond appropriately. Machine learning is one way of implementing robot intelligence. It provides the ability to automatically learn and improve from experience instead of explicitly telling the robot what to do. Social skills can also be learned through watching human–human interaction videos. However, human–human interaction datasets are relatively scarce to learn interactions that occur in various situations. Moreover, we aim to use service robots in the elderly care domain; however, there has been no interaction dataset collected for this domain. For this reason, we introduce a human–human interaction dataset for teaching non-verbal social behaviors to robots. It is the only interaction dataset that elderly people have participated in as performers. We recruited 100 elderly people and 2 college students to perform 10 interactions in an indoor environment. The entire dataset has 5,000 interaction samples, each of which contains depth maps, body indexes, and 3D skeletal data that are captured with three Microsoft Kinect v2 sensors. In addition, we provide the joint angles of a humanoid NAO robot which are converted from the human behavior that robots need to learn. The dataset and useful Python scripts are available for download at https://github.com/ai4r/AIR-Act2Act . It can be used to not only teach social skills to robots but also benchmark action recognition algorithms.
Flow-Edge Guided Unsupervised Video Object Segmentation

Recently, deep learning techniques have achieved significant improvements in unsupervised video object segmentation (UVOS). However, many of existing approach cannot accurately identify the foreground objects and the background as they commonly use the coarse temporal features (e.g., optical flow and multi-frames attention). In this paper, we present a novel model termed Flow Edge-based Motion-Attentive Network (FEM-Net), to address the unsupervised video object segmentation problem. Firstly, a motion-attentive encoder is used to jointly learn the spatial and temporal features. Then, a Flow Edge Connect (FEC) module is designed to hallucinate edges of the ambiguous or missing region in the optical flow. During the segmentation stage, the complementary temporal feature composed by the motion-attentive feature and flow edge is fed into a decoder to infer the salient foreground objects. Experimental results on two challenging public benchmarks (i.e. DAVIS-16 and FBMS) demonstrate that the proposed FEM-Net compares favorably against the state-of-the-art methods.
Sensors Anomaly Detection of Industrial Internet of Things Based on Isolated Forest Algorithm and Data Compression

Aiming at solving network delay caused by large chunks of data in industrial Internet of Things, a data compression algorithm based on edge computing is creatively put forward in this paper. The data collected by sensors need to be handled in advance and are then processed by different single packet quantity K and error threshold e for multiple groups of comparative experiments, which greatly reduces the amount of data transmission under the premise of ensuring the instantaneity and effectiveness of data. On the basis of compression processing, an outlier detection algorithm based on isolated forest is proposed, which can accurately identify the anomaly caused by gradual change and sudden change and control and adjust the action of equipment, in order to meet the control requirement. As is shown by experimental simulation, the isolated forest algorithm based on partition outperforms box graph and K-means clustering algorithm based on distance in anomaly detection, which verifies the feasibility and advantages of the former in data compression and detection accuracy.
Safety-Assured Design and Adaptation of Learning-Enabled Autonomous Systems

Future autonomous systems will employ sophisticated machine learning techniques for the sensing and perception of the surroundings and the making corresponding decisions for planning, control, and other actions. They often operate in highly dynamic, uncertain and challenging environment, and need to meet stringent timing, resource, and mission requirements. In particular, it is critical and yet very challenging to ensure the safety of these autonomous systems, given the uncertainties of the system inputs, the constant disturbances on the system operations, and the lack of analyzability for many machine learning methods (particularly those based on neural networks). In this paper, we will discuss some of these challenges, and present our work in developing automated, quantitative, and formalized methods and tools for ensuring the safety of autonomous systems in their design and during their runtime adaptation. We argue that it is essential to take a holistic approach in addressing system safety and other safety-related properties, vertically across the functional, software, and hardware layers, and horizontally across the autonomy pipeline of sensing, perception, planning, and control modules. This approach could be further extended from a single autonomous system to a multi-agent system where multiple autonomous agents perform tasks in a collaborative manner. We will use connected and autonomous vehicles (CAVs) as the main application domain to illustrate the importance of such holistic approach and show our initial efforts in this direction.
Coordinate Methods for Matrix Games

We develop primal-dual coordinate methods for solving bilinear saddle-point problems of the form minx ∈ Xmaxy ∈ Yy <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sup> Ax which contain linear programming, classification, and regression as special cases. Our methods push existing fully stochastic sublinear methods and variance-reduced methods towards their limits in terms of per-iteration complexity and sample complexity. We obtain nearly-constant per-iteration complexity by designing efficient data structures leveraging Taylor approximations to the exponential and a binomial heap. We improve sample complexity via low-variance gradient estimators using dynamic sampling distributions that depend on both the iterates and the magnitude of the matrix entries. Our runtime bounds improve upon those of existing primal-dual methods by a factor depending on sparsity measures of the m by n matrix A. For example, when rows and columns have constant l1/l2 norm ratios, we offer improvements by a factor of m+n in the fully stochastic setting and √{m+n} in the variance-reduced setting. We apply our methods to computational geometry problems, i.e. minimum enclosing ball, maximum inscribed ball, and linear regression, and obtain improved complexity bounds. For linear regression with an elementwise nonnegative matrix, our guarantees improve on exact gradient methods by a factor of √{nnz(A)/(m+n)}.
An Evaluation Methodology for Interactive Reinforcement Learning with Simulated Users

Interactive reinforcement learning methods utilise an external information source to evaluate decisions and accelerate learning. Previous work has shown that human advice could significantly improve learning agents’ performance. When evaluating reinforcement learning algorithms, it is common to repeat experiments as parameters are altered or to gain a sufficient sample size. In this regard, to require human interaction every time an experiment is restarted is undesirable, particularly when the expense in doing so can be considerable. Additionally, reusing the same people for the experiment introduces bias, as they will learn the behaviour of the agent and the dynamics of the environment. This paper presents a methodology for evaluating interactive reinforcement learning agents by employing simulated users. Simulated users allow human knowledge, bias, and interaction to be simulated. The use of simulated users allows the development and testing of reinforcement learning agents, and can provide indicative results of agent performance under defined human constraints. While simulated users are no replacement for actual humans, they do offer an affordable and fast alternative for evaluative assisted agents. We introduce a method for performing a preliminary evaluation utilising simulated users to show how performance changes depending on the type of user assisting the agent. Moreover, we describe how human interaction may be simulated, and present an experiment illustrating the applicability of simulating users in evaluating agent performance when assisted by different types of trainers. Experimental results show that the use of this methodology allows for greater insight into the performance of interactive reinforcement learning agents when advised by different users. The use of simulated users with varying characteristics allows for evaluation of the impact of those characteristics on the behaviour of the learning agent.
Hydrogeochemical, microbial and compositional analysis of data from surface and groundwater sources in Southeastern Nigeria

Surface and groundwaters are the major sources of drinking and irrigation water in most developing countries. Hydrogeochemical data, being compositional in nature require adequate statistical treatment in order to provide dependable information needed for decision making. In this study, water samples were collected from 100 groundwaters and 24 surface waters within the principal Benin and Bende-Ameki lithostratigraphic units in Southeastern Nigeria, and analyzed for major ions, microbiological and other physicochemical parameters using standard methods. An approach based on the isometric logratio (ilr) transformation, leading to the ostensible symmetric coordinates was applied for multivariate statistics. Source 'fit for purpose' for drinking and irrigation was assessed using a modified water quality index (WQI), TDS-pE graph, sodium hazard (SAR), magnesium hazard (MAR), and other ionic models. Most surface and groundwater sources were acidic with 84% and 98% of the samples respectively having pH below WHO permissible limit for drinking water. Compositional data (CoDa) analysis produced robust principal components (PCs) and clusters that were key in explaining important hydrogeochemical processes in the water sources, whereas heatmaps of the correlated variables clearly showed how spurious effects due to the constant sum constraint of compositional data could occur using non-CoDa statistical approaches. The first two PCs of surface and groundwater data explained 74.8% and 67.0% of the total variance respectively, which indicated relative dominance of SO42− and HCO3− respectively. Results showed that all surface water sources were suitable for irrigation by SAR, MAR, Mg:Ca, and Wilcox method, whereas some groundwater sources were unsuitable. TDS-pE graph and WQI classified water sources as "excellent", with the former being simple and generic for instant monitoring of water quality. More so, minimal treatment is recommended to meet the microbial criteria for domestic use of the water sources.
The effects of Virtual Reality technology application in the aircraft pilot training process

Abstract In this study, we focus on some problems related to virtual reality technology usage in supporting the educational process. The main goal is to obtain the optimal duration of exposure to virtual reality without the appearance of physiological problems for the user affected the achievement of teaching outcomes. Most of the characteristics of virtual reality software at the same time point out some issues in the areas of technological innovations, especially in the health context. The presented example of the VR environment’s training application shows the effectiveness of VR methods and their possibilities in training aircraft pilots. Teaching involving VR allowed to achieve, on average, as much as 90% correct answers, which in comparison with traditional methods resulted in an increase of 21 to 32.7% of correct answers in comparison with training using VR. The research confirmed that a critical challenge in the context of the broader use of VR in education is to appropriately adapt the learning materials and content to the technical requirements. Then, in certain areas, the use of VR is beneficial.
On Privacy of Dynamical Systems: An Optimal Probabilistic Mapping Approach

We address the problem of maximizing privacy of stochastic dynamical systems whose state information is released through quantized sensor data.In particular, we consider the setting where information about the system state is obtained using noisy sensor measurements.This data is quantized and transmitted to a (possibly untrustworthy) remote station through a public/unsecured communication network.We aim at keeping (part of) the state of the system private; however, because the network (and/or the remote station) might be unsecure, adversaries might have access to sensor data, which can be used to estimate the system state.To prevent such adversaries from obtaining an accurate state estimate, before transmission, we randomize quantized sensor data using additive random vectors, and send the corrupted data to the remote station instead.We design the joint probability distribution of these additive vectors (over a time window) to minimize the mutual information (our privacy metric) between some linear function of the system state (a desired private output) and the randomized sensor data for a desired level of distortion-how different quantized sensor measurements and distorted data are allowed to be.We pose the problem of synthesising the joint probability distribution of the additive vectors as a convex program subject to linear constraints.Simulation experiments are presented to illustrate our privacy scheme.