Reviewerly/exHarmony huggingface dataset:
SugarCoat: Programmatically Generating Privacy-Preserving, Web-Compatible Resource Replacements for Content Blocking

Content blocking systems today exempt thousands of privacy-harming scripts. They do this because blocking these scripts breaks the Web sites that rely on them. In this paper, we address this privacy/functionality trade-off with SugarCoat, a tool that allows filter list authors to automatically patch JavaScript scripts to restrict their access to sensitive data according to a custom privacy policy. We designed SugarCoat to generate resource replacements compatible with existing content blocking tools, including uBlock Origin and the Brave Browser, and evaluate our implementation by automatically replacing scripts exempted by the 6,000+ exception rules in the popular EasyList, EasyPrivacy, and uBlock Origin filter lists. Crawling a sample of pages from the Alexa 10k, we find that SugarCoat preserves the functionality of existing pages-our replacements result in Web-compatibility properties similar to exempting scripts-while providing privacy properties most similar to blocking those scripts. SugarCoat is intended for real-world practical deployment, to protect Web users from privacy harms current tools are unable to protect against. Our design choices emphasize compatibility with existing tools, policy flexibility, and extensibility. SugarCoat is open source and is being integrated into Brave's content blocking tools: an initial set of SugarCoat-generated resource replacements are already shipping to users in the Brave Browser.
An Improved Pure Pursuit Algorithm for Tractor Automatic Navigation

In this paper, an improved pure pursuit (IPP) for tractor autonomous navigation is proposed. The kinematics model is based on simplified two wheeled vehicle Ackermann model. By detecting the trend of deviation to the planned path, the looking-ahead distance, a key parameter in the pure tracking algorithm, is automatically adjusted to improve the accuracy of tracking the planned path. The simulation and field test show that the new method has higher accuracy and is easy to implement.
Derivation of the Kalman filter in a Bayesian filtering perspective

The Kalman filter is popularly known to be an optimal recursive implementation of the Bayesian prediction and correction in the sense that it minimises the estimated error covariance. The filter has been originally derived in this error minimising framework and there is extensive literature on the same. The Kalman filter has also been derived under other frameworks, like the maximum likelihood approach, etc., which all converge to the true posterior. In this paper we present a purely Bayesian filtering approach to the Kalman filter. We first build an analogy to the principles of Bayesian estimation and then present a step-by-step derivation for the Kalman filter following the Bayesian principles. From this derivation, we show that the Kalman filter gives a tractable solution to the Bayesian filtering process by computing the underlying probability densities exactly. This derivation is known to some in the research community but no formal article in the literature presents it in detail. This paper fills this gap and will be a good read for Bayesian enthusiasts. The filter is simulated in the proposed framework on a simple 4-D linear Gaussian model.
Bayesian inference for skew-wrapped Cauchy mixture model using a modified Gibbs sampler

More and more datasets on the circle tend to exhibit non-trivial features such as skewness and multimodality. Therefore, there is a growing demand for flexible models to capture these characteristics. For analyzing data with skew and multimodal patterns, we focus on the skew mixture models, specially skew-wrapped Cauchy mixture model as the underlying distribution. We incorporate prior knowledge about the parameters into analysis to improve results. A modified Gibbs sampling is used to obtain the estimates of the parameters. A simulation study is conducted to assess the performance of the proposed Bayesian approach for skew-wrapped Cauchy mixture model. A real dataset illustrates the usefulness of this approach.
The tree-generative capacity of combinatory categorial grammars

Linear context-free tree languageThe generative capacity of combinatory categorial grammars (CCGs) as generators of tree languages is investigated.It is demonstrated that the tree languages generated by CCGs can also be generated by simple monadic context-free tree grammars.However, the important subclass of pure combinatory categorial grammars cannot even generate all regular tree languages.Additionally, the tree languages generated by combinatory categorial grammars with limited rule degrees are characterized: If only application rules are allowed, then these grammars can generate only a proper subset of the regular tree languages, whereas they can generate exactly the regular tree languages once first-degree composition rules are permitted.
A Stochastic Multivariate Irregularly Sampled Time Series Imputation Method for Electronic Health Records

Electronic health records (EHRs) can be very difficult to analyze since they usually contain many missing values. To build an efficient predictive model, a complete dataset is necessary. An EHR usually contains high-dimensional longitudinal time series data. Most commonly used imputation methods do not consider the importance of temporal information embedded in EHR data. Besides, most time-dependent neural networks such as recurrent neural networks (RNNs) inherently consider the time steps to be equal, which in many cases, is not appropriate. This study presents a method using the gated recurrent unit (GRU), neural ordinary differential equations (ODEs), and Bayesian estimation to incorporate the temporal information and impute sporadically observed time series measurements in high-dimensional EHR data.
Phenotypic Niching Using Quality Diversity Algorithms

Here we describe quality diversity algorithms, a recent and powerful class of evolutionary algorithms that produces a diverse set of high-performing solutions. The optimization paradigm emphasizes phenotypic niching and egalitarian treatment of quality and diversity. We ground quality diversity in ecology, describe the historical development, and give an intuition and formalization of the algorithms. We present a practical example that we refer to for engineers and laymen readers to understand how and why quality diversity can be used. The main insights from research of quality diversity, performance metrics, and benchmarks are discussed. Finally, the open challenges are presented.
A Streaming Data Collection and Analysis for Cryptocurrency Price Prediction using LSTM

Big data analysis for accurate predictions requires adherence to systematic procedures. This study shows an entire data analysis phase from the data collection to model evaluation using the Long Short-Term memory(LSTM) for cryptocurrency price prediction. Three different coin prices are directly collected from the CoinMarketCap in nearly real-time by applying the web scraping technique. The LSTM model trained with this data varying random seed or static seed parameters to find optimal conditions, leading to better accuracy of the LSTM model. Our model evaluated their accuracy in terms of MAE, RMSE, and SMAPE indicators. As a result of this experiment, most of the best candidate parameters are classified at the fixed seed trail in terms of the RMSE for Bit coin, Ethereum, and Lite Coin.
Recognition of insulator based on yolov5 algorithm

The pollution flashover accident of insulator strings in substation is the main threat to the safety of power systems, water washing operations on the surface of insulator strings can effectively eliminate this risk, and use intelligent robots to perform water washing tasks instead of manual labor is gradually becoming mainstream. In order to accomplish water washing task, the robot needs to find out the position of the insulator string by using vision sensors. In this paper, we build an insulator strings dataset with 150 images, train and compare the performance differences of four models of Yolov5 for insulator strings detection. Experiments show that when performing the insulator string detection, the difference of detection precision between the four Yolov5 models is small, Yolov5x having 0.02 higher precision than Yolov5s. While the difference in inference speed is large, with Yolov5s being 5 times faster than Yolov5x and can reach 43 FPS on GTX1060.
MVDI25K: A large-scale dataset of microscopic vaginal discharge images

With the widespread application of artificial intelligence technology in the field of biomedical images, the deep learning-based detection of vaginal discharge, an important but challenging topic in medical image processing, has drawn an increasing amount of research interest. Although the past few decades have witnessed major advances in object detection of natural scenes, such successes have been slow to medical images, not only because of the complex background and diverse cell morphology in the microscope images, but also due to the scarcity of well-annotated datasets of objects in medical images. Until now, in most hospitals in China, the vaginal diseases are often checked by observation of cell morphology using the microscope manually, or observation of the color reaction experiment by inspectors, which are time-consuming, inefficient and easily interfered by subjective factors. To this end, we elaborately construct the first large-scale dataset of microscopic vaginal discharge images, named MVDI25K, which consists of 25,708 images covering 10 cell categories related to vaginal discharge detection. All the images in MVDI25K dataset are carefully annotated by experts with bounding-box and object-level labels. In addition, we conduct a systematical benchmark experiments on MVDI25K dataset with 10 representative state-of-the-art (SOTA) deep models focusing on two key tasks, i.e., object detection and object segmentation. Our research offers the community an opportunity to explore more in this new field.
Drone Model Identification by Convolutional Neural Network from Video Stream

We present a convolutional neural network model that correctly identifies drone models in real-life video streams of flying drones. To achieve this, we show a method of generating synthetic drone images. To create a diverse dataset, the simulation parameters (such as drone textures, lighting, and orientation) are randomized. This synthetic dataset is used to train a convolutional neural network to identify the drone model: DJI Phantom, DJI Mavic, or DJI Inspire. The model is then tested on a real-life Anti-UAV dataset of flying drones. The benchmark results show that the DenseNet201 architecture performed the best. Adding Gaussian noise to the training dataset and performing full training (as opposed to freezing layers) shows the best results. The model shows an average accuracy of 92.4%, and an average precision of 88.6% on the test dataset.
Towards Human-Centered AI: Psychological concepts as foundation for empirical XAI research

Abstract Human-Centered AI is a widely requested goal for AI applications. To reach this is explainable AI promises to help humans to understand the inner workings and decisions of AI systems. While different XAI techniques have been developed to shed light on AI systems, it is still unclear how end-users with no experience in machine learning perceive these. Psychological concepts like trust, mental models, and self-efficacy can serve as instruments to evaluate XAI approaches in empirical studies with end-users. First results in applications for education, healthcare, and industry suggest that one XAI does not fit all. Instead, the design of XAI has to consider user needs, personal background, and the specific task of the AI system.
The Cybersecurity Awareness Training Model (CATRAM)

This chapter presents the outcome of one empirical research study that assess the implementation and validation of the cybersecurity awareness training model (CATRAM), designed as a multiple-case study in a Canadian higher education institution. Information security awareness programs have become unsuccessful to change people's attitudes in recognizing, stopping, or reporting cyberthreats within their corporate environment. Therefore, human errors and actions continue to demonstrate that we as humans are the weakest links in cybersecurity. The chapter studies the most recent cybersecurity awareness programs and its attributes. Furthermore, the author compiled recent awareness methodologies, frameworks, and approaches. The cybersecurity awareness training model (CATRAM) has been created to deliver training to different corporate audiences, each of these organizational units with peculiar content and detached objectives. They concluded their study by addressing the necessity of future research to target new approaches to keep cybersecurity awareness focused on the everchanging cyberthreat landscape.
Cognitive Modelling-driven Time Series Forecasting for Predicting Target Indicators in Non-stationary Processes

Analysis and forecasting of time series is a popular predictive tool for working with large amounts of data reflecting the patterns of processes’ behavior under study in the economic, financial, socio-political and other spheres. However, their potential is not enough to effectively forecasting of the situation development in non-stationary processes, which are a characteristic feature and trend of our time in various society spheres. Such processes are characterized by unpredictability of behavior, for example, in cases of: (i) an abrupt transition from one state to another, due to an event that causes an abrupt (jump) change in the process values; (ii) violation or weak severity of seasonality in the processes during the transition from a stable state to a crisis. In this paper we propose a cognitive modelling-driven approach to time series forecasting for predicting target indicators in non-stationary processes. The approach implementation is aimed at improving the forecast quality of target indicators in such processes by (i) building and correcting competing models based on time series, and (ii) activating dominant models from among the competing ones by taking into account the correcting signals. These signals are formed (in monitoring mode) as a result of the analysis of qualitative information (judgments and opinions of the decision makers and experts) using a fuzzy cognitive map of the situation - a model for representing causal influences between system-forming factors in such processes.
Toward Deconfounding the Effect of Entity Demographics for Question Answering Accuracy

The goal of question answering (QA) is to answer any question.However, major QA datasets have skewed distributions over gender, profession, and nationality.Despite that skew, model accuracy analysis reveals little evidence that accuracy is lower for people based on gender or nationality; instead, there is more variation on professions (question topic).But QA's lack of representation could itself hide evidence of bias, necessitating QA datasets that better represent global diversity.1 For NQ, we only consider questions with short answers. 2 https://cloud.google.com/natural-language/docs/analyzing-entities 3 We analyze the dev fold, which is consistent with the training fold (Table 1 and2), as we examine accuracy.
RF-Eletter: A Cross-Domain English Letter Recognition System Based on RFID

As the basis of human-computer interaction (HCI), gesture recognition interprets user-performed gestures as commands, followed by the content execution expressed by users’ gestures. Gesture recognition through wireless signals denotes a novel branch of human perception. Despite the recent popularity of Radio Frequency Identification (RFID) following specific advantages (lightweight, low-cost, and universality), several intricacies remain unresolved in RFID sensing research. First, most studies performed simplified body movements assessments instead of identifying complex and fine-grained or subtle gestures. Second, users require extensive training in a novel discipline to collect training data in a specific pattern. Given the paucity of an intuitive and effective means of identifying user gestures, the RF-E-letter proposed in this study denotes an RFID recognition system for complex, fine-grained, and domain-independent gestures. A multi-label array was utilized to gather gesture signals. Fine-grained gesture data could be obtained pre-processing with a novel data-processing method. Seemingly irregular RFID phase data could be converted into intuitive images for the deep learning module input as convolutional neural networks (CNNs) encompass automatic extraction characteristics for complex space-time features. The average accuracy of new environments for novel users is 95.6% and 96.6%, respectively (significantly better than current RFID-based solutions), thus demonstrating effectiveness and versatility.
Trust Management for Reliable Cross-Platform Cooperation Based on Blockchain

With the rise of crossover services, service providers usually cooperate with each other on different platforms to expand their service value. However, in cross-platform cooperation, insufficient understanding and malicious competition between different platforms would lead to inaccurate trust establishment and unreliable trust recommendations. In this paper, we propose a trust management framework of cross-platform based on blockchain to establish, store and recommend trust securely for cross-platform cooperation. Firstly, we take into account the contextual background information to enhance interaction and understanding between platforms to achieve accurate trust establishment. Secondly, the trust recommendation algorithm is written into the blockchain in the form of smart contracts, which can ensure the security of trust recommendation. Finally, experiments are used to demonstrate the superiority and reliability of the framework.
Control Barrier Function Based Assist Control with Shifting Sensor and Footprint Positions

An indoor autonomous mobile robot is required to avoid contacting building walls and fixtures in the course of its operation. This problem is known as a collision avoidance problem. The method based on control barrier functions has recently been proposed as one of the solutions. We have also designed the control barrier function for point cloud dynamics in our previous work. In this paper, we present the results of an implementation of the control barrier function based controller on an actual robot. The method is extended to handle shifting sensor position and collision area. The effectiveness of the proposed controller is confirmed by experimental results.
Hand-written Hindi Character Recognition - A Comprehensive Review

Character recognition is a technology that facilitates the conversion of different types of scanned documents into searchable and editable data. Since the decade of years, many researchers work on character recognition. It can be classified into hand-written character recognition and printed character recognition. Hand-written character recognition is considered to be a demanding research area in the field of pattern recognition. In this paper, we present a comprehensive survey on existing techniques for hand-written character recognition of Hindi scripts with the help of various parameters such as techniques utilized for pre-processing, feature extraction, classification, etc. This paper aims to provide an insight to researchers working in the domain of hand-written Hindi character recognition.
Using Synthetic Data Generation to Probe Multi-View Stereo Networks

Synthetic data is highly useful for training machine learning systems performing image-based 3D reconstruction, as synthetic data has applications in both extending existing generalizable datasets and being tailored to train neural networks for specific learning tasks of interest. In this paper, we introduce and utilize a synthetic data generation suite capable of generating data given existing 3D scene models as input. Specifically, we use our tool to generate image sequences for use with Multi-View Stereo (MVS), moving a camera through the virtual space according to user-chosen camera parameters. We evaluate how the given camera parameters and type of 3D environment affect how applicable the generated image sequences are to the MVS task using five pre-trained neural networks on image sequences generated from three different 3D scene datasets. We obtain generated predictions for each combination of parameter value and input image sequence, using standard error metrics to analyze the differences in depth predictions on image sequences across 3D datasets, parameters, and networks. Among other results, we find that camera height and vertical camera viewing angle are the parameters that cause the most variation in depth prediction errors on these image sequences.
Explaining robot policies

Abstract In order to interact with a robot or make wise decisions about where and how to deploy it in the real world, humans need to have an accurate mental model of how the robot acts in different situations. We propose to improve users' mental model of a robot by showing them examples of how the robot behaves in informative scenarios. We explore this in two settings. First, we show that when there are many possible environment states, users can more quickly understand the robot's policy if they are shown critical states where taking a particular action is important. Second, we show that when there is a distribution shift between training and test environment distributions, then it is more effective to show exploratory states that the robot does not visit naturally.
Ring-Based Identity Based Encryption – Asymptotically Shorter MPK and Tighter Security

This work constructs an identity based encryption from the ring learning with errors assumption (RLWE), with shorter master public keys and tighter security analysis. To achieve this, we develop three new methods: (1) a new homomorphic equality test method using nice algebraic structures of the rings, (2) a new family of hash functions with natural homomorphic evaluation algorithms, and (3) a new insight for tighter reduction analyses. These methods can be used to improve other important cryptographic tasks, and thus are of general interests.Particularly, our homomorphic equality test method can derive a new method for packing/unpacking GSW-style encodings, showing a new non-trivial advantage of RLWE over the plain LWE. Moreover, our new insight for tighter analyses can improve the analyses of all the currently known partition-based IBE designs, achieving the best of the both from prior analytical frameworks of Waters (Eurocrypt ’05) and Bellare and Ristenpart (Eurocrypt ’09).