Reviewerly/exHarmony huggingface dataset:
Main problems of trajectory processing and approaches to their solution within the framework of multitarget tracking

Abstract The main problems of trajectory processing of radar data are considered. Within the framework of the task of multitarget traсking, approaches are proposed that effectively solve the following problems: assignment problems; detection (reset) of trajectories; trajectory measurement of coordinates and motion parameters of the observed objects; highlighting trajectory features.
An ensemble method of feature selection and classification of Odia characters

Offline Handwritten Character Recognition of Odia scripts have drawn remarkable attention by researchers. In the area of Pattern recognition, most challenging task is to classify and recognize characters with human like accuracy. Feature selection methods are widely used in various sectors of machine learning, bioinformatics, and Pattern recognition. In high dimensional datasets, not all the features are relevant to the problem; some of them interfere and reduce accuracy and also the causes of curse of dimensionality. To overcome the above problems, we have experimented different feature selection technique i.e. filter and wrapper methods, to select a subset of relevant and nonredundant features that have the strongest relationships with the output variable. Filter and wrapper based methods are implemented, on vowels of Odia script of OHCS 1.0 database for Odia character recognition, by selecting top `k' important features and their accuracy is analyzed. The Random Forest (RF) technique, which is an ensemble of decision trees, incorporates Gini importance based feature selection and influences greatly the model's accuracy. By varying the number of decision tress in the Random Forest, the accuracy and the execution time for building the tree before feature selection and after feature selection is compared. After discarding many features that do not carry any information for class prediction, the ensemble model results on an average of 99.2% accuracy with reduced feature set.
Developing the Minimum Dataset for the New Mexico Decedent Image Database

A minimum dataset (MDS) can be determined ad hoc by an investigator or small team; by a metadata expert; or by using a consensus method to take advantage of the global knowledge and expertise of a large group of experts. The first method is the most commonly applied.Here, we describe a use of the third approach using a modified Delphi method to determine the optimal MDS for a dataset of full body computed tomography scans. The scans are of decedents whose deaths were investigated at the New Mexico Office of the Medical Investigator and constitute the New Mexico Decedent Image Database (NMDID).The authors initiated the consensus process by suggesting 50 original variables to elicit expert reactions. Experts were recruited from a variety of scientific disciplines and from around the world. Three rounds of variable selection showed high rates of consensus.In total, 59 variables were selected, only 52% of which the original resource authors selected. Using a snowball method, a second set of experts was recruited to validate the variables chosen in the design phase. During the validation phase, no variables were selected for deletion.NMDID is likely to remain more "future proof" than if a single metadata expert or only the original team of investigators designed the metadata.
Are We Already Living with Skynet? Anthropomorphic Artificial Intelligence to Enhance Customer Experience

The main objective of this chapter is broadening the understanding of anthropomorphic artificial intelligence (AI) (e.g. avatars, humanoid robots, chatbots) in both physical and digital environments. The chapter strives to demonstrate how organisations can curate relationship marketing and enhance customer experience by employing anthropomorphic AI. To achieve this, the chapter extends existing understanding in three ways. First, it explains the interconnectivity between relationship marketing and customer experience. Second, it presents anthropomorphic AI along with its different characteristics and technologies. Third, it offers some real-life uses cases and examples of such AI drawing from practical insights into five selected industries. Overall, the chapter provides some food of thoughts concerning the successful application and deployment of anthropomorphic AI in marketing practices.
Cloud Identity and Access Management Solution with Blockchain

Present solutions to problems of Cloud Identity and Access Management are costly, inefficient with information duplication across service providers. It also comes with additional overhead for users requiring to keep track of associated credentials of desired services. Securing same is difficult and has resulted in worldwide huge breaches of personal data in recent times. A comparatively new approach is required for identity management with ownership of cloud user and also controlled by cloud user. Blockchain technology decentralize various services, security, and verifiability, by offering a peer to peer system. Particularly Blockchain operates by enforcing continuous storage of various transaction performed that are secured by digital signatures and affirmed through consensus. This chapter provides a detailed and systematic description of blockchain for identity and access management. The specific characteristic features of this technology that could revolutionize the management process are highlighted. Towards this end, comparative study and critical analysis of many approached and related aspects are incorporated. In the chapter, we bring out various shortcomings identified from the studied literature, limiting ourselves to the aspects of blockchain technology and their relevance. For addressing these gaps, our comprehensive study also points out some future directions and further enhancement to the blockchain technology. Depending on such findings, we pin point several research gaps and associated fields that are significant for research aspects.
Blockchain-Powered Parallel FinTech Regulatory Sandbox Based on the ACP Approach

As a new FinTech innovation regulation tool, regulatory sandbox effectively balances financial innovation and risk, so it is adopted by many regulatory authorities around the world. However, until now, regulatory sandbox is still at the stage of manually submitting data, and artificial intelligence has not been applied to it. Hence, in this paper, we propose the framework of Parallel FinTech Regulatory Sandbox based on the ACP approach (Artificial systems + Computational experiments + Parallel execution). Then, we talk about how to combine the emerging blockchain technology with intelligent regulatory sandbox, thus solving problems such as poor data flow and regulation sluggish. This paper is aimed at providing helpful guidance and reference for the development of FinTech regulatory sandbox.
Inner bounds via simultaneous decoding in quantum network information theory

We prove new inner bounds for several multiterminal channels with classical inputs and quantum outputs. Our inner bounds are all proved in the one-shot setting and are natural analogues of the best classical inner bounds for the respective channels. For some of these channels, similar quantum inner bounds were unknown even in the asymptotic independent and identically distributed setting. We prove our inner bounds by appealing to a new classical–quantum joint typicality lemma established in a companion paper. This lemma allows us to lift to the quantum setting many inner bound proofs for classical multiterminal channels that use intersections and unions of typical sets.
Feature Generation Based on Knowledge Graph

With the continuous improvement of the status of machine learning in business, scientific research and even life, feature engineering, an indispensable part of machine learning tasks, has attracted more and more researchers attention. In feature engineering, feature engineering is an important component for generating new effective information. Researches in the past hope to operate available data to acquire new features, it does not import extra information. Therefore, this paper puts forward the feature generation method based on knowledge graph. This paper introduces the basic application method of knowledge graph by using the convenience of obtaining structured data in knowledge graph. This paper constructs campus big data mental health assessment data through questionnaires and campus databases, uses feature generation methods based on knowledge maps to generate features, obtains new data generated by collation, and uses the model to verify; finally, the experimental results show that after optimization The recall rate of the data set in the four integrated learning models has increased to more than 60%, which can provide preliminary guidance for judging the mental health status of students.
Human-Robot Collaboration with Variable Autonomy via Gaussian Process

In this paper, we investigate a scenario of human-robot collaboration mainly motivated by agricultural applications, and present a novel control architecture with variable autonomy based on Gaussian Process (GP) regression. The system consists of three components, namely a robot manipulator, system operator, and computer. The operator monitors an image, acquired by the robot, capturing possibly multiple candidates for a target to be reached by the end effector of the robot. He/she chooses a target to be reached among these candidates, and determines a velocity command for the end effector, while the computer also identifies a target through image processing. We first design an automatic controller and a mechanism to tune autonomy online. To this end, we build two GP models from the data on a training system without any uncertain factor that may disturb the operation: one is built from the data of an expert for the system operation and the other is from the operator's own data. The former model is utilized as the automatic controller and the autonomy determination mechanism is designed based on the variance information provided by the GP models so that the controller supports the manual control only when the expert's model is reliable, the computer correctly identifies the human target, and the real environment does not contain any uncertain factor that affects manual control. We finally demonstrate the mechanism through experiments from the viewpoints of the performance and human workload.
Home Assistant of The Future

Information and communication technologies are changing the life of a modern person in many aspects. One of the recent major achievements is the opportunity to communicate with modern technological appliances in natural language. The purpose of this study is to analyze the expectations of people about their future home assistant. The authors analyzed 220 comments - requests for improvement of Virtual Personal Assistant (VPI), which is becoming an increasingly popular home technology. The main research method is netnography, which made it possible to identify the main expectations about the home assistant of the future. In addition, the method of heuristic forecasting, expert method, the interpreted method and the case analysis were used. It was revealed that there is satisfaction with the VPA's communication skills, but full-fledged communication lacks individualization. VPA proved to be the technology that could extend the scope of new technological solutions for smart home.
General construction principles and performance features of trajectory processing by data from one radar data source

Abstract The general principles of construction and features of the operation of the trajectory processing device based on data from one source of radar information are considered. The tasks performed and structure of the trajectory processing device are discussed.
iffDetector: Inference-Aware Feature Filtering for Object Detection

Modern convolutional neural network (CNN)-based object detectors focus on feature configuration during training but often ignore feature optimization during inference. In this article, we propose a new feature optimization approach to enhance features and suppress background noise in both the training and inference stages. We introduce a generic inference-aware feature filtering (IFF) module that can be easily combined with existing detectors, resulting in our iffDetector. Unlike conventional open-loop feature calculation approaches without feedback, the proposed IFF module performs the closed-loop feature optimization by leveraging high-level semantics to enhance the convolutional features. By applying the Fourier transform to analyze our detector, we prove that the IFF module acts as a negative feedback that can theoretically guarantee the stability of the feature learning. IFF can be fused with CNN-based object detectors in a plug-and-play manner with little computational cost overhead. Experiments on the PASCAL VOC and MS COCO datasets demonstrate that our iffDetector consistently outperforms state-of-the-art methods with significant margins.
Comparative Evaluation of the Supervised Machine Learning Classification Methods and the Concept Drift Detection Methods in the Financial Business Problems

Machine Learning methods are key tools for aiding in the decision making of financial business problems, such as risk analysis, fraud detection, and credit-granting evaluations, reducing the time and effort and increasing accuracy. Supervised machine learning classification methods learn patterns in data to improve prediction. In the long term, the data patterns may change in a process known as concept drift, with the changes requesting retraining the classification methods to maintain their accuracies. We conducted a comparative study using twelve classification methods and seven concept drift detection methods. The evaluated methods are Gaussian and Incremental Naïve Bayes, Logistic Regression, Support Vector Classifier, k-Nearest Neighbors, Decision Tree, Random Forest, Gradient Boosting, XGBoost, Multilayer Perceptron, Stochastic Gradient Descent, and Hoeffding Tree. The analyzed concept drift detection methods are ADWIN, DDM, EDDM, HDDMa, HDDMw, KSWIN, and Page Hinkley. We used the next-generation hyperparameter optimization framework Optuna and applied the non-parametric Friedman test to infer hypotheses and Nemeyni as a posthoc test to validate the results. We used five datasets in the financial domain. With the performance metrics of F1 and AUROC scores for classification, XGBoost outperformed other methods in the classification experiments. In the data stream experiments with concept drift, using accuracy as performance metrics, Hoeffding Tree and XGBoost showed the best results with the HDDMw, KSWIN, and ADWIN concept drift detection methods. We conclude that XGBoost with HDDMw is the recommended combination when financial datasets that exhibit concept drift.
Eye, Robot: Calibration Challenges and Potential Solutions for Wearable Eye Tracking in Individuals with Eccentric Fixation

Loss of the central retina, including the fovea, can lead to a loss of visual acuity and oculomotor deficits, and thus have profound effects on day-to-day tasks. Recent advances in head-mounted, 3D eye tracking have allowed researchers to extend studies in this population to a broader set of daily tasks and more naturalistic behaviors and settings. However, decreases in fixational stability, multiple fixational loci and their uncertain role as oculomotor references, as well as eccentric fixation all provide additional challenges for calibration and collection of eye movement data. Here we quantify reductions in calibration accuracy relative to fixation eccentricity, and suggest a robotic calibration and validation tool that will allow for future developments of calibration and tracking algorithms designed with this population in mind.
On the Performance of Convolutional Neural Networks Under High and Low Frequency Information

Convolutional neural networks (CNNs) have shown very promising performance in recent years for different problems, including object recognition, face recognition, medical image analysis, etc. However, generally the trained CNN models are tested over the test set which is very similar to the trained set. The generalizability and robustness of the CNN models are very important aspects to make it to work for the unseen data. In this letter, we study the performance of CNN models over the high and low frequency information of the images. We observe that the trained CNN fails to generalize over the high and low frequency images. In order to make the CNN robust against high and low frequency images, we propose the stochastic filtering based data augmentation during training. A satisfactory performance improvement has been observed in terms of the high and low frequency generalization and robustness with the proposed stochastic filtering based data augmentation approach. The experimentations are performed using ResNet50 model over the CIFAR-10 dataset and ResNet101 model over Tiny-ImageNet dataset.
Weighted Polynomial-Based Secret Image Sharing Scheme with Lossless Recovery

In some particular scenes, the shadows need to be given different weights to represent the participants’ status or importance. And during the reconstruction, participants with different weights obtain various quality reconstructed images. However, the existing schemes based on visual secret sharing (VSS) and the Chinese remainder theorem (CRT) have some disadvantages. In this paper, we propose a weighted polynomial-based SIS scheme in the field of GF (257). We use <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mfenced open="(" close=")" separators="|"> <mrow> <mi>k</mi> <mo>,</mo> <mi>k</mi> </mrow> </mfenced> </math> threshold polynomial-based secret image sharing (SIS) to generate <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>k</mi> </math> shares and assign them corresponding weights. Then, the remaining <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>n</mi> <mo>−</mo> <mi>k</mi> </math> shares are randomly filled with invalid value 0 or 255. When the threshold is satisfied, the number and weight of share can affect the reconstructed image’s quality. Our proposed scheme has the property of lossless recovery. And the average light transmission of shares in our scheme is identical. Experiments and theoretical analysis show that the proposed scheme is practical and feasible. Besides, the quality of the reconstructed image is consistent with the theoretical derivation.
Graph visual tracking using conditional uncertainty minimization and minibatch Monte Carlo inference

In this paper, we propose a novel visual tracking method based on conditional uncertainty minimization (CUM), minibatch Monte Carlo (MMC), and non-nested sampling (NNS). We represent a target as a Markov network with nodes and edges, where each node corresponds to the corresponding pixel of the target and each edge describes the relations among the pixels. The nodes are then grouped into optimal cliques using the proposed CUM, which minimizes the conditional uncertainty (i.e. the variance of the conditional expectation) between two cliques. The aforementioned minimization process is facilitated using the proposed NNS. During visual tracking, Markov networks evolve across frames and describe the geometrically varying appearances of the target. In many cases, these networks cannot represent the targets perfectly; however, the configurations of the target can be inferred accurately using the CUM and the best configuration can be found at an early stage of the Monte Carlo sampling using the proposed MMC. The numerical results demonstrate that our method qualitatively and quantitatively outperforms other state-of-the-art trackers on standard benchmark datasets. In particular, our method accurately tracks deformable objects in realtime.
Adaptive Extractors and Their Application to Leakage Resilient Secret Sharing

We introduce Adaptive Extractors, which unlike traditional randomness extractors, guarantee security even when an adversary obtains leakage on the source after observing the extractor output. We make a compelling case for the study of such extractors by demonstrating their use in obtaining adaptive leakage in secret sharing schemes. Specifically, at FOCS 2020, Chattopadhyay, Goodman, Goyal, Kumar, Li, Meka, Zuckerman, built an adaptively secure leakage resilient secret sharing scheme (LRSS) with both rate and leakage rate being $$\mathcal {O}(1/n)$$ , where $$n$$ is the number of parties. In this work, we build an adaptively secure LRSS that offers an interesting trade-off between rate, leakage rate, and the total number of shares from which an adversary can obtain leakage. As a special case, when considering t-out-of-n secret sharing schemes for threshold $$t = \alpha n$$ (constant $$0<\alpha <1$$ ), we build a scheme with a constant rate, constant leakage rate, and allow the adversary leakage from all but $$t-1$$ of the shares, while giving her the remaining $$t-1$$ shares completely in the clear. (Prior to this, constant rate LRSS scheme tolerating adaptive leakage was unknown for any threshold.) Finally, we show applications of our techniques to both non-malleable secret sharing and secure message transmission.
China in the eyes of news media: a case study under COVID-19 epidemic

As one of the early COVID-19 epidemic outbreak areas, China attracted the global news media's attention at the beginning of 2020. During the epidemic period, Chinese people united and actively fought against the epidemic. However, in the eyes of the international public, the situation reported about China is not optimistic. To better understand how the international public portrays China, especially during the epidemic, we present a case study with big data technology. We aim to answer three questions: (1) What has the international media focused on during the COVID-19 epidemic period? (2) What is the media's tone when they report China? (3) What is the media's attitude when talking about China? In detail, we crawled more than 280 000 pieces of news from 57 mainstream media agencies in 22 countries and made some interesting observations. For example, international media paid more attention to Chinese livelihood during the COVID-19 epidemic period. In March and April, "progress of Chinese vaccines," "specific drugs and treatments," and "virus outbreak in U.S." became the media's most common topics. In terms of news attitude, Cuba, Malaysia, and Venezuela had a positive attitude toward China, while France, Canada, and the United Kingdom had a negative attitude. Our study can help understand China's image in the eyes of the international media and provide a sound basis for image analysis.