Reviewerly/exHarmony huggingface dataset:
Material design of intelligent plastic products with sound generation function due to snap-through buckling

Abstract Audible sound generation due to snap-through buckling during bending deformation was studied using an injection-molded product with a hinge. The sound was clearly generated in a rigid plastic product with a specific shape of the hinge that was markedly deformed. The large elastic energy stored in the hinge by the bending deformation, which was suddenly released during buckling deformation, was responsible for the increased sound pressure. This phenomenon may be exploited as a sensor to control the maximum deformation at bending in intelligent plastic products.
Vulnerability Analysis as Trustworthiness Evidence in Security Benchmarking: A Case Study on Xen

Hypervisors govern the resources of virtualized systems and are a crucial component of many cloud solutions. As a critical component, cloud providers should assess the hypervisor’s security to mitigate risk before adoption. Ideally, a benchmark should be applied to compare the security of different systems objectively, but security benchmarking is still an open problem. Notwithstanding, the evaluation of the system’s trustworthiness has been adopted as a promising approach as part of this complex evaluation process. In this work, we present a vulnerability data analysis of the Xen hypervisor. Additionally, we address the problem of how to apply this analysis results as trustworthiness evidence that can be applied in security benchmarks. Our results present an insightful characterization of Xen’s vulnerabilities evaluating their lifespan, distribution, and modeling. We also show that vulnerability data analysis can qualitatively characterize the Xen hypervisor’s trustworthiness and possibly reflect the security development efforts into its codebase.
Uncertainty-based information measures on the approximate non-parametric predictive inference model

The Non-Parametric Predictive Inference Model for Multinomial Data (NPI-M) is an imprecise probabilities model used to represent the available information about a categorical variable. It presents some advantages over another imprecise probabilities model frequently used in the literature called Imprecise Dirichlet Model (IDM), which assumes previous knowledge about the data through a parameter. The Approximate Non-Parametric Predictive Inference Model for Multinomial Data (A-NPI-M) is a model similar to the NPI-M that can be expressed by reachable sets of probability intervals, is easier to manage, and is non-parametric. As a novelty, in this work, we analyze the main properties of A-NPI-M credal sets, comparing them with the properties of credal sets associated with the IDM. Moreover, we present procedures to calculate the most important uncertainty measures on A-NPI-M credal sets. Those procedures represent useful tools to make the A-NPI-M very suitable to be used in practical applications.
Robust Tracking under Measurement Model Mismatch via Linearly Constrained Extended Kalman Filtering

Standard state estimation techniques, ranging from the linear Kalman filter to nonlinear sigma-point or particle filters, assume a perfectly known system model, that is, process and measurement functions and system noise statistics (both the distribution and its parameters). This is a strong assumption which may not hold in practice, reason why several approaches have been proposed for robust filtering. In the context of linear filtering, a solution to cope with a possible system matrices mismatch is to use linear constraints. In this contribution we further explore the extension and use of recent results on linearly constrained Kalman filtering (LCKF) for robust tracking/localization under measurement model mismatch. We first derive the natural extension of the LCKF to nonlinear systems, and its use to mitigate parametric modelling errors in the nonlinear measurement function. A tracking problem where a set of sensors at possibly mismatched (unknown to a certain extent) positions track a moving object from time of arrival measurements is used to support the discussion.
Identity Document to Selfie Face Matching Across Adolescence

Matching live images (“selfies”) to images from ID documents is a problem that can arise in various applications. A challenging instance of the problem arises when the face image on the ID document is from early adolescence and the live image is from later adolescence. We explore this problem using a private dataset called Chilean Young Adult (CHIYA) dataset, where we match live face images taken at age 18-19 to face images on scanned ID documents created at ages 9 to 18. State-of-the-art deep learning face matchers (e.g., ArcFace) have relatively poor accuracy for document-to-selfie face matching. To achieve higher accuracy, we fine-tune the best available open-source model with triplet loss for a few-shot learning. Experiments show that our approach achieves higher accuracy than the DocFace+ model recently developed for this problem. Our fine-tuned model was able to improve the true acceptance rate for the most difficult (largest age span) subset from 62.92% to 96.67% at a false acceptance rate of 0.01%. Our fine-tuned model is available for use by other researchers.
Clustered Dynamic Graph CNN for Biometric 3D Hand Shape Recognition

The research in biometric recognition using hand shape has been somewhat stagnating in the last decade. Meanwhile, computer vision and machine learning have experienced a paradigm shift with the renaissance of deep learning, which has set the new state-of-the-art in many related fields. Inspired by successful applications of deep learning for other biometric modalities, we propose a novel approach to 3D hand shape recognition from RGB-D data based on geometric deep learning techniques. We show how to train our model on synthetic data and retain the performance on real samples during test time. To evaluate our method, we provide a new dataset NNHand RGB- D of short video sequences and show encouraging performance compared to diverse baselines on the new data, as well as current benchmark dataset HKPolyU. Moreover, the new dataset opens door to many new research directions in hand shape recognition.
Hyperspectral anomaly detection method based on linear background removal

Abstract In view of the fact that the existing hyperspectral image anomaly detection algorithms only pay attention to distinguish between target and background by using spectral differences and ignore the correlation between pixels, and that local RX algorithm is easy to detect global non-anomalies as anomalies when the background is relatively complex, a hyperspectral anomaly detection method based on linear background removal is proposed. In this method, the edge information of hyperspectral image is obtained first, then the line part of edge image is extracted by Hough transform, and finally the local anomaly detection is carried out based on the linear background removal. By comparing the ROC curve and AUC value of this algorithm with RX algorithm and local RX algorithm, it shows that the algorithm of this paper can effectively reduce the false alarm rate of detection and has a better detection effect.
Blockchain-based trusted sharing of electric energy privacy data

The process of sharing private power data has problems of exposing user behavior and leaking trade secrets, and data storage faces the risk of data tampering, duplication, and leakage. Using the decentralized, anti-tampering, and highly scalable features of blockchain, we propose a trustworthy sharing scheme of electric energy privacy data based on blockchain by building an Consortium blockchain of electric energy metadata storage. The standardized metadata uplink description, combined with the open source distributed file system FastDFS, realizes the on-chain aggregation and storage of electrical energy privacy data, solves the problems of high timing requirements and large data volume faced by electrical energy privacy data storage, ensures the trustworthy sharing of data through the data access on-chain verification mechanism, and provides a technical basis for the safe use of electrical energy data.
An innovative algorithm for data differencing

We are presenting a novel algorithm to create software updates using Normalized Compression Distance (NCD) where we define the Z compression function as a modified LZ77[1] compression algorithm. Our test results presented are based on three algorithmic strategies and they surpass commercial software available on the market with a factor of almost 2:1.
Deep Hybrid Recommender System

With the spread of e-commerce platforms in recent years, recommendation systems have become quite popular. Traditional recommendation systems are mostly based on useritem interactions. However, recommendation systems which are based on only user-item interactions are often underperforming due to the data sparsity problem. Therefore, beyond user-item interactions, the rich side information of the product or user is a notable source for improving recommendation quality. For many years, artificial neural networks have been used in many computer science fields and have gained popularity in recommendation systems in recent years. In this study, two different deep hybrid learning architectures are presented. Thanks to the feed forward neural network we use in our architectures, performance in learning the nonlinear, complex relationship between useritem interactions is increased. By adding side information to the collaborative filtering process, solutions are provided for the cold start and data sparsity problems. By making use of the strengths of deep learning and side information, it has been ensured that the constraints of collaborative and content-based methods are mitigated and the recommendation performance is increased. The success of the developed method has been compared with other studies in this field.
Exploration and Visualization of the Hidden Information from the Congestive Heart Failure Patients Data in MIMIC-III Database

In recent times, the systematic collection of clinical data (diagnosis, demographics, scans, hospital emergency admission, blood sample test results, etc.) using electronic devices called as electronic medical record (EMR) is increasing exponentially. EMR contains an enormous amount of information that is hidden from actual users and could be extracted only by doing proper data exploration and analysis. Irrespective of its unrestricted availability, the medical professionals who have lesser computer proficiency are unable to explore the hidden information in it. One such database is MIMIC-III which is a very big, freely available relational database, single centered and consists of details of patients admitted in the intensive care unit at Beth Israel Deaconess Medical Center with 15,682 unique diagnoses. The objective of this study is to show the importance of data exploration and visualization and thereby discover the hidden information in any dataset. For this study, an in-depth exploratory data analysis (EDA) is performed on congestive heart failure (CHF) patients in the benchmark MIMIC-III dataset. Finding the percentage of mortality in male and female patients and effective analysis on age attribute helps the physician in making critical decisions. Calculating the length of stay (LoS) in various units in the hospital helps in preplanned management of beds and drugs available in the hospital.
Factors of Open Science Data Sharing and Reuse in the COVID-19 Crisis: A Case Study of the South Korea R&amp;D Community

Semi-structured interviews with South Korean experts were conducted to explore enabling and limiting factors influencing open commutation of scholarly outputs and data in public health emergencies, such as the COVID-19 outbreak. The study provided a set of contextual/external, institutional/regulatory, resource, and individual/motivational factors with some relevant examples. The results revealed the highest importance of institutional/regulatory factors in such situations. The findings might be useful for a country's comprehensive Open Science policy development as a component of future outbreak preparedness.
Privacy-Preserving Dialogues Between Agents: A Contract-Based Incentive Mechanism for Distributed Meeting Scheduling

Meeting scheduling (MS) is a practical task in everyday life that involves independent agents with different calendars and preferences. In this paper, we consider the distributed MS problem where the host exchanges private information with each attendee separately. Since each agent aims to protect its own privacy and attend the meeting at a time slot that it prefers, it is necessary to design a distributed scheduling mechanism where the privacy leakage can be minimized and as many agents are satisfied with the outcome as possible. To achieve this, we propose an intelligent two-layer mechanism based on contract theory where the host motivates each agent to reveal its true preferences by providing different rewards without knowing the costs of each agent to attend the meeting. We first model the privacy leakage by measuring the difference between the revealed information of an agent’s calendar and other agents’ prior beliefs. An optimal control problem is then formulated such that the reward function and privacy leakage level can be jointly designed for each agent. Through theoretical analysis, we show that our proposed mechanism guarantees the incentive compatibility with respect to all agents. Compared to the state of the art, empirical evaluations show that our proposed mechanism achieves lower privacy leakage and higher social welfare within a small number of rounds.
Linking Data for Ontology Based Advising in Healthcare

Along with the massive influence of computational technologies in medical research and practice, the wide generation of patient, clinical and lab tests data make the assistance of intelligent information systems very important for correct therapy, surveillance and advising of the patient. Medical data is highly heterogeneous and with different structure and formats. Principal tasks of the work are focused at intelligent integration of all data related to a particular patient. Aside with the methodological aspects of linking medical data, an important part of the discussed study focuses on the design and development of a special purpose decision support system. This system enriches a specific ontology for diabetes mellitus with a set of newly defined rules, supporting the development of broad and more precise personalized dietary recommendations as a part of digitized healthcare assistance.
Beyond Point-to-Point Quantum Key Distribution

In this chapter we present the recently-derived theoretical limits on the secret key rate that can be extracted by any point-to-point QKD protocol (Sect. 6.1). Subsequently, we present in detail the (arguably) simplest solution found by researchers to overcome such limitations, which is twin-field (TF) QKD. TF-QKD overcomes the point-to-point private capacity by exploiting single-photon interference in an intermediate untrusted station (Sects. 6.2 and 6.3). This is followed by a detailed investigation of the performance of TF-QKD in realistic conditions, namely finite number of decoy intensity settings and asymmetric channels (Sect. 6.4). Finally, in Sect. 6.5 we generalize the founding idea of TF-QKD to a multiparty scenario by describing a quantum conference key agreement (CKA) based on single-photon interference events.
Out of focus multi-spectral image de-blurring using texture extraction and modified fourier transform

Multi-spectralimages suffers from out-of-focusblur due to well focused camera at the reference imaging channel. A framework for out-of-focus images de-blurring using texture extraction and modified Fourier transform is proposed. The texture is extracted from the blurred image using region covariance. Fourier transform is modified by modification of guided image (as prior) using L0 gradient projection followed by detail amplification which is used as an input for Fourier transform. For further enhancement, the multi-spectral de-blurred images are smooth, and ringing artifacts are minimized by combining with the textures extracted to ensure maximum edge preservation. Comparison of proposed and existing schemes on different multi-spectral images explains the advantage of the proposed scheme for de-blurring in terms of edge preservation, noise and artifacts removal.
Multi-domain Language Understanding of Task Oriented Dialogue Based on Intent Enhancement

The language understanding module in Task oriented Dialogue system includes two tasks: intent detection and slot filling. Typical joint language understanding models model the interaction between intent information and slot information through parameter sharing. These models may cause the problem that slot prediction values do not belong to the set of candidate slots under their corresponding intentions (This paper calls it slot field disorder). In order to reduce the impact of this problem, this paper proposed a joint model based on intent information enhancement. Intent information is explicitly introduced into the prediction process of slot location in the form of vectors. Three construction schemes of intent information vectors: bidirectional long-short term memory network, attention-based bidirectional long-short term memory network and convolutional neural network are successively tried. Finally, the experimental results on snips data set show that the joint model based on intent information enhancement has achieved 88.85%, 97.41% and 76.24% respectively in slot filling F-measure, intent recognition accuracy and semantic framework accuracy.
Automatic Video Indexing and Retrieval System for Turkish Videos

A continual increase in multimedia data needs a sophisticated automatic video indexing and retrieval system based on its content. This paper exploited statistical feature along with some morphological operations to detect horizontally aligned artificial textual fields in Turkish video frames, which are then extracted for content-based video indexing and retrieval system. First, projection analysis was performed to find the edges in the images and then morphological operations to convert the textual regions in images into lines. Later, false positives were eradicated by geometrical constraints and heuristics-based method. The detected candidate text regions were fed to optical character recognition (OCR) system to recognize and output the text. Finally, the recognized words were stored in database as keys for automatic content-based video indexing, which can be retrieved through provided web interface. For evaluation, a ground-truth preparation software is prepared to manually localize the text in images. Experimental results showed that our proposed method performed well on Turkish videos with overall f-measure of 95%.
Proposal for a Chatbot Prototype in the Plant Health Department of Brazilian Ministry of Agriculture

Around the world, governments are implementing services combined with artificial intelligence to improve service to the population. The main objective of this study is to develop a chatbot prototype able to answer the most frequently asked questions about the Plant Health department of the Brazilian Ministry of Agriculture, Livestock, and Supply (MAPA), reducing the workload dedicated to responding emails and phone calls, in addition to providing face-to-face care. Initially, investigations were made on the concepts of artificial intelligence, the different types of chatbots and the tools needed to build the prototype. Subsequently, an analysis of the chosen tool and programming language was elaborated. After that, the development of the application and the configuration of the platforms began. Considering how a chatbot can spread information, including good practices about plant health and agriculture, this project can contribute to food security, increasing productivity and fighting against waste, helping to achieve UN SDG number 2 - zero hunger.
A Near-Optimal Analytic Solution to the Optimal Lead Turn Problem

This research addresses path following error which may develop when an aircraft transitions between following straight and circular path segments. The optimal lead turn problem is introduced which seeks to determine the optimal control and initial position which minimizes the path error during the transition between two path segments with constraints on terminal position and maneuver performance. We derive a sub-optimal analytic solution which assumes the aircraft maneuvers to achieve the terminal bank angle constraints in minimum time. We then show that the analytic solution is near-optimal by comparing it to numerically derived optimal solutions.
Coordinate basis adaptation of cataloging of unified immovable complexes included into the Russian-Chinese initiative project “One Belt – One Way” (by example railway transport)

Abstract Coordinate support is a fundamental basis to study the characteristics of objects and phenomena which occur in the surrounding space. Parameterization of the characteristics is impossible if the rules for describing objects either within themselves or concerning their topological properties are not established. The rules for the metric description of the terrain objects at the level of general state geodetic and cartographic support tasks are determined. Nevertheless, there are quite important tasks to describe some elements of the sets for which the established rules are not considered as optimal, so it will be a more efficient solution to switch to alternative parameters of metric systems. Such objects include linear objects (such as real estate, buildings, natural objects) on the Earth’s surface which are characterized by a significant accumulation of errors in their description in the established coordinate systems (projections). If considered applied, there is some certain necessity to determine (calculate) the optimal rules (parameters) in a number of cases in order to form the metric system of their description for the design and departmental cataloging.
WITHDRAWN: Personalization of data using machine learning

The algorithm presents work on the personalization of data in our digital everyday life based on content. While the algorithms that designed this content have tremendous power to influence the perceptions of users, they’re often concealed behind the GUI, leaving users unaware of their existence. The algorithm provides more personalized feed based on user’s recent history. Machine learning algorithm does not categorize the posts in a particular number of set or based on its parameters but it displays similar posts by analyzing the actual content in it. The algorithm takes an input as a post in which the user is interested and shown feed accordingly. This algorithm does not take the parameters attached to the post such as no. of likes, shares or the person posted, but the actual content itself. The Algorithm would be more helpful for increasing the user’s confidence and experience with the system.