Reviewerly/exHarmony huggingface dataset:
Recursive block splitting in feature‐driven decoder‐side depth estimation

This paper presents a study on the use of encoder-derived features in decoder-side depth estimation. The scheme of multiview video encoding does not require the transmission of depth maps (which carry the geometry of a three-dimensional scene) as only a set of input views and their parameters are compressed and packed into the bitstream, with a set of features that could make it easier to estimate geometry in the decoder. The paper proposes novel recursive block splitting for the feature extraction process and evaluates different scenarios of feature-driven decoder-side depth estimation, performed by assessing their influence on the bitrate of metadata, quality of the reconstructed video, and time of depth estimation. As efficient encoding of multiview sequences became one of the main scopes of the video encoding community, the experimental results are based on the “geometry absent” profile from the incoming MPEG Immersive video standard. The results show that the quality of synthesized views using the proposed recursive block splitting outperforms that of the state-of-the-art approach.
Responsible governance of civilian unmanned aerial vehicle (UAV) innovations for Indian crop insurance applications

Civilian Unmanned Aerial Vehicle (UAV) is an emerging technology in Indian crop insurance applications. The technology is new to an agro-based country like India with diverse socio-cultural norms and values. However, in such a diverse democracy, UAV governance and deployment pose a significant challenge and risk. In other words, charting out a proper framework for a risk-free implementation of this governance has emerged as a leading research topic in the concerned discipline. In innovations literature, Responsible Innovation (RI) takes care of emerging technology governance; thus, RI becomes significant as a theoretical framework. The study is intended to find out how the framework of RI enables responsible governance and also who are the main actors and stakeholders of governance and deployment of civilian UAVs in crop insurance applications in India? An in-depth interview method and snowball sampling technique have been employed to identify interviewees from Delhi, Gujarat, and Rajasthan. Findings suggest that civilian UAV is effective in handling risk, crop damage assessment, and claim settlement. The RI approach, through its dimensions and steps, enables equal participation and deliberation among all the actors and stakeholders of UAV governance that consists of government bodies, research organizations, insurance agencies, local administration, and farmers. Effective regulations, adhering to accountability, and responsibility promote responsible governance.
Analysis of polarized diffraction images of human red blood cells: a numerical study

We carried out a systematic study on cross-polarized diffraction image (p-DI) pairs of 3098 mature red blood cells (RBCs) using optical cell models with varied morphology, refractive index (RI), and orientation. The influence of cell rotation on texture features of p-DI pairs characterized by the gray-level co-occurrence matrix (GLCM) algorithm was quantitatively analyzed. Correlations between the transverse diameters of RBCs with different RI values and scattering efficiency ratios of s- and p-polarized light were also investigated. The correlations remain strong even for RBCs with significant orientation variations. In addition, we applied a minimum redundancy maximum relevance (mRMR) algorithm to improve the performance of support vector machine (SVM) classifiers. It was demonstrated that a set of selected GLCM parameters allowed for an efficient solution of classification problems of RBCs based on morphology. For 1598 RBCs with varied shapes corresponding to normal or pathological cases, the accuracy of the SVM based classifications increased from 83.8% to 96.8% with the aid of mRMR. These results indicate the strong potential of p-DI data for rapid and accurate screening examinations of RGC shapes in routine clinical tests.
Covert Communication via Dynamic Spectrum Control-Assisted Transmission Scheme

To realize secure communication and prevent eaves-droppers from detecting the existence of communication activities, covert communication has attracted substantial research interests. In this paper, we propose a dynamic spectrum control (DSC)-assisted scheme to achieve covert and reliable data transmission. Specifically, by constructing time-frequency division channels, the proposed DSC-assisted scheme generates sequences with iterative and orthogonal transformations. Authorized users can orderly occupy different frequency slots in each time slot under the guidance of these sequences, thus achieving simultaneous data transmission without interfering with each other. Then, the covert performance of the proposed transmission scheme is analyzed to provide the closed-form expressions of covert transmission rate and the reliable transmission probability. Simulation results are provided to validate the accuracy of the theoretical analysis and demonstrate that the proposed scheme can achieve better covert and reliable transmission performances when compared with the existing scheme.
A Reputation-based Framework for Honest Provenance Reporting

Given the distributed, heterogenous, and dynamic nature of service-based IoT systems, capturing circumstances data underlying service provisions becomes increasingly important for understanding process flow and tracing how outputs came about, thus enabling clients to make more informed decisions regarding future interaction partners. Whilst service providers are the main source of such circumstances data, they may often be reluctant to release it, e.g., due to the cost and effort required, or to protect their interests. In response, this article introduces a reputation-based framework, guided by intelligent software agents, to support the sharing of truthful circumstances information by providers. In this framework, assessor agents , acting on behalf of clients, rank and select service providers according to reputation, while provider agents , acting on behalf of service providers, learn from the environment and adjust provider’s circumstances provision policies in the direction that increases provider profit with respect to perceived reputation. The novelty of the reputation assessment model adopted by assessor agents lies in affecting provider reputation scores by whether or not they reveal truthful circumstances data underlying their service provisions, in addition to other factors commonly adopted by existing reputation schemes. The effectiveness of the proposed framework is demonstrated through an agent-based simulation including robustness against a number of attacks, with a comparative performance analysis against FIRE as a baseline reputation model.
Seismic vulnerability assessment of buildings of Patna by rapid visual screening

Preliminary assessment method or simplified assessmentIn this method models of buildings are drawn along with layout of columns and beams.This method includes various type of strength checks and in-depth evaluations of buildings are done.
Deep Learning on Small Tabular Dataset: Using Transfer Learning and Image Classification

Deep Learning is a subset of machine learning inspired by the human brain. It uses multiple layers of representation to extract specific knowledge from raw input. It is best suited for large image or sound-based datasets. Deep learning methods are generally avoided for small datasets because they tend to overfit. Transfer learning can be one approach used to solve this problem. However, in the case of tabular datasets, their heterogeneous nature makes transfer learning algorithms inapplicable. This paper aims to discuss a few approaches using a literature review to convert tabular data into images to overcome such limitations. The paper provides a 2-part study wherein we first give a brief overview of transfer learning enhancing the efficiency of deep learning algorithms and drastically reducing the training time for small datasets. Secondly, we provide a detailed study of different techniques available to convert tabular data into images for image classification such as SuperTML, IGTD, and REFINED approach. Furthermore, we propose a novel approach inspired by IGTD to create a blocked image representation of the tabular data on which we apply transfer learning to demonstrate the application of deep learning methods on small tabular datasets (with less than 1000 data points).
FLOM: Toward Efficient Task Processing in Big Data with Federated Learning

With the diversification and individuation of user requirements as well as the rapid development of computing technology, the large-scale tasks processing for big data in edge computing environment has become a research focus nowadays. Many recent efforts for task processing are designed and implemented based on some traditional protocols and optimization methods. Therefore, it is more difficult to explore the task allocation strategy that maximizes the overall system revenue from the perspective of global load balancing. In order to overcome this problem, a large-scale tasks processing approach called Federated Learning based Optimization Methodology (FLOM) for large-scale tasks processing was presented to achieve accurate task classification and overall load balancing while satisfying task allocation requirements. FLOM performs the data aggregation and establishes the personalized models by federated learning. The deep network model is designed for deep feature learning of task requests and hosts in the substrate network. The experimental results show the capability of FLOM in terms of large-scale task classification as well as allocation.
Optimal dynamic treatment regime estimation using information extraction from unstructured clinical text

The wide-scale adoption of electronic health records (EHRs) provides extensive information to support precision medicine and personalized health care. In addition to structured EHRs, we leverage free-text clinical information extraction (IE) techniques to estimate optimal dynamic treatment regimes (DTRs), a sequence of decision rules that dictate how to individualize treatments to patients based on treatment and covariate history. The proposed IE of patient characteristics closely resembles "The clinical Text Analysis and Knowledge Extraction System" and employs named entity recognition, boundary detection, and negation annotation. It also utilizes regular expressions to extract numerical information. Combining the proposed IE with optimal DTR estimation, we extract derived patient characteristics and use tree-based reinforcement learning (T-RL) to estimate multistage optimal DTRs. IE significantly improved the estimation in counterfactual outcome models compared to using structured EHR data alone, which often include incomplete data, data entry errors, and other potentially unobserved risk factors. Moreover, including IE in optimal DTR estimation provides larger study cohorts and a broader pool of candidate tailoring variables. We demonstrate the performance of our proposed method via simulations and an application using clinical records to guide blood pressure control treatments among critically ill patients with severe acute hypertension. This joint estimation approach improves the accuracy of identifying the optimal treatment sequence by 14-24% compared to traditional inference without using IE, based on our simulations over various scenarios. In the blood pressure control application, we successfully extracted significant blood pressure predictors that are unobserved or partially missing from structured EHR.
An Embedded Machine Learning System For Real-time Face Mask Detection And Human Temperature Measurement

In this paper, an efficient embedded machine learning system is proposed to automatically detect face masks and measure human temperature in a real-time application. In particular, our system uses a Raspberry-Pi camera to collect realtime video and detect face masks by implementing a classification model on Raspberry Pi 3 in public places. The face mask detector is built based on MobileNetV2, with ImageNet pre-trained weights, to detect three cases of correctly wearing, incorrectly wearing and not wearing a mask. We also design a human temperature measurement framework by deploying a temperature sensor on the Raspberry Pi 3. The numerical results prove the practicality and effectiveness of our embedded systems compared to some state-of-the-art researches. The results of accuracy rate in detecting three cases of wearing a face mask are 98.61% based on the training results and 97.63% for validation results. Meanwhile, our proposed system needs a short time of 6 seconds for each person to be tested through the whole process of face mask detection and human forehead temperature measurement.
User Experience Design on Visualization of Mobile-Based Land Monitoring System Using a User-Centered Design Approach

The use of information and communication technology could increase the quantity and quality of agricultural production, including horticulture. This study focuses on the development of a mobile-based interface that makes it easier for farmers to view the data obtained from the results of land monitoring. In designing mobile devices, applications, and user interfaces, it is important to consider the user experience. This paper focuses on UX design which is which is based on User-Centered Design approach that focuses on user needs and prioritizes empathy for users, so as to accommodate the identification of user needs and produce high fidelity pro-totypes. Based on the System Usability Scale and the User Experience Questionnaire testing on the prototype, it was found that the level of acceptance of horticultural farmers was positive with the level of the value of all UX elements above the average
A Comparative Study of Sine Cosine Optimizer and Its Variants for Engineering Design Problems

Sine Cosine Algorithm (SCA) is one of the simplest optimization algorithms and is used to solve a wide range of problems due to using two simple mathematical equations. However, it faces local optima stagnation because of the constraints in its exploration and exploitation mechanism. To solve this problem, many researchers proposed new versions of sine cosine algorithm (SCA). The main concept of developing SCA performance is to add some methods or layers to original SCA, edit the SCA parameters, or only hybridize it with other optimization algorithms to improve SCA’s exploration and exploitation. SCA and three new SCA variants were applied to solve three constrained engineering design problems in this study. The outcomes show that SCA was still able to report a good result more than some of its variants.
Reverse-Engineering CNN Models Using Side-Channel Attacks

This article demonstrates that architectural side-channel attacks (i.e., memory access patterns) can help leak the parameters of a convolutional neural network. — <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Jeyavijayan “JV” Rajendran, Texas A&amp;M University</i>
The Problematic Internet Use Among Students: Prevelance of Internet Addiction towards Indonesian Hospital Management Information System Readiness

This case study discusses the problems and challenges faced on the utilization of the computerized hospital management system in order to improve service quality from several hospitals in Indonesia. Many patients have complained that hospital do not fulfil the minimum satisfaction level as expected. Indeed, the appropriate facilities and infrastructure should support every health process to be automatic, integrative, customized and personalized. The development of a Hospital Management Information System is required to accommodate the registration process, allowing easy payment process, storing medical record securely, generating order quickly, handling the existing resource concussively and managing internal and external data. Based on the investigation towards several Hospital Management Information Systems used, the problems and challenges are identified to depict the prevalence of Internet addiction. It should be considered as the new addiction that high potentiality to become the new emerging threats to the community as new wave of pandemic.
Securing APIs and Chaos Engineering

Suppose information security starts to embrace the reality that failure will happen. In that case, we can move from trying to build the perfectly secure system to continue asking questions like “how much vulnerability do I have and what control do I need to be effective?” This paper proposes Security Chaos Engineering as a method to expose API vulnerabilities and enhance API security. RESTful API has gained popularity in recent years due to its reusability, flexibility and natural adaptation to modern web application, mobile application, and cloud computing. However, ensuring secure API/data access and hence mitigating reputational and/or financial damage to the organization is still in its early stage. Foundational security protection mechanisms include transport layer security, authentication / authorization of the consumer (either individual or application). To complete the spectrum of secure API access and provide advanced protection, there is much more to consider: mitigation of API specific vulnerabilities at design and implementation time. API Security using Chaos Engineering is an approach for learning about system security behavers when using APIs by applying empirical exploration. Security Chaos Engineering is the discipline of experimenting to build confidence in the system’s security and see how a system can withstand threats in production. Security Chaos Engineering isn’t about creating chaos. It is about making the security chaos inherent in the system visible.
Multiview Human Action Recognition System Based on OpenPose and KNN Classifier

Human action recognition is one of the trending research topics in the field of computer vision. Human-computer interaction and video monitoring are broad applications that aid in the understanding of human action in a video. The problem with action recognition algorithms such as 3D CNN, Two-stream network, and CNN-LSTM is that they have highly complex models including a lot of parameters resulting in difficulty while training them. Such models require high configuration machines for real-time human action recognition. Therefore, present research proposes the use of 2D skeleton features along with a KNN classifier based HAR system to overcome the aforementioned problems of complexity and response time.
ALGORITMA CLUSTERING K-MEDOIDS PADA E-GOVERNMENT BIDANG INFORMATION AND COMMUNICATION TECHNOLOGY DALAM PENENTUAN STATUS EDGI

Abstract E-Government is a tool to improve relations with the community, now the development of E-Government in various world governments are monitored directly by the UN through the United Nations E-Government Survey. This monitoring uses the framework where in this EGDI there are 3 factors considered: Online Service Index, Telecommunication Infrastructure Index and Human Capital Index. But the determination of EDGI status is less accurate because it must be based on knowledge and processing of the number of existing data so that required a calculation that applies clustering method with data mining techniques. Kmedoids using clustering techniques, capable of producing optimal Bouldin Index values and this study also determines the medoid distance calculation to obtain optimal algorithm, the value obtained from Bouldin Index on the Chebyshev K-medoids method 0.593. Thus, the optimal clustering scheme with distance ratio is the minimum index value of K-medoids Chebyshev.
Perspectives on p-ary bent functions

Over a period of several years, the authors tried to generalize the Dillon and Bernasconi et al. characterizations of Boolean bent functions to the p-ary case, with various degrees of success. A lot of our work was based on a massive number of computational experiments and examples, usually done in SageMath and, for independent confirmation, in Mathematica. Our main theoretical results will be surveyed below.
Putting Data Protection by Design on the Blockchain

The principle of data protection by design, as it is enshrined in article 25 of the GDPR, is difficult to apply in blockchains.This article will assess how the reliance on asymmetric encryption and other privacy enhancing technological architectures -necessary in a blockchainbased system-approach both user control and data protection by design compliance from the single scope of anonymization and unlinkability.Data subjects' rights, accountability, and the potential shortcomings of applied technological constraints are thus sidelined.Ultimately, this limited understanding of technological privacy, acts as a misguiding set of principles for technological co-regulation through standardisation in blockchains.The standardization of these choices without a holistic analysis of data protection by design imperatives could ultimately weaken the position of data subjects, whose trust in the technological protections of personal data might prove to be relatively misplaced.
Once Upon a Time: A Kit of Tools for Reading and Telling Stories

This paper discusses the contribution of Design in the development of a kit of tangible and multimedia tools for children to collaboratively read and create stories about the world. Here, we detail the design process, including the investigation of visual concepts, the prototyping and the user studies carried out during the development of the tools. We discuss the contribution of Design in the development of each step both through the adoption of theoretical concepts from the field, and by following a participatory Design and a User Centered Design approach. In the context of an investigation that combines a range of different fields, we argue that the Design has acted as the element that materializes and unifies the multidisciplinary contributions, leading to the creation of compelling engaging materials, which have the potential to involve users in the creation of multicultural narratives.
Detection of Fake Reviews on Online Products Using Machine Learning Algorithms

Internet reviews may affect a customer’s selection, and they can assess a product by correlating it to other brands. If the reviews are authentic, the customer may only pick a product that fulfils their demands. If the reviews, on the other hand, are phoney, the buyer is duped. It is vital to obtain the identification of fake consumer opinions in order to address this issue. To evaluate if a review is fraudulent or not, the actions of reviewers are extracted based on a semantic analysis of his review content. In this study, a data set for a mixed product was retrieved from the web, along with reviews and other information about the reviewers, to identify false reviewers using four algorithms: Support Vector Machine, Logistic Regression, K-Nearest Neighbour and Decision Tree. The accuracy rate along with precision rate of the aforementioned four methods are used to validate the significance of the features on the choice. Experiments were carried out on a large number of reviews gathered from the internet, demonstrating the efficacy of the proposed method.