Reviewerly/exHarmony huggingface dataset:
Development of 3D Breast Measurement System Using Structured Light for Bra Design

Female breast size is an essential parameter to the designing, production, and selection of bra. To determine thoracic dimensions, designers use manual measurement with a ruler or 3D measurement with measuring devices. In Vietnam, the research and development of 3D measuring devices to serve the design and manufacture of clothing is still new and limited. This article presents the results of the study on designing and manufacturing the Scan3D MB2019 female breast measurement system. This device operates based on using the structured light method, white light source (creating from 3 combined R-G-B LED sources), which is not harmful to human eyes and skin, with a measurement accuracy of ±0.4 mm, and measuring-scanning range 800 ×1000 × 600mm (length × width × depth).The Scan3D MB2019 measuring device is designed to measure the shape and size of the breast to meet following goals: low cost, fast measurement speed, high accuracy, and convenience. The 3D breast measurement results are equivalent to those of the manual measurement results. The study shows that the Scan3D MB2019 system can be used to measure breast for design and manufacturing in the Garment and Fashion industry.
Factors Shaping Customer Satisfaction with Residential Flats: Evidence from Sulaymaniyah City

Service quality is a critical element of customer loyalty. To guarantee customer loyalty companies must ensure customer satisfaction when selling products and/or providing services. Over the past few years, Kurdistan Regions of Iraq’s construction industry has witnessed a dramatic elevation in the number of skyrocket apartments in Sulaymaniyah city. Hence, the objective of this study is to investigate the factors affecting customer satisfaction on buying residential apartments. Construction industry experts need to understand the needs of residents to continuously improve their products and services associated with the products. The study uses survey responses collected from customers to examine the main factors that may affect their satisfaction. A convenience sample of 94 survey responses received from customers. An analysis was conducted to examine the effect of buying residential apartments and customer satisfaction and the impact of indoor and outdoor quality. The findings of this study state that there is a positive impact which indicates that any improvement in the indoor and outdoor factors will increase the satisfaction of residents and consequently results in a greater demand of purchasing residential apartments.
Conditional Measurements with Silicon Photomultipliers

Nonclassical states of light can be efficiently generated by performing conditional measurements. An experimental setup including Silicon Photomultipliers can currently be implemented for this purpose. However, these devices are affected by correlated noise, the optical cross talk in the first place. Here we explore the effects of cross talk on the conditional states by suitably expanding our existing model for conditional measurements with photon-number-resolving detectors. We assess the nonclassicality of the conditional states by evaluating the Fano factor and provide experimental evidence to support our results.
Image Quality Assessment without Reference by Combining Deep Learning-Based Features and Viewing Distance

An abundance of objective image quality metrics have been introduced in the literature. One important essential aspect that perceived image quality is dependent on is the viewing distance from the observer to the image. We introduce in this study a novel image quality metric able to estimate the quality of a given image without reference for different viewing distances between the image and the observer. We first select relevant patches from the image using saliency information. For each patch, a feature vector is extracted from a convolutional neural network model and concatenated at the viewing distance, for which the quality is predicted. The resulting vector is fed to fully connected layers to predict subjective scores for the considered viewing distance. The proposed method was evaluated using the Colourlab Image Database: Image Quality and Viewing Distance-changed Image Database. Both databases provide subjective scores at two different viewing distances. In the Colourlab Image Database: Image Quality we obtain a Pearson correlation of 0.87 at both 50 cm and 100 cm viewing distances, while in the Viewing Distance-changed Image Database we obtained a Pearson correlation of 0.93 and 0.94 at viewing distance of four and six times the image height. The results show the efficiency of our method and its generalization ability.
Intelligent Negotiation Agent Architecture for SLA Negotiation Process in Cloud Computing

Cloud service provider (CSP) is providing several services over network and selection of resources opt through intelligent matching agent between CSP and consumer. In the cloud market, consumers and CSP are negotiating with each other for requirements of resources. Due to the large number of CSPs in the market, competition is very high. The automated negotiation agent is in high demand to do negotiation in minimum time with maximum success rate. Negotiation must be on price, resources, reliable CSP, maximum utility value, and other quality measures factor. Due to vibrant changes in the market and increasing CSPs’ competition, false negotiation, unreliable servers, and high prices, problem occurs. An automated intelligent agent produces a solution which understands the opponent's behavior and requirements of both sides, and these factors proceed to negotiation and provide the best match between consumer and CSP. The proposed architecture of negotiation agent enhances the negotiation process perceptively by reducing the negotiation time and high success rate and also helps to deliver reliable services.
Hybrid RRT-A*: An Improved Path Planning Method for an Autonomous Mobile Robots

Although the Basic RRT algorithm is considered a traditional search method, it has been widely used in the field of robot path planning (manipulator and mobile robot), especially in the past decade. This algorithm has many features that give it superiority over other methods. On the other hand, the Basic RRT suffers from a bad convergence rate (it takes a long time until finding the goal point), especially in environments with cluttered obstacles, or whose targets are located in narrow passages. Many studies have discussed this problem in recent years. This paper introduces an improved method called (Hybrid RRT-A*) to overcome the shortcomings of the original RRT, specifically slow convergence and cost rate. The heuristic function of A-star algorithm is combined with RRT to decrease tree expansion and guide it towards the goal with less nodes and time. Various experiments have been conducted with different environment scenarios to compare the proposed method with the Basic RRT and A-star under the same conditions, which have shown remarkable performance. The time consumed to find the path of the worst one of these scenarios is about 4.9 seconds, whereas it is 18.3 and 34 for A-star and RRT, respectively.
Morse Code Detection Using Eye Blinks

A novel approach for Morse code generation with its interpretation in English language is done.The Morse code is generated with the help of eyes.Every time a person/user blinks his/her eyes an output of dash and dot is generated which is Morse code.OpenCV and Dlib libraries are used with facial landmark detection for eye blink detection.There are approximately 21 million people of India suffering from one or the other kind of disability.Within a blink these people can express themselves to the world more gracefully.This system allows user to be understand easily by anyone who understands human language well.
NISP: A Multi-lingual Multi-accent Dataset for Speaker Profiling

Many commercial and forensic applications of speech demand the extraction of information about the speaker characteristics, which falls into the broad category of speaker profiling. The speaker characteristics needed for profiling include physical traits of the speaker like height, age, and gender of the speaker along with the native language of the speaker. Many of the datasets available have only partial information for speaker profiling. In this paper, we attempt to overcome this limitation by developing a new dataset which has speech data from five different Indian languages along with English. The meta-data information for speaker profiling applications like linguistic information, regional information, and physical characteristics of a speaker are also collected. We call this dataset as NITK-IISc Multilingual Multi-accent Speaker Profiling (NISP) dataset. The description of the dataset, potential applications, and baseline results for speaker profiling on this dataset are provided in this paper.
Adaptive Savitzky-Golay filtering and its applications

Noise reduction is a central issue of the theory and practice of signal processing. The Savitzky-Golay (SG) smoothing and differentiation filter is widely acknowledged as a simple and efficient method for denoising. However only few book on signal processing contain this method. As is well known, the performance of the classical SG-filter depends on the appropriate setting of the window length and the polynomial degree, which should match the scale of the signal since, in the case of signals with high rate of change, the performance of the filter may be limited. This paper presents a new adaptive strategy to smooth irregular signals based on the Savitzky-Golay algorithm. The proposed technique ensures high precision noise reduction by iterative multi-round smoothing and correction. In each round the parameters dynamically change due to the results of the previous smoothing. Our study provides additional support for data compression based on optimal resolution of the signal with linear approximation. Here, simulation results validate the applicability of the novel method.
Containing Future Epidemics With Trustworthy Federated Systems for Ubiquitous Warning and Response

In this paper, we propose a global digital platform to avoid and combat epidemics by providing relevant real-time information to support selective lockdowns. It leverages the pervasiveness of wireless connectivity while being trustworthy and secure. The proposed system is conceptualized to be decentralized yet federated, based on ubiquitous public systems and active citizen participation. Its foundations lie on the principle of informational self-determination. We argue that only in this way it can become a trustworthy and legitimate public good infrastructure for citizens by balancing the asymmetry of the different hierarchical levels within the federated organization while providing highly effective detection and guiding mitigation measures toward graceful lockdown of the society. To exemplify the proposed system, we choose a remote patient monitoring as use case. This use case is evaluated considering different numbers of endorsed peers on a solution that is based on the integration of distributed ledger technologies and NB-IoT (narrowband IoT). An experimental setup is used to evaluate the performance of this integration, in which the end-to-end latency is slightly increased when a new endorsed element is added. However, the system reliability, privacy, and interoperability are guaranteed. In this sense, we expect active participation of empowered citizens to supplement the more usual top-down management of epidemics.
Anchor-free network with guided attention for ship detection in aerial imagery

For their high maneuverability, unmanned aerial vehicles (UAVs) are widely used in object detection, including the detection of ships. However, object detection in aerial images taken by UAV remains a challenge due to the arbitrary shooting perspectives and small proportion of targets. Existing anchor-based detectors, whose performance could be easily affected by the aspect ratios and scales of anchor boxes, could get into difficulties in handling candidate targets with wide shape variations. We propose an efficient anchor-free detector to replace a set of predefined anchor boxes. Specifically, guided attention module, embedded in the feature pyramid structure, is put forward to help low-level feature maps acquire the guiding information of high-level feature maps in the multi-scale fusion stage. Then an intersection-over-union (IoU) prediction head is added to predict the IoU for each predicted box. The output from IoU prediction and classification branches is then evaluated to dynamically generate soft labels without sacrificing the effiency in an attempt to improve the performance of the proposed detector. The results of extensive experiments demonstrate that the performance of our proposed detector is better than that of several current mainstream detectors.
Coherence-protected nonadiabatic geometric quantum computation

Because of using geometric phases, nonadiabatic geometric gates have robustness against control errors. On the other hand, decoherence still affects nonadiabatic geometric gates, which is a key factor in reducing their fidelities. In this paper, we show that based on the system Hamiltonian that realizes a nonadiabatic geometric gate, one may construct a system Hamiltonian by the use of which not only the geometric feature of the nonadiabatic geometric gate is preserved, but also the system's coherence is protected. As a result, a coherence-protected nonadiabatic geometric gate is realized with this system Hamiltonian, and this gate has robustness against both control errors and decoherence. We further implement our scheme with nitrogen-vacancy centers and show that a universal set of coherence-protected nonadiabatic geometric gates can be realized. Our scheme does not need auxiliary systems or the encoding of logical qubits with physical qubits, which saves resources for the implementation. Due to the robustness against both control errors and decoherence, our scheme provides a promising way to realize high-fidelity quantum gates.Received 21 October 2020Accepted 6 April 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.023104Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasGeometric & topological phasesQuantum computationQuantum gatesGeneral Physics
Evaluation of E-Learning Implementation During the Covid-19 with the DeLone and McLean Models

During the Covid pandemic the learning process was carried out using e-learning media. SMK TI Airlangga Samarinda is one of the schools that has used e-learning in its learning process both before the Covid pandemic. Furthermore, an evaluation of the success of e-learning implementation will be carried out using the DeLone and McLean information system success model (D&amp;M IS Success Model). In this model, there are six measurement variables, namely system quality, information quality, service quality, usage, user satisfaction, and net benefits. The purpose of this study is to provide an overview of the use of e-learning at SMK TI Airlangga Samarinda to the school and to find out the factors that influence the successful implementation of e-learning at SMK TI Airlangga Samarinda. To achieve this goal, SEM-PLS analysis (structural equation model - partial least square) is used. The subjects used were 336 students of SMK TI Airlangga Samarinda. The sampling technique used in this study was purposive sampling, with consideration of the availability of data obtained a sample of 192. Data collection techniques in this study using questionnaires, interviews, and literature study. The results of this study indicate that the quality of information and system quality in e-learning has a positive effect on the satisfaction of using e-learning which in turn has an effect on the benefits felt by students while using e-learning. In addition, the quality of the e-learning system also has a positive effect on the intensity of e-learning use, which means that the better the quality of the system will increase the intensity of e-learning use.
AutoCluster: Meta-learning Based Ensemble Method for Automated Unsupervised Clustering

Automated clustering automatically builds appropriate clustering models. The existing automated clustering methods are widely based on meta-learning. However, it still faces specific challenges: lacking comprehensive meta-features for meta-learning and general clustering validation index (CVI) as objective function. Therefore, we propose a novel automated clustering method named AutoCluster to address these problems, which is mainly composed of Clustering-oriented Meta-feature Extraction (CME) and Multi-CVIs Clustering Ensemble Construction (MC\(^2\)EC). CME captures the meta-features from spatial randomness and different learning properties of clustering algorithms to enhance meta-learning. MC\(^2\)EC develops a collaborative mechanism based on clustering ensemble to balance the measuring criterion of different CVIs and construct more appropriate clustering model for given datasets. Extensive experiments are conducted on 150 datasets from OpenML to create meta-data and 33 test datasets from three clustering benchmarks to validate the superiority of AutoCluster. The results show the superiority of AutoCluster for building an appropriate clustering model compared with classical clustering algorithms and CASH method.
Bars Behind Bars: Digital Technology in the Prison System

With little opportunity to engage with technology while behind bars, returning citizens are finding themselves on the far side of the digital divide and increasingly vulnerable to recidivism. Investing in a well-run digital literacy program for our prison system is an innovative solution to a persistent problem and a rare win-win situation for inmates, prison officials, and American taxpayers.We begin by discussing how inmate tablet distribution programs mutually benefit both inmates and prison officials. We then explore prison profiteering by technology companies as a potential obstacle to the successful administration of technology programs, discussing the emergence of virtual monopolies in the prison technology space, their history of controversial pricing practices, and how these practices are perpetuated through prison tablet programs. We then present novel insights into how competitive bidding can be used as a public policy instrument to regulate competition, specifically in the context of prison technology. We argue that a traditional bidding framework is insufficient to act as a policy instrument and propose an alternative incentive-based framework toward this end. We conclude by outlining several practical recommendations that prison officials should consider when administering digital literacy programs in their facilities.
Food packaging permeability and composition dataset dedicated to text‐mining

This dataset is composed of symbolic and quantitative entities concerning food packaging composition and gas permeability. It was created from 50 scientific articles in English registered in html format from several international journals on the ScienceDirect website. The files were annotated independently by three experts on a WebAnno server. The aim of the annotation task was to recognize all entities related to packaging permeability measures and packaging composition. This annotation task is driven by an Ontological and Terminological Resource (OTR). An annotation guideline was designed in a collective and iterative approach involving the annotators. This dataset can be used to train or evaluate natural language processing (NLP) approaches in experimental fields, such as specialized entity recognition (e.g. terms and variations, units of measure, complex numerical values) or sentence level binary relation (e.g. value to unit, term to acronym).
Digital soil morphometrics of coarse fragments and horizon delineation in soil profiles from Central Mexico

In this study, we developed a method that allows the delimitation of horizons and the quantification of coarse fragments in soil profiles from volcanic areas using digital images. The delineation of the soil profile horizons included the following phases: pre-processing of the digital image, extraction of color systems from the pre-processed image, k-means segmentation of the HSV and CIE L*a*b* color systems, delineation of the horizons, and determination of the horizon characteristics. The coarse fragments were quantified in the following three phases: superpixel analysis of the soil profile image, histogram classification of the image objects, and extraction and quantification of the coarse fragments. For horizon delineation, the HSV color system performed better for the Eutric Andic Cambisol (Loamic, Ochric), and the CIE L*a*b* system performed better for the Eutric Skeletic Mollic Silandic Andosol (Loamic). The RGB image and the S component of the HSV system demonstrated similar coarse fragment volume calculation performance for the Eutric Andic Cambisol (Loamic, Ochric), whereas the S component worked best for the Eutric Skeletic Mollic Silandic Andosol (Loamic). We created a graphic decision-making system for the delineation of soil horizons and for the quantification of coarse fragments in digital images of soil profiles.
A novel classification model for feature-based sentimental analysis on public attention towards covid-19

Covid-19 is the word which is horrifying the world which has infringed socio-economic problems. The main purpose of this article is to build a model which predicts sentiments on Covid-19 reviews which has been taken from Coronavirus Reviews from Kaggle repository. It is originally known as Corona Virus Diseases effected in 2019 and has been declared as pandemic by WHO. With the increase in cases through out the globe people getting panic and worry about their health. Physically and mental state of the people is becoming comparatively proportional for this pandemic situation. So in this case there is a need of a classification model which is to be implemented based on various measures to protect the countries by interpreting the facts and info about this pandemic. So in this paper we performed sentimental analysis using a Novel Classifier (SADBM) named as Sentimental Data Base Miner algorithm to classify the reviews or reviews given by various people from all over the globe, and then we have validated the accuracy of the model which is compared with basic classifiers like Nave-Bayes and SVM where the Novel algorithm gave best accuracy when compared with the other 2 models i.e., 95%.Index TermsSADBM, Reviews, Sentimental Analysis, SVM.
The e-Banknote as a ‘Banknote’: A Monetary Law Interpreted

Abstract This article discusses whether electronic banknotes can properly be characterised as ‘banknotes’, their design as such and what architectural models are available for their issuance by central banks. Issues are addressed under general principles governing the interpretation of monetary laws in the context of evolving technologies and institutional arrangements. The article proposes a clear terminology to address the concepts underlying digital currencies and access to central bank money, and argues that a banknote may be ‘written’ electronically. The stance adopted in the article is critical of both account-based retail central bank digital currency and cryptocurrencies, and highlights the salient features of non-blockchain token-based alternatives. It sheds light on the considerations that influence the selection of a design that is appropriate from both the functional and legal perspectives.