Reviewerly/exHarmony huggingface dataset:
High-Performance Hardware 3D Medical Imaging using Wavelets in the Residue Number System

Discrete wavelet transform (DWT) is used in medical imaging to solve many problems. The practical DWT implementation requires a lot of time. We propose parallelizing computations by using the non-positional residue number system (RNS) to improve the performance of medical imaging systems. We use RNS moduli of a special type which allows us to reduce the computational complexity of the arithmetic operations used for 3D tomographic imaging. In mathematical and hardware modeling, all calculations are performed with fixed-point numbers. Hardware implementation is carried out in Xilinx Vivado 2019.2 for the target device FPGA Kintex UltraScale xcku115-flvf1924-3-e and allows to increase the performance of medical imaging devices by 1.57 – 2.70 times.
The Effect of Naturalness of Voice and Empathic Responses on Enjoyment, Attitudes and Motivation for Interacting with a Voice User Interface

In human-computer interaction much attention is given to the development of natural and intuitive Voice User Interfaces (VUI). However, previous research has shown that humanlike systems will not necessarily be perceived positive by users. The study reported here examined the effect of human likeness on users’ rating of enjoyment, attitudes and motivation to use VUI in a Wizard-of-Oz experiment. Two attributes of human likeness, voice of the system (humanlike vs. machinelike) and social behavior of the system (expressing empathy vs. neutral) were manipulated. Regression analyses confirmed that perceived empathy of the VUI improved interaction enjoyment, attitude towards the system, and intrinsic motivation but no effect of voice was found. Session order also affected participants’ evaluation. In the second session, participants rated the VUI as more negative than in the first session. The results indicate that a VUI that expresses social behavior (e.g. showing empathy) is perceived as more favorable by the user. Furthermore, changing user expectations pose a challenge for the design of the VUI. The dynamics of user interactions must be taken into account when designing the VUI.
Design of a Tangible Programming Tool for Students with Visual Impairments and Low Vision

This article presents the design of a tangible tool for teaching basic programming concepts to students with visual impairments and low vision. Meeting the preliminary requirements of this ongoing project (Tangible input, cost-efficient, and minimal maintenance), the paper describes the design of the hardware of this prototype with inexpensive materials, such as Lego blocks, 3.5 mm stereo audio jack connectors, relays, resistors, and an Arduino Mega microcontroller. To identify the Lego blocks that represent the code instructions, the system provides three different methods of identification; color, 3D printed labels, and braille labels. To acquire feedback and validate this prototype, a preliminary study with nine participants with Visual Impairment and Low Vision has been carried out. All participants completed the task successfully, provided feedback about the prototype, and all recommended this prototype to teach basic programming concepts. Currently, the project is focused on the implementation of music as an output method of programs created by users. The objective is to create a programming tool completely independent of visual graphics or information.
Unequal‐interval based loosely coupled control method for auto‐scaling heterogeneous cloud resources for web applications

Summary Most existing quality of service (QoS) control algorithms of Web applications take into account Web Server or database connections which can be released immediately. However, many applications are deployed on virtual machines (VMs) or even Spot VMs elastically rented from public Clouds. To save costs, interval‐priced VMs are not released until the ends of rented intervals. Such delays of control effects make existing methods rent or release excess VMs leading to overcontrol. Fluctuated prices make Spot VMs unreliable due to unexpected termination which makes fault‐tolerant strategies crucial. In this article, an unequal‐interval‐based loosely coupled control method is proposed to improve the quality of service (QoS) control ability of fault‐tolerant strategies. A queuing model with arrival‐rate‐adjustment coefficient is used to predict required capacity as a feedforward controller. Another two‐threshold and queuing‐model‐based method is applied to update the coefficient as a loosely coupled feedback controller. Meanwhile, unequal‐interval controller collaborating method is proposed to avoid overcontrol and react quickly to workload changes. Our approach is evaluated on both a simulation platform and a real Kubernetes Cluster. Experimental results illustrate that our approach decreases the percentage of waiting times larger than service level agreements with similar or lower rental costs compared with existing algorithms.
Investigation of the Interlaminar Shear Properties of Fiber-Reinforced Polymers via Flexural Testing Using Digital Image Correlation

Abstract The determination of the interlaminar properties of laminate composites to date has included a few direct measurement techniques: the standardized short-beam test via three-point flexural testing and the double beam shear test via five-point flexural testing. These test methods are limited to a determination of the apparent interlaminar strength, whereas the full stress–strain behavior cannot be determined. The double beam shear test allows for a calculation of the shear modulus, provided that four additional elastic, in-plane properties have been determined in advance via three additional types of tests. The present work includes a comparison of the three-point short-beam test, the nonstandardized four-point flexural test, and the double beam shear test (requiring small span-length-to-thickness ratios for the shear loading to become dominant). The tests are carried out on unidirectional carbon fiber–reinforced composite (CFRP-UD) with a symmetric layup. The experiments are accompanied by photomechanical investigations using digital image correlation (DIC) to visualize the phenomenology of shear deformation. The novelty of this approach includes the use of the DIC-determined shear strains, which show a parabolic distribution across the specimen’s thickness. The DIC-calculated maximum shear strain across the thickness is used for a full description of the stress–strain behavior with a focus on the elastic loading range to evaluate the shear modulus. Interlaminar shear strengths and shear moduli determined from all three types of tests are compared with the intralaminar shear properties.
Timed Signatures and Zero-Knowledge Proofs—Timestamping in the Blockchain Era—

Timestamping is an important cryptographic primitive with numerous applications. The availability of a decentralized blockchain such as that offered by the Bitcoin protocol offers new possibilities to realise timestamping services. Even though there are blockchain-based timestamping proposals, they are not formally defined and proved in a universally composable (UC) setting. In this work, we put forth the first formal treatment of timestamping cryptographic primitives in the UC framework with respect to a global clock. We propose timed versions of primitives commonly used for authenticating information, such as digital signatures, non-interactive zero-knowledge proofs, and signatures of knowledge. We show how they can be UC-securely constructed by a protocol that makes ideal (blackbox) access to a transaction ledger. Our definitions introduce a fine-grained treatment of the different timestamping guarantees, namely security against postdating and backdating attacks; our results treat each of these cases separately and in combination, and shed light on the assumptions that they rely on. Our constructions rely on a relaxation of an ideal beacon functionality, which we construct UC-securely. Given many potential use cases of such a beacon in cryptographic protocols, this result is of independent interest.
Intelligent Sports performance Scoring and Analysis system Based on Deep Learning Network

In the past, sports analysis is always conducted by specialized devices like sensors or depth camera, which are hard to promote. In this essay, we use computer vision technology to establish a sport detection and analysis system with the use of an RGB camera and deep learning models. It can recognize the posture from an RGB image, make an overall evaluation, and return the details real-time.The RGB image will be processed by two sub-systems sequentially: Human detection system and Performance analysis system. The human detection system is used to detect the user's posture, while the performance analysis system is designed to return feedback on the details of the user's posture.The results of the system are satisfactory:1)In the human detection system, the overall accuracy can reach more than 99%, meaning that almost the 4 actions can be classified accurately.2)As for the performance analysis system, the score and details can be returned correspondingly based on tiny differences within the centimeter level.3)The delay of the whole system is within milliseconds, meaning that the system can achieve real-time detection and analysis.In this essay, golf driving is taken as an example to illustrate how the system works. Also, the system can be promoted to other sports.
“Talking” Triples to Museum Chatbots

The paper presents recent work on the design and development of AI chatbots for museums using Knowledge Graphs (KGs). The utilization of KGs as a key technology for implementing chatbots raises not only issues related to the representation and structuring of exhibits' knowledge in suitable formalism and models, but also issues related to the translation of natural language dialogues to and from the selected technology for the formal representation and structuring of information and knowledge. Moreover, such a translation must be as transparent as possible to visitors, towards a realistic human-like question-answering process. The paper reviews and evaluates a number of recent approaches for the use of KGs in developing AI chatbots, as well as key tools that provide solutions for natural language translation and the querying of Knowledge Bases and Linked Open Data sources. This evaluation aims to provide answers to issues that are identified within the proposed MuBot approach for designing and implementing AI chatbots for museums. The paper also presents Cretan MuBot, the first experimental KG/Ontology-based AI chatbot of the MuBot Platform, which is under development in the Heracleum Archaeological Museum.
DIAC

This article tackles the problem of detecting and solving potential conflicts among independently developed apps that are to be installed into an open Internet-of-Things (IoT) environment. It provides a new set of definitions and categorizations of the conflicts to more precisely characterize the nature of the problem, and it proposes a representation named “IA Graphs” for formally representing IoT controls and inter-app interplays. Based on the definitions and representation, it then describes an efficient conflict detection algorithm. Combining conflict categories, seriousness indicator, and conflict frequency, an innovative solution policy for solving various detected conflicts is developed, which also takes into account user preference and interest by providing interactive process. It implements a compiler and runtime software system that integrates all the proposed techniques together into a comprehensive solution. Experiments on SmartThings apps validate its significantly better detection efficacy over prior methods and effectiveness of conflict solution with user preference.
Evolving models for incrementally learning emerging activities

Ambient Assisted Living (AAL) systems are increasingly being deployed in real-world environments and for long periods of time. This significantly challenges current approaches that require substantial setup investment and cannot account for frequent, unpredictable changes in human behaviours, healt h conditions, and sensor deployments. The state-of-the-art methodology in studying human activity recognition is cultivated from short-term lab or testbed experimentation, i.e., relying on well-annotated sensor data and assuming no change in activity models. This paper propose a technique, EMILEA, to evolve an activity model over time with new types of activities. This technique novelly integrates two recent advances in continual learning: Net2Net – expanding the architecture of a model while transferring the knowledge from the previous model to the new model and Gradient Episodic Memory – controlling the update on the model parameters to maintain the performance on recognising previously learnt activities. This technique has been evaluated on two real-world, third-party, datasets and demonstrated promising results on enhancing the learning capacity to accommodate new activities that are incrementally introduced to the model while not compromising the accuracy on old activities.
Approach to Determining the Boundaries of the Search Range for the Number of Trees in the Random Forest Algorithm

The problem of determining the search ranges for the optimal values of the trees' number for the random forest (RF) in order to reduce the time spent on its development has been considered. The aim of the work is to obtain the formulas for determining the search range for the number of trees. The formulas have been obtained based on the results of the experimental studies on the development of the RF classifiers based on various datasets from the machine learning data repositories. The results of experimental studies on the development of the RF classifiers using the training and test sets based on the analyzed datasets have been presented. The formulas for the graphical dependencies have been obtained in the general form for assessing the classification quality at the test set and the development time.
Application of PCA and HCA in geochemical parameters to distinguish depositional paleoenvironments from source rocks

Oil-producing rocks can be characterized by organic, inorganic and palynological geochemical parameters to assess the quantity and quality of organic matter. In the present study, samples of source rocks collected from different formations (Fm) from the sedimentary basins of the Parnaíba (Pimentairas Formation) and Paraná (Irati and Ponta Grossa Formations) were evaluated through the geochemical correlation between the types of organic matter and their different depositional paleoenvironments using chemometric tools. Among the inorganic geochemical parameters used in this evaluation are the concentrations and the diagnostic ratios between the transition elements (Co, Ni, V, and Mo) and internal transition (lanthanides and U). The analysis of the transition and internal transition elements were performed by ICP OES (Ni and V) and ICP MS (Co, Mo, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Tm, Yb, Lu, and U). The organic geochemical parameters selected for the characterization of the quantity and quality of the organic matter present in the samples of source rocks involved the determination of their total carbon content (%TOC) and sulfur (%TS), of the potential generator (S2) the amount of hydrocarbons produced by Rock-Eval pyrolysis and the calculation of the hydrogen (HI) and oxygen (OI) indexes. To compose the matrix of the multivariate analysis (PCA), were used ratios Ni/Co, V/Ni, U/Mo, V/V + Ni, TOC/TS the values of S2, HI, OI and the sum of rare earth elements (REE). The kerogen type, obtained through the HI and OI data, were also included in the matrix, information on the origin of the organic matter, and on the type of depositional paleoenvironment of each sample, obtained from previous works. It was observed the formation of three groups of data characterizing the formations Irati, Ponta Grossa and Pimenteiras according to the type of kerogen and organic matter, and also of its depositional paleoenvironment, showing that the exploratory data analysis allows the obtaining of rapid and efficient information on the chemical similarity between the complex samples from each formation.
Prediction Framework with Kalman Filter Algorithm

The article describes the autonomous open data prediction framework, which is in its infancy and is designed to automate predictions with a variety of data sources that are mostly external. The framework has been implemented with the Kalman filter approach, and an experiment with road maintenance weather station data is being performed. The framework was written in Python programming language; the frame is published on GitHub with all currently available results. The experiment is performed with 34 weather station data, which are time-series data, and the specific measurements that are predicted are dew points. The framework is published as a Web service to be able to integrate with ERP systems and be able to be reusable.
The Next Gen Election: Design and Development of E-Voting Web Application

The elevated utilization of in formation technology seems to revolutionize both the provision of governmental services and the vivacity of democracy. E-voting or Electronic voting symbolizes modern democracy. E-voting will be at its best when complied with the existing legal and regulatory framework. “Vote”, the word means to determine or to elect or select from a list or who will run the country or the organization or a group. To find leaders selected by people is the prime aim of voting (Scenario: Citizens electing their country leaders). Most countries, India is no exception, have trouble voting. Some of the issues at stake are incorrect voting during elections, inexperienced personnel, inaccessible or insecure polling stations, and inadequate voting equipment. The new indigenous flagship internet-based voting system solves this exact problem. It should be noted that users, in this case, citizens, have a large time frame during the voting period with the system running. The objective of this paper is to come up with a new solution, does come with a small learning curve, citizens will have to be trained on how to exercise their right to vote online.
Understanding Abstraction in Deep CNN: An Application on Facial Emotion Recognition

Facial Emotion Recognition (FER) is the automatic processing of human emotions by means of facial expression analysis []. The most common approach exploits 3D Face Descriptors (3D-FD) [], which derive from depth maps [] by using mathematical operators. In recent years, Convolutional Neural Networks (CNNs) have been successfully employed in a wide range of tasks including large-scale image classification systems and to overcome the hurdles in facial expression classification. Based on previous studies, the purpose of the present work is to analyze and compare the abstraction level of 3D face descriptors with abstraction in deep CNNs. Experimental results suggest that 3D face descriptors have an abstraction level comparable with the features extracted in the fourth layer of CNN, the layer of the network having the highest correlations with emotions.
Sparse regressions and particle swarm optimization in training high-order Takagi–Sugeno fuzzy systems

Abstract This paper proposes a method for training Takagi–Sugeno fuzzy systems using sparse regressions and particle swarm optimization. The fuzzy system is considered with Gaussian fuzzy sets in the antecedents and high-order polynomials in the consequents of the inference rules. The proposed method can be applied in two variants: without or with particle swarm optimization. In the first variant, ordinary least squares, ridge regression, or sparse regressions (forward selection, least angle regression, least absolute shrinkage and selection operator, and elastic net regression) determine the polynomials in the fuzzy system in which the fuzzy sets are known. In the second variant, we have a hybrid method in which particle swarm optimization determines the fuzzy sets, while ordinary least squares, ridge regression, or sparse regressions determine the polynomials. The first variant is simpler to implement but less accurate, the second variant is more complex, but gives better results. A new quality criterion is proposed in which the goal is to make the validation error and the model density as small as possible. Experiments showed that: (a) the use of sparse regression and/or particle swarm optimization can reduce the validation error and (b) the use of sparse regression may simplify the model by zeroing some of the coefficients.
Computational modelling of an optical character recognition system for Yorùbá printed text images

This study acquired a dataset of scanned images of Standard Yorùbá printed text and formulated a Yorùbá character image recognition model. The model formulated was implemented and the performance of the model evaluated to develop an Optical Character Recognition (OCR) model for Yorùbá printed text images. The image dataset at 300 dots per inches (dpi) was acquired by generating image text-line from Yorùbá New Testament Bible (Bibeli Mimo) corpus using Unicode UTF8. The Long Short Term Memory (LSTM) model, a variant of Recurrent Neural Network (RNN) was used to formulate the Standard Yorùbá character image recognition model. The Python OCRopus framework was used to implement the model designed. The performance of the model designed was evaluated using character error rate based on Levenshtein Edit Distance algorithm. The results show that the Character Error Rate (CER) of 3.138% for the font Times New Roman which gives better recognition than the other font style metric performance. The model achieved an OCR result of (7.435% CER) DejaVuSans font style image dataset, while for Ariel font image dataset, a result of 15.141% was achieved. The introduction of Language model-based Standard Yorùbá a spell-checker corrector show a reduction in the Character Error Rate. The Times New Roman font recorded an error rate of 1.182%, the DejaVuSans font style at an error rate of 4.098% while the Ariel font at 5.87%. The study concluded that the performance of the model shows that the farther away an image text font is from the font(s) used in training the network, the higher the character error rate of the recognition and that the inclusion of a post-processing stage shows a reduction in the Character Error Rates.
Stakeholder Journey Analysis for Innovation

When analysing how the information-technological innovation of a startup company is perceived to affect the market, we encountered challenges when using existing customer journey analysis frameworks. In particular, we identified a need to express critical events for technology adoption, a need to express universal access principles and a need to express journeys for multiple stakeholders in the same diagram; all in an easily readable manner. To do this, we extended an existing stripped-down customer journey framework with elements for the above aspects. We present this extended framework as a stakeholder journey framework. Based on the initial application of this framework on the startup company’s innovation product, we conclude that the stakeholder journey framework aided in uncovering issues within technology adoption and universal access that would otherwise not have been addressed.