Reviewerly/exHarmony huggingface dataset:
An Attribute-Based Searchable Encryption Scheme for Non-Monotonic Access Structure

Attribute based encryption (ABE) is a widely used technique with tremendous application in cloud computing because it provides fine-grained access control capability. Owing to this property, it is emerging as a popular technique in the area of searchable encryption where the fine-grained access control is used to determine the search capabilities of a user. But, in the searchable encryption schemes developed using ABE it is assumed that the access structure is monotonic which contains AND, OR and threshold gates. Many ABE schemes have been developed for non-monotonic access structure which supports NOT gate, but this is the first attempt to develop a searchable encryption scheme for the same. The proposed scheme results in fast search and generates secret key and search token of constant size and also the ciphertext components are quite fewer than the number of attributes involved. The proposed scheme is proven secure against chosen keyword attack (CKA) in selective security model under Decisional Bilinear Diffie-Hellman (DBDH) assumption.
Bringing JSON signatures to ETSI AdES framework: Meet JAdES signatures

This paper proposes a set of JSON signed and unsigned attributes which, if incorporated into a JSON signature, will convert it in a digital signature (JAdES) with identical capabilities (for instance, feasibility of validating them long after their generation, even after the signing certificate has expired, or has been revoked) as the digital signatures in ETSI AdES family, namely CAdES, PAdES, and XAdES. The paper also proposes two different mechanisms for incorporating the aforementioned attributes into a JWS signature (i.e. building a JAdES signature on JWS), with special emphasis in the computation and checking of archive time-stamps (i.e. time-stamp tokens that assure integrity and time of existence of all the contents of the digital signature). Finally, the paper summarizes the results obtained by a program developed to ascertain the correctness of the technical approaches taken, and that served as proof of concept. This paper has served as the starting point for building a formal proposal for producing an ETSI (European Telecommunications Standards Institute) Technical Specification (ETSI TS) to ETSI Electronic Signatures and Infrastructure (ESI) Technical Committee, responsible, within ETSI, of developing European Standards for digital signatures and public key infrastructure in general, and of the standardization of AdES digital signatures family in particular.
Use of Knowledge Management and SCRUM techniques to increase the reusability in software development

In this era of modern technology, every organization concerning healthcare is focusing on increasing the use of healthcare systems. Reason of using IT based healthcare systems are twofold. First of all, they provide a much better source and reliable means of sharing information among organizations. Secondly, they offer economic benefits and make life easier for health providers as well as patients. Healthcare is a wide and complex domain. During the development of such complex software using agile methodologies most of the softwares lose their quality standards. The main focus of software developers is to develop and release a build as early as possible, which obviously impacts the quality of software. Our research proposes a SCRUM based model that helps software developers to build healthcare systems, in parallel it will enhance and maintain desired quality standards with the use of knowledge management techniques. In our study, we conducted an experimental research to test the correctness and validity of proposed approach. Results revealed that the proposed approach significantly improves the quality of healthcare systems and enhance the satisfaction level of the product owner and the development team.
Architecture of an Enterprise Project Life Cycle using Hyperledger platform

Block chain technology is a distributed or decentralized system that consist of nodes also called participants that communicate with each other to perform transactions. These transactions are then securely stored as blocks in the form of a chain. This is a continuous growing list of blocks and the transactions are executed by using cryptographic protocols. The point to be noted is that there is no need of any third party to perform the transaction, only the concerned stakeholders are involved. The study in this paper suggests the innovative use of Block chain platform and discusses the implementation process in the Project Management office (PMO) scenario. The main responsibility of PMO in an organization is to deliver projects on time while overcoming the risks and mitigations. Similarly, Financial Capitalization and Cash flow are also significant processes. So, different key roles of PMO are presented along with the concerned stakeholders. Each stakeholder is referred to as a particular node of our Block Chain system. In this study we would discuss and share the proof of implementation of Project Life Cycle with an existing Block chain ledger platform. It is concluded after reviewing the latest stuff in the subjected area that Block chain based Hyperledger platforms by Linux Foundation are getting maturity day-by-day. Thus, contributing towards another revolution in Financial, Management and Technical processes. Our end result brief the significant outcomes with the advantages of the Project Life Cycle proposed architecture implemented on Hyperledger platform.
Repeat-Authenticate Scheme for Multicasting of Blockchain Information in IoT Systems

We study the problem of efficiently disseminating authenticated blockchain information from blockchain nodes (servers) to Internet of Things (IoT) devices, through a wireless base station (BS). In existing blockchain protocols, upon generation of a new block, each IoT device receives a copy of the block header, authenticated via digital signature by one or more trusted servers. Since it relies on unicast transmissions, the required communication resources grow linearly with the number of IoT devices. We propose a more efficient scheme, in which a single copy of each block header is multicasted, together with the signatures of servers. In addition, if IoT devices tolerate a delay, we exploit the blockchain structure to amortize the authentication in time, by transmitting only a subset of signature in each block period. Finally, the BS sends redundant information, via a repetition code, to deal with the unreliable wireless channel, with the aim of decreasing the amount of feedback required from IoT devices. Our analysis shows the trade-off between timely authentication of blocks and reliability of the communication, depending on the packet loss rate offered by the channel. The numerical results show that the performance benefits of the proposed scheme makes it a viable starting point for designing new lightweight protocols for blockchains.
Feedback Particle Filter with Correlated Noises

Motivated by the mean-field game theory, the feedback particle filter (FPF) for the signal-observation nonlinear filtering (NLF) model with independent white noises, has been developed in [23] for the first time. In this paper, we shall extend this algorithm to the case where the scalar signal process is correlated with the scalar observation process. The equation that the control inputs (K, u) satisfied has been derived by minimizing the Kullback-Leibler (K-L) divergence of the conditional density and the conditional posterior empirical distribution of the controlled particles. Then we show rigorously that the control inputs obtained is consistent, in the sense that if the initial conditional density and the empirical distribution are the same, so are the posterior ones. The explicit expression for the control input u is given if K is obtained. The numerical simulation of a scalar NLF problem with transition phenomenon has been solved by our algorithm with satisfactory performance not only in accuracy but also in efficiency.
Service Robot Navigation and Computer Vision Application in a Banquet Hall Setting

This paper describes a low computation system for a surveying service robot in a banquet hall setting. Automating this process will allow staff to focus on other tasks which will improve the overall efficiency of the event setup. The robot platform used was FURO, a two-wheeled service robot with an on board computer as well as a camera. Image processing algorithms were developed using the open source Open CV library to detect a total item count and ellipse tracker program. Potential fields was the navigation algorithm implemented to generate a collision free path towards the edge of the table. The results were successful as the computer vision algorithms worked 90% of the time in a controlled environment with a normal setup. It experienced failure when items were overlapping or too close to each other. Differences in lighting also came into effect when detecting the contents on the table. Nonetheless, this experiment presents a service robot application for table monitoring in a ballroom.
A Distributed PHD Filter for On-line Joint Sensor Registration and Multi-target Tracking

In this paper, we address the joint sensor registration and multi-target tracking problem using PHD filter and generalized Covariance Intersection (GCI) fusion rule. Each sensor is assumed to have no knowledge about the positions (referred to as the drift parameters) of its neighboring nodes in its local coordinate system. In our method, the drift parameters are estimated first, then the multi-target states are obtained based on the estimated drift parameters at each common global reference time step. The GCI divergence is used to measure the degree of discrepancy between multiple local posterior densities, and the drift parameters are obtained by minimizing the GCI divergence. On account of the optimization problem constructed by GCI divergence is a non-convex optimization problem when Gaussian mixture (GM) implementation is adopted, we provide a fast but effective optimization method on the basis of particle swarm optimization (PSO) algorithm. The efficiency of the proposed method is demonstrated in the numerical results.
An Intelligent Interface for Automatic Grading of Sketched Free Body Diagrams

Sketching free body diagrams is an important skill that students learn in introductory physics and engineering classes; however, university class sizes are growing and often have hundreds of students in a single class. This creates a grading challenge for instructors as there is simply not enough time nor resources to provide adequate feedback on every problem. We have developed an intelligent user interface called Mechanix to provide automated, real-time feedback on hand-drawn free body diagrams for students. The system is driven by novel sketch recognition algorithms developed for recognizing and comparing trusses, general shapes, and arrows in diagrams. We have also discovered trends in how the students utilize extra submissions for learning through deployment to five universities with 350 students completing homework on the system over the 2018 and 2019 school year. A study with 57 students showed the system allowed for homework scores similar to other homework mediums while requiring and automatically grading the free body diagrams in addition to answers.
Click-Fraud Detection for Online Advertising

In affiliate marketing, an affiliate offers to handle the marketing effort selling products of other companies. Click-fraud is damaging to affiliate marketers as they increase the cost of internet traffic. There is a need for a solution that has an economic incentive to protect marketers while providing them with data they need to reason about the traffic quality. In our solution, we propose a set of interpretable flags explainable ones to describe the traffic. Given the different needs of marketers, differences in traffic quality across campaigns and the noisy nature of internet traffic, we propose the use of equality testing of two proportions to highlight flags which are important in certain situations. We present measurements of real-world traffic using these flags.
Multi-band Spectral Entropy Information for Detection of Replay Attacks

Replay attacks have been proven to be a potential threat to practical automatic speaker verification systems. In this work, we explore a novel feature based on spectral entropy for the detection of replay attacks. The spectral entropy is a measure to capture spectral distortions and flatness. It is found that the replay speech carries artifacts in the process of recording and playback. We hypothesize that spectral entropy can be a useful information to capture such artifacts. In this regard, we explore multi-band spectral entropy feature for replay attack detection. The studies are conducted on ASVspoof 2017 Version 2.0 database that deals with replay speech attacks. A baseline system with popular constant-Q cepstral coefficient (CQCC) feature is also developed. Finally, a combined system is proposed with multi-band spectral entropy and CQCC features that outperforms the baseline. The experiments validate the idea of multi-band spectral entropy feature.
Integration of Digital Technology Applications into the Pre-gradual Teacher Training

Due to the fast development of digital technology applications, there is a need to innovate continually content of those parts of teacher trainees study programs which are aimed at the teacher trainee didactic technological competences. To find an optimal model of pre-gradual teachers training in the area of their didactic technological competence development, a research which is described in the paper has been done. Within the research, significance of incorporation of different kinds of software/hardware products into the teacher training study programs was examined. There were examined 9 kinds of digital products, in particular: ActivInspire, SMART Notebook, Flow!Works; Prezi; FreeMind, Mindomo, XMind; ActivExpression2, ActiVote, QRF700/900, Turning Point; Socrative 2.0; Google documents; Microsoft PowerPoint; Microsoft Excel; Microsoft Word. Necessary research data were obtained based on a screening of teacher trainees’ opinions on several aspects relating to each of the given kinds of digital software/hardware products, e.g. on significance of incorporation digital products of the relevant kind into the teacher trainee study programs (teacher pre-gradual preparation).
Digital Literacy in Special Education

Digital literacy is essential for individuals entering college and the workplace. Students with disabilities experience a greater challenge in acquiring the skills necessary to succeed. This chapter explores the disability digital divide, success factors for acquiring digital skills, and the implications of a digital literacy curriculum developed for special education classrooms in Idaho. It demonstrates how leveraging human performance improvement (HPI) models, incorporating universal design for learning (UDL) principles, and supporting classroom teachers resulted in a curriculum to help young people with disabilities to acquire the digital skills they need to be prepared for college and the workplace.
Variable Exponential Forgetting for Estimation of the Statistics of the Normal-Wishart Distribution with a Constant Precision

The problem of estimating normal regression-type models with possibly time-varying regression parameters and constant noise precision is considered and examined from the Bayesian viewpoint. The solution we propose exploits a collaborative decision in order to face the incomplete model of parameter variations. Under this approach, a loss functional evaluating two prediction alternatives is constructed, which allows us to merge both alternatives, complying with the principles of optimization theory. Specifically, the posterior probability density function (pdf) and its flattened variant are combined by means of the geometric mean with automatically adjusted weights. The result is an automatic rescaling of the covariance matrix through the forgetting factor in response to empirically confirmed performance.
A Wavelet Neural Network Model for Hourly Solar Radiation Forecasting from Daily Solar Radiation

This paper represents a framework for forecasting solar radiation data per hour using mean of solar radiation on daily basis. The proposed model which is used for forecasting is wavelet neural network. Sunset hour angle, hour angle and daily solar radiation are the three inputs of this model. The output layer has one node which is mean hourly solar radiation (HSR). The proposed model has been trained and developed using MATLAB software. The outcomes achieved here have better prediction precision in comparison to some previous empirical models. To evaluate the proposed model, two error statistical method such as root mean square error and mean absolute percentage error has been implemented. Finally R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> estimation of 96% of the proposed model demonstrates that it can be implemented easily and can increase the preciseness of estimation.
Blockchain of Custody, BoC

Ensuring continuity of evidence in any investigation is problematic, even more so when multiple agencies are involved. Ensuring there is consistency between paperwork concerning the seizure of artefacts is difficult, and can often be scrutinized in court. The development of this prototype blockchain is for the recording of the transfer of evidence during an investigation, with the aim of providing a guarantee for the continuity of evidence. The implementation of the blockchain is completed using Hyperledger and is referred to in this paper as Blockchain of Custody, or BoC. To test BoC two simple mock investigations are carried out and then evaluated. The evaluation includes querying the evidence, access control, development, security, and auditing. The implementation of BoC is considered a success and the future improvements, disadvantages and advantages are discussed.
Research on Tibetan Text Classification Method Based on Neural Network

Text categorization is an important task in natural language processing, and it has a wide range of applications in real life. In this paper, two N-Gram feature models (MLP, FastText) and two sequential models (sepCNN, Bi-LSTM) are used to study the automatic classification for Tibetan text based on syllables and vocabulary. The experiment on Tibetan language data collected by China Tibet News Network shows that the classification accuracy is about 85%.
Modular Facial Expression Recognition on Noisy Data Using Robust PCA

This paper proposes an efficient facial expression recognition approach based on Robust Principal Component Analysis (RPCA). Facial images are susceptible to image noise and varying illuminations. Standard Principal Component Analysis (PCA) when used on such data alters the quality of the approximation. To handle this, we use RPCA to first remove the noise from the images and then transform it to a lower subspace. To reduce the computational complexity, we have used the modular approach in which the regions of the eyes, nose, mouth and forehead are automatically detected and cropped out. RPCA is used on these regions and 1-NN classifier is used for recognition. Experiments performed on CK+, JAFFE and Oulu CASIA datasets show that our method improves the recognition rate over standard PCA when noise is present.
Rotoscope Automation with Deep Learning

We present a deep learning-based algorithm that can automatically rotoscope people in a given scene, without any user input. Current approaches to image matting require a significant amount of human input, irrespective of whether it is manually rotoscoped or through a chroma key. This study shows that this algorithm can perform as well as and even surpass the rotoscoping capabilities of the Adobe After Effects’ RotoBrush tool, in a variety of scenes comprising different lighting conditions, movements, and subjects. This makes it suitable for integration within a visual effects (VFX) pipeline.
Designing Mechatronic Musical Instruments: The Guitar

Since the earliest times, humans have sought the ability to produce rhythms and tones using devices external to the human body. As technology developed, so too did the desire for this sound production to become automated. Initially peaking around the Industrial Revolution, traditional automated musical production devices went into a steep decline with the arrival of the phonograph. However, factors such as the ubiquitous acceptance of the microcontroller led to a resurgent interest in this field. This paper investigates the design considerations and development of the stringed chordophone and specifically the guitar. The challenge is to produce a mechatronic device capable of speedy and reliable note selection, string actuation, string damping and expressiveness. In the same manner that there is not one best way to play a guitar and no best guitar design, so too is there no “best” chordophone design. Rather, the competing factors of speed, precision, reliability, portability, expressiveness and timbral variation can be given different weightings and result in different designs. Therefore, rather than presenting a single chordophone development, this paper provides a multitude of design options providing an interested reader with the background and suggestions to create their own bespoke design. This paper concludes with the presentation of the authors' final design as an integration of the presented ideas and design techniques. We demonstrate that this chordophone introduces expressivity at a level not achieved before, is modular yet portable, is mechanically quiet and can play at a speed beyond that of even the best human player.
Convolutional Dictionary Learning in Hierarchical Networks

Filter banks are a popular tool for the analysis of piecewise smooth signals such as natural images. Motivated by the empirically observed properties of scale and detail coefficients of images in the wavelet domain, we propose a hierarchical deep generative model of piecewise smooth signals that is a recursion across scales: the low pass scale coefficients at one layer are obtained by filtering the scale coefficients at the next layer, and adding a high pass detail innovation obtained by filtering a sparse vector. This recursion describes a linear dynamic system that is a non-Gaussian Markov process across scales and is closely related to multilayer-convolutional sparse coding (ML-CSC) generative model for deep networks, except that our model allows for deeper architectures, and combines sparse and non-sparse signal representations. We propose an alternating minimization algorithm for learning the filters in this hierarchical model given observations at layer zero, e.g., natural images. The algorithm alternates between a coefficient-estimation step and a filter update step. The coefficient update step performs sparse (detail) and smooth (scale) coding and, when unfolded, leads to a deep neural network. We use MNIST to demonstrate the representation capabilities of the model, and its derived features (coefficients) for classification.