Reviewerly/exHarmony huggingface dataset:
DAMBNFT: Document Authentication Model through Blockchain and Non-fungible Tokens

Hard copies of documents can easily be forged, resulting in the decrease of credibility of issuing institutions and unfair use of forged documents by certain individuals as well. The model that we propose aims to authenticate documents through the use of blockchain technology, non-fungible tokens, and interplanetary file system. When a document is stored on the blockchain, a non-fungible token is created, which contains the unique address of the issuing institution and the hash of the document itself. The ownership of this token is then transferred to the document holder by the corresponding issuing authority. In this way, when someone wants to verify the authenticity of the document, they can use the address mentioned in the token to trace back the creator. If the document’s hash differs from the one stored in the token, we know that the document has been altered. Even when unauthorized users are successful in adding the forged documents to the blockchain, they will not have the same unique signature as that of the authorized institution. The proposed model allows autonomous authentication of documents using public blockchain technology.
A Wind Tunnel Setup for Fluid-Structure Interaction Measurements Using Optical Methods

The design of rotor blades is based on information about aerodynamic phenomena. An important one is fluid-structure interaction (FSI) which describes the interaction between a flexible object (rotor blade) and the surrounding fluid (wind). However, the acquisition of FSI is complex, and only a few practical concepts are known. This paper presents a measurement setup to acquire real information about the FSI of rotating wind turbines in wind tunnel experiments. The setup consists of two optical measurement systems to simultaneously record fluid (PIV system) and deformation (photogrammetry system) information in one global coordinate system. Techniques to combine both systems temporally and spatially are discussed in this paper. Furthermore, the successful application is shown by several experiments. Here, different wind conditions are applied. The experiments show that the new setup can acquire high-quality area-based information about fluid and deformation.
Orthonormal Matrix Codebook Design for Adaptive Transform Coding

We present a novel algorithm for designing a transform codebook for adaptive transform coding a non-stationary source, where the codebook contains a set of or-thonormal transform matrices. The non-stationary source is modeled by a block-wise (locally) stationary process so that all vectors in a given block can be coded using a single transform-matrix chosen from the codebook. The transform-matrix codebook is designed such that the mean-square error (MSE) of coding locally stationary blocks, averaged over the ensemble of blocks (AMSE) is minimized. First, a sequence of training vectors from the non-stationary source (such as blocks of pixels extracted from images) is segmented into locally stationary blocks of vectors. Our design algorithm starts with an initial codebook and iteratively updates the codebook for the training set, similar to the well-known generalized Lloyd algorithm for vector quantizer design. Each iteration consists of two steps: 1) the training set is partitioned into subsets by encoding the training set using the current transform codebook, and 2) the code-book is updated by computing a centroid transform-matrix for each subset. A major difference compared to the standard Lloyd algorithm however is the requirement to enforce an orthonormality constraint on the matrices in the codebook during the computation of centroids. This is accomplished by mapping the constrained problem in Euclidean space to an unconstrained problem on the Stiefel manifold. Gradient-descent on the Stiefel manifold is then used to compute the centroids. Essential to the implementation of manifold gradient-descent is an expression for the AMSE which is differentiable with respect to the transform matrix. Towards this end, we present two expressions whose matrix derivatives can be analytically obtained: 1) an approximate high-rate expression assuming Gaussian distributed source vectors, and 2) an exact finite-rate expression assuming Laplace distributed transform coefficients.
On different variants of the Burrows-Wheeler-Transform of string collections

The extended Burrows- Wheeler- Transform (eBWT), introduced by Mantaci et al. [Theor. Comput. Sci., 2007], is a generalization of the Burrows-Wheeler-Transform (BWT) to multisets of strings. Similarly to the classic BWT, the eBWT consists of one string, which is a permutation of the characters of all the input strings. A number of tools are available that compute the BWT of string collections; however, the data structures they generate in all but one case differ from the one originally defined, as well as from each other.
Cloud computing applications in library services of Bangladesh: a study on librarians’ perceptions

Purpose This study aims to find out the current state of cloud-based library services in selected academic and special libraries of Bangladesh. The study also explored some pertinent problems faced by librarians in implementing this new technology in library services and activities. Design/methodology/approach The study used a mixed-method approach with a range of exploratory research questions. Purposive sampling technique was used to select the libraries. A set of structured questionnaires was designed to address the objectives of the study. For in-depth analysis, short interviews with the library professionals were also conducted. Findings Only four libraries made use of cloud computing services, and they maintained a partial cloud environment. Most responding librarians treated cloud computing as a dynamically scalable infrastructure for applications, data and file storage with charges imposed. Most of the respondents (90%) preferred cloud computing for “Software-as-a-Service”. Due to the high subscription rate (70%), unreliable online payment system (60%) and trading system (60%), librarians treated cloud computing as a burden in providing library services and activities. Respondent librarians were also interviewed for exploring their views on cloud computing in library services. For adopting cloud computing in library services of Bangladesh, “sufficient financial allocation” and “organizations’ positive decision over cloud computing” were mentioned as challenging tasks by the respondents. Finally, the study suggested some solutions to the problem and proposed a model for a cloud-based library system that can be replicated in libraries and information centres of other developing countries too. Originality/value This paper encapsulates the overall scenario of cloud-based library activities and services in Bangladesh. Based on the available secondary sources, no empirical study on librarians’ perceptions of cloud computing in library services of Bangladesh has been carried out before this study. Thus, this is perhaps the first attempt to quantitatively measure cloud-based services in Bangladeshi libraries.
Deployable and Continuable Meta-learning-Based Recommender System with Fast User-Incremental Updates

User cold-start is a major challenge in building personalized recommender systems. Due to the lack of sufficient interactions, it is difficult to effectively model new users. One of the main solutions is to obtain an initial model through meta-learning (mainly gradient-based methods) and adapt it to new users with a few steps of gradient descent. Although these methods have achieved remarkable performance, they are still far from being usable in real-world applications due to their high-demand data processing, heavy computational burden, and inability to perform effective user-incremental update. In this paper, we propose a d eployable and c ontinuable m eta-learning-based r ecommendation (DCMR) approach, which can achieve fast user-incremental updating with task replay and first-order gradient descent. Specifically, we introduce a dual-constrained task sampler, distillation-based loss functions, and an adaptive controller in this framework to balance the trade-off between stability and plasticity in updating. In summary, DCMR can be updated while serving new users; in other words, it learns continuously and rapidly from a sequential user stream and is able to make recommendations at any time. The extensive experiments conducted on three benchmark datasets illustrate the superiority of our model.
Deep Dilated Convolutional Neural Network for Crowd Density Image Classification with Dataset Augmentation for Hajj Pilgrimage

Almost two million Muslim pilgrims from all around the globe visit Mecca each year to conduct Hajj. Each year, the number of pilgrims grows, creating worries about how to handle such large crowds and avoid unpleasant accidents or crowd congestion catastrophes. In this paper, we introduced deep Hajj crowd dilated convolutional neural network (DHCDCNNet) for crowd density analysis. This research also presents augmentation technique to create additional dataset based on the hajj pilgrimage scenario. We utilized a single framework to extract both high-level and low-level features. For creating additional dataset we divide the process of images augmentation into two routes. In the first route, we utilized magnitude extraction followed by the polar magnitude. In the second route, we performed morphological operation followed by transforming the image into skeleton. This paper presented a solution to the challenge of measuring crowd density using a surveillance camera pointed at a distance. An FCNN-based technique for crowd analysis is included in the proposed methodology, particularly for classifying crowd density. There are several obstacles in video analysis when there are a large number of pilgrims moving around the tawaf area, with densities of between 7 and 8 per square meter. The proposed DHCDCNNet method has achieved accuracy of 97%, 89% and 100% for the JHU-CROWD dataset, the UCSD dataset and the proposed Hajj-Crowd dataset, respectively. The proposed Hajj-Crowd dataset, the UCSD dataset, and the JHU-CROW dataset all had accuracy of 98%, 97% and 97%, respectively, using the VGGNet approach. Using the ResNet50 approach, the proposed Hajj-Crowd dataset, the UCSD dataset, and the JHU-CROW dataset all had an accuracy of 99%, 91% and 97%, respectively.
Blink: Lightweight Sample Runs for Cost Optimization of Big Data Applications

Distributed in-memory data processing engines accelerate iterative applications by caching datasets in memory rather than recomputing them in each iteration. Selecting a suitable cluster size for caching these datasets plays an essential role in achieving optimal performance. We present Blink, an autonomous sampling-based framework, which predicts sizes of cached datasets and selects optimal cluster size without relying on historical runs. We evaluate Blink on iterative, real-world, machine learning applications. With an average sample runs cost of 4.6% compared to the cost of optimal runs, Blink selects the optimal cluster size, saving up to 47.4% of execution cost compared to average cost .
Wireless Sensor Technology-Based Physical Education Teaching Evaluation

A wireless sensor network (WSN) is a group of geographically scattered and specialized sensors to monitor and record variables related to environmental and storing the obtained data in a vital location. These networks have applications and can be utilized in different research domains including physical education where error prediction is assumed as one of the core issues. Thus, careful attention is required from the researcher to provide reliable and accurate prediction models. Thus, aiming the shortage of large prediction error in the physical education evaluation, which is based on the BP neural network and wireless sensor technology, a combination of AFP and questionnaire survey method is proposed in order to improve the accuracy and predictability of evaluation, according to the characteristics of different evaluation subjects. We select the evaluation index system as the input of wireless sensor technology and then use the principle of genetic algorithm to select the optimal individual and optimize the initial parameters of wireless sensor technology to establish the evaluation model of physical education quality. Through the training and testing of sample data, it is shown that the model greatly improves the accuracy of physical education quality evaluation and has a good application prospect in physical education evaluation.
NESC: Robust Neural End-2-End Speech Coding with GANs

Neural networks have proven to be a formidable tool to tackle the problem of speech coding at very low bit rates. However, the design of a neural coder that can be operated robustly under real-world conditions remains a major challenge. Therefore, we present Neural End-2-End Speech Codec (NESC) a robust, scalable end-to-end neural speech codec for high-quality wideband speech coding at 3 kbps. The encoder uses a new architecture configuration, which relies on our proposed Dual-PathConvRNN (DPCRNN) layer, while the decoder architecture is based on our previous work Streamwise-StyleMelGAN. Our subjective listening tests on clean and noisy speech show that NESC is particularly robust to unseen conditions and signal perturbations.
Adaptive Image Reconstruction for Defense Against Adversarial Attacks

Adversarial attacks can fool convolutional networks and make the systems vulnerable to fraud and deception. How to defend against malicious attacks is a critical challenge in practice. Adversarial attacks are often conducted by adding tiny perturbations on images to cause network misclassification. Noise reduction can defend the attacks; however, it is not suited for all the cases. Considering that different models have different tolerance abilities on adversarial attacks, we develop a novel detecting module to remove noise by adaptive process and detect adversarial attacks without modifying the models. Experimental results show that by comparing the classification results on adversarial samples of MNIST and two subclasses of ImageNet datasets, our models can successfully remove most of the noise and obtain detection accuracies of 97.71% and 92.96%, respectively. Furthermore, our adaptive module can be assembled into different networks to achieve detection accuracies of 70.83% and 71.96%, respectively, on the white-box adversarial attacks of ResNet18 and SCD01MLP images. The best accuracy of 62.5% is obtained for both networks when dealing with the black-box attacks.
The materialities and embodiments of mundane software: exploring how apps come to matter in everyday life

Purpose In this article, the authors aim to explore mobile apps as both mundane and extraordinary digital media artefacts, designed and promoted to improve or solve problems in people's lives. Drawing on their “App Stories” project, the authors elaborate on how the efficiencies and affordances credited to technologies emerge and are performed through the specific embodied practices that constitute human–app relationships. Design/methodology/approach The project involved short written accounts in an online survey from 200 Australian adults about apps. Analysis was conducted from a sociomaterial perspective, surfacing the emotional and embodied responses to and engagements with the apps; the relational connections described between people and their apps or with other people or objects; and what the apps enabled or motivated people to do. Findings Findings point to three salient concerns about apps: (1) the need for efficiency; (2) the importance and complexity of human relationships and maintaining these connections; and (3) the complex relationships people have with their bodies. These concerns are expressed through themes that reflect how everyday efficiencies are produced through human–app entanglements; apps as relational agents; apps' ability to know and understand users; and future app imaginaries. Originality/value This project explores the affective and embodied dimensions of app use and thinks through the tensions between the extraordinary and mundane dimensions of contemporary techno-social landscapes, reflecting on how apps “matter” in everyday life. Our analysis surfaces the active role of the body and bodily performances in the production of app efficiencies and underlines the ways mobile apps are always situated in relation to other media and materialities.
Enhancing Burp Suite with Machine Learning Extension for Vulnerability Assessment of Web Applications

Today’s web represents the most extensive engineered system ever created by humankind. Web security is critical to web application providers and end-users. Burp Suite is established as a state-of-the-art and fully featured set of tools for web vulnerability scanners. This paper presents a novel approach using state of the art Machine Learning algorithms applied to the Burp Suite extension. These algorithms were used to scan for: SQL injection, Cross-Site Request Forgery, and XML External Entity vulnerabilities in university web applications. The results show that the best algorithm is Long Short-Term Memory and that the targeted website is safe to use.
Attack-Resilient Blockchain-Based Decentralized Timed Data Release

Timed data release refers to protecting sensitive data that can be accessed only after a pre-determined amount of time has passed. While blockchain-based solutions for timed data release provide a promising approach for decentralizing the process, designing a reliable and attack-resilient timed-release service that is resilient to malicious adversaries in a blockchain network is inherently challenging. A timed-release service on a blockchain network is inevitably exposed to the risk of post-facto attacks where adversaries may launch attacks after the data is released in the blockchain network. Existing incentive-based solutions for timed data release in Ethereum blockchains guarantee protection under the assumption of a fully rational adversarial environment in which every peer acts rationally. However, these schemes fail invariably when even a single participating peer node in the protocol starts acting maliciously and deviates from the rational behavior. In this paper, we propose an attack-resilient and practical blockchain-based solution for timed data release in a mixed adversarial environment, where both malicious adversaries and rational adversaries exist. The proposed mechanism incorporates an effective decentralized reputation model to evaluate the behaviors of the peer in the network. Based on the reputation model, we design a suite of novel reputation-aware timed-release protocols that effectively handles the mixed adversarial environment consisting of both malicious adversaries and rational adversaries. We implement a prototype of the proposed approach using Smart Contracts and deploy it on the Ethereum official test network, Rinkeby. For extensively evaluating the proposed techniques at scale, we perform simulation experiments to validate the effectiveness of the reputation-aware timed data release protocols. The results demonstrate the effectiveness and strong attack resilience of the proposed mechanisms and incurs only a modest gas cost.
Federated learning for heart segmentation

Training deep learning models for medical imaging requires access to large volumes of sensitive patient data. To this end, the models are generally trained on centralized, de-identified databases that are hard to collect because of privacy requirements. Federated learning proposes an alternative approach, in which a coalition of hospitals collaboratively trains a central model without exchanging any clinical data. This paper explores the combination of federated learning with U-Net models, and applies it to the task of image segmentation of the heart. A variant of federated learning referred to as federated equal-chances that improves segmentation performance on unbalanced datasets is introduced as well.
Clock-G: A temporal graph management system with space-efficient storage technique

IoT applications can be naturally modeled as a graph where the edges represent the interactions between devices, sensors, and their environment. Thing'in <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://www.thinginthefuture.com/ is a platform, initiated by Orange <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Orange is a French multinational telecommunication operator. The platform manages a graph of millions of connected and non-connected objects using a commercial graph database. The graph of Thing'in is dynamic because loT devices create temporary connections between each other. Analyzing the history of these connections paves the way to new promising applications such as object tracking, anomaly detection, and forecasting the future behavior of devices. However, existing com-mercial graph databases are not designed with native temporal support which limits their usability in such use cases. In this paper, we discuss the design of a temporal graph management system Clock-G and introduce a new space-efficient storage technique δ-Copy+Log, Clock-G is designed by the devel-opers of the Thing'in platform and is currently being deployed into production. It differentiates from existing temporal graph management systems by adopting the δ-Copy+Log technique. This technique targets the mitigation of the apparent trade-off between the conflicting goals of the reduction of space usage and acceleration of query execution time. Our experimental results demonstrate that the δ-Copy+Log presents an overall better performance as compared to traditional storage methods in terms of space usage and query evaluation time.
Exploring Mobile Co-design in the Context of Use Continuous Elicitation and Evaluation of Design Suggestions

This study explores a method for the co-design of mobile applications in the context of use. In 36 sessions with future users, synchronous co-design of a mobile navigation application was conducted in the intended use environment – a hospital – using an interactive Wizard-of-Oz-controlled prototype. The results show that co-design in the intended use environment contributes to the elicitation of design suggestions. Concerning the co-design method, the results show that by using interactive prototyping the user is actively involved as a co-designer, which empowers the user and enables the continuous evaluation of design suggestions.
Case Studies to Enhance Collectively Sharing Human Hearing: Ambient Sounds Memory and Mindful Speaker

Our everyday life is filled with a variety of serendipitous and beautiful sounds that are both natural and artificial. We consciously also unconsciously enjoy these sounds. The sounds are one of the most important factor in our daily life, thus it is a promising direction to use such sounds to develop innovative digital services. In this paper, after presenting a brief overview of our digital platform named CollectiveEars, we show two case studies to use CollectiveEars as an underlying platform to exploit to use the captured diverse sounds in the world. The first case study is named Ambient Sounds Memory and the second case study is named Mindful Speaker. These case studies show the feasibilities to use CollectiveEars to develop future innovative digital services.