Reviewerly/exHarmony huggingface dataset:
Patch-Based Image Hallucination for Super Resolution With Detail Reconstruction From Similar Sample Images

Image hallucination and super-resolution have been studied for decades, and many approaches have been proposed to upsample low-resolution images using information from the images themselves, multiple example images, or large image databases. However, most of this work has focused exclusively on small magnification levels because the algorithms simply sharpen the blurry edges in the upsampled images - no actual new detail is typically reconstructed in the final result. In this paper, we present a patch-based algorithm for image hallucination which, for the first time, properly synthesizes novel high frequency detail. To do this, we pose the synthesis problem as a patch-based optimization which inserts coherent, high-frequency detail from contextually-similar images of the same physical scene/subject provided from either a personal image collection or a large online database. The resulting image is visually plausible and contains coherent high frequency information. We demonstrate the robustness of our algorithm by testing it on a large number of images and show that its performance is considerably superior to all state-of-the-art approaches, a result that is verified to be statistically significant through a randomized user study.
Water Demand Prediction for Housing Apartments Using Time Series Analysis

Water management has always been a topic of serious discussion since infrastructure, rural, and industrial development flourished. Due to the depleting water resources, this is now even a bigger challenge. So, here is developed an IoT-based water management system where ultrasonic sensors are employed for predicting the depth of water in the tank and accordingly pumping the water to the sub tank of the apartment. In addition, the time series analysis Auto Regressive Integrative Moving Average (ARIMA) and Least Square Linear Regression (LSLR) algorithms were employed and compared for predicting the water demand for next six months based on the historical water consumption record of the main reservoir/tank. The information on the amount of water consumed from the main reservoir is pushed to the cloud and to the mobile application developed for utilities. The purpose is to access the water consumption pattern and predict water demand for the next six months from the cloud.
A Systematic Evaluation of Wavelet-Based Attack Framework on Random Delay Countermeasures

Random delay countermeasure is a commonly used defense against side-channel attacks, which brings certain interference and disturbance to those calculation sequences in the time domain. Data alignment and frequency attack are considered as typical techniques to counteract the random delay countermeasure. However, these attacks have limitations from the perspectives of both efficiency and performance. In comparison, facing those delays, wavelet analysis is considered as a more efficient technique due to its detailed and comprehensive interpretation of a signal. This paper applies different wavelet techniques to three attack components: noise reduction, trace alignment and key extraction. For the first time, the unified wavelet-based attack framework against random delays is proposed where wavelet analysis is fully applied in the entire attack life cycle. In particular, a novel method of trace alignment at the wavelet level is proposed in this framework, which is based on wavelet pattern detection to synchronize the misaligned power traces. Most importantly, the overall wavelet-based attack framework is systematically evaluated over three random delay strategies, after the respective contribution of each component is investigated through a series of comparative experiments. Experimental results show that the performance of the wavelet-based attack framework is significantly improved compared to standard attack procedures and frequency ones, which can be regarded as a unified and effective solution to conquer random delay countermeasures.
A Framework for Website Security Assessment

Nowadays, the Internet plays a crucial role in our society. Among Internet services, web-based services are very popular that become the target of security attacks. Hence, securing websites and connection to the users is important. If we own or manage a website, we certainly concern about how secure it is. For assessing the security level of a website, we usually take some action, including testing the website using security scanning tools. Unfortunately, most of scanning tools have limitations and need to be updated frequently for new vulnerabilities. Using only one scanning tool is sometime not enough to determine security level of a website. In this paper we propose a framework supporting website security assessment. The idea of this framework is to integrate different scanning tools into the framework. We then write a program to implement this framework with a real website. We guide the users how to add a new scanning tool to this framework, manage it and generate a final report.
University: Toward “Beyond the Horizon” Strategy

The article shows that the entrenched orientation of higher education towards the demands of the labor market requires rethinking and adjustment, since in modern conditions it does not guarantee the effectiveness of university’s activities and the ability to meet the challenges of the future. The real basis and guarantee of the university’s success is now the “synchronization” of its development strategy with the historical and epistemological trends in the development of knowledge, its functions and forms of being. In this context, the role and place of techno-science at university and university processes – research, design, and educational – is becoming especially important. It is argued that the current trends in the development of knowledge and science bring to life (and have already provoked) a new “projective-personal” paradigm of education. It is not focused on mastering the maximum academic knowledge (as it has been so far), but on individualizing the trainee’s educational trajectory and mastering the methods of synthesizing knowledge and project activities.
Breaking Text-Based CAPTCHA with Sparse Convolutional Neural Networks

CAPTCHA is an automated test designed to check if the user is human. Though other approaches are explored (such as object recognition), the text-based CAPTCHA is still the main test used by many web service providers, to separate human users from bots. In this paper, a sparse Convolutional Neural Network (CNN) to break text-based CAPTCHA is proposed. Unlike previous CNN solutions, which mainly use fine-tuning and transfer learning from pre-trained models, the proposed framework does not require a pre-trained model. The sparsity constraint deactivates connections between neurons in the CNN fully connected layers that leads to improved accuracy compared to the baseline approach. Visualization of the spatial distribution of neuron activity shed light on the internal learning and the effect of the sparsity constraint.
Impact of Pre-Processing on Recognition of Cursive Video Text

Recognition of text appearing in videos offers a number of interesting applications including retrieval systems, generation of user alerts on keywords and news summarization systems. Thanks to the recent advancements in deep learning, high text recognition rates have been reported in the recent years. An important step in training such systems is the pre-processing of images for effective feature learning and classification. This study investigates the impact of pre-processing on recognition of cursive video text using Urdu as a case study. The recognition engine relies on a combination of convolutional and long short-term memory networks followed by a connectionist temporal classification layer for sequence alignment. The system is fed with gray scale text line images directly as well as by segmenting the text from background using various thresholding techniques. Experimental study on a dataset of 12,000 text lines in cursive Urdu text reveals that appropriately pre-processing the text line images significantly improves the recognition rates.
A robust and forensic transform for copy move digital image forgery detection based on dense depth block matching

Copy-move forgery is one of the most popular tampering artefacts in digital images. However, tampering effect in digital images makes the authentication of the processing as untrustworthy. In this paper, a combination of Fourier-Mellin and Zernike moments (FMZM) Transform is proposed which detects the copy-move region with high-speed and low-computational complexity. Here, initially an image is segmented into various blocks using marker controlled watershed management and from that proposed FMZM feature extraction is used which detects duplication. The detected regions are matched with the Dense Depth Reconstruction based lexicographically sorting. Finally, tampered outliers presented at the data are removed through RANSAC (RANdom Sample Consensus) algorithm, in which removed false matches are verified with the morphological operators. The efficiency of proposed method is measured by various performance metrics and this method earned up to 97.56%, 99.98%, and 97.12% for precision, recall, and F1-score performance, respectively.
Continuous vocoder applied in deep neural network based voice conversion

Abstract In this paper, a novel vocoder is proposed for a Statistical Voice Conversion (SVC) framework using deep neural network, where multiple features from the speech of two speakers (source and target) are converted acoustically. Traditional conversion methods focus on the prosodic feature represented by the discontinuous fundamental frequency (F0) and the spectral envelope. Studies have shown that speech analysis/synthesis solutions play an important role in the overall quality of the converted voice. Recently, we have proposed a new continuous vocoder, originally for statistical parametric speech synthesis, in which all parameters are continuous. Therefore, this work introduces a new method by using a continuous F0 (contF0) in SVC to avoid alignment errors that may happen in voiced and unvoiced segments and can degrade the converted speech. Our contribution includes the following. (1) We integrate into the SVC framework the continuous vocoder, which provides an advanced model of the excitation signal, by converting its contF0, maximum voiced frequency, and spectral features. (2) We show that the feed-forward deep neural network (FF-DNN) using our vocoder yields high quality conversion. (3) We apply a geometric approach to spectral subtraction (GA-SS) in the final stage of the proposed framework, to improve the signal-to-noise ratio of the converted speech. Our experimental results, using two male and one female speakers, have shown that the resulting converted speech with the proposed SVC technique is similar to the target speaker and gives state-of-the-art performance as measured by objective evaluation and subjective listening tests.
A Blockchain Based Secure E-Commerce Transaction System

Traditional centralized e-commerce transaction systems have security problems such as untrustworthy third-party services, high cost of keeping funds, easy leakage of users’ privacy. This paper designs and implements a decentralized secure e-commerce transaction system based on blockchain technology called “BSETS”. The system adopts B/S and distributed architecture, and all the transactions are completed through blockchains to ensure security and convenience. The key techniques include user information encryption based on MD5 abstract algorithm to ensure anonymity; transaction process management based on smart contract to ensure security; transaction confirmation based on user’s private key signature to ensure the non-repudiation of transactions. The blockchain can only be appended and read to protect the system from tampering. The use of smart contract realizes the decentralization of transactions and solves the problem of third-party untrustworthiness. Finally, the function and performance experiments verify that BSETS has completed transaction function and has good operation efficiency on the basis of ensuring information security.
Exploring Mobile Biometric Performance Through Identification of Core Factors and Relationships

Biometrics, as a form of authentication, has existed for several decades and shows no signs of slowing down. Extensive research has been carried out into enhancing systems either by improving error rates or ease of adoption by examining barriers to use. In this paper, we investigate factors of a biometric system that is likely to affect performance, in particular, focusing on mobile device implementation. By surveying the area, we have identified seven core factors that help to form a clearer understanding of what changes the performance of a system. These seven factors are Users, Modality, Environments, Diversity of Scenarios, System Constraints, Hardware and Algorithms and form `The Core Factors Affecting Mobile Biometric Performance'. We utilise these factors to illustrate the practicalities of mobile implementations and indicate future considerations to explore future performance enhancements and provide an informative overview to developers, implementers and testers of biometrics systems, enabling the binning of performance alterations within one of these factors.
Ubiquitous Computing and Ambient Intelligence—UCAmI

The Ubiquitous Computing (UC) idea envisioned by Weiser in 1991 [...].
Instance Generation via Generator Instances

Access to good benchmark instances is always desirable when developing new algorithms, new constraint models, or when comparing existing ones. Hand-written instances are of limited utility and are time-consuming to produce. A common method for generating instances is constructing special purpose programs for each class of problems. This can be better than manually producing instances, but developing such instance generators also has drawbacks. In this paper, we present a method for generating graded instances completely automatically starting from a class-level problem specification. A graded instance in our present setting is one which is neither too easy nor too difficult for a given solver. We start from an abstract problem specification written in the Essence language and provide a system to transform the problem specification, via automated type-specific rewriting rules, into a new abstract specification which we call a generator specification. The generator specification is itself parameterised by a number of integer parameters; these are used to characterise a certain region of the parameter space. The solutions of each such generator instance form valid problem instances. We use the parameter tuner irace to explore the space of possible generator parameters, aiming to find parameter values that yield graded instances. We perform an empirical evaluation of our system for five problem classes from CSPlib, demonstrating promising results.
Hardware-Software Co-design Approach for Deep Learning Inference

Hardware implementation of machine learning algorithms is a promising solution for higher performance and improved throughput. However, the most challenging task lies in the design of power, energy, and area efficient architectures that can be deployed in tightly constrained embedded systems. In this paper, we investigate the concept of intermediate exit branches in a CNN architecture aiming to reduce latency and energy. We propose a heterogeneous hardware-software based architecture for performing inference in feed-forward neural networks which is capable of early exiting through intermediate exits according to the difficulty level of the input. Our proposed architecture can be reconfigured to support different kernel sizes and is fully scalable. Our experiments show an increase in classification accuracy by 0.27% for MNIST dataset (LeNet) with 2.2× decrease in average computations and 1.07% for Cifar-10 dataset (AlexNet) with 1.55× decrease in average computations along with 1.39× reduction in energy consumption for LeNet architecture.
The DAta Protection REgulation COmpliance Model

Understanding whether certain technical measures comply with the General Data Protection Regulation's (GDPR's) principles is complex legal work. This article describes a model of the GDPR that allows for a semiautomatic processing of legal text and the leveraging of state-of-the-art legal informatics approaches, which are useful for legal reasoning, software design, information retrieval, or compliance checking.
Reversible Data Hiding Scheme Based on Quad-Tree and Pixel Value Ordering

This study proposes a reversible data hiding (RDH) scheme based on pixel value ordering (PVO). RDH schemes based on PVO technique generally realize the prediction procedure by employing the sorted pixels in a block-wise manner, and then embedding secret data by expanding the prediction errors. Since these operations are carried out based on the block, and pixel intensity has spatial correlation such that the neighboring pixels have similar values, the block patterns and sizes certainly affect the embedding performance of the PVO-based RDH method. The original PVO-based RDH schemes use fixed block size. However, due to the differences in image complexity, fixed-size blocks have many limitations. Thus far, several methods that break the limitations have been proposed, such as setting a threshold value, and determine the size from the complexity of the block, using sliding window instead of fixed blocks. These methods significantly enhanced the embedding performance of PVO-based RDH schemes, but this can still be improved. The proposed scheme introduces the quad-tree structure, which combines the advantages of several previously proposed methods to design a flexible block patterns, thus fully utilizing the characteristics of the image itself in embedding secret data. Experimental results indicate that the proposed scheme is flexible and better than previous approaches.
Utilizing Neural Networks for Fully Adaptive Radar

This paper discusses the use, of neural networks to replace the non-linear optimization at the core of the fully adaptive radar (FAR) framework. Replacement of the optimizer with the neural network reduces computational complexity, leading to faster run times. It also prepares the FAR framework for the future inclusion of learning and attention. A feedforward neural networks was trained using the Levenberg-Marquardt and Bayesian regularization algorithms along with the radial basis and generalized regression neural network architectures in order to perform parameter adaption based on the forward propagation of the prior probabilities of the target state (expected: target range, velocity and signal to noise ratio & the target range and velocity variance). The neural network was trained using data from simulated runs of the optimizer based FAR framework tracking a single target in range and Doppler. The trained neural network could approximate the optimization solutions with a lower cumulative optimization time, by an order of magnitude, and a better cumulative measurement cost. As such we concluded neural network could be used to replace the FAR framework optimizations.
An Algorithm for Extracting Shape Expression Schemas from Graphs

Unlike traditional data such as relational databases and XML documents, most of graphs do not have their own schema. However, schema is a concise representation of a graph, and if we can extract a "good" schema from a graph, we can take advantage of the extracted schema for effective graph data management. In this paper, we focus on Shape Expression Schemas (ShEx) and consider extracting ShEx schemas from RDF/graph data. To manage both efficiency and quality of extracted schema, our algorithm consists of two schema extraction steps: (i) edge-label based clustering and (ii) type-merge method for target nodes of outgoing edges. We made preliminary experiments, which result suggests that our algorithm can extract ShEx schemas appropriately.
Professional Development of Educational Leaders

The role of educational leaders has been undergoing many changes in the era of globalization due to diverse needs and expectations of the stakeholders of education.This increases the need for professional development of educational leaders to fulfill their roles.Educational leaders have high impact on shaping school culture, school improvement, student learning, and achievement, so that their professional development is critical to their continued success as leaders.Educational leaders who participate in professional development programs update and extend their knowledge and improve their performance on the job by applying new knowledge and skills to implement the best educational practices in schools.This book chapter intends to provide reader with a comprehensive overview of expected responsibilities and features of school leaders in the context of the different models of educational leadership and also presents a review of the articles about the need for professional development of educational leaders and finally gives professional development ideas which will help school leaders continue to strengthen their practices throughout their career.
A security modeling and verification method of embedded software based on Z and MARTE

The existing modeling and verification methods for embedded software are insufficient towards the increasingly prominent security requirements. In this paper, aiming at high security requirements of embedded software, a security modeling and verification framework of embedded software based on semi-formal and formal methods is proposed. An extensible security model ZMsec (Z-MARTE security model), which extends Z with elements of MARTE (Modeling and Analysis of Real-Time and Embedded systems) and FSA (Finite State Automata), is presented to describe three dimensions of software: security usecases, static structures and dynamic behaviors. Further on, this paper designs ZMsecTL (ZMsecTemporal Logic) formulas to describe security properties, and proposes ZMCA (ZMsec Model Checking Algorithm), a security model checking algorithm on ZMsec model, to verify security properties in ZMsecTL formulas. In order to provide an intuitive state transition diagram for model checking, a depth-first ZMsecSD (ZMsecState Diagram) generation algorithm is designed. Drawing on the abstract framework, this paper develops a prototype implementation of ZMV (ZMsec Modeling and Verification tool), a tool for ZMsec integrated with modeling and verification phases. We finally discuss an embedded software example with ZMV, which illustrates and validates the security modeling and verification method proposed in this paper.
Study of diffraction data sets using factor analysis: a new technique for comparing mineralogical and geochemical data and rapid diagnostics of the mineral composition of large collections of rock samples

This paper presents an example of comparing geochemical and mineralogical data by means of the statistical analysis of the X-ray diffraction patterns and the chemical compositions of bulk samples. The proposed methodology was tested on samples of metasomatic rocks from two geologically different objects. Its application allows us to mathematically identify all the main, secondary and some accessory minerals, to qualitatively estimate the contents of these minerals, as well as to assess their effect on the distribution of all petrogenic and investigated trace elements in a short period of time at the earliest stages of the research. We found that the interpretation of the results is significantly influenced by the number of samples studied and the quality of diffractograms.