Reviewerly/exHarmony huggingface dataset:
High-precision Registration Algorithm and Parallel Design Method for High-Resolution Optical Remote Sensing Images

In optical remote sensing image reconstruction, image registration is an important issue to address in order to ensure satisfactory reconstruction performance. In this study, a multi-frame image registration algorithm for high-resolution images and its parallel design method are proposed. The algorithm realizes an improved feature point detection method based on an adaptive gradient bilateral tensor filter and carries out weighted Gaussian surface sub-pixel interpolation to obtain more accurate corner positions, which better guarantees the registration accuracy. On this basis, multi-scale expansion is carried out to generate descriptors for image registration. In addition, the operation-level parallel analysis and design are carried out on a GPU platform based on compute unified device architecture (CUDA), and the memory model of the GPU is utilized reasonably. The task-level parallel analysis and design are carried out based on the GPU stream model. Moreover, based on the open multi-processing (OpenMP) platform, a multi-core CPU carries out parallel design at the operation level and task level, which realizes post-processing operations such as optical remote sensing images loading, accurate matching, and coordinate mapping, thereby effectively improving registration speed. Compared with feature point algorithms and deep learning algorithm, our algorithm and its parallel design significantly improve the registration accuracy and speed of high-resolution optical remote sensing images.
TSFnet: a new two-stream fusion framework for scene text detection

Abstract In order to solve the problem of algorithm robustness caused by scale change and imbalanced distribution of classes in the scene text detection task, we propose a new two-stream fusion framework TSFnet. It is constructed by the Detection Stream, the Judge Stream and the Merge output algorithm. In the Detection Stream, we propose a loss balance factor (LBF), which is used to optimize region proposal network. Then, the Regression-net and the Segmentation-net are used to predict text global segmentation map and its corresponding coordinates probability score. In Judge Stream, we use the feature pyramid network to extract the Judge map. In the process, the LBF is calculated to support the Detection Stream. Finally, we design a novel algorithm to fuse the outputs of the two-stream, and the precise position of the text is localized. Extensive experiments are conducted on the ICDAR 2015 and the ICDAR2017-MLT standard datasets. The results demonstrate that the framework performance is comparable with the sate-of-the-art approaches.
Machine learning interpretability through contribution-value plots

The field of explainable artificial intelligence aims to help experts understand complex machine learning models. One key approach is to show the impact of a feature on the model prediction. This helps experts to verify and validate the predictions the model provides. However, many challenges remain open. For example, due to the subjective nature of interpretability, a strict definition of concepts such as the contribution of a feature remains elusive. Different techniques have varying underlying assumptions, which can cause inconsistent and conflicting views. In this work, we introduce Local and Global Contribution-Value plots as a novel approach to visualize feature impact on predictions and the relationship with feature value. We discuss design decisions, and show an exemplary visual analytics implementation that provides new insights into the model.
Feature engineering combined with 1-D convolutional neural network for improved mortality prediction

Abstract Objectives The appropriate care for patients admitted in Intensive care units (ICUs) is becoming increasingly prominent, thus recognizing the use of machine learning models. The real-time prediction of mortality of patients admitted in ICU has the potential for providing the physician with the interpretable results. With the growing crisis including soaring cost, unsafe care, misdirected care, fragmented care, chronic diseases and evolution of epidemic diseases in the domain of healthcare demands the application of automated and real-time data processing for assuring the improved quality of life. The intensive care units (ICUs) are responsible for generating a wealth of useful data in the form of Electronic Health Record (EHR). This data allows for the development of a prediction tool with perfect knowledge backing. Method We aimed to build the mortality prediction model on 2012 Physionet Challenge mortality prediction database of 4,000 patients admitted in ICU. The challenges in the dataset, such as high dimensionality, imbalanced distribution and missing values, were tackled with analytical methods and tools via feature engineering and new variable construction. The objective of the research is to utilize the relations among the clinical variables and construct new variables which would establish the effectiveness of 1-Dimensional Convolutional Neural Network (1-D CNN) with constructed features. Results Its performance with the traditional machine learning algorithms like XGBoost classifier, Light Gradient Boosting Machine (LGBM) classifier, Support Vector Machine (SVM), Decision Tree (DT), K-Neighbours Classifier (K-NN), and Random Forest Classifier (RF) and recurrent models like Long Short-Term Memory (LSTM) and LSTM-attention is compared for Area Under Curve (AUC). The investigation reveals the best AUC of 0.848 using 1-D CNN model. Conclusion The relationship between the various features were recognized. Also, constructed new features using existing ones. Multiple models were tested and compared on different metrics.
Modeling of Distributed Ledger Deployment View

The Distributed Ledger Technology (DLT) is a peerto-peer model of sharing data among collaborating parties in a decentralized manner.An example of DLT is a blockchain where data form blocks in an append-only chain.Software architecture description usually comprises multiple views.The paper concentrates on the Deployment view of the DLT solution within the 1+5 architectural views model.The authors have proposed Unified Modeling Language (UML) extensibility mechanisms to describe the needed additional semantic notation to model deployment details.The paper covers both the network and node levels.The proposed stereotypes and tagged values have enriched UML Deployment diagram.We have gathered those modeling elements in dedicated UML Profile for Distributed Ledger Deployment.We have applied the profile to model Deployment view of a renewable energy management system that uses R3 Corda framework.The system records information about inbound and outbound energy to/from renewable energy grid.
Can mass-count syntax be derived from semantics?

Analysing aspects of how our brain processes language may provide, even before the language faculty is really understood, useful insights into higher order cognitive functions. We have taken initial steps in this direction, focusing on the mass-count distinction. The mass-count distinction relates to the countability or un-countability of nouns in terms of their syntactic usage. Our first results show that the mass-count distinction, across a number of natural languages, is far from bimodal, and exhibits in fact complex fuzzy relations between syntax and semantics. We then tried to test the ability of a standard, biologically plausible self-organising neural network to learn such associations between syntax and semantics. A neural network that expresses competition amongst output neurons with lateral inhibition can identify the basic classes of mass and count in the syntactic markers and produce a graded distribution of the nouns along the mass-count spectrum. The network however fails to successfully map the semantic classes of the nouns to their syntactic usage, thus corroborating the hypothesis that the syntactic usage of nouns in the mass-count domain is not simply predicted by the semantics of the noun.
Challenge of Anomaly Detection in IoT Analytics

Many studies applied anomaly detection technology to varied areas such as fraud detection for finance activities, fault detection in industrial systems, and so on. However, big data rises to the challenge of performing a large scale of anomaly analytics in IoT. In this paper, we adopt several methods to analyze a realworld dataset on anomalous events in paper and pulp industry. By experiments, we discuss the findings and illustrate the difficulty in identifying anomalies, which provide useful information for further study.
Vista

With the increasing prevalence and power of building IoT sensors and devices, there is a growing need for intuitive, accessible, and meaningful data visualization tools that are specific and tailored for building IoT. Existing tools that incorporate spatial contextualization are insulated to specific applications with predefined visualization goals. We present Vista, a front end tool, that presents dynamic data visualizations of building IoT data on an interface that allows for context and deeper evaluation of information. This framework proposes a method to transform any static building floor plan into a dynamic one with which one can create unique and intuitive data representations tailored to their own objectives. The modular design of this tool enables an authoring system flexible to heterogeneous data by separating the visual elements of the floor plan and the relevant data, enabling diverse visual presentations of various types of IoT information and inputs.
Modulation recognition based on spectral correlation function

As a very important inherent characteristic of modulated signals, Cyclostationarity can be exploited for various signal processing tasks such as detection, classification, especially for signal buried in noise or masked interference. In this paper, cyclostationarity of some typical digitally modulated signals are analyzed and Spectral correlation functions (SCF) of these signals are simulated. Based on two features extracted from cycle frequency profile of SCF, three common digitally modulated signals (BPSK, QPSK, MSK) are recognized with a decision tree classifier. The proposed scheme for modulation recognition is easy for implementation and doesn't require any prior knowledge such as carrier frequency or symbol rate. Simulations prove that the proposed scheme is able to achieve 90% recognition rate when signal to noise ratio (SNR) higher than -3dB.
Self-adaptive extreme learning machine-based prediction of roller length of hydraulic jump on rough bed

In this study, the roller length of hydraulic jumps occurring on rough beds is modeled using the Self-Adaptive Extreme Learning Machine (SAELM) method. For this purpose, the parameters influencing the roller length are specified and four different SAELM models are developed based on them. A superior model is also established by analyzing the modeling results. For the superior model, the statistical values of the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and correlation coefficient are calculated to be 1.720, 6.369 and 0.969, respectively. Also, the results of the SAELM superior model are compared with the Multilayer Perceptron Neural Network (MLPNN) and Support Vector Machine (SVM) methods. The analysis of the SVM, MLPNN and SVM models results reveals the effectiveness of the SAELM model. In this study, the uncertainty analysis of the SAELM, MLPNN and SVM models is also performed and the prediction error interval of 95% for the SAELM model is obtained which varies from −0.112 to +0.134.
High-Speed Hardware Implementation of PQC Algorithm LAC

As the computing power of quantum computers continues to improve, the security of the mathematical problems on current cryptographic algorithm used today will be challenged. It is necessary to formulate the standard of post-quantum cryptographic algorithm. LAC is one of 26 candidates for the Round 2 of NIST PQC contest. In this paper, we implement the high-speed anti-SPA hardware implementation of security level 1. Finally, since our work is the first implementation of LAC on Zynq-7000, there is no preliminary comparison, so we have a horizontal comparison with implementation of the same type of algorithm NewHope-512.
Usability Assessment on Symmetric Cryptography Learning Module

Teaching information security requires lecturer to introduce several tools to encourage students performing hands on practices in better way in order to enhance their understanding. In this study, a novel learning module as a tool for student to perform symmetric cryptography exercises is introduced. It is called NgeXTEA, a novel learning module to describe how a simple yet powerful symmetric cryptography called Extended Tiny Encryption Algorithm (XTEA) works. The main objective of the study is to conduct assessment from usability point of view on NgeXTEA learning module using System Usability Scale (SUS) method. Finally, SUS based assessment indicated that the learning module is considered useful to enhance students' understanding and skills on the given topic.
Cross-Jurisdictional Analysis of Damage Awards in Copyright Infringement Cases

Copyright infringement is a widespread phenomenon that produces massive financial losses to stakeholders. This article, based on an extensive examination of damage awards in copyright infringement cases from the United States, Canada, Singapore, and Italy, proposes a comprehensive cross-jurisdictional analysis. The analysis regards types, factors, methodologies, and arguments. The findings of this article can be used to improve the litigation of such cases, to elaborate educational materials, for professional programs or law school clinics, and to develop better prevention policies. The proposed improvements could lead to a more unified approach to damage award, increase the litigation outcome predictability, and better commensurate the award with the infringement. While this article considered only four jurisdictions, the findings can be of major interest to a global context.
Learning 3D Face Reconstruction with a Pose Guidance Network

We present a self-supervised learning approach to learning monocular 3D face reconstruction with a pose guidance network (PGN). First, we unveil the bottleneck of pose estimation in prior parametric 3D face learning methods, and propose to utilize 3D face landmarks for estimating pose parameters. With our specially designed PGN, our model can learn from both faces with fully labeled 3D landmarks and unlimited unlabeled in-the-wild face images. Our network is further augmented with a self-supervised learning scheme, which exploits face geometry information embedded in multiple frames of the same person, to alleviate the ill-posed nature of regressing 3D face geometry from a single image. These three insights yield a single approach that combines the complementary strengths of parametric model learning and data-driven learning techniques. We conduct a rigorous evaluation on the challenging AFLW2000-3D, Florence and FaceWarehouse datasets, and show that our method outperforms the state-of-the-art for all metrics.
Improvements on Easypaysy: The Bitcoin’s Layer-2 Accounts Protocol

Bitcoin employs multiple technologies from various fields of science and engineering to provide a solution for decentralized digital currency. However, it can be used as an infrastructure to build many other schemes. Protocols over the Bitcoin's blockchain use the power of decentralization to achieve interesting goals. These protocols often improve Bitcoin's properties or increase its capabilities.Easypaysy is one of these protocols that are considered as blockchain layer-2. The goal of Easypaysy is to add user-friendliness of having accounts to Bitcoin while maintaining the level of privacy it already provides. In this paper, two improvements over Easypaysy protocol are proposed. In the first one, we suggest a new type of payment that can offer deniable authentication property. The other proposal provides hierarchical account systems. We discuss the scenarios in which each one can be useful. Then, the security features of each proposed scheme will be explained.
Improving Emergency Healthcare Response using Real-Time Collaborative Technology

Harnessing geospatial technologies (GPS, GIS, internet mapping and remote sensing) can promote collaboration of humans, environmental, and healthcare resources-for real-time decision support in emergency healthcare scenarios. Oftentimes, high death rates are recorded by emergency rescue workers due to infrastructural deficit and sparsity of ambulatory services within the patients' location. Also, acute shortage of health personnel may pose great risk to patients requiring emergency healthcare services, as limited knowledge of healthcare data can prevent proactive, just-in-time response to emergency, and dearth of appropriate healthcare policies. This study therefore proposes a geospatial recommender framework that connects patients with healthcare providers and other relevant stakeholders; to exploit and filter in context, emergency healthcare information and knowledge-for enhanced response strategy that minimizes fatalities among patients. We examine case study data within the African context and discuss infrastructural challenges, opportunities and design implications, to demonstrate the feasibility of our framework. The immediate benefit of the framework is enhanced specialty care for home-bound patients or in emergency cases where distance appears prohibitive. Our strategy will certainly scale up complex healthcare interventions to large populations, as a new paradigm for evidence-based intervention with state-of-the-art technology for optimized services is guaranteed.
Improved Distance Functions for Instance-Based Text Classification

Text classification has many applications in text processing and information retrieval. Instance-based learning (IBL) is among the top-performing text classification methods. However, its effectiveness depends on the distance function it uses to determine similar documents. In this study, we evaluate some popular distance measures' performance and propose new ones that exploit word frequencies and the ordinal relationship between them. In particular, we propose new distance measures that are based on the value distance metric (VDM) and the inverted specific-class distance measure (ISCDM). The proposed measures are suitable for documents represented as vectors of word frequencies. We compare these measures' performance with their original counterparts and with powerful Naïve Bayesian-based text classification algorithms. We evaluate the proposed distance measures using the kNN algorithm on 18 benchmark text classification datasets. Our empirical results reveal that the distance metrics for nominal values render better classification results for text classification than the Euclidean distance measure for numeric values. Furthermore, our results indicate that ISCDM substantially outperforms VDM, but it is also more susceptible to make use of the ordinal nature of term-frequencies than VDM. Thus, we were able to propose more ISCDM-based distance measures for text classification than VDM-based measures. We also compare the proposed distance measures with Naïve Bayesian-based text classification, namely, multinomial Naïve Bayes (MNB), complement Naïve Bayes (CNB), and the one-versus-all-but-one (OVA) model. It turned out that when kNN uses some of the proposed measures, it outperforms NB-based text classifiers for most datasets.
Towards Automatic Image Exposure Level Assessment

The quality of acquired images can be surely reduced by improper exposures. Thus, in many vision-related industries, such as imaging sensor manufacturing and video surveillance, an approach that can routinely and accurately evaluate exposure levels of images is in urgent need. Taking an image as input, such a method is expected to output a scalar value, which can represent the overall perceptual exposure level of the examined image, ranging from extremely underexposed to extremely overexposed. However, studies focusing on image exposure level assessment (IELA) are quite sporadic. It should be noted that blind NR-IQA (no-reference image quality assessment) algorithms or metrics used to measure the quality of contrast-distorted images cannot be used for IELA. The root reason is that though these algorithms can quantify quality distortion of images, they do not know whether the distortion is due to underexposure or overexposure. This paper aims to resolve the issue of IELA to some extent and contributes to two aspects. Firstly, an Image Exposure Database (IEpsD) is constructed to facilitate the study of IELA. IEpsD comprises 24,500 images with various exposure levels, and for each image a subjective exposure score is provided, which represents its perceptual exposure level. Secondly, as IELA can be naturally formulated as a regression problem, we thoroughly evaluate the performance of modern deep CNN architectures for solving this specific task. Our evaluation results can serve as a baseline when the other researchers develop even more sophisticated IELA approaches. To facilitate the other researchers to reproduce our results, we have released the dataset and the relevant source code at https://cslinzhang.github.io/imgExpo/.
Human Activity Recognition and Temporal Action Localization Based on Depth Sensor Skeletal Data

Abundance in inertia measurement units and wide usage of depth sensors has led to the effortless acquisition of significant activity data. As a result, studies in the area of human activity recognition and datasets related to this area have risen. In this paper, skeletonized action sequence data; obtained by a depth sensor, is used to classify among twenty seven different class of activities. A three-layer Long-Short Term Memory(LSTM) architecture was used to analyze the mentioned skeleton data. LSTM cells are a special type of recurrent neural network which can remember long time relationship between sequences of related data. Also, another application was carried out using a recurrent neural network(RNN) architecture to address where a specific action occurs on a video stream. This architecture consists of four layers. Thanks to this deep learning structures; which led to the automated extraction of features, high classification accuracy rates around %93 can be achieved.
Estimation of the Distance to Moving Vehicles in a Traffic Stream

This work is devoted to estimating the distance between moving cars to prevent dangerous traffic situations. The solution to this problem uses the approach of computer vision and calculating a depth map based on a stereoscopic pair. Using the Viola-Jones algorithm, the system detects vehicles while the vehicle is moving and calculates the distance to the objects moving in front, combining the received information with a depth map.
QWERTY Keyboard? }.?BZQ is Better!

In this work, we provide a Genetic-based algorithm that is used to quickly find a placement for a set of objects within a given layout such that access to these objects is optimized. The given layout describes the free locations of the objects and the object handles and the access is done through a corpus of object requests. The proposed algorithm optimizes the placement of the objects by searching through a small fraction of the search space. As a case study, we use the algorithm to find a better placement for the keyboard characters than QWERTY and Dvorak Simplified characters placements. The algorithm finds a placement that is better than both QWERTY and Dvorak Simplified by 32.68% and 15.79% respectively on the training set, and 32.71% and 15.84% respectively on the testing set. This result is achieved after searching through only 500K possible solutions, which is about 1.23 × 10-19percent only of the total search space. Both training and testing sets are extracted randomly from TED2013 v1.1 English corpus. Moreover, we release the dataset, code and experimental results on our GitHub repository.