Reviewerly/exHarmony huggingface dataset:
Accurate and Efficient LIF-Nets for 3D Detection and Recognition

3D object detection and recognition are crucial tasks for many spatiotemporal processing applications, such as computer-aided diagnosis and autonomous driving. Although prevalent 3D Convolution Nets (ConvNets) have continued to improve the accuracy and sensitivity, excessive computing resources are required. In this paper, we propose Leaky Integrate and Fire Networks (LIF-Nets) for 3D detection and recognition tasks. LIF-Nets have rich inter-frame sensing capability brought from membrane potentials, and low power event-driven mechanism, which make them excel in 3D processing and save computational cost at the same time. We also develop ResLIF Blocks to solve the degradation problem of deep LIF-Nets, and employ U-LIF structure to improve the feature representation capability. As a result, we carry out experiments on the LUng Nodule Analysis 2016 (LUNA16) public dataset for chest CT automated analysis and conclude that the LIF-Nets achieve 94.6% detection sensitivity at 8 False Positives per scan and 94.14% classification accuracy while the LIF-detection net reduces 65.45% multiplication operations, 65.12% addition operations, and 65.32% network parameters. The results show that LIF-Nets have extraordinary time-efficient and energy-saving performance while achieving comparable accuracy.
Cryptanalyzing an Image Cryptosystem Based on Circular Inter-Intra Pixels Bit-Level Permutation

Recently, a new image encryption algorithm based on chaos has been proposed [Circular inter-intra pixels bit-level permutation and chaos-based image encryption, Information Sciences, 355-356: 314-327, 2016]. In this paper, this image encryption algorithm was crypt-analyzed in detail. Some defects of it, such as its row-based scrambling being invalid for the special images and the lacking of diffusion operations in its processes, were pointed out. Meanwhile, two crack methods were presented in this paper. They are using only a pair of chosen plain-cipher images or a pair of known plain-cipher images to crack the image encryption algorithm. That is enough to demonstrate the image cryptosystem using this image encryption algorithm is insecure.
A Multilayer Fusion Network With Rotation- Invariant and Dynamic Feature Representation for Multiview Low-Altitude Image Registration

Due to human and natural factors, when the small unmanned aerial vehicles (UAVs) are monitoring the ground, multiview transformation problems such as image distortion and low overlap will occur, which will inhibit the accuracy of low-altitude image registration and limit the subsequent application. In this letter, we propose a mismatch removal method based on the Siamese architecture to solve the issues of multiview images. A dynamic neighbor-guided patch representation is designed to enhance the representation of each feature point. Meanwhile, a multilayer fusion is used to obtain more comprehensive information on feature points, and whether a pair of points correspond depends on the similarity of its descriptors. The network is trained by adding a rotation-invariant layer to solve the inevitable rotation and image distortion in multiview scenarios. The experimental results prove that our method can deal with the scenarios of the horizontal rotation, vertical rotation, mixture, scaling, and extreme, and is better than the other five state-of-the-art methods in most scenarios.
Deep Learning and Unsupervised Fuzzy C-Means Based Level-Set Segmentation for Liver Tumor

In this paper, we propose and validate a novel level-set method integrating an enhanced edge indicator and an automatically derived initial curve for CT based liver tumor segmentation. In the beginning, a 2D U-net is used to localize the liver and a 3D fully convolutional network (FCN) is used to refine the liver segmentation as well as to localize the tumor. The refined liver segmentation is used to remove non-liver tissues for subsequent tumor segmentation. Given that the tumor segmentation obtained from the aforementioned 3D FCN is typically imperfect, we adopt a novel level-set method to further improve the tumor segmentation. Specifically, the probabilistic distribution of the liver tumor is estimated using fuzzy c- means clustering and then utilized to enhance the object indication function used in level-set. The proposed segmentation pipeline was found to have an outstanding performance in terms of both liver and liver tumor.
Firefly-Based Approaches of Image Recognition

The main aim of the reported work is to solve the registration problem for recognition purposes. We introduce two new evolutionary algorithms (EA) consisting of population-based search methods, followed by or combined with a local search scheme. We used a variant of the Firefly algorithm to conduct the population-based search, while the local exploration was implemented by the Two-Membered Evolutionary Strategy (2M-ES). Both algorithms use fitness function based on mutual information (MI) to direct the exploration toward an appropriate candidate solution. A good similarity measure is the one that enables us to predict well, and with the symmetric MI we tie similarity between two objects A and B directly to how well A predicts B, and vice versa. Since the search landscape of normalized mutual information proved more amenable for evolutionary computation algorithms than simple MI, we use normalized mutual information (NMI) defined as symmetric uncertainty. The proposed algorithms are tested against the well-known Principal Axes Transformation technique (PAT), a standard evolutionary strategy and a version of the Firefly algorithm developed to align images. The accuracy and the efficiency of the proposed algorithms are experimentally confirmed by our tests, both methods being excellently fitted to registering images.
Swap: A Replacement-based Text Revision Technique for Mobile Devices

Text revision is an important task to ensure the accuracy of text content. Revising text on mobile devices is cumbersome and time-consuming due to the imprecise caret control and the repetitive use of the backspace. We present Swap, a novel replacement-based technique to facilitate text revision on mobile devices. We conducted two user studies to validate the feasibility and the effectiveness of Swap compared to traditional text revision techniques. Results showed that Swap reduced efforts in caret control and repetitive backspace pressing during the text revision process. Most participants preferred to use the replacement-based technique rather than backspace and caret. They also commented that the new technique is easy to learn, and it makes text revision rapid and intuitive.
The Converging Triangle of Cultural Content, Cognitive Science, and Behavioral Economics

How online cultural content is chosen based on conscious or subconscious criteria is an central question across a broad spectrum of sciences and for the entertainment industry, including content providers and distributors. To this end, a number of tailored analytics forming the backbone of recommendation engines specialized for retrieving cultural content are proposed. Their strength derives directly from well-established principles of cognitive science and behavioral economics, both scientific fields exploring aspects of human decision making. Another novel contribution of this conference paper is that these analytics are implemented in Neo4j expressed as Cypher queries. Various aspects of the cultural content and digital consumers can be naturally represented by appropriately configured vertices, whereas edges represent various connections indicating content delivery preferences. Early experiments conducted over a synthetic dataset mimicking the distributions of preferences and ratings of well-known movie datasets are encouraging as the proposed analytics outperformed the baseline of a multilayer feedforward neural network of various configurations. The synthetic dataset contains enriched preferences of mobile digital consumers of cultural content regarding literature of the Greek region of Ionian Islands.
An Innovative Graph-Based Approach to Advance Feature Selection from Multiple Textual Documents

This paper introduces a novel graph-based approach to select features from multiple textual documents. The proposed solution enables the investigation of the importance of a term into a whole corpus of documents by utilizing contemporary graph theory methods, such as community detection algorithms and node centrality measures. Compared to well-tried existing solutions, evaluation results show that the proposed approach increases the accuracy of most text classifiers employed and decreases the number of features required to achieve ‘state-of-the-art’ accuracy. Well-known datasets used for the experimentations reported in this paper include 20Newsgroups, LingSpam, Amazon Reviews and Reuters.
Contact Tracing and Quarantine for Covid 19: Challenges in community surveillance

Covid 19 caused by SARS-coV-2 is a novel corona virus. This began in Wuhan city, China at the end of December 2019 and had spread to the rest of the world. World Health Organization (WHO) declared Covid 19 as Public Health Emergency of International Concern (PHEIC) on 30th Jan 2020 and later declared as pandemic on 11th march 2020. 1 The disease is mainly spread from human to human through small droplets from nose or mouth when a person with Covid 19 coughs or exhales and through the surface contact. Community surveillance plays significant role in prevention of spread of disease. It includes isolation of the positive case, quarantine of the high risk and low risk contacts and community disinfection.1, 2&#x0D; The period of communicability is estimated with the current data to be from 2 days before the onset of symptoms and up to 2 weeks after onset. Hence the initial few asymptomatic days turns out to be crucial period in containing the spread of infection. By the time a Covid 19 patient is diagnosed and isolated, there are quite a number of primary and secondary contacts. Government of India focus has been on Community Surveillance activities which mainly comprises of Contact Tracing and Quarantine.3, 4 This article focuses on the various measures taken to trace the contacts, quarantine measures and on the challenges faced.
LiDAR-camera system extrinsic calibration by establishing virtual point correspondences from pseudo calibration objects

Extrinsic calibration on LiDAR-camera system without specific calibration objects is a challenging task, for it is difficult to find point correspondences from RGB image and sparse LiDAR point cloud. In a natural scene, some objects if satisfying three conditions can be regarded as pseudo calibration objects. In this paper, we propose the virtual point correspondence at the first time. It is established from the 2D box of one pseudo calibration object in RGB image and its corresponding 3D frustum box in point cloud. Based on virtual point correspondence, we present a novel LiDAR-camera extrinsic calibration method without specific calibration objects. It requires two calibration conditions that easily satisfied in the practical application. A normal guided foreground detection method is proposed to automatically extract 3D frustum box. After that, a geometrical optimization scheme is presented to estimate the extrinsic parameters with the virtual point correspondences. Simulations and real data experiments demonstrate that our method is accurate, robust, and outperforms state-of-the-art calibration object based method.
Single color image super-resolution using sparse representation and color constraint

Color image super-resolution reconstruction based on the sparse representation model usually adopts the regularization norm (e.g., L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> or L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ). These methods have limited ability to keep image texture detail to some extent and are easy to cause the problem of blurring details and color artifacts in color reconstructed images. This paper presents a color super-resolution reconstruction method combining the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> / <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> sparse regularization model with color channel constraints. The method converts the low-resolution color image from RGB to YCbCr. The L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> / <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> sparse regularization model is designed to reconstruct the brightness channel of the input low-resolution color image. Then the color channel-constraint method is adopted to remove artifacts of the reconstructed high-resolution image. The method not only ensures the reconstruction quality of the color image details, but also improves the removal ability of color artifacts. The experimental results on natural images validate that our method has improved both subjective and objective evaluation.
INTEGRATING TEACHING AND LEARNING IN GRADUATE STUDIES: ECONOMIC DEVELOPMENT COURSE

Development economics is a field of study, trying to explore the reason of economic success and failures of different countries or regions in the world. It deals with important issues and gaps of 21st Century, such as poverty, inequality, education, health, demographic changes, migration, trade and globalization. Moreover, the basic issues of development economics have been in focus since 1776 within the famous book of Adam Smith “An Inquiry into the Nature and Causes of the Wealth of Nations”. Development economics, it can be argued, has to be concerned not only with protecting its "own" territory, but also with keeping alive the foundational motivation of the subject of economics in general. All the above-mentioned aspects on development economics are the main motivation of this paper. Furthermore, this study tries to analyze, synthesize and recapitulate the integration of teaching and learning in graduate studies. It is the case of an actual course, termed “Development and Growth”, offered in the economics department of the Master of Science program. The study will cover two main analyses: the first analysis, dealing with the specifics of course organization and management and the second one, exploring the students’ projects and their integration with the course.
A Bayesian framework for functional calibration of expensive computational models through non-isometric matching

We study statistical calibration, i.e., adjusting features of a computational model that are not observable or controllable in its associated physical system. We focus on functional calibration, which arises in many manufacturing processes where the unobservable features, called calibration variables, are a function of the input variables. A major challenge in many applications is that computational models are expensive and can only be evaluated a limited number of times. Furthermore, without making strong assumptions, the calibration variables are not identifiable. We propose Bayesian Non-isometric Matching Calibration (BNMC) that allows calibration of expensive computational models with only a limited number of samples taken from a computational model and its associated physical system. BNMC replaces the computational model with a dynamic Gaussian process whose parameters are trained in the calibration procedure. To resolve the identifiability issue, we present the calibration problem from a geometric perspective of non-isometric curve to surface matching, which enables us to take advantage of combinatorial optimization techniques to extract necessary information for constructing prior distributions. Our numerical experiments demonstrate that in terms of prediction accuracy BNMC outperforms, or is comparable to, other existing calibration frameworks.
Smart Contracts Contracts

This paper explores the connection between software contracts and smart contracts. Despite the assonance, these two terms denote quite different concepts: software contracts are logical properties of software components, while smart contracts are programs executed on blockchains. What is the relation between them? We answer this question by discussing how to integrate software contracts in the design of programming languages for smart contracts.
HiVision: Rapid visualization of large-scale spatial vector data

Rapid visualization of large-scale spatial vector data is a long-standing challenge in Geographic Information Science. In existing methods, the computation overheads grow rapidly with data volumes, leading to the incapability of providing real-time visualization for large-scale spatial vector data, even with parallel acceleration technologies. To fill the gap, we present HiVision, a display-driven visualization model for large-scale spatial vector data. Different from traditional data-driven methods, the computing units in HiVision are pixels rather than spatial objects to achieve real-time performance, and efficient spatial-index-based strategies are introduced to estimate the topological relationships between pixels and spatial objects. HiVision can maintain exceedingly good performance regardless of the data volume due to the stable pixel number for display. In addition, an optimized parallel computing architecture is proposed in HiVision to ensure the ability of real-time visualization. Experiments show that our approach outperforms traditional methods in rendering speed and visual effects while dealing with large-scale spatial vector data, and can provide interactive visualization of datasets with billion-scale points/segments/edges in real-time with flexible rendering styles. The HiVision code is open-sourced at https://github.com/MemoryMmy/HiVision with an online demonstration.
Time-Dependent Performance Prediction System for Early Insight in Learning Trends

Performance prediction systems allow knowing the learning status of students during a term and produce estimations on future status, what is invaluable information for teachers.The majority of current systems statically classify students once in time and show results in simple visual modes.This paper presents an innovative system with progressive, time-dependent and probabilistic performance predictions.The system produces byweekly probabilistic classifications of students in three groups: high, medium or low performance.The system is empirically tested and data is gathered, analysed and presented.Predictions are shown as point graphs over time, along with calculated learning trends.Summary blocks are with latest predictions and trends are also provided for teacher efficiency.Moreover, some methods for selecting best moments for teacher intervention are derived from predictions.Evidence gathered shows potential to give teachers insights on students' learning trends, early diagnose learning status and selecting best moment for intervention.
A Study on Extreme Learning Machine for Gasoline Engine Torque Prediction

This research presents an extreme learning machine (ELM) based neural network modeling technique for gasoline engine torque prediction.The technique adopts a single-hidden layer feedforward neural network (SLFN) structure which has the potential to approximate any continuous function with high accuracy.To verify the robustness of this technique, over 3300 data points collected from a real-world gasoline engine are used to train, validate, and test the model.These data points cover a wide spectrum of normal engine operating conditions, with the engine speed from 1000 rpm to 4500 rpm, and the engine torque from idle to full load.The experiment results demonstrate that the model can predict the gasoline engine torque with high accuracy.Moreover, this research proposes a weight factor approach to further improve the prediction accuracy of the model in the desired data regions without modifying the input data set.The evaluation shows that the weight factor approach can reduce the overall prediction errors in the regions significantly.This feature is particularly useful in tuning the performance of the model when the significance of the individual data points varies, or when the distribution of the data points is imbalanced.In practice, the modeling approaches presented in this research will help reduce the engine test and verification time.
Quantum correlations and quantum Fisher information of two qubits in the presence of the time-dependent coupling effect

In this paper, we consider two separate Jaynes–Cummings (JC) nodes with a nonidentical qubit-field system in the presence of dissipation terms. We reveal the influence of the time variation of the coupling terms on some important measures when the qubits are immersed in a vacuum. The density matrix for the two qubits initially in Bell states are obtained. The dynamical behavior of the quantum discord (QD), classical correlation (CC), qubit-qubit entanglement, and quantum Fisher information (QFI) is investigated. We explore the relationship among QD, CC, qubit-qubit entanglement, and QFI in the absence and presence of the dissipation effect during the time evolution. Furthermore, we show the main optimal conditions for obtaining a high level of correlation and coherence between the two qubits.
Double-Spending Analysis of Bitcoin

Bitcoin is a distributed online payment system that facilitates anonymous transactions using a peer-to-peer network without a central trusted authority. Every peer in the Bitcoin network keeps the collection of all transactions which is referred to as a ledger. This public ledger will work effectively for honest peers, however, one well-known attack is the fifty-one percent or majority attack. This paper provides an Uppaal model of the Bitcoin protocol focusing on its three important components namely transactions, blocks and the blockchain. It presents a probability analysis for two scenarios of the fifty-one percent attacks. Two Phase Proof-of-Work (2PPoW) is a proposed solution to address attacks of this type, and we will extend the model to include 2PPoWand calculate the probability of a successful attack. The analysis shows that a traditional fifty-one percent attacks can be successful even if the attacker has less than a majority of the processing pool.
Application for Iraqi sign language translation on Android system

Deaf people suffer from difficulty in social communication, especially those who have been denied the blessing of hearing before the acquisition of spoken language and before learning to read and write. For the purpose of employing mobile devices for the benefit of these people, their teachers and everyone who has contact with them, this research aims to design an application for social communication and learning by translating Iraqi sign language into text in Arabic and vice versa. Iraqi sign language has been chosen because of a lack of applications for this field. The current research, to the best of our knowledge, is the first of its kind in Iraq. The application is open source; words that are not found in the application database can be processed by translating them into letters alphabetically. The importance of the application lies in the fact that it is a means of communication and e-learning through Iraqi sign language, reading and writing in Arabic. Similarly, it is regarded as a means of social communication between deaf people and those with normal hearing. This application is designed by using JAVA language and it was tested on several deaf students at Al-Amal Institute for Special Needs Care in Mosul, Iraq. It was well comprehended and accepted.