Reviewerly/exHarmony huggingface dataset:
Knowledge transfer between brain lesion segmentation tasks with increased model capacity

Convolutional neural networks (CNNs) have become an increasingly popular tool for brain lesion segmentation in recent years due to its accuracy and efficiency. However, CNN-based brain lesion segmentation generally requires a large amount of annotated training data, which can be costly for medical imaging. In many scenarios, only a few annotations of brain lesions are available. One common strategy to address the issue of limited annotated data is to transfer knowledge from a different yet relevant source task, where training data is abundant, to the target task of interest. Typically, a model can be pretrained for the source task, and then fine-tuned with the scarce training data associated with the target task. However, classic fine-tuning tends to make small modifications to the pretrained model, which could hinder its adaptation to the target task. Fine-tuning with increased model capacity has been shown to alleviate this negative impact in image classification problems. In this work, we extend the strategy of fine-tuning with increased model capacity to the problem of brain lesion segmentation, and then develop an advanced version that is better suitable for segmentation problems. First, we propose a vanilla strategy of increasing the capacity, where, like in the classification problem, the width of the network is augmented during fine-tuning. Second, because unlike image classification, in segmentation problems each voxel is associated with a labeling result, we further develop a spatially adaptive augmentation strategy during fine-tuning. Specifically, in addition to the vanilla width augmentation, we incorporate a module that computes a spatial map of the contribution of the information given by width augmentation in the final segmentation. For demonstration, the proposed method was applied to ischemic stroke lesion segmentation, where a model pretrained for brain tumor segmentation was fine-tuned, and the experimental results indicate the benefit of our method.
Visual Analysis and Exploration of COVID-19 Based on Multi-source Heterogeneous Data

The novel coronavirus pneumonia (COVID-19) has raged in many countries around the world.In the process of fighting against the COVID-19, unprecedented large-scale epidemic data have been produced such as case data, spatio-temporal data, public opinion data and so on.The increasingly complex data poses a significant challenge to understand.A two-level interactive visualization system named COVID-19Vis is proposed in this paper, which collects epidemic data from multiple sources and provides an interactive mode of multi-graph linkage.Users can not only easily analyze and interpret the spatial-temporal characteristics and potential rules of the epidemic, but also find the relationship between policy, online public opinion and the development of the epidemic situation.Through a large number of visualization effects and user feedback, the effectiveness and practicability of the COVID-19Vis are further verified.
Designing the Repository of Documentary Cultural Heritage

Digitization of communication processes generates the need to design repositories of documentary cultural heritage, providing an open access to historical artifacts. The process of forming repositories is to combine information resources, which is carried out to meet qualitatively the information users’ needs. To preserve such large arrays of information resources, the authors have proposed an information technology based on the creation of data warehouses where the resources of social memory institutions are consolidated. The problem of certification of electronic data warehouses, which is currently carried out by CoreTrustSeal, is analyzed and the requirements for the design of such data warehouses are determined. A prototype of an information technology platform for a repository of documentary cultural heritage is proposed.
Domain Alignment Embedding Network for Sketch Face Recognition

Sketch face recognition refers to the process of matching sketches to photos. Recently, there has been a growing interest in using deep learning to learn discriminative features for sketch face recognition. However, the success of deep learning relies on the large-scale paired images to counteract effects such as over-fitting, since the amount of the paired training data is relatively small, the discriminative power of the deeply learned features will inevitably be reduced. This paper proposes a novel deep metric learning method termed domain alignment embedding network for sketch face recognition. Specifically, a training episode strategy is designed to alleviate the small sample problem, and a domain alignment embedding loss is proposed to guide the feature embedding network to learn discriminative features. Extensive experimental results on the UoM-SGFSv2 and PRIP-VSGC datasets are verified to show the effectiveness of the proposed method.
Learning Latent Semantic Attributes for Zero-Shot Object Detection

Zero-shot Object Detection (ZSD) aims to locate and classify instances of unseen categories. Existing methods focus on learning the mapping from visual space to semantic space, while the learning of discriminative representations for ZSD has not gained enough attention. In this paper, we demonstrate the necessity to learn discriminative semantic representations for ZSD, and propose a new end-to-end framework for this task. Our framework is able to learn discriminative semantic representations in an augmented space introduced for both user-defined and latent attributes, and refine the user-defined attributes with the help of unseen and external classes. The proposed method is extensively evaluated on two challenging ZSD datasets, and the experimental results show that our method significantly outperforms several existing methods.
On the prediction of long-lived bugs: An analysis and comparative study using FLOSS projects

Software evolution and maintenance activities in today’s Free/Libre Open Source Software (FLOSS) rely primarily on information extracted from bug reports registered in bug tracking systems. Many studies point out that most bugs that adversely affect the user’s experience across versions of FLOSS projects are long-lived bugs. However, proposed approaches that support bug fixing procedures do not consider the real-world lifecycle of a bug, in which bugs are often fixed very fast. This may lead to useless efforts to automate the bug management process. This study aims to confirm whether the number of long-lived bugs is significantly high in popular open-source projects and to characterize the population of long-lived bugs by considering the attributes of bug reports. We also aim to conduct a comparative study evaluating the prediction accuracy of five well-known machine learning algorithms and text mining techniques in the task of predicting long-lived bugs. We collected bug reports from six popular open-source projects repositories (Eclipse, Freedesktop, Gnome, GCC, Mozilla, and WineHQ) and used the following machine learning algorithms to predict long-lived bugs: K-Nearest Neighbor, Naïve Bayes, Neural Networks, Random Forest, and Support Vector Machines. Our results show that long-lived bugs are relatively frequent (varying from 7.2% to 40.7%) and have unique characteristics, confirming the need to study solutions to support bug fixing management. We found that the Neural Network classifier yielded the best results in comparison to the other algorithms evaluated. Research efforts regarding long-lived bugs are needed and our results demonstrate that it is possible to predict long-lived bugs with a high accuracy (around 70.7%) despite the use of simple prediction algorithms and text mining methods.
Path Planning of Mobile Robots based on Improved Genetic Algorithm

In this paper, the path planning algorithm is proposed based on the improved genetic algorithm. Firstly, the global environment for the grid model on mobile robot movement is built using the grid method. Then the classic genetic algorithm is applied into the grid map of path planning, and the gray wolf optimization algorithm is used into the grid map of path planning. Thereby, the genetic algorithm is optimized by gray wolf optimization and finally applied into path planning. The improved genetic algorithm is used into path planning of mobile robots and compared with the classic genetic algorithm and the gray wolf optimization algorithm. Finally, the simulation results confirm the improved algorithm has better convergence and effectiveness and is very valuable for path planning.
Augmented Reality for Smarter Bangladesh

Augmented Reality (AR) is an emerging form of experience where the real world is enhanced by computer-generated content. It is often considered as a type of futuristic technology, but a part of it has been reality for years. It is a step between reality and virtual reality. Over the past several years AR technology has become more accessible and is starting to branch out into many different fields. AR can already be found in news, entertainment, sports, e-commerce, scientific research, education, marketing, and travel etc. But in Bangladesh there are virtually no applications of AR. In order to find out the potential of AR technology in Bangladesh this paper gives an overview of the technology, examines recent development of the technology abroad, and explores its sustainability and implications in Bangladesh from the education, medical and traffic management perspectives.
Modeling Across-Context Attention For Long-Tail Query Classification in E-commerce

Product query classification is the basic component for query understanding, which aims to classify the user queries into multiple categories under a predefined product category taxonomy for the E-commerce search engine. It is a challenging task due to the tremendous amount of product categories. And a slight modification to a query will change its corresponding categories entirely, e.g., appending the "button" to the query "shirt". The problem is more severe for the tail queries which lack enough supervision information from customers. Motivated by this phenomenon, this paper proposes to model the contrasting/similar relationships between such similar queries. Our framework is composed of a base model and an across-context attention module. The across-context attention module plays the role of deriving and extracting external information from these variant queries by predicting their categories. We conduct both offline and online experiments on the real-world E-commerce search engine. Experimental results demonstrate the effectiveness of our across-context attention module.
Exploiting Classical Planning Grounding in Hybrid PDDL+ Planning Engines

Hybrid PDDL+ models are amongst the most advanced models of systems and the resulting problems are notoriously difficult for planners to cope with due to nonlinear behaviours and immense search spaces. This difficulty is exacerbated by the potentially huge size of the fully ground representations that are used by modern planners in order to effectively explore the search space, which can make some problems impossible to tackle, with the result that in several situations the grounding phase has to be done externally or manually. This not only produces a much less compact problem description, but also complicates debugging and model reuse. To overcome the aforementioned limit, in this paper we investigate two simple grounding techniques for PDDL+ problems. The former method we propose extends the simple mechanism of invariance analysis to limit the groundings of operators upfront. The latter proposes to tackle the grounding process by means of a PDDL+ to Classical Planning abstraction. A preliminary experimental analysis over benchmarks coming from real case study shows that not only the grounding can be sped up, but that also problems that were out of the reach before can now be efficiently solved in an automated manner.
Semantic classification of qualitative conditionals and calculating closures of nonmonotonic inference relations

Qualitative conditionals of the form "If A, then usually B" are often used to model nonmonotonic inference relations. Evaluating conditionals as three valued logical objects, allows for a classification of all conditionals over a given propositional signature. These classes of conditionals and their properties in terms of nonmonotonic inference are useful for the task of calculating the closures of concrete nonmonotonic inference relations. In this paper, we present a rigorous classification of conditionals in terms of semantic properties. We also present and evaluate an approach for calculating closures of inference relations by exploiting our classification of conditionals. We discuss the usefulness of the availability of the complete closure of inference relations by using the results of our approach to systematically evaluate several inference relations with respect to postulates proposed for nonmonotonic inference relations.
Small Marker Tracking with Low-Cost, Unsynchronized, Movable Consumer Cameras For Augmented Reality Surgical Training

Surgeons improve their skills through repetition of training tasks in order to operate on living patients, ideally receiving timely, useful, and objective performance feedback. However, objective performance measurement is currently difficult without 3D visualization, with effective surgical training apparatus being extremely expensive or limited in accessibility. This is problematic for medical students, especially in situations such as the COVID-19 pandemic in which they are needed by the community but have few ways of practicing without lab access. In this work, we propose and prototype a system for augmented reality (AR) visualization of laparoscopic training tasks using cheap and widely-compatible borescopes, which can track small objects typical of surgical training. We use forward kinematics for calibration and multi-threading to attempt synchronization in order to increase compatibility with consumer applications, resulting in an effective AR simulation with low-cost devices and consumer software, while also providing dynamic camera and marker tracking. We test the system with a typical peg transfer task on the HoloLens 1 and MagicLeap One.
An Introduction to Deep Learning Research for Alzheimer’s Disease

This tutorial explains the evolving approaches on deep learning (DL) modeling and their dependence on statistically comprehensive datasets as input in various brain scan neuroimages. Powerful visual modalities, e.g., magnetic resonance images and positron emission tomography, can show neural changes during Alzheimer's disease (AD) development. Computer vision's recent success has lent impetus to numerous DL modeling publications reporting accuracy above 90%, using AD NeuroImage (ADNI) datasets. However, several limitations exist when using DL for AD image interpretation. Due to the lack of a comprehensive dataset and medical images' complexity, there is little to no clinical value in such DL approaches. Furthermore, many of the published research results in the field are not comparable in experimenting with the ADNI datasets without well-accepted evaluation criteria. This tutorial describes the fundamentals and gaps in applying DL methodology over ADNI datasets.
Graph Signal Processing on Complex Networks for Structural Health Monitoring

In this work, we demonstrate the application of a framework targeting Complex Networks and Graph Signal Processing (GSP) for Structural Health Monitoring (SHM). By modeling and analyzing a large bridge equipped with strain and vibration sensors, we show that GSP is capable of selecting the most important sensors, investigating different optimization techniques for selection. Furthermore, GSP enables the detection of graph signal patterns (mode shapes), grasping the physical function of the sensors in the network. Our results indicate the efficacy of GSP on complex sensor data modeled in complex networks.
Analysis of regional economic evaluation based on machine learning

The regional economic evaluation and analysis has guiding significance for the subsequent economic strategy formulation. Due to the influence of various factors, the volatility of some current economic evaluation models is relatively large. According to the needs of regional economic evaluation, this study uses computer technology combined with regional economic development to build an economic development evaluation model to evaluate and analyze the regional economy. Through comparative analysis, this study selects the entropy weight-TOPSIS model as the comprehensive evaluation model of regional economy, uses the entropy weight method to determine the weight of each index, and then uses the TOPSIS method to conduct comprehensive evaluation. In addition, this study designs a control experiment to analyze the performance of this study model. Moreover, this study uses the model proposed in this study to conduct regional economic evaluation in recent years, and compares it with real data, and observes the test results with statistical charts and table data. The research results show that this research model has a certain effect, which can provide analytical tools for the follow-up economic strategy research and analysis.
An Efficient Forensic Approach for Copy-move Forgery Detection via Discrete Wavelet Transform

The following topics are dealt with: security of data; Internet; invasive software; telecommunication security; learning (artificial intelligence); computer network security; Internet of Things; cryptographic protocols; computer crime; authorisation.
Scalar and Vector Quantization for Learned Image Compression: A Study on the Effects of MSE and GAN Loss in Various Spaces

Recently, learned image compression by means of neural networks has experienced a performance boost by the use of adversarial loss functions. Typically, a generative adversarial network (GAN) is designed with the generator being an autoencoder with quantizer in the bottleneck for compression and reconstruction. It is well known from rate-distortion theory that vector quantizers provide lower quantization errors than scalar quantizers at the same bitrate. Still, learned image compression approaches often use scalar quantization instead. In this work we provide insights into the image reconstruction quality of the often-employed uniform scalar quantizers, non-uniform scalar quantizers, and the rarely employed but bitrate-efficient vector quantizers, all being integrated into backpropagation and operating under the exact same bitrate. Further interesting insights are obtained by our investigation of an MSE loss and a GAN loss. We show that vector quantization is always beneficial for the compression performance both in the latent space and the reconstructed image space. However, image samples demonstrate that the GAN loss produces the more pleasing reconstructed images, while the non-adversarial MSE loss provides better quality scores of various instrumental measures both in the latent space and on the reconstructed images.
Space reconstruction of audiovisual media based on artificial intelligence and virtual reality

In the electronic age, the changes in the mirror space constructed by movies are also brought about by changes in the media. Cinematic space is a four-dimensional space illusion including time created by the use of light and shadow, color, perspective, sound, and the movement of characters and cameras. It is not a real four-dimensional space, but a reflection and reproduction of the real space. With the goal of improving the accuracy of spatially coded structured light 3D reconstruction, this paper conducts in-depth research on several key technologies that affect the reconstruction accuracy and makes corresponding innovations and improvements. Moreover, this paper respectively proposes an adaptive structured light spatial coding algorithm based on geometric features and a color structured light decoding algorithm based on color shift technology. In addition, this paper implements a spatially coded structured light three-dimensional reconstruction system and calibration system. The simulation research shows that the method in this paper has certain reliability.
A Principle Solution for Enroll-Test Mismatch in Speaker Recognition

Mismatch between enrollment and test conditions causes serious performance degradation on speaker recognition systems. This paper presents a statistics decomposition (SD) approach to solve this problem. This approach decomposes the PLDA score into three components that corresponding to enrollment, prediction and normalization respectively. Given that correct statistics are used in each component, the resultant score is theoretically optimal. A comprehensive experimental study was conducted on three datasets with different types of mismatch: (1) physical channel mismatch, (2) long-term speaker characteristics mismatch, (3) near-far recording mismatch. The results demonstrated that the proposed SD approach is highly effective, and outperforms the ad-hoc multi-condition training approach that is commonly adopted but not optimal in theory.
Enhanced Semantic-Aware Multi-Keyword Ranked Search Scheme Over Encrypted Cloud Data

Traditional searchable encryption schemes based on the Term Frequency-Inverse Document Frequency (TF-IDF) model adopt the presence of keywords to measure the relevance of documents to queries, which ignores the latent semantic meanings that are concealed in the context. Latent Dirichlet Allocation (LDA) topic model can be utilized for modeling the semantics among texts to achieve semantic-aware multi-keyword search. However, the LDA topic model treats queries and documents from the perspective of topics, and the keywords information is ignored. In this article, we propose a privacy-preserving searchable encryption scheme based on the LDA topic model and the query likelihood model. We extract the feature keywords from the document using the LDA-based Information Gain (IG) and Topic Frequency-Inverse Topic Frequency (TF-ITF) model. With feature keyword extraction and the query likelihood model, our scheme can achieve a more accurate semantic-aware keyword search. A special index tree is used to enhance search efficiency. The secure inner product operation is utilized to implement the privacy-preserving ranked search. The experiments on real-world datasets demonstrate the effectiveness of our scheme.
A Creative Approach to Humour Degree Calculation for Utterances

Humour is valued by people for its many positive social functions, which is also of great merit for computing exploration. Especially in this era, many advanced computing science and technologies allow new perspectives to address this topic. Among them, creative computing, which aims to offer new and surprising computing solutions, can lend its theoretical and technical supports to humour generation and exploitation. Based on it, the proposed research first raises the concept of the humour degree to bridge the more qualitative topic of humour with the quantitative nature of computing, so as to build a new basis for the following computing work. Secondly, a creative system is designed to calculate the humour degree for utterances. Thirdly, the experiment is conducted to validate the effectiveness of the system. The results show that it is feasible and significant to calculate the humour degree for utterances through a creative computing approach.