Reviewerly/exHarmony huggingface dataset:
A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super-Resolution

This paper presents a transfer domain strategy to tackle the limitations of low-resolution thermal sensors and generate higher-resolution images of reasonable quality. The proposed technique employs a CycleGAN architecture and uses a ResNet as an encoder in the generator along with an attention module and a novel loss function. The network is trained on a multi-resolution thermal image dataset acquired with three different thermal sensors. Results report better performance benchmarking results on the 2nd CVPR-PBVS-2021 thermal image super-resolution challenge than state-of-the-art methods. The code of this work is available online.
HDR video coding with MPEG-5 LCEVC

High Dynamic Range (HDR) video content is continuing to gain market relevance for both streaming and brodacasting services, providing video with improved contrast and colour depth. However, the predominance of 8-bit based codecs and the wide availability of Standard Dynamic Range (SDR) devices still poses challenges regarding effective deployment of HDR content.
Barriers to Academic Career Development for University Teachers

Barriers to Academic Career Development for University TeachersThe relevance of the issue in the present study is determined by the fact that it is difficult to engage and retain employees (including young ones) in the academic sphere because of various career barriers, which complicate pursuing a career in higher education. The study was conducted using the method of expert interviews with 108 full-time employees of Russian universities including administration, management, academic and teaching staff. These interviews identified the key barriers to academic careers for researchers and also classified these barriers. Presented findings can be useful for HR departments of higher education institutions for building various career paths for the employees of different ages, or for identifying specific types of barriers in career development to be overcome. The results of the study are relevant for academic and teaching staff interested in career development. The prospect of further research is to conduct a quantitative analysis based on the questionnaire results received from interviewing academic and teaching staff with the use of the proposed classification of academic career barriers.
On the Frontiers of Software Science and Software Engineering

Advances in software engineering, software science, computational intelligence, and intelligent mathematics have led to the establishment of Frontiers in Computer Science—Software (FCSS). FCSS aims to promote transdisciplinary research on software science and engineering (SSE), autonomous systems, and computational intelligence. FCSS covers not only classical empirical software engineering and industrial processes, but also contemporary topics of software science, intelligent programming languages, autonomous code generation, mathematical foundations of software, and programming knowledge bases. FCSS reports empirical studies and emerging topics in software engineering including tools, development platforms, industrial processes, management infrastructures, quality assurance schemes, big data systems, and software migrations across languages and platforms.
Sentiment Analysis of Madura Tourism in New Normal Era using Text Blob and KNN with Hyperparameter Tuning

Tourism during the Covid-19 pandemic has paralysis, even though tourism is a source of regional income. In the new normal period, tourism began to rise again. Madura Tourism Sentiment Analysis is needed for regional parties and tourism developers to find a public opinion about tourism places in Madura that have been vacuumed for a long time. The dataset used is opinion data on Twitter for nature, culinary and religious tourism in Madura. Data was taken during the New Normal period between April 2020 to August 2021. This research compared Manual Lexicon Based and TextBlob for labeling data. TF-IDF for term weighting. SVM, Na&#x00EF;ve Bayes, and KNN methods with Tuning Parameters are compared for classification methods in sentiment analysis. Based on this research, the best Accuracy value is 94% for SVM Method or KNN Method using Manhattan measure and K-Value = 1. The most positive labels are obtained for three tourism categories: nature, culinary, and religious.
A novel textual data augmentation method for identifying comparative text from user-generated content

Mining user-generated content on e-commerce platforms and social media is timely and more objective compared with other information access channels for gaining competitive intelligence. Identifying comparative text from large volumes of non-comparative text is an important but challenging task. On one hand, existing methods are time-consuming and not generalizable across different domains. On the other hand, the datasets for the task generally suffer from the severe imbalance issue. To address abovementioned problems, we propose a framework adopting advanced deep learning methods to automatically learn features and a novel textual data augmentation method named TA3S to deal with the data imbalance issue. Specifically, the TA3S method simultaneously considers the syntactic structure and semantic information of comparative text samples. Moreover, in order to support the successful implementation of TA3S, we develop a novel method based on word embedding and label propagation algorithm to distinguish between synonymous and antonymous substitute words. The experiments on two real-world datasets demonstrate the feasibility and effectiveness of our framework, and present that our framework outperforms state-of-the-art methods in identifying comparative text from user-generated content.
A collaborative deep multitask learning network for face image compliance to ISO/IEC 19794-5 standard

The face is considered the primary biometric trait for machine-readable travel documents, like passports. In this context, the ISO/IEC 19794-5 standard defines a set of photographic requirements to ensure image quality and simplify the face recognition process. However, the assessment of face image compliance to the ISO/ICAO standard is still mostly performed by humans today due to the lack of automatic evaluation systems to perform this task. In this paper, we present the first deep multitask learning-based method designed for automatic evaluation of the photographic requirements of the ISO/IEC 19794-5 standard, called ICAONet. We extended undercomplete Autoencoders to employ a multi-and-collaborative learning approach, where both supervised and unsupervised learning is performed concurrently and in a collaborative manner. The method is trained using an ad hoc image dataset and evaluated by an official benchmark system also used by other approaches presented in the literature. The results show that our method achieves the best results in terms of Equal Error Rate for 9 out of the 23 photographic requirements of ISO/IEC 19794-5, which was not achieved by any other individual method evaluated. Therefore, the proposed method can be considered the best overall solution among academic works published in the literature and private SDKs. Overall, the median Equal Error Rate (3.3%) is also competitive. Finally, in terms of running time, the proposed method stands out among the fastest to evaluate all 23 requirements according to the official benchmark.
Creative Probes, Proxy Feelers, and Speculations on Interactive Skin

This paper critically discusses the combination of creative and social research methods to generate a novel approach to explore the multimodal technoscape. This paper draws on an interdisciplinary exploratory case study on interactive skin—an emergent technology that augments and/or interacts with the skin. This paper shows how concepts from skin studies and the HCI literature can be used to draw on creative methods to think about and with the body. We describe the use of an online probe pack, a speculative research workshop and sensory research interviews using ‘proxy feelers’ to agitate the design space of interactive skin futures. We show how combining these methods provoked and expanded the scope of interactive skin from the technological to the sensory and the social. We discuss the opportunities and challenges of the research dialogues that this approach facilitated, make the case for creative methodological improvisation and exploration of emergent technologies and show how creative and social research methods can be combined to explore the interconnection between technology, society and design.
A novel self-learning approach to overcome incompatibility on TripAdvisor reviews

Among social media networks, TripAdvisor acts as the main role because everyone is eager to share and review their thoughts on their travel experiences in different destinations. Sentiment analysis is a method that can be used to analyze people's behaviors and opinions on public and social media platforms. In this study, hotel reviews are extracted from the five most attractive Sri Lankan cities, and user-written reviews are compared over user bubble ratings, which define overall travelers' experiences as a numerical scale that ranks from 1 to 5. We find that the compatibility between user-written reviews and bubble ratings has a low correlation because bubble ratings may not represent the overall idea of users' genuine opinions expressed in their reviews. To address this problem, a two-phase approach is proposed: (1) the ensemble method to improve the performance of lexicon-based outputs and identify the correctly matching user review and bubble rating; (2) the self-learning approach to finding the sentiment of a review that does not properly label by the user. The performance is studied by considering reviews incompatible with the sentiment of user bubble rating and the sentiment generated by the proposed model. For example, regardless of bigram “not good”, the average percentages of the word “good” for each negatively identified review from the proposed model and bubble rating are 25.63% and 38.85%, respectively. Thereby, it is apparent that the negative sentiments derived by bubble rating have significantly more positive words compared to the proposed model.
A novel deep classifier framework for automated molecular subtyping of breast carcinoma using immunohistochemistry image analysis

Breast carcinoma has various subtypes based on the genetic factors involved in the pathogenesis of the malignancy. Identifying the exact subtype and providing targeted treatment to the patient can improve the survival chances. Molecular subtyping through immunohistochemistry analysis is a pathology procedure to determine the subtype of breast cancer. The existing manual procedure is tedious and involves assessing the status of the four vital molecular biomarkers present in the tumor tissues. In this paper, a deep learning-based framework for automated molecular subtyping of breast cancer is proposed. Digital slide images of the four biomarkers are separately processed by the proposed framework. In the preprocessing stage, the non-informative background regions from the images are separated. The patches extracted from the foreground regions are classified into target classes using convolutional neural network models trained for this purpose. Classification results are post-processed to predict the status of all the four biomarkers. The predictions for the individual biomarkers are finally consolidated as per clinical guidelines to determine the subtype of the cancer. The proposed system is evaluated for the performance of individual biomarker status prediction and patient-level subtype classification.For patient-level evaluation of biomarkers ER, PR, K67, and HER2, the proposed method gives F1 Scores 1.00, 1.00, 0.90, and 0.94 respectively, whereas for molecular subtyping an F1 score of 0.89 is obtained. In both these aspects, the proposed framework has given significant results that show the effectiveness of our approach.
Unsupervised anomaly detection in 3D brain MRI using deep learning with multi-task brain age prediction

Lesion detection in brain Magnetic Resonance Images (MRIs) remains a challenging task. MRIs are typically read and interpreted by domain experts, which is a tedious and time-consuming process. Recently, unsupervised anomaly detection (UAD) in brain MRI with deep learning has shown promising results to provide a quick, initial assessment. So far, these methods only rely on the visual appearance of healthy brain anatomy for anomaly detection. Another biomarker for abnormal brain development is the deviation between the brain age and the chronological age, which is unexplored in combination with UAD. We propose deep learning for UAD in 3D brain MRI considering additional age information. We analyze the value of age information during training, as an additional anomaly score, and systematically study several architecture concepts. Based on our analysis, we propose a novel deep learning approach for UAD with multi-task age prediction. We use clinical T1-weighted MRIs of 1735 healthy subjects and the publicly available BraTs 2019 data set for our study. Our novel approach significantly improves UAD performance with an AUC of 92.60% compared to an AUC-score of 84.37% using previous approaches without age information.
Super resolution reconstruction method for infrared images based on pseudo transferred features

High-quality infrared images are limited by optical diffraction limits, device material, and technology with a high acquisition cost. Improving image quality with super-resolution reconstruction is an important way to obtain high-resolution infrared images. Based on pseudo texture transfer, an infrared image super-resolution reconstruction algorithm is proposed in this paper. First, an image-to-image transfer learning framework is used to generate pseudo-infrared images from the high-resolution visible image set. Second, the feature maps of pseudo-infrared and low-resolution infrared images are extracted simultaneously, and the feature matching module is designed to calculate the similarity of feature maps with different layers between pseudo-infrared and low-resolution infrared images. The local feature maps with the maximum similarity replace the feature map of low-resolution infrared images. Finally, the original low-resolution image and the corresponding exchanged feature map are used in the proposed feature transfer model to gradually reconstruct the super-resolution image from high level to low level. Experimental results prove that the proposed method has improved performance in terms of quantitative indicators and visual quality.
Integrating annotations into multidimensional visual dashboards

Multidimensional data is often visualized using coordinated multiple views in an interactive dashboard. However, unlike in infographics where text is often a central part of the presentation, there is currently little knowledge of how to best integrate text and annotations in a visualization dashboard. In this paper, we explore a technique called FacetNotes for presenting these textual annotations on top of any visualization within a dashboard irrespective of the scale of data shown or the design of visual representation itself. FacetNotes does so by grouping and ordering the textual annotations based on properties of (1) the individual data points associated with the annotations, and (2) the target visual representation on which they should be shown. We present this technique along with a set of user interface features and guidelines to apply it to visualization interfaces. We also demonstrate FacetNotes in a custom visual dashboard interface. Finally, results from a user study of FacetNotes show that the technique improves the scope and complexity of insights developed during visual exploration.
Auto-splitting D* lite path planning for large disaster area

Abstract This research introduces a new path planning method for rescue robots in a dynamic and partially known area when the robots are performing tasks in a large area. The path planning of the rescue robots that move in the dynamic area is introduced to solve the issue of unnecessary areas, which are the disadvantages of the existing D*-based algorithms. This paper proposes a method to eliminate unnecessary expanded nodes of the dynamic and partially known environment by segmenting a map with an auto-clustering algorithm, which is able to achieve a faster execution time than conventional algorithms. Furthermore, to show the effectiveness of the proposed algorithms, an expected value of re-planned nodes in the dynamic and partially known area is introduced using a probability-based approach.
Designing efficient convolutional neural network structure: A survey

As a powerful machine learning method, deep learning has attracted the attention of numerous researchers. While exploring a high-performance neural network model, the floating-point operations of a neural network model are also increasing. In recent years, many researchers have noticed that efficiency is also one of important indicators to measure the property of neural network models. Obviously, the efficient neural network model is more helpful to deploy on mobile and embedded devices. Therefore, the efficient neural network model becomes a hot research spot. In this paper, we review the methods related to the structural design of efficient convolution neural networks in recent years. According to the characteristics of these methods, we divide them into three kinds of methods: model pruning, efficient architecture, and neural architecture search. Detailed analyses of each method are presented to demonstrate their advantages and disadvantages. Then, we comprehensively compare them in detail and propose many suggestions about the design of the efficient convolution neural network model structure. Inspired by these suggestions, we built a new efficient neural network model, SharedNet. And the SharedNet obtains the best accuracy of manually-designed efficient CNN models on the ImageNet dataset.
Enhancing Front-Vehicle Detection in Large Vehicle Fleet Management

Transportation safety has been widely discussed for avoiding forward collisions. The broad concept of remote sensing can be applied to detect the front of vehicles without contact. The traditional Haar features use adjacent rectangular areas for many ordinary vehicle studies to detect the front vehicle images in practice. This paper focused on large vehicles using a front-installed digital video recorder (DVR) with a near-infrared (NIR) camera. The views of large and ordinary vehicles are different; thus, this study used a deep learning method to process progressive improvement in moving vehicle detection. This study proposed a You Only Look Once version 4 (YOLOv4) supplemented with the fence method, called YOLOv4(III), to enhance vehicle detection. This method had high detection accuracy and low false omission rates using the general DVR equipment, and it provided comparison results. There was no need to have a high specification front camera, and the proposed YOLOv4(III) was found to have competitive performance. YOLOv4(III) reduced false detection rates and had a more stable frame per second (FPS) performance than with Haar features. This improved detection method can give an alert for large vehicle drivers to avoid serious collisions, leading to a reduction in the waste of social resources.
An adaptive kernelized correlation filters with multiple features in the tracking application

Automatic target detection and tracking systems are used extensively in complex scenes. In long-term tracking, some visual attributes of objects are changing, such as illumination, size, profile, and so on. To address the issue, it is particularly important to describe the essential properties of the objects in tracking. An enhanced kernelized correlation filter tracking strategy fused multiple features with location prediction is proposed. To make the object appearance models more accuracy and robustness, based on the original histogram of oriented gradient features, we integrate the hue, saturation, value, and grayscale information to construct a new descriptor to represent the target appearance. Moreover, location prediction and bi-linear interpolation are employed to obtain the more accurate target position. Experiments show that the proposed strategy can obtain superior or competitive performance in challenging benchmark data sets. In practice, the algorithm is applied to track shuttle bus targets in the airport apron.
Multi‐Traffic Targets Tracking Based on an Improved Structural Sparse Representation with Spatial‐Temporal Constraint

Vehicles or pedestrians tracking is an important task in intelligent transportation system. In this paper, we propose an online multi-object tracking for intelligent traffic platform that employs improved sparse representation and structural constraint. We first build the spatial-temporal constraint via the geometric relations and appearance of tracked objects, then we construct a robust appearance model by incorporating the discriminative sparse representation with weight constraint and local sparse appearance with occlusion analysis. Finally, we complete data association by using maximum a posteriori in a Bayesian framework in the pursuit for the optimal detection estimation. Experimental results in two challenging vehicle tracking benchmark datasets show that the proposed method has a good tracking performance.
A2BCF: An Automated ABC-Based Feature Selection Algorithm for Classification Models in an Education Application

Feature selection is an essential step of preprocessing in Machine Learning (ML) algorithms that can significantly impact the performance of ML models. It is considered one of the most crucial phases of automated ML (AutoML). Feature selection aims to find the optimal subset of features and remove the noninformative features from the dataset. Feature selection also reduces the computational time and makes the data more understandable to the learning model. There are various heuristic search strategies to address combinatorial optimization challenges. This paper develops an Automated Artificial Bee Colony-based algorithm for Feature Selection (A2BCF) to solve a classification problem. The application domain evaluating our proposed algorithm is education science, which solves a binary classification problem, namely, undergraduate student success. The modifications made to the original Artificial Bee Colony algorithm make the algorithm a well-performed approach.
An adaptive background subtraction approach based on frame differences in video surveillance

In the past decades, the insertion of cameras for the aim of surveillance is increased. Hence, a huge amount of data is produced by cameras. It is impossible to categorize and store all data. Therefore, algorithms that automatically process big data and track objects of interest are needed. Many methods are based on the opinion that the movement of objects causes differences in frames of a video, and the background would remain motionless during the video. Continuous dynamic behavior in the background deteriorates object detection performance.On the other hand, an excellent background extraction model can help to gain beneficent foreground detection results. The target of this paper is to model an algorithm that provides the pure background from video sequences. The idea of the proposed approach is to extract the background of complex and crowded scenes by using the differences of two consecutive frames’ pixels. Our experimental results show that the proposed approach provides significant performance in comparison with some previous techniques.