Reviewerly/exHarmony huggingface dataset:
Cascaded Deeply Supervised Convolutional Networks for Liver Lesion Segmentation

Liver lesion segmentation from abdomen computed tomography (CT) with deep neural networks remains challenging due to the small volume and the unclear boundary. To effectively tackle these problems, in this paper, we propose a cascaded deeply supervised convolutional networks (CDS-Net). The cascaded deep supervision (CDS) mechanism uses auxiliary losses to construct a cascaded segmentation method in a single network, focusing the network attention on pixels that are more difficult to classify, so that the network can segment the lesion more effectively. CDS mechanism can be easily integrated into standard CNN models and it helps to increase the model sensitivity and prediction accuracy. Based on CDS mechanism, we propose a cascaded deep supervised ResUNet, which is an end-to-end liver lesion segmentation network. We conduct experiments on LiTS and 3DIRCADb dataset. Our method has achieved competitive results compared with other state-of-the-art ones.
Mask-based Style-Controlled Image Synthesis Using a Mask Style Encoder

In recent years, the advances in Generative Adversarial Networks (GANs) have shown impressive results for image generation and translation tasks. In particular, the image-to-image translation is a method of learning mapping from a source domain to a target domain and synthesizing an image. Image-to-image translation can be applied to a variety of tasks, making it possible to quickly and easily synthesize realistic images from semantic segmentation masks. However, in the existing image-to-image translation method, there is a limitation on controlling the style of the translated image, and it is not easy to synthesize an image by controlling the style of each mask element in detail. Therefore, we propose an image synthesis method that controls the style of each element by improving the existing image-to-image translation method. In the proposed method, we implement a mask style encoder that extracts style features for each mask element. The extracted style features are concatenated to the semantic mask in the normalization layer, and used the style-controlled image synthesis of each mask element. In the experiments, we performed style-controlled images synthesis using the datasets consisting of semantic segmentation masks and real images. The results show that the proposed method has excellent performance for style-controlled images synthesis for each element.
Deep Learning for Medical Image Segmentation

Alzheimer's disease is recognized as a progressive loss of memory and often leads to a total loss of autonomy, which renders it difficult to tolerate. Hippocampus is among the first cerebral regions to be infected in AD. An accurate diagnosis at an early stage of AD is crucial for the intervention process. The low contrast of supporting tissues and organs makes segmentation of the CC from brain MRI very difficult. In this paper we introduced both automatic brain image segmentation methods to extract the Corpus Callosum (CC), the first based on SLIC using parallel implementation which gives accelerated results over classical SLIC, the second method motivated by using Deep Learning approach. Finally, we compare the outcome given by the previous approaches.
Robust LAD Based Occluded Face Recognition using a Support Map

Recently, face recognition has been widely studied. Many works use different fidelity terms to gain better robust performance in the case of occlusion. Most of them use least-squares-based estimation as the fidelity term, which is not far from the real situation especially under the occlusions. Moreover, these methods ignore the knowledge that occlusion has local spatial continuity. To get the more robust result, this paper proposes a novel method, namely robust least-sum of absolute deviations (RLAD) based occluded face recognition using a support map. Although LAD regression is more robust than LS regression, we propose a support map as the weight vector to gain robustness. The RLAD alternatively updates the support map and the representation code. Then, the classification stage is present based on the support map and the representation code. Extensive experiments on the Extended Yale B public face database demonstrate the effectiveness and robustness of the RLAD in face recognition against occlusion.
Web Application Software Engineering Technology and Process

With the rapid development of network technology and the rapid growth of e-commerce, complex wide-area distributed Web applications are emerging. Many traditional information management and database systems are being migrated to the Internet. The development of Web engineering is increasing gradually, and the proportion in software engineering technology and project development process will be larger and larger. Web engineering is an emerging and rapidly developing discipline. In order to ensure that Web engineering can develop as efficiently as traditional software engineering so as to avoid Web crisis, this paper will illustrate with the development and process examples of Web engineering technology. Combined with the software development and project management process and related results of a Web engineering project example, namely the driver training management information system (JPMIS), this paper proposes some applications based on the Web engineering software development and project management technology. This paper puts forward a concrete implementation method for the application of Web engineering in China's small and medium-sized software enterprises.
Deep Learning Based Question Answering Search Engine

Domain based Question Answering is concerned with building systems which provide answers to natural language questions that are asked specific to a domain. It comes under Information Retrieval and Natural language processing. Using Information Retrieval, one can search for the relevant documents which may contain the answer but it won’t give the exact answer for the question asked. In the presented work, a question answering search engine has been developed which first finds out the relevant documents from a huge textual document data of a construction company and then goes a step beyond to extract answer from the extracted document. The robust question answering system developed uses Elastic Search for Information Retrieval [paragraphs extraction] and Deep Learning for answering the question from the short extracted paragraph. It leverages BERT Deep Learning Model to understand the layers and representations between the question and answer. The research work also focuses on how to improve the search accuracy of the Information Retrieval based Elastic Search engine which returns the relevant documents which may contain the answer.
Adversarial Learning via Probabilistic Proximity Analysis

We consider the problem of designing a robust classifier in the presence of an adversary who aims to degrade classification performance by elaborately falsifying the test instance. We propose a model-agnostic defense approach wherein the true class label of the falsified instance is inferred by analyzing its proximity to each class as measured based on class-conditional data distributions. We present a k-nearest neighbors type approach to perform a sample-based approximation of the aforementioned probabilistic proximity analysis. The proposed approach is evaluated on three different real-world datasets in a game-theoretic setting, in which the adversary is assumed to optimize the attack design against the employed defense approach. In the game-theoretic evaluation, the proposed defense approach significantly outperforms benchmarks in various attack scenarios, demonstrating its efficacy against optimally designed attacks.
Estimating the Perceived Audio Quality Based on Multigene Symbolic Regression for Broadcasting Systems and Web-Casting Applications

In these challenging times of pandemic, people are increasingly using various broadcasting systems and webcasting applications. For this reason, the importance of evaluating the perceived quality from the perspective of the end user of these applications is also growing. In this paper we present a design and performance evaluation of parametric models estimating the audio quality perceived by the end users of broadcasting systems and web-casting applications. We used a concept of symbolic regression (SR) by Multi-Gene Genetic Programming (MGGP). Symbolic regression (SR) is used to discover mathematical expressions of functions that are multigene in nature, i.e. linear combinations of the input variables. Multigene symbolic regression was validated as an effective method by the results obtained by the designed parametric audio quality estimation models, providing good accuracy and generalisation capabilities.
The Importance of Sociocultural-Based Reflective Picture Storybook Media to Increase Reading Interest and Social Skills of Elementary School Students

Abstract Introduction: Issues of urgency in learning include interest in reading and social skills in primary schools. Interest in reading and social skills are some of the basic things that are always integrated into all learning. Therefore, learning requires appropriate media to overcome these problems, one of which is a sociocultural-based reflective picture storybook (SRPS) media. Purpose: The purpose of this article is to know the importance of SRPS media to increase the reading interest and social skills of elementary school students. Methods: In the paper, the method of a comparative literature review is applied. Conclusion: This concludes that SRPS media is important to increase the reading interest and social skills of elementary school students. To improve social skills and reading fondness, a media that is suitable for the characteristics of students is needed. Students’ interest in pictorial stories can be used as a solution to overcome the problem of low social skills and students’ fondness of reading characters that need to be improved. SRPS media is important to be developed in future research.
Efficient Clinical Data Sharing Framework Based on Blockchain Technology

While electronic health records have been collected for many years in Taiwan, their interoperability across different health care providers has not been entirely achieved yet. The exchange of clinical data is still inefficient and time consuming.This study proposes an efficient patient-centric framework based on the blockchain technology that makes clinical data accessible to patients and enable transparent, traceable, secure, and effective data sharing between physicians and other health care providers.Health care experts were interviewed for the study, and medical data were collected in collaboration with Ministry of Health and Welfare (MOHW) Chang-Hua hospital. The proposed framework was designed based on the detailed analysis of this information. The framework includes smart contracts in an Ethereum-based permissioned blockchain to secure and facilitate clinical data exchange among different parties such as hospitals, clinics, patients, and other stakeholders. In addition, the framework employs the Logical Observation Identifiers Names and Codes (LOINC) standard to ensure the interoperability and reuse of clinical data.The prototype of the proposed framework was deployed in Chang-Hua hospital to demonstrate the sharing of health examination reports with many other clinics in suburban areas. The framework was found to reduce the average access time to patient health reports from the existing next-day service to a few seconds.The proposed framework can be adopted to achieve health record sharing among health care providers with higher efficiency and protected privacy compared to the system currently used in Taiwan based on the client-server architecture.
Siamese Dynamic Mask Estimation Network for Fast Video Object Segmentation

Video object segmentation(VOS) has been a fundamental topic in recent years, and many deep learning-based methods have achieved state-of-the-art performance on multiple benchmarks. However, most of these methods rely on pixel-level matching between the template and the searched frames on the whole image while the targets only occupy a small region. Calculating on the entire image brings lots of additional computation cost. Besides, the whole image may contain some distracting information resulting in many false-positive matching points. To address this issue, motivated by one-stage instance object segmentation methods, we propose an efficient siamese dynamic mask estimation network for fast video object segmentation. The VOS is decoupled into two tasks, i.e., mask feature learning and dynamic kernel prediction. The former is responsible for learning high-quality features to preserve structural geometric information, and the latter learns a dynamic kernel that is used to convolve with the mask feature to generate a mask output. We use Siamese neural network as a feature extractor and directly predict masks after correlation. In this way, we can avoid using pixel-level matching, making our framework more simple and efficient. Experiment results on DAVIS 2016 /2017 datasets show that our proposed methods can run at 35 frames per second on NVIDIA RTX TITAN while preserving competitive accuracy.
Sensitivities and block sensitivities of elementary symmetric Boolean functions

Abstract Boolean functions have important applications in molecular regulatory networks, engineering, cryptography, information technology, and computer science. Symmetric Boolean functions have received a lot of attention in several decades. Sensitivity and block sensitivity are important complexity measures of Boolean functions. In this paper, we study the sensitivity of elementary symmetric Boolean functions and obtain many explicit formulas. We also obtain a formula for the block sensitivity of symmetric Boolean functions and discuss its applications in elementary symmetric Boolean functions.
The validity of multimedia edupark physics learning with a scientific approach based on the beach destination of Painan Beach

Abstract The background of developing edupark multimedia is a following-up to the preliminary research. Based on preliminary research that carried out to reveal several facts: the first principle, the concept of physics related to the tourist attraction of Carocok Painan Beach, Indonesia. Efforts to overcome every problem, especially in learning physics as the times evolve, in the 21st century the world of education is in a time of knowledge that requires the world community to be able to understand and utilize information and communication technology. The purpose of this research is to develop a physics edupark multimedia using a scientific approach to the Carocok Painan Beach tourist destination that is valid. This type of research is Educational design research (EDR) using the Plomp model, starting from the preliminary research stage (preliminary research), development or prototyping phase (development and design), and assessment phase (assessment). Edupark Physics multimedia validity tests were conducted by several experts. Its validation was carried out on the instrument sheet and learning multimedia by three validated materials, languages, and constructions from the lecturers. This research produces multimedia learning that belongs to a valid category. The conclusion of the results of this study is multimedia learning could make a best-matched platform for a user in physics learning activities as a source of learning physics at high school.
Error-Driven Fixed-Budget ASR Personalization for Accented Speakers

We consider the task of personalizing ASR models while being constrained by a fixed budget on recording speaker specific utterances. Given a speaker and an ASR model, we propose a method of identifying sentences for which the speaker’s utterances are likely to be harder for the given ASR model to recognize. We assume a tiny amount of speaker-specific data to learn phoneme-level error models which help us select such sentences. We show that speaker’s utterances on the sentences selected using our error model indeed have larger error rates when compared to speaker’s utterances on randomly selected sentences. We find that fine-tuning the ASR model on the sentence utterances selected with the help of error models yield higher WER improvements in comparison to fine-tuning on an equal number of randomly selected sentence utterances. Thus, our method provides an efficient way of collecting speaker utterances under budget constraints for personalizing ASR models.
Personalized Regularization Learning for Fairer Matrix Factorization

Matrix factorization is a canonical method for modeling user preferences for items. Regularization of matrix factorization models often uses a single hyperparameter tuned globally based on metrics evaluated on all data. However, due to the differences in the structure of per-user data, a globally optimal value may not be locally optimal for each individual user, leading to an unfair disparity in performance. Therefore, we propose to tune individual regularization parameters for each user. Our approach, personalized regularization learning (PRL), solves a secondary learning problem of finding the per-user regularization parameters by back-propagating through alternating least squares. Experiments on a benchmark dataset with different user group splits show that PRL outperforms existing methods in improving model performance for disadvantaged groups. We also analyze the learned parameters, finding insights into the effect of regularization on subpopulations with varying properties.
A Rank-Constrained Clustering Algorithm with Adaptive Embedding

Most spectral clustering algorithms obtain firstly the embedding based on the similarity matrix of the input data and then discretize the embedding to get the final clustering result. So, their performance is greatly affected by the noise of the similarity matrix, and the two-step strategy may lead to a suboptimal result. In this article, a novel Rank-Constrained clustering algorithm with Adaptive Embedding called RCAE is proposed, where the spectral embedding and the clustering structure are learned simultaneously, so, the influence of noise on performance is greatly reduced. In addition, a rank constraint is adopted in our model, thus, the connectivity matrix with exactly c (the number of clusters to construct) connected components can be learned, therefore, the final clustering result can be obtained according to the connected components. Experiments on several benchmark datasets validate the superiority of the proposed methods, compared to the several state-of-the-art clustering algorithms [GitHub].
ReADS: A Rectified Attentional Double Supervised Network for Scene Text Recognition

In recent years, scene text recognition is always regarded as a sequence-to-sequence problem. Connectionist Temporal Classification (CTC) and Attentional sequence recognition (Attn) are two very prevailing approaches to tackle this problem while they may fail in some scenarios respectively. CTC concentrates more on every individual character but is weak in text semantic dependency modeling. Attn based methods have better context semantic modeling ability while tends to overfit on limited training data. In this paper, we elaborately design a Rectified Attentional Double Supervised Network (ReADS) for general scene text recognition. To overcome the weakness of CTC and Attn, both of them are applied in our method but with different modules in two supervised branches which can make a complementary to each other. Moreover, effective spatial and channel attention mechanisms are introduced to eliminate background noise and extract valid foreground information. Finally, a simple rectified network is implemented to rectify irregular text. The ReADS can be trained end-to-end and only word-level annotations are required. Extensive experiments on various benchmarks verify the effectiveness of ReADS which achieves state-of-the-art performance.
Mutative BFO-Based Scheduling Algorithm for Cloud Environment

Scheduling jobs in cloud computing is a challenging issue. Utilizing the resources efficiently and gaining maximum in cloud computing is one of the goals of the cloud service provider, whereas the end-user likes to minimize the cost and time while scheduling a job on the cloud environment. The rapid increase in the demand for high computational resources has led the path for the growth of data centers in cloud data centers. With an increase in data centers, energy consumption has become a concern to the environment. The cloud environment is also characterized by unpredictable demand from users leading to a challenge to map virtual resources with jobs while satisfying the user, service provider, and environmental constraints. In this paper, a job scheduling algorithm for mapping resources to the job is proposed by applying the mutation operator to Bacterial Foraging Optimization (BFO) algorithm named BFO-Mutative algorithm. The BFO algorithm is characterized by slow search space and local convergence. The proposed algorithm overcomes these issues by mutating each particle in every iteration that improves convergence rate and thus efficiency. The proposed algorithm maps job to resources considering deadline as a constraint and reduces execution cost and time. The proposed algorithm also optimizes energy consumption in cloud data centers. Through experiments implemented in CloudSim, the results verify that the proposed algorithm reduces makespan and energy efficiency while increasing throughput.
A New Approach Based on Steganography to Face Facial Recognition Vulnerabilities Against Fake Identities

By the huge use of biometric identification and authentication systems, securing user's images is one of the major recent researches topics. In this paper we aim to present a new approach which is based on detecting face in any picture (even with low quality) a detection that can goes to 93%. As a second work we aim to test the ability of this algorithm to identify people while wearing medical masks. In fact, with the spread of Covid-19, people are now obliged to wear medical masks. These masks cover almost 60% of persons faces, this lack of information can prevent the identification of the person or can create some confusions. And To secure images and prevent identity theft we propose an approach that consists of hiding a generated key in each person's image. This unique key is based on user's personal information, the key will be verified by Luhn algorithm, which is considered as a widely used algorithm to verify generated IDs. As a third work, we aim to hide the person's ID in the image using a steganographic algorithm. Our work main objective is to protect pictures and prevent any attempt of creating a fake model of the owners. Therefore, the ID hidden in the picture will be destructed in every attempt of creating a fake image or model.
Pedestrian Detection on Multispectral Images in Different Lighting Conditions

Autonomous vehicles keep evolving in years. Pedestrian detection is a significant function for autonomous vehicles. However, there is an issue that affects the performance of pedestrian detection, which is the lighting condition. Pedestrian detection performance usually decreases at nighttime. This research utilized Deep Neural Network as a tool to accomplish a pedestrian detection task in different lighting conditions. RGB, thermal infrared, and multispectral images were inputted to the DNN for pedestrian detection and it was evaluated afterward. The results imply that the multispectral image-based pedestrian detection can achieve better performance than the RGB image-based or thermal image-based pedestrian detections.