Reviewerly/exHarmony huggingface dataset:
Orthogonal Deep Models as Defense Against Black-Box Attacks

Deep learning has demonstrated state-of-the-art performance for a variety of challenging computer vision tasks. On one hand, this has enabled deep visual models to pave the way for a plethora of critical applications like disease prognostics and smart surveillance. On the other, deep learning has also been found vulnerable to adversarial attacks, which calls for new techniques to defend deep models against these attacks. Among the attack algorithms, the black-box schemes are of serious practical concern since they only need publicly available knowledge of the targeted model. We carefully analyze the inherent weakness of deep models in black-box settings where the attacker may develop the attack using a model similar to the targeted model. Based on our analysis, we introduce a novel gradient regularization scheme that encourages the internal representation of a deep model to be orthogonal to another, even if the architectures of the two models are similar. Our unique constraint allows a model to concomitantly endeavour for higher accuracy while maintaining near orthogonal alignment of gradients with respect to a reference model. Detailed empirical study verifies that controlled misalignment of gradients under our orthogonality objective significantly boosts a model's robustness against transferable black-box adversarial attacks. In comparison to regular models, the orthogonal models are significantly more robust to a range of l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> norm bounded perturbations. We verify the effectiveness of our technique on a variety of large-scale models.
AI-enabled Microscopic Blood Analysis for Microfluidic COVID-19 Hematology

Microscopic blood cell analysis is an important methodology for medical diagnosis, and complete blood cell counts (CBCs) are one of the routine tests operated in hospitals.Results of the CBCs include amounts of red blood cells, white blood cells and platelets in a unit blood sample.It is possible to diagnose diseases such as anemia when the numbers or shapes of red blood cells become abnormal.The percentage of white blood cells is one of the important indicators of many severe illnesses such as infection and cancer.The amounts of platelets are decreased when the patient suffers hemophilia.Doctors often use these as criteria to monitor the general health conditions and recovery stages of the patients in the hospital.However, many hospitals are relying on expensive hematology analyzers to perform these tests, and these procedures are often time consuming.There is a huge demand for an automated, fast and easily used CBCs method in order to avoid redundant procedures and minimize patients' burden on costs of healthcare.In this research, we investigate a new CBC detection method by using deep neural networks, and discuss state of the art machine learning methods in order to meet the medical usage requirements.The approach we applied in this work is based on YOLOv3 algorithm, and our experimental results show the applied deep learning algorithms have a great potential for CBCs tests, promising for deployment of deep learning methods into microfluidic point-of-care medical devices.As a case of study, we applied our blood cell detector to the blood samples of COVID-19 patients, where blood cell clots are a typical symptom of COVID-19.
No room for error

Developers of quantum computers face their true challenge: taming the noise that plagues the intertwined quantum bits
Using gradient boosting with stability selection on health insurance claims data to identify disease trajectories in chronic obstructive pulmonary disease

Objective We propose a data-driven method to detect temporal patterns of disease progression in high-dimensional claims data based on gradient boosting with stability selection. Materials and methods We identified patients with chronic obstructive pulmonary disease in a German health insurance claims database with 6.5 million individuals and divided them into a group of patients with the highest disease severity and a group of control patients with lower severity. We then used gradient boosting with stability selection to determine variables correlating with a chronic obstructive pulmonary disease diagnosis of highest severity and subsequently model the temporal progression of the disease using the selected variables. Results We identified a network of 20 diagnoses (e.g. respiratory failure), medications (e.g. anticholinergic drugs) and procedures associated with a subsequent chronic obstructive pulmonary disease diagnosis of highest severity. Furthermore, the network successfully captured temporal patterns, such as disease progressions from lower to higher severity grades. Discussion The temporal trajectories identified by our data-driven approach are compatible with existing knowledge about chronic obstructive pulmonary disease showing that the method can reliably select relevant variables in a high-dimensional context. Conclusion We provide a generalizable approach for the automatic detection of disease trajectories in claims data. This could help to diagnose diseases early, identify unknown risk factors and optimize treatment plans.
D-Star Algorithm Modification

One of the most effective methods for solving a navigation problem is the method of constructing a navigation system based on the simultaneous localization and mapping algorithm and obstacle avoidance algorithms. One of the most effective obstacles avoidance algorithms is the D-star algorithm [1, 2, 3], which, despite its effectiveness, has some drawbacks. This modification allows to eliminate some problems arising during the implementation of the navigation system.
Type IV codes over a non-local non-unital ring

There is a local ring H of order 4, without identity for the multiplication, defined by generators and relations as H =〈a, b | 2a = 2b = 0, a2 = 0, b2 = b, ab = ba = 0〉. We classify self orthogonal codes of length n and size 2n (called here quasi self-dual codes or QSD) up to the length n = 6. In particular, we classify quasi Type IV codes (a subclass of Type IV codes, viz QSD codes with even weights) up to n = 6.
A New Secret Sharing Scheme Based on Polynomials over Finite Fields

In this paper, we examine a secret sharing scheme based on polynomials over finite fields. In the presented scheme, the shares can be used for the reconstruction of the secret using polynomial multiplication. This scheme is both ideal and perfect.
Automated badminton smash recognition using convolutional neural network on the vision based data

Abstract Sport performance analysis in sports practice cannot be separable. It is important to help coach analyse and improve the performance of their athletes through training or game session. Due to the advancement of technology nowadays, the notational analysis of the video content using various software packages has become possible. Unluckily, the coach needs to recognize the actions manually before doing further analysis. The purpose of this study is to formulate an automated system for badminton smash recognition on widely available broadcasted videos using pre-trained Convolutional Neural Network (CNN) method. Smash and other badminton actions such as clear, drop, lift and net from the video were used to formulate the CNN models. Therefore, two experiments were conducted in this study. The first experiment is the study on the performance between four different existing pre-trained models which is AlexNet, GoogleNet, Vgg-16 Net and Vgg-19 Net in recognizing five actions. The results show that the pre-trained AlexNet model has the highest performance accuracy and fastest training period among the other models. The second experiment is the study on the performance of two different pre-trained models which is AlexNet and GoogleNet to recognize smash and non-smash action only. The results show that the pre-trained GoogleNet model produces the best performance in recognizing smash action. In conclusion, pre-trained AlexNet model is suitable to be used to automatically recognize the five badminton actions while GoogleNet model is excellent at recognizing smash action from the broadcasted video for further notational analysis.
Brecha digital en tiempo del COVID-19

espanolCon la crisis sanitaria que estamos viviendo debido al COVID-19, de un dia para otro, la comunidad educativa se ha visto obligada a cambiar las aulas de nuestros centros por las habitaciones de casa, donde ensenanza se mantiene gracias a las Tecnologias de la Informacion y la Comunicacion (TIC) en sus multiples formas. Educacion en Extremadura ha puesto a disposicion de los docentes diversos recursos para la ensenanza a traves de plataformas y paquetes de herramientas. Pero todo este amplio sistema informatico esta desvelando serias dificultades para el alumnado, donde todas las familias no disponen de soporte tecnico ni las mismas oportunidades para acceder a ellos. Esta experiencia refleja la realidad de tres centros de Educacion Primaria, en contextos bien diferenciados: rural, urbano de clase social baja y urbano de clase social media - alta, donde acceder a la educacion con la ayuda de las TIC puede ser algo cotidiano, o por el contrario, algo que resulta imposible. Planteamos las dificultades en estos tres contextos, desde la vision de docentes que trabajan en los mismos, mostrando con ello, la inevitable brecha digital que encuentran nuestro alumnado, el estres que sufren las familias o los horarios lectivos interminables de los docentes. EnglishWith the health crisis that we are experiencing due to COVID-19, from one day to the next, the educational community has been forced to change the classrooms of our centers for rooms at home, where teaching is maintained thanks to Information Technologies. and Communication (ICT) in its many forms. Education in Extremadura has made various teaching resources available to teachers through platforms and toolkits. But all this extensive computer system is revealing serious difficulties for students, where all families do not have technical support or the same opportunities to access them. This experience reflects the reality of three primary education centers, in well-differentiated contexts: rural, urban low-social class and urban middle-upper social class, where access to education with the help of ICT can be something everyday, or on the contrary, something that is impossible. We raise the difficulties in these three contexts, from the perspective of teachers who work in them, thereby showing the inevitable digital divide that our students encounter, the stress families suffer or the teachers' endless teaching hours.
Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning

Named Entity Recognition (NER) in domains like e-commerce is an understudied problem due to the lack of annotated datasets.Recognizing novel entity types in this domain, such as products, components, and attributes, is challenging because of their linguistic complexity and the low coverage of existing knowledge resources.To address this problem, we present a bootstrapped positiveunlabeled learning algorithm that integrates domain-specific linguistic features to quickly and efficiently expand the seed dictionary.The model achieves an average F1 score of 72.02% on a novel dataset of product descriptions, an improvement of 3.63% over a baseline BiL-STM classifier, and in particular exhibits better recall (4.96% on average).
SV-NET: A Deep Learning Approach to Video Based Human Activity Recognition

The automatic identification of physical activities performed by human beings is referred to as Human Activity Recognition (HAR). It aims to infer the actions of one or more persons from a set of observations captured by sensors, videos or still images. Recognizing human activities from video sequences is a much challenging task due to problems such as background clutter, partial occlusion, changes in scale, viewpoint, lighting, and appearance etc. In this paper, we propose a Convolutional Neural Network (CNN) model named as SV-NET, in order to classify human activities obtained directly from RGB videos. The proposed model has been tested on three benchmark video datasets namely, KTH, UCF11 and HMDB51. The results of the proposed model demonstrate improved performance over some existing deep learning based models.
Summarizing Online Movie Reviews: A Machine Learning Approach to Big Data Analytics

Information is exploding on the web at exponential pace, and online movie review over the web is a substantial source of information for online users. However, users write millions of movie reviews on regular basis, and it is not possible for users to condense the reviews. Classification and summarization of reviews is a difficult task in computational linguistics. Hence, an automatic method is demanded to summarize the vast amount of movie reviews, and this method will permit the users to speedily distinguish between positive and negative features of a movie. This work has proposed a classification and summarization method for movie reviews. For movie review classification, bag-of-words feature extraction technique is used to extract unigrams, bigrams, and trigrams as a feature set from given review documents and represent the review documents as a vector. Next, the Na¨ıve Bayes algorithm is employed to categorize the movie reviews (signified as a feature vector) into negative and positive reviews. For the task of movie review summarization, word2vec model is used to extract features from classified movie review sentences, and then semantic clustering technique is used to cluster semantically related review sentences. Different text features are employed to compute the salience score of all review sentences in clusters. Finally, the best-ranked review sentences are picked based on top salience scores to form a summary of movie reviews. Empirical results indicate that the suggested machine learning approach performed better than benchmark summarization approaches.
DCSegNet: Deep Learning Framework Based on Divide-and-Conquer Method for Liver Segmentation

Image segmentation plays a vital role in the medical diagnosis and intervention field. The segmentation methods can be classified as fully automated, semiautomated or manual. Among them, manual segmentation can best improve the quality of the results, but it is time-consuming and tedious, and it may lead to operator bias. A continuity-aware probabilistic network based on the divide-and-conquer method was proposed in the current work. The proposed network comprised backbone network, local segmentation and a weight network. The backbone network extracts the features from image. The local segmentation divides the data space, whereas the weight network provides the continuity-aware weights. Therefore, combining those results of the weighted segments can eventually yield precise estimations. In this study, the proposed model was evaluated against several recent methods on the three datasets, and a several performance indexes of segmentation were evaluated for liver segmentation, the results showing that it is the most advanced liver segmentation approach. The source code of this work is publicly shared at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/licongsheng/DCSegNet</uri> for others to easily reproduce the work and build their own models with the introduced mechanisms.
The role of waveform monitoring in Sepsis identification within the first hour of Intensive Care Unit stay

The latest extensive development of machine learning models in healthcare, and in particular their application to data from the intensive care unit (ICU), is directed towards the main objective to help clinicians in making more timely diagnoses and efficient decisions. Many studies have been focused on the identification of Sepsis in a complex environment such as the ICU by using the data collected in electronic health records. However, only a few studies have investigated associations between the patients' continuously monitored vital signs and their Sepsis status. This work aims at demonstrating that machine learning algorithms considering measures extracted from 103 patients from the publicly available MIMIC-III clinical and waveform databases are able to adequately identify Sepsis just within the first hour of stay in the ICU. A bagged tree classifier showed AUC=0.86, Specificity=0.85 and Sensitivity=0.86 on the test set, when trained using only the information extracted from the recorded electrocardiogram and arterial blood pressure waveforms, showing that the information coming from waveform monitoring may help in detecting Sepsis within the first hour of ICU stay.
Selection of optimal hyper-parameter values of support vector machine for sentiment analysis tasks using nature-inspired optimization methods

Sentiment analysis and classification task is used in recommender systems to analyze movie reviews, tweets, Facebook posts, online product reviews, blogs, discussion forums, and online comments in social networks. Usually, the classification is performed using supervised machine learning methods such as support vector machine (SVM) classifier, which have many distinct parameters. The selection of the values for these parameters can greatly influence the classification accuracy and can be addressed as an optimization problem. Here we analyze the use of three heuristics, nature-inspired optimization techniques, cuckoo search optimization (CSO), ant lion optimizer (ALO), and polar bear optimization (PBO), for parameter tuning of SVM models using various kernel functions. We validate our approach for the sentiment classification task of Twitter dataset. The results are compared using classification accuracy metric and the Nemenyi test.
Machine Learning and its Emergence in the Modern World and its Contribution to Artificial Intelligence

Machine learning is known as the scientific study of various algorithms and statistics as well as models which can be used to create or perform certain tasks. These tasks are often based upon the dependability of the Interface as well as the patterns. As machine learning is also known as the subset of Artificial Intelligence, it enables the system to create or perform several tasks without the need for any manual changes. This mechanism relatively related to automatic performance and self- learning as it can be used to detect various faults within a inbuilt system or a software and take necessary steps to debug and run diagnostics to reduce errors as much as it can. Due to the emergence of Artificial Intelligence, various industries and private firms such as Space X and Tesla have induced Machine Learning into their workspace and especially when it comes to industrial usage, Corporations such as Tesla have developed AI based vehicles which run under electricity and automatic debugging. As the emergence of Artificial Intelligence have given ways to Machine Learning, it is inevitable that we may or may even be quite close to be perceiving futuristic technology much earlier than intended.
An Attention-Based Latent Information Extraction Network (ALIEN) for High-Order Feature Interactions

One of the primary tasks for commercial recommender systems is to predict the probabilities of users clicking items, e.g., advertisements, music and products. This is because such predictions have a decisive impact on profitability. The classic recommendation algorithm, collaborative filtering (CF), still plays a vital role in many industrial recommender systems. However, although straight CF is good at capturing similar users’ preferences for items based on their past interactions, it lacks regarding (1) modeling the influences of users’ sequential patterns from their individual history interaction sequences and (2) the relevance of users’ and items’ attributes. In this work, we developed an attention-based latent information extraction network (ALIEN) for click-through rate prediction, to integrate (1) implicit user similarity in terms of click patterns (analogous to CF), and (2) modeling the low and high-order feature interactions and (3) historical sequence information. The new model is based on the deep learning, which goes beyond the capabilities of econometric approaches, such as matrix factorization (MF) and k-means. In addition, the approach provides explainability to the recommendation by interpreting the contributions of different features and historical interactions. We have conducted experiments on real-world datasets that demonstrate considerable improvements over strong baselines.
Open Science Graphs Must Interoperate!

Open Science Graphs (OSGs) are Scientific Knowledge Graphs whose intent is to improve the overall FAIRness of science, by enabling open access to graph representations of metadata about people, artefacts, institutions involved in the research lifecycle, as well as the relationships between these entities, in order to support stakeholder needs, such as discovery, reuse, reproducibility, statistics, trends, monitoring, impact, validation, and assessment. The represented information may span across entities such as research artefacts (e.g. publications, data, software, samples, instruments) and items of their content (e.g. statistical hypothesis tests reported in publications), research organisations, researchers, services, projects, and funders. OSGs include relationships between such entities and sometimes formalised (semantic) concepts characterising them, such as machine-readable concept descriptions for advanced discoverability, interoperability, and reuse. OSGs are generally valuable individually, but would greatly benefit from information exchange across their collections, thereby improving their efficacy to serve stakeholder needs. They could, therefore, reuse and exploit the data aggregation and added value that characterise each OSG, decentralising the effort and capitalising on synergies, as no one-size-fits-all solution exists. The RDA IG on Open Science Graphs for FAIR Data is investigating the motivation and challenges underpinning the realisation of an Interoperability Framework for OSGs. This work describes the key motivations for i) the definition of a classification for OSGs to compare their features, identify commonalities and differences, and added value and for ii) the definition of an Interoperability Framework, specifically an information model and APIs that enable a seamless exchange of information across graphs.
Paillier-based Fuzzy Multi-Keyword Searchable Encryption Scheme with Order-Preserving

Efficient multi-keyword fuzzy search over encrypted data is a desirable technology for data outsourcing in cloud storage. However, the current searchable encryption solutions still have deficiencies in search efficiency, accuracy and multiple data owner support. In this paper, we propose an encrypted data searching scheme that can support multiple keywords fuzzy search with order preserving (PMS). First, a new spelling correction algorithm-(Possibility-Levenshtein based Spelling Correction) is proposed to correct user input errors, so that fuzzy keywords input can be supported. Second, Paillier encryption is introduced to calculate encrypted relevance score of multiple keywords for order preserving. Then, a queue-based query method is also applied in this scheme to break the linkability between the query keywords and search results and protect the access pattern. Our proposed scheme achieves fuzzy matching without expanding the index table or sacrificing computational efficiency. The theoretical analysis and experiment results show that our scheme is secure, accurate, error-tolerant and very efficient.