Reviewerly/exHarmony huggingface dataset:
Exploring Common and Label-Specific Features for Multi-Label Learning With Local Label Correlations

In multi-label learning, instances can be associated with a set of class labels. The existing multi-label feature selection (MLFS) methods generally adopt either of these two strategies, namely, selecting a subset of features that is shared by all labels (common features) or exploring the most discriminative features for each label (label-specific features). However, both of them can play a key role in the discrimination of different labels. For example, common features can distinguish all labels, and label-specific features contribute to discriminating label’s differences. They are important for the discriminability of selected features. On the other hand, it is well-known that exploiting label correlations can advance the performance of MLFS, and label correlations are local and only shared by a data subset in most cases. How to effectively learn and exploit local label correlations in the selection process is significant. In this paper, to address these problems, we propose a novel MLFS framework. Specially, common and label-specific features are simultaneously considered by introducing both <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{2,1}$ </tex-math></inline-formula> -norm and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> -norm regularizers, local label correlations are automatically learned with probability and learned correlation information is efficiently exploited to help feature selection by constraining label correlations on the output of labels. A comparative study with seven state-of-the-art methods manifests the efficacy of our framework.
Clusterization Of Paddy Field Farmers Condition In Kota Metro Lampung Province Indonesia Using K-Means Clustering Algorithm

Metro City is one of the administrative cities in Lampung province, Indonesia and also one of important rice producing regions in Lampung. Application of intensive agriculture in trend of declining area, low quality of land and differences of farmers internal characteristics that requires special treatment as solutions. Clustering farmers using the K-Means Cluster algorithm and Elbow Method can be used to facilitate policy makers determine programs and activities must be taken. Results showed that farmers are ideally grouped into 5 clusters (C1, C2, C3, C4 and C5). C1 members having most family members (4,54 persons). C2 members are the oldest age (68 years old) with longest farming experience (52.00 years) but have lowest formal education (7.67 years), least family members (3.33 person) and lowest total area (0.37 hectare). C3 having highest formal education (14.60 years) and largest paddy fields (0.80 hectare) but don't use any pesticides in paddy cropping management. Whereas farmers in C4 have largest family members helped (2.00 people). Farmers in C5 are the youngest (45.50 years old) and having the shortest experience (29.50 years) but use the most types (4 brands) and amounts of pesticides (400.00 mm.hectare.rotation-1) in paddy field management practices in Metro City.&#x0D;
Smart Privacy Protection for Big Video Data Storage Based on Hierarchical Edge Computing

Recently, the rapid development of the Internet of Things (IoT) has led to an increasing exponential growth of non-scalar data (e.g., images, videos). Local services are far from satisfying storage requirements, and the cloud computing fails to effectively support heterogeneous distributed IoT environments, such as wireless sensor network. To effectively provide smart privacy protection for video data storage, we take full advantage of three patterns (multi-access edge computing, cloudlets and fog computing) of edge computing to design the hierarchical edge computing architecture, and propose a low-complexity and high-secure scheme based on it. The video is divided into three parts and stored in completely different facilities. Specifically, the most significant bits of key frames are directly stored in local sensor devices while the least significant bits of key frames are encrypted and sent to the semi-trusted cloudlets. The non-key frame is compressed with the two-layer parallel compressive sensing and encrypted by the 2D logistic-skew tent map and then transmitted to the cloud. Simulation experiments and theoretical analysis demonstrate that our proposed scheme can not only provide smart privacy protection for big video data storage based on the hierarchical edge computing, but also avoid increasing additional computation burden and storage pressure.
Corpora of Russian Spontaneous Speech as a Tool for Modelling Natural Speech Production and Recognition

The paper presents two corpora of spontaneous Russian. The aim of the study is to describe the speech signal in a way close to the one a listener has to cope with while processing natural speech and to use the corpora for further computer simulation of spoken word recognition. The corpus of adult speech includes around two hours of recordings provided with the orthographic and acoustic-phonetic transcription performed manually by trained phoneticians. The word list imitating the mental lexicon of a listener where each phonetic realization corresponds to all possible variants of its interpretation found in the corpus was created based on the corpus. The analysis of the adult speech shows how often reduced word forms occur in spontaneous speech and allows to develop and check an algorithm of the restoration of grammatical information in noun phrases. The corpus of children's speech includes both longitudinal and experimental data (around 18 hours all together) and is the first example of the corpus of Russian children's speech provided with phonetic annotation. The preliminary analysis of the children's speech shows that at least some reduced variants can be stored in the mental lexicon of a native speaker.
Authenticating Motor Insurance Documents using QR Codes

In Jamaica there has been a significant rise in the level of fraudulent activities over the years relating to the forging of insurance cover notes and certificate papers. To address this problem, this research presents a quantitative case study aimed at authenticating and verifying these paper-based insurance document templates. The approach applied the use of a 2D barcode authentication scheme known as Quick Response (QR) Codes within to support the authentication and verification points for such insurance papers. In the context of the local insurance market, as far as we know this is the first research study conducted within Caribbean regional institutions as it relates to the security of the digitization of insurance documents. In conducting this proof of concept study our delimitation is that we do not seek to support any continuous online and network-access as forms of real time communication for the security of these digital insurance documents, as that would be the subject to a larger enterprise wide study on the issue and in of itself represents independent ongoing work.
Rare Itemsets Selector with Association Rules for Revenue Analysis by Association Rare Itemset Rule Mining Approach

Background: Knowledge discovering rare itemsets mining, and association rules are significant in a transactional dataset. Be that as it may, rare association rules are now and again more intriguing than frequent association rules since rare rules indicate an unforeseen or obscure association. The utilization of rare itemsets mining with item selector is unavoidable and has turned into an emerging field of research; therefore, this subject has numerous challenges. Objective: To perform the revenue examination of the marketing sector by rare itemsets selector by threshold and time series-based prediction technique. Methods: This paper gives the revenue examination of marketing sector by rare itemsets selector by threshold and time series-based prediction technique. A new algorithm for locating the rare itemsets by Association Rare itemset Rule Mining (ARIRM) to produced rules then utility itemsets discover by the threshold. When the rare patterns are analyzed, the ARIMA model used to anticipate the revenue. Based on the investigation of rare showcasing data with rules of the mining space, this methodology uses a tree structure to learn the rare items. Results: The test results in the "K" transactions with high revenues discovered utilizing the proposed model contrasted with other existing procedures; this forecast procedure is helpful for upcoming transactions. Conclusion: Based on the investigation of rare showcasing data with rules of the mining space, this methodology uses a tree structure to learn the rare items.
Learning numerical observers using unsupervised domain adaptation

Medical imaging systems are commonly assessed by use of objective image quality measures. Supervised deep learning methods have been investigated to implement numerical observers for task-based image quality assessment. However, labeling large amounts of experimental data to train deep neural networks is tedious, expensive, and prone to subjective errors. Computer-simulated image data can potentially be employed to circumvent these issues; however, it is often difficult to computationally model complicated anatomical structures, noise sources, and the response of real-world imaging systems. Hence, simulated image data will generally possess physical and statistical differences from the experimental image data they seek to emulate. Within the context of machine learning, these differences between the sets of two images is referred to as <i>domain shift</i>. In this study, we propose and investigate the use of an adversarial domain adaptation method to mitigate the deleterious effects of domain shift between simulated and experimental image data for deep learning-based numerical observers (DL-NOs) that are trained on simulated images but applied to experimental ones. In the proposed method, a DL-NO will initially be trained on computer-simulated image data and subsequently adapted for use with experimental image data, without the need for any labeled experimental images. As a proof of concept, a binary signal detection task is considered. The success of this strategy as a function of the degree of domain shift present between the simulated and experimental image data is investigated.
The Impact of SMOTE and Grid Search on Maintainability Prediction Models

Software maintainability has gained more attention in recent years. It can be defined as the ease with which modifications can be performed. In this study, we are interested in the changes of a class due to bug fixing. We performed an experimental study using five Machine learning techniques; K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Trees (DT), Multilayer Perceptron (MLP), and Naïve Bayes (NB). The main focus in this study is to propose the use of Grid search method for tuning hyper-parameters and to balance datasets using SMOTE technique. The results show that there is no evidence concerning the best ML technique in all datasets. However, we found that balanced data and tuning parameters are suitable in order to obtain the best performance of ML techniques.
Similarity detection design using Winnowing Algorithm as an effort to apply green computing

Abstract The website is a media that is currently widely used as a tool to provide services and information. In this study, the application of a Winnowing Algorithm to detect the similarity of proposal documents through the website as an effort to apply green computing on campus. With the development of similarity detection is expected to use paper, computer resources will be reduced. Some test scenarios are carried out by changing the values of the gram and window parameters to obtain optimal values. This optimal value will be applied to the application to be built. The results of the implementation of the Winnowing Algorithm on the proposal management information system in the form of a plagiarism checking feature that is part of the system. The plagiarism checking feature will be able to produce output in the form of a percentage of the plagiarism rate that will be used as a recommendation for the department as an effort to apply green computing.
Digital embodiment and improving health outcomes

This chapter focuses on the influence of avatar use and interaction with agents or virtual characters on users' health behaviors and attitudes. Avatar and agent-based interactions are becoming increasingly common. For instance, people play video games on phones, consoles, and computers that depict digital characters onscreen and also have access to augmented reality apps that overlay digital characters on the physical world (e.g., Pokémon GO). This chapter discusses how users experience virtual situations and characters as if they were “real” and then reviews studies investigating how avatars and agents may increase health-related outcomes including pain and recovery, physical activity, psychological well-being, food consumption, and body image. The chapter also offers theoretical integration and critique, along with future directions for this area of research.
Simulating breast mammogram using Conditional Generative Adversarial Network: application towards finding mammographically-occult cancer

We are developing a computerized method to detect mammographically-occult (MO) breast cancers in screening mammograms. The technique exploits asymmetries between mammograms of the left and right breasts. In this study we investigated whether using a conditional generative adversarial network (CGAN) to produce simulated images of the contralateral breast can provide additional information as to breast cancer occurrence (supplementing the left-right mammogram comparison). We trained the CGAN using 1366 normal screening mammograms to simulate the opposite breast, by using the left-right pair as input. After training, we found increased similarity (mean squared error (MSE) and 2D-correlation) between the pair of simulated contralateral and actual (real) mammograms (SR) compared to that of the pair of actual (real left and real right) mammograms (RR). We then applied the CGAN on the independent screening mammogram dataset of 333 women with dense breasts, containing 97 unilateral MO cancer. We computed the similarity measures on the SR and RR pairs. The similarity between the SR pairs of MO cases was smaller than that of controls, while the similarity between the RR pairs of MO cases and controls was similar to each other. We trained a logistic regression classifier using similarity measures as markers for finding MO cancer. Using 10-folder cross-validation, the AUC of the SR+RR classifier was 0.67&plusmn;0.09 compared to 0.57&plusmn;0.1 for the RR classifier (p=0.032). We conclude that by comparing a mammogram with simulated images can provide additional information than obtained by comparing pairs of actual mammograms.
Finding novelty with uncertainty

Medical images are often used to detect and characterize pathology and disease; however, automatically identifying and segmenting pathology in medical images is challenging because the appearance of pathology across diseases varies widely. To address this challenge, we propose a Bayesian deep learning method that learns to translate healthy computed tomography images to magnetic resonance images and simultaneously calculates voxel-wise uncertainty. Since high uncertainty occurs in pathological regions of the image, this uncertainty can be used for unsupervised anomaly segmentation. We show encouraging experimental results on an unsupervised anomaly segmentation task by combining two types of uncertainty into a novel quantity we call scibilic uncertainty.
Multi-tier Authentication for Cloud Security

Cloud computing is one of the latest fields of Computer Science Engineering that deals with providing services to the users of the system/network as per their requirements and on pay-per-use basis. It provides a simple, flexible, heterogeneous, and architecture-neutral platform from where the user can access the desired services with ease. It can be considered as a modified form of distributed computing. The entire operation of cloud computing is based on Internet; hence, we can consider it as Internet-based computing. Security for cloud systems is an important factor that ensures trust between the cloud service provider and the users. As cloud provides a centralized repository where all services and resources reside, it is very important to ensure the authorized access to them. As cloud is based on Pay-per-Use model, it is necessary that the user should access only those services and resources for which they had subscribed. Any type of unauthorized access will lead to some type of loss. In this paper, a technique to ensure authorized access by users to cloud resources and services has been proposed, so as to overcome the above-stated issues.
Wavelet Scattering Transform and Ensemble Methods for Side-Channel Analysis

Recent works in side-channel analysis have been fully relying on training classification models to recover sensitive information from traces. However, the knowledge of an attacker or an evaluator is not taken into account and poorly captured by solely training a classifier on signals. This paper proposes to inject prior information in preprocessing and classification in order to increase the performance of side-channel attacks (SCA). First we propose to use the Wavelet Scattering Transform, recently proposed by Mallat, for mapping traces into a time-frequency space which is stable under small translation and diffeomorphism. That way, we address the issues of desynchronization and deformation generally present in signals for SCA. The second part of our paper extends the canonical attacks over byte and Hamming weight by introducing a more general attack. Classifiers are trained on different labelings of the sensitive variable and combined by minimizing a cross-entropy criterion so as to find the best labeling strategy. With these two key ideas, we successfully increase the performance of Template Attacks on artificially desynchronized traces and signals from a jitter-protected implementation.
The four biggest malware threats to UK businesses

It's official – cyber criminals are steadily switching their focus from consumers to organisations, with global business malware detection rates up 13% during 2019, and no sign this trend will slow during 2020. Cyber criminals are switching their focus from consumers to organisations, with business malware detection rates up 13% during 2019 and no sign this trend will slow down. Attackers are increasingly bold about the ways they monetise the information they steal. And the UK retains the unenviable honour of being the cybercrime epicentre of Europe, with the UK service sector the most-targeted industry, followed by education, retail and manufacturing. Roy Reynolds of Vodat examines the four biggest malware threats for UK businesses, how they can infect your digital networks and the steps necessary to remain malware-free.
Hardware Root Of Trust for IoT Security In Smart Home Systems

Authentication is a key challenge in Internet of Things (IoT), and it is a foundation for data integrity and confidentiality. Security and privacy preservation are crucial for the integration of IoT in our daily life. Due to the resource-constrained nature of IoT sensors, it is not applicable to utilize traditional cryptography techniques in IoT domains. Wireless network devices are used for IoT in a wide range of applications. In this paper, we propose an efficient and secure authentication architecture for IoT. Compared to the existing constrained applications Protocols, the proposed architecture increases efficiency by minimizing the number of message exchanges and implementing lightweight cryptosystem. Also, the proposed architecture increases security by eliminating any key storage on device memory as well as third party certificate authority. The main idea of the proposed architecture is to integrate lightweight cryptography and hardware security approaches to be the basis to face the current security challenges.
Se-DenseNet

Clinically, the doctor judges the degree of tumor infiltration, and identifies whether it is metastasized by observing the histopathological section of the patient, and conducts targeted treatment for patients accordingly. Pathologists must be highly focused on the diagnostic process, but it is still prone to missed diagnosis. This paper presents a complete diagnostic system for the classification of pathological images of metastatic breast cancer, which can automatically identify metastatic breast cancer cells. In this paper, we use convolutional neural networks to extract images intensively and add attention mechanisms to score and re-learn the features to ensure that the effective features of metastatic cancer can be learned quickly and effectively. Furthermore, the generalization performance of the system is improved by using super-resolution to supplement image details. Eventually, the area under the corresponding curve (AUC) for a single model was 0.993, and the auc score on the kaggle test set was 0.001 higher than when the super-resolution treatment was not used. A final score of 0.975 was achieved on the KAGGLE Histopathologic Cancer Detection competition test set. This method can be used to assist doctors in examining pathological sections, and make the diagnosis results relatively objective.
Semantically Corroborating Neural Attention for Biomedical Question Answering

Biomedical question answering is a great challenge in NLP due to complex scientific vocabulary and lack of massive annotated corpora, but, at the same time, is full of potential in optimizing in critical ways the biomedical practices. This paper describes the work carried out as a part of the BioASQ challenge (Task-7B Phase-B), and targets an integral step in the question answering process: extractive answer selection. This deals with the identification of the exact answer (words, phrases or sentences) from given article snippets that are related to the question at hand. We address this problem in the context of factoid and summarization question types, using a variety of deep learning and semantic methods, including various architectures (e.g., Dynamic Memory Networks and Bidirectional Attention Flow), transfer learning, biomedical named entity recognition and corroboration of semantic evidence. On the top of candidate answer selection module, answer prediction to yes/no question types is also addressed by incorporating a sentiment analysis approach. The evaluation with respect to Rouge, MRR and F1 scores, in relation to the type of question answering task being considered, exhibits the potential of this hybrid method in extracting the correct answer to a question. In addition, the proposed corroborating semantics module can be added on top of the typical QA pipeline to gain a measured 5% improvement in identifying the exact answer with respect to the gold standard.