Reviewerly/exHarmony huggingface dataset:
CoLV: A Collaborative Latent Variable Model for Knowledge-Grounded Dialogue Generation

Knowledge-grounded dialogue generation has achieved promising performance with the engagement of external knowledge sources.Typical approaches towards this task usually perform relatively independent two sub-tasks, i.e., knowledge selection and knowledge-aware response generation.In this paper, in order to improve the diversity of both knowledge selection and knowledge-aware response generation, we propose a collaborative latent variable (CoLV) model to integrate these two aspects simultaneously in separate yet collaborative latent spaces, so as to capture the inherent correlation between knowledge selection and response generation.During generation, our proposed model firstly draws knowledge candidate from the latent space conditioned on the dialogue context, and then samples a response from another collaborative latent space conditioned on both the context and the selected knowledge.Experimental results on two widely-used knowledge-grounded dialogue datasets show that our model outperforms previous methods on both knowledge selection and response generation.
Multi-label Diagnosis Classification of Swedish Discharge Summaries – ICD-10 Code Assignment Using KB-BERT

The International Classification of Diseases (ICD) is a system for systematically recording patients' diagnoses.Clinicians or professional coders assign ICD codes to patients' medical records to facilitate funding, research, and administration.In most health facilities, clinical coding is a manual, time-demanding task that is prone to errors.A tool that automatically assigns ICD codes to free-text clinical notes could save time and reduce erroneous coding.While many previous studies have focused on ICD coding, research on Swedish patient records is scarce.This study explored different approaches to pairing Swedish clinical notes with ICD codes.KB-BERT, a BERT model pre-trained on Swedish text, was compared to the traditional supervised learning models Support Vector Machines, Decision Trees, and K-nearest Neighbours used as the baseline.When considering ICD codes grouped into ten blocks, the KB-BERT was superior to the baseline models, obtaining an F 1 -micro of 0.80 and an F 1 -macro of 0.58.When considering the 263 full ICD codes, the KB-BERT was outperformed by all baseline models at an F 1 -micro and F 1 -macro of zero.Wilcoxon signed-rank tests showed that the performance differences between the KB-BERT and the baseline models were statistically significant.
Certified Robustness of Nearest Neighbors against Data Poisoning and Backdoor Attacks

Data poisoning attacks and backdoor attacks aim to corrupt a machine learning classifier via modifying, adding, and/or removing some carefully selected training examples, such that the corrupted classifier makes incorrect predictions as the attacker desires. The key idea of state-of-the-art certified defenses against data poisoning attacks and backdoor attacks is to create a majority vote mechanism to predict the label of a testing example. Moreover, each voter is a base classifier trained on a subset of the training dataset. Classical simple learning algorithms such as k nearest neighbors (kNN) and radius nearest neighbors (rNN) have intrinsic majority vote mechanisms. In this work, we show that the intrinsic majority vote mechanisms in kNN and rNN already provide certified robustness guarantees against data poisoning attacks and backdoor attacks. Moreover, our evaluation results on MNIST and CIFAR10 show that the intrinsic certified robustness guarantees of kNN and rNN outperform those provided by state-of-the-art certified defenses. Our results serve as standard baselines for future certified defenses against data poisoning attacks and backdoor attacks.
Moving Beyond the Crypto-Currency Success of Blockchain: A Systematic Survey

Blockchain (BC) is a technology whose value today is estimated by the success of Bitcoin. However, the spectrum of Blockchain applications goes beyond the financial sector. It has displayed enormous potential for revamping the customary industry with its key merits like decentralization, persistency, anonymity, and auditability. In this paper we conduct a comprehensive survey on the blockchain technology, explaining its structure and functioning. This work has analyzed the potential of BC in seven crucial sectors vis. voting systems, supply chain management, the security of Internet of Things (IoT), healthcare, intelligent transportation systems, government services, and tourism. Moreover, this paper has critically evaluated the traditional technologies used in various sectors, the problems in them, and the benefits that will be provided by the employment of BC. With its future directions, this paper will help researchers to create and realize new value for various sectors that is beyond anything we can imagine with existing technologies.
Lexicon-Based Methods vs. BERT for Text Sentiment Analysis

The performance of sentiment analysis methods has greatly increased in recent years. This is due to the use of various models based on the Transformer architecture, in particular BERT. However, deep neural network models are difficult to train and poorly interpretable. An alternative approach is rule-based methods using sentiment lexicons. They are fast, require no training, and are well interpreted. But recently, due to the widespread use of deep learning, lexicon-based methods have receded into the background. The purpose of the article is to study the performance of the SO-CAL and SentiStrength lexicon-based methods, adapted for the Russian language. We have tested these methods, as well as the RuBERT neural network model, on 16 text corpora and have analyzed their results. RuBERT outperforms both lexicon-based methods on average, but SO-CAL surpasses RuBERT for four corpora out of 16.
Digital Forensics Use Case for Glaucoma Detection Using Transfer Learning Based on Deep Convolutional Neural Networks

In parallel with the development of various emerging fields such as computer vision and related technologies, e.g., iris identification and glaucoma detection, criminals are developing their methods. It is the foremost reason for the blindness of human beings that affects the eye’s optic nerve. Fundus photography is carried out to examine this eye disease. Medical experts evaluate fundus photographs, which is a time-consuming visual inspection. Most current systems for automated glaucoma detection in fundus images rely on segmentation-based features nuanced by the underlying segmentation methods. Convolutional neural networks (CNNs) are powerful tools for solving image classification tasks, as they can learn highly discriminative features from raw pixel intensities. However, their applicability to medical image analysis is limited by the nonavailability of large sets of annotated data required for training. In this work, we aim to accelerate this process using a computer-aided diagnosis of this severe disease with the help of transfer learning based on deep convolutional neural networks. We have suggested the Inception V-3 approach for image classification based on convolution neural networks. Our developed model has the potential to address this CNN model’s problem of classification accuracy, and with imaging data, our proposed method outperforms recent state-of-the-art approaches. The case study for digital forensics is an essential component of emerging technologies, and hence glaucoma detection plays a vital role in it.
Optimized Breast Cancer Classification using Feature Selection and Outliers Detection

Breast cancer is the second most commonly diagnosed cancer in women throughout the world. It is on the rise, especially in developing countries, where the majority of cases are discovered late. Breast cancer develops when cancerous tumors form on the surface of the breast cells. The absence of accurate prognostic models to assist physicians recognize symptoms early makes it difficult to develop a treatment plan that would help patients live longer. However, machine learning techniques have recently been used to improve the accuracy and speed of breast cancer diagnosis. If the accuracy is flawless, the model will be more efficient, and the solution to breast cancer diagnosis will be better. Nevertheless, the primary difficulty for systems developed to detect breast cancer using machine-learning models is attaining the greatest classification accuracy and picking the most predictive feature useful for increasing accuracy. As a result, breast cancer prognosis remains a difficulty in today's society. This research seeks to address a flaw in an existing technique that is unable to enhance classification of continuous-valued data, particularly its accuracy and the selection of optimal features for breast cancer prediction. In order to address these issues, this study examines the impact of outliers and feature reduction on the Wisconsin Diagnostic Breast Cancer Dataset, which was tested using seven different machine learning algorithms. The results show that Logistic Regression, Random Forest, and Adaboost classifiers achieved the greatest accuracy of 99.12%, on removal of outliers from the dataset. Also, this filtered dataset with feature selection, on the other hand, has the greatest accuracy of 100% and 99.12% with Random Forest and Gradient boost classifiers, respectively. When compared to other state-of-the-art approaches, the two suggested strategies outperformed the unfiltered data in terms of accuracy. The suggested architecture might be a useful tool for radiologists to reduce the number of false negatives and positives. As a result, the efficiency of breast cancer diagnosis analysis will be increased.
Electrical spectroscopy of the spin-wave dispersion and bistability in gallium-doped yttrium iron garnet

Yttrium iron garnet (YIG) is a magnetic insulator with record-low damping, allowing spin-wave transport over macroscopic distances. Doping YIG with gallium ions greatly reduces the demagnetizing field and introduces a perpendicular magnetic anisotropy, which leads to an isotropic spin-wave dispersion that facilitates spin-wave optics and spin-wave steering. Here, we characterize the dispersion of a gallium-doped YIG (Ga:YIG) thin film using electrical spectroscopy. We determine the magnetic anisotropy parameters and Gilbert damping from the frequency and linewidth of the ferromagnetic resonance (FMR). Next, we use propagating spin wave spectroscopy in the Damon–Eshbach configuration to detect the small spin-wave magnetic fields of this ultrathin weak magnet over a wide range of wavevectors, enabling the extraction of the exchange constant αex=1.32×10−12 J/m. We observe foldover of the FMR with increasing drive power, leading to frequency shifts of the spin-wave modes and a bistable region in the spin-wave spectra. Our results shed light on isotropic spin-wave transport in Ga:YIG and highlight the potential of electrical spectroscopy to map out the dispersion and bistability of propagating spin waves in magnets with a low saturation magnetization.
Clustering Interval-Censored Time-Series for Disease Phenotyping

Unsupervised learning is often used to uncover clusters in data. However, different kinds of noise may impede the discovery of useful patterns from real-world time-series data. In this work, we focus on mitigating the interference of interval censoring in the task of clustering for disease phenotyping. We develop a deep generative, continuous-time model of time-series data that clusters time-series while correcting for censorship time. We provide conditions under which clusters and the amount of delayed entry may be identified from data under a noiseless model. On synthetic data, we demonstrate accurate, stable, and interpretable results that outperform several benchmarks. On real-world clinical datasets of heart failure and Parkinson's disease patients, we study how interval censoring can adversely affect the task of disease phenotyping. Our model corrects for this source of error and recovers known clinical subtypes.
Developing Countries’ Concerns Regarding Blockchain Adoption in Maritime

This paper deals with challenges of implementing blockchain (BC) technology in maritime at developing countries, with a research focus on Montenegro and South Africa. Research design and categories analyzed in the paper are chosen due to the search of relevant secondary literature resources. Selected experts in Information Technology (IT) and maritime from aforementioned developing countries were asked about their perception of BC as disruptive technology, its implementation, and implications on maritime and other industries, through a questionnaire, which contains both quantitative and qualitative parts. The results should give the readers insights into the experts’ standpoints concerning rational blockchain adoption in maritime and other industries in developing and transitional economies. The paper is organized into six sections: (1) introduction, (2) literature review on blockchain in maritime, (3) research problem and design, (4) results, (5) discussion, and (6) conclusions.
Neural Network Used for the Fusion of Predictions Obtained by the K-Nearest Neighbors Algorithm Based on Independent Data Sources

The article concerns the problem of classification based on independent data sets-local decision tables. The aim of the paper is to propose a classification model for dispersed data using a modified k-nearest neighbors algorithm and a neural network. A neural network, more specifically a multilayer perceptron, is used to combine the prediction results obtained based on local tables. Prediction results are stored in the measurement level and generated using a modified k-nearest neighbors algorithm. The task of neural networks is to combine these results and provide a common prediction. In the article various structures of neural networks (different number of neurons in the hidden layer) are studied and the results are compared with the results generated by other fusion methods, such as the majority voting, the Borda count method, the sum rule, the method that is based on decision templates and the method that is based on theory of evidence. Based on the obtained results, it was found that the neural network always generates unambiguous decisions, which is a great advantage as most of the other fusion methods generate ties. Moreover, if only unambiguous results were considered, the use of a neural network gives much better results than other fusion methods. If we allow ambiguity, some fusion methods are slightly better, but it is the result of this fact that it is possible to generate few decisions for the test object.
Content Based Movie Scene Retrieval using Spatio Temporal Features

Thousands of movies along with TV shows, documentaries are being produced each year around the world with different genres and languages. Making a movie scene impactful as well as original is challenging task for the director. On the other hand, users demands to retrieve similar scenes from their queries is also challenging task as there is no proper maintenance of database of movie scene videos with proper semantic tags associated with it. So to fulfill the requirement of these two (but not the least) application areas there is a need of content based retrieval system for movie scenes. Content based video retrieval is a problem of retrieving most similar videos to a given query video by analyzing the visual contents of videos. Traditional video level features based on key frame level hand engineered features which does not exploit rich dynamics present in the video. In this paper we propose a Content based Movie Scene Retrieval (CB-MSR) framework using spatio-temporal features learned by deep learning. Specifically deep CNN along with LSTM is deploy to learn spatio-temporal representations of video. On the basis of these learned features similar movie scenes can be retrieve from the collection of movies. Hollywood2 dataset is used to test the proposed system. Two types of features: spatial and spatio-temporal features are used to evaluate the proposed framework.
Research on college English teaching based on data mining technology

Abstract To improve the efficiency and quality of college English teaching, we analyzed the feasibility and application process of data mining technology in college English teaching. The entire process of data classification mining was fully realized. A new teaching program was proposed. The object and target of data mining were determined. Online surveys were used to collect data. Data integration, data cleaning, data conversion, data reduction and other pre-processing technologies were adopted. The decision tree was generated by using the C4.5 algorithm, and the pruning was carried out. The result analysis decision tree model was completed. A detailed survey of the students' English learning in University was made in detail. The results showed that the qualified rate of students' English performance was increased from 20–30% to 50–60%. Therefore, the classification rules provide theoretical support for the school teaching decision. This method can improve the quality of English teaching.
Intelligent SPARQL Query Generation for Natural Language Processing Systems

Developing question answering (QA) systems that process natural language is a popular research topic. Conventionally, when QA systems receive a natural language question, they choose useful words or phrases based on their parts-of-speech (POS) tags. In general, words tagged as nouns are mapped to class entities, words tagged as verbs are mapped to property entities, and words tagged as proper nouns are mapped to named entities, although the accuracy of entity type identification remains low. Afterward, the relationship between entity types as RDF types determines the first element to be a pivot word to generate the SPARQL (acronym for SPARQL protocol and RDF query language) query on the basis of the sequences by a specific graph or tree structure, such as dependence tree or directed acyclic graph (DAG). However, the generated SPARQL query is difficult to adapt to the given query request in that the sequences are decided by a fixed structure. Unlike in previous research, SPARQL generation occurs automatically according to the entity type identification and RDF type identification results. This study attempts to design a method that leverages machine learning to learn human experiences in entity type identification as well as RDF-type identification. We approach the problem as a multiclass classification problem and propose a two-stage maximum-entropy Markov model (MEMM). The first stage identifies the entity type and the second identifies the RDF type for the purpose of generating appropriate SPARQL queries to meet the query request. Along with the templates designed for the two-stage MEMM model, we develop an automatic question answering prototype system called QAWizard. The experimental results show that QAWizard outperforms all other systems in question answering when evaluated on Linked Data version 8 (QALD-8) metrics.
A Decentralized Identity-Based Blockchain Solution for Privacy-Preserving Licensing of Individual-Controlled Data to Prevent Unauthorized Secondary Data Usage

This paper presents a design for a blockchain solution aimed at the prevention of unauthorized secondary use of data. This solution brings together advances from the fields of identity management, confidential computing, and advanced data usage control. In the area of identity management, the solution is aligned with emerging decentralized identity standards: decentralized identifiers (DIDs), DID communication and verifiable credentials (VCs). In respect to confidential computing, the Cheon-Kim-Kim-Song (CKKS) fully homomorphic encryption (FHE) scheme is incorporated with the system to protect the privacy of the individual’s data and prevent unauthorized secondary use when being shared with potential users. In the area of advanced data usage control, the solution leverages the PRIV-DRM solution architecture to derive a novel approach to licensing of data usage to prevent unauthorized secondary usage of data held by individuals. Specifically, our design covers necessary roles in the data-sharing ecosystem: the issuer of personal data, the individual holder of the personal data (i.e., the data subject), a trusted data storage manager, a trusted license distributor, and the data consumer. The proof-of-concept implementation utilizes the decentralized identity framework being developed by the Hyperledger Indy/Aries project. A genomic data licensing use case is evaluated, which shows the feasibility and scalability of the solution.
Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection

Deep neural networks have reached high accuracy on object detection but their success hinges on large amounts of labeled data. To reduce the labels dependency, various active learning strategies have been proposed, based on the confidence of the detector. However, these methods are biased towards high-performing classes and lead to acquired datasets that are not good representatives of the testing set data. In this work, we propose a unified frame-work for active learning, that considers both the uncertainty and the robustness of the detector, ensuring that the network performs well in all classes. Furthermore, our method leverages auto-labeling to suppress a potential distribution drift while boosting the performance of the model. Experiments on PASCAL VOC07+12 and MS-COCO show that our method consistently outperforms a wide range of active learning methods, yielding up to a 7.7% improvement in mAP, or up to 82% reduction in labeling cost. Code is available at https://github.com/NVlabs/AL-SSL.
Automatic Generation Mechanism of Cause-Effect Graph with Informal Requirement Specification Based on the Korean Language

In requirement engineering, an important issue is how to transform and tailor the informal system requirements of users or customers into more structured specification documents, which are then used by the software developers. In addition, it is both challenging and necessary to redefine and analyze, from ill-defined or unknown requirements, specifications correctly and automatically generate test cases with them. There are few kinds of research in Korea for automatically reducing requirement complexity and developing test cases of the Korean language-based requirement specifications. Why do we need requirement simplification? Requirement complexity causes analyzers less readability and low understandability. To do this, we propose the automatic cause-effect generation via a requirement simplification mechanism of informal requirement specifications with the Korean language, which works the following procedures: (1) the automatic simplification of informal requirement sentences, (2) the generation of Condition/Conjunction/Clause Tree (C3Tree) Model, (3) and the Cause-effect generation.
Detecting The Speaker Language Using CNN Deep Learning Algorithm

Many languages identification (LID) systems rely on language models that use machine learning (ML) approaches, LID systems utilize rather long recording periods to achieve satisfactory accuracy. This study aims to extract enough information from short recording intervals in order to successfully classify the spoken languages under test. The classification process is based on frames of (2-18) seconds where most of the previous LID systems were based on much longer time frames (from 3 seconds to 2 minutes). This research defined and implemented many low-level features using MFCC (Mel-frequency cepstral coefficients), containing speech files in five languages (English. French, German, Italian, Spanish), from voxforge.org an open-source corpus that consists of user-submitted audio clips in various languages, is the source of data used in this paper. A CNN (convolutional Neural Networks) algorithm applied in this paper for classification and the result was perfect, binary language classiﬁcation had an accuracy of 100%, and five languages classiﬁcation with six languages had an accuracy of 99.8%.