Reviewerly/exHarmony huggingface dataset:
Pre-trained Language Model for Biomedical Question Answering

The recent success of question answering systems is largely attributed to pre-trained language models. However, as language models are mostly pre-trained on general domain corpora such as Wikipedia, they often have difficulty in understanding biomedical questions. In this paper, we investigate the performance of BioBERT, a pre-trained biomedical language model, in answering biomedical questions including factoid, list, and yes/no type questions. BioBERT uses almost the same structure across various question types and achieved the best performance in the 7th BioASQ Challenge (Task 7b, Phase B). BioBERT pre-trained on SQuAD or SQuAD 2.0 easily outperformed previous state-of-the-art models. BioBERT obtains the best performance when it uses the appropriate pre-/post-processing strategies for questions, passages, and answers.
Attention-Based Radar PRI Modulation Recognition With Recurrent Neural Networks

Analyzing radar signals is a critical task in modern Electronic Warfare (EW) environments. However, the pulse streams emitted by radars have flexible features and complex patterns which are difficult to be identified from a statistical perspective. To solve this problem, pulse repetition interval (PRI) is used as a distinguishing parameter of emitters to be identified. However, traditional PRI modulation recognition methods can only deal with simple PRI modulations and their performance will further degrade with the increasing number of emitters or noisy environments. In this paper, we introduce an attention-based recognition framework based on recurrent neural network (RNN) to categorize pulse streams with complex PRI modulations and in environments with high ratios of missing and spurious pulses. Simulation results show that our model is robust to noisy environments and has a better performance than conventional methods.
Optimal Broadcast Encryption from Pairings and LWE

Boneh, Waters and Zhandry (CRYPTO 2014) used multilinear maps to provide a solution to the long-standing problem of public-key broadcast encryption (BE) where all parameters in the system are small. In this work, we improve their result by providing a solution that uses only bilinear maps and Learning With Errors (LWE). Our scheme is fully collusion-resistant against any number of colluders, and can be generalized to an identity-based broadcast system with short parameters. Thus, we reclaim the problem of optimal broadcast encryption from the land of "Obfustopia". Our main technical contribution is a ciphertext policy attribute based encryption (CP-ABE) scheme which achieves special efficiency properties – its ciphertext size, secret key size, and public key size are all independent of the size of the circuits supported by the scheme. We show that this special CP-ABE scheme implies BE with optimal parameters; but it may also be of independent interest. Our constructions rely on a novel interplay of bilinear maps and LWE, and are proven secure in the generic group model.
Are SonarQube Rules Inducing Bugs?

The popularity of tools for analyzing Technical Debt, and particularly the popularity of SonarQube, is increasing rapidly. SonarQube proposes a set of coding rules, which represent something wrong in the code that will soon be reflected in a fault or will increase maintenance effort. However, our local companies were not confident in the usefulness of the rules proposed by SonarQube and contracted us to investigate the fault-proneness of these rules. In this work we aim at understanding which SonarQube rules are actually fault-prone and to understand which machine learning models can be adopted to accurately identify fault-prone rules. We designed and conducted an empirical study on 21 well-known mature open-source projects. We applied the SZZ algorithm to label the fault-inducing commits. We analyzed the fault-proneness by comparing the classification power of seven machine learning models. Among the 202 rules defined for Java by SonarQube, only 25 can be considered to have relatively low fault-proneness. Moreover, violations considered as “bugs” by SonarQube were generally not fault-prone and, consequently, the fault-prediction power of the model proposed by SonarQube is extremely low. The rules applied by SonarQube for calculating technical debt should be thoroughly investigated and their harmfulness needs to be further confirmed. Therefore, companies should carefully consider which rules they really need to apply, especially if their goal is to reduce fault-proneness.
Dynamical Channel Pruning by Conditional Accuracy Change for Deep Neural Networks

Channel pruning is an effective technique that has been widely applied to deep neural network compression. However, many existing methods prune from a pretrained model, thus resulting in repetitious pruning and fine-tuning processes. In this article, we propose a dynamical channel pruning method, which prunes unimportant channels at the early stage of training. Rather than utilizing some indirect criteria (e.g., weight norm, absolute weight sum, and reconstruction error) to guide connection or channel pruning, we design criteria directly related to the final accuracy of a network to evaluate the importance of each channel. Specifically, a channelwise gate is designed to randomly enable or disable each channel so that the conditional accuracy changes (CACs) can be estimated under the condition of each channel disabled. Practically, we construct two effective and efficient criteria to dynamically estimate CAC at each iteration of training; thus, unimportant channels can be gradually pruned during the training process. Finally, extensive experiments on multiple data sets (i.e., ImageNet, CIFAR, and MNIST) with various networks (i.e., ResNet, VGG, and MLP) demonstrate that the proposed method effectively reduces the parameters and computations of baseline network while yielding the higher or competitive accuracy. Interestingly, if we Double the initial Channels and then Prune Half (DCPH) of them to baseline's counterpart, it can enjoy a remarkable performance improvement by shaping a more desirable structure.
Politician hate speech and domestic terrorism

Does hate speech – rhetoric that targets, vilifies or is intended to intimidate minorities and other groups in society – fuel domestic terrorism? This question is, unfortunately, relevant given the convergence of the use of hate speech by political figures and domestic terrorist incidents in a variety of countries, including the United States. In this study I theorize that hate speech by politicians deepens political polarization and that this, in turn, produces conditions under which domestic terrorism increases. I test this proposition using terrorism and hate speech data for 135 to 163 countries for the period 2000 to 2017. I produce two findings. First, hate speech by political figures boosts domestic terrorism. Second, the impact of political hate speech on domestic terrorism is mediated through increased political polarization.
LEAF-QA: Locate, Encode &amp; Attend for Figure Question Answering

We introduce LEAF-QA, a comprehensive dataset of 250 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">,</sub> 000 densely annotated figures/charts, constructed from real-world open data sources, along with 2 million question-answer (QA) pairs querying the structure and semantics of these charts. LEAF-QA highlights the problem of multimodal QA, which is notably different from conventional visual QA (VQA), and has recently gained interest in the community. Furthermore, LEAF-QA is significantly more complex than previous attempts at chart QA, viz. FigureQA and DVQA, which present only limited variations in chart data. LEAF-QA being constructed from real-world sources, requires a novel architecture to enable question answering. To this end, LEAF-Net, a deep architecture involving chart element localization, question and answer encoding in terms of chart elements, and an attention network is proposed. Different experiments are conducted to demonstrate the challenges of QA on LEAF-QA. The proposed architecture, LEAF-Net also considerably advances the current state-of-the-art on FigureQA and DVQA.
BOFFIN TTS: Few-Shot Speaker Adaptation by Bayesian Optimization

We present BOFFIN TTS (Bayesian Optimization For FIne-tuning Neural Text To Speech), a novel approach for few-shot speaker adaptation. Here, the task is to fine-tune a pre-trained TTS model to mimic a new speaker using a small corpus of target utterances. We demonstrate that there does not exist a one-size-fits-all adaptation strategy, with convincing synthesis requiring a corpus-specific configuration of the hyper-parameters that control fine-tuning. By using Bayesian optimization to efficiently optimize these hyper-parameter values for a target speaker, we are able to perform adaptation with an average 30% improvement in speaker similarity over standard techniques. Results indicate, across multiple corpora, that BOFFIN TTS can learn to synthesize new speakers using less than ten minutes of audio, achieving the same naturalness as produced for the speakers used to train the base model.
An Intelligent Chatbot System Based on Entity Extraction Using RASA NLU and Neural Network

Abstract Intelligent chatbot systems are popular issues in the application fields of robot system and natural language processing. As the development of natural language processing and neural network algorithms, the application of artificial intelligence is increasing in Chatbot systems, which are typically used in dialog systems for various practical purposes including customer service or information acquisition. This paper designs the functional framework and introduces the principle of RASA NLU for the Chatbot system, then it integrates RASA NLU and neural network (NN) methods and implements the system based on entity extraction after intent recognition. With the experimental comparison and validation, our developed system can realize automatic learning and answering the collected questions about finance. The system analysis of two methods also validate that RASA NLU outperforms NN in accuracy for a single experiment, but NN has better integrity to classify entities from segmented words.
An efficient <scp>radix trie</scp> ‐based semantic visual indexing model for <scp>large‐scale</scp> image retrieval in cloud environment

Summary In recent years, massive growth in the number of images on the web has raised the requirement of developing an effective indexing model to search digital images from a large‐scale database. Though cloud service offers effective indexing of compressed images, it remains a major issue due to the semantic gap between the user query and diverse semantics of large‐scale database. This article presents a radix trie indexing (RTI) model based on semantic visual indexing for retrieving the images from cloud platforms. Initially, an interactive optimization model is applied to identify the joint semantic and visual descriptor space. Next, an RTI model is applied to integrate the semantic visual joint space model for finding an effective solution for searching large‐scale sized dataset. Finally, a Spark distributed model is applied for deploying the online image retrieval service. The performance of the proposed method is validated on two standard dataset, namely, Holidays 1 M and Oxford 5 K in terms of mean average precision (mAP) and processing time under varying dataset sizes. During experimentation, the presented RTI model shows the maximum mAP value of 0.83 under the dataset size of 1000. Similarly, under the sample count of 1000, it is noted that the standalone server requires a maximum of 118 minutes to complete the process, whereas the spark cluster requires a minimum of around only 19 minutes to finish the process. The experimental outcome showed improvement in terms of various measures over the best rivals in the literature.
Generalized end-to-end detection of spoofing attacks to automatic speaker recognizers

As automatic speaker recognizer systems become mainstream, voice spoofing attacks are on the rise. Common attack strategies include replay, the use of text-to-speech synthesis, and voice conversion systems. While previously-proposed end-to-end detection frameworks have shown to be effective in spotting attacks for one particular spoofing strategy, they have relied on different models, architectures, and speech representations, depending on the spoofing strategy. In practice, however, one does not have a priori information regarding the strategy an attacker might employ to fool a speaker recognizer, thus it is necessary to devise approaches which are able to detect attacks regardless of the strategy employed to generate them. In this work, we introduce an end-to-end ensemble based approach such that two models – previously shown to perform well on each considered attack strategy – are trained jointly, while a third model learns how to mix their outputs yielding a single score. Experimental results with replay and text-to-speech/voice conversion attacks show the proposed ensemble method achieving similar or superior performance when compared to systems specialized on each spoofing strategy separately.
An Autoencoder Based Model for Detecting Fraudulent Credit Card Transaction

With the rapid growth in credit card based financial transactions, it has become important to identify the fraudulent ones. In this work, a two stage model is proposed to identify such fraudulent transactions. To make a fraud detection system trustworthy, both miss in fraud detection and false alarms are to minimized. Understanding and learning the complex associations among the transaction attributes is a major problem. To address this issue, at the first stage of the proposed model an autoencoder is used to transform the transaction attributes to a feature vector of lower dimension. The feature vector thus obtained is used as the input to a classifier at the second stage. Experiment is done on a benchmarked dataset. It is observed that in terms of F1-measure, proposed two stage model performs better than the systems relying on only classifier and other autoencoder based systems.
Accessible conversational user interfaces

Conversational user interfaces (CUIs), such as chatbots and voice assistants, are increasingly common in areas of day-to-day life, and can be expected to become ever more pervasive in the future. These interfaces are being designed for ever more complex interactions, and they appear to have potential to be beneficial to people with disabilities to interact through the web and with technologies embedded in the environment. However, to fulfil this promise they need to be designed to be accessible.
COVID-19 infection and death rates: the need to incorporate causal explanations for the data and avoid bias in testing

COVID-19 testing strategies are primarily driven by medical need - focusing on people already hospitalized with significant symptoms or on people most at risk. However, such testing is highly biased because it fails to identify the extent to which COVID-19 is present in people with mild or no symptoms. If we wish to understand the true rate of COVID-19 infection and death, we need to take full account of the causal explanations for the resulting data to avoid highly misleading conclusions about infection and death rates. We describe how causal (Bayesian network) models can provide such explanations and the need to combine these with more random testing in order to achieve reliable data and predictions for the both policy makers and the public.
Joint multi-level attentional model for emotion detection and emotion-cause pair extraction

Emotion detection (ED) and emotion-cause pair extraction (ECPE) have drawn extensive research interests due to their wide applications in real-world scenarios. However, existing work fails to capture the implicit connection between two tasks. This limits the performances of these tasks. To address this issue, we propose a novel joint framework to take full advantage of the clause-level and word-level information about ED and ECPE tasks. Specifically, we explore a multi-level attentional module to model the relationship between two clauses in an emotion-cause pair. Results on two benchmark datasets show that our proposed model achieves the current best performance, outperforming the previous methods and strong neural baselines by a large margin. Our code is available at https://github.com/tomsonsgs/LVE-joint-MANN-master.
On Cloud Storage Optimization of Blockchain With a Clustering-Based Genetic Algorithm

With the rise of cryptocurrencies, blockchain, which is the underlying technology of them, has gained more attention and been used in the Internet of Things (IoT) and other fields. However, there are bottlenecks that hinder its application, such as the storage capacity. Due to the large number of IoT devices which always act as data generators in many systems, the transactions will be generated at a high rate. The storage problem will be more serious in IoT. In this article, to expand the capacity of blockchain, for each peer, we select old blocks which are created previously and less likely to be queried and store them in the cloud. Based on this idea, we develop objective functions related to query probability, storage cost, and local space occupancy, which naturally narrows down the problem to block selection. The results can be obtained by solving a multiobjective optimization problem. We design a nondominated sorting genetic algorithm with clustering (NSGA-C), which changes the method of selecting individuals from the critical dominance layer by adding clustering to ensure diversity. A suitable solution can be selected from the Pareto set to fulfil the requirements of different users. We then compare the algorithm with the improved NSGA-II and NSGA-III, which both add integer constraints to the decision variables. The results show that our method is better than NSGA-II and NSGA-III in terms of local space occupancy in the blockchain application. In addition, it can also effectively avoid the risk of block overflow.
A Comparative Study of Support Vector Machine and Naive Bayes Classifier for Sentiment Analysis on Amazon Product Reviews

This paper represents a comparison between two machine learning approaches for analyzing the sentiment of the customers' reviews on Amazon products. Eventually, reviews of a product help the customers to understand the product quality. Incorporating multiple product review factors, including product quality, content, time of the review related to product durability, and historically older positive customer reviews will affect product rankings accordingly. Conversely, the manual approach on a large scale comment is time-consuming leading to an inefficient and unproductive way. In this era of artificial intelligence, machine learning is the most convenient way to train the neural network. So it would be much easier to go through thousands of comments if a model were adopted to polarize those reviews and learn from them. In this research work, firstly, the sentiment of the consumer has been analyzed by the Naive Bayes classifier. Meanwhile, the support vector machine(SVM) has classified the sentiments of the users in binary categories. Hence, the data has been passed through the network model after the preprocessing method named term frequency(TF) and inverse document frequency(IDF) lead to evaluate the feature. To sum up, the goal of this research is to find comparatively better machine learning approaches among SVM and Naive Bayes classifier based on statistical measurement.
Network-Based Bag-of-Words Model for Text Classification

The rapidly developing internet and other media have produced a tremendous amount of text data, making it a challenging and valuable task to find a more effective way to analyze text data by machine. Text representation is the first step for a machine to understand the text, and the commonly used text representation method is the Bag-of-Words (BoW) model. To form the vector representation of a document, the BoW model separately matches and counts each element in the document, neglecting much correlation information among words. In this paper, we propose a network-based bag-of-words model, which collects high-level structural and semantic meaning of the words. Because the structural and semantic information of a network reflects the relationship between nodes, the proposed model can distinguish the relation of words. We apply the proposed model to text classification and compare the performance of the proposed model with different text representation methods on four document datasets. The results show that the proposed method achieves the best performance with high efficiency. Using the Eccentricity property of the network as features can get the highest accuracy. We also investigate the influence of different network structures in the proposed method. Experimental results reveal that, for text classification, the dynamic network is more suitable than the static network and the hybrid network.
Sentiment Analysis using Machine Learning and Deep Learning

With the increasing rate at which data is created by internet users on various platforms, it becomes necessary to analyze and make use of the data by the Defense and other Government Organizations and know the sentiment of the people. This shall help the organizations take control of their actions and decide the steps to be taken shortly. Added to it, when something crucial is happening in the nation, it is of paramount importance to decide every step without hurting/violating the sentiments of the people. In the era of Microblogging, which has become quite a popular tool of communication, millions of users share their views and opinions on various day-to-day life issues concerning them directly or indirectly through social media platforms like Twitter, Reddit, Tumblr, Facebook. Data from these sites can be efficiently used for marketing or social studies. In this paper, we have taken into account various methods to perform sentiment analysis. Sentiment Analysis has been performed by using Machine Learning Classifiers. Polarity-based sentiment analysis, and Deep Learning Models are used to classify user's tweets as having ‘positive’ or ‘negative’ sentiment. The idea behind taking in various model architectures was to account for the variance in the opinions and thoughts existing on such social media platforms. These classification models can further be implemented to classify live tweets on twitter on any topic.
Many-objective optimization of feature selection based on two-level particle cooperation

Feature selection (FS) plays a crucial role in classification, which aims to remove redundant and irrelevant data features.unknown However, for high-dimensional FS problems, Pareto optimal solutions are usually sparse, signifying that most of the decision variables are zero. Solving such problems using most existing evolutionary algorithms is difficult. In this paper, we reformulate FS as a many-objective optimization problem comprising three objectives to be minimized. To solve this problem, we proposed a binary particle swarm optimization with a two-level particle cooperation strategy. In the first level, to maintain rapid convergence, randomly generated ordinary particles and strict particles filtered by ReliefF are combined as the initialized particles. In the second level, under the decomposition multiobjective optimization framework, cooperation between particles is conducted during the update process to search for Pareto solutions more efficiently. In addition, a many-objective reset operation is proposed to enable the algorithm to jump out of the local optimum. Experimental studies on 10 real-world benchmark data sets revealed that our proposed algorithm could effectively reduce the number of features and achieve a competitive classification accuracy compared with some state-of-the-art evolutionary FS methods and non-evolutionary approaches.