Reviewerly/exHarmony huggingface dataset:
An Information-Theoretic Proof of the Streaming Switching Lemma for Symmetric Encryption

Motivated by a fundamental paradigm in cryptography, we consider a recent variant of the classic problem of bounding the distinguishing advantage between a random function and a random permutation. Specifically, we consider the problem of deciding whether a sequence of q values was sampled uniformly with or without replacement from [N], where the decision is made by a streaming algorithm restricted to using at most s bits of internal memory. In this work, the distinguishing advantage of such an algorithm is measured by the KL divergence between the distributions of its output as induced under the two cases. We show that for any s = Ω(logN) the distinguishing advantage is upper bounded by O(q · s/N), and even by O(q·s/N logN) when q ≤ N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-ε</sup> for any constant ε > 0 where it is nearly tight with respect to the KL divergence.
HQADeepHelper: A Deep Learning System for Healthcare Question Answering

It is challenging to generate high quality answers for healthcare queries in online platforms. Recent studies proposed deep models for healthcare question answering (HQA) tasks. However, these models have not been thoroughly compared, and they were only tested on self-created datasets. This paper demonstrates a novel system, denoted by HQADeepHelper, to facilitate the learning and practicing of deep models for HQA. We have implemented a wide spectrum of state-of-the-art deep models for HQA retrieval. Users can upload self-collected HQA datasets and knowledge graphs, and do simple configurations by selecting datasets, knowledge graphs, neural network models, and evaluation metrics. Based on user’s configuration specified, the system can automatically train and test the model, conduct extensive experimental evaluation of the models selected, and report comprehensive findings. The reports provide new insights about the strengths and weaknesses of deep models that can guide practitioners to select appropriate models for various scenarios. Moreover, users can download the datasets, knowledge graphs, experimental reports and source codes of neural network models for their own practice and evaluations further.
Arabic Sentiment Analysis Based on Salp Swarm Algorithm with S-shaped Transfer Functions

Social media has played a significant role in marketing and advertising. Monitoring the attitude of customers and analyzing their written sentiments to evaluate their opinions toward a particular product, topic or situation becomes essential to improve the product quality and customer services. Due to the importance of sentiment analysis (SA), a plethora of tools and systems have been developed for analyzing the polarities of people’s sentiments. However, SA is a difficult task, especially when dealing with massive data resources. Feature selection (FS) algorithms are needed for the machine learning (ML) process to reduce the high dimensionality space. In this paper, we propose an enhancement of a bio-inspired optimizer, called the salp swarm algorithm (SSA) designed for feature selection to solve the problem of Arabic sentiment analysis. Our proposed algorithm operates in two phases: The first phase employs a filtering technique based on the information gain (IG) metric to reduce the number of features. The second phase employs a wrapper technique which combines the basic SSA optimizer with four variants of S-shaped transfer functions. Experimental results show that the SSA combined with the S-shaped transfer functions outperformed the particle swarms optimizer (PSO) and the grey wolf optimizer (GWO) in term of classification accuracy.
Genetic Programming Symbolic Regression: What Is the Prior on the Prediction?

In the context of Genetic Programming Symbolic Regression, we empirically investigate the prior on the output prediction, that is, the distribution of the output prior to observing data. We distinguish between the prior due to initialisation and due to evolutionary search. We also investigate the effect on the prior of maximum tree depth and the effect of different function sets and different independent variable distributions. We find that priors are highly diffuse and sometimes include support for extreme values. We compare priors to values for dependent variables observed in benchmarks and real-world problems, finding that mismatches occur and can affect algorithm behaviour and performance. As a further application of our results, we investigate the behaviour of mutation operators in semantic space.
<i>AdaNS</i>: Adaptive Non-Uniform Sampling for Automated Design of Compact DNNs

This paper introduces an adaptive sampling methodology for automated compression of Deep Neural Networks (DNNs) for accelerated inference on resource-constrained platforms. Modern DNN compression techniques comprise various hyperparameters that require per-layer customization. Our objective is to locate an optimal hyperparameter configuration that leads to lowest model complexity while adhering to a desired inference accuracy. We design a score function that evaluates the aforementioned optimality. The optimization problem is then formulated as searching for the maximizers of this score function. To this end, we devise a non-uniform adaptive sampler that aims at reconstructing the band-limited score function. We reduce the total number of required objective function evaluations by realizing a targeted sampler. We propose three adaptive sampling methodologies, i.e., AdaNS-Zoom, AdaNS-Genetic, and AdaNS-Gaussian, where new batches of samples are chosen based on the history of previous evaluations. Our algorithms start sampling from a uniform distribution over the entire search-space and iteratively adapt the sampling distribution to achieve highest density around the function maxima. This, in turn, allows for a low-error reconstruction of the objective function around its maximizers. Our extensive evaluations corroborate AdaNS effectiveness by outperforming existing rule-based and Reinforcement Learning methods in terms of DNN compression rate and/or inference accuracy.
Learning sums of powers of low-degree polynomials in the non-degenerate case

We develop algorithms for writing a polynomial as sums of powers of low degree polynomials in the non-degenerate case. This problem generalizes symmetric tensor decomposition which is widely studied, having many applications in machine learning. Our algorithm for this more general problem allows us to solve the moment problem for mixtures of zero-mean Gaussians in the nondegenerate case. Our algorithm is based on a scheme for obtaining a learning algorithm for an arithmetic circuit model from lower bound for the same model, provided certain non-degeneracy conditions hold. The scheme reduces the learning problem to the problem of decomposing two vector spaces under the action of a set of linear operators, where the spaces and the operators are derived from the input circuit and the complexity measure used in a typical lower bound proof. The non-degeneracy conditions are certain restrictions on how the spaces decompose. Such a scheme is present in a rudimentary form in an earlier work of Kayal and Saha. Here, we make it more general and detailed, and potentially applicable to learning other circuit models. An exponential lower bound on the representation above is known using the shifted partials measure. However, the number of linear operators in shifted partials is exponential and also the non-degeneracy condition emerging out of this measure is unlikely to be satisfied by a random such circuit when the number of variables is large with respect to the degree. We bypass this hurdle by proving a lower bound (which is nearly as strong as the previous bound) using a novel variant of the partial derivatives measure, namely affine projections of partials (APP). The non-degeneracy conditions appearing from this new measure are satisfied by a random circuit of the above kind. The APP measure could be of independent interest for proving other lower bounds.
Hardware Implementation of Softmax Function Based on Piecewise LUT

Deep neural networks (DNN) achieve great results in many fields. While softmax function is widely used in DNN, how to implement it on hardware considering the accuracy, speed, area, and power, is a critical issue. This paper proposes a piecewise exponential lookup table (LUT) method, which reduces the LUT size of the exponential function in DNN's softmax layer. The experiment results show that the hardware using this method consumes less area and power resources than the previous work. The experiment input has a wide range and high accuracy, the absolute error of the calculation result is up to 4.5×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> . The experiment results prove the proposed design is suitable for the softmax layer in most hardware implementation of DNN.
ADFNet: accumulated decoder features for real‐time semantic segmentation

Semantic segmentation is one of the important technologies in autonomous driving, and ensuring its real‐time and high performance is of utmost importance for the safety of pedestrians and passengers. To improve its performance using deep neural networks that operate in real‐time, the authors propose a simple and efficient method called ADFNet using accumulated decoder features, ADFNet operates by only using the decoder information without skip connections between the encoder and decoder. They demonstrate that the performance of ADFNet is superior to that of the state‐of‐the‐art methods, including that of the baseline network on the cityscapes dataset. Further, they analyse the results obtained via ADFNet using class activation maps and RGB representations for image segmentation results.
Analysis the Issue of Increasing National Health Insurance (BPJS Kesehatan) Rates through Community Perspectives on Social Media: A Case Study of Drone Emprit

Mobile phones are devices that always embedded in the society of Indonesian people, around 91% of Indonesians population use mobile phones and around 60% are smartphones. The most widely used smartphone application is social media, around 60% of Indonesia's population uses social media. The number of social media users in Indonesia opens up opportunities to analyze these social media data. Social media data mining, commonly referred to as text mining, can help stakeholders implement a data driven policy. In this paper will discuss the public perception on social media related to the issue of rising fees national health insurance or Badan Penyelenggara Jaminan Sosial (BPJS Kesehatan). The social media analyzed is Twitter because it can provide a comprehensive sentiment and discussion of an issue. We used Drone Emprit system to analyze social media data, Drone Emprit is a big data system that captures and analyzes discussion and sentiments on social media platforms, in this case Twitter. The data analyzed are all conversations that took place over 60 days in the time span of 22 September to 22 November 2019. During that time period 360,820 total conversations occurred with a percentage of sentiment of 91% showing negative sentiment, 5% positive and 4% neutral. This data shows that the majority of Indonesian people do not agree with the increase in fees of national health insurances (BPJS Kesehatan).
A Bibliometric Analysis of Communication Research on Artificial Intelligence and Big Data

The development of artificial intelligence (AI), big data and other communication technologies have multiple implications for the consumption and production of media content.Communication researchers have recently discussed several methodological and thematic challenges, ranging from the ways in which they disrupt the news production and consumption, to the digital transformation of the media and content ecosystems.With the aim to provide a systematic overview of the latest development of communication research in relation to AI and Big Data, this paper has mapped out the main sources, disciplines, and keywords based on a bibliometric analysis of 685 articles collected from Web of Science database.The findings show that the main clusters are "Communication and Sociology", "Journalism", and "Information and Telecommunications", with the cluster of "Communication and Political Science" being in-between them.Also, the visualization of the author keywords indicates the main "Communication and Sociology" cluster includes the main research topics such as surveillance, algorithms, datafication, privacy, ethics, etc., and the "Journalism" cluster includes computational journalism, data journalism, automated journalism.Although future work is needed to provide detailed synthesis, the paper nonetheless provides a succinct summary for communication researchers and professionals to have an overview of the latest discussions.
The Scientific Psychological Knowledge in the Structure of Autocompetence of a Student of a Technical University as a Factor in His Professional Development

The article is devoted to the successful professional activity’s problem of novice specialists. Autopsychological competence is considered as the basis for designing the trajectory of personal and professional development. The work aimed to study the sources of psychological knowledge from future engineers. The article provides an overview of competence studies and basic approaches to the understanding of autocompetence and its role in self-development. Using the methods of interrogation, content analysis and experiment, the cognitive component of autocompetence was investigated, which correlates with the existence in the individual mind of reliable scientific psychological information. The mass media can act as a source of such knowledge, primarily the works of scientific journalists and popularizers of science. The results of the study showed that the information presented in the mass media by scientific journalists is unknown to most students. It was established that most participants have erroneous ideas about the laws of mental activity. Only an insignificant part of the participants of the study showed an interest in the perception of the laws of the person’s mental life as a whole and his own in particular. The research procedure had not only diagnostic but also had a developing value. A third of the respondents had been formed a motivation to improve the quality of their knowledge of human mental life. Thus, the results of the study can become the basis for developing activities.
Language Identification and Translation of English and Gujarati code-mixed data

Code-mixing is a growing field of research in the domain of Natural Language Processing. Communication on social media involves code-mixed texts, colloquial language, and variations in spellings. An amalgamation of different languages in the conversation results in transliteration and romanization. Presently there is a deficit amount of open-source resources for code-mixed English-Gujarati data, while the amount of such data is exponentially increasing every-day with growing social-media users. In this paper, we present our creation of a linguistic resource for English and Gujarati. We also present our approach of language identification and normalization of data along with the translation of the transliterated text into native language form.
Feature Extraction from Text Images to Study Individuality of Handwriting

Individuality of handwriting originates from interpersonal variations in handwriting. In this work, individuality of handwriting is studied using the following features: skew of writing, baseline, zone, slant and pen-pressure. Algorithms to extract the features are described and experimental results on controlled dataset collected from specific age groups are given. Experimental results are also given for sample images from IAM dataset. It is observed that for pre-defined text, skew-angle and baseline do not show much variation, whereas zone, slant and pen pressure show enough variation and hence ascertain individuality.
Deep Learning Approaches to Text Production

Text production has many applications. It is used, for instance, to generate dialogue turns from dialogue moves, verbalise the content of knowledge bases, or generate English sentences from rich lingu
A Hybrid Approach for the Recommendation of Scholarly Journals

The increasing number of scholarlyjournals have made it difficult for authors to select the most suitable journal that publishes their research. Existing search systems that recommend journals for manuscript submission are either based on author 's profile, bibliographic data or the copublication network. These approaches are not useful for beginner researchers who have no publication records or for those who are interested in new research domains. The present work proposes a hybrid approach that combines clustering and document similarity for the recommendation of scholarly venues. The proposal was evaluated both objectively and subjectively using domain experts. The results of mean average precision (0.84) and normalized discounted cumulative gain (0.89) shows positive recommendations made by the proposed approach.
Optimizing dichotomic local phase measurement settings for multipartite quantum systems

We provide a method to describe quantum nonlocality for $n$-qubit systems. By treating the correlation function as an $n$-index tensor, we derive a generalized Bell inequality. Taking generalized Greenberger-Horne-Zeilinger (GHZ) state for example, we calculate quantum prediction under a series of measurement settings involving various angle parameters. We reveal the exact relationship between quantum prediction and the angle parameters. We show that there exists a set of optimal measurement settings and find the corresponding maximal quantum prediction for $n$-qubit generalized GHZ states. As an example, we consider an interesting situation involving only two angle parameters. Finally, we obtain a criterion for the violation of the generalized Bell inequality.
ENHANCED SVM CLASSIFIER FOR BREAST CANCER DIAGNOSIS

Breast cancer is the leading disease to cause death especially in women. In this paper, a frame work based algorithm for the classification of cancerous/non-cancerous data is developed using application of supervised machine learning. In feature selection, we derive basis set in the kernel space and then we extend the margin based feature selection algorithm. We are trying to explore several feature selection, extraction techniques and combine the optimal feature subsets with various learning classification methods such as KNN, PNN and Support Vector Machine (SVM) classifiers. The best classification performance for breast cancer diagnosis is attained equal to 99.17% between radius and compact features using SVM classifier. And also derive the features of a breast image in the WBCD dataset.
Topic extraction to provide an overview of research activities: The case of the high-temperature superconductor and simulation and modelling

For those who are not experts in a particular scientific field, it is difficult to understand scientific research trends. Although studies on the extraction of research trends have been conducted, most focus on extracting global trends from large-scale data, and the methods are often complicated. The purpose of this study is to develop a method of obtaining overviews of a scientific field for non-experts by capturing research trends simply and then to verify the method. To extract research topics which should express research trends, text analysis was performed using abstracts over 12 years of articles on high-temperature superconductors. We characterised three topics for the extracted word groups that frequently occurred. For these topics, we studied their appropriateness using a method that has been little used: examining research articles, review literature and co-citations among research articles used to extract the words, comparisons with controlled index terms assigned to the articles and confirming that there were no contradictions. Based on the established method, we have also applied this method to another research field: ‘simulation and modelling’. Although the method used in this article is simple, important topics were extracted, and the relations with the original articles are clear, which can lead to further investigation of the extracted topics.
Named Entity Recognition for Terahertz Domain Knowledge Graph based on Albert-BiLSTM-CRF

Named entity recognition is a vital part of the question answering system. The current methods for identifying named entities are mostly for short entities. In our terahertz domain question answering system, there are both long and short entities in questions. In this paper, an entity recognition method based on Albert-BiLSTM-CRF is applied to recognize long entity completely. Specifically, a pre-trained model of deep bidirectional representation is applied to fully understand the semantics of the entire sentence. The experimental results on the terahertz domain dataset show that the proposed method improves the accuracy of long domain entity recognition and improves the performance of the terahertz domain question answering system.
Q-BERT: A BERT-based Framework for Computing SPARQL Similarity in Natural Language

In this paper, we present a pre-trained transformer network Q-BERT, in which siamese network architecture is employed to produce semantically meaningful embeddings of SPARQL queries to be compared via cosine-similarity. A core idea of Q-BERT is to put SPARQL query into the category of natural language to ensure that each entity mention or relation phrase in different knowledge bases has the same vector representation. Moreover, based on Q-BERT, we present a practical approach for the SPARQL query-empty-answer problem by accessing the RDF repository. The experiments on real datasets show the effectiveness and efficiency of Q-BERT.
Selecting a Secure Cloud Provider—An Empirical Study and Multi Criteria Approach

Security has become one of the primary factors that cloud customers consider when they select a cloud provider for migrating their data and applications into the Cloud. To this end, the Cloud Security Alliance (CSA) has provided the Consensus Assessment Questionnaire (CAIQ), which consists of a set of questions that providers should answer to document which security controls their cloud offerings support. In this paper, we adopted an empirical approach to investigate whether the CAIQ facilitates the comparison and ranking of the security offered by competitive cloud providers. We conducted an empirical study to investigate if comparing and ranking the security posture of a cloud provider based on CAIQ’s answers is feasible in practice. Since the study revealed that manually comparing and ranking cloud providers based on the CAIQ is too time-consuming, we designed an approach that semi-automates the selection of cloud providers based on CAIQ. The approach uses the providers’ answers to the CAIQ to assign a value to the different security capabilities of cloud providers. Tenants have to prioritize their security requirements. With that input, our approach uses an Analytical Hierarchy Process (AHP) to rank the providers’ security based on their capabilities and the tenants’ requirements. Our implementation shows that this approach is computationally feasible and once the providers’ answers to the CAIQ are assessed, they can be used for multiple CSP selections. To the best of our knowledge this is the first approach for cloud provider selection that provides a way to assess the security posture of a cloud provider in practice.