Reviewerly/exHarmony huggingface dataset:
A Hybrid of Dual and Meet-in-the-Middle Attack on Sparse and Ternary Secret LWE

The dual attack is one of the most efficient attack algorithms for learning with errors (LWE) problem. Recently, an efficient variant of the dual attack for sparse and small secret LWE was reported by Albrecht (Eurocrypt 2017), which forces some LWE-based cryptosystems, especially fully homomorphic encryptions (FHE), to change parameters. In this paper, we propose a new hybrid of dual and meet-in-themiddle (MITM) attack, which outperforms the improved variant on the same LWE parameter regime. To this end, we adapt the MITM attack for NTRU due to Odlyzko to LWE and give a rigorous analysis for it. The performance of our MITM attack depends on the relative size of error and modulus, and hence, for a large modulus LWE samples, our MITM attack works well for quite large error. We then combine our MITM attack with Albrecht's observation that understands the dual attack as a dimension-error tradeoff, which finally yields our hybrid attack. We also implement a sage module that estimates the attack complexity of our algorithm upon LWE-estimator, and our attack shows significant performance improvement for the LWE parameter for FHE. For example, for the LWE problem with dimension n = 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">15</sup> , modulus q = 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">628</sup> , and ternary secret key with Hamming weight 64 which is one parameter set used for HEAAN bootstrapping (Eurocrypt 2018), our attack takes 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">112.5</sup> operations and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">70.6</sup> bit memory, while the previous best attack requires 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">127.2</sup> operations as reported by the LWE-estimator.
THE ROLE OF ARTIFICIAL INTELLIGENCE ON ENHANCING CUSTOMER EXPERIENCE

The main aim of the study is to examine the role of Artificial Intelligence (AI) on Enhancing Customer Experience in Palestine through different industries, such as banks and telecommunication companies. Interviews and a structured questionnaire were the primary data of this study. The results of the study revealed that there is a positive significant relationship between Artificial Intelligence and Customer Experience. Artificial Intelligence explained 26.4% of the variance of the Customer Experience (R²= 0.264, F (1,89) =28.634, P&lt; 0.05). Customer Experience has two dimensions; Customer Service and After-Sale Support, the study shows that Artificial Intelligence predicted 22.9% of the variance of Customer Service, whereas it predicted 7% of After-Sale Support. Moreover, providing Personalized Customer Service throughout the customer's buying journey has a great impact on Customer Experience. The study recommends enterprises to offer more personalized services for customers which it influences their overall experience with the enterprise. Likewise, it's highly recommended to employ Artificial Intelligence in call centers and the other after-sales support services to shortening the customers waiting time.Keywords: Customer Experience, Artificial Intelligence, Personalized Customer Service.JEL Classifications: M30, M31, M10DOI: https://doi.org/10.32479/irmm.8166
Sentiment Analysis on Mobile Phone Reviews Using Supervised Learning Techniques

Opinion Mining or Sentiment Analysis is the process of mining emotions, attitudes, and opinions automatically from speech, text, and database sources through Natural Language Processing (NLP).Opinions can be given on anything.It may be a product, feature of a product or any sentiment view on a product.In this research, Mobile phone products reviews, fetched from Amazon.com, are mined to predict customer rating of the product based on its user reviews.This is performed by the sentiment classification of unlocked mobile reviews for the sake of opinion mining.Different opinion mining algorithms are used to identify the sentiments hidden in the reviews and comments for a specific unlocked mobile.Moreover, a performance analysis of Sentiment Classification algorithms is performed on the data set of mobile phone reviews.Results yields from this research provide the comparative analysis of eight different classifiers on the evaluation parameters of accuracy, recall, precision and F-measure.The Random Forest Classifiers offers more accurate predictions than others but LSTM and CNN also give better accuracy.
Compressing CNN-DBLSTM models for OCR with teacher-student learning and Tucker decomposition

Integrated convolutional neural network (CNN) and deep bidirectional long short-term memory (DBLSTM) based character models have achieved excellent recognition accuracies on optical character recognition (OCR) tasks, along with large amount of model parameters and massive computation cost. To deploy CNN-DBLSTM model in products with CPU server, there is an urgent need to compress and accelerate it as much as possible, especially the CNN part, which dominates both parameters and computation. In this paper, we study teacher-student learning and Tucker decomposition methods to reduce model size and runtime latency for CNN-DBLSTM based character model for OCR. We use teacher-student learning to transfer the knowledge of a large-size teacher model to a small-size compact student model, followed by Tucker decomposition to further compress the student model. For teacher-student learning, we design a novel learning criterion to bring in the guidance of succeeding LSTM layer when matching the CNN-extracted feature sequences of the large teacher and small student models. Experimental results on large scale handwritten and printed OCR tasks show that, using teacher-student learning alone achieves 9.90 × footprint reduction and 15.23 × inference speedup yet without degrading recognition accuracy. Combined with Tucker decomposition method, we can compress and accelerate the model further. The decomposed model achieves 11.89 × footprint reduction and 22.16 × inference speedup while suffering no or only a small recognition accuracy degradation against the large-size baseline model.
Ensemble of evolving optimal granular experts, OWA aggregation, and time series prediction

This paper presents an online-learning ensemble framework for nonstationary time series prediction. Optimal granular fuzzy rule-based models with different objective functions and constraints are evolved from data streams. Evolving optimal granular systems (eOGS) consider multiobjective optimization, the specificity of information, model compactness, and variability and coverage of the data within the process of modeling data streams. Forecasts of individual base eOGS models are combined using averaging aggregation functions: ordered weighted averaging (OWA), weighted arithmetic mean, median, and linear non-inclusive centered OWA. Some aggregation functions use specific weights for the relevance of the base models and exclude extreme values and outliers. The weights of other aggregation functions are adapted over time based on a quadratic programming problem and the data within a sliding window. This paper investigates whether an online-learning ensemble can outperform individual eOGS models, and which aggregation function provides the most accurate forecasts. Real multivariate weather time series, particularly time series of daily mean temperature, air humidity, and wind speed from different weather stations, such as Paris–Orly, Frankfurt–Main, Reykjavik, and Oslo–Blindern, are used for evaluation. The results show that ensemble schemes outperform individual models. The proposed linear non-inclusive centered OWA function provided the most accurate numerical predictions.
What if We Simply Swap the Two Text Fragments? A Straightforward yet Effective Way to Test the Robustness of Methods to Confounding Signals in Nature Language Inference Tasks

Nature language inference (NLI) task is a predictive task of determining the inference relationship of a pair of natural language sentences. With the increasing popularity of NLI, many state-of-the-art predictive models have been proposed with impressive performances. However, several works have noticed the statistical irregularities in the collected NLI data set that may result in an over-estimated performance of these models and proposed remedies. In this paper, we further investigate the statistical irregularities, what we refer as confounding factors, of the NLI data sets. With the belief that some NLI labels should preserve under swapping operations, we propose a simple yet effective way (swapping the two text fragments) of evaluating the NLI predictive models that naturally mitigate the observed problems. Further, we continue to train the predictive models with our swapping manner and propose to use the deviation of the model’s evaluation performances under different percentages of training text fragments to be swapped to describe the robustness of a predictive model. Our evaluation metrics leads to some interesting understandings of recent published NLI methods. Finally, we also apply the swapping operation on NLI models to see the effectiveness of this straightforward method in mitigating the confounding factor problems in training generic sentence embeddings for other NLP transfer tasks.
Joint Representation and Estimator Learning for Facial Action Unit Intensity Estimation

Facial action unit (AU) intensity is an index to characterize human expressions. Accurate AU intensity estimation depends on three major elements: image representation, intensity estimator, and supervisory information. Most existing methods learn intensity estimator with fixed image representation, and rely on the availability of fully annotated supervisory information. In this paper, a novel general framework for AU intensity estimation is presented, which differs from traditional estimation methods in two aspects. First, rather than keeping image representation fixed, it simultaneously learns representation and intensity estimator to achieve an optimal solution. Second, it allows incorporating weak supervisory training signal from human knowledge (e.g. feature smoothness, label smoothness, label ranking, and positive label), which makes our model trainable even fully annotated information is not available. More specifically, human knowledge is represented as either soft or hard constraints which are encoded as regularization terms or equality/inequality constraints, respectively. On top of our novel framework, we additionally propose an efficient algorithm for optimization based on Alternating Direction Method of Multipliers (ADMM). Evaluations on two benchmark databases show that our method outperforms competing methods under different ratios of AU intensity annotations, especially for small ratios.
Rethinking Transport Layer Design for Distributed Machine Learning

Motivated by the increasing scale of data, we see a growing need of high performance distributed machine learning systems. Many research works are being proposed to improve distributed machine learning performance.
Group-based Learning of Disentangled Representations with Generalizability for Novel Contents

Sensory data are often comprised of independent content and transformation factors. For example, face images may have shapes as content and poses as transformation. To infer separately these factors from given data, various ``disentangling'' models have been proposed. However, many of these are supervised or semi-supervised, either requiring attribute labels that are often unavailable or disallowing for generalization over new contents. In this study, we introduce a novel deep generative model, called group-based variational autoencoders. In this, we assume no explicit labels, but a weaker form of structure that groups together data instances having the same content but transformed differently; we thereby separately estimate a group-common factor as content and an instance-specific factor as transformation. This approach allows for learning to represent a general continuous space of contents, which can accommodate unseen contents. Despite the simplicity, our model succeeded in learning, from five datasets, content representations that are highly separate from the transformation representation and generalizable to data with novel contents. We further provide detailed analysis of the latent content code and show insight into how our model obtains the notable transformation invariance and content generalizability.
A novel clustering technique for efficient clustering of big data in Hadoop Ecosystem

Big data analytics and data mining are techniques used to analyze data and to extract hidden information. Traditional approaches to analysis and extraction do not work well for big data because this data is complex and of very high volume. A major data mining technique known as data clustering groups the data into clusters and makes it easy to extract information from these clusters. However, existing clustering algorithms, such as k-means and hierarchical, are not efficient as the quality of the clusters they produce is compromised. Therefore, there is a need to design an efficient and highly scalable clustering algorithm. In this paper, we put forward a new clustering algorithm called hybrid clustering in order to overcome the disadvantages of existing clustering algorithms. We compare the new hybrid algorithm with existing algorithms on the bases of precision, recall, F-measure, execution time, and accuracy of results. From the experimental results, it is clear that the proposed hybrid clustering algorithm is more accurate, and has better precision, recall, and F-measure values.
Sentiment-Controllable Chinese Poetry Generation

Expressing diverse sentiments is one of the main purposes of human poetry creation. Existing Chinese poetry generation models have made great progress in poetry quality, but they all neglected to endow generated poems with specific sentiments. Such defect leads to strong sentiment collapse or bias and thus hurts the diversity and semantics of generated poems. Meanwhile, there are few sentimental Chinese poetry resources for studying. To address this problem, we first collect a manually-labelled sentimental poetry corpus with fine-grained sentiment labels. Then we propose a novel semi-supervised conditional Variational Auto-Encoder model for sentiment-controllable poetry generation. Besides, since poetry is discourse-level text where the polarity and intensity of sentiment could transfer among lines, we incorporate a temporal module to capture sentiment transition patterns among different lines. Experimental results show our model can control the sentiment of not only a whole poem but also each line, and improve the poetry diversity against the state-of-the-art models without losing quality.
Vantage

Social live video streaming (SLVS) applications are becoming increasingly popular with the rise of platforms such as Facebook-Live, YouTube-Live, Twitch and Periscope. A key characteristic that differentiates this new class of applications from traditional live streaming is that these live streams are watched by viewers at different delays; while some viewers watch a live stream in real-time, others view the content in a time-shifted manner at different delays. In the presence of variability in the upload bandwidth, which is typical in mobile environments, existing solutions silo viewers into either receiving low latency video at a lower quality or a higher quality video with a significant delay penalty, without accounting for the presence of diverse time-shifted viewers.
Quantification of histopathological findings using a novel image analysis platform

Digital pathology, including image analysis and automatic diagnosis of pathological tissue, has been developed remarkably. HALO is an image analysis platform specialized for the study of pathological tissues, which enables tissue segmentation by using artificial intelligence. In this study, we used HALO to quantify various histopathological changes and findings that were difficult to analyze using conventional image processing software. Using the tissue classifier module, the morphological features of degeneration/necrosis of the hepatocytes and muscle fibers, bile duct in the liver, basophilic tubules and hyaline casts in the kidney, cortex in the thymus, and red pulp, white pulp, and marginal zone in the spleen were learned and separated, and areas of interest were quantified. Furthermore, using the cytonuclear module and vacuole module in combination with the tissue classifier module, the number of erythroblasts in the red pulp of the spleen and each area of acinar cells in the parotid gland were quantified. The results of quantitative analysis were correlated with the histopathological grades evaluated by pathologists. By using artificial intelligence and other functions of HALO, we recognized morphological features, analyzed histopathological changes, and quantified the histopathological grades of various findings. The analysis of histopathological changes using HALO is expected to support pathology evaluations.
A Gated Recurrent Convolutional Neural Network for Robust Spoofing Detection

Automatic speaker verification (ASV) systems are exposed to spoofing attacks which may compromise their security. While anti-spoofing techniques have been mainly studied for clean scenarios, it has also been shown that they perform poorly in noisy environments. In this work, we aim at improving the performance of spoofing detection for ASV in clean and noisy scenarios. To achieve this, we first propose the use of Gated Recurrent Convolutional Neural Networks (GRCNNs) as a deep feature extractor to robustly represent speech signals as utterance-level embeddings, which are later used by a back-end recognizer for the final genuine/spoofed classification. Then, to enhance the robustness of the system in noisy conditions, we propose the use of signal-to-noise masks (SNMs) as new input features to inform the anti-spoofing system about the time-frequency regions of the input spectral features that are mostly affected by noise and, hence, should be neglected when computing the embeddings. To evaluate our proposals, experiments were carried out on the clean and noisy versions of the ASVspoof 2015 corpus for detecting logical access attacks, as well as on the ASVspoof 2017 database to detect replay attacks. Additional results are provided for the ASVspoof 2019 corpus, including both logical and physical scenarios. The experimental results show that our proposal clearly outperforms some well-known methods based on classical features and other similar deep feature based systems for both clean and noisy conditions.
Lost in Evaluation: Misleading Benchmarks for Bilingual Dictionary Induction

The task of bilingual dictionary induction (BDI) is commonly used for intrinsic evaluation of cross-lingual word embeddings.The largest dataset for BDI was generated automatically, so its quality is dubious.We study the composition and quality of the test sets for five diverse languages from this dataset, with concerning findings: (1) a quarter of the data consists of proper nouns, which can be hardly indicative of BDI performance, and (2) there are pervasive gaps in the gold-standard targets.These issues appear to affect the ranking between cross-lingual embedding systems on individual languages, and the overall degree to which the systems differ in performance.With proper nouns removed from the data, the margin between the top two systems included in the study grows from 3.4% to 17.2%.Manual verification of the predictions, on the other hand, reveals that gaps in the gold standard targets artificially inflate the margin between the two systems on English to Bulgarian BDI from 0.1% to 6.7%.We thus suggest that future research either avoids drawing conclusions from quantitative results on this BDI dataset, or accompanies such evaluation with rigorous error analysis.
The upsurge of deep learning for computer vision applications

Artificial intelligence (AI) is additionally serving to a brand new breed of corporations disrupt industries from restorative examination to horticulture. Computers can’t nevertheless replace humans, however, they will work superbly taking care of the everyday tangle of our lives. The era is reconstructing big business and has been on the rise in recent years which has grounded with the success of deep learning (DL). Cyber-security, Auto and health industry are three industries innovating with AI and DL technologies and also Banking, retail, finance, robotics, manufacturing. The healthcare industry is one of the earliest adopters of AI and DL. DL accomplishing exceptional dimensions levels of accurateness to the point where DL algorithms can outperform humans at classifying videos &amp; images. The major drivers that caused the breakthrough of deep neural networks are the provision of giant amounts of coaching information, powerful machine infrastructure, and advances in academia. DL is heavily employed in each academe to review intelligence and within the trade-in building intelligent systems to help humans in varied tasks. Thereby DL systems begin to crush not solely classical ways, but additionally, human benchmarks in numerous tasks like image classification, action detection, natural language processing, signal process, and linguistic communication process.
Quality Estimation and Translation Metrics via Pre-trained Word and Sentence Embeddings

We propose the use of pre-trained embeddings as features of a regression model for sentencelevel quality estimation of machine translation.In our work we combine freely available BERT and LASER multilingual embeddings to train a neural-based regression model.In the second proposed method we use as an input features not only pre-trained embeddings, but also log probability of any machine translation (MT) system.Both methods are applied to several language pairs and are evaluated both as a classical quality estimation system (predicting the HTER score) as well as an MT metric (predicting human judgements of translation quality).
Learning to combine Grammatical Error Corrections

The field of Grammatical Error Correction (GEC) has produced various systems to deal with focused phenomena or general text editing.We propose an automatic way to combine black-box systems.Our method automatically detects the strength of a system or the combination of several systems per error type, improving precision and recall while optimizing F score directly.We show consistent improvement over the best standalone system in all the configurations tested.This approach also outperforms average ensembling of different RNN models with random initializations.In addition, we analyze the use of BERT for GEC -reporting promising results on this end.We also present a spellchecker created for this task which outperforms standard spellcheckers tested on the task of spellchecking.This paper describes a system submission to Building Educational Applications 2019 Shared Task: Grammatical Error Correction (Bryant et al., 2019).Combining the output of top BEA 2019 shared task systems using our approach, currently holds the highest reported score in the open phase of the BEA 2019 shared task, improving F 0.5 by 3.7 points over the best result reported.