Reviewerly/exHarmony huggingface dataset:
Unsupervised dialogue intent detection via hierarchical topic model

One of the challenges during a taskoriented chatbot development is the scarce availability of the labeled training data.The best way of getting one is to ask the assessors to tag each dialogue according to its intent.Unfortunately, performing labeling without any provisional collection structure is difficult since the very notion of the intent is ill-defined.In this paper, we propose a hierarchical multimodal regularized topic model to obtain a first approximation of the intent set.Our rationale for hierarchical models usage is their ability to take into account several degrees of the dialogues relevancy.We attempt to build a model that can distinguish between subject-based (e.g.medicine and transport topics) and action-based (e.g.filing of an application and tracking application status) similarities.In order to achieve this, we divide set of all features into several groups according to part-of-speech analysis.Various feature groups are treated differently on different hierarchy levels.
A Novel Learning Model Based on Trust Diffusion and Global Item for Recommender Systems

Recommender systems can provide users with an ordered list of various items, which greatly assists users to purchase products that they are satisfied with. However, item recommendation has been confronted with some inherent problems, such as sparse ratings and long-tail distribution, resulting in low accuracy of recommendations and insignificant marketing. In this paper, we propose a novel learning model based on trust diffusion and global item (TDGIL) to improve the accuracy of item rating prediction for recommender systems. Specifically, first, the rating information on items is mined and aggregated to the greatest extent based on trust diffusion characteristics among users. The benchmark prediction of item recommendation is updated by a user trust neighbor set and its item ratings, which are obtained by a trust diffusion algorithm. Then, the difference weights and compensation coefficients for all items are defined to learn users' potential preferences in the proposed global item model. Finally, the TDGIL learning algorithm is presented to train and learn the target networks by random gradient descent. The extensive experiments and results on two real-world datasets demonstrated that our proposed model can achieve significant improvements in the accuracy of rating prediction compared with some state-of-the-art methods.
Edge-Boosted U-Net for 2D Medical Image Segmentation

Automatic medical image segmentation has always been a heated study in computer-assisted diagnosis (CAD).It is a quite challenging task due to the diversity and complexity of medical images.In this paper, we propose an Edge-boosted U-Net (EU-Net) to address the problem of medical image segmentation.The architecture is basically a U-shape network, combined with three main parts: Edge Aggregation Path, Feature Fusion Block, and Feature Attention Block.The Edge Aggregation Path is to extract multilevel edge-relevant information.The Feature Fusion Block is designed to fuse features from different paths.And the Feature Attention Block is embedded in the network to generate more informative feature maps.The collaboration of these three parts effectively boosts the performance of the whole network.We verified the importance of each part by conducting several experiments.Meanwhile, we compared the proposed method with other state-of-the-art methods on three different modalities of public medical image datasets.Our method achieves the superiority with IoU and dice coefficient respectively on all the datasets.Notably, it attains 2% accuracy improvement over other methods on the challenging datasets.
CascadeGAN: A category-supervised cascading generative adversarial network for clothes translation from the human body to tiled images

With the popularity and development of the Internet, purchasing items that are similar to those that appear in videos or on fashion websites has gradually become an established trend in online commerce. Currently, determining how to accurately locate these similar items in a huge e-commerce database poses a major challenge. When processing images that contain clothes worn on the human body, traditional methods usually detect the clothes in these images first, add them to a database for clothing retrieval, and finally identify a few pieces of clothing with a high degree of similarity for recommendation. Generative adversarial networks (GANs) have been widely and successfully utilized in the field of image-to-image translation. In this paper, GAN is used to transfer human body images into tiled clothing images, which can be directly used for clothing retrieval. To achieve this, a category-supervised GAN under a cascading structure is proposed. For model training, a large-scale dataset was compiled that contains 39,521 image pairs. Experimental results demonstrate that the tiled clothing images generated by our proposed method deliver higher quality, as well as performance superiority, for clothing retrieval in comparison to other existing methods.
A novel approach for big data classification based on hybrid parallel dimensionality reduction using spark cluster

The big data concept has elicited studies on how to accurately and efficiently extract valuable information from such huge dataset. The major problem during big data mining is data dimensionality due to a large number of dimensions in such datasets. This major consequence of high data dimensionality is that it affects the accuracy of machine learning (ML) classifiers; it also results in time wastage due to the presence of several redundant features in the dataset. This problem can be possibly solved using a fast feature reduction method. Hence, this study presents a fast HP-PL which is a new hybrid parallel feature reduction framework that utilizes spark to facilitate feature reduction on shared/distributed-memory clusters. The evaluation of the proposed HP-PL on KDD99 dataset showed the algorithm to be significantly faster than the conventional feature reduction techniques. The proposed technique required 1 minute to select 4 dataset features from over 79 features and 3,000,000 samples on a 3-node cluster (total of 21 cores). For the comparative algorithm, more than 2 hours was required to achieve the same feat. In the proposed system, Hadoop’s distributed file system (HDFS) was used to achieve distributed storage while Apache Spark was used as the computing engine. The model development was based on a parallel model with full consideration of the high performance and throughput of distributed computing. Conclusively, the proposed HP-PL method can achieve good accuracy with less memory and time compared to the conventional methods of feature reduction. This tool can be publicly accessed at https://github.com/ahmed/Fast-HP-PL.
Browser Extension based Hybrid Anti-Phishing Framework using Feature Selection

Phishing is one of the socially engineered cybersecurity attacks where the attacker impersonates a genuine and legitimate website source and sends emails with the intention of stealing sensitive personal information.The phishing websites' URLs are usually spread through emails by luring the users to click on them or by embedding the link to fake website replicating any genuine e-commerce website inside the invoice or other documents.The phishing problem is very wide and no single solution exists to mitigate all the vulnerabilities properly.Thus, multiple techniques are often combined and implemented to mitigate specific attacks.The primary objective of this paper is to propose an efficient and effective anti-phishing solution that can be implemented at the client-side in the form of a browser extension and should be capable to handle real-time scenarios and zero-day attacks.The proposed approach works efficiently for any phishing link carrier mode as the execution on clicking on any link or manually entering URL in the browser doesn't proceed unless the proposed framework approves that the website associated with that URL is genuine.Also, the proposed framework is capable to handle DNS cache poisoning attacks even if the system's DNS cache is somehow infected.This paper first presents a comprehensive review that broadly discusses the phishing life cycle and available anti-phishing countermeasures.The proposed framework considers the pros and cons of existing methodologies and presents a robust solution by combining the best features to ensure that a fast and accurate response is achieved.The effectiveness of the approach is tested in a real-time dataset consisting of live phishing and legitimate website URLs and the framework is found to be 98.1% accurate in identifying websites correctly in very less time.
Enet-CRF-Lidar: Lidar and Camera Fusion for Multi-Scale Object Recognition

Object recognition, which mainly includes object detection and semantic segmentation, is one of the critical challenges for intelligent vehicles. In most cases, cameras and Lidar are the most common sensors used for object recognition. However, both cameras and Lidar suffer from some inherent drawbacks. Therefore, the fusion of camera and Lidar becomes a natural solution to overcome the inherent defects of each single sensing modality. With the boost of deep learning-based algorithms, multi-sensor fusion methodologies employ deep-learning methods as their fusion strategy, which has made impressive accomplishments on large-scale objects such as vehicles and buses. However, most existing sensor-fusion strategies have the problem of ignoring detailed information caused by down-sampling operations in deep learning, which results in poor detection performance on small-scale objects such as pedestrians and cyclists. In this paper, we propose a real-time multi-sensor (Lidar and color camera) fusion strategy for multi-scale object recognition at the semantic level named Enet-CRF-Lidar. Firstly, a multi-module Enet is designed to adapt both large-scale objects and small-scale ones. Then, the CRF-RNN module is integrated with the multi-module Enet to introduce the low-level details of the input data, which leads to a significant improvement in small-scale object recognition. The experimental results show that the proposed Enet-CRF-Lidar module can provide reliable detection performance on multi-scale objects and can be adapted to complex scenarios.
TWSVC+: Improved Twin Support Vector Machine-Based Clustering

Based on twin support vector machines (TWSVM) model, the twin support vector clustering (TWSVC) is a planar clustering model that increases inter-cluster separation.Because the TWSVC is not a standard model for some variables, its solving algorithm consumes lots of time and does not always converge to the optimal solution.To solve the problem, this paper proposes a novel clustering model, denoted as TWSVC+, based on twin support vector machines (TWSVM).The TWSVC+ is convex and standard with respect to each variable.Therefore, it is possible to solve this model rapidly with an algorithm that converges to a global optimal solution relative to each variable.The author presented linear TWSVC+ and non-linear TWSVC+ for clustering linear separable clusters and linear inseparable clusters, respectively.Experimental results on real datasets of UCI repository show that the TWSVC+ was better than TWSVC and support vector clustering (SVC) in accuracy and training time.
A Closer Look at Arabic Text Classification

The world has witnessed an information explosion in the past two decades.Electronic devices are now available in many varieties such as PCs, Laptops, book readers, mobile devices and with relatively affordable prices.This and the ubiquitous use of software applications such as social media and cloud applications, and the increasing trend towards digitalization, the amount of information on the global cloud has surged to an unprecedented level.Therefore, a dire need exists in order to mine this massively large amount of data and produce meaningful information.Text Classification is one of the known and well established data mining techniques that has been used and reported in the literature.Text classification methods include statistical and machine learning algorithms such as Naive Baysian, Support Vector Machines and others have widely been used.Many works have been reported regarding text classification of various languages including English, Chinese, Russian, and many others.Arabic is the fifth most spoken language in the world.There has been many works in the literature for Arabic text classification.However, and to the best of our knowledge, there is no recent work that presents a good, critical and comprehensive survey of the Arabic text classification for the past two decades.The aim of this paper is to present a concise and yet comprehensive review of the Arabic text classification.We have covered over 50 research papers covering the past two decades (2000 -2019).The main focus of this paper is to address the following issues: 1) The techniques reported in the literature including.2) New Techniques.3) Most claimed efficient technique.4) Datasets used and which ones are most popular.5) Which feature selection techniques are used?6) Popular classes/categories used.7) Effect of stemming techniques on classification results.
A Barrage Sentiment Analysis Scheme Based on Expression and Tone

Most of existing methods do not consider the influence of expression and tone on barrage sentiment analysis.This decreases the effect and accuracy of barrage sentiment analysis.Therefore, we propose a barrage sentiment analysis scheme based on expression and tone.First, we propose a new sentiment dictionary based on expression and tone to increase the effect of barrage sentiment analysis.Second, we propose a new calculation method of sentiment value based on expression and tone for barrage sentiment analysis to increase the accuracy.Meanwhile, we change a single threshold to a threshold range, to expand the scope of neutral barrages.Finally, the experimental results show that our scheme is more effective and practical than the existing methods in the scenario of barrage sentiment analysis.
Building Deep Equivariant Capsule Networks

Capsule networks are constrained by the parameter-expensive nature of their layers, and the general lack of provable equivariance guarantees. We present a variation of capsule networks that aims to remedy this. We identify that learning all pair-wise part-whole relationships between capsules of successive layers is inefficient. Further, we also realise that the choice of prediction networks and the routing mechanism are both key to equivariance. Based on these, we propose an alternative framework for capsule networks that learns to projectively encode the manifold of pose-variations, termed the space-of-variation (SOV), for every capsule-type of each layer. This is done using a trainable, equivariant function defined over a grid of group-transformations. Thus, the prediction-phase of routing involves projection into the SOV of a deeper capsule using the corresponding function. As a specific instantiation of this idea, and also in order to reap the benefits of increased parameter-sharing, we use type-homegenous group-equivariant convolutions of shallower capsules in this phase. We also introduce an equivariant routing mechanism based on degree-centrality. We show that this particular instance of our general model is equivariant, and hence preserves the compositional representation of an input under transformations. We conduct several experiments on standard object-classification datasets that showcase the increased transformation-robustness, as well as general performance, of our model to several capsule baselines.
On Generalization Error Bounds of Noisy Gradient Methods for Non-Convex Learning

Generalization error (also known as the out-of-sample error) measures how well the hypothesis learned from training data generalizes to previously unseen data. Proving tight generalization error bounds is a central question in statistical learning theory. In this paper, we obtain generalization error bounds for learning general non-convex objectives, which has attracted significant attention in recent years. We develop a new framework, termed Bayes-Stability, for proving algorithm-dependent generalization error bounds. The new framework combines ideas from both the PAC-Bayesian theory and the notion of algorithmic stability. Applying the Bayes-Stability method, we obtain new data-dependent generalization bounds for stochastic gradient Langevin dynamics (SGLD) and several other noisy gradient methods (e.g., with momentum, mini-batch and acceleration, Entropy-SGD). Our result recovers (and is typically tighter than) a recent result in Mou et al. (2018) and improves upon the results in Pensia et al. (2018). Our experiments demonstrate that our data-dependent bounds can distinguish randomly labelled data from normal data, which provides an explanation to the intriguing phenomena observed in Zhang et al. (2017a). We also study the setting where the total loss is the sum of a bounded loss and an additiona l`2 regularization term. We obtain new generalization bounds for the continuous Langevin dynamic in this setting by developing a new Log-Sobolev inequality for the parameter distribution at any time. Our new bounds are more desirable when the noise level of the processis not very small, and do not become vacuous even when T tends to infinity.
Extracting Word Synonyms from Text using Neural Approaches

Extracting synonyms from textual corpora using computational techniques is an interesting research problem in the Natural Language Processing (NLP) domain. Neural techniques (such as Word2Vec) have been recently utilized to produce distributional word representations (also known as word embeddings) that capture semantic similarity/relatedness between words based on linear context. Nevertheless, using these techniques for synonyms extraction poses many challenges due to the fact that similarity between vector word representations does not indicate only synonymy between words, but also other sense relations as well as word association or relatedness. In this paper, we tackle this problem using a novel 2-step approach. We first build distributional word embeddings using Word2Vec then use the induced word embeddings as an input to train a feed-forward neutral network using annotated dataset to distinguish between synonyms and other semantically related words
Educational Resources: Saturation or Satiety?

The article explores the methodological issues of education system evaluation. Such evaluation, based on the comparative analysis of national and regional education systems, is an important source of information for education policy design and implementation through educational development programs. The existing approaches to and methods of education system evaluation are discussed. It is shown that they are based on internal indicators, not those perceived by end users or the effects external to the system. Even though there have been some objective reasons for using such approach, it remains unclear to what extent its results reflect the educational outcomes for the end users― that is, individuals, society, and economy? Statistical analysis methods are applied to OECD education statistics to investigate the relations between the education indicators characterizing the level of educational attainment, education accessibility, and the amount and quality of the resources involved, on the one hand, and the outcome effects for individuals, society, and economy. Where such relations are observed, they tend to be non-smooth and only manifest themselves up to a certain point in the vast majority of cases. Such cessation of growth in the outcome indicators that happens after achieving certain levels of resources involved, educational attainment, and other education indicators can be described as oversaturation or satiety effect. Inferences about the limitations and conditions of applying education indicators in education system evaluation are drawn from the findings.
Stream sediment geochemical survey of rare elements in an arid region of the Hamadat area, central Eastern Desert, Egypt

The present work deals with the possibility of conducting orientated geochemical survey exploration in an arid area using stream sediments. These sediments were drained from the Neoproterozoic metavolcanic and granitic rocks of the Hamadat area, Egypt. The fine size fraction of <63 µm, which is most appropriate for the exploration in the arid desert conditions, has been considered. Fourty four samples, including 35 clay and silt fractions and three size fractions of three samples, were analyzed by ICP-MS for major and trace elements. The heavy mineral content was separated from the stream sediments and identified. Based on the geochemical and mineralogical features, the catchment area of the studied stream sediments is split into two primary domains: namely first and second. The first domain drains monzogranite and associated pegmatite and quartz veins, whereas the second domain drains a metasomatized riebeckite granite. The stream sediments of the first domain are enriched in LREE (and ΣREE), Th, Zr and Hf, whereas the second domain has a clear signature of Nb, Ta, Th, U, Y and HREE. These rare metals are predominantly accommodated in the structure of detrital minerals representative of the clastic aureole around the source rocks and are absorbed to a lesser extent. The obtained data are utilized for constructing high-resolution geochemical maps to show the distribution of different rare metals. The anomaly aureoles have been delineated for Y, Yb, Nb, Th and U on a base map of the catchment area. The distribution maps propose possible potential mineralization of rare metals, especially REE, Nb, Ta, Zr, and Hf, in the study area. The HREE + Y appear to be prospective in the second domain, whereas Th, Zr and LREE are possible resources in the first domain. Further studies to authenticate the potentiality of these resources are strongly recommended.
Bijective, Non-Bijective and Semi-Bijective Translations on the Triangular Plane

The triangular plane is the plane which is tiled by the regular triangular tessellation. The underlying discrete structure, the triangular grid, is not a point lattice. There are two types of triangle pixels. Their midpoints are assigned to them. By having a real-valued translation of the plane, the midpoints of the triangles may not be mapped to midpoints. This is the same also on the traditional square grid. However, the redigitized result on the square grid always gives a bijection (gridpoints of the square grid are mapped to gridpoints in a bijective way). This property does not necessarily hold on to the triangular plane, i.e., the redigitized translated points may not be mapped to the original points by a bijection. In this paper, we characterize the translation vectors that cause non bijective translations. Moreover, even if a translation by a vector results in a bijection after redigitization, the neighbor pixels of the original pixels may not be mapped to the neighbors of the resulting pixel, i.e., a bijective translation may not be digitally ‘continuous’. We call that type of translation semi-bijective. They are actually bijective but do not keep the neighborhood structure, and therefore, they seemingly destroy the original shape. We call translations strongly bijective if they are bijective and also the neighborhood structure is kept. Characterizations of semi- and strongly bijective translations are also given.