Reviewerly/exHarmony huggingface dataset:
Deep Attentive Factorization Machine for App Recommendation Service

Recommendation service in mobile app markets decently helps users choose their preferred apps. Though a lot of recommendation service models are proposed in recent years, it is still challenging to tackle extreme sparse app data and get a relatively satisfactory recommendation performance. The reason can be concluded as that traditional recommendation models either focus on limited features or stand aside from deep training. In this paper, we propose knowledge-based deep factorization machine (KDFM), a recommendation model inspired by techniques of factorization machine and attentive deep learning, and apply it in the recommendation service for mobile apps. The KDFM aims to make full use of the rich categorical and textual knowledge in the app market for better performance. To achieve this goal, a topical attention representation component, which contains three typical parts (Word2Vec, BiLSTM and Topical Attention), is constructed. Such representation not only avoids the dimension explosion brought by traditional models, but also preserves the textual semantics for better recommendation. Through extensive experiments conducted on a large number of collected app samples, the KDFM achieves better performance compared with state-of-art rating recommendation models in terms of the rating prediction. In addition, the benefits brought by the usage of attention mechanism and topical representation are confirmed through the comparison experiments.
A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text

Ontologies are used to model knowledge in several domains of interest, such as the biomedical domain. Conceptualization is the basic task for ontology building. Concepts are identified, and then they are linked through their semantic relationships. Recently, ontologies have constituted a crucial part of modern semantic webs because they can convert a web of documents into a web of things. Although ontology learning generally occupies a large space in computer science, Arabic ontology learning, in particular, is underdeveloped due to the Arabic language’s nature as well as the profundity required in this domain. The previously published research on Arabic ontology learning from text falls into three categories: developing manually hand-crafted rules, using ordinary supervised/unsupervised machine learning algorithms, or a hybrid of these two approaches. The model proposed in this work contributes to Arabic ontology learning in two ways. First, a text mining algorithm is proposed for extracting concepts and their semantic relations from text documents. The algorithm calculates the concept frequency weights using the term frequency weights. Then, it calculates the weights of concept similarity using the information of the ontology structure, involving (1) the concept’s path distance, (2) the concept’s distribution layer, and (3) the mutual parent concept’s distribution layer. Then, feature mapping is performed by assigning the concepts’ similarities to the concept features. Second, a hybrid genetic-whale optimization algorithm was proposed to optimize ontology learning from Arabic text. The operator of the G-WOA is a hybrid operator integrating GA’s mutation, crossover, and selection processes with the WOA’s processes (encircling prey, attacking of bubble-net, and searching for prey) to fulfill the balance between both exploitation and exploration, and to find the solutions that exhibit the highest fitness. For evaluating the performance of the ontology learning approach, extensive comparisons are conducted using different Arabic corpora and bio-inspired optimization algorithms. Furthermore, two publicly available non-Arabic corpora are used to compare the efficiency of the proposed approach with those of other languages. The results reveal that the proposed genetic-whale optimization algorithm outperforms the other compared algorithms across all the Arabic corpora in terms of precision, recall, and F-score measures. Moreover, the proposed approach outperforms the state-of-the-art methods of ontology learning from Arabic and non-Arabic texts in terms of these three measures.
Monocular Segment-Wise Depth: Monocular Depth Estimation Based on a Semantic Segmentation Prior

Monocular depth estimation using novel learning-based approaches has recently emerged as a promising potential alternative to more conventional 3D scene capture technologies within real-world scenarios. Many such solutions often depend on large quantities of ground truth depth data, which is rare and often intractable to obtain. Others attempt to estimate disparity as an intermediary step using a secondary supervisory signal, leading to blurring and other undesirable artefacts. In this paper, we propose a monocular depth estimation approach, which employs a jointly-trained pixel-wise semantic understanding step to estimate depth for individually-selected groups of objects (segments) within the scene. The separate depth outputs are efficiently fused to generate the final result. This creates more simplistic learning objectives for the jointly-trained individual networks, leading to more accurate overall depth. Extensive experimentation demonstrates the efficacy of the proposed approach compared to contemporary state-of-the-art techniques within the literature.
Sunflowers of convex open sets

A sunflower is a collection of sets {U1,…,Un} such that the pairwise intersection Ui∩Uj is the same for all choices of distinct i and j. We study sunflowers of convex open sets in Rd, and provide a Helly-type theorem describing a certain “rigidity” that they possess. In particular we show that if {U1,…,Ud+1} is a sunflower in Rd, then any hyperplane that intersects all Ui must also intersect ⋂i=1d+1Ui. We use our results to describe a combinatorial code Cn for all n≥2 which is on the one hand minimally non-convex, and on the other hand has no local obstructions. Along the way we further develop the theory of morphisms of codes, and establish results on the covering relation in the poset PCode.
Direction-aware neural style transfer with texture enhancement

Neural learning methods have been shown to be effective in style transfer. These methods, which are called NST, aim to synthesize a new image that retains the high-level structure of a content image while keeps the low-level features of a style image. However, these models using convolutional structures only extract local statistical features of style images and semantic features of content images. Since the absence of low-level features in the content image, these methods would synthesize images that look unnatural and full of traces of machines. In this paper, we find that direction, that is, the orientation of each painting stroke, can capture the soul of image style preferably and thus generates much more natural and vivid stylizations. According to this observation, we propose a direction-aware neural style transfer with texture enhancement. There are four major innovations. First, we separate the style transfer method into two stage, namely, NST stage and texture enhancement stage. Second, for the NST stage, a novel direction field loss is proposed to steer the direction of strokes in the synthesized image. And to build this loss function, we propose novel direction field loss networks to generate and compare the direction fields of content image and synthesized image. By incorporating the direction field loss in neural style transfer, we obtain a new optimization objective. Through minimizing this objective, we can produce synthesized images that better follow the direction field of the content image. Third, our method provides a simple interaction mechanism to control the generated direction fields, and further control the texture direction in synthesized images. Fourth, with a texture enhancement module, our method can add vivid texture details to the synthesized image. Experiments show that our method outperforms state-of-the-art style transfer method.
Feature Fusion of Face and Body for Engagement Intensity Detection

Online learning has grown rapidly in recent years. Automatically detecting student engagement plays a vital role in gauging the learning progress of each student.In this paper we propose a novel approach to detect student engagement. We fuse facial and body features into a single long short-term memory (LSTM) model to detect the temporal dynamics of student engagement. In contrast to other CNN models that use only facial or body features, we enhance detection accuracy with a compact feature set by merging facial and body features. Our single model generates state-of-the-art results on an engagement database from the EmotiW 2018 Challenge, where it achieves a 0.0439 mean squared error on the validation set, competitive with ensemble methods.
Ascertaining Public Opinion Through Sentiment Analysis

Over the years, the evolution of the internet has played an important role in interconnectivity amongst people from every walk of life. One of the consequences to arise from this evolution is the emergence of online web forums and social media outlets. These forums act as a resource full of rich text-based data ready to be mined. Sentiment analysis is a concept that deals with people's opinion, emotions, attitudes towards any particular field or topic. As of 2019 it is one of the most discussed and researched area in the NLP (Natural Language Processing) and is also studied (widely) in data/web/text-based mining. Sentiment analysis at its current stage is widespread among many other fields (let alone computer science) including management and social media marketing due to its importance to business and society. This paper focuses on the workings of a machine learning based model that monitors public opinion on trending topics on the social media platform, Twitter, to arrive at the emotional aspect of the opinion that is being conveyed. This is in turn achieved by the NLP approach, that mines the data for emotional cues based on predefined keywords and ascertains the polarity of the public view on the topic.
How to Discover IoT Devices When Network Traffic Is Encrypted

Managing Internet of Things (IoT) devices should be easy. Yet, the increasing use of encrypted network traffic in IoT devices is complicating their management, for example during device audits or security scans. While desirable from a network security point of view, the use of encrypted traffic allows less visibility to IT environments looking to manage IoT devices. In this paper, we focus on the problem of identifying IoT device types by analyzing their encrypted traffic. We examine the TLS traffic of IoT devices and derive fingerprints from their session initialization message exchanges (i.e., ClientHello and ServerHello messages). We identify key features of the TLS handshake protocol that can serve as strong indicators for identifying IoT devices. We then build term frequency-inverse document frequency (TF-IDF) based models for identifying IoT devices based on their TLS fingerprints. In our experimental setup, we train on 71 IoT devices in 15 distinct categories over a range of three months; we derive TF-IDF classifiers for testing using two different feature sets. One feature set representing a greedy strategy contains ten prominent features extracted from the TLS handshake protocol. The other feature set contains the four features representing the most unique values in the training dataset. Experimental results show that the 4-feature set classifiers have similar classification performance as the 10- feature set, generating accuracy, precision and F1-score of over 90%.
A Voting-Based Sentiment Classification Model

Sentiment analysis is used to depictMungra,Dhara sentiments present in the textAgrawal,Anjali structures, including news, reviews, and articles, and classify them as positive, or negative. It hasThakkar,Ankit gained significant attention due to the increase in individuals utilizing social media platforms to express sentiments about organizations, products, and administrations. Many methods are being devised to improve the efficacy of automated sentiment classification. The study proposes a voting-based ensemble model Majority Voting (MV) using five supervised machine learning classifiers named Logistic Regression (LR), Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), and Random Forest (RF) as base classifiers and a majority voting rule-based mechanism to get the final prediction. The performance of the proposed method is assessed using minimum, maximum, mean, and median values of precision, recall, f-score, and accuracy. The results of 900 values of the classification accuracy (3 datasets * 6 (classification methods) * 10 data subsets (k-fold cross-validation for $$k=10$$) * 5 runs), indicates that the proposed approach outperforms the individual classifiers in majority of the cases.
Joint Object and State Recognition Using Language Knowledge

The state of an object is an important piece of knowledge in robotics applications. States and objects are intertwined together, meaning that object information can help recognize the state of an image and vice versa. This paper addresses the state identification problem in cooking related images and uses state and object predictions together to improve the classification accuracy of objects and their states from a single image. The pipeline presented in this paper includes a CNN with a double classification layer and the Concept-Net language knowledge graph on top. The language knowledge creates a semantic likelihood between objects and states. The resulting object and state confidences from the deep architecture are used together with object and state relatedness estimates from a language knowledge graph to produce marginal probabilities for objects and states. The marginal probabilities and confidences of objects (or states) are fused together to improve the final object (or state) classification results. Experiments on a dataset of cooking objects show that using a language knowledge graph on top of a deep neural network effectively enhances object and state classification.
An Ensemble Approach to Cross-Domain Authorship Attribution

This paper presents an ensemble approach to cross-domain authorship attribution that combines predictions made by three independent classifiers, namely, standard character n-grams, character n-grams with non-diacritic distortion and word n-grams. Our proposal relies on variable-length n-gram models and multinomial logistic regression to select the prediction of highest probability among the three models as the output for the task. The present approach is compared against a number of baseline systems, and we report results based on both the PAN-CLEF 2018 test data, and on a new corpus of song lyrics in English and Portuguese.
Multi-Level Attention Based BLSTM Neural Network for Biomedical Event Extraction

Biomedical event extraction is an important and challenging task in Information Extraction, which plays a key role for medicine research and disease prevention. Most of the existing event detection methods are based on shallow machine learning methods which mainly rely on domain knowledge and elaborately designed features. Another challenge is that some crucial information as well as the interactions among words or arguments may be ignored since most works treat words and sentences equally. Therefore, we employ a Bidirectional Long Short Term Memory (BLSTM) neural network for event extraction, which can skip handcrafted complex feature extraction. Furthermore, we propose a multi-level attention mechanism, including word level attention which determines the importance of words in a sentence, and the sentence level attention which determines the importance of relevant arguments. Finally, we train dependency word embeddings and add sentence vectors to enrich semantic information. The experimental results show that our model achieves an F-score of 59.61% on the commonly used dataset (MLEE) of biomedical event extraction, which outperforms other state-of-the-art methods.
Museum in Your Living Room: Recreating the Peace Corps Experience in Mixed Reality

We describe the design and implementation of a mixed reality exhibit prototype for the Museum of the Peace Corps Experience. The Museum exhibits are reconstructed using the photogrammetry and made interactive using real-time physics simulation and freehand interaction. We recorded the volumetric captures of Returned Peace Corps Volunteers telling stories related to the artifacts. The prototype was evaluated in a pilot study with positive response of the audience.
DPSAaS

Differential privacy has emerged as the de facto standard for privacy definitions, and been used by, e.g ., Apple, Google, Uber, and Microsoft, to collect sensitive information about users and to build privacy-preserving analytics engines. However, most of such advanced privacy-protection techniques are not accessible to mid-size companies and app developers in the cloud. We demonstrate a lightweight middleware DPSAaS , which provides &lt;u&gt;d&lt;/u&gt; ifferentially &lt;u&gt;p&lt;/u&gt; rivate data-&lt;u&gt;s&lt;/u&gt;haring-and-&lt;u&gt;a&lt;/u&gt;nalytics functionality &lt;u&gt;a&lt;/u&gt; s cloud &lt;u&gt;s&lt;/u&gt;ervices. We focus on multi-dimensional analytical (MDA) queries under local differential privacy (LDP) in this demo. MDA queries against a fact table have predicates on (categorical or ordinal) dimensions and aggregate one or more measures. In the absence of a trusted agent, sensitive dimensions and measures are encoded in a privacy-preserving way locally using our LDP data sharing service, before being sent to the data collector. The data collector estimates the answers to MDA queries from the encoded data, using our data analytics service. We will highlight the design decisions of DPSAaS and twists made to LDA algorithms to fit the design, in order to smoothly connect DPSAaS to the data processing platform and analytics engines, and to facilitate efficient large-scale processing.
Parallel probability density approximation

Probability density approximation (PDA) is a nonparametric method of calculating probability densities. When integrated into Bayesian estimation, it allows researchers to fit psychological processes for which analytic probability functions are unavailable, significantly expanding the scope of theories that can be quantitatively tested. PDA is, however, computationally intensive, requiring large numbers of Monte Carlo simulations in order to attain good precision. We introduce Parallel PDA (pPDA), a highly efficient implementation of this method utilizing the Armadillo C++ and CUDA C libraries to conduct millions of model simulations simultaneously in graphics processing units (GPUs). This approach provides a practical solution for rapidly approximating probability densities with high precision. In addition to demonstrating this method, we fit a piecewise linear ballistic accumulator model (Holmes, Trueblood, & Heathcote, 2016) to empirical data. Finally, we conducted simulation studies to investigate various issues associated with PDA and provide guidelines for pPDA applications to other complex cognitive models.
Identification of Available Trunking Communication Systems in Heterogeneous Cognitive Radio Access Networks

The article discusses the algorithms for determining the following parameters: symbol rate, frame length, modulation type, bit period and sync word. Based on the described algorithms, a software complex for identification of trunking radio signals in heterogeneous cognitive radio systems has been implemented. The paper also describes an experiment, the result of which allows to confirm the efficiency of the developed software complex and the algorithms described in the article. Based on the analysis of the results of the experiment, possible further ways to study this problem were identified.
TimeML Annotation of Events and Temporal Expressions in Arabic Texts

Events and temporal expressions are crucial elements in the construction of the text. In this article, we present the first free Arabic corpus for events and temporal expressions by adapting the TimeML standard. For this, we first present the concept of events and temporal expressions as well as their main ontological and grammatical characteristics for the Arabic language and according to the TimeML standard. Second, we describe our annotation scheme and annotated corpus.
Anomaly Detection over Streaming Data: Indy500 Case Study

Sports racing is attracting billions of audiences each year. It is powered and transformed by the latest data analysis technologies, from race car design, driving skill improvements to audience engagement on social media. However, most of the data processing are off-line and retrospective analysis. The emerging real-time data analysis from the Internet of Things (IoT) result in fast data streams generated from distributed sensors. Applying advanced Machine Learning/Artificial Intelligence over such data streams to discover new information, predict future insights and make control decision is a crucial process. In this paper, we start by articulating racing car big data characteristics and present time-critical anomaly detection of the racing cars with the real-time sensors of cars and the tracks from actual racing events. We build a scalable system infrastructure based on neuro-morphic Hierarchical Temporal Memory Algorithm (HTM) algorithm and Storm stream processing engine. By courtesy of historical Indy500 racing logs, evaluation experiments on this prototype system demonstrate good performance in terms of anomaly detection accuracy and service level objective (SLO) of latency for a real-world streaming application.
Object Recognition Under Multifarious Conditions: A Reliability Analysis and a Feature Similarity-Based Performance Estimation

In this paper, we investigate the reliability of online recognition platforms, Amazon Rekognition and Microsoft Azure, with respect to changes in background, acquisition device, and object orientation. We focus on platforms that are commonly used by the public to better understand their real-world performances. To assess the variation in recognition performance, we perform a controlled experiment by changing the acquisition conditions one at a time. We use three smartphones, one DSLR, and one webcam to capture side views and overhead views of objects in a living room, an office, and photo studio setups. Moreover, we introduce a framework to estimate the recognition performance with respect to backgrounds and orientations. In this framework, we utilize both handcrafted features based on color, texture, and shape characteristics and data-driven features obtained from deep neural networks. Experimental results show that deep learning- based image representations can estimate the recognition performance variation with a Spearman's rank-order correlation of 0.94 under multifarious acquisition conditions.