Reviewerly/exHarmony huggingface dataset:
Zero-knowledge arguments for matrix–vector relations and lattice-based group encryption

Group encryption (GE) is the natural encryption analogue of group signatures in that it allows verifiably encrypting messages for some anonymous member of a group while providing evidence that the receiver is a properly certified group member. Should the need arise, an opening authority is capable of identifying the receiver of any ciphertext. As introduced by Kiayias, Tsiounis and Yung (Asiacrypt'07), GE is motivated by applications in the context of oblivious retriever storage systems, anonymous third parties and hierarchical group signatures. This paper provides the first realization of group encryption under lattice assumptions. Our construction is proved secure in the standard model (assuming interaction in the proving phase) under the Learning-With-Errors (LWE) and Short-Integer-Solution (SIS) assumptions. As a crucial component of our system, we describe a new zero-knowledge argument system allowing to demonstrate that a given ciphertext is a valid encryption under some hidden but certified public key, which incurs to prove quadratic statements about LWE relations. Specifically, our protocol allows arguing knowledge of witnesses consisting of X∈Zqm×n, s∈Zqn and a small-norm e∈Zm which underlie a public vector b=X⋅s+e∈Zqm while simultaneously proving that the matrix X∈Zqm×n has been correctly certified. We believe our proof system to be useful in other applications involving zero-knowledge proofs in the lattice setting.
L1-Depth Revisited: A Robust Angle-Based Outlier Factor in High-Dimensional Space

Angle-based outlier detection (ABOD) has been recently emerged as an effective method to detect outliers in high dimensions. Instead of examining neighborhoods as proximity-based concepts, ABOD assesses the broadness of angle spectrum of a point as an outlier factor. Despite being a parameter-free and robust measure in high-dimensional space, the exact solution of ABOD suffers from the cubic cost $$O(n^3)$$ regarding the data size n, hence cannot be used on large-scale data sets. In this work we present a conceptual relationship between the ABOD intuition and the L1-depth concept in statistics, one of the earliest methods used for detecting outliers. Deriving from this relationship, we propose to use L1-depth as a variant of angle-based outlier factors, since it only requires a quadratic computational time as proximity-based outlier factors. Empirically, L1-depth is competitive (often superior) to proximity-based and other proposed angle-based outlier factors on detecting high-dimensional outliers regarding both efficiency and accuracy. In order to avoid the quadratic computational time, we introduce a simple but efficient sampling method named SamDepth for estimating L1-depth measure. We also present theoretical analysis to guarantee the reliability of SamDepth. The empirical experiments on many real-world high-dimensional data sets demonstrate that SamDepth with $$\sqrt{n}$$ samples often achieves very competitive accuracy and runs several orders of magnitude faster than other proximity-based and ABOD competitors. Data related to this paper are available at: https://www.dropbox.com/s/nk7nqmwmdsatizs/Datasets.zip . Code related to this paper is available at: https://github.com/NinhPham/Outlier .
Assessing Seismic Hazard in Chile Using Deep Neural Networks

Earthquakes represent one of the most destructive yet unpredictable natural disasters around the world, with a massive physical, psychological, and economical impact in the population. Earthquake events are, in some cases, explained by some empirical laws such as Omori’s law, Bath’s law, and Gutenberg-Richter’s law. However, there is much to be studied yet; due to the high complexity associated with the process, nonlinear correlations among earthquake occurrences and also their occurrence depend on a multitude of variables that in most cases are yet unidentified. Therefore, having a better understanding on occurrence of each seismic event, and estimating the seismic hazard risk, would represent an invaluable tool for improving earthquake prediction. In that sense, this work consists in the implementation of a machine learning approach for assessing the earthquake risk in Chile, using information from 2012 to 2018. The results show a good performance of the deep neural network models for predicting future earthquake events.
#autism

We present an initial qualitative exploration of Twitter data on autism spectrum disorders (ASDs) and acquired immunodeficiency syndrome (AIDS) from India and the United States, conducted with the goal of learning more about the nature of existing online awareness around ASDs in India. We collected tweets from both locations during and around World Autism Awareness Day and World AIDS Day. We then qualitatively analyzed these tweets, finding differences in how each country talks about stigmatized health issues and how Indian users talk about ASDs. We use these findings to discuss potential avenues for research on supporting and extending existing levels of autism awareness in India.
Predicting Illusory Contours Without Extracting Special Image Features

Boundary completion is one of the desired properties of a robust object boundary detection model, since in real-word images the object boundaries are commonly not fully and clearly seen. An extreme example of boundary completion occurs in images with illusory contours, where the visual system completes boundaries in locations without intensity gradient. Most illusory contour models extract special image features, such as L and T junctions, while the task is known to be a difficult issue in real-world images. The proposed model uses a functional optimization approach, in which a cost value is assigned to any boundary arrangement to find the arrangement with minimal cost. The functional accounts for basic object properties, such as alignment with the image, object boundary continuity, and boundary simplicity. The encoding of these properties in the functional does not require special features extraction, since the alignment with the image only requires extraction of the image edges. The boundary arrangement is represented by a border ownership map, holding object boundary segments in discrete locations and directions. The model finds multiple possible image interpretations, which are ranked according to the probability that they are supposed to be perceived. This is achieved by using a novel approach to represent the different image interpretations by multiple functional local minima. The model is successfully applied to objects with real and illusory contours. In the case of Kanizsa illusion the model predicts both illusory and real (pacman) image interpretations. The model is a proof of concept and is currently restricted to synthetic gray-scale images with solid regions.
Fast large scale deep face search

Towards the large scale face search problem, this paper proposes a fast deep face search method which is realized by combination of deep convolution neural network (CNN), semantic hashing, and hash-based similarity search. First of all, to boost the performance in accuracy of face search, the residual network (Resnet) is exploited to construct a deep face feature model and then train it over the cleaned MS-Celeb-1M, which is used to extract real-valued face feature. Next, by imposing PCA and binarization operations, we convert the real-valued feature into compact hash code used for speeding up the face search. Based on the extracted dual features, the face search can be efficiently performed by adopting two-stage matching (i.e., coarse matching and fine matching) strategy. The coarse matching is implemented under the support of efficient hash indexing technique for yielding a small number of candidates while the fine stage is to filter out the unrelated images by cosine distance comparison of real-valued features. It is worth noting that we offer two coarse matching methods, such as GPU-hash and M-index-hash based matching, which are suitable for tens-of million and billion scale scenarios respectively. The experimental results demonstrate that the proposed method is very effective for large scale face search in both aspects of accuracy and real time property.
The content disseminated on social media by public secondary school libraries as a reflection of society

Purpose The purpose of this paper is to analyze the type of content disseminated by school libraries through social media and what topics are the most commonly used, to discover if, in addition to topics concerning libraries, information with a wide social scope is also disseminated. Design/methodology/approach This paper reviews the use of social media as well as the implementation of a content analysis on the most used 2.0 platforms, to locate which content is the most relevant in the school libraries of public secondary schools in Extremadura. Findings In the Extremadura region of Spain, the results of the 752 publications posted during the period 2014-2017 by the libraries of the 86 public secondary schools on six selected social media platforms generated a total of 4 categories and 14 subcategories, and point to a predominance of topics related to encouraging and promoting reading and writing, and to the library’s support function for both the classroom and the school. However, shortcomings are detected in content related to social aspects of special interest, such as bullying or education in equality. Originality/value The originality of this paper lies in the fact that although there are some works on the analysis of the content of the social web of libraries in general, especially university libraries, the same does not apply to the evaluation of social media in school libraries, and much less about the analysis of social media content in these types of libraries.
Extracting Potentially High Profit Product Feature Groups by Using High Utility Pattern Mining and Aspect Based Sentiment Analysis

As a subproblem of sentiment analysis topic, aspect based sentiment analysis aims to extract distinct opinions for different aspects of a case in a given text. When the case is product review, it is possible to understand reviewer’s opinion on features of the product, rather than the product in general. Then, a product feature can be associated with a sentiment score denoting user satisfaction value by that feature. Modeling features mentioned in a review as items in a transaction may provide better insight into questions such as how to market products more effectively through analyzing properties that are more preferred to exist together in products. Sentiments behind feature groups enable decision makers further to understand and rank the feature groups that can lead to better marketing decisions, which constitutes the main motivation behind our work. In this paper, we propose a method that combines high utility pattern mining and aspect based sentiment analysis in order to extract groups of features that potentially increase profit and that need to be improved in order to increase user satisfaction. Experiments performed on patterns extracted by the proposed approach in comparison to the baselines show the potential to reveal high profit feature groups.
ISOO<sup>V2</sup> <sub>DL</sub> - Semantic Instance Segmentation of Touching and Overlapping Objects

We present ISOO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DL</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V2</sup> - a method for semantic instance segmentation of touching and overlapping objects. We introduce a series of design modifications to the prior framework, including a novel mixed 2D-3D segmentation network and a simplified post-processing procedure which enables segmentation of touching objects without relying on object detection. For the case of overlapping objects where detection is required, we upgrade the bounding box parametrization and allow for smaller reference point distances. All these novelties lead to substantial performance improvements and enable the method to deal with a wider range of challenging practical situations. Additionally, our framework can handle object sub-part segmentation. We evaluate our approach on both real-world and synthetically generated biological datasets and report state-of-the-art performance.
Three-Stream Convolution Networks After Background Subtraction for Action Recognition

Action recognition has vital significance for computer vision. Recently, deep learning has made breakthrough progress in action recognition. However, as two important branches of deep learning, Two-Stream relies on optical flow with complex computation and 3D convolution network is difficult for training. In this paper, we propose a novel Three-Stream Convolution networks after feature extraction for action recognition. For feature, we introduce three input features: RGB images, background subtraction feature with low complexity and historical contour feature. In order to optimize the discriminability of long-term actions, the historical contour feature is superimposed by background subtraction feature. For network structure, we present a convolution network stream for each feature input: RGB net, background subtraction sequence net and historical contour net. Finally, we merge three streams into one network with automatic network learning mechanism for action recognition to obtain a better recognition performance. We conduct experiments on two large main action recognition datasets UCF-101 and HMDB-51. Comparing the mainstream methods, the results verify the accuracy and high efficiency of our framework.
FICOBU: Filipino WordNet Construction Using Decision Tree and Language Modeling

The paper discusses the approach in creating a Filipino WordNet.A semi-supervised learning approach using Decision Tree and Language Modeling.This will take advantage on the information found on the web.It will help future NLP researchers in Filipino language.The approach uses words from a dictionary as preliminary data and as seed for the search engine to start crawling the WWW.To decide if the word is part of Filipino language, the word will first undergo in Code-Switching Points Module (CSPD).CSPD scores the word by using the frequency counts of word bigrams and unigrams from language models which were trained from an existing and available corpus.After scoring, Filipino Stemmer will get the stem of the word and examine if the stem word is part of the said language.Once the words were scored and stemmed, the archive will evaluate if the word is Filipino.To test the accuracy of the system, we collected different articles around the web and then grouped it into two groups -Plain Filipino and Bilingual.The result shows the F-measure for Plain Filipino Category range between 65.65% -96.85% with an average of 85.64% while for Bilingual range between 60% -100% with an average of 88.17%.
Omniscient DevOps Analytics

DevOps predicates the continuity between Development and Operations teams at an unprecedented scale. Also, the continuity does not stop at tools, or processes but goes beyond into organizational practices, collaboration, co-located and coordinated effort. We conjecture that this unprecedented scale of continuity requires predictive analytics which are omniscient, that is (i) transversal to the technical, organizational, and social stratification in software processes and (ii) correlate all strata to provide a live and holistic snapshot of software development, its operations, and organization. Elaborating this conjecture, we illustrate a set of metrics to be used in the DevOps scenario and overview challenges and future research directions.
Anomaly Detection in Crowd Using Optical Flow and Textural Feature

This paper aims at solving real-world surveillance problems using computer vision and motion estimation techniques. It focuses on detecting abnormal crowd behaviour and locating it in dynamic crowd condition. In this paper, a combined approach is the proposed using the crowd motion analysis and texture-based analysis. Lucas–Kanade optical flow method is used for the estimation of motion in the scene. Also, texture-based feature and entropy give the statistical measure of randomness which is used for localization of crowd. The University of Minnesota (UMN) database has been used for testing.
Modeling and optimization for selecting LED to synthesize solar spectrum using residual-guided evolution algorithms

LED has proven to be promising artificial light source to match solar spectrum in photovoltaic field. Selection of LED type from product database and working parameters determination for each type LED are essential problems to be solved. In this paper, a spectral match residual-guided evolution algorithm (SMREA) was proposed to solve the problems. Dependence of spectral match and correlation index of synthesized spectrum on LED type number are investigated from our LED type database. The results reveal that spectral synthesis performance dramatically improves firstly with increasing LED type number and after combination reached a certain number, it begins to stabilize. In our study, 4 types of LED can achieve spectral match deviation 21.45% in range of 400 nm–800 nm with low correlation index 0.7606. When the number of LED types added to 7, 11, and 13, the spectral match deviation can decreased to be 5.94%, 2.65%, 3.65% respectively and the correlation index increases to be 0.9039, 0.9691, 0.9841. SMREA can also output working parameters of LED at corresponding evolved type number. The feasibility of SMREA to solve the dual optimization problem was effectively validated. The reported work provides a significant value for LED type selection of LED-based solar spectral synthesis.
Identifying Multi-dimensional Information from Microblogs During Epidemics

Microblogging sites like Twitter and Weibo have been shown to provide important real-time information during epidemics and disease outbreaks. During such situations, different types of stakeholders look for different types of information, such as symptoms, prevention, treatment schemes, death reports, and many more. Additionally, lots of personal opinions, sentiments are also posted on social media along with factual contents. In this work, we propose a method to automatically classify tweets posted during an epidemic into various informative categories. To this end, we utilize features derived from a medical knowledge base (UMLS) as well as features based on syntactic and lexical structure of tweets. We apply the classifier over tweets related to several diseases (Ebola, Dengue, and MERS), and show that, the proposed approach yields better classification performance as compared to earlier works. We also identify some interesting directions of future work, e.g., applying the classifier over drug addictions like Opioid.
RETRACTED: An iterative propagation based co-saliency framework for RGBD images

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the Editor-in-Chief. The authors have plagiarized part of a paper that had already appeared in IEEE Transactions on Cybernetics, Volume: 49, Issue: 1, Jan. 2019. DOI: 10.1109/TCYB.2017.277148810.1109/TCYB.2017.2771488. One of the conditions of submission of a paper for publication is that authors declare explicitly that their work is original and has not appeared in a publication elsewhere. Re-use of any data should be appropriately cited. As such this article represents a severe abuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission process.
Experimental Evaluation of Big Data Analytical Tools

Due to the extensive use of SQL, the number of SQL-on-Hadoop systems has significantly increased, transforming Big Data Analytics in a more accessible practice and allowing users to perform ad-hoc querying and interactive analysis. Therefore, it is of upmost importance to understand these querying tools and the specific contexts in which each one of them can be used to accomplish specific analytical needs. Due to the high number of available tools, this work performs a performance evaluation, using the well-known TPC-DS benchmark, of some of the most popular Big Data Analytical tools, analyzing in more detail the behavior of Drill, Hive, HAWQ, Impala, Presto, and Spark.
Single-Plane Scene Classification Using Deep Convolution Features

Scene classification is considered as one of the challenging tasks of computer vision. Due to the availability of powerful graphics processing unit and millions of images, deep learning techniques such as convolutional neural networks (CNNs) have become popular in the image classification. This paper proposes the use of a pre-trained CNN model known as Places CNN, which was trained on scene-centric images. In this work, the pre-trained CNN is used as a feature extractor. The features are then used as input data for support vector machines (SVMs). The effect of grayscale images on the performance of pre-trained CNN-based scene classification system is analyzed by means of classification accuracy and equal error rate. The dataset used for this purpose is Oliva Torralba (OT) scene dataset, which consists of eight classes. The classification experiments are conducted using the feature vector from the ‘fc7’ layer of the CNN model for RGB, RGB2Gray, and SVD decolorized images. The classification experiment was also done using a dimensionality reduction technique known as principal component analysis (PCA) on the feature vector. The results obtained from classification experiments show that RGB2Gray and SVD decolorized images were able to give results similar to that of RGB images. The grayscale images were able to retain the required shape and texture information from the original RGB images and were also sufficient to categorize the classes of scene images.
MyEvents: A Personal Visual Analytics Approach for Mining Key Events and Knowledge Discovery in Support of Personal Reminiscence

Abstract Reminiscence is an important aspect in our life. It preserves precious memories, allows us to form our own identities and encourages us to accept the past. Our work takes the advantage of modern sensor technologies to support reminiscence, enabling self‐monitoring of personal activities and individual movement in space and time on a daily basis. This paper presents MyEvents, a web‐based personal visual analytics platform designed for non‐computing experts, that allows for the collection of long‐term location and movement data and the generation of event mementos. Our research is focused on two prominent goals in event reminiscence: (1) selection subjectivity and human involvement in the process of self‐knowledge discovery and memento creation; and (2) the enhancement of event familiarity by presenting target events and their related information for optimal memory recall and reminiscence. A novel multi‐significance event ranking model is proposed to determine significant events in the personal history according to user preferences for event category, frequency and regularity. The evaluation results show that MyEvents effectively fulfils the reminiscence goals and tasks.