Reviewerly/exHarmony huggingface dataset:
Deep Learning With Conceptual View in Meta Data for Content Categorization

Data gathered from various devices have to be observed by human operators manually for extended durations which is not viable and may lead to imprecise results. Data are analyzed only when any unwanted event occurs. Machine-learning technology powers many aspects of modern society, from web searches to content filtering on social networks to recommendations on e-commerce websites, and it is increasingly present in consumer products. Machine-learning systems are used to identify objects in different forms of data. For decades, constructing a pattern-recognition, machine-learning system required careful engineering and domain expertise to design a feature extractor that transformed the raw data into a suitable internal representation, which the learning subsystem could detect patterns in the input by making use of and integrating ideas such as backpropagation, regularization, the softmax function, etc. This chapter will cover the importance of representations and metadata appendage and feature vector construction for the training deep models optimization.
A New Method for Detecting Altered Text in Document Images

As more and more office documents are captured, stored, and shared in digital format, and as image editing software becomes increasingly more powerful, there is a growing concern about document authenticity. For example, texts in property documents can be altered to make an illegal deal, or the date on an airline ticket can be altered to gain entry to airport terminals by breaching security. To prevent such illicit activities, this paper presents a new method for detecting altered text in a document. The proposed method explores the relationship between positive and negative coefficients of a DCT to extract the effect of distortions caused by tampering operations. Here we divide DCT coefficients into positive and negative classes, then reconstructs images from the inverse DCT of the respective positive and negative coefficients. Next, we perform Laplacian filtering over reconstructed images for widening the gap between the values of text and other pixels. Then filtered images of positive and negative coefficients are fused by an average operation. For a fused image, we generate Canny and Sobel edge images in order to investigate the effect of distortion through quality measures, namely, MSE, PSNR and SSIM used as features. In addition, for the fused image, the proposed method extracts features based on histograms over the residual images. The features are then passed on to a deep Convolutional Neural Network for classification. The proposed method is tested on our own dataset as well as two standard datasets, namely IMEI and the ICPR 2018 Fraud Contest dataset. The results show that the proposed method is effective and outperforms existing methods.
Protecting the FPGA IPs against Higher-order Side Channel Attacks using Dynamic Partial Reconfiguration

Power Attacks have become a serious concern for secure FPGA-based embedded system design. However, considerable countermeasures have been introduced to improve the resistance of FPGA devices against Differential Power Analysis (DPA) and Correlation Power Analysis (CPA) attacks during recent years. In this work, we proposed a novel countermeasure which utilizes the Dynamic Partial Reconfiguration (DPR) property of the FPGA devices to obfuscate the leaked information. DPR capability allows updating a certain part of the running design on the device while other parts remain functioning without interruption. We proposed a new masking scheme using this technology in which masking function is changed dynamically at runtime to violate the assumptions needed for a successful power analysis attack. The proposed method not only overcomes to the first-order DPA attacks but also increases the resistance against the second-order DPA attacks. Our experimental results show that the proposed method has considerable security improvement against first and second order SCA attacks with negligible overheads. Moreover it can be complemented with any other desired countermeasures as well.
Formalising Mathematics – in Praxis; A Mathematician’s First Experiences with Isabelle/HOL and the Why and How of Getting Started

Abstract This is an account of a mathematician’s first experiences with the proof assistant (interactive theorem prover) Isabelle/HOL, including a discussion on the rationale behind formalising mathematics and the choice of Isabelle/HOL in particular, some instructions for new users, some technical and conceptual observations focussing on some of the first difficulties encountered, and some thoughts on the use and potential of proof assistants for mathematics.
Accurate 3D hand pose estimation network utilizing joints information

In this paper, a method is proposed to improve the accuracy of 3D hand pose estimation. The existing methods make poor use of the depth information of hand joints and have difficulties of estimating the 3D coordinates accurately. To solve this problem, a method that utilizing the information between adjacent joints of each finger is proposed to estimate the depth coordinates of joints. In order to make full use of 2D information for depth estimation, this paper divides hand pose estimation into two sub-tasks (2D hand joints estimation and depth estimation). In depth estimation, a multi-stage network is proposed. We first estimate the depth of a part of hand joints, and then with the help of it and 2D information, the depth coordinates of adjacent joints can be well estimated. The method proposed in this paper has been proved to be effective on three public hand pose datasets through Self-comparisons. Compared with the methods that based on 2D CNN, our method achieves state-of-the-art performance on ICVL and NYU datasets, and also has a good result on MSRA dataset.
Recognition of Named Entities in the Russian Subcorpus Google Books Ngram

This paper describes how to build a recognizer to identify named entities that occur in the Google Books Ngram corpus. In the previous studies, the text was usually input to the recognizer to solve the task of named entities recognition. In this paper, the decision is made based on the analysis of the word co-occurrence statistics. The recognizer is a neural network. A vector of frequencies of bigrams or syntactic bigrams including the studied word is fed at the input. The task is to recognize named entities denoted by one word. However, the proposed method can be further applied to recognize two- or multi-word named entities. The recognition error probability obtained on the test sample of 10 thousand words, which are free from homonymy, was 2.71% (F1-score is 0.963). Solving the problem of word classification in Google Books Ngram will allow one to create large dictionaries of named entities that will improve recognition quality of named entities in texts by existing algorithms.
DISKSHIELD: A Data Tamper-Resistant Storage for Intel SGX

With the increasing importance of data, the threat of malware which destroys data has been increasing. If malware acquires the highest software privilege, any attempt to detect and remove malware can be disabled. In this paper, we propose DISKSHIELD, a secure storage framework. DISKSHIELD uses Intel SGX to provide Trusted Execution Environment (TEE) to the host, implements the file system into SSD firmware that provides a Trusted Computing Base (TCB), and uses a two-way authentication mechanism to securely transfer data from the host TEE to the SSD TCB against data tampering attacks. This design frees DISKSHIELD from attacks to the kernel. To show the efficacy of DISKSHIELD, we prototyped a DISKSHIELD system by modifying Intel IPFS and developing a device file system on the Jasmine OpenSSD Platform in a Linux environment. Our results show that DISKSHIELD provides strong data tamper resistance the throughput of read and write is on average to 28%, 19% lower than IPFS.
Compact StereoNet: Stereo Disparity Estimation via Knowledge Distillation and Compact Feature Extractor

Stereo disparity estimation is a difficult and crucial task in computer vision. Although many experimental techniques have been proposed in recent years with the flourishing of deep learning, very few studies take into account the optimization of computational complexity and memory consumption. Most previous works take advantage of stacked 3D convolutional block to generate fine disparity, but with a high computational cost and a large memory consumption. Considering the aforementioned problem, in this paper, we proposed an efficient convolutional neural architecture for stereo disparity estimation. In particular, a compact and efficient multi-scale extractor named MCliqueNet with stacked CliqueBlock was proposed to extract the more refined features for constructing multi-scale cost volume. In order to reduce the computational cost and maintain the accuracy of disparity, we utilized knowledge distillation scheme to transfer contextual features from a teacher network to a student network. Furthermore, we present a novel adaptive Smooth <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L1</sub> (ASL) Loss for calculating the similarity between the contextual features of the teacher network and those of the student network, resulting in a more robust distillation process. Experimental results have shown that our method achieves competitive performance on the challenging Scene Flow and KITTI benchmarks while maintaining a very fast running speed.
A Novel Initialization Algorithm for Fuzzy C-means Problem

The fuzzy C-means problem belongs to soft clustering problem, where each given point has relationship to every center point. This problem is different from the k-means problem, where each point should belong to only one cluster. In this paper, we design one seeding algorithm for fuzzy C-means problem and obtain performance ratio \(O(k\mathrm{ln}k)\). We also give the performance guarantee \(O(k^2\mathrm{ln}k)\) of the seeding algorithm based on k-means++ for fuzzy C-means problem. At last, we present our numerical experiment to show the validity of the algorithms.
Chinese Question Classification Based on ERNIE and Feature Fusion

Question classification (QC) is a basic task of question answering (QA) system. This task effectively narrows the range of candidate answers and improves the operating efficiency of the system by providing semantic restrictions for the subsequent steps of information retrieval and answer extraction. Due to the small number of words in the question, it is difficult to extract deep semantic information for the existing QC methods. In this work, we propose a QC method based on ERNIE and feature fusion. We approach this problem by first using ERNIE to generate word vectors, which we then use to input into the feature extraction model. Next, we propose to combine the hybrid neural network (CNN-BILSTM, which extracts features independently), highway network and DCU (Dilated Composition Units) module as the feature extraction model. Experimental results on Fudan university's question classification data set and NLPCC(QA)-2018 data set show that our method can improve the accuracy, recall rate and F1 of the QC task.
Agro-AI Education

The importance of sustainable and efficient food production has driven the need for applying increasingly advanced approaches in farming and agriculture. To keep up with this need, farmers and future farmers need to be trained in the ways of precision agriculture. With the widespread use of artificial intelligence across all industries, it is not surprising to find AI at the heart of precision agriculture. While AI has brought a lot of promise, it has also brought a lot of dread, due to the lack of understanding about what it is and what it can do. To help farmers buy into applying AI, a new educational program is needed. To this end, we have developed a simple active learning system to illustrate AI, particularly, machine learning. We present an overview of the system and discuss how it can contribute to future farmers' understanding of how AI works.
A Novel Approach for Detecting Near-Duplicate Web Documents by Considering Images, Text, Size of the Document and Domain

Web mining is a part of data mining in which the web consists of enormous amount of data. The search engines faces large amount of problems due to the presence of Near duplicate documents in web which leads to irrelevant answers. The performance and reliability of search engines are critically affecting since the near duplicate documents present in web. For detection of near duplicate web documents two attempts are found in the literature. The former considered domain and size of the document and the later considered text and image as the search parameters. This article proposes a novel approach combining the parameters such as text, image, size and domain of the document to detect near duplicate documents. The approach extracts the keywords and images of the crawled document and compares them with the existing documents for similarity measure. If the similarity score measure value is less than 19.5 and image comparison value is greater than 70%, then it is detected as near duplicate document.
Networked Mixed Reality Framework for the Internet of Things

The Internet of Things (IoT) world is developing rapidly, yet seamless integration into our daily lives is lacking. Vendor-specific products tend to restrict customers to their software applications, due to their inherent steep learning curve. We develop the Mixed Reality Control Panel (MRCP) system to address this gap by offering a cross-platform, open-source, vendor-neutral, mixed reality software application capable of interacting with headless, IoT devices. The MRCP application, Smart Parking Meter, and Smart Lock are developed and tested against existing solutions. The duration to perform each action is compared as well as the benefits of additional smart features. Having a mixed reality interface allows for a agnostic and quick interaction with the IoT devices. Early development decisions on cross-platform and vendor-neutral enable future scalability. As a result, both devices are deemed viable solutions for future smart city integrations. Further development will take place for the MRCP system. While others can benefit from the project by incorporating mixed reality as a solution for Smart environments to bridge the gap between IoT and our daily lives.
Automatic digital modulation recognition in the presence of alpha-stable noise

In this paper, we introduce a novel modulation recognition method of digital communication signals with alpha-stable noise to solve the problem of low recognition performance in mixed signal-to-noise-ratio environment. The received signals are separated into signals and alpha-stable noise by employing fractional lower order fast independent component analysis (FLO-Fast-ICA), and then the separated signals are interpolated based on the global average local mean decomposition. With these processing, two classification features that are the segmented instantaneous frequency standard deviation and the segmental instantaneous amplitude standard deviation are extracted. Thereafter, a decision tree classifier is developed to recognize the digital communication signals including minimum shift keying (MSK), 2 amplitude shift keying (2ASK), quadrature phase shift keying (QPSK) and 16 quadrature amplitude modulation (16QAM). Simulation results show that the proposed method has a promising performance without prior information.
Mobile Internet Throughput Prediction using Machine Learning Techniques

Data streaming and internet consumption are some of the most common activities in this 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century. Streaming can be deployed in many different ways, it can be music, movie and even video game streaming. All these activities rely on the user's internet throughput. While a broadband or wired connection remains ideal, many people use mobile data to access the services. When the users access internet using mobile data, it may not always have the same internet conditions or speed and thus the content they intend to access should remain dynamic and adaptable to their network changes. The paper explores the techniques that can be used to predict a user's internet throughput by utilizing various basic parameters, which are easily accessible. This would help the server to predict a user's throughput beforehand and serve data or content at a bit rate, which suits the throughput and leads to lower buffering/excessive loading and ultimately achieving a better quality of experience (QoE).
A hardware accelerator for entropy estimation using the top-k most frequent elements

Estimating the empirical entropy of the elements in a dataset is an important task in data analysis. In particular, empirical entropy can be effectively used to detect anomalies in network traffic. However, computing the empirical entropy of a large dataset is computationally expensive and requires a large amount of memory. This is particularly important in high-speed network traffic analysis, where computing the entropy of a data flow in real time requires using hardware accelerators with restricted on-chip memory and arithmetic resources. In this work, we propose a method to estimate the entropy using a streaming algorithm with sublinear space requirements. Our approach uses a sketch to estimate the frequency of the elements in the stream, and a priority queue to store the top-k most frequent elements. We show that our method can provide a good approximation of the entropy of the dataset, and present the design of a hardware accelerator that can compute the entropy of the stream with a throughput of one packet per clock cycle. Implemented on a Xilinx Zynq UltraScale + MPSoC ZCU102 FPGA, our accelerator can operate at line rates above 181 Gbps, consuming 511 mW and using less than 24% of the resources available on the device.
Lying About Lying on Social Media: A Case Study of the 2019 Canadian Elections

This paper analyzes a new social media phenomenon in which users are lying about not being bots or about real news being fake news. Twitter data were collected throughout the 2019 Canadian federal election cycle, and we investigated the use of the #FakeNews and #NotABot hashtags. Twitter users connected the #FakeNews hashtag more often to mainstream news sources and reporters rather than actual fake news sites, often as a way to discredit certain reporters or viewpoints. We also found that users of the #NotABot hashtag were no more likely to be human than other users participating in political discourse in our data set. Bots that attempt to pass as human have been reportedly used to amplify misinformation campaigns in the past. This new type of online defensive strategy shows how these campaigns continue to evolve and illustrates how they may be run in the future.
Incorporating Lexicon for Named Entity Recognition of Traditional Chinese Medicine Books

Little research has been done on the Named Entity Recognition (NER) of Traditional Chinese Medicine (TCM) books and most of them use statistical models such as Conditional Random Fields (CRFs). However, in these methods, lexicon information and large-scale of unlabeled corpus data are not fully exploited. In order to improve the performance of NER for TCM books, we propose a method which is based on biLSTM-CRF model and can incorporate lexicon information into representation layer to enrich its semantic information. We compared our approach with several previous character-based and word-based methods. Experiments on "Shanghan Lun" dataset show that our method outperforms previous models. In addition, we collected 376 TCM books to construct a large-scale of corpus to obtain the pre-trained vectors since there is no large available corpus in this field before. We have released the corpus and pre-trained vectors to the public.
Learning Multi-Scale Features and Batch-Normalized Global Features for Person Re-Identification

In recent years, person re-identification based on deep learning approaches has made great progress and achieved good results. However, many of the latest network design methods, which usually deploy ResNet or SENet as the backbone network, were originally designed for classification tasks. Since the person re-identification task is essentially different from the classification task, the structure of the backbone network should be modified accordingly. In this paper, we propose a retrieval network based on a multi-scale backbone architecture, which is specifically suitable for the person re-identification task. By constructing hierarchical residual-like connections within a single residual block, the model learns multi-scale discriminative features of pedestrian images. Unlike many state-of-the-art methods that use complex network structures and concatenate multi-branch features, our proposed retrieval network is implemented using only global features, simple triplet loss, and softmax with cross-entropy loss. The results of extensive experiments show that the proposed network has stronger fine-grained pedestrian representation ability, leading to performance gains for person re-identification tasks. Our proposed network achieves a rank-1 accuracy of 96.03% on the Market-1501 and 92.11% on DukeMTMC-reID datasets while only using global features.
XSS Vulnerabilities in Cloud-Application Add-Ons

Many cloud-application vendors open their APIs for third-party developers to easily extend the functionality of their applications. The features implemented with these APIs are called add-ons (also called add-ins or apps). This is a relatively new phenomenon, and its effects on the application security have not been widely studied. It seems likely that some of the add-ons have lower code quality than the core applications themselves and, thus, may bring in security vulnerabilities. In this work, we found that many of such add-ons are vulnerable to cross-site scripting (XSS). The attacker can take advantage of the document-sharing and messaging features of the cloud applications to send malicious input to them. The vulnerable add-ons then execute client-side JavaScript from the carefully crafted malicious input. In a major analysis effort, we systematically studied 300 add-ons for three popular application suites, namely Microsoft Office Online, G Suite and Shopify, and discovered a significant percentage of vulnerable add-ons among them. We present the results of this study, as well as analyze the add-on architectures to understand how the XSS vulnerabilities can be exploited and how the threat can be mitigated.
Improving the representation of image descriptions for semantic image retrieval with RDF

The past few years have witnessed a surge of interest in many topics at the intersection of natural language processing and computer vision. In particular, using objects together with their attributes and relations to represent images or interpret languages has been proved useful across a wide variety of applications. The goal of this work is to provide an improved RDF-based model to represent images for enhancing textual based image retrieval. We use natural language processing tools to obtain a set of objects, attributes and relations; and then model them into graphical structures with RDF-based model. We also conduct some preliminary experiments to show how to handle textual based image retrieval for complex queries or multilingual queries. The experimental results show that our approach improves the representation of image descriptions, which is suitable for enhancing image retrieval with high-level semantics.