Reviewerly/exHarmony huggingface dataset:
Interlinking Heterogeneous Data for Smart Energy Systems

Smart energy systems in general, and solar energy analysis in particular, have recently gained increasing interest. This is mainly due to stronger focus on smart energy saving solutions and recent developments in photovoltaic (PV) cells. Various data-driven and machine-learning frameworks are being proposed by the research community. However, these frameworks perform their analysis - and are designed on - specific, heterogeneous and isolated datasets, distributed across different sites and sources, making it hard to compare results and reproduce the analysis on similar data. We propose an approach based on Web (W3C) standards and Linked Data technologies for representing and converting PV and weather records into an Resource Description Framework (RDF) graph-based data format. This format, and the presented approach, is ideal in a data integration scenario where data needs to be converted into homogeneous form and different datasets could be interlinked for distributed analysis.
Multimodal Sequential Fashion Attribute Prediction

We address multimodal product attribute prediction of fashion items based on product images and titles. The product attributes, such as type, sub-type, cut or fit, are in a chain format, with previous attribute values constraining the values of the next attributes. We propose to address this task with a sequential prediction model that can learn to capture the dependencies between the different attribute values in the chain. Our experiments on three product datasets show that the sequential model outperforms two non-sequential baselines on all experimental datasets. Compared to other models, the sequential model is also better able to generate sequences of attribute chains not seen during training. We also measure the contributions of both image and textual input and show that while text-only models always outperform image-only models, only the multimodal sequential model combining both image and text improves over the text-only model on all experimental datasets.
A Deep Learning Approach to Web Bot Detection Using Mouse Behavioral Biometrics

Web bots are automated scripts that perform online tasks like human. Abuse of bot technology poses various threats to the security of websites. Recently, mouse dynamics has been applied to bot detection by analyzing whether recorded mouse operations are consistent with human operational patterns. In this paper, we introduce a deep neural network approach to bot detection. We propose a new representation method for mouse movement data, which converts every mouse movement into an image containing its spatial and kinematic information. This representation method makes it possible to utilize CNN models to automate feature learning from mouse movement data. Experimental results demonstrate that our method is able to detect 96.2% of bots with statistical attack ability while traditional detection methods using hand-crafted features or RNN can only detect less than 30% of them.
APDPk-Means: A New Differential Privacy Clustering Algorithm Based on Arithmetic Progression Privacy Budget Allocation

How to protect users' private data during network data mining has become a hot issue in the fields of big data and network information security. Most current researches on differential privacy k-means clustering algorithms focus on optimizing the selection of initial centroids. However, the traditional privacy budget allocation has the problem that the random noise becomes too large as the number of iterations increases, which will reduce the performance of data clustering. To solve the problem, we improved the way of privacy budget allocation in differentially private clustering algorithm DPk-means, and proposed APDPk-means, a new differential privacy clustering algorithm based on arithmetic progression privacy budget allocation. APDPk-means decomposes the total privacy budget into a decreasing arithmetic progression, allocating the privacy budgets from large to small in the iterative process, so as to ensure the rapid convergence in early iteration. The experiment results show that compared with the other differentially private k-means algorithms, APDPk-means has better performance in availability and quality of the clustering result under the same level of privacy protection.
Blockchain: From Industry 4.0 to the Machine Economy

The extreme automation of our factories is necessary in order to face the Fourth Industrial Revolution. This new industrial paradigm will force our industries to manufacture much shorter and customized series at increasingly competitive prices, even tackling the manufacture of thousands of different configurations of a single base product. In order to achieve this, our production processes must have a flexibility in their configuration that has never been imagined before. This flexibility and ability to adapt automatically to demand are the essence of the Fourth Industrial Revolution and are part of the Western strategy to recover an industrial sector increasingly threatened by the Eastern production of large series at really competitive prices. Based on our participation in more than a dozen proofs of concept in the automotive, aeronautics, agri-food, or energy sectors, we describe the scenarios in which blockchain technology brings the greatest benefits to Industry 4.0. After finishing different experimentations, we carried out an in-depth analysis of the true added value of blockchain in the industry and contrasted our conclusions through interviews with more than 20 people in charge of innovation from different industries. As a result, we have obtained the principal four values of blockchain technology applied to Industry 4.0.
Attention-Aware Multi-Task Convolutional Neural Networks

Multi-task deep learning methods learn multiple tasks simultaneously and share representations amongst them, so information from related tasks improves learning within one task. The generalization capabilities of the produced models are substantially enhanced. Typical multi-task deep learning models usually share representations of different tasks in lower layers of the network, and separate representations of different tasks in higher layers. However, different groups of tasks always have different requirements for sharing representations, so the required design criterion does not necessarily guarantee that the obtained network architecture is optimal. In addition, most existing methods ignore the redundancy problem and lack the pre-screening process for representations before they are shared. Here, we propose a model called Attention-aware Multi-task Convolutional Neural Network, which automatically learns appropriate sharing through end-to-end training. The attention mechanism is introduced into our architecture to suppress redundant contents contained in the representations. The shortcut connection is adopted to preserve useful information. We evaluate our model by carrying out experiments on different task groups and different datasets. Our model demonstrates an improvement over existing techniques in many experiments, indicating the effectiveness and the robustness of the model. We also demonstrate the importance of attention mechanism and shortcut connection in our model.
When There is No Progress with a Task-Oriented Chatbot

Task-oriented chatbots are increasingly prevalent in our daily life. Research effort has been devoted to advancing our understanding of users' interaction with conversational agents, including conversation breakdowns. However, most research attempts were limited to obversions from a relatively short duration of user interaction with chatbots, where users were aware of being studied. In this study, we conducted a conversation analysis on a three-month conversation log of users conversing with a chatbot of a banking institution. The log consisted of 1,837 users' conversations with this chatbot with 19,449 message exchanges. From this analysis, we show that users more often failed to make a progress in a conversation when they requested information than when they provided information. Furthermore, we uncovered five kinds of intention gaps unexpected to the chatbot, and five major behaviors users adopted to cope with non-progress.
A bias-variance based heuristic for constructing a hybrid logistic regression-naïve Bayes model for classification

Discriminative classifiers tend to have lower asymptotic classification errors, while generative classifiers can be more accurate when the training set size is small. In this paper, we examine the construction of hybrid models from categorical data, where we use logistic regression (LR) as a discriminative component, and naïve Bayes (NB) as a generative component. We adopt a bias-variance tradeoff based strategy, with the objective of minimizing the sum of these two errors. Specifically, the proposed heuristic consists of functions of training sample size and conditional dependence among features. These functions serve as proxies for model variance and model bias. We implement our method on 25 different classification datasets, and find that the hybrid model does better than pure LR and pure NB. Our proposed method is competitive with random forest. Although the hybrid model fails to beat LASSO in predictive performance, as suggested by the experimental results, the difference appears to be insignificant when the number of features is small. Also, the hybrid model requires less training time than LASSO, which makes it more attractive when the training time is a big concern.
Urban Area Vehicle Re-Identification With Self-Attention Stair Feature Fusion and Temporal Bayesian Re-Ranking

Vehicle re-identification (Re-ID) plays a key role in many smart traffic management systems. Re-identifying a vehicle can be very challenging because the differences in visual appearances between pairs of vehicles are sometimes extremely subtle if they have the same colour and the same model. Given an image of a vehicle, most existing techniques adopt a global feature representation where details may be ignored. In this paper, we propose an Self-Attention Stair Feature Fusion model to learn the discriminative features for vehicle Re-ID. The model is designed to extract multi-level features in order to capture as much small details as possible. We also propose a Temporal Bayesian Re-Ranking method to exploit the spatial-temporal information in the vehicles' travel patterns. Our algorithm has been tested against state-of-the-art techniques on popular benchmarks. The results show that our algorithm outperforms other state-of-the-art techniques by a large margin.
Bidirectional Independently Long Short-Term Memory and Conditional Random Field Integrated Model for Aspect Extraction in Sentiment Analysis

Aspect extraction or feature extraction is a crucial and challenging task of opinion mining that aims to identify opinion targets from opinion text. Especially, how to explore these aspects or features from unstructured comments is a matter of concern. In this paper, we propose a novel supervised learning approach using deep learning technique for the above-mentioned aspect extraction task. Our model combines a bidirectional independently long short-term memory (Bi-IndyLSTM) with a Conditional Random Field (CRF). This integrated model is trained on labelled data to extract feature sets in opinion text. We employ a Bi-IndyLSTM with word embeddings achieved by training GloVe on the SemEval 2014 data set. There are 6086 training reviews and 1600 testing reviews on two domains, Laptop and Restaurant of the SemEval 2014 data set. Experimental results showed that our proposed Bi-IndyLSTM-CRF aspect extraction model in sentiment analysis obtained considerably better accuracy than the state-of-the-art methods.
Extracting Tables from Documents using Conditional Generative Adversarial Networks and Genetic Algorithms

Extracting information from tables in documents presents a significant challenge in many industries and in academic research. Existing methods which take a bottom-up approach of integrating lines into cells and rows or columns neglect the available prior information relating to table structure. Our proposed method takes a top-down approach, first using a generative adversarial network to map a table image into a standardised `skeleton' table form denoting the approximate row and column borders without table content, then fitting renderings of candidate latent table structures to the skeleton structure using a distance measure optimised by a genetic algorithm.
Using Winning Lottery Tickets in Transfer Learning for Convolutional Neural Networks

Neural network pruning can be an effective method for creating more efficient networks without incurring a significant penalty in accuracy. It has been shown that the topology induced by pruning after training can be used to re-train a network from scratch on the same data set, with comparable or better performance. In the context of convolutional neural networks, we build on this work to show that not only can networks be pruned to 10% of their original parameters, but that these sparse networks can also be re-trained on similar data sets with only a slight reduction in accuracy. We use the Lottery Ticket Hypothesis as the basis for our pruning method and discuss how this method can be an alternative to transfer learning, with positive initial results. This paper lays the groundwork for a transfer learning method that reduces the original network to its essential connections and does not require freezing entire layers.
Indoor Home Scene Recognition Using Capsule Neural Networks

This paper presents the use of a class of Deep Neural Networks for recognizing indoor home scenes so as to aid Intelligent Assistive Systems (IAS) in performing indoor services to assist elderly or infirm people. Identifying exact indoor location is important so that objects associated with particular tasks can be located speedily and efficiently irrespective of position or orientation. In this way, IAS developed for providing services may become more efficient in accomplishing designated tasks satisfactorily. There are many Convolutional Neural Networks (CNNs) which have been developed for outdoor scene classification and, also, for interior (not necessarily indoor home) scene classification. However, to date, there are no CNNs which are trained, validated and tested on indoor home scene datasets as there appears to be an absence of sufficiently large databases of home scenes. Nonetheless, it is important to train systems which are meant to operate within home environments with the correct relevant data. To counteract this problem, it is proposed that a different type of network is used, which is not very deep (i.e., a network which does not have too many layers) but which can attain sufficiently high classification accuracy using smaller training datasets. A type of neural network likely to help achieve this is a Capsule Neural Network (CapsNet). In this paper, 20,000 indoor home scenes were used for training the CapsNet, and 5000 images were used for testing it. The validation accuracy achieved is 71% and testing accuracy achieved is 70%.
Integrating HCI Perspective into a Mobile Software Development Team

Software engineers are traditionally concerned with performance and capabilities of mobile applications, while Human-Computer Interaction (HCI) approaches remain underused in many cases of industry everyday practice, especially in smaller companies, running on a tight schedule.
Deep learning long-range information in undirected graphs with wave networks

Graph algorithms are key tools in many fields of science and technology. Some of these algorithms depend on propagating information between distant nodes in a graph. Recently, there have been a number of deep learning architectures proposed to learn on undirected graphs. However, most of these architectures aggregate information in the local neighborhood of a node, and therefore they may not be capable of efficiently propagating long-range information. To solve this problem we examine a recently proposed architecture, wave, which propagates information back and forth across an undirected graph in waves of nonlinear computation. We compare wave to graph convolution, an architecture based on local aggregation, and find that wave learns three different graph-based tasks with greater efficiency and accuracy. These three tasks include (1) labeling a path connecting two nodes in a graph, (2) solving a maze presented as an image, and (3) computing voltages in a circuit. These tasks range from trivial to very difficult, but wave can extrapolate from small training examples to much larger testing examples. These results show that wave may be able to efficiently solve a wide range of tasks that require long-range information propagation across undirected graphs. An implementation of the wave network, and example code for the maze task are included in the tflon deep learning toolkit (https://bitbucket.org/mkmatlock/tflon).
Towards Automated Application-Specific Software Stacks

Software complexity has increased over the years. One common way to tackle this complexity during development is to encapsulate features into a shared library. This allows developers to reuse already implemented features instead of reimplementing them over and over again. However, not all features provided by a shared library are actually used by an application. As a result, an application using shared libraries loads unused code into memory, which an attacker can use to perform code-reuse and similar types of attacks. The same holds for applications written in a scripting language such as PHP or Ruby: The interpreter typically offers much more functionality than is actually required by the application and hence provides a larger overall attack surface. In this paper, we tackle this problem and propose a first step towards automated application-specific software stacks. We present a compiler extension capable of removing unneeded code from shared libraries and—with the help of domain knowledge—also capable of removing unused functionalities from an interpreter’s code base during the compilation process. Our evaluation against a diverse set of real-world applications, among others Nginx, Lighttpd, and the PHP interpreter, removes on average 71.3% of the code in musl-libc, a popular libc implementation. The evaluation on web applications show that a tailored PHP interpreter can mitigate entire vulnerability classes, as is the case for OpenConf. We demonstrate the applicability of our debloating approach by creating an application-specific software stack for a Wordpress web application: we tailor the libc library to the Nginx web server and PHP interpreter, whereas the PHP interpreter is tailored to the Wordpress web application. In this real-world scenario, the code of the libc is decreased by 65.1% in total, thereby reducing the available code for code-reuse attacks.
Mesoscale characterization of continuous fiber reinforced composites through machine learning: Fiber chirality

A method of quantifying fiber chirality, the twist of continuous fibers through a volume, is defined and applied to both phantom data and real data. Specifically, a field quantity termed the fiber chirality based on the anti-symmetric part of the gradient of the fiber orientation is introduced. For this method of estimation, the input is the set of fiber positions gathered from the stack of images which represent the sample volumes. The phantom sample is generated and several different real continuous fiber reinforced matrix composites are experimentally characterized. Each phantom dataset contains a bundle of fibers that rotate about a center axis with a user-defined angle at each step in the z-direction. The chirality of the fibers is calculated based on the pre-characterized positions using a machine learning algorithm. To validate the method of quantification, our chirality estimation method results in a colormap with an angle of rotation that becomes increasingly more similar to the user-defined angle with decreasing inter-slice distance. Fiber positions from real data are then input into the estimation method and the results are compared.
Tweet Act Classification : A Deep Learning based Classifier for Recognizing Speech Acts in Twitter

Speech act provides valuable insight for understanding the communicative intention and behaviour of a user utterance. This applies for conversation on any platform which includes social media network such as Twitter. This paper presents a deep learning based tweet act classifier (speech acts for Twitter) for understanding the content and intention of tweets and for discovering the valuable communications amongst the tweeters. A generic taxonomy of seven speech acts has been created for the task. A Convolution Neural Network (CNN) based Deep Learning (DL) architecture is integrated with different classifiers at the final layer such as softmax or linear Support Vector Machine (SVM) to develop a tweet act classifier. To make the classifier even more robust, various hand-crafted features are added to the system to boost its effectiveness towards improving the performance of the system. Experimental results indicate that the proposed model achieved good results with an average F1-score of 0.71 and outperformed state of the art approaches.
Regression Wavelet Analysis for Near-Lossless Remote Sensing Data Compression

Regression wavelet analysis (RWA) is one of the current state-of-the-art lossless compression techniques for remote sensing data. This article presents the first regression-based near-lossless compression method. It is built upon RWA, a quantizer, and a feedback loop to compensate the quantization error. Our near-lossless RWA (NLRWA) proposal can be followed by any entropy coding technique. Here, the NLRWA is coupled with a bitplane-based coder that supports progressive decoding. This successfully enables gradual quality refinement and lossless and near-lossless recovery. A smart strategy for selecting the NLRWA quantization steps is also included. Experimental results show that the proposed scheme outperforms the state-of-the-art lossless and the near-lossless compression methods in terms of compression ratios and quality retrieval.
Exploring the Potential of Augmented Reality in Domestic Environments

While Augmented Reality (AR) technologies are becoming increasingly available, our understanding of AR is primarily limited to controlled experiments which address use at work or for entertainment. Little is known about how it could enhance everyday interaction from a user's perspective. Personal use of AR at home may improve how users' interface with information on a daily basis. Through an online survey, we investigated attitudes towards domestic AR. We further explored the opportunities for AR at home in a technology probe. We first introduced the users to AR by offering an AR experience presented through mixed reality smart glasses. We then used a tailor-made tablet application to elicit photos illustrating how users imagine future AR experiences. Finally, we conducted semi-structured interviews based on elicited photos. Our results show that users are eager to benefit from on-demand information, assistance, enhanced sensory perception, and play offered by AR across many locations at home. We contribute insights for future AR systems designed for domestic environments.