Reviewerly/exHarmony huggingface dataset:
“Fixing the curse of the bad product descriptions” – Search-boosted tag recommendation for E-commerce products

Various e-commerce platforms allow sellers to register, describe and organize their own products, using tags and other textual metadata. The quality of these textual descriptors is essential for the effectiveness of e-commerce information services such as search and product recommendation, and thus, for the ability of consumers to find desired products. In this paper, we focus on a particular, widely used textual descriptors of products, tags. We argue that sellers may not be the “best” providers of tag information for products either because of their inability to do so (they were not “trained” for that) or due to an explicit intent to fool the system in order to promote their products with inadequate or imprecise tags (tag spam). To deal with these issues, we may rely on automatic tag recommendation techniques to improve the quality of the tags suggested to describe a given product. In this context, the main novel contribution of our work is a set of new tag recommendation techniques that take advantage of product search result data (in particular the search queries and product clicks from these queries) to improve the quality of the recommended tags. Our main hypothesis is that the set of queries collectively issued by the consumers of the e-market place, along with corresponding clicks, reflect a more trustworthy view of the products; thus those queries and clicks can be exploited as a source of high quality (e.g., more diverse) tags to describe the products. We propose new solutions, including some based on deep learning, that translate this main hypothesis into new features and methods for recommending tags for products. Our manual and automatic evaluations, using real data from one of the largest e-commerce sites in Brazil, show that indeed tags created by sellers contain a lot of noise. On the other hand, our proposed search-boosted tag recommenders are highly effective in suggesting relevant tags, with gains of more than 16% in recommendation effectiveness against the state-of-the-art. Even more, our experiments show that the suggested tags provide a potentially better data source for e-commerce search than the original tags assigned by product sellers.
The Study on the Text Classification for Financial News Based on Partial Information

The goal of this paper is to conduct the study on the text classification for financial news based on partial information.By a fact that an indispensable step for the efficient use of topic information embedded in financial news is the text classification, a new neural network called ''All Dataset based on CharCNN (Character Convolutional Neural Networks) and GRU (Gated Recurrent Unit)'' (in short, AD-CharCGNN) which extracts a part of the financial article and incorporates both time domain and spatial domain to classify financial texts is proposed.In the study of this paper, we first build a character level vocabulary by reading all characters of the financial dataset, part of each financial text which will be classified is mapped to a high-dimensional spatial vector based on the vocabulary.Then, the vectors are convoluted in the spatial domain to get the text local features, and next, the features are processed by the gated recurrent units to get the features contained time information.Finally, the features which contain spatial and time information will be classified through softmax function to get the text classification results.Our results on the experiments confirm that the network proposed in this paper works effectively with the accuracy of 96.45%, and it seems that the text classification algorithm with the feature by taking only partial text part is more suitable for the application of the practice.Meanwhile, for the input with character level vector, the network is not only suitable for Chinese but also for other languages.
Conversational Interfaces for Information Search

Recent progress in machine learning has given rise to a plethora of tools and applications that rely on conversational interactions, from chatbots, speech-controlled devices to robots and virtual agents. Conversational interfaces are becoming widely accepted for utility tools, where a common function is to serve users' information needs. Albeit with much excitement, we are only starting to understand how users' information-seeking behaviors and design opportunities may transform moving from traditional graphical user interfaces to conversational user interfaces. In this chapter, we start by reviewing recent work in the emerging area of conversational interfaces and lay out their opportunities for supporting information search tasks. We then present insights from our experience deploying a chatbot supporting information search in a large enterprise, demonstrating how a conversational interface impacts user behaviors and offers new opportunities for improving search experience, in particular for user modeling.
IT2-Based Hybrid Approach for Sustainable Economic Equality: A Case of E7 Economies

This study aims to identify the factors affecting economic inequality in emerging seven (E7) countries. Within this framework, first, a wide literature review has been made and 12 different criteria which are thought to affect economic inequality have been determined. These criteria were weighted according to their importance with the help of interval type-2 (IT2) fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) method. Then, by using these weighted criteria, the E7 countries were ranked according to economic equality performance with IT2 fuzzy Multi-Objective Optimization by Ratio Analysis (MOORA) and IT2 fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. The findings show that fair taxation, the equality of education provided by the state, and the uncontrolled growth of financial markets have a significant effect on the economic inequality. On the other side, Russia and China are the most successful countries in terms of economic equality, whereas India and Brazil have the worst performances. While considering these results, it can be said that the taxation system must be fair to eliminate the economic inequality. For this purpose, E7 countries should implement direct taxes, such as wealth tax to transfer funds from the rich to middle- and low-income groups. In addition, these countries should offer equal education opportunities to all citizens which affects their future income level. Finally, legal authorities should make the necessary arrangements and prevent excessive risk of financial system in the country so that the financial system in the country can be maintained in a healthy manner.
A Comprehensive Analysis of Preprocessing for Word Representation Learning in Affective Tasks

Affective tasks such as sentiment analysis, emotion classification and sarcasm detection have been popular in recent years due to abundance of user-generated data, accurate computational linguistic models, and broad range of relevant applications in various domains.At the same time, many studies have highlighted the importance of text preprocessing, as an integral step to any natural language processing prediction model and downstream task.While preprocessing in affective systems is well-studied, preprocessing in word vector based models applied to affective systems, is not.To address this limitation, we conduct a comprehensive analysis of the role of preprocessing techniques in affective analysis based on word vector models.Our analysis is the first of its kind and provides useful insights of the importance of each preprocessing technique when applied at the training phase, commonly ignored in pretrained word vector models, and/or at the downstream task phase.
The Tree Ensemble Layer: Differentiability meets Conditional Computation

Neural networks and tree ensembles are state-of-the-art learners, each with its unique statistical and computational advantages. We aim to combine these advantages by introducing a new layer for neural networks, composed of an ensemble of differentiable decision trees (a.k.a. soft trees). While differentiable trees demonstrate promising results in the literature, they are typically slow in training and inference as they do not support conditional computation. We mitigate this issue by introducing a new sparse activation function for sample routing, and implement true conditional computation by developing specialized forward and backward propagation algorithms that exploit sparsity. Our efficient algorithms pave the way for jointly training over deep and wide tree ensembles using first-order methods (e.g., SGD). Experiments on 23 classification datasets indicate over 10x speed-ups compared to the differentiable trees used in the literature and over 20x reduction in the number of parameters compared to gradient boosted trees, while maintaining competitive performance. Moreover, experiments on CIFAR, MNIST, and Fashion MNIST indicate that replacing dense layers in CNNs with our tree layer reduces the test loss by 7-53% and the number of parameters by 8x. We provide an open-source TensorFlow implementation with a Keras API.
Second-Order Provable Defenses against Adversarial Attacks

A robustness certificate is the minimum distance of a given input to the decision boundary of the classifier (or its lower bound). For {\it any} input perturbations with a magnitude smaller than the certificate value, the classification output will provably remain unchanged. Exactly computing the robustness certificates for neural networks is difficult since it requires solving a non-convex optimization. In this paper, we provide computationally-efficient robustness certificates for neural networks with differentiable activation functions in two steps. First, we show that if the eigenvalues of the Hessian of the network are bounded, we can compute a robustness certificate in the $l_2$ norm efficiently using convex optimization. Second, we derive a computationally-efficient differentiable upper bound on the curvature of a deep network. We also use the curvature bound as a regularization term during the training of the network to boost its certified robustness. Putting these results together leads to our proposed {\bf C}urvature-based {\bf R}obustness {\bf C}ertificate (CRC) and {\bf C}urvature-based {\bf R}obust {\bf T}raining (CRT). Our numerical results show that CRT leads to significantly higher certified robust accuracy compared to interval-bound propagation (IBP) based training. We achieve certified robust accuracy 69.79\%, 57.78\% and 53.19\% while IBP-based methods achieve 44.96\%, 44.74\% and 44.66\% on 2,3 and 4 layer networks respectively on the MNIST-dataset.
Semi-Dynamic Hypergraph Neural Network for 3D Pose Estimation

This paper proposes a novel Semi-Dynamic Hypergraph Neural Network (SD-HNN) to estimate 3D human pose from a single image. SD-HNN adopts hypergraph to represent the human body to effectively exploit the kinematic constrains among adjacent and non-adjacent joints. Specifically, a pose hypergraph in SD-HNN has two components. One is a static hypergraph constructed according to the conventional tree body structure. The other is the semi-dynamic hypergraph representing the dynamic kinematic constrains among different joints. These two hypergraphs are combined together to be trained in an end-to-end fashion. Unlike traditional Graph Convolutional Networks (GCNs) that are based on a fixed tree structure, the SD-HNN can deal with ambiguity in human pose estimation. Experimental results demonstrate that the proposed method achieves state-of-the-art performance both on the Human3.6M and MPI-INF-3DHP datasets.
Towards a Deep Attention-Based Sequential Recommender System

With the availability of a large amount of user-generated online data, discovering users' sequential behaviour has become an integral part of a Sequential Recommender System (SRS).Combining the recent observed items (i.e., short-term preferences) with prior interacted items (i.e., long-term preferences) has gained increasing attention in recent years.However, the existing methods mostly assume that all the adjacent items in a sequence are highly dependent, which may not be practical in real-world scenarios due to the uncertainty of customers' shopping behaviours.A user-item interaction sequence may contain some irrelevant items which may in turn lead to false dependencies between items.Moreover, current studies usually assign a static representation to each item when modeling a user's long-term preferences.Therefore, they cannot differentiate the contributions of the items.Specifically, these two types of users' preferences have been separately modeled and then linearly combined, which may fail to model complicated user-item interactions.In order to overcome the above mentioned problems, we propose a novel Deep Attention-based Sequential (DAS) model.DAS consists of three different blocks, (i) an embedding block: which embeds users and items into low-dimensional spaces; (ii) an attention block: which aims to discriminatively learn dependencies among items in both users' long-term and short-term item sets; and (iii) a fully-connected block: which first learns a mixture of users' preferences representation through a nonlinear way and then combines it with users' embeddings to have a personalized recommendation.Extensive experiments demonstrate the superiority of our proposed model compared to the state-of-the-art approaches in SRSs.
Agent Architecture for Adaptive Behaviors in Autonomous Driving

Evolution has endowed animals with outstanding adaptive behaviours which are grounded in the organization of their sensorimotor system. This paper uses inspiration from these principles of organization in the design of an artificial agent for autonomous driving. After distilling the relevant principles from biology, their functional role in the implementation of an artificial system are explained. The resulting Agent, developed in an EU H2020 Research and Innovation Action, is used to concretely demonstrate the emergence of adaptive behaviour with a significant level of autonomy. Guidelines to adapt the same principled organization of the sensorimotor system to other agents for driving are also obtained. The demonstration of the system abilities is given with example scenarios and open access simulation tools. Prospective developments concerning learning via mental imagery are finally discussed.
Day-Ahead Forecasting for Small-Scale Photovoltaic Power Based on Similar Day Detection with Selective Weather Variables

As photovoltaic (PV) power plants are an essential component of modern smart grids, the PV generation forecasting of such plants has recently been gaining interest. The forecasting results of PV power often suffer from large errors because of unusual weather conditions. In a learning-based forecasting model, the forecasting accuracy can be enhanced by using carefully selected data for training rather than all the data without any screening. That is, using a training set that only contains information obtained from similar days can help enhance the accuracy of learning-based PV forecasting. This paper proposes a forecasting method for small-scale PV generation. This method is based on long short-term memory; further, it detects similar days considering the different impacts of weather variables on PV power according to the day. This method can address issues caused by unnecessary learning from non-similar historical days. The simulation results demonstrate that the proposed method exhibits better performance than do existing similar day detection methods.
Separation logic for sequential programs (functional pearl)

This paper presents a simple mechanized formalization of Separation Logic for sequential programs. This formalization is aimed for teaching the ideas of Separation Logic, including its soundness proof and its recent enhancements. The formalization serves as support for a course that follows the style of the successful Software Foundations series, with all the statement and proofs formalized in Coq. This course only assumes basic knowledge of lambda-calculus, semantics and logics, and therefore should be accessible to a broad audience.
BIM-Based Spatial Augmented Reality (SAR) for Architectural Design Collaboration: A Proof of Concept

Three-dimensional (3D) visualization technology, such as augmented reality (AR), has served as the display for building information modeling (BIM)-based architectural design collaboration to provide more effective design observation and communication for stakeholders. That said, AR has several technical limitations in terms of personal device issues, user experience, and visualization quality. A new form of AR called spatial augmented reality (SAR) has been introduced to address these issues, which uses a digital projector to present graphics on physical objects for augmenting real-world objects. Therefore, SAR has great benefits and potentials to combine with BIM for design collaboration. This paper introduces a BIM-based SAR operational framework, where 3D building models generated from BIM software are imported to projection mapping tools to display building surface textures on physical white building models. A case study using Revit and 3ds Max as the BIM software, and MadMapper as the projection mapping tool, was conducted to demonstrate the feasibility of the proposed framework and to evaluate the projection performance of SAR. The case study showed that the texture of BIM models could be projected on the objects clearly and realistically. Additionally, the proposed SAR method potentially offers intuitive observation of building models and comfortable wear-free experience for collaborative design, and the qualitative analysis by changing the parameters was conducted to test the different projection conditions. Since it is expected that the use of SAR can be promoted by overcoming the discussed technical limitations and possible solution application, this study aims to traceability provide the whole process of BIM-based SAR for architectural design collaboration.
A Generative Steganography Method Based on WGAN-GP

With the development of Generative Adversarial Networks (GAN), GAN-based steganography and steganalysis techniques have attracted much attention from researchers. In this paper, we propose a novel image steganography method without modification based on Wasserstein GAN Gradient Penalty (WGAN-GP). The proposed architecture has a generative network, a discriminative network, and an extractor network. The Generator is used to generate the cover image (also is the stego image), and the Extractor is used to extract secret information. During the process of stego image generation, no modification operations are required. To make full use of the learning ability of convolutional neural networks and GAN, we synchronized the training of Generator and Extractor. Experiment results show that the proposed method has the advantages of higher recovery accuracy and higher training efficiency.
A novel method to identify the flow pattern of oil–water two-phase flow

Abstract This paper presents a novel method combining extreme learning machine (ELM) and multiple empirical mode decomposition (MEMD) to identify flow patterns of oil–water two-phase flow. The proposed method can recognize accurately five typical flow patterns of horizontal oil–water two-phase flow. Taking the Lorenz system as an example, we verify the MEMD is more suitable for simultaneous decomposition of multi-channel signals than empirical mode decomposition and ensemble empirical mode decomposition. In the proposed method, we employ the MEMD to decompose the multivariate conductance signal of oil–water two-phase flow to obtain the same intrinsic mode function modes, select the normalized energy of the high-frequency components as the eigenvalue, and utilize the trained ELM to achieve a good recognition result. The experimental results show that the proposed method is not only fast and generalized, but also has high accuracy in identifying flow patterns of oil–water two-phase flow.
A Novel Categorical Approach to Semantics of Relational First-Order Logic

We present a categorical formalization of a variant of first-order logic. Unlike other texts on this topic, the goal of this paper is to give a very transparent and self-contained account without requiring more background than basic logic and set theory. Our focus is to show how the semantics of first-order formulas can be derived from their usual deduction rules. For understanding the core ideas, it is not necessary to investigate the internal term structure of atomic formulas, thus we abstract atomic formulas to (syntactically opaque) relations; in this sense, our variant of first-order logic is “relational”. While the derived semantics is based on categorical principles (even the duality that arises from a symmetry between two ways of looking at something where there is no reason to choose one over the other), it is nevertheless “constructive” in that it describes explicit computations of the truth values of formulas. We demonstrate this by modeling the categorical semantics in the RISCAL (RISC Algorithm Language) system which allows us to validate the core propositions by automatically checking them in finite models.
Image restoration of optical sparse aperture systems based on a dual target network

Optical sparse aperture (OSA) systems show great potential for the next generation astronomical telescope system due to its excellent high resolution with low volume and weight. However, the sparse arrangement causes its mid-frequency modulation transfer function to be lower compared with a single fully-filled aperture system, which further leads to blurred images and reduced contrast. Therefore, image restoration becomes an indispensable part for OSA systems. In this paper, a dual target network (DTN) is proposed for the image restoration of OSA systems. The noise in a raw image is estimated with interpolation and difference calculation. A block matching 3D filter is used as a denoiser. A denoised image is regarded as a degraded image which cannot be accurately modeled. To cope with the restoration problem, a dual target (negative structural similarity and the sum of fidelity and regularization term) network is trained. A function determined by the filling factor and the aperture distribution is trained as a correction term of the network. The trained network is used to deconvolve the denoised image. Simulation and experiment results show that the proposed method has good peak signal-to-noise ratio and structure similarity. For a Golay-6 system with a filling factor of 0.3245, when the signal-to-noise ratio is 30 dB, the DTN method increases the average peak signal to noise ratio from 22.6 dB to 31.7 dB and improves the average structural similarity from 0.77 to 0.90.