Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation methodologies in Automatic Question Generation 2013-2018

In the last few years Automatic Question Generation (AQG) has attracted increasing interest. In this paper we survey the evaluation methodologies used in AQG. Based on a sample of 37 papers, our research shows that the systems’ development has not been accompanied by similar developments in the methodologies used for the systems’ evaluation. Indeed, in the papers we examine here, we find a wide variety of both intrinsic and extrinsic evaluation methodologies. Such diverse evaluation practices make it difficult to reliably compare the quality of different generation systems. Our study suggests that, given the rapidly increasing level of research in the area, a common framework is urgently needed to compare the performance of AQG systems and NLG systems more generally.
BrainT at IEST 2018: Fine-tuning Multiclass Perceptron For Implicit Emotion Classification

We present BrainT, a multi-class, averaged perceptron tested on implicit emotion prediction of tweets. We show that the dataset is linearly separable and explore ways in fine-tuning the baseline classifier. Our results indicate that the bag-of-words features benefit the model moderately and prediction can be improved with bigrams, trigrams, skip-one-tetragrams and POS-tags. Furthermore, we find preprocessing of the n-grams, including stemming, lowercasing, stopword filtering, emoji and emoticon conversion generally not useful. The model is trained on an annotated corpus of 153,383 tweets and predictions on the test data were submitted to the WASSA-2018 Implicit Emotion Shared Task. BrainT attained a Macro F-score of 0.63.
The E2E NLG Challenge: A Tale of Two Systems

This paper presents the two systems we entered into the 2017 E2E NLG Challenge: TemplGen, a templated-based system and SeqGen, a neural network-based system. Through the automatic evaluation, SeqGen achieved competitive results compared to the template-based approach and to other participating systems as well. In addition to the automatic evaluation, in this paper we present and discuss the human evaluation results of our two systems.
Logic Synthesis of Binarized Neural Networks for Efficient Circuit Implementation

Neural networks (NNs) are key to deep learning systems. Their efficient hardware implementation is crucial to applications at the edge. Binarized NNs (BNNs), where the weights and output of a neuron are of binary values {–1, +1} (or encoded in {0, 1}), have been proposed recently. As no multiplier is required, they are particularly attractive and suitable for hardware realization. Most prior NN synthesis methods target on hardware architectures with neural processing elements (NPEs), where the weights of a neuron are loaded and the output of the neuron is computed. The load-and-compute method, though area efficient, requires expensive memory access, which deteriorates energy and performance efficiency. In this work we aim at synthesizing BNN dense layers into dedicated logic circuits. We formulate the corresponding matrix covering problem and propose a scalable algorithm to reduce the area and routing cost of BNNs. Experimental results justify the effectiveness of the method in terms of area and net savings on FPGA implementation. Our method provides an alternative implementation of BNNs, and can be applied in combination with NPE-based implementation for area, speed, and power tradeoffs.
Classification of diagnostic subcategories for obesity and diabetes based on eating patterns

AIM To investigate whether eating patterns of specific food groups can be used to predict and classify Mexican adults who have been diagnosed as having obesity, diabetes or both, when compared to those without a diagnosis. Additionally, we aim to show the benefit of data mining techniques in nutritional studies. METHODS Statistical analysis of self-reported eating patterns based on designated food groups is conducted. Predictive models for health status based on dietary patterns are built using a naïve Bayes classifier. RESULTS Clear patterns emerge in the model building where adults are categorised as having obesity, diabetes or both. The model for diabetics showed the greatest degree of predictability, producing sensitivity results 2.4 times higher than the average, using score decile testing. The models for people with obesity and for those with both obesity and diabetes both reported sensitivity doubling the average. Coverage also showed greatest response for the diabetic model, the first decile containing 24% of all diabetics. CONCLUSIONS Classifier models using dietary habits as inputs succeed in subcategorising Mexican adults based on health status. Diabetics are associated with a very different, and more appropriate dietary pattern (significantly less sugar consumption) for their condition, relative to the non-diagnosed group. Adults with obesity are also associated with a very different, but inappropriate (higher overall consumption), dietary pattern. We hypothesise that obesity, unlike diabetes, is not seen as a sufficiently serious condition, leading to an inadequate response to the diagnosis. Furthermore, data mining techniques can provide new results in nutritional studies.
SDN/NFV, Machine Learning, and Big Data Driven Network Slicing for 5G

5G networks are expected to be able to satisfy a variety of vertical services for mobile users, business demands, and automotive industry. Network slicing is a promising technology for 5G to provide a network as a service (NaaS) for a wide range of services that run on different virtual networks deployed on a shared network infrastructure. Moreover, the SON (self-organizing network) in 5G is expected as a significant evolution to guarantee for full intelligence, automatic, and faster management and optimization. To deal with those requirements, recently, software-defined networking (SDN), network functions virtualization (NFV), big data, and machine learning have been proposed as emerging technologies and the necessary tools for 5G, especially, for network slicing. This study aims to integrate various machine learning (ML) algorithms, big data, SDN, and NFV to build a comprehensive architecture and an experimental framework for the future SONs and network slicing. Finally, based on this framework, we successfully implemented an early state traffic classification and network slicing for mobile broadband traffic applications implemented at Broadband Mobile Lab (BML), National Chiao Tung University (NCTU).
Urban Land Use and Land Cover Classification Using Novel Deep Learning Models Based on High Spatial Resolution Satellite Imagery

Urban land cover and land use mapping plays an important role in urban planning and management. In this paper, novel multi-scale deep learning models, namely ASPP-Unet and ResASPP-Unet are proposed for urban land cover classification based on very high resolution (VHR) satellite imagery. The proposed ASPP-Unet model consists of a contracting path which extracts the high-level features, and an expansive path, which up-samples the features to create a high-resolution output. The atrous spatial pyramid pooling (ASPP) technique is utilized in the bottom layer in order to incorporate multi-scale deep features into a discriminative feature. The ResASPP-Unet model further improves the architecture by replacing each layer with residual unit. The models were trained and tested based on WorldView-2 (WV2) and WorldView-3 (WV3) imageries over the city of Beijing. Model parameters including layer depth and the number of initial feature maps (IFMs) as well as the input image bands were evaluated in terms of their impact on the model performances. It is shown that the ResASPP-Unet model with 11 layers and 64 IFMs based on 8-band WV2 imagery produced the highest classification accuracy (87.1% for WV2 imagery and 84.0% for WV3 imagery). The ASPP-Unet model with the same parameter setting produced slightly lower accuracy, with overall accuracy of 85.2% for WV2 imagery and 83.2% for WV3 imagery. Overall, the proposed models outperformed the state-of-the-art models, e.g., U-Net, convolutional neural network (CNN) and Support Vector Machine (SVM) model over both WV2 and WV3 images, and yielded robust and efficient urban land cover classification results.
Syntactic Manipulation for Generating more Diverse and Interesting Texts

Natural Language Generation plays an important role in the domain of dialogue systems as it determines how users perceive the system. Recently, deep-learning based systems have been proposed to tackle this task, as they generalize better and require less amounts of manual effort to implement them for new domains. However, deep learning systems usually adapt a very homogeneous sounding writing style which expresses little variation. In this work, we present our system for Natural Language Generation where we control various aspects of the surface realization in order to increase the lexical variability of the utterances, such that they sound more diverse and interesting. For this, we use a Semantically Controlled Long Short-term Memory Network (SC-LSTM), and apply its specialized cell to control various syntactic features of the generated texts. We present an in-depth human evaluation where we show the effects of these surface manipulation on the perception of potential users.
A Study on the Sensitivity-Based Discriminative Hyperspectral Image Content Representation

In this paper, we study the importance of spectral sensitivity functions in constructing discriminative representation of hyperspectral images (HSI). The main goal of a such representation is to improve image content recognition by focusing the processing only on the most relevant spectral channels. The underlying hypothesis is that for a given category, each image content is better extracted through a specific set of spectral sensitivity functions. In this study, we fixed the number of spectral sensitivity functions to 3 for displaying purposes. Deep features are then extracted from the obtained trichromatic representation of HSI data to build a discriminative image signature. Finally, spectral sensitivity functions are compared in a Content-Based Image Retrieval (CBIR) paradigm. Exhaustive experiments have been conducted on a hyperspectral dataset. Obtained results show the usefulness of the whole spectrum to obtain a discriminative image representation compared to the RGB representation.
HLSPredict: Cross Platform Performance Prediction for FPGA High-Level Synthesis

FPGA application developers must explore increasingly large design spaces to identify regions of code to accelerate. High-Level Synthesis (HLS) tools automatically derive FPGA-based designs from high-level language specifications, which improves designer productivity; however, HLS tool run-times are cost-prohibitive for design space exploration, preventing designers from adequately answering cost-value decisions without expert guidance. To address this concern, this paper introduces a machine learning framework to predict FPGA performance and power consumption without relying on analytical models or HLS tools in-the-loop. For workloads that were manually optimized by appropriately setting pragmas, the framework obtains a worst-case relative error of 9.08% while running 43.78x faster than HLS; for unoptimized workloads, the framework obtains a worst-case relative error of 9.79% while running 36.24x faster than HLS.
HGSGNLP at IEST 2018: An Ensemble of Machine Learning and Deep Neural Architectures for Implicit Emotion Classification in Tweets

This paper describes our system designed for the WASSA-2018 Implicit Emotion Shared Task (IEST). The task is to predict the emotion category expressed in a tweet by removing the terms angry, afraid, happy, sad, surprised, disgusted and their synonyms. Our final submission is an ensemble of one supervised learning model and three deep neural network based models, where each model approaches the problem from essentially different directions. Our system achieves the macro F1 score of 65.8%, which is a 5.9% performance improvement over the baseline and is ranked 12 out of 30 participating teams.
Analysis of dynamic complexity feature of information system data based on visualization

The volume and complexity of the data in the information system continues to increase during the operation of the information system. The phenomenon leads to the fact that decision-makers are faced with data-rich and information-deficient situations. How to intuitively grasp the dynamic complexity of information system data has become a concern of scholars. Therefore, this paper collected and organized the system log data of two shipping companies, adopted multiple visualization forms, and combined the dynamic complexity measurement methods of information systems, focusing on the overall distribution status and development trend of the data dynamic complexity and internal information system and the influence of complexity of subsystems within the information system on overall data dynamic operation complexity, and then analyzed the overall feature of the dynamic complexity of information system data, and summarized the feature regulars of the complexity of the visualization method. The results show that the visual feature analysis of the dynamic complexity of information systems can improve the ability of enterprises to analyze and make decisions, and provide a basis for enterprise managers to develop corresponding management measures.
Unlocking Fine-Grain Parallelism for AIG Rewriting

Parallel computing is a trend to enhance scalability of electronic design automation (EDA) tools using widely available multicore platforms. In order to benefit from parallelism, well-known EDA algorithms have to be reformulated and optimized for multicore implementation. This paper introduces a set of principles to enable a fine-grain parallel AND-inverter graph (AIG) rewriting. It presents a novel method to discover and rewrite in parallel parts of the AIG, without the need for graph partitioning. Experiments show that, when synthesizing large designs composed of millions of AIG nodes, the parallel rewriting on 40 physical cores is up to 36x and 68x faster than ABC commands rewrite −l and drw, respectively, with comparable quality of results in terms of AIG size and depth.
Language Independent Sentiment Analysis with Sentiment-Specific Word Embeddings

Data annotation is a critical step to train a text model but it is tedious, expensive and time-consuming. We present a language independent method to train a sentiment polarity model with limited amount of manually-labeled data. Word embeddings such as Word2Vec are efficient at incorporating semantic and syntactic properties of words, yielding good results for document classification. However, these embeddings might map words with opposite polarities, to vectors close to each other. We train Sentiment Specific Word Embeddings (SSWE) on top of an unsupervised Word2Vec model, using either Recurrent Neural Networks (RNN) or Convolutional Neural Networks (CNN) on data auto-labeled as “Positive” or “Negative”. For this task, we rely on the universality of emojis and emoticons to auto-label a large number of French tweets using a small set of positive and negative emojis and emoticons. Finally, we apply a transfer learning approach to refine the network weights with a small-size manually-labeled training data set. Experiments are conducted to evaluate the performance of this approach on French sentiment classification using benchmark data sets from SemEval 2016 competition. We were able to achieve a performance improvement by using SSWE over Word2Vec. We also used a graph-based approach for label propagation to auto-generate a sentiment lexicon.
Application-based power saving approach for IoT CoAP protocol

IoT devices are gaining more importance as they are involved in solving modern world problems in many industries like health, agriculture, manufacturing and many others. The limited power capability of IoT devices drives the research to find new methods of extending the battery lifetime. In this work, we introduce a new approach that helps in reducing the power consumption driven by the IoT device information. The proposed approach allows the IoT device to send its data to the CoAP (Constrained application protocol) server only if its current reading is different from the old one. We used the Intel Lab Data to conduct experiments using the proposed approach on real-world sensor readings. The experiments have shown that the proposed approach allowed device lifetime extension by 33% to 155% with respect to the standard CoAP protocol and device lifetime extension by 8% and 14% with respect to the dynamic tuning approach.
Self-Consumption Optimization of Renewable Energy Production in Distributed Clouds

The growing appetite of new technologies, such as Internet-of-Things, for Cloud resources leads to an unprecedented energy consumption for these infrastructures. In order to make these energy-hungry distributed systems more sustainable, Cloud providers resort more and more to on-site renewable energy production facilities like photovoltaic panels. Yet, this intermittent and variable electricity production is often uncorrelated with the Cloud consumption induced by its workload. Geographical load balancing, virtual machine (VM) migration and consolidation can be used to exploit multiple Cloud data centers' locations and their associated photovoltaic panels for increasing their renewable energy consumption. However, these techniques cost energy and network bandwidth, and this limits their utilization. In this paper, we propose to rely on the flexibility brought by Smart Grids to exchange renewable energy between distributed sites and thus, to further increase the overall Cloud's self-consumption of the locally-produced renewable energy. Our solution is named SCORPIUS: Self-Consumption Optimization of Renewable energy Production In distribUted cloudS. It takes into account telecommunication network constraints and electrical grid requirements to optimize the Cloud's self-consumption by trading-off between VM migration and renewable energy exchange. Our simulation-based results show that SCORPIUS outperforms existing solutions on various workload traces of production Clouds in terms of both renewable self-consumption and overall energy consumption.
Optimizing the Constrained Estimate of Random Walks

We introduce the problem of optimizing the constrained estimate of random walks on the probability networks (which are formally defined as the weighted directed graphs in which the total outgoing weight of any node is at most 1). In this problem, it is required to find the size-constrained set of nodes, which maximized the hitting probability (before the time limit) of the randomly initialized random walk. The problem is proved NP-Hard, and we propose an algorithm with polynomial time complexity and constant approximation ratio.
Big Trajectory Data: A Survey of Applications and Services

The rapid development of wireless infrastructure and data acquisition technologies contributes to the explosive growth of data, especially trajectory data with rich information. Trajectory data, which records locations of moving objects at certain moments, has long been an important means of studying human behavior and solving traffic problems. In this paper, we mainly introduce the trajectory data from the perspective of applications and services. According to the degree of data structured, we divide the trajectory data into explicit trajectory data and implicit trajectory data, and describe each type in detail. Then, we introduce the applications of trajectory data from travel behavior, travel patterns, and other aspects. Combined with case studies, we provide a description to the services of trajectory data in terms of transportation administration and commercial service. Finally, we focus on challenges in trajectory data, including privacy protection, human mobility causality, and emission reduction.
Single-call mechanisms

Truthfulness is fragile and demanding. It is oftentimes computationally harder than solving the original problem. Even worse, truthfulness can be utterly destroyed by small uncertainties in a mechanism's outcome. One obstacle is that truthful payments depend on outcomes other than the one realized, such as the lengths of non-shortest-paths in a shortest-path auction. Single-call mechanisms are a powerful tool that circumvents this obstacle --- they implicitly charge truthful payments, guaranteeing truthfulness in expectation using only the outcome realized by the mechanism. The cost of such truthfulness is a trade-off between the expected quality of the outcome and the risk of large payments. We largely settle when and to what extent single-call mechanisms are possible. The first single-call construction was discovered by Babaioff, Kleinberg, and Slivkins [2010] in single-parameter domains. They give a transformation that turns any monotone, single-parameter allocation rule into a truthful-in-expectation single-call mechanism. Our first result is a natural complement to[Babaioff et al. 2010]: we give a new transformation that produces a single-call VCG mechanism from any allocation rule for which VCG payments are truthful. Second, in both the single-parameter and VCG settings, we precisely characterize the possible transformations, showing that that a wide variety of transformations are possible but that all take a very simple form. Finally, we study the inherent trade-off between the expected quality of the outcome and the risk of large payments. We show that our construction and that of [Babaioff et al. 2010] simultaneously optimize a variety of metrics in their respective domains. Our study is motivated by settings where uncertainty in a mechanism renders other known techniques untruthful. As an example, we analyze pay-per-click advertising auctions, where the truthfulness of the standard VCG-based auction is easily broken when the auctioneer's estimated click-through-rates are imprecise.
TGPA: Tile-Grained Pipeline Architecture for Low Latency CNN Inference

FPGAs are more and more widely used as reconfigurable hardware accelerators for applications leveraging convolutional neural networks (CNNs) in recent years. Previous designs normally adopt a uniform accelerator architecture that processes all layers of a given CNN model one after another. This homogeneous design methodology usually has dynamic resource underutilization issue due to the tensor shape diversity of different layers. As a result, designs equipped with heterogeneous accelerators specific for different layers were proposed to resolve this issue. However, existing heterogeneous designs sacrifice latency for throughput by concurrent execution of multiple input images on different accelerators. In this paper, we propose an architecture named Tile-Grained Pipeline Architecture (TGPA) for low latency CNN inference. TGPA adopts a heterogeneous design which supports pipelining execution of multiple tiles within a single input image on multiple heterogeneous accelerators. The accelerators are partitioned onto different FPGA dies to guarantee high frequency. A partition strategy is designd to maximize on-chip resource utilization. Experiment results show that TGPA designs for different CNN models achieve up to 40% performance improvement than homogeneous designs, and 3X latency reduction over state-of-the-art designs.
Sentence Packaging in Text Generation from Semantic Graphs as a Community Detection Problem

An increasing amount of research tackles the challenge of text generation from abstract ontological or semantic structures, which are in their very nature potentially large connected graphs. These graphs must be “packaged” into sentence-wise subgraphs. We interpret the problem of sentence packaging as a community detection problem with post optimization. Experiments on the texts of the VerbNet/FrameNet structure annotated-Penn Treebank, which have been converted into graphs by a coreference merge using Stanford CoreNLP, show a high F1-score of 0.738.