Miracle-dz/UniFAR-FTUs huggingface dataset:
Telling the Whole Story: A Manually Annotated Chinese Dataset for the Analysis of Humor in Jokes

Humor plays important role in human communication, which makes it important problem for natural language processing. Prior work on the analysis of humor focuses on whether text is humorous or not, or the degree of funniness, but this is insufficient to explain why it is funny. We therefore create a dataset on humor with 9,123 manually annotated jokes in Chinese. We propose a novel annotation scheme to give scenarios of how humor arises in text. Specifically, our annotations of linguistic humor not only contain the degree of funniness, like previous work, but they also contain key words that trigger humor as well as character relationship, scene, and humor categories. We report reasonable agreement between annota-tors. We also conduct an analysis and exploration of the dataset. To the best of our knowledge, we are the first to approach humor annotation for exploring the underlying mechanism of the use of humor, which may contribute to a significantly deeper analysis of humor. We also contribute with a scarce and valuable dataset, which we will release publicly.
Desarrollo y evaluación de una herramienta basada en corpus para identificar errores y realizar propuestas de corrección en las colocaciones del español

The topic of collocation has drawn attention for the past three decades in the lexical area of theoretical and applied linguistics. Our research team developed and evaluated a corpus-based assisted tool for collocation learning in Spanish. Based on the two constructed corpora (CEATE and CPEIC) and a Spanish collocation extraction tool (HCE), this computer-assisted learning tool, SpColEDRS, is easy to use to detect errors and also suggests revisions for Spanish collocations. Based on the evaluation, the research results indicated that the tool developed in this research can assist learners effectively, especially in the case of beginners. In addition, the results of the satisfaction survey provided positive confirmation of the effectiveness of this tool in assisting the learning of Spanish collocations. Finally, this study shed light on pedagogical applications of the constructed corpora and the learning of Spanish collocation with a corpus-based approach in a multilingual acquisition setting.
Robust Principal Component Analysis with Adaptive Neighbors

Suppose certain data points are overly contaminated, then the existing principal component analysis (PCA) methods are frequently incapable of filtering out and eliminating the excessively polluted ones, which potentially lead to the functional degeneration of the corresponding models. To tackle the issue, we propose a general framework namely robust weight learning with adaptive neighbors (RWL-AN), via which adaptive weight vector is automatically obtained with both robustness and sparse neighbors. More significantly, the degree of the sparsity is steerable such that only exact k well-fitting samples with least reconstruction errors are activated during the optimization, while the residual samples, i.e., the extreme noised ones are eliminated for the global robustness. Additionally, the framework is further applied to PCA problem to demonstrate the superiority and effectiveness of the proposed RWL-AN model.
NeuCheck: A more practical Ethereum smart contract security analysis tool

Ethereum is one of the currently popular trading platform, where any one can exchange, buy, or sell cryptocurrencies. Smart contract, a computer program, can help Ethereum to encode rules or scripts for processing transactions. Because the smart contract usually handles large number of cryptocurrencies worth billions of dollars apiece, its security has gained considerable attention. In this paper, we first investigate the security of smart contracts running on the Ethereum and introduce several new security vulnerabilities that allow adversaries to exploit and gain financial benefits. Then, we propose a more practical smart contract analysis tool termed NeuCheck, in which we introduce the syntax tree in the syntactical analyzer to complete the transformation from source code to intermediate representation, and then adopt the open source library working with XML to analyze such tree. We have built a prototype of NeuCheck for Ethereum and evaluate it with over 52 000 existing Ethereum smart contracts. The results show that (1) our new documented vulnerabilities are prevalent; (2) NeuCheck improves the analysis speed by at least 17.2 times compared to other popular analysis tools (eg, Securify and Mythril; and (3) allows for cross‐platform deployment.
Cooperative Overlay Cognitive Radio NOMA Network with Channel Errors and Imperfect SIC

: we propose an overlay cooperative cognitive radio (CR) non-orthogonal multiple access (NOMA) network which has channel estimation errors and imperfect successive interference cancellation (SIC). To further performance enhancement, spatial diversity gains can be offered by relay selection which is chosen based on channel state information (CSI) among the candidate secondary transmitters. We derive an analytical expression of the outage probability of the primary receiver (PR) and the secondary receiver (SR) as a function of the channel errors and imperfect SIC, respectively. And the analytical outage probability expressions are validated through simulations. Numerical results show that when the number of relays ( N ) is fixed, the performance of the PR degrades as the increase of the channel estimation errors. In the case of N =2, to satisfy the required outage probability of 1×10 -4 , 8.2 dB of the transmit SNR degradation is noticed with correlation coefficient ρ=0.98 in comparison with ρ=1. Moreover, the SNR degradation caused by the less correlation coefficient decreases as the number of relays increases. On the other hand, as further imperfect SIC, the error floor that the performance enhancement does not expect with the increase of the transmit power is noticed. The optimum power allocation which maintains the minimum outage probability of SR is realigned by the error floor.
Domain Adaptation for Person-Job Fit with Transferable Deep Global Match Network

Person-job fit has been an important task which aims to automatically match job positions with suitable candidates. Previous methods mainly focus on solving the match task in single-domain setting, which may not work well when labeled data is limited. We study the domain adaptation problem for person-job fit. We first propose a deep global match network for capturing the global semantic interactions between two sentences from a job posting and a candidate resume respectively. Furthermore, we extend the match network and implement domain adaptation in three levels, sentence-level representation, sentence-level match, and global match. Extensive experiment results on a large real-world dataset consisting of six domains have demonstrated the effectiveness of the proposed model, especially when there is not sufficient labeled data.
A policy-driven, model-based software and services deployment architecture for heterogeneous environments (Propuesta de una arquitectura de despliegue de software y servicios para entornos heterogéneos basado en modelos y dirigido por políticas)

Software deployment focuses on the group of activities associated with the transition of software from software production sites to software consumers' sites. Modern systems are created by combination of multiple software components at runtime. Furthermore, they are very frequently changing. Systems are dynamically updated, extended and removed. These changes may be related to many different causes, such as including new features, improving performance or correcting failures. In consequence; software deployment issues have an undeniable impact on ITC systems' operation and maintenance costs. Despite this fact, deployment challenges have traditionally been neglected by Software Engineering. At least, if we compare it with the attention drawn by other development-time activities such as design, coding, testing or development processes. Unfortunately, there are many factors that complicate the task of automating software deployment. Deployment units exhibit complex dependency patterns that must be understood and solved. Dependencies span elements at different but interrelated levels; software, hardware and networking. All of them must be satisfied in order to carry out a proper deployment action. This dissertation's contributes to the area of software deployment with a software deployment architecture that supports context adaptation and distributed deployment targets. A deployment system is context aware if it is capable of adapting its operation to the current status of the deployment target, i.e. its hardware, software and networking available resources. Deployment activities can be initiated from remote locations and deployment units can be fetched from many distributed units' repositories. I have intended to handle an additional aspect of distribution; distribution at the deployment target. This implies that the deployment target can be a networked set of nodes. Eventually, this implies that units' which collaborate to provide a certain service can be allocated
Using a budget approach for decision-support in the design process

The use of Life Cycle Assessment (LCA) during the design phase can help to improve the environmental performance of buildings. However, designers and clients find it difficult to set environmental performance targets and interpret the results obtained through LCA in order to improve the building design. Therefore, performance levels or benchmarks are needed that provide design guidance towards reducing the environmental impacts of buildings in the life cycle. This paper uses a dual benchmark approach. The main concept consists in combining building-related top-down targets with building component-related bottom-up benchmarks. The overall top-down targets per capita and year are derived from the capacity of the global eco system. The bottom-up benchmarks for building elements are calculated following a best-in-class (top 5%) approach. A workflow of applying these benchmarks is proposed. It provides guidance on how to optimize the environmental performance of a building and its components efficiently by differentiating between material and design-related options. The approach is exemplified by means of a case study of a multi-family house.
Smart grid and energy district mutual interactions with demand response programs

The bi-directional energy flow between prosumers (wind energy) and smart grid (SG) provides pertinent benefits, such as (i) load-sharing, (ii) peak-load shaving, (iii) load reduction with energy market programs, (iv) ancillary services-based energy transactions, and (v) mutual beneficial frameworks based on rewards and penalties. However, the load variations of SG, intermittent wind speed in energy district (ED) of prosumers, and stochastic energy price are the major constraints that must be considered in wind energy prosumers (WEPs) interaction with utility. Further, the interfacing and interactions of WEPs with SG incur an enormous volume of data to be processed, stored, accessed, and managed. Therefore, the authors proposed a stochastic bi-directional energy management model (BEMM) to manage the aforementioned constraints. Moreover, the BEMM is empowered with cloud-based service level agreement (C-SLA) that provides massive storage capabilities to the enormous data incurred due to WEPs interactions with SG. Two sub-models of BEMM are incorporated, namely stochastic wind estimation model and stochastic energy pricing model. The wind estimation model deals the stochasticity of wind speed for energy generation, while energy price model manages and controls the uncertainty of pricing tariffs based on real-time pricing and day-a-head pricing mechanisms for efficient energy trade between SG and WEPs under the principle of C-SLA.
Enabling Fast Exploratory Analyses Over Voluminous Spatiotemporal Data Using Analytical Engines

Fueled by the proliferation of IoT devices and increased adoption of sensing environments the collection of spatiotemporal data has exploded in recent years. Disk based storage systems provide reliable archives but are far too slow for efficient analytics. Furthermore, spatiotemporal datasets quickly exceed the memory capacity of cluster environments. Current solutions focused on in-memory analytics suffer from memory contention and unnecessary network I/O, failing to provide a suitable platform for iterative, exploratory analytics in shared environments. In this work we propose Anamnesis, the first in-memory, sketch aligned, HDFS compliant storage system. Data sketching algorithms reduce dataset sizes by summarizing feature values and inter-feature relationships. Anamnesis leverages data sketches to alleviate memory contention and vastly reduce network I/O during analytics. Upon request, we generate accurate full-resolution datasets with negligible resource and time costs. Datasets are available using a fully HDFS compliant interface allowing Anamnesis to achieve unprecedented compatibility with popular analytics engines. This facilitates adoption into existing workflows by serving as a “drop-in” replacement for canonical HDFS. We evaluate the system using 2 spatiotemporal datasets, a variety of popular analytics engines, and real-world analytical operations.
Emergent Overlays for Adaptive MANET Broadcast

Mobile Ad-Hoc Networks (MANETs) allow distributed applications where no fixed network infrastructure is available. MANETs use wireless communication subject to faults and uncertainty, and must support efficient broadcast. Controlled flooding is suitable for highly-dynamic networks, while overlay-based broadcast is suitable for dense and more static ones. Density and mobility vary significantly over a MANET deployment area. We present the design and implementation of emergent overlays for efficient and reliable broadcast in heterogeneous MANETs. This adaptation technique allows nodes to automatically switch from controlled flooding to the use of an overlay. Interoperability protocols support the integration of both protocols in a single heterogeneous system. Coordinated adaptation policies allow regions of nodes to autonomously and collectively emerge and dissolve overlays. Our simulation of the full network stack of 600 mobile nodes shows that emergent overlays reduce energy consumption, and improve reliability and coverage compared to single protocols and to two previously-proposed adaptation techniques.
An Efficient Semantic based Clustering Algorithm for Textual Documents

Documents that are classified into different categories gets flooded in the internet every day. These documents have many links or associations with the other documents in the web. The terms in the document are open to multiple interpretations which are vague and unclear. Hence there is a need to find the semantic understanding of the terms. One of the major application in identifying and applying such semantic measure lies in clustering the related textual documents. However, the traditional clustering algorithms may exhibit reduced performances due to the existence of irrelevant terms in the raw documents. Hence, the proposed algorithm in this paper exploits the use of a feature selection algorithm in order to increase the performance of the clustering algorithm. In this paper, a feature selection algorithm with booster technique is used. Moreover, clustering algorithm based on a fuzzy linguistic variable measure that uses separation and dominance value is used in this paper for precise clustering. Experimental analysis shows that the three performance measures that evaluates the clustering algorithm increases, in comparison to the other existing algorithms.
G2SAT: Learning to Generate SAT Formulas

The Boolean Satisfiability (SAT) problem is the canonical NP-complete problem and is fundamental to computer science, with a wide array of applications in planning, verification, and theorem proving. Developing and evaluating practical SAT solvers relies on extensive empirical testing on a set of real-world benchmark formulas. However, the availability of such real-world SAT formulas is limited. While these benchmark formulas can be augmented with synthetically generated ones, existing approaches for doing so are heavily hand-crafted and fail to simultaneously capture a wide range of characteristics exhibited by real-world SAT instances. In this work, we present G2SAT, the first deep generative framework that learns to generate SAT formulas from a given set of input formulas. Our key insight is that SAT formulas can be transformed into latent bipartite graph representations which we model using a specialized deep generative neural network. We show that G2SAT can generate SAT formulas that closely resemble given real-world SAT instances, as measured by both graph metrics and SAT solver behavior. Further, we show that our synthetic SAT formulas could be used to improve SAT solver performance on real-world benchmarks, which opens up new opportunities for the continued development of SAT solvers and a deeper understanding of their performance.
AMPERSAND: Argument Mining for PERSuAsive oNline Discussions

Argumentation is a type of discourse where speakers try to persuade their audience about the reasonableness of a claim by presenting supportive arguments. Most work in argument mining has focused on modeling arguments in monologues. We propose a computational model for argument mining in online persuasive discussion forums that brings together the micro-level (argument as product) and macro-level (argument as process) models of argumentation. Fundamentally, this approach relies on identifying relations between components of arguments in a discussion thread. Our approach for relation prediction uses contextual information in terms of fine-tuning a pre-trained language model and leveraging discourse relations based on Rhetorical Structure Theory. We additionally propose a candidate selection method to automatically predict what parts of one’s argument will be targeted by other participants in the discussion. Our models obtain significant improvements compared to recent state-of-the-art approaches using pointer networks and a pre-trained language model.
3D face recognition using depth filtering and deep convolutional neural network

In this paper, we first estimate the accuracy of 3D facial surface reconstruction from real RGB-D depth maps using various depth filtering algorithms. Next, a new 3D face recognition algorithm using deep convolutional neural network is proposed. With the help of 3D face augmentation techniques different facial expressions from a single 3D face scan are synthesized and used for network learning. The performance of the proposed algorithm is compared in terms of 3D face recognition metrics and processing time with that of common 3D face recognition algorithms.
Recent Techniques for Regularization in Partial Differential Equations and Imaging

Inverse problems model real world phenomena from data, where the data are often noisy and models contain errors. This leads to instabilities, multiple solution vectors and thus ill-posedness. To solve ill-posed inverse problems, regularization is typically used as a penalty function to induce stability and allow for the incorporation of a priori information about the desired solution. In this thesis, high order regularization techniques are developed for image and function reconstruction from noisy or misleading data. Specifically the incorporation of the Polynomial Annihilation operator allows for the accurate exploitation of the sparse representation of each function in the edge domain. This dissertation tackles three main problems through the development of novel reconstruction techniques: (i) reconstructing one and two dimensional functions from multiple measurement vectors using variance based joint sparsity when a subset of the measurements contain false and/or misleading information, (ii) approximating discontinuous solutions to hyperbolic partial differential equations by enhancing typical solvers with `1 regularization, and (iii) reducing model assumptions in synthetic aperture radar image formation, specifically for the purpose of speckle reduction and phase error correction. While the common thread tying these problems together is the use of high order regularization, the defining characteristics of each of these problems create unique challenges. Fast and robust numerical algorithms are also developed so that these problems can be solved efficiently without requiring fine tuning of parameters. Indeed, the numerical experiments presented in this dissertation strongly suggest that the new methodology provides more accurate and robust solutions to a variety of ill-posed inverse problems.
TABLAS DE COMPOSICIÓN DE ALIMENTOS DEL CESNID

Las Tablas de Composición de Alimentos (TCA) son una herramienta imprescindible en los ámbitos de la Alimentación y la Nutrición. Este libro, en edición bilingüe castellano-catalán, recoge las Tablas de Composición de Alimentos del CESNID y también se aporta (como gran novedad) la metodología y la fuente de los datos utilizados. Elaboradas por profesionales de la Nutrición y la Dietética de gran prestigio, pertenecientes al CESNID (Centro adscrito a la Universidad de Barcelona, dedicado a la formación de profesionales cualificados dentro de estas disciplinas.
KnowledgeNet: A Benchmark Dataset for Knowledge Base Population

KnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge base (Wikidata) with facts expressed in natural language text on the web. KnowledgeNet provides text exhaustively annotated with facts, thus enabling the holistic end-to-end evaluation of knowledge base population systems as a whole, unlike previous benchmarks that are more suitable for the evaluation of individual subcomponents (e.g., entity linking, relation extraction). We discuss five baseline approaches, where the best approach achieves an F1 score of 0.50, significantly outperforming a traditional approach by 79% (0.28). However, our best baseline is far from reaching human performance (0.82), indicating our dataset is challenging. The KnowledgeNet dataset and baselines are available at https://github.com/diffbot/knowledge-net
Is Acyclic Directed Graph Partitioning Effective for Locality-Aware Scheduling?

We investigate efficient execution of computations, modeled as Directed Acyclic Graphs (DAGs), on a single processor with a two-level memory hierarchy, where there is a limited fast memory and a larger slower memory. Our goal is to minimize execution time by minimizing redundant data movement between fast and slow memory. We utilize a DAG partitioner that finds localized, acyclic parts of the whole computation that can fit into fast memory, and minimizes the edge cut among the parts. We propose a new scheduler that executes each part one-by-one, obeying the dependency among parts, aiming at reducing redundant data movement needed by cut-edges. Extensive experimental evaluation shows that the proposed DAG-based scheduler significantly reduces redundant data movement.