Miracle-dz/UniFAR-FTUs huggingface dataset:
A Metamodel and Model-based Design Rule Checking DSL for Verification and Validation of Electronic Circuit Designs

Development of embedded systems depends on both software and hardware design activities. Quality management of development artifacts is of crucial importance for the overall function and safety of the final product. This paper introduces a metamodel and model-based approach and accompanying domain-specific language for validation of electronic circuit designs. The solution does not depend on any particular electronic design automation (EDA) software, instead we propose a workflow that is integrated into a modular, general-purpose model analysis framework. The paper illustrates both the underlying concepts as well as possible application scenarios for the developed validation domain-specific language, MBDRC (Model-Based Design Rule Checking). It also discusses fields for further research and transfer of the initial results.
Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation

Inducing diversity in the task of paraphrasing is an important problem in NLP with applications in data augmentation and conversational agents. Previous paraphrasing approaches have mainly focused on the issue of generating semantically similar paraphrases while paying little attention towards diversity. In fact, most of the methods rely solely on top-k beam search sequences to obtain a set of paraphrases. The resulting set, however, contains many structurally similar sentences. In this work, we focus on the task of obtaining highly diverse paraphrases while not compromising on paraphrasing quality. We provide a novel formulation of the problem in terms of monotone submodular function maximization, specifically targeted towards the task of paraphrasing. Additionally, we demonstrate the effectiveness of our method for data augmentation on multiple tasks such as intent classification and paraphrase recognition. In order to drive further research, we have made the source code available.
HPSM: a programming framework to exploit multi-CPU and multi-GPU systems simultaneously

This paper presents a high-level C++ framework to explore multi-CPU and multi-GPU systems called HPSM. HPSM enables execution of parallel loops and reductions simultaneously over CPUs and GPUs using three parallel backends: Serial, OpenMP, and StarPU. We analysed HPSM development effort with AXPY program through two standard metrics (NCLOC and ES). In addition, we evaluated performance and energy with three parallel benchmarks: N-Body, Hotspot, and CFD solver. HPSM reduced code effort by up to 56.9% compared to StarPU C interface, although it resulted in 2.5× more lines of code compared to OpenMP. The CPU-GPU combination attained speedup results with Hotspot of up to 92.7× on a X86-based system with four GPUs and up to 108.2× on an IBM POWER8+ system with two GPUs. On both systems, the addition of GPUs improved energy efficiency.
Co-Representation Network for Generalized Zero-Shot Learning

Generalized zero-shot learning is a significant topic but faced with bias problem, which leads to unseen classes being easily misclassified into seen classes. Hence we propose an embedding model called co-representation network to learn a more uniform visual embedding space that effectively alleviates the bias problem and helps with classification. We mathematically analyze our model and find it learns a projection with high local linearity, which is proved to cause less bias problem. The network consists of a cooperation module for representation and a relation module for classification. It is simple in structure and can be easily trained in an end-to-end manner. Experiments show that our method outperforms existing generalized zero-shot learning methods on several benchmark datasets.
Adversarial Category Alignment Network for Cross-domain Sentiment Classification

Cross-domain sentiment classification aims to predict sentiment polarity on a target domain utilizing a classifier learned from a source domain. Most existing adversarial learning methods focus on aligning the global marginal distribution by fooling a domain discriminator, without taking category-specific decision boundaries into consideration, which can lead to the mismatch of category-level features. In this work, we propose an adversarial category alignment network (ACAN), which attempts to enhance category consistency between the source domain and the target domain. Specifically, we increase the discrepancy of two polarity classifiers to provide diverse views, locating ambiguous features near the decision boundaries. Then the generator learns to create better features away from the category boundaries by minimizing this discrepancy. Experimental results on benchmark datasets show that the proposed method can achieve state-of-the-art performance and produce more discriminative features.
Combining Discourse Markers and Cross-lingual Embeddings for Synonym–Antonym Classification

It is well-known that distributional semantic approaches have difficulty in distinguishing between synonyms and antonyms (Grefenstette, 1992; Padó and Lapata, 2003). Recent work has shown that supervision available in English for this task (e.g., lexical resources) can be transferred to other languages via cross-lingual word embeddings. However, this kind of transfer misses monolingual distributional information available in a target language, such as contrast relations that are indicative of antonymy (e.g. hot . while . cold). In this work, we improve the transfer by exploiting monolingual information, expressed in the form of co-occurrences with discourse markers that convey contrast. Our approach makes use of less than a dozen markers, which can easily be obtained for many languages. Compared to a baseline using only cross-lingual embeddings, we show absolute improvements of 4–10% F1-score in Vietnamese and Hindi.
Practical, Efficient, and Customizable Active Learning for Named Entity Recognition in the Digital Humanities

Scholars in inter-disciplinary fields like the Digital Humanities are increasingly interested in semantic annotation of specialized corpora. Yet, under-resourced languages, imperfect or noisily structured data, and user-specific classification tasks make it difficult to meet their needs using off-the-shelf models. Manual annotation of large corpora from scratch, meanwhile, can be prohibitively expensive. Thus, we propose an active learning solution for named entity recognition, attempting to maximize a custom model’s improvement per additional unit of manual annotation. Our system robustly handles any domain or user-defined label set and requires no external resources, enabling quality named entity recognition for Humanities corpora where such resources are not available. Evaluating on typologically disparate languages and datasets, we reduce required annotation by 20-60% and greatly outperform a competitive active learning baseline.
Product Reliability Management throughout the Life Cycle on Transition to Industry 4.0

The article deals with the actual problem: ensuring the reliability of technical systems in the digital era and at the transition to Industry 4.0. The authors reviewed the existing positive international experience and opportunities for digitalization, as well as particular qualities of the factories of the future and production during the transition to Industry 4.0. It is shown that focusing on customer needs can create problems in the field of ensuring the technical systems reliability. In addition, in such conditions it is important to shorten the duration of various processes. As a particular example, the authors consider the process of conducting strength tests. To reduce the time of testing, the article authors developed a DSS for document management in a central strength laboratory of an automotive company. Although the authors investigated a specific example, but this technique is universal and can be used for similar processes and for other sectors of the economy.
Multi-Channel Convolutional Neural Network for Twitter Emotion and Sentiment Recognition

The advent of micro-blogging sites has paved the way for researchers to collect and analyze huge volumes of data in recent years. Twitter, being one of the leading social networking sites worldwide, provides a great opportunity to its users for expressing their states of mind via short messages which are called tweets. The urgency of identifying emotions and sentiments conveyed through tweets has led to several research works. It provides a great way to understand human psychology and impose a challenge to researchers to analyze their content easily. In this paper, we propose a novel use of a multi-channel convolutional neural architecture which can effectively use different emotion and sentiment indicators such as hashtags, emoticons and emojis that are present in the tweets and improve the performance of emotion and sentiment identification. We also investigate the incorporation of different lexical features in the neural network model and its effect on the emotion and sentiment identification task. We analyze our model on some standard datasets and compare its effectiveness with existing techniques.
A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation

We present a corpus of anaphoric information (coreference) crowdsourced through a game-with-a-purpose. The corpus, containing annotations for about 108,000 markables, is one of the largest corpora for coreference for English, and one of the largest crowdsourced NLP corpora, but its main feature is the large number of judgments per markable: 20 on average, and over 2.2M in total. This characteristic makes the corpus a unique resource for the study of disagreements on anaphoric interpretation. A second distinctive feature is its rich annotation scheme, covering singletons, expletives, and split-antecedent plurals. Finally, the corpus also comes with labels inferred using a recently proposed probabilistic model of annotation for coreference. The labels are of high quality and make it possible to successfully train a state of the art coreference resolver, including training on singletons and non-referring expressions. The annotation model can also result in more than one label, or no label, being proposed for a markable, thus serving as a baseline method for automatically identifying ambiguous markables. A preliminary analysis of the results is presented.
Unifying Orthogonal Monte Carlo Methods

Many machine learning methods making use of Monte Carlo sampling in vector spaces have been shown to be improved by conditioning samples to be mutually orthogonal. Exact orthogonal coupling of samples is computationally intensive, hence approximate methods have been of great interest. In this paper, we present a unifying perspective of many approximate methods by considering Givens transformations, propose new approximate methods based on this framework, and demonstrate the ﬁrst statistical guarantees for families of approximate methods in kernel approximation. We provide extensive empirical evaluations with guidance for practitioners.
Lorentzian Distance Learning for Hyperbolic Representations

We introduce an approach to learn representations based on the Lorentzian distance in hyperbolic geometry. Hyperbolic geometry is especially suited to hierarchically-structured datasets, which are prevalent in the real world. Current hyperbolic representation learning methods compare examples with the Poincaré distance. They try to minimize the distance of each node in a hierarchy with its descendants while maximizing its distance with other nodes. This formulation produces node representations close to the centroid of their descendants. To obtain efficient and interpretable algorithms, we exploit the fact that the centroid w.r. t the squared Lorentzian distance can be written in closed-form. We show that the Euclidean norm of such a centroid decreases as the curvature of the hyperbolic space decreases. This property makes it appropriate to represent hierarchies where parent nodes minimize the distances to their descendants and have smaller Euclidean norm than their children. Our approach obtains state-of-the-art results in retrieval and classification tasks on different datasets.
Label Noise Cleansing with Sparse Graph for Hyperspectral Image Classification

In a real hyperspectral image classification task, label noise inevitably exists in training samples. To deal with label noise, current methods assume that noise obeys the Gaussian distribution, which is not the real case in practice, because in most cases, we are more likely to misclassify training samples at the boundaries between different classes. In this paper, we propose a spectral–spatial sparse graph-based adaptive label propagation (SALP) algorithm to address a more practical case, where the label information is contaminated by random noise and boundary noise. Specifically, the SALP mainly includes two steps: First, a spectral–spatial sparse graph is constructed to depict the contextual correlations between pixels within the same superpixel homogeneous region, which are generated by superpixel image segmentation, and then a transfer matrix is produced to describe the transition probability between pixels. Second, after randomly splitting training pixels into “clean” and “polluted,” we iteratively propagate the label information from “clean” to “polluted” based on the transfer matrix, and the relabeling strategy for each pixel is adaptively adjusted along with its spatial position in the corresponding homogeneous region. Experimental results on two standard hyperspectral image datasets show that the proposed SALP over four major classifiers can significantly decrease the influence of noisy labels, and our method achieves better performance compared with the baselines.
Box of Lies: Multimodal Deception Detection in Dialogues

Deception often takes place during everyday conversations, yet conversational dialogues remain largely unexplored by current work on automatic deception detection. In this paper, we address the task of detecting multimodal deceptive cues during conversational dialogues. We introduce a multimodal dataset containing deceptive conversations between participants playing the Box of Lies game from The Tonight Show Starring Jimmy Fallon, in which they try to guess whether an object description provided by their opponent is deceptive or not. We conduct annotations of multimodal communication behaviors, including facial and linguistic behaviors, and derive several learning features based on these annotations. Initial classification experiments show promising results, performing well above both a random and a human baseline, and reaching up to 69% accuracy in distinguishing deceptive and truthful behaviors.
Large-Scale Sparse Kernel Canonical Correlation Analysis

This paper presents gradKCCA, a large-scale sparse non-linear canonical correlation method. Like Kernel Canonical Correlation Analysis (KCCA), our method ﬁnds non-linear relations through kernel functions, but it does not rely on a kernel matrix, a known bottleneck for scaling up kernel methods. gradKCCA corresponds to solving KCCA with the additional constraint that the canonical projection directions in the kernel-induced feature space have preimages in the original data space. Firstly, this modiﬁcation allows us to very efﬁciently maximize kernel canonical correlation through an alternating projected gradient algorithm working in the original data space. Secondly, we can control the sparsity of the projection directions by constraining the (cid:96) 1 norm of the preimages of the projection directions, facilitating the interpretation of the discovered patterns, which is not available through KCCA. Our empirical experiments demonstrate that gradKCCA out-performs state-of-the-art CCA methods in terms of speed and robustness to noise both in simulated and real-world datasets.
Join the Park!: Exploring Opportunities to Lower the Participation Divide in Park Communities

The current work explores the participation divide that is oftentimes at play within local citizen communities. The studied case illustrates a common situation where the majority of local citizens does not participate in public space improvement and maintenance activities organised by local community activists. The presented research involved semi-structured interviews supported by interactive service design probes. It has led to two strategies for stimulating community participation, namely 1) increasing transparency around community activities, and 2) embedding community participation in citizens' daily social practices.
Deobfuscating Android Native Binary Code

In this paper, we propose an automated approach to facilitate the deobfuscation of Android native binary code. Specifically, given a native binary obfuscated by Obfuscator-LLVM (the most popular native code obfuscator), our deobfuscation system is capable of recovering the original Control Flow Graph. To the best of our knowledge, it is the first work that aims to tackle the problem. We have applied our system in different scenarios, and the experimental results demonstrate the effectiveness of our system based on generic similarity comparison metrics.
A Personalized Affective Memory Neural Model for Improving Emotion Recognition

Recent models of emotion recognition strongly rely on supervised deep learning solutions for the distinction of general emotion expressions. However, they are not reliable when recognizing online and personalized facial expressions, e.g., for person-specific affective understanding. In this paper, we present a neural model based on a conditional adversarial autoencoder to learn how to represent and edit general emotion expressions. We then propose Grow-When-Required networks as personalized affective memories to learn individualized aspects of emotion expressions. Our model achieves state-of-the-art performance on emotion recognition when evaluated on \textit{in-the-wild} datasets. Furthermore, our experiments include ablation studies and neural visualizations in order to explain the behavior of our model.
IoT-DMCP: An IoT Data Management and Control Platform for Smart Cities

This paper presents a design and implementation of a data management platform to monitor and control smart objects in the Internet of Things (IoT). This is through IPv4/IPv6, and by combining IoT specific features and protocols such as CoAP, HTTP and WebSocket. The platform allows anomaly detection in IoT devices and real-time error reporting mechanisms. Moreover, the platform is designed as a standalone application, which targets at extending cloud connectivity to the edge of the network with fog computing. It extensively uses the features and entities provided by the Capillary Networks with a micro-services based architecture linked via a large set of REST APIs, which allows developing applications independently of the heterogeneous devices. The platform addresses the challenges in terms of connectivity, reliability, security and mobility of the Internet of Things through IPv6. The implementation of the platform is evaluated in a smart home scenario and tested via numeric results. The results show low latency, at the order of few ten of milliseconds, for building control over the implemented mobile application, which confirm realtime feature of the proposed solution.
Subword-based Compact Reconstruction of Word Embeddings

The idea of subword-based word embeddings has been proposed in the literature, mainly for solving the out-of-vocabulary (OOV) word problem observed in standard word-based word embeddings. In this paper, we propose a method of reconstructing pre-trained word embeddings using subword information that can effectively represent a large number of subword embeddings in a considerably small fixed space. The key techniques of our method are twofold: memory-shared embeddings and a variant of the key-value-query self-attention mechanism. Our experiments show that our reconstructed subword-based embeddings can successfully imitate well-trained word embeddings in a small fixed space while preventing quality degradation across several linguistic benchmark datasets, and can simultaneously predict effective embeddings of OOV words. We also demonstrate the effectiveness of our reconstruction method when we apply them to downstream tasks.
A Containerized Tool to Deploy Scientific Applications over SoC-based Systems: The Case of Meteorological Forecasting with WRF

Container-based virtualization represents a flexible and scalable solution for HPC environments, allowing a simple and efficient management of scientific applications. Recently, Systems-on-a-Chip (SoC) have emerged as an alternative to traditional HPC clusters, with a good computing power and low costs. In this paper, we present how we developed a container-based solution for SoC clusters, and study the performance of WRF (Weather Research and Forecasting) in such environments. The results demonstrate that although the peak performance of SoC clusters is still limited, these environments are more than suitable to scientific application that have relaxed QoS constraints.
Helicobacter pylori modulates host cell responses by CagT4SS-dependent translocation of an intermediate metabolite of LPS inner core heptose biosynthesis

Highly virulent Helicobacter pylori cause proinflammatory signaling inducing the transcriptional activation and secretion of cytokines such as IL-8 in epithelial cells. Responsible in part for this signaling is the cag pathogenicity island (cagPAI) that codetermines the risk for pathological sequelae of an H. pylori infection such as gastric cancer. The Cag type IV secretion system (CagT4SS), encoded on the cagPAI, can translocate various molecules into cells, the effector protein CagA, peptidoglycan metabolites and DNA. Although these transported molecules are known to contribute to cellular responses to some extent, a major part of the cagPAI-induced signaling leading to IL-8 secretion remains unexplained. We report here that biosynthesis of heptose-1,7-bisphosphate (HBP), an important intermediate metabolite of LPS inner heptose core, contributes in a major way to the H. pylori cagPAI-dependent induction of proinflammatory signaling and IL-8 secretion in human epithelial cells. Mutants defective in the genes required for synthesis of HBP exhibited a more than 95% reduction of IL-8 induction and impaired CagT4SS-dependent cellular signaling. The loss of HBP biosynthesis did not abolish the ability to translocate CagA. The human cellular adaptor TIFA, which was described before to mediate HBP-dependent activity in other Gram-negative bacteria, was crucial in the cagPAI- and HBP pathway-induced responses by H. pylori in different cell types. The active metabolite was present in H. pylori lysates but not enriched in bacterial supernatants. These novel results advance our mechanistic understanding of H. pylori cagPAI-dependent signaling mediated by intracellular pattern recognition receptors. They will also allow to better dissect immunomodulatory activities by H. pylori and to improve the possibilities of intervention in cagPAI- and inflammation-driven cancerogenesis.