Miracle-dz/UniFAR-FTUs huggingface dataset:
Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text Generation

Existing text generation methods tend to produce repeated and ”boring” expressions. To tackle this problem, we propose a new text generation model, called Diversity-Promoting Generative Adversarial Network (DP-GAN). The proposed model assigns low reward for repeatedly generated text and high reward for ”novel” and fluent text, encouraging the generator to produce diverse and informative text. Moreover, we propose a novel language-model based discriminator, which can better distinguish novel text from repeated text without the saturation problem compared with existing classifier-based discriminators. The experimental results on review generation and dialogue generation tasks demonstrate that our model can generate substantially more diverse and informative text than existing baselines.
Sanskrit Word Segmentation Using Character-level Recurrent and Convolutional Neural Networks

The paper introduces end-to-end neural network models that tokenize Sanskrit by jointly splitting compounds and resolving phonetic merges (Sandhi). Tokenization of Sanskrit depends on local phonetic and distant semantic features that are incorporated using convolutional and recurrent elements. Contrary to most previous systems, our models do not require feature engineering or extern linguistic resources, but operate solely on parallel versions of raw and segmented text. The models discussed in this paper clearly improve over previous approaches to Sanskrit word segmentation. As they are language agnostic, we will demonstrate that they also outperform the state of the art for the related task of German compound splitting.
Contextual Inter-modal Attention for Multi-modal Sentiment Analysis

Multi-modal sentiment analysis offers various challenges, one being the effective combination of different input modalities, namely text, visual and acoustic. In this paper, we propose a recurrent neural network based multi-modal attention framework that leverages the contextual information for utterance-level sentiment prediction. The proposed approach applies attention on multi-modal multi-utterance representations and tries to learn the contributing features amongst them. We evaluate our proposed approach on two multi-modal sentiment analysis benchmark datasets, viz. CMU Multi-modal Opinion-level Sentiment Intensity (CMU-MOSI) corpus and the recently released CMU Multi-modal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) corpus. Evaluation results show the effectiveness of our proposed approach with the accuracies of 82.31% and 79.80% for the MOSI and MOSEI datasets, respectively. These are approximately 2 and 1 points performance improvement over the state-of-the-art models for the datasets.
An Artificial Arms Race: Could it Improve Mobile Malware Detectors?

On the Internet today, mobile malware is one of the most common attack methods. These attacks are usually established via malicious mobile apps. To combat this threat, one technique used is the deployment of mobile malware detectors. As the mobile threats evolve, designing and developing mobile malware detectors remains a challenging task. In this paper, we aim to explore whether creating an artificial arms race between mobile malware and detectors could improve the ability of the detector to adapt to the evolving threats. To better model this interaction, we present a co-evolution of both sides of the arms race using genetic algorithms. The experimental evaluations on publicly available malicious and non-malicious mobile apps and their variants generated by the artificial arms race show that this approach improves the detectors understanding of the problem.
Data-Specific Adaptive Threshold for Face Recognition and Authentication

Many face recognition systems boost the performance using deep learning models, but only a few researches go into the mechanisms for dealing with online registration. Although we can obtain discriminative facial features through the state-of-the-art deep model training, how to decide the best threshold for practical use remains a challenge. We develop a technique of adaptive threshold mechanism to improve the recognition accuracy. We also design a face recognition system along with the registering procedure to handle online registration. Furthermore, we introduce a new evaluation protocol to better evaluate the performance of an algorithm for real-world scenarios. Under our proposed protocol, our method can achieve a 22% accuracy improvement on the LFW dataset.
A Hardware Architecture for Cell-Based Feature-Extraction and Classification Using Dual-Feature Space

Many computer-vision and machine-learning applications in robotics, mobile, wearable devices, and automotive domains are constrained by their real-time performance requirements. This paper reports a dual-feature-based object recognition coprocessor that exploits both histogram of oriented gradient (HOG) and Haar-like descriptors with a cell-based parallel sliding-window recognition mechanism. The feature extraction circuitry for HOG and Haar-like descriptors is implemented by a pixel-based pipelined architecture, which synchronizes to the pixel frequency from the image sensor. After extracting each cell feature vector, a cell-based sliding window scheme enables parallelized recognition for all windows, which contain this cell. The nearest neighbor search classifier is, respectively, applied to the HOG and Haar-like feature space. The complementary aspects of the two feature domains enable a hardware-friendly implementation of the binary classification for pedestrian detection with improved accuracy. A proof-of-concept prototype chip fabricated in a 65-nm SOI CMOS, having thin gate oxide and buried oxide layers (SOTB CMOS), with 3.22-mm2 core area achieves an energy efficiency of 1.52 nJ/pixel and a processing speed of 30 fps for $1024\times 1616$ -pixel image frames at 200-MHz recognition working frequency and 1-V supply voltage. Furthermore, multiple chips can implement image scaling, since the designed chip has image-size flexibility attributable to the pixel-based architecture.
P2SNet: Can an Image Match a Video for Person Re-Identification in an End-to-End Way?

We address a new person re-identification problem, in which our goal is to directly match image-based scenarios with video-based ones. This differs significantly from the conventional person re-identification problem, which aims to match two image-based scenarios (and it is assumed that the available video frames have been manually selected to form the image-based scenarios). To solve this more challenging and realistic problem without the implicit assumption of manual selection, we propose an end-to-end matching framework called a point-to-set network (P2SNet), which consists of: 1) a ${k}$ -nearest neighbor triplet module, which functions as a “denoiser” by letting the network sequentially focus on the available frames, while ignoring the other useless frames in a video and 2) a novel deep neural network that uses videos and images as input to jointly learn the feature representations and a point-to-set distance metric in a unified way. Our P2SNet is evaluated on three new image-to-video person re-identification data sets, i-LIDS-VID-P2S, PRID2011-P2S, and MARS-P2S, which are modified from i-LIDS-VID, PRID 2011, and MARS, respectively. The experimental results demonstrate the superior performance of our model over the other state-of-the-art methods.
Investigation of Four-Wave-Mixing Crosstalk in Phase-Sensitive Fiber Optical Parametric Amplifier

Four-wave-mixing (FWM) crosstalk in frequency nondegenerate phase-sensitive fiber optical parametric amplifiers (FOPAs) applied for wavelength-division-multiplexed (WDM) signals is investigated experimentally and theoretically. We analyze significant FWM interactions that can generate crosstalk components in the signal band. Approximate expressions are developed for the FWM crosstalk to conveniently estimate how it scales with the system parameters. The analytical results agree well with the experimental results and simulations. The results show that the FWM crosstalk grows dramatically with the increase of the system parameters, including input signal power per channel, signal gain, the length of nonlinear fiber, and signal channel number. We also compare the FWM crosstalk in the phase-sensitive FOPA and the phase-insensitive FOPA experimentally and explain the difference of their crosstalk behaviors through theoretical analysis and simulations. The results show that, due to the presence of idlers at the input, the phase-sensitive FOPA produces higher crosstalk than the phase-insensitive FOPA even when they have equivalent signal gains. It indicates that the crosstalk in the phase-sensitive FOPA is more detrimental so that careful design of the amplifier to suppress the impact of the FWM crosstalk is necessary. The analytical solutions obtained in this paper are useful in the design of PS-FOPAs for amplifying WDM signals.
Topic Intrusion for Automatic Topic Model Evaluation

Topic coherence is increasingly being used to evaluate topic models and filter topics for end-user applications. Topic coherence measures how well topic words relate to each other, but offers little insight on the utility of the topics in describing the documents. In this paper, we explore the topic intrusion task — the task of guessing an outlier topic given a document and a few topics — and propose a method to automate it. We improve upon the state-of-the-art substantially, demonstrating its viability as an alternative method for topic model evaluation.
Adaptive Lightweight Regularization Tool for Complex Analytics

Deep Learning and Machine Learning models have recently been shown to be effective in many real world applications. While these models achieve increasingly better predictive performance, their structures have also become much more complex. A common and difficult problem for complex models is overfitting. Regularization is used to penalize the complexity of the model in order to avoid overfitting. However, in most learning frameworks, regularization function is usually set as some hyper parameters, and therefore the best setting is difficult to find. In this paper, we propose an adaptive regularization method, as part of a large end-to-end healthcare data analytics software stack, which effectively addresses the above difficulty. First, we propose a general adaptive regularization method based on Gaussian Mixture (GM) to learn the best regularization function according to the observed parameters. Second, we develop an effective update algorithm which integrates Expectation Maximization (EM) with Stochastic Gradient Descent (SGD). Third, we design a lazy update algorithm to reduce the computational cost by 4x. The overall regularization framework is fast, adaptive and easy-to-use. We validate the effectiveness of our regularization method through an extensive experimental study over 13 standard benchmark datasets and three kinds of deep learning/machine learning models. The results illustrate that our proposed adaptive regularization method achieves significant improvement over state-of-the-art regularization methods.
Identification of Parallel Sentences in Comparable Monolingual Corpora from Different Registers

Parallel aligned sentences provide useful information for different NLP applications. Yet, this kind of data is seldom available, especially for languages other than English. We propose to exploit comparable corpora in French which are distinguished by their registers (specialized and simplified versions) to detect and align parallel sentences. These corpora are related to the biomedical area. Our purpose is to state whether a given pair of specialized and simplified sentences is to be aligned or not. Manually created reference data show 0.76 inter-annotator agreement. We exploit a set of features and several automatic classifiers. The automatic alignment reaches up to 0.93 Precision, Recall and F-measure. In order to better evaluate the method, it is applied to data in English from the SemEval STS competitions. The same features and models are applied in monolingual and cross-lingual contexts, in which they show up to 0.90 and 0.73 F-measure, respectively.
Anatomy-Aware Spinal Cord Stimulation in Magnetotherapeutical Applications

The presented article details anatomy-aware spinal cord stimulation based on patient-specific spinal cord coordinates computed from computer tomography data. Digital image processing methods and algorithms are used to extract spatial points representing the spinal cord canal and are subsequently used to precisely guide the electromagnetic applicator (e.g. figure-of-eight coil) along the patient back so as to focus the electromagnetic energy within the spinal cord canal and minimize surrounding tissue exposure. A comparison between manually (e.g. physician, doctor) and computer guided applicator is presented based on numerical simulation results.
Are they Our Brothers? Analysis and Detection of Religious Hate Speech in the Arabic Twittersphere

Religious hate speech in the Arabic Twittersphere is a notable problem that requires developing automated tools to detect messages that use inflammatory sectarian language to promote hatred and violence against people on the basis of religious affiliation. Distinguishing hate speech from other profane and vulgar language is quite a challenging task that requires deep linguistic analysis. The richness of the Arabic morphology and the limited available resources for the Arabic language make this task even more challenging. To the best of our knowledge, this paper is the first to address the problem of identifying speech promoting religious hatred in the Arabic Twitter. In this work, we describe how we created the first publicly available Arabic dataset annotated for the task of religious hate speech detection and the first Arabic lexicon consisting of terms commonly found in religious discussions along with scores representing their polarity and strength. We then developed various classification models using lexicon-based, n-gram-based, and deep-learning-based approaches. A detailed comparison of the performance of different models on a completely new unseen dataset is then presented. We find that a simple Recurrent Neural Network (RNN) architecture with Gated Recurrent Units (GRU) and pre-trained word embeddings can adequately detect religious hate speech with 0.84 Area Under the Receiver Operating Characteristic curve (AUROC).
A Sliding-Window Framework for Representative Subset Selection

Representative subset selection (RSS) is an important tool for users to draw insights from massive datasets. A common approach is to model RSS as the submodular maximization problem because the utility of extracted representatives often satisfies the "diminishing returns" property. To capture the data recency issue and support different types of constraints in real-world problems, we formulate RSS as maximizing a submodular function subject to a d-knapsack constraint (SMDK) over sliding windows. Then, we propose a novel KnapWindow framework for SMDK. Theoretically, KnapWindow is 1-ε/1+d-approximate for SMDK and achieves sublinear complexity. Finally, we evaluate the efficiency and effectiveness of KnapWindow on real-world datasets. The results show that it achieves up to 120x speedups over the batch baseline with at least 94% utility assurance.
NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval

Pseudo relevance feedback (PRF) is commonly used to boost the performance of traditional information retrieval (IR) models by using top-ranked documents to identify and weight new query terms, thereby reducing the effect of query-document vocabulary mismatches. While neural retrieval models have recently demonstrated strong results for ad-hoc retrieval, combining them with PRF is not straightforward due to incompatibilities between existing PRF approaches and neural architectures. To bridge this gap, we propose an end-to-end neural PRF framework that can be used with existing neural IR models by embedding different neural models as building blocks. Extensive experiments on two standard test collections confirm the effectiveness of the proposed NPRF framework in improving the performance of two state-of-the-art neural IR models.
Decision Tree for Early Detection of Cognitive Impairment by Community Pharmacists

Purpose: The early detection of Mild Cognitive Impairment (MCI) is essential in aging societies where dementia is becoming a common manifestation among the elderly. Thus our aim is to develop a decision tree to discriminate individuals at risk of MCI among non-institutionalized elderly users of community pharmacy. A more clinically and patient-oriented role of the community pharmacist in primary care makes the dispensation of medication an adequate situation for an effective, rapid, easy, and reproducible screening of MCI. Methods: A cross-sectional study was conducted with 728 non-institutionalized participants older than 65. A total of 167 variables were collected such as age, gender, educational attainment, daily sleep duration, reading frequency, subjective memory complaint, and medication. Two screening tests were used to detect possible MCI: Short Portable Mental State Questionnaire (SPMSQ) and the Mini-Mental State Examination (MMSE). Participants classified as positive were referred to clinical diagnosis. A decision tree and predictive models are presented as a result of applying techniques of machine learning for a more efficient enrollment. Results: One hundred and twenty-eight participants (17.4%) scored positive on MCI tests. A recursive partitioning algorithm with the most significant variables determined that the most relevant for the decision tree are: female sex, sleeping more than 9 h daily, age higher than 79 years as risk factors, and reading frequency. Moreover, psychoanaleptics, nootropics, and antidepressants, and anti-inflammatory drugs achieve a high score of importance according to the predictive algorithms. Furthermore, results obtained from these algorithms agree with the current research on MCI. Conclusion: Lifestyle-related factors such as sleep duration and the lack of reading habits are associated with the presence of positive in MCI test. Moreover, we have depicted how machine learning provides a sound methodology to produce tools for early detection of MCI in community pharmacy. Impact of findings on practice: The community of pharmacists provided with adequate tools could develop a crucial task in the early detection of MCI to redirect them immediately to the specialists in neurology or psychiatry. Pharmacists are one of the most accessible and regularly visited health care professionals and they can play a vital role in early detection of MCI.
Development and Testing of a Novel Cylindrical Permanent Magnet Linear Generator

Vernier hybrid machines are known for their simple salient translator structure and inherent magnetic gearing, making them a strong candidate for low speed, high force applications. As the armature coil and the magnets are both mounted on the stator, linear versions of this machine are attractive for high amplitude systems. In most configurations, single sided or double sided flat cross sections are considered, giving a two dimensional flux path. In this paper, a cylindrical variant of the double sided configuration is presented, where the flux path is now three dimensional. Guiding operating equations are given as well as the three dimensional finite element analysis (FEA) used to design a prototype. Details and test results of a soft magnetic composite (SMC) based laboratory prototype for a cylindrical Vernier hybrid machine are given which demonstrate the concept and validate the simulation results.
PrivTrie: Effective Frequent Term Discovery under Local Differential Privacy

A mobile operating system often needs to collect frequent new terms from users in order to build and maintain a comprehensive dictionary. Collecting keyboard usage data, however, raises privacy concerns. Local differential privacy (LDP) has been established as a strong privacy standard for collecting sensitive information from users. Currently, the best known solution for LDP-compliant frequent term discovery transforms the problem into collecting n-grams under LDP, and subsequently reconstructs terms from the collected n-grams by modelling the latter into a graph, and identifying cliques on this graph. Because the transformed problem (i.e., collecting n-grams) is very different from the original one (discovering frequent terms), the end result has poor utility. Further, this method is also rather expensive due to clique computation on a large graph. In this paper we tackle the problem head on: our proposal, PrivTrie, directly collects frequent terms from users by iteratively constructing a trie under LDP. While the methodology of building a trie is an obvious choice, obtaining an accurate trie under LDP is highly challenging. PrivTrie achieves this with a novel adaptive approach that conserves privacy budget by building internal nodes of the trie with the lowest level of accuracy necessary. Experiments using real datasets confirm that PrivTrie achieves high accuracy on common privacy levels, and consistently outperforms all previous methods.
Improved Dependency Parsing using Implicit Word Connections Learned from Unlabeled Data

Pre-trained word embeddings and language model have been shown useful in a lot of tasks. However, both of them cannot directly capture word connections in a sentence, which is important for dependency parsing given its goal is to establish dependency relations between words. In this paper, we propose to implicitly capture word connections from unlabeled data by a word ordering model with self-attention mechanism. Experiments show that these implicit word connections do improve our parsing model. Furthermore, by combining with a pre-trained language model, our model gets state-of-the-art performance on the English PTB dataset, achieving 96.35% UAS and 95.25% LAS.
Micobactérias não-tuberculosas

Estudo realizado na Disciplina de Pneumologia e Laboratório de Microbiologia do HC Unicamp. Foram analisados todos os casos de micobacterioses não-tuberculosas detectados durante os anos de 2012 a 2017, identificados por meio dos resultados de cultura positivos e de revisão de prontuário destes pacientes.
Violent Video Games in Virtual Reality: Re-Evaluating the Impact and Rating of Interactive Experiences

Research shows that bespoke Virtual Reality (VR) laboratory experiences can be differently affecting than traditional display experiences. With the proliferation of at-home VR headsets, these effects need to be explored in consumer media, to ensure the public are adequately informed. As yet, the organizations responsible for content descriptions and age-based ratings of consumer content do not rate VR games differently to those played on TV. This could lead to experiences that are more intense or subconsciously affecting than desired. To test whether VR and non-VR games are differently affecting, and so whether game ratings are appropriate, our research examined how participant (n=16) experience differed when playing the violent horror video game "Resident Evil 7", viewed from a first-person perspective in PlayStation VR and on a 40" TV. The two formats led to meaningfully different experiences, suggesting that current game ratings may be unsuitable for capturing and conveying VR experiences. The public must be better informed by ratings bodies, but also protected by developers and researchers conscious of the effects their designs may have.