Miracle-dz/UniFAR-FTUs huggingface dataset:
Reducing Class Overlapping in Supervised Dimension Reduction

Dimension reduction is to find a low-dimensional subspace to project high-dimensional data on, such that the discriminative property of the original higher-dimensional data is preserved. In supervised dimension reduction, class labels are integrated into the lower-dimensional representation, to produce better results on classification tasks. The supervised dimension reduction (SDR) framework by [17] is one of the state-of-the-art methods that takes into account not only the class labels but also the neighborhood graphs of the data, and have some advantages in preserving the within-class local structure and widening the between-class margin. However, the reduced-dimensional representation produced by the SDR framework suffers from the class overlapping problem - in which, data points lie closer to a different class rather than the class they belong to. The class overlapping problem can hurt the quality on the classification task. In this paper, we propose a new method to reduce the overlap for the SDR framework in [17]. The experimental results show that our method reduces the size of the overlapping set by an order of magnitude. As a result, our method outperforms the pre-existing framework on the classification task significantly. Moreover, visualization plots show that the reduced-dimensional representation learned by our method is more scattered for within-class data and more separated for between-class data, as compared to the pre-existing SDR framework.
Improving NER Tagging Performance in Low-Resource Languages via Multilingual Learning

Existing supervised solutions for Named Entity Recognition (NER) typically rely on a large annotated corpus. Collecting large amounts of NER annotated corpus is time-consuming and requires considerable human effort. However, collecting small amounts of annotated corpus for any language is feasible, but the performance degrades due to data sparsity. We address the data sparsity by borrowing features from the data of a closely related language. We use hierarchical neural networks to train a supervised NER system. The feature borrowing from a closely related language happens via the shared layers of the network. The neural network is trained on the combined dataset of the low-resource language and a closely related language, also termed Multilingual Learning. Unlike existing systems, we share all layers of the network between the two languages. We apply multilingual learning for NER in Indian languages and empirically show the benefits over a monolingual deep learning system and a traditional machine-learning system with some feature engineering. Using multilingual learning, we show that the low-resource language NER performance increases mainly due to (1) increased named entity vocabulary, (2) cross-lingual subword features, and (3) multilingual learning playing the role of regularization.
A Study of Security Requirements in Wireless Sensor Networks for Smart Home Healthcare Systems

Smart home healthcare are systems used to monitor patient in home by expecting and responding to their needs and granting their independence. This remote patient's monitoring is becoming more feasible as specialized sensors can be placed inside homes. In fact, wireless sensor network are used to collect medical data such as vital signs and personal data to transmit it to the caregiver. Therefore, the preserving security and privacy of this sensitive information is very crucial. This is particularly challenging because of the specific characteristics of WSN (e.g. limited resources: energy, storage, computation, communication) and the specific smart home environment. This paper tends to analyze the security related issues in smart home healthcare systems and proposes security requirements for these systems. We highlight the security threats for networks in healthcare, discuss issues related to the existing security systems and also present the proposed and derived requirements of security in this context.
3D Human Pose Estimation from RGB+D Images with Convolutional Neural Networks

In this paper, we explore 3D human pose estimation on the RGB+D images. While many researchers try to directly predict 3D pose from single RGB image, we propose a simple framework that could predict 3D pose predictions with the RGB image and depth image. Our approach is based on two aspects. On the one hand, we predicted accurate 2D joint locations from RGB image by applying the stacked hourglass networks based on the improved residual architecture. On the other hand, in view of obtained 2D joint locations, we could estimate 3D pose with the depth after calculating depth image patches. In general, compared with the state-of-the-art approaches, our model achieves signification improvement on benchmark dataset.
Optical Parameters of Tissue Phantoms Measurement Based on DPDW with Multi-frequency Modulation

The measurement of the optical parameters of tissues plays an important role in detecting human diseases and tissue function. On the basis of the frequency-domain near-infrared spectroscopy method, this study obtained the optical parameters of the phantoms with the technology of Diffuse Photon Density Waves (DPDW) under the multi-frequency modulation to detect the optical parameters of tissues, which is based on the light transmission process and the diffuse transmission theory. A DPDW optical parameter measurement system, which uses Intralipid solutions as the research objects, was built. The output amplitude and phase were fitted by the optical radiation equation to obtain the absorption coefficient and the reduced scattering coefficient of the phantoms. The experimental results show that DPDW system with multi-frequency modulation can detect the optical parameters of the tissues quickly and accurately, which is helpful for the research of the composition and internal structure of tissues.
Flexible Sentence Analysis Model for Visual Question Answering Network

Nowadays, visual question answering (VQA) has attracted much attention in both computer vision and natural language processing. Generally, a VQA system adopts sentence analysis models that decompose the sentence to short parts to analyze the user's attempt and merge partial results to get final answer. Despite the success of those models, the correct analysis of long length questions still remains as a key problem in VQA case. Especially, when a sentence produces comprehensive deviation due to different situation or customs of questioners, the sentence analysis model might output a wrong answer and lead to severe performance drop of the VQA system. To tackle the problem, a new sentence comprehension model has been proposed in this paper. The model is named flexible analysis model and is mainly used to deal with the sentences related to object counting. In human dialogue case, when the first answer went wrong, people would change a way to comprehend the sentence for finding the correct answer. Inspired by the mechanism, the flexible sentence analysis model tries another different way to comprehend the sentence after the sentence is given a wrong number answer, and the VQA system can generate a new answer according to the new output. Our model was tested on CLEVR dataset, and the experiment result shows that our method improved the accuracy nearly 10.5% in long sentence cases. It proves that our network has better performance on both correctness and robustness.
Detecting Attacks on Web Applications using Autoencoder

Web attacks have become a real threat to the Internet. This paper proposes the use of autoencoder to detect malicious pattern in the HTTP/HTTPS requests. The autoencoder is able to operate on the raw data and thus, does not require the hand-crafted features to be extracted. We evaluate the original autoencoder and its variants and end up with the Regularized Deep Autoencoder, which can achieve an F1-score of 0.9463 on the CSIC 2010 dataset. It also produces a better performance with respect to OWASP Core Rule Set and other one-class methods, reported in the literature. The Regularized Deep Autoencoder is then combined with Modsecurity in order to protect a website in real time. This algorithm proves to be comparable to the original Modsecurity in terms of computation time and is ready to be deployed in practice.
You Cannot Improve What You Do not Measure

Recently, deep learning (DL) has become best-in-class for numerous applications but at a high computational cost that necessitates high-performance energy-efficient acceleration. The reconfigurability of FPGAs is appealing due to the rapid change in DL models but also causes lower performance and area-efficiency compared to ASICs. In this article, we implement three state-of-the-art computing architectures (CAs) for convolutional neural network (CNN) inference on FPGAs and ASICs. By comparing the FPGA and ASIC implementations, we highlight the area and performance costs of programmability to pinpoint the inefficiencies in current FPGA architectures. We perform our experiments using three variations of these CAs for AlexNet, VGG-16 and ResNet-50 to allow extensive comparisons. We find that the performance gap varies significantly from 2.8× to 6.3×, while the area gap is consistent across CAs with an 8.7 average FPGA-to-ASIC area ratio. Among different blocks of the CAs, the convolution engine, constituting up to 60% of the total area, has a high area ratio ranging from 13 to 31. Motivated by our FPGA vs. ASIC comparisons, we suggest FPGA architectural changes such as increasing DSP block count, enhancing low-precision support in DSP blocks and rethinking the on-chip memories to reduce the programmability gap for DL applications.
Aspect Based Sentiment Analysis Using NeuroNER and Bidirectional Recurrent Neural Network

Nowadays, understanding sentiments of what customers say, think and review plays an important part in the success of every business. In consequence, Sentiment Analysis (SA) has been becoming a vital part in both academic and commercial standpoint in recent years. However, most of the current sentiment analysis approaches only focus on detecting the overall polarity of the whole sentence or paragraph. That is the reason why this work focuses on another approach to this task, which is Aspect Based Sentiment Analysis (ABSA). The proposed ABSA system in this paper has two main phases: aspect term extraction and aspect sentiment prediction. For the first phase, as to deal with the named-entity recognition (NER) task, it is performed by reusing the NeuroNER [1] program without any modifications because it is currently one of the best NER tool available. For the sentiment prediction task, a bidirectional gated recurrent unit (BiGRU) Recurrent Neural Network (RNN) model which processes 4 features as input: word embeddings, SenticNet [2], Part of Speech and Distance is implemented. However, this network architecture performance on SemEval 2016 [3] dataset showed some drawbacks and limitations that influenced the polarity prediction result. For this reason, this work proposes some adjustments to the mentioned model to solve the current problems and improve the accuracy of the second task.
A graph based model for multiple biological data sources integration

Nowadays, recent technologies in biology has gained a lot of attention, because of the massive data they produced with different types, very complex structures and various interaction categories. They allowed to perform deep analysis on cell structure and it's sub-system. Moreover, They enabled construction of complex networks that represent the extracted data and the mutual interactions between biological entities of diverse types. However, most of users, especially researchers and biologists, find it difficult to do their experiments on a set of data of various types stored in multiple databases. In this paper, we present the state of the art for data integration based on collective mining, using various types of networked biological data. Moreover, we propose a new approach to make it possible to integrate heterogeneous data in the MicroCancer platform, recently developed by our laboratory, to deal with micro-array data.
Scaling Exponential Backoff

Randomized exponential backoff is a widely deployed technique for coordinating access to a shared resource. A good backoff protocol should, arguably, satisfy three natural properties: (1) it should provide constant throughput, wasting as little time as possible; (2) it should require few failed access attempts, minimizing the amount of wasted effort; and (3) it should be robust, continuing to work efficiently even if some of the access attempts fail for spurious reasons. Unfortunately, exponential backoff has some well-known limitations in two of these areas: it can suffer subconstant throughput under bursty traffic, and it is not robust to adversarial disruption. The goal of this article is to “fix” exponential backoff by making it scalable, particularly focusing on the case where processes arrive in an online, worst-case fashion. We present a relatively simple backoff protocol, Re-Backoff, that has, at its heart, a version of exponential backoff. It guarantees expected constant throughput with dynamic process arrivals and requires only an expected polylogarithmic number of access attempts per process. Re-Backoff is also robust to periods where the shared resource is unavailable for a period of time. If it is unavailable for D time slots, Re-Backoff provides the following guarantees. For n packets, the expected number of access attempts for successfully sending a packet is O(log2(n + D)). For the case of an infinite number of packets, we provide a similar result in terms of the maximum number of processes that are ever in the system concurrently.
CitationLDA++: an Extension of LDA for Discovering Topics in Document Network

Along with rapid development of electronic scientific publication repositories, automatic topics identification from papers has helped a lot for the researchers in their research. Latent Dirichlet Allocation (LDA) model is the most popular method which is used to discover hidden topics in texts basing on the co-occurrence of words in a corpus. LDA algorithm has achieved good results for large documents. However, article repositories usually only store title and abstract that are too short for LDA algorithm to work effectively. In this paper, we propose CitationLDA++ model that can improve the performance of the LDA algorithm in inferring topics of the papers basing on the title or/and abstract and citation information. The proposed model is based on the assumption that the topics of the cited papers also reflects the topics of the original paper. In this study, we divide the dataset into two sets. The first one is used to build prior knowledge source using LDA algorithm. The second is training dataset used in CitationLDA++. In the inference process with Gibbs sampling, CitationLDA++ algorithm use topics distribution of prior knowledge source and citation information to guide the process of assigning the topic to words in the text. The use of topics of cited papers helps to tackle the limit of word co-occurrence in case of linked short text. Experiments with the AMiner dataset including title or/and abstract of papers and citation information, CitationLDA++ algorithm gains better perplexity measurement than no additional knowledge. Experimental results suggest that the citation information can improve the performance of LDA algorithm to discover topics of papers in the case of full content of them are not available.
Usability Assessments for Augmented Reality Motor Rehabilitation Solutions: A Systematic Review

This article aims to understand which methods and user assessment approaches are most commonly used in motor rehabilitation studies that use Augmented Reality (AR) applications. The way in which this was performed and discussed was through a systematic review of the area. Firstly, the different uses of AR in these treatments, and the importance of improving usability studies to evaluate these interfaces and their interactions with their users, were discussed. Then, the systematic review of the literature was structured according to previous studies and covered the period from 2007 to September 2017, using the main scientific journals of the area. Of the 666 results collected in the initial search, 32 articles were selected within the planned requirements and scope. These publications were classified by relevance, using the QualSyst evaluation tool for health technology research, and the type of evaluation, approach, and methods used were catalogued. It was found that most of the studies prioritise methods of data collection, such as task execution and performance analysis. However, through QualSyst, it was verified that the best-evaluated studies chose to use a combination of quantitative and qualitative analysis, through the application of methods such as user preference questionnaires, interviews, and observations. The results presented in this review may benefit designers who intend to design and conduct usability assessments for AR within this context.
ITAmoji 2018: Emoji Prediction via Tree Echo State Networks

English. For the “ITAmoji” EVALITA 2018 competition we mainly exploit a Reservoir Computing approach to learning, with an ensemble of models for trees and sequences. The sentences for the models of the former kind are processed by a language parser and the words are encoded by using pretrained FastText word embeddings for the Italian language. With our method, we ranked 3rd out of 5 teams. Italiano. Per la competizione EVALITA 2018 sfruttiamo principalmente un approccio Reservoir Computing, con un ensemble di modelli per sequenze e per alberi. Le frasi per questi ultimi sono elaborate da un parser di linguaggi e le parole codificate attraverso degli embedding FastText preaddestrati per la lingua italiana. Con il nostro metodo ci siamo classificati terzi su un totale di 5 team.
Cross-domain & In-domain Sentiment Analysis with Memory-based Deep Neural Networks

Cross-domain sentiment classifiers aim to predict the polarity, namely the sentiment orientation of target text documents, by reusing a knowledge model learned from a different source domain. Distinct domains are typically heterogeneous in language, so that transfer learning techniques are advisable to support knowledge transfer from source to target. Distributed word representations are able to capture hidden word relationships without supervision, even across domains. Deep neural networks with memory (MemDNN) have recently achieved the state-of-the-art performance in several NLP tasks, including cross-domain sentiment classification of large-scale data. The contribution of this work is the massive experimentations of novel outstanding MemDNN architectures, such as Gated Recurrent Unit (GRU) and Differentiable Neural Computer (DNC) both in cross-domain and in-domain sentiment classification by using the GloVe word embeddings. As far as we know, only GRU neural networks have been applied in cross-domain sentiment classification. Sentiment classifiers based on these deep learning architectures are also assessed from the viewpoint of scalability and accuracy by gradually increasing the training set size, and showing also the effect of fine-tuning, an explicit transfer learning mechanism, on cross-domain tasks. This work shows that MemDNN based classifiers improve the state-of-the-art on Amazon Reviews corpus with reference to document-level cross-domain sentiment classification. On the same corpus, DNC outperforms previous approaches in the analysis of a very large in-domain configuration in both binary and fine-grained document sentiment classification. Finally, DNC achieves accuracy comparable with the state-of-the-art approaches on the Stanford Sentiment Treebank dataset in both binary and fine-grained single-sentence sentiment classification.
Remotely Monitoring Cancer-Related Fatigue Using the Smart-Phone: Results of an Observational Study

Cancer related fatigue is a chronic disease that may persist up to 10 years after successful cancer treatment and is one of the most prevalent problems in cancer survivors. Cancer related fatigue is a complex symptom that is not yet explained completely and there are only a few remedies with proven evidence. Patients do not necessarily follow a treatment plan with regular follow ups. As a consequence, physicians lack of knowledge how their patients are coping with their fatigue in daily life. To overcome this knowledge gap, we developed a smartphone-based monitoring system. A developed Android app provides activity data from smartphone sensors and applies experience based sampling to collect the patients’ subjective perceptions of their fatigue and interference of fatigue with the patients’ daily life. To evaluate the monitoring system in an observational study, we recruited seven patients suffering from cancer related fatigue and tracked them over two to three weeks. We collected around 2700 h of activity data and over 500 completed questionnaires. We analysed the average completion of answering the digital questionnaires and the wearing time of the smartphone. A within-subject analysis of the perceived fatigue, its interference and measured physical activity yielded in patient specific fatigue and activity patterns depending on the time of day. Physical activity level correlated stronger with the interference of fatigue than with the fatigue itself and the variance of the acceleration correlates stronger than absolute activity values. With this work, we provide a monitoring system used for cancer related fatigue. We show with an observational study that the monitoring system is accepted by our study cohort and that it provides additional details about the perceived fatigue and physical activity to a weekly paper-based questionnaire.
Local and Holistic Feature Fusion for Occlusion-Robust 3D Ear Recognition

Occlusion over ear surfaces results in performance degradation of ear registration and recognition systems. In this paper, we propose an occlusion-resistant three-dimensional (3D) ear recognition system consisting of four primary components: (1) an ear detection component, (2) a local feature extraction and matching component, (3) a holistic matching component, and (4) a decision-level fusion algorithm. The ear detection component is implemented based on faster region-based convolutional neural networks. In the local feature extraction and matching component, a symmetric space-centered 3D shape descriptor based on the surface patch histogram of indexed shapes (SPHIS) is used to generate a set of keypoints and a feature vector for each keypoint. Then, a two-step noncooperative game theory (NGT)-based method is proposed. The proposed symmetric game-based method is effectively applied to determine a set of keypoints that satisfy the rigid constraints from initial keypoint correspondences. In the holistic matching component, a proposed variant of breed surface voxelization is used to calculate the holistic registration error. Finally, the decision-level fusion algorithm is applied to generate the final match scores. Evaluation results from experiments conducted show that the proposed method produces competitive results for partial occlusion on a dataset consisting of natural and random occlusion.
A virtual filter based fast assessment methodology for fault tolerant NoCs

Network on Chips (NoCs) as the compromising communication infrastructures in many-core system, are suffering the serious Multi-Cell Upsets (MCU) impacts. To accelerate the accurate assessment of the increasing and complex MCU in fault tolerant NoCs, we propose a virtual filter based analytical methodology. The approach converts an original MCU model to a new regenerated MCU model via fault tolerance filtering, and then maps the new MCU information into an existing boundary model to estimate the effectiveness of fault tolerant schemes fast. The diverse results demonstrate that the virtual filter based assessment achieves 473.75× speedup and only 11.454% accuracy loss over the latest fault injection.