Miracle-dz/UniFAR-FTUs huggingface dataset:
MONITORING AND CONTROL DEVICES: THE WAY TO IMPROVE EFFICIENCY AND RELIABILITY

As the industry rushes towards the full optimization of processes and methods, the terms "control" and "monitor" are recognized as of vital importance even for small manufacturing companies and not only in major industrial installations. The high equipment cost and the growing aggregated value of products means that every downtime will reflect in large losses, thus concrete means of monitoring and preventive detection of fails are a vital step towards the maintenance of profitable activities. This paper focuses in means of monitoring motors performance and several other variables that are valuable to access the operation condition of the motors and map the correct maintenance plan for the equipment. It is shown also how this monitoring paired with control mechanisms that allow for automatic star-delta commutation may increase the performance of electric motors and it´s driven equipment. Finally, it is presented the method to the full integration of this monitor and control devices in the motor without changing its footprint and the advantages to end user installations.
A Self-adaptive Agent-based System for Cloud Platforms

Cloud computing is a model for enabling on-demand network access to a shared pool of computing resources, that can be dynamically allocated and released with minimal effort. However, this task can be complex in highly dynamic environments with various resources to allocate for an increasing number of different users requirements. In this work, we propose a Cloud architecture based on a multi-agent system exhibiting a self-adaptive behavior to address the dynamic resource allocation. This self-adaptive system follows a MAPE-K approach to reason and act, according to QoS, Cloud service information, and propagated run-time information, to detect QoS degradation and make better resource allocation decisions. We validate our proposed Cloud architecture by simulation. Results show that it can properly allocate resources to reduce energy consumption, while satisfying the users demanded QoS.
Aggregation of Time Series Forecasts via Cacoullos Copula

The simplest linear combination of time series forecasters has shown a better performance than individual models. Thus, many researchers have sought to combine models for improving the forecasting process. This paper introduces a copulas-based approach (CB) for aggregating forecasters. The CB is based on the Cacoullos copula, with architecture divided into three parts: Single Modelling, Marginal Probability Distribution Computational and Joint Probability Distribution Computation. In the first part, the forecasts of individual models are obtained. In the second part, the residuals of the individuals models are calculated. In the third part, the models are combined via Cacoullos copula, based on the obtained residuals. The paper also evaluates the performance of the CB via simulated as well as financial time series (e.g. Google Stock Value). Thus, a comparative analysis is presented between CB and individual models (e.g. Artificial Neural Networks) and alternative combined forecasters (e.g. Simple Average-SA and Normal copula-CN). This study showed that the CB model produces better results when compared with the individual models, SA and CN.
Clinical Characteristic and Management of Acquired Uterine Arteriovenous Malformation

To the Editor: Arteriovenous malformations (AVM), also referred to as cirsoid aneurysm, arteriovenous aneurysm, arteriovenous fistula, and cavernous hemangioma, can occur in any organ in the body. Uterine AVM is a rare entity that is classified as either congenital or acquired. Congenital uterine AVM is thought to develop from a defect during embryologic differentiation leading to abnormal vascular connections, whereas acquired uterine AVM has been reported to occur due to a previous uterine trauma, such as curettage or cesarean delivery, or to be associated with neoplastic disorders, including gestational trophoblastic disease (GTD) and endometrial adenocarcinoma. [1]
Paraphrase Recognition via Combination of Neural Classifier and Keywords

Paraphrases are sentences or phrases that convey the same meaning using different words. Paraphrase recognition is of interest for many current Natural Language Processing (NLP) tasks. As understood in linguistics, thephenomenon ofparaphrases is difficult to characterize. In this article, we present a novel approach to the task of paraphrase identification. The proposed approach measures similarity between two sentences based on both the lexical and semantic levels, via combining neural networks and keywords jointly. In particular, we employ a vector offset, which implies the relation of given inputs in vector space, as the representation of a neural classifier. We conduct experiments on the Microsoft Research Paraphrase Corpus (MSRP)1 and SICK dataset, which are both standard datasets for evaluating approaches to paraphrase identification. The experiments showed that our proposed approach makes much progress and achieves state-of-the-art results.1https://www.microsoft. com/en-us/download/details.aspx? id=52398
Some Experiences on Applying Deep Learning to Speech Signal and Natural Language Processing

With the advent of deep learning there has been a major paradigm shift in how speech signal and text processing techniques work. In this paper, we would like to share our experiences/recent works on applying deep neural networks (DNNs) to adaptive microphone array speech enhancement, sentiment analysis, speech recognition, language recognition and text-to-speech (TTS). In those tasks, many different DNN structures and learning strategies were involved including DNN, convolutional neural network (CNN), recurrent neural network (RNN) and long-short-term memory (LSTM). Experimental results all show DNNs are quite promising.
Use of Machine Learning Techniques in Real-Time Strategy Games

Despite excessive amount of research done in the field of automated RTS gameplay, subtle changes in strategies are often ignored leading to non-optimal results. Researchers often consider obvious strategical decisions in order to cover as many gameplay scenarios as possible. In this paper, we focus on creating a dataset of atypical strategies which are often overlooked and using machine learning methods for detection of these strategies during the gameplay. Such information could be used to predict the strategies before they occur, or to correspond which adaptive behavior is able to answer them. In work presented in this paper, we approached the strategy recognition in StarCraft game using a set of classifiers trained on data obtained from the various replays. As there was not a proper dataset available to solve such task during our work, the dataset was created and annotated to cover four selected strategies for our experiments. Binary classification models were trained to detect each particular strategy and evaluated in a set of replay data using cross-validation technique. Then the overall platform architecture to train the models, export them and use in runtime during the gameplay was designed. Best performed models were then applied to real games to detect the covered strategies in replays or matches by bots or human players.
Intelligent Asset Allocation via Market Sentiment Views

The sentiment index of market participants has been extensively used for stock market prediction in recent years. Many financial information vendors also provide it as a service. However, utilizing market sentiment under the asset allocation framework has been rarely discussed. In this article, we investigate the role of market sentiment in an asset allocation problem. We propose to compute sentiment time series from social media with the help of sentiment analysis and text mining techniques. A novel neural network design, built upon an ensemble of evolving clustering and long short-term memory, is used to formalize sentiment information into market views. These views are later integrated into modern portfolio theory through a Bayesian approach. We analyze the performance of this asset allocation model from many aspects, such as stability of portfolios, computing of sentiment time series, and profitability in our simulations. Experimental results show that our model outperforms some of the most successful forecasting techniques. Thanks to the introduction of the evolving clustering method, the estimation accuracy of market views is significantly improved.
An empirical analysis of Combined Dissimilarity Spaces

Text categorization can be applied in many areas, such as Business Intelligence, sentiment analysis, social network monitoring, and security. Considering textual analysis techniques’ wide applications, it is important to improve these techniques to obtain more precise and reliable results. Combined Dissimilarity Spaces (CoDiS) model implements a system for text categorization with multiple classifiers trained on different dissimilarity spaces, aiming to overcome Bag-of-Words’ limitations. This study evaluates CoDiS performance when applying some parametric variations, such as the classifier, the data input format, and the dissimilarity measure. Macro F1 and Micro F1 are used to evaluate the performance, and the results show a significant difference among different configurations tested on CoDiS. Based on the statistical tests’ results, it is possible to conclude that the best scenario is to apply TFIDF to represent the datasets, Cosine as the dissimilarity measure, and SVM as the classifier. This scenario also outperformed literature multiple classifier systems: Bagging and Random Subspace.
“Find Your Fit”: Wearable technology and the cultural politics of disability

By examining advertisements, technological design, workplace wellness programs, and legal discourses involving Fitbit activity trackers, this article examines how cultural ideas about disability infuse the representation, use, study, and implementation of wearable technology. Although Fitbit features wheelchair users prominently in advertising, Fitbit only measures movements in steps, and its use in workplace wellness programs has been accompanied by legal concerns about wellness programs’ potential weakening of workplace protections afforded to US workers by the Americans with Disabilities Act (ADA) and the Genetic Information Nondiscrimination Act (GINA). This article shows that inspirational and tragic representations of disability work to depoliticize wearable technology and argues that disability needs to be a more central category of analysis for cultural studies and sociological studies of the cultural impacts of fitness tracking and wellness culture.
Vascularization Strategies for Peripheral Nerve Tissue Engineering

Vascularization plays a significant role in treating nerve injury, especially to avoid the central necrosis observed in nerve grafts for large and long nerve defects. It is known that sufficient vascularization can sustain cell survival and maintain cell integration within tissue‐engineered constructs. Several studies have also shown that vascularization affects nerve regeneration. Motivated by these studies, vascularized nerve grafts have been developed using various different techniques, although donor site morbidity and limited nerve supply remain significant drawbacks. Tissue engineering provides an exciting alternative approach to prefabricate vascularized nerve constructs which could overcome the limitations of grafts. In this review article, we focus on the role of vascularization in nerve regeneration, discussing various approaches to generate vascularized nerve constructs and the contribution of tissue engineering and mathematical modeling to aid in developing vascularized engineered nerve constructs, illustrating these aspects with examples from our research experience. Anat Rec, 301:1657–1667, 2018. © 2018 The Authors. The Anatomical Record published by Wiley Periodicals, Inc. on behalf of American Association of Anatomists.
A New Modeling for Item Ratings Using Landmarks

Collaborative Filtering CF has been a widely used approach for personalized recommendations. In this context, model-based CF algorithms have been studied extensively in the literature and have shown higher accuracy in rating prediction than memory-based ones. The major approach regarding model- based CF is Matrix Factorization (MF). It uses the item ratings given by users to predict unknown ratings, which are posteriorly used in recommendations. Usually, MF learns a data model through optimization algorithms by requiring too much time to process. To overcome this issue, we propose a novel modeling for item ratings that allows one to apply Supervised Learning (SL) algorithms to predict unknown ratings. This modeling consists in computing item similarities to a preselected set of items, namely landmarks. Then, we build a model for each user using his/her ratings as labels and the corresponding vector of item similarities to landmarks as samples. In this work, it was applied Support Vector Regression to learn the data model. We compared our proposal against 5 state-of-the-art CF techniques in 6 different databases. The results show the proposal is able to reduce computational time and still keep competitive accuracy compared to the experimented CF algorithms.
A systematic review of non-pharmacological interventions for BPSD in nursing home residents with dementia: from a perspective of ergonomics

ABSTRACT Background: Non-pharmacological interventions for Behavioral and Psychological Symptoms of Dementia (BPSD) have been developed; however, a systematic review on the effectiveness of this type of intervention from a perspective of ergonomics is lacking. According to ergonomics, the capabilities of Persons with Dementia (PwD) should be considered in the interventions for the outcomes to be reliable. We aimed to systematically review the non-pharmacological interventions for BPSD in nursing home residents with an additional assessment criterion based on ergonomics, specifically, capability consideration. Methods: The electronic databases MEDLINE, EMBASE, and PsycINFO were searched for non-pharmacological interventions treating BPSD in nursing homes. The interventions were categorized according to the capabilities of PwD required to participate. Study quality was assessed by National Health and Medical Research Council (NHMRC) evidence hierarchy and the capability consideration. Results: Sixty-four clinical trials met the inclusion criteria; 41 trials reported a significant reduction in at least one BPSD symptom; 20 trials reported no significant reduction in BPSD symptoms; three trials reported adverse effects after the intervention. Interventions were categorized into sensory-, cognition-, and movement-oriented. Capabilities of PwD were not considered in 28 trials, especially for sensory capabilities. Conclusions: The majority of the clinical trials reported a significant reduction in BPSD. The quality of evidence for nonpharmacological interventions in these trials is low due to the lack of capability consideration, data inhomogeneity, and inadequate study design and reporting. Future studies should focus on improving the quality of evidence by including capability consideration and examining if a relationship between capability consideration and effectiveness of non-pharmacological interventions exists.
Curved Gabor Projection Entropy for Face Recognition

The purpose of face recognition is to identify a person based on their face images. There are still some challenges and problems to be overcome, although several improvements have been achieved recently. These difficulties are mainly due to environment conditions, such as lighting changes, occlusion, changes in racial expressions and head position. This work presents an approach to face recognition based on combination of the curved Gabor filter, entropy and Support Vector Machine (SVM). The curved Gabor filter is used to perform the feature vector extraction of an image. Then, the results from the Gabor response curve are segmented into non-overlapping blocks to reduce interference from local variations in the image. The entropy is used to maintain the most representative image data and to provide a reduction in the feature vector. As classifier we use the SVM. A set of experiments was performed to evaluate this approach based on the characteristics of scenarios encountered in a real environment, using the AR face database. The results obtained from the experiments exceed the state-of-the-art approaches available in the literature in 4 of the 5 tests and the two additional final tests.
Urinary Glycopeptide Analysis for the Investigation of Novel Biomarkers

Urine is a rich source of potential biomarkers, including glycoproteins. Glycoproteomic analysis remains difficult due to the high heterogeneity of glycans. Nevertheless, recent advances in glycoproteomics software solutions facilitate glycopeptide identification and characterization. The aim is to investigate intact glycopeptides in the urinary peptide profiles of normal subjects using a novel PTM‐centric software—Byonic.
Extract Generalization Ability from Convolutional Neural Networks

With the development of deep learning, the trend of mobile artificial intelligent is emerging at a rapid pace. Over time, a series of mobile devices such as smart speakers and smartphones gradually have become the first choice of artificial intelligent landing. However, the hardware resources of mobile devices are very limited. In terms of time, memory, or energy consumption, large neural networks cannot be deployed on mobile devices. Therefore, it is necessary to explore a new compression method. Studies have shown that much redundancy exists in convolutional neural networks. This means that the neural network structure can be trimmed without affecting the accuracy. Inspired by the autoencoder, this paper presents a new compression method. The method can remove redundant neurons and convolution kernels. It extracts the generalization ability of convolutional neural networks onto smaller models. Experimental results show that the pruning rate is approximately 2x to 5x.
A Computational Approach for Authorship Attribution on Multiple Languages

In this paper, we describe a computational approach for authorship attribution of literary texts in a multilingual environment. First, we define a set of syntactical features at text structural levels by considering the internal grammatical structure of sentences. The main idea is to extract syntactic functions from every word necessary to make up a sentence, such as subject and predicate. Such elements denote a writing pattern, tracking a stylometric profile for each author. Second, we gather literary texts in five languages: Portuguese, Spanish, French, German, and English. We analyze distinct scenarios through a series of experiments, taking into account writer-dependent and writer-independent approaches while using authorship verifica- tion and identification strategies. Furthermore, we evaluate the model behavior when the amount of information in each sample is varied and the impact of the reference samples. We use a support vector machine classifier in this process.
Rank-order principal components. A separation algorithm for ordinal data exploration.

In most research studies, much of the information gathered is of qualitative nature. This paper concentrates on items for which multiple rankings exist that shall be combined optimally. This work presents a unsupervised deterministic approach that can be applied to rank-order data for decomposing them into composite signals. On the opposite to other techniques, it does not requite any prior knowledge about the distribution of the signal components. Typically the variables are necessarily dependent because of the inequality relations among them. The mathematical relationships found are of interest in themselves and in the theory of blind source separation (BSS). Multiple rankings of an item set are decomposed into other rankings of these items, each ranking being orthogonal to the others, hence the name of rank-order principal components. We transform the original ranking data into matrices that linearize the optimization problem for its resolution by linear programming.The resolution is sensitive to the distance between the items that is used. Several distances have been tested: the Euclidean distance (Spearman), the rank absolute deviation distance, the Hölder distance, the $\chi ^{2}$ distance, etc. The results are concordant.
REPT: Reverse Debugging of Failures in Deployed Software

Debugging software failures in deployed systems is important because they impact real users and customers. However, debugging such failures is notoriously hard in practice because developers have to rely on limited information such as memory dumps. The execution history is usually unavailable because high-fidelity program tracing is not affordable in deployed systems. In this paper, we present REPT, a practical system that enables reverse debugging of software failures in deployed systems. REPT reconstructs the execution history with high fidelity by combining online lightweight hardware tracing of a program's control flow with offline binary analysis that recovers its data flow. It is seemingly impossible to recover data values thousands of instructions before the failure due to information loss and concurrent execution. REPT tackles these challenges by constructing a partial execution order based on timestamps logged by hardware and iteratively performing forward and backward execution with error correction. We design and implement REPT, deploy it on Microsoft Windows, and integrate it into WinDbg. We evaluate REPT on 16 real-world bugs and show that it can recover data values accurately (92% on average) and efficiently (in less than 20 seconds) for these bugs. We also show that it enables effective reverse debugging for 14 bugs.
On Learning Community-specific Similarity Metrics for Cold-start Link Prediction

This paper studies a cold-start problem of inferring new edges between vertices with no demonstrated edges but vertex content by learning vertex-based similarity metrics. Existing metric learning methods for link prediction fail to consider communities which can be observed in real-world social networks. Because communities imply the existence of local homogeneities, learning a global similarity metric is not appropriate. In this paper, we thus learn community-specific similarity metrics by proposing a community-weighted formulation of metric learning model. To better illustrate the community-weighted formulation, we instantiate it in two models, which are community-weighted ranking (CWR) model and community-weighted probability (CWP) model. Experiments on three real-world networks show that community-specific similarity metrics are meaningful and that both models perform better than those leaning global metrics in terms of prediction accuracy.