Miracle-dz/UniFAR-FTUs huggingface dataset:
Polarity classification of political tweets in Mexico using a lexicon-based approach

Lexicon-based approaches are employed despite their low performance because they can be used in many scenarios i.e. their performance is consistent despite the sentiment analysed. In these approaches, the valence of words is usually employed to determine the level of sentiment in a sentence e.g. to determine whether a tweet is positive, negative or neutral in a certain context (i.e. the tweet’s polarity). In this paper, we explore the effect in the performance of lexicon-based approaches when a model like Vector Space Models (VSMs) is employed by the increment in the dimension of valence. We explored this proposal in the polarity classification of tweets in the political context in Mexico. We found that the performance is as good as with lexicon-based approaches.
Selecting local ensembles for multi-class imbalanced data classification

Learning from imbalanced data is a challenge that machine learning community is facing over last decades, due to its ever-growing presence in real-life problems. While there is a significant number of works addressing the issue of handling binary and skewed datasets, its multi-class counterpart have not received as much attention. This problem is much more difficult, as presence of multiple imbalanced classes can significantly deteriorate the predictive power of any classifier. The relationship among classes are no longer clearly established and there are many difficulties embedded in the nature of such data that needs to be properly addressed. In this work, we discuss the issue of forming effective ensembles for multi-class imbalanced data based on static classifier selection approach. We propose a fully adaptive learning scheme that splits the original feature space into a number of competence areas and modifies their size and location in order to most effectively exploit the supplied pool of base classifiers. Additionally, for each established cluster we perform a weighted classifier combination, where weights are set individually for each cluster and each considered class. This allows for exploiting local competencies of each base learner in given part of feature space, as well as for each of considered classes. These two tasks are combined together in a single hybrid training scheme guided by an evolutionary algorithm. The optimization criterion is formulated in order to achieve skew-insensitive ensemble of local ensembles able to tackle highly imbalanced and multi-class problems. Experimental study proves the high efficacy of the proposed method and its superiority to other ensemble selection methods.
CoNNA – Compressed CNN Hardware Accelerator

In this paper we propose a novel Convolutional Neural Network hardware accelerator, called CoNNA, capable of accelerating pruned, quantized, CNNs. In contrast to most existing solutions, CoNNA offers a complete solution to the full, compressed CNN acceleration, being able to accelerate all layer types commonly found in contemporary CNNs. CoNNA is designed as a coarse-grained reconfigurable architecture, which uses rapid, dynamic reconfiguration during CNN layer processing. Furthermore, by being able to directly process compressed feature and kernel maps, CoNNA is able to achieve higher CNN processing efficiency than some of the previously proposed solutions. Results of the experiments indicate that CoNNA architecture is up to 14.10 times faster than previously proposed MIT's Eyeriss CNN accelerator, up to 6.05 times faster than NullHop CNN accelerator, and up to 4.91 times faster than NVIDIA's Deep Learning Accelerator (NVDLA), while using identical number of computing units and operating at the same clock frequency.
Controls and Cooperation in Dynamic Environments: The Role of Involvement in Control Design

The modern business environment is a dynamic one, and organizations must be adaptive. In addition, organizations rely increasingly upon groups. This study provides an examination of the effectiveness of controls in dynamic, cooperative environments. As control, we study a mechanism that provides immediate feedback about peer behavior which has been shown to be effective in facilitating cooperation across the accounting, economics, and management literature. However, in a dynamic environment, feedback mechanisms can become outdated and lose their effectiveness. We argue that this likely happens when feedback is incomplete, i.e., feedback is not provided for the full set of employees’ strategies. We predict and find that while incomplete feedback is effective in static environments, it is only effective in dynamic environments that require employees to adapt their strategy when employees were initially involved in the development of the feedback system. Involvement matters because it increases employees’ understanding of the consequences of everyone’s actions and increases trust. Implications for the literature and practice are discussed.
Renal Replacement Therapy Modalities in Critically Ill Children*

Objectives: The objective of this study is to describe the relative frequency of use of continuous renal replacement therapy, intermittent hemodialysis, and peritoneal dialysis and to analyze characteristics and outcomes of critically ill children receiving renal replacement therapies admitted to PICUs that participate in the Virtual PICU (VPS LLC, Los Angeles, CA) registry. Design: Retrospective, database analysis. Setting: PICUs that participate in the Virtual PICU (VPS LLC) registry. Patients: Critically ill children admitted to PICUs that participate in the Virtual PICU (VPS LLC) registry and received renal replacement therapy from January 1, 2009, to December 31, 2015. Interventions: None. Measurements and Main Results: A total of 7,109 cases (53% males) received renal replacement therapy during the study period. The median age was 72.3 months (interquartile range, 8.4–170 mo) and median length of stay was 8.7 days (interquartile range, 3.3–21.2 d). Caucasians comprised 42% of the cohort and blacks and Hispanics were 16% each. Continuous renal replacement therapy was used in 46.5%, hemodialysis in 35.5% and peritoneal dialysis in 18%. Of the 7,109 cases, 1,852 (26%) were postoperative cases (68% cardiac surgical) and 981 (14%) had a diagnosis of cancer. Conventional mechanical ventilation was used in 64%, high-frequency oscillatory ventilation in 12%, noninvasive ventilation in 24%, and extracorporeal membrane oxygenation in 5.8%. The overall mortality was 22.3%. Patients who died were younger 40.8 months (interquartile range, 1.5–159.4 mo) versus 79.9 months (interquartile range, 12.6–171.7 mo), had a longer length of stay 15 days (interquartile range, 7–33 d) versus 7 days (interquartile range, 3–18 d) and higher Pediatric Index of Mortality 2 score –2.84 (interquartile range, –3.5 to –1.7) versus –4.2 (interquartile range, –4.7 to –3.0) (p < 0.05). On multivariate logistic regression analysis, higher mortality was associated with the presence of cancer (32.7%), previous ICU admission (32%), requiring mechanical ventilation (33.7%), receiving high-frequency oscillatory ventilation (67%), or extracorporeal membrane oxygenation (58.4%), admission following cardiac surgical procedure (29.4%), and receiving continuous renal replacement therapy (38.8%), and lower mortality was associated with hemodialysis (9.8%), and peritoneal dialysis (12.3%) (p < 0.0001). Conclusions: Continuous renal replacement therapy is an increasingly prevalent renal replacement therapy modality used in critically ill children admitted to an ICU. Higher mortality rate with the use of continuous renal replacement therapy should be interpreted with caution.
The evaluation of technology-based course materials developed by mathematics teachers

The change in technology manifests itself in the restructuring of mathematics teaching and learning processes. Effective use of information and communications technology (ICT) in learning environments has gained importance. The ability to prepare technology-based course materials is currently one of the competencies of teachers. In this study, within the scope of the Erasmus+ project titled ‘Math Teachers’ Adventure of ICT Integration’, mathematics teachers were offered an online course called ‘Teaching Mathematics with GeoGebra,’ which combines geometry, algebra, graphs, statistics and calculus in a single interface. It is both a dynamic geometry software program and a computer algebra system. The aim of this study is to examine the course materials developed by teachers using GeoGebra and determine the errors in their materials. The technology-pedagogy-content knowledge model was used as the framework for the content analysis. Based on the results, suggestions for both in-service training of teachers and future research were presented.
KETCube – The Universal Prototyping IoT Platform

The IoT (Internet of Things) devices continue to penetrate into new areas of our daily lives as well as industry. The evolution of IoT devices comes with the necessity of operation in heterogeneous environments. This evolution brings new challenges in the areas of R&D and education. We identify important features beneficial for R&D engineers as well as for educationalists and students and we propose a novel open platform for rapid development of IoT nodes. This platform is easy to employ in the educational process at the same time.
Mixture of Non-homogeneous Hidden Markov Models for Clustering and Prediction of Water Consumption Time Series

In the domain of water, companies are deploying innovative infrastructures to instrument buildings with smart meters that collect in particular consumption readings with a fine granularity. Advanced analytics are investigated on these massive and complex data to address different objectives e.g., water consumption forecasting, water leak detection and consumption behavior analysis. This paper is an extension of a previous study aiming to model the dynamics of water consumption time series issued from smart meters in order to predict the future consumption behaviors. We propose a mixture of non-homogeneous hidden Markov models (MixNHMM) to cluster the consumption behavior series in appropriate groups, which share the same transition dynamics, and to forecast the future behaviors in each group of consumers separately. The proposed methodology is applied on a real data gathered during one year and provided by a water utility in France. The experimentation results demonstrate the effectiveness of the proposed approach for the forecasting task. In addition, the clustering capability of the proposed method is confirmed using numeric metrics and visualization.
Reliability Driven Mixed Critical Tasks Processing on FPGAs Against Hardware Trojan Attacks

The property of dynamic partial reconfiguration of modern field programmable gate arrays (FPGAs) has made it feasible to execute various mixed critical tasks on the same platform. This requires partitioning the FPGA fabric into several virtual portions (VPs) and a scheduling methodology to determine which task is to be executed when and in which FPGA VP. Executing a task in an FPGA VP requires runtime configuring of the VP with a bitstream or a reconfigurable intellectual property, procured from a third party intellectual property (3PIP) vendor. Recent literature has exposed the presence of malicious elements like hardware trojan horses (HTHs) in such 3PIP bitstreams. Such HTH is particularly dangerous as these remain dormant during testing and initial stages of operation, but gets activated suddenly at runtime to jeopardize the basic security primitives of the system. Thus, reliability driven mixed critical tasks processing on FPGAs against HTH attacks is important. Firstly, reliability driven mixed critical periodic task schedule generation against HTH attacks is focused. Secondly, reliability ensured execution of mixed critical aperiodic and sporadic tasks in the generated periodic task schedule is considered. Experimentation is carried out with a variety of bitstreams and performance evaluation is performed via metrics like task success rate, task rejection rate and task preemption rate.
Factors Affecting Student Time to Examination Completion

Objective. To investigate factors (prior or current knowledge, metacognitive accuracy, and personality) that might impact the time it takes students to complete an examination. Methods. On the final examination, the time to complete the examination was recorded. Prior to the course, students completed the five-factor personality assessment. During the semester, students completed four cumulative assessments that included prospective judgments of performance to improve their metacognitive accuracy. Measures of metacognitive accuracy were calculated from the difference between the students’ prospective judgments of performance and their actual assessment performance for the final examination. Two weeks prior to the final examination, students completed a cumulative assessment, which served as prior knowledge; this was similar in content to the final examination. Results. The time to complete the final examination was significantly negatively correlated with examination score and positively correlated with Agreeableness, and degree of metacognitive bias. However, only current knowledge (β=-.35) and Agreeableness (β=.12) predicted the time to complete the final examination. These two factors explained about 14% of the variability in completion times. Examining the scale for the time to complete the examination, there were some regional differences between the slowest, intermediate and fastest completers. Conclusion. Current knowledge and to a lesser extent, pro-social behavior (agreeableness) influenced examination completion time. Metacognitive accuracy had limited predictability in time to complete the examination.
Pattern graph for sparse Hessian matrix determination†

In a recent work, we have proposed the pattern graph as a unifying framework for methods that exploit sparsity by matrix compression: row compression, column compression or a combination of the two in sparse Jacobian matrix determination. Utilization of structural similarity between the matrix and its graph has been found to be beneficial. In this paper, we show that an important structural property, symmetry, can be exploited in the formulation of sparse Hessian matrix calculations using the pattern graph model. Using the notion of ‘direct cover’, we present a new general direct method for the determination of sparse Hessian matrices with fixed sparsity pattern and a multicolouring interpretation of it on the pattern graph associated with the matrix. A heuristic procedure for finding direct covers is sketched and some preliminary numerical test results are provided.
Learning Transferable Features For Open-Domain Question Answering

Corpora used to learn open-domain QuestionAnswering (QA) models are typically collected from a wide variety of topics or domains. Since QA requires understanding natural language, open-domain QA models generally need very large training corpora. A simple way to alleviate data demand is to restrict the domain covered by the QA model, leading thus to domain-specific QA models. While learning improved QA models for a specific domain is still challenging due to the lack of sufficient training data in the topic of interest, additional training data can be obtained from related topic domains. Thus, instead of learning a single open-domain QA model, we investigate domain adaptation approaches in order to create multiple improved domain-specific QA models. We demonstrate that this can be achieved by stratifying the source dataset, without the need of searching for complementary data unlike many other domain adaptation approaches. We propose a deep architecture that jointly exploits convolutional and recurrent networks for learning domain-specific features while transferring domain-shared features. That is, we use transferable features to enable model adaptation from multiple source domains. We consider different transference approaches designed to learn span-level and sentence-level QA models. We found that domainadaptation greatly improves sentence-level QA performance, and span-level QA benefits from sentence information. Finally, we also show that a simple clustering algorithm may be employed when the topic domains are unknown and the resulting loss in accuracy is negligible.
A dynamic transcriptomic atlas of cytokine-induced killer cells

Several clinical immunotherapy trials with cytokine-induced killer (CIK) cells have been reported. However, molecular evidence of cell expansion, acquisition of tumor cytotoxicity, and safety of CIK cells is required before putting them to clinical use. Here, we performed dynamic transcriptomic analyses of CIKs generated from primary peripheral blood mononuclear cells exposed to interferon-γ, OKT3, and interleukin-2. CIK mRNAs were extracted and sequenced at days 0, 1, 7, and 14 and subjected to bioinformatics analyses. Using weighted correlation network analysis (WGCNA), we identified two major gene modules that mediate immune cell activation and mitosis. We found that activation and cytotoxicity of CIK cells likely rely on cluster of differentiation 8 (CD8) and its protein partner LCK proto-oncogene, Src family tyrosine kinase (LCK). A time-course series analysis revealed that CIK cells have relatively low immunogenicity because of decreased expression of some self-antigens. Importantly, we identified several crucial activating receptors and auxiliary adhesion receptors expressed on CIK cells that may function as tumor sensors. Interestingly, cytotoxicity-associated genes, including those encoding PRF1, GZMB, FASL, and several cytokines, were up-regulated in mature CIK cells. Most immune-checkpoint molecules and inflammatory tumor-promoting factors were down-regulated in the CIK cells, suggesting efficacy and safety in future clinical trials. Notably, insulin-like growth factor 1 (IGF-1) was highly expressed in CIK cells and may promote cytotoxicity, although it also could facilitate tumorigenesis. The transcriptomic atlas of CIK cells presented here may inform efforts to improve CIK-associated tumor cytotoxicity and safety in clinical trials.
Security Matters: A Survey on Adversarial Machine Learning

Adversarial machine learning is a fast growing research area, which considers the scenarios when machine learning systems may face potential adversarial attackers, who intentionally synthesize input data to make a well-trained model to make mistake. It always involves a defending side, usually a classifier, and an attacking side that aims to cause incorrect output. The earliest studies on the adversarial examples for machine learning algorithms start from the information security area, which considers a much wider varieties of attacking methods. But recent research focus that popularized by the deep learning community places strong emphasis on how the "imperceivable" perturbations on the normal inputs may cause dramatic mistakes by the deep learning with supposed super-human accuracy. This paper serves to give a comprehensive introduction to a range of aspects of the adversarial deep learning topic, including its foundations, typical attacking and defending strategies, and some extended studies.
Effects of Data Sparsity on Recommender Systems based on Collaborative Filtering

E-commerce has shown to be a promising platform for nowadays business. Customers are now provided a wider set of items to choose from and a lot of companies are migrating from shelf-stores to online markets and offering their products on the web. Because of this broader online catalog, Recommender Systems have been widely used to exhibit the most appropriate items to users given their past consumption preferences. Nonetheless, available data tends to be highly sparse since users only evaluate a small fraction of available items. Recent techniques such as Matrix Factorization and Deep Learning’s Autoencoders have demonstrated to be effective on recommendations, yet sparsity effects on such techniques are still unclear. In order to provide the effects of sparsity changes on recommender systems, this paper compares three different algorithms, namely Non-negative Matrix Factorization, Singular Value Decomposition and Stacked Autoencoders, under specific sparsity scenarios of the MovieLens 100k dataset.
Recognition of Endangered Pantanal Animal Species using Deep Learning Methods

Pantanal is one of the most important biomes of the world, with a large number of wild animal species, some of them are in extinction. The automatic identification of wild animals is extremely important for the estimation of the species’ population within Pantanal. However, digital processing techniques for the identification and tracking of species have faced great challenges due to clumsy light and pose conditions present in images taken in the wild. To overcome such problems, we propose a methodology that, by combining regular RGB images and thermal images, improves the identilication of species even in images taken in rough circumstances. We use the SLIC segmentation algorithm to identify the regions of the images where animals are present; after that, we apply convolutional neural networks to classify the identified regions according to eight possible animal species. We experiment on a real-world dataset composed of 1,600 images. Our results showed an average gain between 6% and 10% when compared to the method Fast R-CNN.
Neural Network based Distributed Adaptive Time-varying Formation Control for Multi-UAV Systems with Varying Time Delays

This paper investigates the time-varying formation control problem for multiple unmanned aerial vehicle (multi- UAV) systems with unknown uncertainties and varying time delays. Firstly, a radial basis function neural network (RBFNN) is adopted to estimate the lumped model uncertainties online for compensation. Then, a novel RBFNN-based fully distributed adaptive control scheme consisting of control law to stabilize the system and adaptive law to adjust RBFNN weights is developed to tackle the time-varying formation tracking problem in the presence of varying time delay. The uniformly ultimately boundedness (UUB) of the formation tracking errors is theoretically analyzed through Lyapunov approach. Comparative simulation results demonstrate the effectiveness of the control and adaptive laws proposed in this paper.
Predicting Nodule Malignancy using a CNN Ensemble Approach

Lung cancer is the leading cause of cancer-related deaths globally, which makes early detection and diagnosis a high priority. Computed tomography (CT) is the method of choice for early detection and diagnosis of lung cancer. Radiomics features extracted from CT-detected lung nodules provide a good platform for early detection, diagnosis, and prognosis. In particular when using low dose CT for lung cancer screening, effective use of radiomics can yield a precise non-invasive approach to nodule tracking. Lately, with the advancement of deep learning, convolutional neural networks (CNN) are also being used to analyze lung nodules. In this study, our own trained CNNs, a pre-trained CNN and radiomics features were used for predictive analysis. Using subsets of participants from the National Lung Screening Trial, we investigated if the prediction of nodule malignancy could be further enhanced by an ensemble of classifiers using different feature sets and learning approaches. We extracted probability predictions from our different models on an unseen test set and combined them to generate better predictions. Ensembles were able to yield increased accuracy and area under the receiver operating characteristic curve (AUC). The best-known AUC of 0.96 and accuracy of 89.45% were obtained, which are significant improvements over the previous best AUC of 0.87 and accuracy of 76.79%.