Miracle-dz/UniFAR-FTUs huggingface dataset:
DESCRIÇÃO DE SINTOMAS VISUAIS EM FUNÇÃO DAS DEFICIÊNCIAS DE MACRONUTRIENTES EM MUDAS DE CEREJEIRA ( Amburana acreana )

Description of visual symptoms under macronutrients deficiency in seedlings of cerejeira (Amburana acreana ducke). This study aimed to identify the visual symptoms of deficiencies of nutrients and dry mass production in seedlings of cherry ( Amburana acreana ), grown in nutrient solution. Cherry seedlings were grown in tubes 180 cm≥ and later tra nsplanted into plastic bags (17 x 10 cm). Upon reaching 20 cm height were transplanted to PVC tubes filled with sand and subjected to 30 days to complete nutritive solution, corresponding to the p eriod of adaptation. Then the seedlings were subjected for 90 days, to complete nutrient solutio ns and with the omission of N, P, K, Ca, Mg, S, constituting seven treatments and three replication s in a randomized design. Every 30 days, the seedlings were assessed for height and diameter, an d deficiency symptoms were identified and photographed. After 90 days of application of nutri ent solution, the seedlings were dried and the dry biomass was determinate. It was concluded that P, S and N were the macronutrients that limit the growth of seedlings Amburana acreana and P and S, the dry mass production. There was gr eater absorption of N, P and K in shoots of the seedlings and N and Mg by root.
K-means++ clustering-based active contour model for fast image segmentation

Abstract. Intensity inhomogeneity often occurs in real-life images because of nonuniform illumination, device operating, and technical limitation. We propose a K-means++ clustering-based active contour model for fast image segmentation. The key point is two fitting functions whose value computed by K-means++ clustering algorithm before level set function evolution. At first, we set up a rectangular local window. Two fitting functions represent the center points of brighter and darker subregions in the moving rectangular local windows. The method avoids repeating calculation of the fitting functions during curve evolution compared with the traditional region-based active contour models. Therefore, the proposed model has lower computational costs, and we can obtain correct segmentation results in less time and fewer iterations. The proposed model can efficiently segment images with intensity inhomogeneity. In addition, the experiments have proved that the proposed model has strong robustness to initialization.
A Data Cleaning Framework for Water Quality Based on NLDIW-PSO Based Optimal SVR

Water quality monitoring is an essential part of water big data analysis. Spatiotemporal variations of water quality and constraints on measurement make it very complex. The objective of this study is to establish a water quality data cleaning framework based on time series, in order to clean the water quality data of the Gaobeidian Sewage Treatment Plant inlet in Beijing. Pauta criterion was used to deal with single water quality indicator. For abnormal values and missing values that are discontinuously distributed over time, the average of the non-abnormal data for three days before and after was used to fill it; For abnormal values and missing values that are continuously distributed over time, using the Non-Linear decreasing inertia weight particle swarm algorithm (NLDIW-PSO) based optimal Support Vector Regression (SVR) to forecast. And Pearson's correlation coefficient was used to reduce the dimension of the inputs of the model, k-fold cross validation was also used to train the model. The performance of the model was evaluated in terms of the coefficient of determination (R2), Pearson's correlation coefficient. Water quality data of Gaobeidian wastewater treatment inlet in Beijing, China was taken as the study case to examine effectiveness of this approach. The experiment results also revealed that the proposed model has advantages of stability and time reduction in comparison with other data-driven models including traditional BP ANN, Bayesian network model and Decision Tree model. And this framework can be used as an effective approach to deal with General time series data.
MULTIFOCUS: MULTImodal Learning Analytics FOr Co-located Collaboration Understanding and Support

This PhD research project has multiple focus points. Using the help of Multimodal Learning Analytics with the help of sensors to understand how co-located collaboration takes place, identifying the indicators of collaboration (such as pointing at peer, looking at peer, making constructive interruptions, etc.) and designing a collaboration framework model which defines the aspects of successful collaboration. These insights will help us to build the support framework to enable efficient real-time feedback during a group activity to facilitate co-located
A Survey of Sparse-Learning Methods for Deep Neural Networks

Deep neural networks (DNNs) has drawn considerable attention in recent years as a result of their remarkable performace in many visual and speech recognition assignments. As the scale of tasks that need to solve is increasingly big, the networks used also become wider and deeper, resulting in millions or even billions of parameters needed. Deep and wide networks with large number of parameters bring many problems, including memory requirement, computation cost and overfitting, which severely hinder the application of DNNs in practice. Therefore, a natural thought is to train sparse networks with less parameters and float operators while maintaining comparable performance. During past few years, a mass of research has been proposed in this area. In this paper, we survey sparsity-promoting techniques in DNNs proposed in recent years. These approaches are roughly divided into three categories, including pruning, randomly reducing the complexity and optimizing with sparse regularizer. Pruning techniques will be introduced first and others will be described in the following section. For each kind of methods, we present approaches in this category, strengths and drawbacks. In the final, we will discuss the relationship of these three categories of methods.
Computer-Assisted Diagnosis for Diabetic Retinopathy Based on Fundus Images Using Deep Convolutional Neural Network

Diabetic retinopathy (DR) is a complication of long-standing diabetes, which is hard to detect in its early stage because it only shows a few symptoms. Nowadays, the diagnosis of DR usually requires taking digital fundus images, as well as images using optical coherence tomography (OCT). Since OCT equipment is very expensive, it will benefit both the patients and the ophthalmologists if an accurate diagnosis can be made, based solely on reading digital fundus images. In the paper, we present a novel algorithm based on deep convolutional neural network (DCNN). Unlike the traditional DCNN approach, we replace the commonly used max-pooling layers with fractional max-pooling. Two of these DCNNs with a different number of layers are trained to derive more discriminative features for classification. After combining features from metadata of the image and DCNNs, we train a support vector machine (SVM) classifier to learn the underlying boundary of distributions of each class. For the experiments, we used the publicly available DR detection database provided by Kaggle. We used 34,124 training images and 1,000 validation images to build our model and tested with 53,572 testing images. The proposed DR classifier classifies the stages of DR into five categories, labeled with an integer ranging between zero and four. The experimental results show that the proposed method can achieve a recognition rate up to 86.17%, which is higher than previously reported in the literature. In addition to designing a machine learning algorithm, we also develop an app called “Deep Retina. ” Equipped with a handheld ophthalmoscope, the average person can take fundus images by themselves and obtain an immediate result, calculated by our algorithm. It is beneficial for home care, remote medical care, and self-examination.
SAIFE: Unsupervised Wireless Spectrum Anomaly Detection with Interpretable Features

Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer complexity of the electromagnetic spectrum use. Wireless spectrum anomalies can take a wide range of forms from the presence of an unwanted signal in a licensed band to the absence of an expected signal, which makes manual labeling of anomalies difficult and suboptimal. We present, Spectrum Anomaly Detector with Interpretable FEatures (SAIFE), an Adversarial Autoencoder (AAE) based anomaly detector for wireless spectrum anomaly detection using Power Spectral Density (PSD) data which achieves good anomaly detection and localization in an unsupervised setting. In addition, we investigate the model’s capabilities to learn interpretable features such as signal bandwidth, class and center frequency in a semi-supervised fashion. Along with anomaly detection the model exhibits promising results for lossy PSD data compression up to 120X and semi-supervised signal classification accuracy close to 100% on three datasets just using 20% labeled samples. Finally the model is tested on data from one of the distributed Electrosense sensors over a long term of 500 hours showing its anomaly detection capabilities.
TV white space and its applications in future wireless networks and communications: a survey

In 2008, the Federal Communications Commission issued a ruling permitting the unlicensed usage of TV white spaces (TVWS), i.e. locally vacant TV channels. Due to its low-frequency range (50-698 MHz), the TV spectrum has much better propagation characteristic and higher-spectral efficiency, resulting in a wide range of potentially important applications. However, unlike typical cellular and industrial scientific medical bands, TVWS are subjected to high-spatial variation, temporal variation, and fragmentation, resulting in new challenges in TVWS identification and in implementing a wireless network in this band. Identification and network design are the two key issues required to be addressed while investigating TVWS. These two problems have been widely discussed in several existing literature. Applications in TVWS are also an important topic, which has not been adequately explored. This study provides an up-to-date survey of TVWS and its applications in future wireless networks and communication. Various problems and challenges associated with each use case as well as the possible enabling methods to address these challenges are also presented.
Unsupervised Facial Image De-occlusion with Optimized Deep Generative Models

In recent years, Generative Adversarial Networks (GANs) or various types of Auto-Encoders (AEs) have gained attention on facial image de-occlusion and/or in-painting tasks. In this paper, we propose a novel unsupervised technique to remove occlusion from facial images and complete the occluded parts simultaneously with optimized Deep Convolutional Generative Adversarial Networks (DCGANs) in an iterative way. Generally, GANs, as generative models, can estimate the distribution of images using a generator and a discriminator. DCGANs, as its variant, are proposed to conquer its instability during training. Existing facial image in-painting methods manually define a block of pixels as the missing part and the potential content of this block is semantically generated using generative models, such as GANs or AEs. In our method, a mask is inferred from an occluded facial image using a novel loss function, and then this mask is utilized to in-paint the occlusions automatically by pre-trained DCGANs. We evaluate the performance of our method on facial images with various occlusions, such as sunglasses and scarves. The experiments demonstrate that our method can effectively detect certain kinds of occlusions and complete the occluded parts in an unsupervised manner.
Sentiment Classification on Twitter Data Using Support Vector Machine

Sentiment analysis in Twitter has really engaged interest in field of research. Sentiment classification in Twitter deals with analyzing the tweets in terms of their sentiment polarity. The proposed method deals with twitter sentiment classification by employing a classification model of machine learning domain which makes use of different textual features viz. n-grams of twitter data. Also, we have used three different weighting schemes to understand the impact of weighting on classifier accuracy. Furthermore, a sentiment score vector of tweets is used to provide external knowledge in order to improve the performance of SVM classifier.
Bimodal IT career aspirations in Bangladesh

Saxenian's concept of "brain circulation" suggests that the information technology sectors of developing countries benefit from having their brightest talent move and work abroad, as some then return home with new skills and networks. This accelerates economic growth within their country of origin. A key first step for good brain circulation is the initial emigration of a country's talent. We consider this issue for Bangladesh - which compared with India has seen far less brain circulation - and focus on university students' aspirations for careers abroad. Based on a survey distributed to 595 undergraduate IT students, we find that students' aspirations bifurcate into those hoping to work in English-speaking developed countries and those expecting to remain in Bangladesh.
Work in progress: Fearful users' privacy intentions: an empirical investigation

Background. While recent research has found that the affect dimension of privacy attitude is fear focused [14], fear is known in psychology literature to be asymmetric to one's self-efficacy [5], that is one's belief in successfully solving a problem. Therefore the question arises how a user feeling fear or one warned about privacy, intends to act. In particular whether the user intends to take protective or defensive action. Aim. We investigate the relationship between incidental affect state of [fear/ happiness], self-efficacy and privacy intention. Method. We conduct an observational Study 1, with N = 175 and an experimental Study 2, with N = 200. In both studies, we measure general self-efficacy, affect state and privacy intentions. In Study 2, we induce affect states of [fear/happiness] via autobiographical recall of emotive events and a 150-word free-form report. Results. A meta-analysis of correlation across both studies shows a negative correlation between Fear and Self-Efficacy. Fear is also negatively correlated to information disclosure, protection and refusal to transact intentions. We found a good fit for the data in a structural equation model, with causal links from Fear/Happiness to Self-Efficacy and from Self-Efficacy to protection intention. Conclusions. This research is novel in measuring fear together with self-efficacy and privacy intentions. The findings suggest that while the fearful user has a not-disclosing intention, in parallel, he also does not have a protection intention. While this research contributes to understanding the privacy paradox, it also suggests that a culture of fear online is not conducive to users actively protecting their privacy. In fact fear can lead to user dis-empowerment because of its influence on self-efficacy. These initial findings supports our larger research vision for deeper investigation of human affect in cyber security and privacy [11].
99DOTS: a low-cost approach to monitoring and improving medication adherence

Ensuring that patients adhere to prescribed medication remains an important challenge in global health. While technology has been utilized to monitor and improve adherence, solutions to date have been too costly for large-scale deployment in developing regions. This paper describes 99DOTS, a low-cost approach for tracking adherence using a combination of paper packaging and low-end mobile phones. Every day, patients reveal an unpredictable phone number behind the pills and send a free call to that number to indicate that drugs were dispensed and taken. Within five years of its inception, 99DOTS has become a standard of care for tuberculosis in India and has enrolled over 200,000 patients. We provide a holistic account of the project's evolution, including its iterative design, scaled implementation, and lessons learned along the way. We hope this account will serve as a useful case study for anyone seeking to establish and scale new low-cost technologies for a global audience.
Reducing Writebacks Through In-Cache Displacement

Non-Volatile Memory (NVM) technology is a promising solution to fulfill the ever-growing need for higher capacity in the main memory of modern systems. Despite having many great features, however, NVM’s poor write performance remains a severe obstacle, preventing it from being used as a DRAM alternative in the main memory. Most of the prior work targeted optimizing writes at the main memory side and neglected the decisive role of upper-level cache management policies on reducing the number of writes. In this article, we propose a novel cache management policy that attempts to maximize write-coalescing in the on-chip SRAM last-level cache (LLC) for the sake of reducing the number of costly writes to the off-chip NVM. We decouple a few physical ways of the LLC to have a dedicated and exclusive storage for the dirty blocks after being evicted from the cache and before being sent to the off-chip memory. By displacing dirty blocks in exclusive storage, they are kept in the cache based on their rewrite distance and are evicted when they are unlikely to be reused shortly. To maximize the effectiveness of exclusive storage, we manage it as a Cuckoo Cache to offer associativity based on the various applications’ demands. Through detailed evaluations targeting various single- and multi-threaded applications, we show that our proposal reduces the number of writebacks by 21%, on average, over the state-of-the-art method and enhances both performance and energy efficiency.
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting

We present a large-scale study of gender bias in occupation classification, a task where the use of machine learning may lead to negative outcomes on peoples' lives. We analyze the potential allocation harms that can result from semantic representation bias. To do so, we study the impact on occupation classification of including explicit gender indicators---such as first names and pronouns---in different semantic representations of online biographies. Additionally, we quantify the bias that remains when these indicators are "scrubbed," and describe proxy behavior that occurs in the absence of explicit gender indicators. As we demonstrate, differences in true positive rates between genders are correlated with existing gender imbalances in occupations, which may compound these imbalances.
SynergyFlow

Neural networks (NNs) have achieved great success in a broad range of applications. As NN-based methods are often both computation and memory intensive, accelerator solutions have been proved to be highly promising in terms of both performance and energy efficiency. Although prior solutions can deliver high computational throughput for convolutional layers, they could incur severe performance degradation when accommodating the entire network model, because there exist very diverse computing and memory bandwidth requirements between convolutional layers and fully connected layers and, furthermore, among different NN models. To overcome this problem, we proposed an elastic accelerator architecture, called SynergyFlow, which intrinsically supports layer-level and model-level parallelism for large-scale deep neural networks. SynergyFlow boosts the resource utilization by exploiting the complementary effect of resource demanding in different layers and different NN models. SynergyFlow can dynamically reconfigure itself according to the workload characteristics, maintaining a high performance and high resource utilization among various models. As a case study, we implement SynergyFlow on a P395-AB FPGA board. Under 100MHz working frequency, our implementation improves the performance by 33.8% on average (up to 67.2% on AlexNet) compared to comparable provisioned previous architectures.
Controlling Polarization in Personalization: An Algorithmic Framework

Personalization is pervasive in the online space as it leads to higher efficiency for the user and higher revenue for the platform by individualizing the most relevant content for each user. However, recent studies suggest that such personalization can learn and propagate systemic biases and polarize opinions; this has led to calls for regulatory mechanisms and algorithms that are constrained to combat bias and the resulting echo-chamber effect. We propose a versatile framework that allows for the possibility to reduce polarization in personalized systems by allowing the user to constrain the distribution from which content is selected. We then present a scalable algorithm with provable guarantees that satisfies the given constraints on the types of the content that can be displayed to a user, but -- subject to these constraints -- will continue to learn and personalize the content in order to maximize utility. We illustrate this framework on a curated dataset of online news articles that are conservative or liberal, show that it can control polarization, and examine the trade-off between decreasing polarization and the resulting loss to revenue. We further exhibit the flexibility and scalability of our approach by framing the problem in terms of the more general diverse content selection problem and test it empirically on both a News dataset and the MovieLens dataset.
Cloud Segmentation of Remote Sensing Images on Landsat-8 by Deep Learning

Cloud is always the weak and even uninformative area inevitably existing in the remote sensing images, and greatly limits the development of remote sensing application. Accurate and automatic detection and segmentation of clouds in satellite scenes is a key problem for the application of remote sensing images. In this paper, we propose a fully convolutional network (FCN) based on deep learning framework for cloud segmentation from remote sensing images. First, the obtained remote sensing image is pre-processed by using band merging and size cropping. Second the image is input into the FCN to achieve the cloud segmentation. To demonstrate the effectiveness of the method, we conduct experiments on Landsat-8 satellite dataset. The overall accuracy of our proposed method for cloud segmentation is higher than 88%. Experimental results demonstrate that both thin and thick cloud can be well segmented with higher accuracy and robustness by using our method.