Miracle-dz/UniFAR-FTUs huggingface dataset:
MPB-CNN: a multi-scale parallel branch CNN for choroidal neovascularization segmentation in SD-OCT images

Choroidal neovascularization (CNV) generally appears in the advanced stage of age-related macular degeneration (AMD) and has more complex pathological characteristics comparing with most other retinal diseases. So far, few literatures focused on the CNV segmentation from a machine learning and computer vision perspective. Therefore, accurate CNV segmentation is a challenging problem in SD-OCT images. In this paper, a multi-scale parallel branch CNN (MPB-CNN) is proposed for CNV segmentation. First, three parallel branch networks are used for multi-scale feature extraction. In each branch, standard convolution is replaced with atrous convolution, in which wider and more powerful multi-scale information can be extracted due to the sparsity of convolutional kernels. To further improve the segmentation results, intra-branch connections are introduced to preserve signal transmission and inter-branch connections are introduced to interact multi-scale information. Then, feature maps from different branches are cascaded with low-level features computing by several convolutional layers. The combined feature is used as the input to acquire the final prediction map by stacking three convolutional layers. Besides, extra branch supervisions are applied at the end of each branch to guarantee the discrimination of feature representations from each branch and benefit the network optimization. Finally, gradient constraint is added to loss function to preserve the boundary of the CNV lesion. An effective cross validation is performed on 202 cubes from 12 patients based on patient independence with the mean dice value being 0.757 and the mean overlap ratio being 60.8%. Experiment results indicated that the proposed MPB-CNN can provide reliable segmentations for CNV from SD-OCT images.
Personalized Educational Process Models with Spacing Effect

Teaching new material and reviewing what has already been taught are two important activities in the educational process. Different students with different learning abilities need to review at different rates, that is to say, a personalized teaching process is necessary. Review has a spacing effect, namely it is most useful only if it is executed neither too soon nor too late. How should designers of educational software schedule the learning process to satisfy the need that different students learn different material? We present a mathematical model to capture the issue in idealized form. The learning abilities of students and the spacing effect of review are modeled as some input parameters’ constraints on the schedule. Our results include the optimal scheduling in accordance with which tutoring systems teach one student specific material, characteristics of the rate at which new material can be introduced under different conditions, and the reproduction of ladder-like educational process.
Wind farm simulations using an overset hp-adaptive approach with blade-resolved turbine models

Blade-resolved numerical simulations of wind energy applications using full blade and tower models are presented. The computational methodology combines solution technologies in a multi-mesh, multi-solver paradigm through a dynamic overset framework. The coupling of a finite volume solver and a high-order, hp-adaptive finite element solver is utilized. Additional technologies including in-situ visualization and atmospheric microscale modeling are incorporated into the analysis environment. Validation of the computational framework is performed on the National Renewable Energy Laboratory (NREL) 5MW baseline wind turbine, the unsteady aerodynamics experimental NREL Phase VI turbine, and the Siemens SWT-2.3-93 wind turbine. The power and thrust results of all single turbine simulations agree well with low-fidelity model simulation results and field experiments when available. Scalability of the computational framework is demonstrated using 6, 12, 24, 48, and 96 wind turbine setups including the 48 turbine wind plant known as Lillgrund. The largest case consisting of 96 wind turbines and a total of 385 overset grids are run on 44,928 cores at a weak scaling efficiency of 86%. Demonstration of the coupling of atmospheric microscale and Computational Fluid Dynamics (CFD) solvers is presented using the National Center for Atmospheric Research (NCAR) Weather Research and Forecasting Model (WRF) solver and the NREL Simulator fOr Wind Farm Applications (SOWFA) solver.
Matching Fusion with Conceptual Indexing

Many studies have been addressed the term-mismatch problem, which arises when using different terms or words for expressing the same meaning. We also introduce another problem: over-specialized document, which is caused when IR systems prefer documents that have poor query-document intersection, but with high weighting value, to those that have rich query-document intersection with low weighting value. In this study, we propose to use, simultaneously, multiple types of indexing elements: ngrams, keywords, and concepts, instead of only keywords. We followed a late data-fusion technique to achieve that. Through our proposed model, we also try to overcome the over-specialized document problem. Experiments for model validation have been done by using ImageCLEF2011 test collection, UMLS2009 Meta-thesaurus, and MetaMap tool for mapping text into UMLS concepts.
Accurate Plant Modeling based on the Real Light Incidence

In this paper, we propose a framework for accurate plant modeling constrained to actual plant-light interaction along a time-interval. To this end, several plant models have been generated by using data from different sources such as LiDAR scanning, optical cameras and multispectral sensors. In contrast to previous approaches that mostly focus on realistic rendering purposes, the main objective of our method is to improve the multiview stereo reconstruction of plant structures and the prediction of the growth of existing plants according to the influence of real light incidence. Our experimental results are oriented to olive trees, which are formed by many thin branches and dense foliage. Plant reconstruction is a challenging task due to self-occlusion. Our approach is based on inverse modeling to generate a parametric model which describes how plants evolve in a time interval by considering the surrounding environment. A multispectral sensor has been used to characterize input plant models from reflectance values for each narrow-band. We propose the fusion of heterogeneous data to achieve a more accurate modeling of plant structure and the prediction of the branching fate.
Probabilistic Graphical Model on Detecting Insiders: Modeling with SGD-HMM

This paper presents a novel approach to detect malicious behaviors in computer systems. We propose the use of varying granularity levels to represent users’ log data: Session-based, Day-based, and Week-based. A user’s normal behavior is modeled using a Hidden Markov Model. The model is used to detect any deviation from the normal behavior. We also propose a Sliding Window Technique to identify malicious activity effectively by considering the near history of user activity. We evaluated our results using Receiver Operating Characteristic curves (or ROC curves). Our evaluation shows that the results are superior to existing research by improving the detection ability and reducing the false positive rate. Combining sliding window technique with sessionbased system gives a fast detection performance.
Semantic Segmentation of PolSAR Images Using Conditional Random Field Model Based on Deep Features

Aiming at the problem that the representation ability of traditional features is weakly, this paper proposes a semantic segmentation method based on deep convolutional neural network and conditional random field. The pre-trained VGG-Net-16 model is used to extract more powerful image features, and then the semantic segmentation of images is achieved through the efficient use of multiple features and context information by conditional random fields. The experimental results show that compared with the three methods using traditional classical features, the method achieves the highest overall classification accuracy and Kappa coefficient, indicating that VGG-Net-16 can extract more effective features.
Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network

Weight pruning has been introduced as an efficient model compression technique. Even though pruning removes significant amount of weights in a network, memory requirement reduction was limited since conventional sparse matrix formats require significant amount of memory to store index-related information. Moreover, computations associated with such sparse matrix formats are slow because sequential sparse matrix decoding process does not utilize highly parallel computing systems efficiently. As an attempt to compress index information while keeping the decoding process parallelizable, Viterbi-based pruning was suggested. Decoding non-zero weights, however, is still sequential in Viterbi-based pruning. In this paper, we propose a new sparse matrix format in order to enable a highly parallel decoding process of the entire sparse matrix. The proposed sparse matrix is constructed by combining pruning and weight quantization. For the latest RNN models on PTB and WikiText-2 corpus, LSTM parameter storage requirement is compressed 19× using the proposed sparse matrix format compared to the baseline model. Compressed weight and indices can be reconstructed into a dense matrix fast using Viterbi encoders. Simulation results show that the proposed scheme can feed parameters to processing elements 20 % to 106 % faster than the case where the dense matrix values directly come from DRAM.
Word-Based POMDP Dialog Management via Hybrid Learning

Dialog management plays an important role in the task-oriented dialog system. Most of the previous works divide dialog management into state tracker and action selector. The two parts are modeled separately and implemented in a pipelined way, which suffers from the problem of error accumulation, and the feedback signal from action selector cannot be propagated to state tracker and natural language understanding module. This paper proposes a word-based partially observable Markov decision processes’ dialog management that integrates natural language understanding, state tracker, and action selector into an end-to-end architecture. Our proposed dialog management takes the words from user utterances as inputs and then produces optimal action as well as slot values of natural language understanding which are necessary for response generation. To this end, we propose a hybrid learning method, which integrates reinforcement learning and supervised learning, to optimize the action selector and slot filler jointly. In addition, we develop a high-return prioritized experience replay to speed up the convergence of the training process. The experimental results show that the proposed dialog management outperforms four strong baselines in a series of different dialog tasks. A human user’s evaluation also shows the same results. The high-return prioritized experience replay accelerates the convergence effectively, especially in the scenario in which the proposed dialog management works on more complex tasks.
Vowel Recognition using Facial Movement (SEMG) for Speech Control based HCI

This paper examines the use of facial muscle activity (Surface Electromyogram) to recognize speech based commands in English without any audio signals. The system is designed for applications based on speech control for Human Computer Interaction (HCI). The paper presents an effective technique that uses the facial muscle activity of the articulatory muscles and human factors for recognition. In these investigations, three English vowels were used as recognition variables. The moving root mean square (RMS) of surface electromyogram (SEMG) of four facial muscles is used to segment the signal and to identify the start and end of a silently spoken utterance. The relative muscle activity is computed by integrating and normalizing the RMS values of the signals between the detected start and end markers. The featured data of this is classified using a back propagation neural network to identify the voiceless speech. The experimental results show that this technique gives high recognition rate when used for each of the participants. The investigations also show that the system is easy to train for a new user.
Active multiple matrix completion with adaptive confidence sets

In this work, we formulate a new multi-task active learning setting in which the learner's goal is to solve multiple matrix completion problems simultaneously. At each round, the learner can choose from which matrix it receives a sample from an entry drawn uniformly at random. Our main practical motivation is market segmentation, where the matrices represent different regions with different preferences of the customers. The challenge in this setting is that each of the matrices can be of a different size and also of a different rank which is unknown. We provide and analyze a new algorithm, MALocate that is able to adapt to the unknown ranks of the different matrices. We then give a lower-bound showing that our strategy is minimax-optimal and demonstrate its performance with synthetic experiments.
Forecasting Hotel Room Sales within Online Travel Agencies by Combining Multiple Feature Sets

Hotel Room Sales prediction using previous booking data is a prominent research topic for the online travel agency (OTA) sector. Various approaches have been proposed to predict hotel room sales for different prediction horizons, such as yearly demand or daily number of reservations. An OTA website includes offers of many companies for the same hotel, and the position of the company’s offer in OTA website depends on the bid amount given for each click by the company. Therefore, the accurate prediction of the sales amount for a given bid is a crucial need in revenue and cost management for the companies in the sector. In this paper, we forecast the next day’s sales amount in order to provide an estimate of daily revenue generated per hotel. An important contribution of our study is to use an enriched dataset constructed by combining the most informative features proposed in various related studies for hotel sales prediction. Moreover, we enrich this dataset with a set of OTA specific features that possess information about the relative position of the company’s offers to that of its competitors in a travel metasearch engine website. We provide a real application on the hotel room sales data of a large OTA in Turkey. The comparative results show that enrichment of the input representation with the OTA-specific additional features increases the generalization ability of the prediction models, and tree-based boosting algorithms perform the best results on this task.
Clustering Honeybees by Its Daily Activity

In this work, we analyze the activity of bees starting at 6 days old. The data was collected at the INRA (France) during 2014 and 2016. The activity is counted according to whether the bees enter or leave the hive. After data wrangling, we decided to analyze data corresponding to a period of 10 days. We use clustering method to determine bees with similar activity and to estimate the time during the day when the bees are most active. To achieve our objective, the data was analyzed in three different time periods in a day. One considering the daily activity during in two periods: morning and afternoon, then looking at activities in periods of 3 hours from 8:00am to 8:00pm and, finally looking at the activities hourly from 8:00am to 8:00pm. Our study found two clusters of bees and in one of them clearly the bees activity increased at the day 5. The smaller cluster included the most active bees representing about 24 percent of the total bees under study. Also, the highest activity of the bees was registered between 2:00pm until 3:00pm. A Chi-square test shows that there is a combined effect Treatment× Colony on the clusters formation.
Ontology and HMAX Features-based Image Classification using Merged Classifiers

Bag-of-Viusal-Words (BoVW) model has been widely used in the area of image classification, which rely on building visual vocabulary. Recently, attention has been shifted to the use of advanced architectures which are characterized by multilevel processing. HMAX model (Hierarchical Max-pooling model) has attracted a great deal of attention in image classification. Recent works, in image classification, consider the integration of ontologies and semantic structures is useful to improve image classification. In this paper, we propose an approach of image classification based on ontology and HMAX features using merged classifiers. Our contribution resides in exploiting ontological relationships between image categories in line with training visual-feature classifiers, and by merging the outputs of hypernym-hyponym classifiers to lead to a better discrimination between classes. Our purpose is to improve image classification by using ontologies. Several strategies have been experimented and the obtained results have shown that our proposal improves image classification. Results based our ontology outperform results obtained by baseline methods without ontology. Moreover, the deep learning network Inception-v3 is experimented and compared with our method, classification results obtained by our method outperform Inception-v3 for some image classes.
Deep convolutional neural networks compression method based on linear representation of kernels

Convolutional Neural Networks (CNNs) are getting larger and deeper, and thus becoming harder to be deployed on systems with limited resources. Though convolutional filters benefit from the concept of receptive field, they still take up lots of resources to store these parameters in the large amounts of filters. Therefore, a compression method of pre-trained CNN models using "Linear Representation" of convolutional kernels is introduced in this paper. First, a codebook of template kernels "Kt". are generated by conducting unsupervised clustering on all convolutional kernels, with Pearson Correlation Coefficient set as distance. Then all the convolutional kernels are represented by the closest templates using linear fitting function a • Kt + b , which means that only two parameters and a codebook index are enough to represent a kernel. After that, the model is retrained with fixed template kernels and only two related parameters need to be finetuned for each kernel. Experiments show that convolutional kernels of a large CNN model can be represented using only a small amount of templates. Thus, this method can reach a compression rate of convolutional layers near 4×, with tiny impact on precision after retraining. Nevertheless, the proposed method can be performed with other compression approaches to get higher compression rate.
A novel hybrid credit scoring model based on ensemble feature selection and multilayer ensemble classification

Credit scoring focuses on the development of empirical models to support the financial decision‐making processes of financial institutions and credit industries. It makes use of applicants' historical data and statistical or machine learning techniques to assess the risk associated with an applicant. However, the historical data may consist of redundant and noisy features that affect the performance of credit scoring models. The main focus of this paper is to develop a hybrid model, combining feature selection and a multilayer ensemble classifier framework, to improve the predictive performance of credit scoring. The proposed hybrid credit scoring model is modeled in three phases. The initial phase constitutes preprocessing and assigns ranks and weights to classifiers. In the next phase, the ensemble feature selection approach is applied to the preprocessed dataset. Finally, in the last phase, the dataset with the selected features is used in a multilayer ensemble classifier framework. In addition, a classifier placement algorithm based on the Choquet integral value is designed, as the classifier placement affects the predictive performance of the ensemble framework. The proposed hybrid credit scoring model is validated on real‐world credit scoring datasets, namely, Australian, Japanese, German‐categorical, and German‐numerical datasets.
Development of a Decision Support System to increase the Tea Crops yield

Pakistan is the largest consumer of tea, but not the biggest producer of it. To fulfill its needs Pakistan expends a large amount of foreign reserve to import tea. To save foreign reserves and fulfill tea needs, we have to make precise decisions at the right time to increase the tea yield to maximize the utilization of land suitable for it. The factors on the basis of which farmers, researchers and the government make decisions to increase tea crop yield are environmental and soil conditions such as soil PH level, humidity level and rainfall level as well as Plucking rounds. We can use these factors to make a right and timely decision by using the decision support system with data mining because it encompasses both classical statistics and modern machine learning techniques. To develop the Decision Support System with Data mining we use different methods for decision making, such as decision trees, naive Bayes, neural network and linear regression. These techniques utilize the dataset for factors affecting the tea yield, which is collected from black tea farms located in Shinkyari, Mansehra to build and train a data mining model to discover hidden patterns and relationships. Once we get a stable data mining model for decision support system using different techniques, we can have accuracy through cross validation for each. In this regard neural network has higher accuracy followed by decision tree and then other techniques. In this study, decision support system is developed to find the best environmental conditions to maximize the tea plan production.
Identifying People through Iris Recognition

— a biometric framework provides automatic identity proof for an individual based on the individual's unique characteristics or features. As the demand for safe identification grows, and the human iris provides an excellent pattern for identification, the use of low-cost equipment could enable iris recognition to become a new standard in security. The most reliable and exact biometric identification framework known is iris recognition. To evaluate the effectiveness of pupil person identification, pixel density, and acceptance rate, a test case based on open source software can be constructed. The image quality of photographs as data from a collection acquired from a typical camera is surveyed in this research, and the most critical issue areas are identified, as well as the underlying recognition performance. The primary goal of this initiative is to investigate the eye's distinctive iris pattern. Antiquities in the image, such as different types of noise and reflections from light sources, artifacts that add mistakes in the iris recognition process, affect the execution while imaging the iris under less than ideal conditions. The main purpose of this project is to investigate the eye's distinctive iris pattern. Antiquities in the image, such as different types of noise and reflects from light sources, artifacts that add mistakes in the iris recognition task, influence the execution while imaging the iris under less than ideal conditions.