Miracle-dz/UniFAR-FTUs huggingface dataset:
The relationship between fish abundance and benthic community structure on artificial reefs in the Mid-Atlantic Bight, and the importance of sea whip corals Leptogorgia virgulata

Autogenic engineers (i.e., biogenic structure) add to habitat complexity by altering the environment by their own physical structures. The presence of autogenic engineers is correlated with increases in species abundance and biodiversity. Biogenic structural communities off the coast of Delaware, Maryland, and Virginia (Delmarva) are comprised of multiple species including boring sponge Cliona celata, various hydroids (i.e., Tubularia sp., Obelia sp., Campanular sp.), northern stone coral Astrangia poculata, sea whips Leptogorgia virgulata, and blue mussels Mytilus edulis. Sea whips are soft corals that provide the majority of vertical height to benthic structure off the coast of the Delmarva peninsula. The mid-Atlantic bight is inhabited by several economically valuable fishes; however, data regarding habitat composition, habitat quality, and fish abundance are scarce. We collected quadrat and sea whip images from 12 artificial reef sites (i.e., shipwrecks) ranging from 10 to 24 m depth to determine proportional coverage of biogenic structures and to assess habitat health, respectively. Underwater video surveys were used to estimate fish abundances on the 12 study sites and determine if fish abundance was related to biogenic coverage and habitat health. Our results showed that higher fish abundance was significantly correlated with higher proportional sea whip coral coverage, but showed no significant relationship to other biogenic structure. Assessment of sea whip condition (as a damage index) showed that sea whip corals on artificial reefs off the Delmarva coast exhibited minor signs of degradation that did not differ significantly among study sites.
Lack of Association between the IL6R Gene Asp358Ala Variant (rs2228145), IL-6 Plasma Levels, and Treatment Resistance in Chilean Schizophrenic Patients Treated with Clozapine

Alterations in neuroinflammatory processes have been suggested to contribute to the development of Schizophrenia (SZ); one component of the inflammatory system that has been linked to this disorder is interleukin-6 (IL-6). The minor allele of rs2228145, a functional polymorphism in the IL-6 receptor gene, has been associated to elevated IL-6 plasma levels and increased inflammatory activity, making it an interesting candidate to study as a possible factor underlying clinical heterogeneity in SZ. We studied a sample of 100 patients undergoing treatment with clozapine. Their symptoms were quantified by Brief Psychotic Rating Scale; those with the lowest scores (“remitted”) were compared with the highest (“clozapine treatment resistant”). We determined allelic frequencies for rs2228145 and IL-6 plasma levels. Our results do not support a role of IL-6 in response to treatment with clozapine. Further studies accounting for potential confounding factors are necessary.
The Tsushima leopard cat exhibits extremely low genetic diversity compared with the Korean Amur leopard cat: Implications for conservation

We examined genetic diversity of the wild Tsushima leopard cat—a regional population of the Amur leopard cat—using microsatellite markers. In addition, we compared genetic diversity of the Tsushima leopard cat with that of the Korean population of Amur leopard cat. Although bias should be considered when applying cross-species amplification, the Tsushima leopard cat showed a lower index of molecular genetic diversity than did the Korean population. These results were consistent with those obtained using other genetic markers, such as mitochondrial DNA and Y chromosome sequences. This low genetic diversity of the wild Tsushima leopard cat may be derived from the founding population. Furthermore, our results suggest that the captive populations held in Japanese zoos may show extremely low genetic diversity, leading to difficulties in genetic management of the Tsushima leopard cat. Moreover, the two regional populations were clearly separated using these marker sets. In the present study, we demonstrated that the genetic diversity of the Tsushima leopard cat is extremely low compared with that of the continental regional population. Importantly, the Japanese captive population for ex situ conservation was derived from a founding population with extremely low genetic diversity; hence, we assume that both the captive and wild populations showed extremely low genetic diversities. Our findings emphasize the need to develop carefully considered management strategies for genetic conservation.
Regularizing Fully Convolutional Networks for Time Series Classification by Decorrelating Filters

Deep neural networks are prone to overfitting, especially in small training data regimes. Often, these networks are overparameterized and the resulting learned weights tend to have strong correlations. However, convolutional networks in general, and fully convolution neural networks (FCNs) in particular, have been shown to be relatively parameter efficient, and have recently been successfully applied to time series classification tasks. In this paper, we investigate the application of different regularizers on the correlation between the learned convolutional filters in FCNs using Batch Normalization (BN) as a regularizer for time series classification (TSC) tasks. Results demonstrate that despite orthogonal initialization of the filters, the average correlation across filters (especially for filters in higher layers) tends to increase as training proceeds, indicating redundancy of filters. To mitigate this redundancy, we propose a strong regularizer, using simple yet effective filter decorrelation. Our proposed method yields significant gains in classification accuracy for 44 diverse time series datasets from the UCR TSC benchmark repository.
Towards Gene Function Prediction via Multi-Networks Representation Learning

Multi-networks integration methods have achieved prominent performance on many network-based tasks, but these approaches often incur information loss problem. In this paper, we propose a novel multi-networks representation learning method based on semi-supervised autoencoder, termed as DeepMNE, which captures complex topological structures of each network and takes the correlation among multinetworks into account. The experimental results on two realworld datasets indicate that DeepMNE outperforms the existing state-of-the-art algorithms.
Robust Multi-Object Detection Based on Data Augmentation with Realistic Image Synthesis for Point-of-Sale Automation

As an alternative to bar-code scanning, we are developing a real-time retail product detector for point-of-sale automation. The major challenge associated with image based object detection arise from occlusion and the presence of other objects in close proximity. For robust product detection under such conditions, it is crucial to train the detector on a rich set of images with varying degrees of occlusion and proximity between the products, which fairly represents a wide range of customer tendencies of placing products together. However, generating a fairly large database of such images traditionally requires a large amount of human effort. On the other hand, acquiring individual object images with their corresponding masks is a relatively easy task. We propose an realistic image synthesis approach which uses individual object images and their corresponding masks to create training images with desired properties (occlusion and congestion among the products). We train our product detector over images thus generated and achieve a consistent performance improvement across different types of test data. With the proposed approach, detector achieves an improvement of 46.2% (from 0.67 to 0.98) and 40% (from 0.60 to 0.84) over precision and recall respectively, compared to using a basic training dataset containing one product per image.
Concurrency Debugging with MaxSMT

Current Maximum Satisfiability (MaxSAT) algorithms based on successive calls to a powerful Satisfiability (SAT) solver are now able to solve real-world instances in many application domains. Moreover, replacing the SAT solver with a Satisfiability Modulo Theories (SMT) solver enables effective MaxSMT algorithms. However, MaxSMT has seldom been used in debugging multi-threaded software.Multi-threaded programs are usually non-deterministic due to the huge number of possible thread operation schedules, which makes them much harder to debug than sequential programs. A recent approach to isolate the root cause of concurrency bugs in multi-threaded software is to produce a report that shows the differences between a failing and a non-failing execution. However, since they rely solely on heuristics, these reports can be unnecessarily large. Hence, reports may contain operations that are not relevant to the bug’s occurrence.This paper proposes the use of MaxSMT for the generation of minimal reports for multi-threaded software with concurrency bugs. The proposed techniques report situations that the existing techniques are not able to identify. Experimental results show that using MaxSMT can significantly improve the accuracy of the generated reports and, consequently, their usefulness in debugging the root cause of concurrency bugs.
T-Center: A Novel Discriminative Feature Extraction Approach for Iris Recognition

For large-scale iris recognition tasks, the determination of classification thresholds remains a challenging task, especially in practical applications where sample space is growing rapidly. Due to the complexity of iris samples, the classification threshold is difficult to determine with the increase of samples. The key issue to solving such threshold determination problems is to obtain iris feature vectors with more obvious discrimination. Therefore, we train deep convolutional neural networks based on a large number of iris samples to extract iris features. More importantly, an optimized center loss function referred to Tight Center (T -Center) Loss is used to solve the problem of insufficient discrimination caused by Softmax loss function. In order to evaluate the effectiveness of our proposed method, we use cosine similarity to estimate the similarity between the features on the published datasets CASIA-IrisV4 and IITD2.0. Our experiment results demonstrate that the T -Center loss can minimize intra-class variance and maximize inter-class variance, which achieve significant performance on the benchmark experiments.
Robust Principal Component Analysis-Based Infrared Small Target Detection

A method based on Robust Principle Component Analysis (RPCA) technique is proposed to detect small targets in infrared images. Using the low rank characteristic of background and the sparse characteristic of target, the observed image is regarded as the sum of a low-rank background matrix and a sparse outlier matrix, and then the decomposition is solved by the RPCA. The infrared small target is extracted from the single-frame image or multi-frame sequence. In order to get more efficient algorithm, the iteration process in the augmented Lagrange multiplier method is improved. The simulation results show that the method can detect out the small target precisely and efficiently.
Machine Learning with Crowdsourcing: A Brief Summary of the Past Research and Future Directions

With crowdsourcing systems, labels can be obtained with low cost, which facilitates the creation of training sets for prediction model learning. However, the labels obtained from crowdsourcing are often imperfect, which brings great challenges in model learning. Since 2008, the machine learning community has noticed the great opportunities brought by crowdsourcing and has developed a large number of techniques to deal with inaccuracy, randomness, and uncertainty issues when learning with crowdsourcing. This paper summarizes the technical progress in this field during past eleven years. We focus on two fundamental issues: the data (label) quality and the prediction model quality. For data quality, we summarize ground truth inference methods and some machine learning based methods to further improve data quality. For the prediction model quality, we summarize several learning paradigms developed under the crowdsourcing scenario. Finally, we further discuss several promising future research directions to attract researchers to make contributions in crowdsourcing.
Quality Assessment of Photographed 3D Printed Flat Surfaces Using Hough Transform and Histogram Equalization

Automatic visual quality assessment of objects created using additive manufacturing processes is one of the hot topics in the Industry 4.0 era. As the 3D printing becomes more and more popular, also for everyday home use, a reliable visual quality assessment of printed surfaces attracts a great interest. One of the most obvious reasons is the possibility of saving time and filament in the case of detected low printing quality, as well as correction of some smaller imperfections during the printing process. A novel method presented in the paper can be successfully applied for the assessment of flat surfaces almost independently on the filament’s colour. Is utilizes the assumption about the regularity of the layers visible on the printed high quality surfaces as straight lines, which can be extracted using Hough transform. However, for various colours of filaments some preprocessing operations should be conducted to allow a proper line detection for various samples. In the proposed method the additional brightness compensation has been used together with Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. Results obtained for the database of 88 photos of 3D printed samples, together with their scans, are encouraging and allow a reliable quality assessment of 3D printed surfaces for various colours of filaments.
Image‐based authoring of herd animations

Animating herds of animals while achieving both convincing global shapes and plausible distributions within the herd is difficult, using simulation methods. In this work, we allow users to rely on photos of real herds, which are widely available, for keyframing their animation. More precisely, we learn global and local distribution features in each photo of the input set (which may depict different numbers of animals) and transfer them to the group of animals to be animated, thanks to a new statistical learning method enabling to analyze distributions of ellipses, as well as their density and orientation fields. The animated herd reconstructs the desired distribution at each keyframe while avoiding obstacles. As our results show, our method offers both high‐level user control and help toward realism, enabling to easily author herd animations.
Amsterdam to Dublin Eventually Delayed? LSTM and Transfer Learning for Predicting Delays of Low Cost Airlines

Flight delays impact airlines, airports and passengers. Delay prediction is crucial during the decision-making process for all players in commercial aviation, and in particular for airlines to meet their on-time performance objectives. Although many machine learning approaches have been experimented with, they fail in (i) predicting delays in minutes with low errors (less than 15 minutes), (ii) being applied to small carriers i.e., low cost companies characterized by a small amount of data. This work presents a Long Short-Term Memory (LSTM) approach to predicting flight delay, modeled as a sequence of flights across multiple airports for a particular aircraft throughout the day. We then suggest a transfer learning approach between heterogeneous feature spaces to train a prediction model for a given smaller airline using the data from another larger airline. Our approach is demonstrated to be robust and accurate for low cost airlines in Europe.
Hybrid Stochastic GA-Bayesian Search for Deep Convolutional Neural Network Model Selection

In recent years, deep convolutional neural networks (DCNNs) have delivered notable successes in visual tasks, and in particular, image classification related applications. However, they are sensitive to the selection of their architectural and learning hyperparameters, which impose an exponentially large search space on modern DCNN models. Traditional hyperparameter selection methods include manual model tuning, grid, or random search but these require expert domain knowledge or are computationally burdensome. On the other hand, Bayesian optimization and evolutionary inspired techniques have surfaced as viable alternatives to the hyperparameter problem. In this work, an alternative automated system that combines the advantages of evolutionary processes and state-of-the-art Bayesian optimization is proposed. Specifically, the search space is first partitioned into separate discrete-architectural, and continuous and categorical learning parameter subspaces, which are then efficiently traversed by a stochastic genetic search applied to the former, combined with a genetic-Bayesian search of the latter. Several sequential experiments on prominent image classification tasks reveal that the proposed method results in overall classification accuracy improvements over several wellestablished techniques, and significant computational costs reductions compared to brute force computation.
Confidence Weighted Multitask Learning

Traditional online multitask learning only utilizes the firstorder information of the datastream. To remedy this issue, we propose a confidence weighted multitask learning algorithm, which maintains a Gaussian distribution over each task model to guide online learning process. The mean (covariance) of the Gaussian Distribution is a sum of a local component and a global component that is shared among all the tasks. In addition, this paper also addresses the challenge of active learning on the online multitask setting. Instead of requiring labels of all the instances, the proposed algorithm determines whether the learner should acquire a label by considering the confidence from its related tasks over label prediction. Theoretical results show the regret bounds can be significantly reduced. Empirical results demonstrate that the proposed algorithm is able to achieve promising learning efficacy, while simultaneously minimizing the labeling cost.
Towards Sequence-to-Sequence Reinforcement Learning for Constraint Solving with Constraint-Based Local Search

This paper proposes a framework for solving constraint problems with reinforcement learning (RL) and sequence-tosequence recurrent neural networks. We approach constraint solving as a declarative machine learning problem, where for a variable-length input sequence a variable-length output sequence has to be predicted. Using randomly generated instances and the number of constraint violations as a reward function, a problem-specific RL agent is trained to solve the problem. The predicted solution candidate of the RL agent is verified and repaired by CBLS to ensure solutions, that satisfy the constraint model. We introduce the framework and its components and discuss early results and future applications.
Method for recognizing human head based on feature of contour

According to the feature of human head,this paper puts forward a method for recognizing human head based on the feature of contour.The method to clear burr of contour in threshold image,whose purpose is to prevent endless loop,is described in detail.One method to extract the contour is put forward,and the detailed steps are given out,too.Primary human head screening is carried on with the size of the area.Then the circularity is used to recognize human head.This method is not only real-time,but also needs less memory.At the end,experiment proves the good effect of the method.
Improving NLTK for Processing Portuguese

Python has a growing community of users, especially in the AI and ML fields. Yet, Computational Processing of Portuguese in this programming language is limited, in both available tools and results. This paper describes NLPyPort, a NLP pipeline in Python, primarily based on NLTK, and focused on Portuguese. It is mostly assembled from pre-existent resources or their adaptations, but improves over the performance of existing alternatives in Python, namely in the tasks of tokenization, PoS tagging, lemmatization and NER. 2012 ACM Subject Classification Computing methodologies → Natural language processing
Diversity in Computing Technologies and Strategies for Dynamic Resource Allocation

High Energy Physics (HEP) is a very data intensive and trivially parallelizable science discipline. HEP is probing nature at increasingly finer details requiring ever increasing computational resources to process and analyze experimental data. In this paper, we discuss how HEP provisioned resources so far using Grid technologies, how HEP is starting to include new resource providers like commercial Clouds and HPC installations, and how HEP is transparently provisioning resources at these diverse providers.
Higher-Order Multi-Layer Community Detection

In this paper, we define a new problem of multi-layer network community detection, namely higher-order multi-layer community detection. A multi-layer motif (M-Motif) approach is proposed, which discovers communities with good intralayer higher-order community quality while preserving interlayer higher-order community consistency. Experimental results have confirmed the superiority of the proposed method.
A Preliminary Report of Integrating Science and Computing Teaching Using Logic Programming

This paper presents a framework to integrate Science and Computing teaching using Logic Programming. We developed two modules: one for chemistry and the other for chemistry and physics. They are implemented in an elective course for 8th graders. Through clinical interviews, video taped class observations, exit interviews and our own experiences with the class, Logic Programming based approach is accessible to the students.
Theme Division and Team Activities Interactive Teaching Method for Software Engineering

With abstract content and complex process relationship in software engineering course, under the traditional classroom teaching method, students find it hard to grasp the core of this subject, let alone apply it in actual software projects. In order to improve the learning outcomes of software engineering, we proposed the theme division and team activity interactive teaching method by which students can truly understand and use the tools, processes and methods of modern software engineering. Based on the content attributes of the software life cycle and each stage, the themes are extracted and constructed. With instructor’s guidance, students carry out team activities, including team form-up, tasks collection, group learning, topic reporting, defense review, and team feedback. The interactive teaching ways between the instructors and the learners promote the latter to actively participate in problem research and discussion, use software engineering principles and methods to solve design, development and management problems in software engineering, and enhance their technique ability and teamwork awareness.
Semi-Supervised Learning for Electron Microscopy Image Segmentation

In the research field called connectomics, it is aimed to investigate the structure and connection of the neural system in the brain and sensory organ of the living things. Earlier studies have been proposed the method to help experts who suffer from labeling for three-dimensional reconstruction, that is important process to observe tiny neuronal structure in detail. In this paper, we proposed semi-supervised learning method, that performs pseudo-labeling. This makes it possible to automatically segment neuronal regions using only a small amount of labeled data. Experimental result showed that our method outperformed normal supervised learning with few labeled samples, while the accuracy was not sufficient yet.