Reviewerly/exHarmony huggingface dataset:
Raindrops on Windshield: Dataset and Lightweight Gradient-Based Detection Algorithm

Autonomous vehicles use cameras as one of the primary sources of information about the environment. Adverse weather conditions such as raindrops, snow, mud, and others, can lead to various image artifacts. These artifacts significantly degrade the quality and reliability of the obtained visual data and can lead to accidents if they are not detected in time. This paper presents ongoing work on a new dataset for training and assessing vision algorithms' performance for different tasks of image artifacts detection on either camera lens or car windshield. At the moment, we present a publicly available set of images containing 8190 images, of which 3390 contain raindrops. Images are annotated with the binary mask representing areas with raindrops. We demonstrate the applicability of the dataset in the problems of raindrops presence detection and raindrop region segmentation. To augment the data, we also propose a data augmentation algorithm that allows the generation of synthetic raindrops on images. Apart from the dataset, we present a novel gradient-based algorithm for raindrop presence detection in a video sequence. The experimental evaluation proves that the algorithm reliably detects raindrops. Moreover, compared with the state-of-the-art cross-correlation-based algorithm, the proposed algorithm showed a higher quality of raindrop presence detection and image processing speed, making it applicable for the self-check procedure of real autonomous systems. The dataset is available at github.com/EvoCargo/RaindropsOnWindshield.
Kohonen map-wise regression applied to interval data

Kohonen maps, also known as self-organizing maps, is a powerful clustering method which groups data using multiple nodes that converge to clusters. Therefore, it is not necessary to know a priori the exact number of clusters in the data. This paper proposes a clusterwise regression method that combines self-organizing maps and a parametrized linear regression approach for interval data in the framework of Symbolic Data Analysis. The linear regression approach adapts itself to use the best set of points inside intervals to build the regression model, generalizing all approaches that select points of interest, such as center, range, minimum and maximum, to provide linear regression for interval data. Here, self-organizing map nodes are responsible for detecting local linear regression structures and the parametrized linear regression builds local regression models, one for each node. Finally, outputs are given as weighted averages of the local model predictions. Since our model fits the linear models using the map nodes, it avoids weak performances that might arise when clusters are not easily separable. As another contribution, we provide a way to extract predictions from an existing clusterwise regression method. Experiments with synthetic and real data sets show the usefulness of the proposed method.
CCEyes: An Effective Tool for Code Clone Detection on Large-Scale Open Source Repositories

Code clone is a common phenomenon in the rapid development of the software ecosystem and open source community, which can improve efficiency, while leading to many negative effects on software maintenance at the same time. In this paper, we propose CCEyes, an effective tool for code clone detection on GitHub. We build a semantic vector representations corpora database of large-scale open sourse codes by the deep learning-based method that can automatically learn program features to address the resource challenge. CCEyes returns the detection results according to the user's input, including similar clone code fragments, similarity analysis and repository address on GitHub. Among which the Recursive Autoencoder is designed to learn code representation and then the comparator network is employed for similarity evaluation. We conduct comprehensive experiments on the ability of the detection method, the effectiveness of database storage. Besides, we also compare CCEyes with other application tools and conduct a user study. The evalution results in that CCEyes outperforms state-of-the-art.
Assessment of Student Attentiveness to E-Learning by Monitoring Behavioural Elements

In the current scenario of the world, most of the learning has been shifted to e-learning modes like online classes. In a live class, a teacher is able to constantly monitor the students by visual analysis and active learning. But due to virtual learning, this skill of the teachers becomes impaired due to the mediums being used. But the constant vigilance of the students' attentiveness in class remains imperative to impart good knowledge and have productive sessions. The proposed work is aimed at providing the teacher a detailed analysis for all the students based on physical and emotional analysis of their state during the class. The model analyses the live videos of the students and utilises factors like the pose of the student, the emotional look on the face, the position of the eyelids, along with the posture of the student to provide the teacher a confidence score which he/she can use to determine the attentiveness of the student during the class. With the successful implementation of our proposed work, the aim is to establish a correlation between the factors that have been chosen and develop this model which will aid the teachers to recognise the output of the students, so that they can bring classroom learning closer to the students, while in the safety of their homes. The use of these kind of technologically advanced instructional techniques and enhanced personal learning analysis will allow for the preparing the workforce of tomorrow to be highly trained and competent.
Learning High-Resolution Domain-Specific Representations with a GAN Generator

In recent years generative models of visual data have made a great progress, and now they are able to produce images of high quality and diversity. In this work we study representations learnt by a GAN generator. First, we show that these representations can be easily projected onto semantic segmentation map using a lightweight decoder. We find that such semantic projection can be learnt from just a few annotated images. Based on this finding, we propose LayerMatch scheme for approximating the representation of a GAN generator that can be used for unsupervised domain-specific pretraining. We consider the semi-supervised learning scenario when a small amount of labeled data is available along with a large unlabeled dataset from the same domain. We find that the use of LayerMatch-pretrained backbone leads to superior accuracy compared to standard supervised pretraining on ImageNet. Moreover, this simple approach also outperforms recent semi-supervised semantic segmentation methods that use both labeled and unlabeled data during training.
Multi-scene ancient Chinese text recognition with deep coupled alignments

The task of multi-scene ancient Chinese text recognition (MACR) is challenging due to large-scale categories, high intra-class variance and inter-class similarity and complicated backgrounds. Little effort has been devoted to MACR research due to insufficient datasets and language barrier. Because the sub-dataset generation process of sub-dataset is mutually blind, there are discrepancies in the class category number, deep feature representation and class center distribution after the dataset statistics and character analysis are performed. The general deep learning method that assumes that data are independent and identically distributed is inappropriate. The deep coupled alignments (CA) module based on domain adaptation is presented to alleviate domain and class center shifts. In addition, a cross-domain fusion (CF) module is proposed to mitigate negative transfer in partial domain adaptation by updating the target domain with the full-class and augmenting the source domain with pseudo labeled samples. Extensive experiments of the proposed method are conducted, and the results illustrate the superiority of CA–CF to previous methods in terms of the model size and recognition accuracy. • A deep coupled alignments (CA) module is presented to alleviate domain and class center shifts. • A cross-domain fusion (CF) module is proposed to relieve negative transfer and realize data augmentation. • Experimental results demonstrate the effectiveness of the proposed CA–CF method on MACR.
A Reinforcement Learning-Based Virtual Machine Placement Strategy in Cloud Data Centers

With the widespread use of cloud computing, energy consumption of cloud data centers is increasing which mainly comes from IT equipment and cooling equipment. This paper argues that once the number of virtual machines on the physical machines reaches a certain level, resource competition occurs, resulting in a performance loss of the virtual machines. Unlike most papers, we do not impose placement constraints on virtual machines by giving a CPU cap to achieve the purpose of energy savings in cloud data centers. Instead, we use the measure of performance loss to weigh. We propose a reinforcement learning-based virtual machine placement strategy(RLVMP) for energy savings in cloud data centers. The strategy considers the weight of virtual machine performance loss and energy consumption, which is finally solved with the greedy strategy. Simulation experiments show that our strategy has a certain improvement in energy savings compared with the other algorithms.
3DRM: Pair-wise relation module for 3D object detection

Context has proven to be one of the most important factors in object layout reasoning for 3D scene understanding. Existing deep contextual models either learn holistic features for context encoding or rely on pre-defined scene templates for context modeling. We argue that scene understanding benefits from object relation reasoning, which is capable of mitigating the ambiguity of 3D object detections and thus helps locate and classify the 3D objects more accurately and robustly. To achieve this, we propose a novel 3D relation module (3DRM) which reasons about object relations at pair-wise levels. The 3DRM predicts the semantic and spatial relationships between objects and extracts the object-wise relation features. We demonstrate the effects of 3DRM by plugging it into proposal-based and voting-based 3D object detection pipelines, respectively. Extensive evaluations show the effectiveness and generalization of 3DRM on 3D object detection. Our source code is available at https://github.com/lanlan96/3DRM.
Early Alert Systems During a Pandemic: A Simulation Study on the Impact of Concept Drift

Predictions from early alert systems are increasingly being used by institutions to assist decision-making and support at-risk individuals. Concept drifts caused by the 2020 SARS-CoV-2 pandemic are threatening the performance and usefulness of the machine learning models that power these systems. In this paper, we present an analytical framework that uses imputation-based simulations to perform preliminary evaluation on the extent to which data quality and availability issues impact the performance of machine learning models. Guided by this framework, we studied how these issues would impact the performance of the high school dropout prediction model implemented in the Early Warning System (EWS). Results show that despite the disruptions, this model can still be reasonably useful in assisting decision-making. We discuss the implications of these findings in more general educational contexts and recommend steps in countering the challenges of using predictions from imperfect machine learning models in early alert systems and, more broadly, learning analytic research that uses longitudinal data.
The Prototype of In-Store Visitor and People Passing Counters using Single Shot Detector Performed by OpenCV

Information related to the power hours of a mall or store is important. By typically knowing it, the manager of the store or the mall can wisely determine the staff planning decision. Without the right decision, it potentially decreases customer satisfaction. The decision can be defined by utilizing in-store visitors and people passing traffic patterns. The other problem also arises when the calculation of in-store visitors and people passing are executed manually, so it requires much effort. This study proposes a prototype design of the system which can automatically calculate visitors by utilizing Single Shot Detector (SSD) method. This method is performed by operating OpenCV library. It is used to detect a human object marked as in-store visitor or people passing. The embedded computer is conducted to process images captured by Pi Camera. Based on the study, the result accuracy is 65.08% for the system counts in-store visitors, and 66.12% for the system marks objects as people pass around in front of the store. Although the accuracy values obtained is not high, but all patterns show that the highest average values of in-store visitors and people passing occur on the days nearing weekend and also on the weekend, such as Friday, Saturday and Sunday. The peak time of in-store visitors (e.g. power hour) on Friday is between 12 PM and 1 PM. The peak time of in-store visitors on Saturday is between 3 PM and 4 PM, and on Monday, it is between 4 PM and 5 PM.
Automatic breast tissue segmentation in MRIs with morphology snake and deep denoiser training via extended Stein’s unbiased risk estimator

Accurate segmentation of the breast tissue is a significant challenge in the analysis of breast MR images, especially analysis of breast images with low contrast. Most of the existing methods for breast segmentation are semi-automatic and limited in their ability to achieve accurate results. This is because of difficulties in removing landmarks from noisy magnetic resonance images (MRI). Especially, when tumour is imaged for scanning, how to isolate the tumour region from chest will directly affect the accuracy for tumour to be detected. Due to low intensity levels and the close connection between breast and chest portion in MRIs, this study proposes an innovative, fully automatic and fast segmentation approach which combines histogram with inverse Gaussian gradient for morphology snakes, along with extended Stein's unbiased risk estimator (eSURE) applied for unsupervised learning of deep neural network Gaussian denoisers, aimed at accurate identification of landmarks such as chest and breast.
Research on the application of big data technology in college students’ innovation and entrepreneurship guidance service

Abstract The concept and mode of innovation and entrepreneurship education are embedded in the research university education system. From the four dimensions of external policy and public opinion environment, internal system module, operation mode and operation mechanism, this paper constructs the framework model of innovation and entrepreneurship education system in research universities. The model not only conforms to the training law of innovation and entrepreneurship talents, but also conforms to the training law of innovation and entrepreneurship talents. At the same time, innovation and entrepreneurship education can also be incorporated into the university education system. It also focuses on the internal system module of innovation and entrepreneurship education and the operation mode of three shift system. It lays a theoretical foundation for the promotion and implementation of the concept of innovation and entrepreneurship education in China. The relevant achievements of the construction of the innovation and entrepreneurship education system are optimized and improved accordingly. After putting it into practice, the innovation and entrepreneurship education level of our universities will be greatly improved.
Detecting Fake News Using Machine Learning Algorithms

Online media cooperation particularly the word getting out around the organization is an incredible wellspring of data these days. From one's point of view, its insignificant effort, direct access, and speedy scattering of data that lead individuals to watch out and global news from web sites. Twitter being a champion among the most notable progressing news sources moreover winds up a champion among the most prevailing news emanating mediums. It is known to cause broad damage by spreading pieces of tattle beforehand. Therefore, motorizing fake news acknowledgment is rudimentary to keep up healthy online media and casual association. We proposes a model for perceiving manufactured news messages from twitter posts, by making sense of how to envision exactness examinations, considering automating fashioned news distinguishing proof in Twitter datasets. Subsequently, we played out a correlation between five notable Machine Learning calculations, similar to Support Vector Machine, Naïve Bayes Method, Logistic Regression and Recurrent Neural Network models, independently to exhibit the effectiveness of the grouping execution on the dataset. Our exploratory outcome indicated that SVM and Naïve Bayes classifier beats different calculation.
Deep Learning Network Intensification for Preventing Noisy-Labeled Samples for Remote Sensing Classification

The deep-learning-network performance depends on the accuracy of the training samples. The training samples are commonly labeled by human visual investigation or inherited from historical land-cover or land-use maps, which usually contain label noise, depending on subjective knowledge and the time of the historical map. Helping the network to distinguish noisy labels during the training process is a prerequisite for applying the model for training across time and locations. This study proposes an antinoise framework, the Weight Loss Network (WLN), to achieve this goal. The WLN contains three main parts: (1) the segmentation subnetwork, which any state-of-the-art segmentation network can replace; (2) the attention subnetwork (λ); and (3) the class-balance coefficient (α). Four types of label noise (an insufficient label, redundant label, missing label and incorrect label) were simulated by dilate and erode processing to test the network’s antinoise ability. The segmentation task was set to extract buildings from the Inria Aerial Image Labeling Dataset, which includes Austin, Chicago, Kitsap County, Western Tyrol and Vienna. The network’s performance was evaluated by comparing it with the original U-Net model by adding noisy training samples with different noise rates and noise levels. The result shows that the proposed antinoise framework (WLN) can maintain high accuracy, while the accuracy of the U-Net model dropped. Specifically, after adding 50% of dilated-label samples at noise level 3, the U-Net model’s accuracy dropped by 12.7% for OA, 20.7% for the Mean Intersection over Union (MIOU) and 13.8% for Kappa scores. By contrast, the accuracy of the WLN dropped by 0.2% for OA, 0.3% for the MIOU and 0.8% for Kappa scores. For eroded-label samples at the same level, the accuracy of the U-Net model dropped by 8.4% for OA, 24.2% for the MIOU and 43.3% for Kappa scores, while the accuracy of the WLN dropped by 4.5% for OA, 4.7% for the MIOU and 0.5% for Kappa scores. This result shows that the antinoise framework proposed in this paper can help current segmentation models to avoid the impact of noisy training labels and has the potential to be trained by a larger remote sensing image set regardless of the inner label error.
On-the-fly simplification of genetic programming models

The last decade has seen amazing performance improvements in deep learning. However, the black-box nature of this approach makes it difficult to provide explanations of the generated models. In some fields such as psychology and neuroscience, this limitation in explainability and interpretability is an important issue. Approaches such as genetic programming are well positioned to take the lead in these fields because of their inherent white box nature. Genetic programming, inspired by Darwinian theory of evolution, is a population-based search technique capable of exploring a high-dimensional search space intelligently and discovering multiple solutions. However, it is prone to generate very large solutions, a phenomenon often called "bloat". The bloated solutions are not easily understandable. In this paper, we propose two techniques for simplifying the generated models. Both techniques are tested by generating models for a well-known psychology experiment. The validity of these techniques is further tested by applying them to a symbolic regression problem. Several population dynamics are studied to make sure that these techniques are not compromising diversity - an important measure for finding better solutions. The results indicate that the two techniques can be both applied independently and simultaneously and that they are capable of finding solutions at par with those generated by the standard GP algorithm - but with significantly reduced program size. There was no loss in diversity nor reduction in overall fitness. In fact, in some experiments, the two techniques even improved fitness.
The Landscape of Ontology Reuse Approaches

Ontology reuse aims to foster interoperability and facilitate knowledge reuse. Several approaches are typically evaluated by ontology engineers when bootstrapping a new project. However, current practices are often motivated by subjective, case-by-case decisions, which hamper the definition of a recommended behaviour. In this chapter we argue that to date there are no effective solutions for supporting developers’ decision-making process when deciding on an ontology reuse strategy. The objective is twofold: (i) to survey current approaches to ontology reuse, presenting motivations, strategies, benefits and limits, and (ii) to analyse two representative approaches and discuss their merits.
Supertagging the Long Tail with Tree-Structured Decoding of Complex Categories

Abstract Although current CCG supertaggers achieve high accuracy on the standard WSJ test set, few systems make use of the categories’ internal structure that will drive the syntactic derivation during parsing. The tagset is traditionally truncated, discarding the many rare and complex category types in the long tail. However, supertags are themselves trees. Rather than give up on rare tags, we investigate constructive models that account for their internal structure, including novel methods for tree-structured prediction. Our best tagger is capable of recovering a sizeable fraction of the long-tail supertags and even generates CCG categories that have never been seen in training, while approximating the prior state of the art in overall tag accuracy with fewer parameters. We further investigate how well different approaches generalize to out-of-domain evaluation sets.