Reviewerly/exHarmony huggingface dataset:
Bidirectional Backpropagation

We extend backpropagation (BP) learning from ordinary unidirectional training to bidirectional training of deep multilayer neural networks. This gives a form of backward chaining or inverse inference from an observed network output to a candidate input that produced the output. The trained network learns a bidirectional mapping and can apply to some inverse problems. A bidirectional multilayer neural network can exactly represent some invertible functions. We prove that a fixed three-layer network can always exactly represent any finite permutation function and its inverse. The forward pass computes the permutation function value. The backward pass computes the inverse permutation with the same weights and hidden neurons. A joint forward-backward error function allows BP learning in both directions without overwriting learning in either direction. The learning applies to classification and regression. The algorithms do not require that the underlying sampled function has an inverse. A trained regression network tends to map an output back to the centroid of its preimage set.
Rule extraction from neural network trained using deep belief network and back propagation

Representing the knowledge learned by neural networks in the form of interpretable rules is a prudent technique to justify the decisions made by neural networks. Heretofore many algorithms exist to extract symbolic rules from neural networks, but among them, a few extract rules from deep neural networks trained using deep learning techniques. So, this paper proposes an algorithm to extract rules from a multi-hidden layer neural network, pre-trained using deep belief network and fine-tuned using back propagation. The algorithm analyzes each node of a layer and extracts knowledge from each layer separately. The process of knowledge extraction from the first hidden layer is different from the other layers. Consecutively, the algorithm combines all the knowledge extracted and refines them to construct a final ruleset consisting of symbolic rules. The algorithm further subdivides the subspace of a rule in the ruleset if it satisfies certain conditions. Results show that the algorithm extracted rules with higher accuracy compared to some existing rule extraction algorithms. Other than accuracy, the efficacy of the extracted rules is also validated with fidelity and various other performance measures.
Exploring Hungarian teachers’ perceptions of inclusive education of SEN students

The purpose of the current study is to identify Hungarian teachers' perceptions of special educational needs (SEN) and to explore their teaching practices in relation to ICT tools involved in classes with both SEN and typically developing students. A hypothesized model was developed and tested based on the relevant literature to map the relations of the variables. The research was conducted among primary and secondary school teachers (N = 121) using an adapted version of the Audit of provision for students with special educational needs self-evaluation scale (Stack, 2007), which was rounded out with ICT issues and background data on teachers' knowledge of SEN practices acquired at university. A path analysis showed significant links between the teaching- and learning-related factors on the scale. We also observed significant relationships between ICT variables, but the effect between the two factors is rather limited. The quality of curriculum provision is the only significant factor which has a significant effect on using ICT teaching material. Our model also highlighted that teachers' knowledge of SEN acquired at university represents a determining factor in using ICT tools in SEN education. Teacher trainees who acquire comprehensive knowledge in their university education courses on teaching SEN students possess more profound skills to deal with them and possess higher-level competences in using ICT in their teaching practice. This study demonstrates that teaching SEN students in an integrated education system poses unique challenges; therefore, it is a relevant topic and an issue to be addressed. Our hope is that the results from this study will be useful in the future for schools and teachers educating students with special educational needs and that the findings will improve the overall understanding of inclusive education among schools and teachers.
Built-in Depth-Semantic Coupled Encoding for Scene Parsing, Vehicle Detection, and Road Segmentation

Recent representative scene parsing methods based on Convolutional Neural Networks (CNN) have greatly improved spatial resolution of pixel-wise labelling by exploiting multi-scale features and refined boundaries. However, the vast majority of previous works only utilize the color or textural information of images, without considering the depth information, which is beneficial for semantic reasoning. In this paper, we take advantages of the mutual benefit and strong correlation between depth information and semantic information in scene parsing by introducing the Built-in Depth-Semantic Coupled Encoding (BDSCE) module, which adaptively fuses RGB and depth features, and selectively highlights the depth-discriminative features. The proposed BDSCE module is compatible with existing CNN based methods, and can greatly improve scene parsing performance, particularly in those categories that have clear depth distinction and might be misclassified with RGB-only features. Furthermore, we also extend our proposed module to other urban scene semantic reasoning tasks such as vehicle detection and road segmentation, which are implemented by effectively learning and exploiting the encoded depth-semantics and transferring the learned representations with fine-tuning. The extensive experiments on the popular datasets Cityscapes and KITTI demonstrate that our method performs quite well and can significantly improve the state-of-the-art methods.
A New Image Compression Algorithm Based on Non-Uniform Partition and U-System

JPEG lossy image compression is a still image compression algorithm model that is currently widely used in major network media. However, it is unsatisfactory in the quality of compressed images at low bit rates. The objective of this paper is to improve the quality of compressed images and suppress blocking artifacts by improving the JPEG image compression model at low bit rates. First, the image texture adaptive non-uniform rectangular partition (ITANRP) algorithm is proposed which partitions the image into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$8\times 8$</tex-math></inline-formula> size image blocks with high texture complexity and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$16\times 16$</tex-math></inline-formula> size image blocks with low texture complexity. Then, a new transform coding based on the complete orthogonal U-system and all-phase digital filter (APDF) is proposed for coding image blocks with different sizes. Next, a flexible adaptive quantization scheme is designed to quantize image blocks with different sizes by considering the sensitivity of the human visual system (HVS) to different texture complexities. Finally, combining the above method with the JPEG model, a novel image compression algorithm model with low algorithm complexity is proposed to solve the problem in JPEG. The experimental results demonstrate that the performance of our algorithm model outperforms the JPEG image compression algorithms, the quality of the compressed image is greatly improved, and the blocking artifacts are also significantly suppressed.
Online Voting System using Cloud

In this research work. Voting is commonly related to politics and is finished with often exploitation and manual approach where voters stand to vote for his or her decisions. Manual voting may lead to malpractices sometimes.so there is a need to implement online voting system. This is for expand the technology from manual voting system to digital voting system. In this specific research our idea is to implement online voting system with features like the schemes that the specific party has implemented, based on the features we are going to vote. The main reason we need to shift from normal voting system to online voting system is that we can consume our time and can vote from anywhere through online. We have implemented this by using C# as a programming language, Microsoft SQL server 2012 and Microsoft azure as a cloud.
Detection of Lightning Damage on Wind Turbine Blades Using the SCADA System

In recent years, there have been several reports of blade damage caused by lightning strikes on wind turbines. A few of these accidents have resulted in more serious secondary damage owing to the continued rotation of the damaged blades. To prevent the initial damage from spreading in this manner, wind farms in the regions of Japan vulnerable to winter lightning were required to introduce lightning detection systems, which can accurately detect when lightning strikes the wind turbine and then immediately stop the rotation of the blades. Normally, when the system detects a lightning strike to a wind turbine and stops operation, the process of restarting is initiated only after the soundness of the blades is confirmed by visual inspection. However, in bad weather, it is often difficult to check the soundness of the blade visually, and the resulting delay in the restart process prolongs the downtime and reduces the availability of the wind turbine. In this paper, we report the results of using the supervisory control and data acquisition (SCADA) system data to check the soundness of the blades after a lightning strike to resume operations more quickly, thereby increasing up-time.
Sparse neighbor constrained co-clustering via category consistency learning

Clustering has long been an enduring and promising task in machine learning. However, developed one-side clustering is still insufficient to explore the context of data, such as texts and genes. Hence, developing two-way clustering has drawn more attention in recent years, which tends to cluster samples and features simultaneously. This paper proposes a sparse neighbor constrained co-clustering via category consistency learning, for alleviating the misclassification of close points. Following an additional observation, samples often fall into the same category as their neighbors, as do features. Accordingly, the co-clustering problem is formulated as nonnegative matrix tri-factorization appended dual regularizers, considering coherence between data affinity and label assignment. Then, a multiplicative alternating scheme is raised for objective optimization, whose convergence and correctness are theoretically guaranteed. Furthermore, the proposed approach is validated on six datasets using three evaluation metrics, whose parameter sensitivity is analyzed as well. Finally, comprehensive experiments show that our algorithm is competitive against existing ones.
A novel online action detection framework from untrimmed video streams

Online temporal action localization from an untrimmed video stream is a challenging problem in computer vision. It is challenging because of i) in an untrimmed video stream, more than one action instance may appear, including background scenes, and ii) in online settings, only past and current information is available. Therefore, temporal priors, such as the average action duration of training data, which have been exploited by previous action detection methods, are not suitable for this task because of the high intra-class variation in human actions. We propose a novel online action detection framework that considers actions as a set of temporally ordered subclasses and leverages a future frame generation network to cope with the limited information issue associated with the problem outlined above. Additionally, we augment our data by varying the lengths of videos to allow the proposed method to learn about the high intra-class variation in human actions. We evaluate our method using two benchmark datasets, THUMOS’14 and ActivityNet, for an online temporal action localization scenario and demonstrate that the performance is comparable to state-of-the-art methods that have been proposed for offline settings.
Learning multi-level domain invariant features for sketch re-identification

Person re-identification (Re-ID) aims to match the photos of suspects in the gallery database by a query photo. Under Re-ID, the input is usually a query photo of the target person, however, in a realistic setting, such a photo is not always available. In this paper, we study the problem of Sketch Re-ID, which is not based on a person's photo but the professional sketch of the target person. We use a full-body sketch of a target person drawn by a professional to find the photo with the same ID in the gallery. This problem is very challenging because photos and sketches belong to two completely different domains. Specifically, a sketch is a highly abstract description of a person, which only contains some rough outline information. We address the Sketch Re-ID problem by proposing a novel framework to jointly model photos and sketches into a common embedding space. Our framework uses a triplet classification network as a base network. We propose to use a spatial attention module and combine high-level and mid-level output features of CNN to represent the input images. Moreover, we design a novel domain-invariant feature by using a gradient reverse layer (GRL) to solve the domain gap problem. We validated our approach on the Sketch Re-ID dataset, which contains 200 persons, each of whom has a sketch and two photos from different cameras associated. To evaluate the generalization of our method, we also performed experiments on some other public Sketch-based Image Retrieval (SBIR) datasets. The extensive experimental results show that our method can get higher performance than state-of-the-art models.
Replay spoofing countermeasure using autoencoder and siamese networks on ASVspoof 2019 challenge

Automatic Speaker Verification (ASV) is authentication of individuals by analyzing their speech signals. Different synthetic approaches allow spoofing to deceive ASV systems (ASVs), whether using techniques to imitate a voice or reconstruct the features. Attackers beat up the ASVs using four general techniques; impersonation, speech synthesis, voice conversion, and replay. The last technique is considered as a common and high potential tool for spoofing purposes since replay attacks are more accessible and require no technical knowledge of adversaries. In this study, we introduce a novel replay spoofing countermeasure for ASVs. Accordingly, we use the Constant Q Cepstral Coefficient (CQCC) features fed into an autoencoder to attain more informative features and to consider the noise information of spoofed utterances for discrimination purpose. Finally, different configurations of the Siamese network are used for the first time in this context for classification. The experiments performed on ASVspoof challenge 2019 dataset using Equal Error Rate (EER) and Tandem Detection Cost Function (t-DCF) as evaluation metrics show that the proposed system improved the results over the baseline by 10.73% and 0.2344 in terms of EER and t-DCF, respectively.
GingerVR: An Open Source Repository of Cybersickness Reduction Techniques for Unity

Virtual reality (VR) technology faces a major road block in the form of cybersickness. Cybersickness describes the collection of negative symptoms sometimes experienced while immersed in virtual environments, such as disorientation, nausea, and fatigue. Many techniques exist to reduce cybersickness, for example, reducing the user’s field of view, or dynamically blurring the user’s vision. But to utilize these techniques, researchers may need to develop them from scratch. To remedy this, we implemented several previously reported reduction techniques in Unity, packaged them in an open source repository, and created tutorials for ease of integration. These techniques are now simple to add, and require no dependencies outside of what comes with a default Unity project. We intend to create an outlet for researchers to submit new techniques as they are developed. GingerVR can help to lessen the work load for developers, and researchers looking to further evaluate these cybersickness reduction techniques.
An Efficient Content-Based Image Retrieval System for the Diagnosis of Lung Diseases

The main problem in content-based image retrieval (CBIR) systems is the semantic gap which needs to be reduced for efficient retrieval. The common imaging signs (CISs) which appear in the patient's lung CT scan play a significant role in the identification of cancerous lung nodules and many other lung diseases. In this paper, we propose a new combination of descriptors for the effective retrieval of these imaging signs. First, we construct a feature database by combining local ternary pattern (LTP), local phase quantization (LPQ), and discrete wavelet transform. Next, joint mutual information (JMI)-based feature selection is deployed to reduce the redundancy and to select an optimal feature set for CISs retrieval. To this end, similarity measurement is performed by combining visual and semantic information in equal proportion to construct a balanced graph and the shortest path is computed for learning contextual similarity to obtain final similarity between each query and database image. The proposed system is evaluated on a publicly available database of lung CT imaging signs (LISS), and results are retrieved based on visual feature similarity comparison and graph-based similarity comparison. The proposed system achieves a mean average precision (MAP) of 60% and 0.48 AUC of precision-recall (P-R) graph using only visual features similarity comparison. These results further improve on graph-based similarity measure with a MAP of 70% and 0.58 AUC which shows the superiority of our proposed scheme.
An improved algorithm for incremental extreme learning machine

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A Lung Dense Deep Convolution Neural Network for Robust Lung Parenchyma Segmentation

Lung parenchyma segmentation is the prerequisite for an automatic diagnosis system to analyze lung CT (computed tomography) images. However, traditional lung segmentation algorithms have poor adaptability and are not effectively robust regarding lung databases with blood vessels and small voids which can interfere the segmentation. The main work of this paper is as follows: Firstly, a lung dense deep convolutional neural network (LDDNet) is proposed, which adopts some popular optimizer methods, such as dense block, batch normalization (BN) and dropout. The performance of LDDNet is tested on the public lung database LIDC-IDRI which contains many cases of interference for segmentation. Secondly, the labeled with blood vessels and small voids are not contained by the public ground-truth masks of the LIDC-IDRI database, therefore these regions are labeled by us with LabelMe software. Thirdly, for the aim of exploring the effect of image preprocessing on segmenting lung CT images with deep neural network, contrast enhancing, median filtering and Laplacian filtering are used to preprocess the image as comparative experiments. Finally, dataset is classified into four classes by the geometrical shapes to test the performance of LDDNet. The accuracy of the segmentation experiment reaches over 99% and the four classes can all reach over 95%. Additionally, blood vessels and small voids are segmented out from the lung parenchyma which is not achieved by other methods. Experimental results confirm that the proposed LDDNet can segment the lung parenchymal area more accurately and has better robustness in comparison with other neural networks and most of the traditional methods.
Cross-cultural comparison of interactive streaming services: Evidence from Twitch

A cross-cultural study of interactive live streaming platforms was conducted to investigate emotional differences in communication behavior. We collected 10,432,423 chat messages from Twitch streams, and our analysis was twofold: (1) classifying viewers by their consumption behavior and (2) analyzing subscribers’ emote usage and emotional expressions. The results showed that there are significant differences in several linguistic and psychological dimensions of emotional expressions between Western and Eastern cultures. Along with these cultural differences, our study addresses the importance of encouraging the social interaction in Twitch for viewer engagement.
Machine Learning Methodology for Management of Shipbuilding Master Data

The continuous development of information and communication technologies has resulted in an exponential increase in data. Consequently, technologies related to data analysis are growing in importance. The shipbuilding industry has high production uncertainty and variability, which has created an urgent need for data analysis techniques, such as machine learning. In particular, the industry cannot effectively respond to changes in the production-related standard time information systems, such as the basic cycle time and lead time. Improvement measures are necessary to enable the industry to respond swiftly to changes in the production environment. In this study, the lead times for fabrication, assembly of ship block, spool fabrication and painting were predicted using machine learning technology to propose a new management method for the process lead time using a master data system for the time element in the production data. Data preprocessing was performed in various ways using R and Python, which are open source programming languages, and process variables were selected considering their relationships with the lead time through correlation analysis and analysis of variables. Various machine learning, deep learning, and ensemble learning algorithms were applied to create the lead time prediction models. In addition, the applicability of the proposed machine learning methodology to standard work hour prediction was verified by evaluating the prediction models using the evaluation criteria, such as the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Logarithmic Error (RMSLE).
A comparison of classification algorithms for hate speech detection

Abstract Freedom of opinion through social media is frequently affect a negative impact that spreads hatred. This study aims to automatically detect Indonesian tweets that contain hate speech on Twitter social media. The data used amounted to 4,002 tweets related to politics, religion, ethnicity and race in Indonesia. The application model uses classification methods with machine learning algorithms such as Naïve Bayes, Multi Level Perceptron, AdaBoost Classifier, Decision Tree and Support Vector Machine. The study also compared the performance of the model using SMOTE to overcome imbalanced data. The results show that the Multinomial Naive Bayes algorithm produces the best model with the highest recall value of 93.2% which has an accuracy value of 71.2% for the classification of hate speech. Therefore, the Multinomial Naïve Bayes algorithm without SMOTE is recommended as the model to detect hate speech on social media.