Reviewerly/exHarmony huggingface dataset:
Design &amp; Analyses of a Novel Real Time Kurdish Sign Language for Kurdish Text and Sound Translation System

The most used code for communication by deaf and dumb people is Sign Language, which involves several gestures where each has a particular meaning. Many researchers and commercial firms seek to help the lifestyle of science in various areas of life for deaf people. Except for necessary steps dealing with only ten integer numeric values or essential Kurdish alphabet representation, there is no adequate Kurdish sign language. This research aims to study and examine a new system to be developed based on the concepts of a system of sign language for Kurdish writings and spoken translation. The proposed approach deems to be acting to interpret signs for a better life for the deaf and the public. The framework proposed falls into three phases: video processing, creation of patterns, and discrimination, and translation of the text. This technique depends on a translator's structure trained in the Kurdish sign language, mainly in the standard Kurdish sign language, and the domain expert's signature gesture.
Two-step phase extraction and random phase shift estimation in phase-shifting profilometry based on least-squared optical flow method

Phase shifting profilometry normally requires at least three-frame fringe patterns to extract the phase of objects. In the dynamic measurement field, it is desirable to reduce the number of fringe patterns and eliminate the phase-shifting error caused by the moving of the test object between fringe patterns. In the field of interferometry, the optical flow technique is an outstanding two-step phase reconstruction method. However, the accuracy of the optical flow (OF) algorithm is limited by the residual background intensity and approximation operation as well as noise. In this paper, a phase reconstruction method based on the optical flow algorithm and two-frame least square algorithm (OF&LSA) is proposed to alleviate the phase error caused by the above problems. The proposed method is employed in dynamic phase-shifting profilometry to achieve 2-frame phase reconstruction. The simulation and experiment results verify that the proposed OF&LSA improves the phase accuracy of OF algorithm significantly, and it also performs better than the 3-step phase shifting method in our dynamic measurement experiment.
Psychological and Didactic Issues of Digital Textbooks Creation

The article substantiates a special approach to the development of a school digital textbook which is based on the idea of synthesis of psychological, didactic, methodological, and subject knowledge with the priority of using the psychic pat- terns of personality development. To denote an interactive electronic textbook, it is proposed to use the term “digital textbook” (DT). From the methodological point of view, DT is regarded as an “enhanced” paper textbook, characterized by such basic properties as interactivity, multimodality, hypertextuality, and personality. The question about the development of the digital textbook concept before the textbook creation is raised. A number of psychological and didactic problems are discussed, such as: 1) the relationship between paper and digital textbooks (an option of the simultaneous use of both textbook formats is proposed); 2) DT as an essential com- ponent of the digital educational environment (the maximum effect is achieved if every DT is integrated into a single system as well as educational environment); 3) the features of reading on screen (non-linear nature, activity and dynamism, superficial understanding, greater fatigue, etc.). Besides, the article analyzes the advantages and disadvantages of DT and presents the results of its effectiveness based on the foreign studies. A conclusion was made that the wide-scale introduction of DT is possible solely when its positive influence not only on academic performance, but also on the cognitive development of students is revealed.
E-SC4R: Explaining Software Clustering for Remodularisation

Maintenance of existing software requires a large amount of time for comprehending the source code. The architecture of a software, however, may not be clear to maintainers if up-to-date documentations are not available. Software clustering is often used as a remodularisation and architecture recovery technique to help recover a semantic representation of the software design. Due to the diverse domains, structure, and behaviour of software systems, the suitability of different clustering algorithms for different software systems are not investigated thoroughly. Research that introduce new clustering techniques usually validate their approaches on a specific domain, which might limit its generalisability. If the chosen test subjects could only represent a narrow perspective of the whole picture, researchers might risk not being able to address the external validity of their findings. This work aims to fill this gap by introducing a new approach, Explaining Software Clustering for Remodularisation (E-SC4R), to evaluate the effectiveness of different software clustering approaches. This work focuses on hierarchical clustering and Bunch clustering algorithms and provides information about their suitability according to the features of the software, which as a consequence, enables the selection of the most suitable algorithm and configuration that can achieve the best MoJoFM value from our existing pool of choices for a particular software system. The E-SC4R framework is tested on 30 open-source software systems with varying sizes and domains, and demonstrates that it can characterise both the strengths and weaknesses of the analysed software clustering algorithms using software features extracted from the code. The proposed approach also provides a better understanding of the algorithms' behaviour by showing a 2D representation of the effectiveness of clustering techniques on the feature space generated through the application of dimensionality reduction techniques.
Mobile Application Encryption Traffic Classification Based On TLS Flow Sequence Network

Traffic classification can detect the source of traffic and can be used for network management and network security. Methods based on manually extracting features and using machine learning have become mainstream. These methods have poor results in classifying applications that use standard web services, which can cause ambiguities in application classification. In this paper, we propose the TLS Flow Sequence Network (TFSN), which can automatically learn representative features from the original TLS flow sequence and complete the classification. In addition, we also used the attention mechanism to reinforce the learned features. Compared with other similar researches, we can further identify the web services corresponding to encrypted flows in detail, and are no longer limited to application classification. We conducted experiments on the real network traffic dataset of 11 types of Google applications and 9 types of Apple applications that contain the same standard web services. It shows that TFSN has excellent performance, and the accuracy of web service recognition is more than 98%.
A data science approach for early-stage prediction of Patient's susceptibility to acute side effects of advanced radiotherapy

The prediction by classification of side effects incidence in a given medical treatment is a common challenge in medical research. Machine Learning (ML) methods are widely used in the areas of risk prediction and classification. The primary objective of such algorithms is to use several features to predict dichotomous responses (e.g., disease positive/negative). Similar to statistical inference modelling, ML modelling is subject to the class imbalance problem and is affected by the majority class, increasing the false-negative rate. In this study, seventy-nine ML models were built and evaluated to classify approximately 2000 participants from 26 hospitals in eight different countries into two groups of radiotherapy (RT) side effects incidence based on recorded observations from the international study of RT related toxicity “REQUITE”. We also examined the effect of sampling techniques and cost-sensitive learning methods on the models when dealing with class imbalance. The combinations of such techniques used had a significant impact on the classification. They resulted in an improvement in incidence status prediction by shifting classifiers’ attention to the minority group. The best classification model for RT acute toxicity prediction was identified based on domain experts' success criteria. The Area Under Receiver Operator Characteristic curve of the models tested with an isolated dataset ranged from 0.50 to 0.77. The scale of improved results is promising and will guide further development of models to predict RT acute toxicities. One model was optimised and found to be beneficial to identify patients who are at risk of developing acute RT early-stage toxicities as a result of undergoing breast RT ensuring relevant treatment interventions can be appropriately targeted. The design of the approach presented in this paper resulted in producing a preclinical-valid prediction model. The study was developed by a multi-disciplinary collaboration of data scientists, medical physicists, oncologists and surgeons in the UK Radiotherapy Machine Learning Network.
Methodology for Multi-aspect Ontology Development

The lack of interoperability observed in modern DSSs becomes even greater when complex systems covering multiple domains are considered. In the present research, the apparatus of multi-aspect ontologies is used as a means to represent knowledge of DSSs based on human-machine collective intelligence for enabling interoperability between the system components and coordinate interrelated processes. The available ontology development methodologies are not quite suitable for the development of multi-aspect ontologies because they leave aside the problem of choosing approaches for integration of reusable ontologies. Since the structure of a multi-aspect ontology imposes some restrictions on the aspects integration, the objective of this research is to propose a methodology for the development of multi-aspect ontologies that incorporates an aspects integration approach. For the research purpose, the existing ontology development methodologies have been analyzed and an ontology development pattern followed by most methodologies has been revealed. The developed four-stage methodology extends it with an aspects integration approach. The methodology is illustrated through the development of a multi-aspect ontology to support semantic interoperability in DSSs based on human-machine collective intelligence.
DeRenderNet: Intrinsic Image Decomposition of Urban Scenes with Shape-(In)dependent Shading Rendering

We propose DeRenderNet, a deep neural network to decompose the albedo and latent lighting, and render shape-(in)dependent shadings, given a single image of an outdoor urban scene, trained in a self-supervised manner. To achieve this goal, we propose to use the albedo maps extracted from scenes in videogames as direct supervision and pre-compute the normal and shadow prior maps based on the depth maps provided as indirect supervision. Compared with state-of-the-art intrinsic image decomposition methods, DeRenderNet produces shadow-free albedo maps with clean details and an accurate prediction of shadows in the shape-independent shading, which is shown to be effective in re-rendering and improving the accuracy of high-level vision tasks for urban scenes.
Self-Attention Mechanism Enhanced User Interests Modeling for Personalized Recommendation Services in Cyber-Physical-Social Systems

Cyber-Physical-Social Systems (CPSS) provide great value to our lives, but they also cause data overload problems. Data-driven personalized recommendation service is one of the most efficient means to solve such problems, which is currently receiving wide attention from research and industrial communities. The most important task of personalized recommender systems is to predict the click-through rate of given items, which is especially true for personalized advertisement recommendation systems. Recently, a number of deep click-through models have been proposed, which obtain low-dimensional dense embedding vectors of features, and then concatenate together and input into multi-layer perceptron to learn the nonlinear relationship between the features. However, the existing models don't dig deep enough into the user preferences and habits in users’ behavior history. In this paper, we propose a new model: Self-attention based Deep Neural Network (DeepSA), which addresses this issue by constructing Ad-related graph and training graph embedding vectors to enhance the representation of the advertisement for capturing user interests, and learns the internal correlation between user behaviors via the self-attention mechanism, which better explores interests and preferences hidden in users’ historical behaviors. Experiments on two public datasets and an industrial dataset demonstrate the proposed method outperforms the state-of-the-art models.
Multi-Modality Deep Restoration of Extremely Compressed Face Videos

Arguably the most common and salient object in daily video communications is the talking head, as encountered in social media, virtual classrooms, teleconferences, news broadcasting, talk shows, etc. When communication bandwidth is limited by network congestions or cost effectiveness, compression artifacts in talking head videos are inevitable. The resulting video quality degradation is highly visible and objectionable due to high acuity of human visual system to faces. To solve this problem, we develop a multi-modality deep convolutional neural network method for restoring face videos that are aggressively compressed. The main innovation is a new DCNN architecture that incorporates known priors of multiple modalities: the video-synchronized speech signal and semantic elements of the compression code stream, including motion vectors, code partition map and quantization parameters. These priors strongly correlate with the latent video and hence they are able to enhance the capability of deep learning to remove compression artifacts. Ample empirical evidences are presented to validate the superior performance of the proposed DCNN method on face videos over the existing state-of-the-art methods.
Quantum genetic selection

This paper proposes an innovative selection operator based on concepts from quantum mechanics. In particular, a quantum state is used to embody genetic individuals and their fitness values, and a quantum algorithm known as amplitude amplification is used to modify this state in order to create a quantum superposition in which the probability to measure an individual is related to its quality. The main peculiarity of this approach is related to the non-zero probability of selecting individuals do not belonging to the current population so as to create new genetic material and reduce the likelihood that genetic evolution will converge to local optima. The suitability of the proposed operator has been proved by an experimental session where a comparison with well-known selection methods has been carried out on a set of benchmark problems.
Five-weight codes from three-valued correlation of M-sequences

In this paper, for each of six families of three-valued $ m $-sequence correlation, we construct an infinite family of five-weight codes from trace codes over the ring $ R = \mathbb{F}_2+u\mathbb{F}_2 $, where $ u^2 = 0. $ The trace codes have the algebraic structure of abelian codes. Their Lee weight distribution is computed by using character sums. Their support structure is determined. An application to secret sharing schemes is given. The parameters of the binary image are $ [2^{m+1}(2^m-1),4m,2^{m}(2^m-2^r)] $ for some explicit $ r. $
The Importance of Thinking Multivariately When Setting Subscale Cutoff Scores

Setting cutoff scores is one of the most common practices when using scales to aid in classification purposes. This process is usually done univariately where each optimal cutoff value is decided sequentially, subscale by subscale. While it is widely known that this process necessarily reduces the probability of "passing" such a test, what is not properly recognized is that such a test loses power to meaningfully discriminate between target groups with each new subscale that is introduced. We quantify and describe this property via an analytical exposition highlighting the counterintuitive geometry implied by marginal threshold-setting in multiple dimensions. Recommendations are presented that encourage applied researchers to think jointly, rather than marginally, when setting cutoff scores to ensure an informative test.
Earthquake Magnitude Estimation Based on Machine Learning: Application to Earthquake Early Warning System

Abstract Indonesia has high level of seismic activity, so determining magnitude of an earthquake is important in the Earthquake Early Warning System. In the Earthquake Early Warning System, the parameter magnitude must be estimated earlier, so that warnings can be disseminated before the S and surface waves arrive. In previous studies machine learning technology can be used to recognized earthquake events and extract hidden information with massive datasets. This study was a preliminary, proposed the alternative methods to calculate the earthquake magnitude as fast as possible, the data was 1s before and 3 seconds after the P wave from the 3-component single station raw seismogram historical data and developed with a classification deep neural network (DNN) model, classical machine learning random forest (RF) algorithm and the regression deep neural network (DNN). Results from the statistical analysis show that the waveform can be modelled by deep neural network (DNN) models. Classification DNN Model that we constructed reaches good pattern which final loss of 0.63. If it benchmarked to another model such as Random forest (RF), Classification DNN was a better model than RF which is determined by final loss of RF. Our recommendation related to estimate the magnitude from seismic raw modelling are better using Classification DNN with larger dataset. In our study, with relatively small dataset, modelling using RF algorithm can be another option. Another suggestion related this work was utilizing the Regression DNN, that resulting best alternative related to estimation of magnitude.
Sparsity-Assisted Signal Denoising and Pattern Recognition in Time-Series Data

We address the problem of signal denoising and pattern recognition in processing batch-mode time-series data by combining linear time-invariant filters, orthogonal multiresolution representations, and sparsity-based methods. We propose a novel approach to designing higher-order zero-phase low-pass, high-pass, and band-pass infinite impulse response filters as matrices, using spectral transformation of the state-space representation of digital filters. We also propose a proximal gradient-based technique to factorize a special class of zero-phase high-pass and band-pass digital filters so that the factorization product preserves the zero-phase property of the filter and incorporates a sparse-derivative component of the input in the signal model. To demonstrate applications of our novel filter designs, we validate and propose new signal models to simultaneously denoise and identify patterns of interest. To denoise or detect a pattern of interest in the signal, our proposed signal models simultaneously combine linear time-invariant (LTI) filters and sparsity-based methods with orthogonal multiresolution representations, such as wavelets and short-time Fourier transform. We illustrate the capabilities of our proposed signal models using sleep-electroencephalography (EEG) data to detect K-complexes and sleep spindles. Reproducible research is available at https://github.com/prateekgv/sasdpr .
StruMonoNet: Structure-Aware Monocular 3D Prediction

Monocular 3D prediction is one of the fundamental problems in 3D vision. Recent deep learning-based approaches have brought us exciting progress on this problem. However, existing approaches have predominantly focused on end-to-end depth and normal predictions, which do not fully utilize the underlying 3D environment’s geometric structures. This paper introduces StruMonoNet, which detects and enforces a planar structure to enhance pixel-wise predictions. StruMonoNet innovates in leveraging a hybrid representation that combines visual feature and a surfel representation for plane prediction. This formulation allows us to combine the power of visual feature learning and the flexibility of geometric representations in incorporating geometric relations. As a result, StruMonoNet can detect relations between planes such as adjacent planes, perpendicular planes, and parallel planes, all of which are beneficial for dense 3D prediction. Experimental results show that StruMonoNet considerably outperforms state-of-the-art approaches on NYUv2 and ScanNet.
GopGAN: Gradients Orthogonal Projection Generative Adversarial Network With Continual Learning

The generative adversarial networks (GANs) in continual learning suffer from catastrophic forgetting. In continual learning, GANs tend to forget about previous generation tasks and only remember the tasks they just learned. In this article, we present a novel conditional GAN, called the gradients orthogonal projection GAN (GopGAN), which updates the weights in the orthogonal subspace of the space spanned by the representations of training examples, and we also mathematically demonstrate its ability to retain the old knowledge about learned tasks in learning a new task. Furthermore, the orthogonal projection matrix for modulating gradients is mathematically derived and its iterative calculation algorithm for continual learning is given so that training examples for learned tasks do not need to be stored when learning a new task. In addition, a task-dependent latent vector construction is presented and the constructed conditional latent vectors are used as the inputs of generator in GopGAN to avoid the disappearance of orthogonal subspace of learned tasks. Extensive experiments on MNIST, EMNIST, SVHN, CIFAR10, and ImageNet-200 generation tasks show that the proposed GopGAN can effectively cope with the issue of catastrophic forgetting and stably retain learned knowledge.
TauMed: test augmentation of deep learning in medical diagnosis

Deep learning has made great progress in medical diagnosis. However, due to data standardization and privacy restriction, the acquisition and sharing of medical image data have been hindered, leading to the unacceptable accuracy of some intelligent medical diagnosis models. Another concern is data quality. If insufficient quantity and low-quality data are used for training and testing medical diagnosis models, it may cause serious medical accidents. We always use data augmentation to deal with it, and one of the most representative ways is through mutation relation. However, although common mutation methods can increase the amount of medical data, the quality of the image cannot be guaranteed due to the particularity of medical image. Therefore, combined with the characteristics of medical images, we propose TauMed, which implements augmentation techniques based on a series of mutation rules and domain semantics on medical datasets to generate sufficient and high-quality images. Moreover, we chose the ResNet-50 model to experiment with the augmented dataset and compared the results with two main popular mutation tools. The experimental result indicates that TauMed can improve the classification accuracy of the model effectively, and the quality of augmented images is higher than the other two tools. Its video is at https://www.youtube.com/watch?v=O8W8I7U_eqk and TauMed can be used at http://121.196.124.158:9500/.