Reviewerly/exHarmony huggingface dataset:
Precise Measurement of the Surface Shape of Silicon Wafer by Using a New Phase-Shifting Algorithm and Wavelength-Tuning Interferometer

In wavelength-tuning interferometry, the surface profile of the optical component is a key evaluation index. However, the systematic errors caused by the coupling error between the higher harmonics and phase shift error are considerable. In this research, a new 10N − 9 phase-shifting algorithm comprising a new polynomial window function and a DFT is developed. A new polynomial window function is developed based on characteristic polynomial theory. The characteristic of the new 10N − 9 algorithm is represented in the frequency domain by Fourier description. The phase error of the new algorithm is also discussed and compared with other phase-shifting algorithms. The surface profile of a silicon wafer was measured by using the 10N − 9 algorithm and a wavelength-tuning interferometer. The repeatability measurement error across 20 experiments was 2.045 nm, which indicates that the new 10N − 9 algorithm outperforms the conventional phase-shifting algorithm.
Perturbation analysis of gradient-based adversarial attacks

After the discovery of adversarial examples and their adverse effects on deep learning models, many studies focused on finding more diverse methods to generate these carefully crafted samples. Although empirical results on the effectiveness of adversarial example generation methods against defense mechanisms are discussed in detail in the literature, an in-depth study of the theoretical properties and the perturbation effectiveness of these adversarial attacks has largely been lacking. In this paper, we investigate the objective functions of three popular methods for adversarial example generation: the L-BFGS attack, the Iterative Fast Gradient Sign attack, and Carlini & Wagner's attack (CW). Specifically, we perform a comparative and formal analysis of the loss functions underlying the aforementioned attacks while laying out large-scale experimental results on ImageNet dataset. This analysis exposes (1) the faster optimization speed as well as the constrained optimization space of the cross-entropy loss, (2) the detrimental effects of using the signature of the cross-entropy loss on optimization precision as well as optimization space, and (3) the slow optimization speed of the logit loss in the context of adversariality. Our experiments reveal that the Iterative Fast Gradient Sign attack, which is thought to be fast for generating adversarial examples, is the worst attack in terms of the number of iterations required to create adversarial examples in the setting of equal perturbation. Moreover, our experiments show that the underlying loss function of CW, which is criticized for being substantially slower than other adversarial attacks, is not that much slower than other loss functions. Finally, we analyze how well neural networks can identify adversarial perturbations generated by the attacks under consideration, hereby revisiting the idea of adversarial retraining on ImageNet.
Sparsity-based recovery of Galactic-binary gravitational waves

The detection of Galactic binaries as sources of gravitational waves promises an unprecedented wealth of information about these systems but also raises several challenges in signal processing. In particular the large number of expected sources and the risk of misdetection call for the development of robust methods. We describe here an original nonparametric recovery of the imprint of Galactic binaries in measurements affected by instrumental noise typical of the space-based gravitational wave observatory LISA. This method, based on a denoising procedure, aims at separating from noise the sum of all signals coming from Galactic binaries. We assess the impact of various approaches based on sparse signal modeling and focus on adaptive structured block sparsity. We carefully show that a sparse representation of the interferometric measurement gives a reliable access to the total signal coming from Galactic binaries. In particular we check the successful fast recovery of the gravitational wave signal on a simple yet realistic example involving verification Galactic binaries recently proposed in LISA data challenges.
Image Classification Considering Probability Density Function based on Simplified Beta Distribution

Method for image classification considering Probability Density Function (PDF) based on simplified beta distributions is proposed.In this paper, image classification for Synthetic Aperture Radar (SAR) data is concerned.In particular, Probability Density Function (PDF) of SAR data is followed by not multivariate normal distribution but Chi-Square like distribution.It, however, is not always true that the PDF of SAR data is followed by Chi-Square distribution.Due to the mismatch between Chi-Square distribution and actual distribution, classification performance gets worth.In this paper, simplified beta distribution is assumed for the PDF of the SAR data.Furthermore, it is used to add texture information to the SAR data when the Maximum Likelihood classification is applied.In the paper, "Contrast" of texture feature is added to the SAR data.Through the experiments with real SAR data, it is found that matching error between real PDF and the proposed simplified beta distribution is smaller than the normal distribution.It is also found that applying the proposed distribution-adaptive maximum likelihood method using the simplified beta-distribution could achieve a classification accuracy improvement of 94.7% and 12.1%.
Drosophila-inspired 3D moving object detection based on point clouds

3D moving object detection is one of the most critical tasks in dynamic scene analysis. In this paper, we propose a novel Drosophila-inspired 3D moving object detection method using Lidar sensors. According to the theory of elementary motion detector, we have developed a motion detector based on the shallow visual neural pathway of Drosophila. This detector is sensitive to the movement of objects and can well suppress background noise. Designing neural circuits with different connection modes, the approach searches for motion areas in a coarse-to-fine fashion and extracts point clouds of each motion area to form moving object proposals. An improved 3D object detection network is then used to estimate the point clouds of each proposal and efficiently generates the 3D bounding boxes and the object categories. We evaluate the proposed approach on the widely-used KITTI benchmark, and state-of-the-art performance was obtained by using the proposed approach on the task of motion detection.
Desa wisma siaga bencana di desa bungurasih sidoarjo

The purpose of this activity is to motivate, improve the understanding of the community or community towards disaster risk (understanding to disaster risk); increase the capacity of the community or community in dealing with disasters, especially in the event of a disaster (emergency response); increase the involvement of women in disaster management programs and activities at the local level; and contributes to the strengthening and / or development of a Disaster Resilient Village (DesTaNa) in a disaster-prone area in the Waru District of Sidoarjo Regency, which is a "subscribed" area for flooding. Dasa Wisma is trained in how to make and install biopores. The results of this activity can reduce standing water that occurs during the rainy season so that the community can be free from flooding.Keywords: Dasa Wisa; DesTaNa; Biopori, Bungurasih
Interpretable Multimodal Routing for Human Multimodal Language.

The human language has heterogeneous sources of information, including tones of voice, facial gestures, and spoken language. Recent advances introduced computational models to combine these multimodal sources and yielded strong performance on human-centric tasks. Nevertheless, most of the models are often black-box, which comes with the price of lacking interpretability. In this paper, we propose Multimodal Routing to separate the contributions to the prediction from each modality and the interactions between modalities. At the heart of our method is a routing mechanism that represents each prediction as a concept, i.e., a vector in a Euclidean space. The concept assumes a linear aggregation from the contributions of multimodal features. Then, the routing procedure iteratively 1) associates a feature and a concept by checking how this concept agrees with this feature and 2) updates the concept based on the associations. In our experiments, we provide both global and local interpretation using Multimodal Routing on sentiment analysis and emotion prediction, without loss of performance compared to state-of-the-art methods. For example, we observe that our model relies mostly on the text modality for neutral sentiment predictions, the acoustic modality for extremely negative predictions, and the text-acoustic bimodal interaction for extremely positive predictions.
Deepfake Video Forensics based on Transfer Learning

Deeplearning has been used to solve complex problems in various domains. As it advances, it also creates applications which become a major threat to our privacy, security and even to our Democracy. Such an application which is being developed recently is the "Deepfake". Deepfake models can create fake images and videos that humans cannot differentiate them from the genuine ones. Therefore, the counter application to automatically detect and analyze the digital visual media is necessary in today world. This paper details retraining the image classification models to apprehend the features from each deepfake video frames. After feeding different sets of deepfake clips of video fringes through a pretrained layer of bottleneck in the neural network is made for every video frame, already stated layer contains condense data for all images and exposes artificial manipulations in Deepfake videos. When checking Deepfake videos, this technique received more than 87 per cent accuracy. This technique has been tested on the Face Forensics dataset and obtained good accuracy in detection.
Learning a gradient grammar of French liaison

In certain French words, an orthgraphically-final consonant is unpronounced except, in certain environments, when it precedes a vowel. This phenomenon, liaison, shows significant interactions with several other patterns in French (including h-aspiré, schwa deletion, and the presence of other morphemes in the liaison context). We present a learning algorithm that acquires a grammar that accounts for these patterns and their interactions. The learned grammar employs Gradient Symbolic Computation (GSC), incorporating weighted constraints and partially-activated symbolic representations. Grammatical analysis in the GSC framework includes the challenging determination of the numerical strength of symbolic constituent activations (as well as constraints). Here we present the first general algorithm for learning these quantities from empirical examples: the Error-Driven Gradient Activation Readjustment (EDGAR). Smolensky and Goldrick (2016) proposed a GSC analysis, with hand-determined numerical strengths, in which liaison derives from the coalescence of partially-activated input consonants. EDGAR allows us to extend this work to a wider range of liaison phenomena by automatically determining the more comprehensive set of numerical strengths required to generate the complex pattern of overall liaison behaviour.
Virtual Reality Speaking Application Utilisation in Combatting Presentation Apprehension

Virtual Reality (VR) application in the field of education has been an ongoing endeavour. It is believed that VR provides an opportunity for students to learn in a real situation through the exposure of artificially created reality and the use of visual which improves the learning by enhancing study focus (Piovesan, Passerion and Pereira, 2012). The accessibility to VR is made even easier with the invention of wireless VR. Incorporating this technology in language learning is a logical step, particularly in combatting students’ apprehension to carry out presentations. Thus, the focus of this study is to investigate UiTM Faculty of Education TESL postgraduates’ attitude in using virtual reality speaking application to ease their presentation apprehension. A mixed-method study is adopted by incorporating both quantitative (questionnaire) and qualitative (interviews) approaches. A purposive sampling of twenty-four TESL postgraduates is used to gather data which was then analysed using SPSS version 23 for the questionnaire and thematic analysis for the interview. Descriptive statistics is used to present the findings. The findings show that the participants have limited knowledge of VR speaking application. Nonetheless, they are very interested and motivated to use the application as part of their presentation preparation especially in easing their apprehension. VR speaking application provides a crucial connection in improving oral presentations skills, but it is not the best approach for teachers to develop speaking skills. As this study is based on the respondents’ perceptions, future work is necessary to statistically study the effectiveness of VR speaking technology in language learning as well as investing if any significant relationship is formed between VR speaking application and language learning and teaching variables.
Risk assessment optimization for decision support using intelligent model based on fuzzy inference renewable rules

Due to the unreliability of wired communications and the risks of controlling the process of transmitting data besides the complications that affecting data protection and the high costs of systems infrastructure, led to use wireless communications instead of wires media, but these networks are vulnerable towards illegal attacks. The side effects of these attacks are modifying data or penetrate the security system and discover its weaknesses, which leads to great material losses. These risks and difficulties led to the reluctance of wires communications and propose intelligent techniques and robust encryption algorithms for preventing data transmitted over wireless networks to keep it safe from cyber security attacks. So, there is a persistent need for providing intelligent techniques and robust algorithms to preserve conveyed information using wireless network. This paper introduces scenario for proposing intelligent technique to increase data reliability and provides a new way to improve high level of protection besides reduces infrastructure cost. The proposed system relies on two models, where the first model based on producing a knowledge base of risk rules while the aim of the second module is a risk assessment outcomes and encryption process according to attacks type. In this system, reducing risks levels based on renewable rules whereas a novel security system established on non-periodic keys with unsystematic operations using fuzzy system. We concluded that the proposed system has the ability to protect the transmitted data, increases its reliability and reduce the potential risks. MATLAB Toolkit 2014 then Weka open source package was used in encryption and data mining for the proposed system.&lt;iframe class="ginger-extension-definitionpopup" style="left: -91.5px; top: -78px; z-index: 100001; display: none;" src="chrome-extension://kdfieneakcjfaiglcfcgkidlkmlijjnh/content/popups/definitionPopup/index.html?title=Abstract&amp;amp;description=a%20concept%20or%20idea%20not%20associated%20with%20any%20specific%20instance"&gt;&lt;/iframe&gt;
Desarrollo de habilidades creativas de los estudiantes como consecuencia del uso de herramientas TIC

The following quasi-experimental research of quantitative, descriptive and comparative order aims to establish the relational existence (status, factors and significant differences) between the use of a 3D CAD modeling ICT tool and the creative skills developed by students of the SENA Mechatronic Automotive Maintenance Technologist training program. A sample composed of two groups (Experimental and Control) was established with a previous test (Pretest ECG), followed by stimulation or treatment to the Experimental group (3D CAD modeling intervention) and to the Control Group without stimulus. A subsequent test (Postest ECG) was finally performed. According to the evaluated indicators by the ECG instrument, the subjects who worked with 3D CAD modeling developed the creative skills associated with the evaluated indicators: elaboration (El), thematic connectivity (Ct), expansive connectivity (Ce), fantasy (Fa), graphic ability (Hg), sense of humor (Sh) and graphic fluidity (Fg). The total score (Pt), is not considered as an indicator of ECG’s graphical ability, but provides a holistic perspective of the results obtained from the tests, where an increase in the mean could be seen, indicating that the use of 3D CAD modeling develops creative skills in individuals.
A prior case study of natural language processing on different domain

In the present state of digital world, computer machine do not understand the human’s ordinary language. This is the great barrier between humans and digital systems. Hence, researchers found an advanced technology that provides information to the users from the digital machine. However, natural language processing (i.e. NLP) is a branch of AI that has significant implication on the ways that computer machine and humans can interact. NLP has become an essential technology in bridging the communication gap between humans and digital data. Thus, this study provides the necessity of the NLP in the current computing world along with different approaches and their applications. It also, highlights the key challenges in the development of new NLP model.
Performance of Levenberg-Marquardt Algorithm in Backpropagation Network Based on the Number of Neurons in Hidden Layers and Learning Rate

One of the supervised learning paradigms in artificial neural networks (ANN) that are in great developed is the backpropagation model. Backpropagation is a perceptron learning algorithm with many layers to change weights connected to neurons in hidden layers. The performance of the algorithm is influenced by several network parameters including the number of neurons in the input layer, the maximum epoch used, learning rate (lr) value, the hidden layer configuration, and the resulting error (MSE). Some of the tests conducted in previous studies obtained information that the Levenberg-Marquardt training algorithm has better performance than other algorithms in the backpropagation network, which produces the smallest average error with a test level of α = 5% which used 10 neurons in a hidden layer. The number of neurons in hidden layers varies depending on the number of neurons in the input layer. In this study an analysis of the performance of the Levenberg-Marquardt training algorithm was carried out with 5 neurons in the input layer, a number of n neurons in hidden layers (n = 2, 4, 5, 7, 9), and 1 neuron in the output layer. Performance analysis is based on network-generated errors. This study uses a mixed method, namely development research with quantitative and qualitative testing using ANOVA statistical tests. Based on the analysis, the Levenberg-Marquardt training algorithm produces the smallest error of 0.00014 ± 0.00018 on 9 neurons in hidden layers with lr = 0.5. Keywords: hidden layer, backpropogation, MSE, learning rate, Levenberg-Marquardt.
HANDWRITTEN RECOGNITION BY USING MACHINE LEARNING APPROACH

Deep learning is the most powerful machine learning technique in current generation.This paper presents the result of handwritten recognition using deep learning.Handwriting is unique to each individual.So the handwriting is differed from one person to another person.Handwritten Recognition can be done in two ways.One is Online Handwritten recognition and another one is Offline Handwritten Recognition.Online Handwritten recognition system, which takes the input at run time and Offline Handwritten Recognition which works on scanned images.Offline handwritten is the hardest to find the handwriting.MNIST data set is used for this handwritten digit recognition process and it has 70000 handwritten digits.Many Machine Learning Algorithms are developed which can be used for this digit classification.This paper performs the analysis of accuracies and performance measures of algorithm Convolutional Neural Networks (CNN).The proposed approach recognizes with overall accuracy is 93%.
BrePartition: Optimized High-Dimensional <i>k</i>NN Search With Bregman Distances

Bregman distances (also known as Bregman divergences) are widely used in machine learning, speech recognition and signal processing, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> NN searches with Bregman distances have become increasingly important with the rapid advances of multimedia applications. Data in multimedia applications such as images and videos are commonly transformed into space of hundreds of dimensions. Such high-dimensional space has posed significant challenges for existing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> NN search algorithms with Bregman distances, which could only handle data of medium dimensionality (typically less than 100). This paper addresses the urgent problem of high-dimensional <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> NN search with Bregman distances. We propose a novel partition-filter-refinement framework. Specifically, we propose an optimized dimensionality partitioning scheme to solve several non-trivial issues. First, an effective bound from each partitioned subspace to obtain exact <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> NN results is derived. Second, we conduct an in-depth analysis of the optimized number of partitions and devise an effective strategy for partitioning. Third, we design an efficient integrated index structure for all the subspaces together to accelerate the search processing. Moreover, we extend our exact solution to an approximate version by a trade-off between the accuracy and efficiency. Experimental results on four real-world datasets and two synthetic datasets show the clear advantage of our method in comparison to state-of-the-art algorithms.
Open Source Software Vulnerability Propagation Analysis Algorithm based on Knowledge Graph

With the extensive reuse of open source components, the scope of vulnerability impact will have cascade expansion. At the level of vulnerability data analysis, aiming at the vulnerability propagation problem, this thesis proposes a hierarchical propagation path search algorithm based on open source software vulnerability knowledge graph, at the same time, proposes a heuristic search strategy in both component layer and class layer to reduce the search space complexity, which is optimized from exponential down to polynomial. Furthermore, we propose the optimal blocking concept to represent the cost of repairing the entire propagation path, in order to measure the severity of the project's vulnerability. As for the purpose of providing effective suggestions on vulnerability repairing, we model the optimal blocking calculation as the network flow minimal separate problem, then calculate the network maximal flux to obtain the key dependencies with risks. Finally, multiple case studies with various vulnerability dependent risks show that the proposed algorithm can find software vulnerabilities affecting specific projects effectively.