Reviewerly/exHarmony huggingface dataset:
DEEP RESIDUAL LEARNING FOR SINGLE-IMAGE SUPER-RESOLUTION OF MULTI-SPECTRAL SATELLITE IMAGERY

Abstract. Analyzing optical remote sensing imagery depends heavily on their spatial resolution. At the same time, this data is adversely affected by fixed sensor parameters and environmental influences. Methods for increasing the quality of such data and concomitantly optimizing its information content are, thus, in high demand. In particular, single-image super-resolution (SISR) approaches aim to achieve this goal solely by observing the individual images.We propose to adapt a generic deep residual neural network architecture for SISR to deal with the special properties of remote sensing satellite imagery, especially taking into account the different spatial resolutions of individual Sentinel-2 bands, i.e., ground sampling distances of 20 m and 10 m. As a result, this method is able to increase the perceived resolution of the 20 m channels and mesh all spectral bands. Experimental evaluation and ablation studies on large datasets have shown superior performance compared to the state-of-the-art and that the model is not bound by its capacity.
The Impact of Experiential Learning Method and Vocabolary Mastery Toward Indonesiaon Students' Reading Comprehension Though Animation Video

The aim of the research is to know the influenced of using experiential learning and animation video as a method toward students’ reading comprehension and vocabulary mastery. The researcher used quantitative experiment used survey Technic. The instrument using test validity and realibility. The respondents of the research is all of students grade 7th in Al Inayah Islamic Junior High School at Cilegon city they are 86 students in Stratefied Random Sampling. Reading comprehension is an intentional, active, interactive process that occurs before, during and after a person reads a particular piece of writing. Therefore vocabulary mastery is not only knowing the words and its meanings, but also knowing about how the words sound and how the words are used in the context. It is that knowing a word by sight and sound and knowing its dictionary definition are not the same as knowing how to use the word correctly and understanding it when it is heard or seen in various contexts. This research combaining the method and media toward reading comprehension and vocabulary mastery. There are experiential learning method and animation video as media.Based on the results of data processing using SPSS 17.0, obtained the value of t count for experiential learning method (X1) of 1.416 and the value of sig. 0.001. And t table is T (α / 2; n-k-1) = T (0.05 / 2; 21-2-1) = T (0.025; 19) = 0.475. Because the value of t count (0.475) is larger t table (1.416), then H0 is rejected and H1 is accepted. Therefore the value of t count for animation video (X3) of 1.078 and the value of sig. 0.000. And t table is T (α / 2; n-k-1) = T (0.05 / 2; 21-2-1) = T (0.025; 19) = 0.475. Because the value of t count (0.475) is larger t table (1.078) Therefore the value of t count for vocabulary mastery (X2) of 1.161 and the value of sig. 0.000. And t table is T (α / 2; n-k-1) = T (0.05 / 2; 21-2-1) = T (0.025; 19) = 0.475. Because the value of t count (0.475) is larger t table (1.161), so it can be conclude that there is significant influence of experiential learning method and animation video toward students’ reading comprehension and students’ vocabulary mastery.
Blinking Extraction in Eye gaze System for Stereoscopy Movies

Abstract The aim of this paper is to present methods for human eye blink recognition. The main function of blinking is to spread tears across the eye and remove irratants from the surface of the cornea and conjuctiva. Blinking can be associated with internal memory processing, fatigue or activation in central nervous system. There are currently many methods for automatic blink detection. The most reliable methods include EOG or EEG signals. These methods, however, are associated with a decrease in the comfort of the examined person. This paper presents a method to detect blinks with the eye-tracker device. There are currently many blink detection methods for this devices. Two popular eye-trackers were tested in this paper. In addition a method for improving detection efficiency was proposed.
Subspace Determination Through Local Intrinsic Dimensional Decomposition

Axis-aligned subspace clustering generally entails searching through enormous numbers of subspaces (feature combinations) and evaluation of cluster quality within each subspace. In this paper, we tackle the problem of identifying subsets of features with the most significant contribution to the formation of the local neighborhood surrounding a given data point. For each point, the recently-proposed Local Intrinsic Dimension (LID) model is used in identifying the axis directions along which features have the greatest local discriminability, or equivalently, the fewest number of components of LID that capture the local complexity of the data. In this paper, we develop an estimator of LID along axis projections, and provide preliminary evidence that this LID decomposition can indicate axis-aligned data subspaces that support the formation of clusters.
Optimal differential trails in lightweight block ciphers ANU and PICO

ANU and PICO are two lightweight block ciphers published by Bansod et al. in 2016. For cryptanalysis, we apply a branch-and-bound based search algorithm to find the least number of active Substitution boxes (S-boxes) in differential trails of these ciphers. Designers provide a lower bound on the number of active S-boxes in any differential trail of ANU up to 4 rounds as 0, 2, 8, and 20. We improve this lower bound to 0, 2, 5, and 9. We present the optimal differential trails in ANU for 7 rounds with probability 2−62. The design specification of PICO claims to generate a large number of active S-boxes in a few rounds. We reduce the lower bound on the number of active S-boxes in any 6-round trail from 12 to 6. We also present the optimal differential trails in PICO for 21 rounds with probability 2−63. These are the best results reported in the literature for differential cryptanalysis of ANU and PICO.
On the Equivalence between Node Embeddings and Structural Graph Representations

This work provides the first unifying theoretical framework for node embeddings and structural graph representations, bridging methods like matrix factorization and graph neural networks. Using invariant theory, we show that the relationship between structural representations and node embeddings is analogous to that of a distribution and its samples. We prove that all tasks that can be performed by node embeddings can also be performed by structural representations and vice-versa. We also show that the concept of transductive and inductive learning is unrelated to node embeddings and graph representations, clearing another source of confusion in the literature. Finally, we introduce new practical guidelines to generating and using node embeddings, which fixes significant shortcomings of standard operating procedures used today.
Hybridizing Dragonfly Algorithm with Differential Evolution for Global Optimization

Due to its simplicity and efficiency, differential evolution (DE) has gained the interest of researchers from various fields for solving global optimization problems. However, it is prone to premature convergence at local minima. To overcome this drawback, a novel hybrid dragonfly algorithm with differential evolution (Hybrid DA-DE) for solving global optimization problems is proposed. Firstly, a novel mutation operator is introduced based on the dragonfly algorithm (DA). Secondly, the scaling factor (F) is adjusted in a self-adaptive and individual-dependent way without extra parameters. The proposed algorithm combines the exploitation capability of DE and exploration capability of DA to achieve optimal global solutions. The effectiveness of this algorithm is evaluated using 30 classical benchmark functions with sixteen state-of-the-art meta-heuristic algorithms. A series of experimental results show that Hybrid DA-DE outperforms other algorithms significantly. Meanwhile, Hybrid DA-DE has the best adaptability to high-dimensional problems.
On Robust Estimates of Correlated Risk in Cyber-Insured IT Firms

In this article, we comment on the drawbacks of the existing AI-based Bayesian network (BN) cyber-vulnerability analysis (C-VA) model proposed in Mukhopadhyay et al. (2013) to assess cyber-risk in IT firms, where this quantity is usually a joint distribution of multiple risk (random) variables (e.g., quality of antivirus, frequency of monitoring, etc.) coming from heterogeneous distribution families. As a major modeling drawback, Mukhopadhyay et al. (2013) assume that any pair of random variables in the BN are linearly correlated with each other. This simplistic assumption might not always hold true for general IT organizational environments. Thus, the use of the C-VA model in general will result in loose estimates of correlated IT risk and will subsequently affect cyber-insurance companies in framing profitable coverage policies for IT organizations. To this end, we propose methods to (1) find a closed-form expression for the maximal correlation arising between pairs of discrete random variables, whose value finds importance in getting robust estimates of copula-induced computations of organizational cyber-risk, and (2) arrive at a computationally effective mechanism to compute nonlinear correlations among pairs of discrete random variables in the correlation matrix of the CBBN model (Mukhopadhyay et al. 2013). We also prove that an empirical computation of MC using our method converges rapidly, that is, exponentially fast, to the true correlation value in the number of samples. Our proposed method contributes to a tighter estimate of IT cyber-risk under environments of low-risk data availability and will enable insurers to better assess organizational risks and subsequently underwrite profitable cyber-insurance policies.
WBCaps: A Capsule Architecture-based Classification Model Designed for White Blood Cells Identification

This pilot study presented a capsule architecture-based classification model named WBCaps to automatically recognize the five types of white blood cells (WBC) from normal peripheral blood smears. The entire WBCs identification workflow including two sections were also reported here. The first section automatically located the WBCs from the whole slice of blood smears through a three-step image segmentation method including color normalization, color deconvolution and cell extraction. In the second section, the extracted cells were fed into WBCaps, which was a network cascaded by traditional convolutional layer, primary capsule layer and representation capsule layer, to generate the type prediction. We employed 3-fold cross validation to validate the proposed WBCs identification method on a small clinical dataset and yielded precision 0.99, recall 0.99 and F1-score 0.99. The result outperformed that of ResNet (precision 0.97, recall 0.97, F1-score 0.97) and VGG (precision 0.97, recall 0.97, F1-score 0.98). The proposed model could be a promising clinical tool for hematology analyzer to facilitate the cytological and morphological examination.
Neural networks on microcontrollers: saving memory at inference via operator reordering.

Designing deep learning models for highly-constrained hardware would allow imbuing many edge devices with intelligence. Microcontrollers (MCUs) are an attractive platform for building smart devices due to their low cost, wide availability, and modest power usage. However, they lack the computational resources to run neural networks as straightforwardly as mobile or server platforms, which necessitates changes to the network architecture and the inference software. In this work, we discuss the deployment and memory concerns of neural networks on MCUs and present a way of saving memory by changing the execution order of the network's operators, which is orthogonal to other compression methods. We publish a tool for reordering operators of TensorFlow Lite models and demonstrate its utility by sufficiently reducing the memory footprint of a CNN to deploy it on an MCU with 512KB SRAM.
Virtual Reality Authoring Tools for Educators

Virtual reality technologies have been demonstrated to produce encouraging results in the educational process. However, adoption of virtual reality-based instruction among educators has yet to see significant numbers. One of the reasons is the technical difficulty in authoring custom-made VR experiences for students. In this paper, we propose criteria for choosing VR authoring tools that are appropriate for usage in education and apply them to such current platforms. We also present a case study of learning collaboration project that revolved around the usage of VR authoring tools. We conclude with some recommendations regarding additional features that would enhance the exploitation of current VR authoring tools in education.
A Low-Cost Design of 2D Median Filter

The median filter plays an important role in image processing for noise removing. To enhance the operating speed, hardware implementation of median filter on field programmable gate arrays (FPGA) or application specific IC (ASIC) are necessary and inevitable. This paper presents a low-cost and high-throughput hardware design for two-dimensional (2D) median filter. Two techniques are employed to reduce circuit area. One is the parallel three-valued sorting method for reducing the number of pipelined registers under constant speed, and the other is the functional sharing method used to implement median value sorting with the least number of comparisons. The proposed 2D median filter is implemented by two different synthesized methods. One is synthesized by Synopsys Design Compiler with the TSMC 90nm cell library (ASIC design) and the other is synthesized by Xilinx FPGA Virtex7 XC7VX690T (FPGA design). Experimental results show that the area cost of the proposed design is reduced more than 30% on average in comparison to the previous designs.
Quantum-enhanced learning of rotations about an unknown direction

We design machines that learn how to rotate a quantum bit about an initially unknown direction, encoded in the state of a spin-j particle. We show that a machine equipped with a quantum memory of O(log j) qubits can outperform all machines with purely classical memory, even if the size of their memory is arbitrarily large. The advantage is present for every finite j and persists as long as the quantum memory is accessed for no more than O(j) times. We establish these results by deriving the ultimate performance achievable with purely classical memories, thus providing a benchmark that can be used to experimentally demonstrate the implementation of quantum-enhanced learning.
A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media

Generated hateful and toxic content by a portion of users in social media is a rising phenomenon that motivated researchers to dedicate substantial efforts to the challenging direction of hateful content identification. We not only need an efficient automatic hate speech detection model based on advanced machine learning and natural language processing, but also a sufficiently large amount of annotated data to train a model. The lack of a sufficient amount of labelled hate speech data, along with the existing biases, has been the main issue in this domain of research. To address these needs, in this study we introduce a novel transfer learning approach based on an existing pre-trained language model called BERT (Bidirectional Encoder Representations from Transformers). More specifically, we investigate the ability of BERT at capturing hateful context within social media content by using new fine-tuning methods based on transfer learning. To evaluate our proposed approach, we use two publicly available datasets that have been annotated for racism, sexism, hate, or offensive content on Twitter. The results show that our solution obtains considerable performance on these datasets in terms of precision and recall in comparison to existing approaches. Consequently, our model can capture some biases in data annotation and collection process and can potentially lead us to a more accurate model.
Unsupervised pre-training for sequence to sequence speech recognition

This paper proposes a novel approach to pre-train encoder-decoder sequence-to-sequence (seq2seq) model with unpaired speech and transcripts respectively. Our pre-training method is divided into two stages, named acoustic pre-trianing and linguistic pre-training. In the acoustic pre-training stage, we use a large amount of speech to pre-train the encoder by predicting masked speech feature chunks with its context. In the linguistic pre-training stage, we generate synthesized speech from a large number of transcripts using a single-speaker text to speech (TTS) system, and use the synthesized paired data to pre-train decoder. This two-stage pre-training method integrates rich acoustic and linguistic knowledge into seq2seq model, which will benefit downstream automatic speech recognition (ASR) tasks. The unsupervised pre-training is finished on AISHELL-2 dataset and we apply the pre-trained model to multiple paired data ratios of AISHELL-1 and HKUST. We obtain relative character error rate reduction (CERR) from 38.24% to 7.88% on AISHELL-1 and from 12.00% to 1.20% on HKUST. Besides, we apply our pretrained model to a cross-lingual case with CALLHOME dataset. For all six languages in CALLHOME dataset, our pre-training method makes model outperform baseline consistently.
Adversarial Regression Training for Visualizing the Progression of Chronic Obstructive Pulmonary Disease with Chest X-Rays

Knowledge of what spatial elements of medical images deep learning methods use as evidence is important for model interpretability, trustiness, and validation. There is a lack of such techniques for models in regression tasks. We propose a method, called visualization for regression with a generative adversarial network (VR-GAN), for formulating adversarial training specifically for datasets containing regression target values characterizing disease severity. We use a conditional generative adversarial network where the generator attempts to learn to shift the output of a regressor through creating disease effect maps that are added to the original images. Meanwhile, the regressor is trained to predict the original regression value for the modified images. A model trained with this technique learns to provide visualization for how the image would appear at different stages of the disease. We analyze our method in a dataset of chest x-rays associated with pulmonary function tests, used for diagnosing chronic obstructive pulmonary disease (COPD). For validation, we compute the difference of two registered x-rays of the same patient at different time points and correlate it to the generated disease effect map. The proposed method outperforms a technique based on classification and provides realistic-looking images, making modifications to images following what radiologists usually observe for this disease. Implementation code is available at https://github.com/ricbl/vrgan.
Real-Time Traffic Rules Infringing Determination Over the Video Stream: Wrong Way and Clearway Violation Detection

Video processing techniques are used in research areas about traffic for many purposes such as measuring traffic density, crash detection, vehicle classification and counting. To control the traffic flow and create a safer environment, vehicle detection, tracking, security lane and wrong way violation detection is achieved with the developed system. As known, security lane violation causes units from security, health or fire departments to not be able to use those lanes when needed. Also, wrong way violation is one of the main causes of car crashes. These types of violations directly or indirectly cause serious numbers of loss of life and property. In order to prevent such problems, it is very important to detect the infringers early. In this paper, the wrong way and clearway violation detection over the video stream in real-time is achieved.
Experimental test of error-disturbance uncertainty relation with continuous variables

The uncertainty relation is one of the fundamental principles in quantum mechanics and plays an important role in quantum information science. We experimentally test the error-disturbance uncertainty relation (EDR) with continuous variables for Gaussian states. Two incompatible continuous-variable observables, amplitude and phase quadratures of an optical mode, are measured simultaneously using a heterodyne measurement system. The EDR values with continuous variables for coherent, squeezed, and thermal states are verified experimentally. Our experimental results demonstrate that Heisenberg’s EDR with continuous variables is violated, while Ozawa’s and Branciard’s EDRs with continuous variables are validated.
The use of Least Significant Bit (LSB) and Knight Tour Algorithm for image steganography of cover image

&lt;span lang="EN-US"&gt;Steganography is one of the method to communicate in a hidden way. In another word, steganography literally means the practice of hiding messages or information within another data. Previous studies have proposed various steganography techniques using different approaches including Least Significant Bit (LSB), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). However, different approaches still have its own weaknesses. Therefore image stenography using Knight Tour Algorithm with Least Significant Bit (LSB) technique is presented. The main objective is to improve the security factor in the stego image. Basically, the proposed technique is divided into two parts which are the sender and receiver side. Then, steganalysis which is a type of attack on stenography algorithm is used to detect the secret message in the cover image by the statistical analysis of pixel values. Chi Square Statistical Attach which is one of the type of steganalysis is used to detect these near-equal Po Vs in images and bases the probability of embedding on how close to equal the even pixel values and their corresponding odd pixel values are in the test image. The Knight Tour Algorithm is applied due to the common Least Significant Bit technique that is weak in security and easily decoded by outsider.&lt;/span&gt;
Correlation between Students’ Cognitive Reading Strategies and Reading Comprehension

This research aims to 1) describe students’ responses toward cognitive reading strategies and their reading comprehension score; 2) find out the correlation between students’ cognitive reading strategies and their reading comprehension. The current research is quantitative in nature. The quantitative is intended to describe the responses of students to cognitive and reading strategies and to explain to what extent the correlation between students’ cognitive reading strategies and reading comprehension. The samples are the fourth semester students of English education as subjects or participants of the research. Data were collected by distributing questionnaires to get responses from students in term of Cognitive Reading Strategies. IELTS Reading Testing was used to get Students’ reading comprehension scores. The quantitative data were analyzed by correlation product moment. The results of the study shows that most of students usually use four cognitive reading strategies included in this research and cognitive reading strategies correlate significantly to students’ reading comprehension with p value of 0.00 and coefficient value of 0.61.