Reviewerly/exHarmony huggingface dataset:
Convolution Neural Network with Dual Tree Complex Wavelet Transform Preprocessor for Universal Image Steganalysis

Recently, deep learning models based on convolutional neural networks (CNN) have been used in image steganalysis problems. In this paper, we present different architecture of CNN with dual tree complex wavelet transform for preprocessing before input images put into system. The main task of this transform is for exploiting the difference between cover and stego images through shift variance property. The net consists of five successive convolutions layers. Each one following by normalization and pooling layers ends with fully connected layer. The performance of system is evaluated through accuracy, precision, recall and f-score measures. The results show effectiveness of it with more than 0.9 precision values. HUGO, WOW and UNIWARD algorithms selected for implementation.
Every positive integer is the order of an ordinary abelian variety over $${{\mathbb {F}}}_2$$

Abstract We show that for every integer $$m &gt; 0$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>m</mml:mi><mml:mo>&gt;</mml:mo><mml:mn>0</mml:mn></mml:mrow></mml:math> , there is an ordinary abelian variety over $${{\mathbb {F}}}_2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>F</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math> that has exactly m rational points.
Global Optimization Method to Multiple Local Optimals with the Surface Approximation Methodology and Its Application for Industry Problems

Although generally speaking, a great number of functional evaluations may be required until convergence, it can be solved by using neural network effectively. Here, techniques to search the region of interest containing the global optimal design selected by random seeds is investigated. Also techniques for finding more accurate approximation using Holographic Neural Network (HNN) improved by using penalty function for generalized inverse matrix is investigated. Furthermore, the mapping method of extrapolation is proposed to make the technique available to general application in structural optimization. Application examples show that HNN may be expected as potential activate and feasible surface functions in response surface methodology than the polynomials in function approximations. Finally, the real design examples of a vehicle performance such as idling vibration, booming noise, vehicle component crash worthiness and combination problem between vehicle crashworthiness and restraint device performance at the head-on collision are used to show the effectiveness of the proposed method.
Deep Multi-type Objects Muli-view Multi-instance Multi-label Learning

Multi-view multi-instance multi-label learning (M3L) can model complex objects (bags) that are composed of multiple instances, represented with heterogeneous feature views and annotated with multiple related semantic labels.Although significant progress has been made toward M3L tasks, the current solutions still focus on a single-type of complex objects, and cannot effectively mine the widely-witnessed interconnected objects of multi-types.To bridge this gap, we propose a Deep Multi-type objects Multi-view Multi-instance Multi-label Learning solution (DeepM4L) based on heterogeneous network embedding.DeepM4L first encodes the inter-and intra-relations among multi-type objects using a heterogeneous network, and performs instance neighbor embedding to learn the representation vectors of instances.Next, it obtains the instance-label score tensor for each view and uses a max pooling operation to induce the bag-label score tensor for each bag.After that, it combines bag-label scores by multi-view learning to guarantee the semantic consistency between bags of different views.Our empirical study on benchmark datasets shows that DeepM4L is significantly superior to the recent advanced baselines.
Deep Convolutional Generative Adversarial Network Application in Batik Pattern Generator

Batik has been alleged as one of the oldest cultural heritages worldwide. Since the initiation, batik has been identified with various types and patterns. Various kinds of batik making techniques have long been popularized by the wider community. DCGAN serves as a new idea for the modern batik world. Such algorithm is capable of reproducing a novel image to produce batik patterns previously unnotified. Hence, this study aims to propose a DCGAN model to craft a new type of batik pattern. By utilizing a large dataset of batik images and a variety of the proposed DCGAN models, the proposed algorithm has succeeded in crafting a novel and diverse synthetic batik pattern.
Peculiarities of formation and assessment of schoolchildren’s mathematical literacy

Introduction. The study investigates the problem of developing mathematical literacy. The purpose of the article is to reveal the specifics of developing and assessing schoolchildren’s mathematical literacy. Materials and Methods. The research is based on the concept and model of mathematical literacy defined for the OECD Programme for International Student Assessment (PISA). The main research methods include an analysis of mathematics teachers’ experiences and an analysis of the structure of mathematical literacy tasks. Results. The article presents an analysis of the conceptual foundations of developing schoolchildren’s mathematical literacy. The authors identified and structured the characteristic features of the tasks aimed at forming mathematical literacy, described the main approaches to their development, based on the use of model schemes and mathematical modeling. The links between the contexts of real-life situations and the content areas of the school mathematics course were traced. The complete process of working with mathematical models is illustrated by tasks arising in life situations. The authors revealed the main approaches to the development of tasks, designed to form mathematical literacy, based on the model schemes and the process of mathematical modeling. Conclusions. In conclusion, the authors summarize stages of designing mathematical literacy tasks and the requirements to the approaches of their development. The conclusion is made about the possibility of using model schemes as a reference for mathematical literacy tasks.
Research on full cycle evaluation of power grid projects based on rough set-multilevel extension theory

Project full-cycle evaluation is the process of evaluating the level of project implementation. The evaluation has a clear purpose orientation and involves the entire cycle of the project. This paper proposes a research method for the full-cycle evaluation of power grid projects based on rough set-multi-level extension theory. First, this article combines various aspects of the project to establish a project evaluation index system involving multiple interests such as the environment and society. Then, the index weight is determined based on the rough set theory, and the multi-level extension theory is used to evaluate the project in the whole cycle. This model provides new ideas for the accurate evaluation of various types of projects and lays a theoretical foundation for improving the level of project management.
Social Media-Based E-learning and Online Assignments on Algebraic Materials

This study aims at evaluating the application of social media-based e-learning and online assignments during Covid-19 pandemic based on: (1) the availability of facilities and infrastructure in implementing social media-based e-learning and online assignments during the Covid-19 pandemic, (2) comprehension and management of e-learning and online assignments by lecturers and students, (3) social media-based e-learning and online assignments. This study is a qualitative descriptive study using the CIPP evaluation by evaluating each component, including context, input, process and product/outcome. The sources of study data involved lecturers and students of Informatics Engineering at Muhammadiyah University Sorong in Matrix Algebra course. The instruments of primary data collection was online assignments and Google Form questionnaires, while secondary data was obtained through observation, literature study, documentation and interviews. The results show that students obtained an overall average score (mean) of 76.4 from the maximum score of 100, and a percentage of assignment collection of 65.78%. This results were categorized as adequate, in meaning it is rather effective for theory comprehension, and was categorized as moderate in terms of boosting students’ motivation in doing social media-based online assignments, depending on the type of assignment. This signifies that the evaluation of CIPP in social media-based e-learning and online assignments in algebra has positive outcome in terms of infrastructure, management, and use.
ARCHES DUNHUANG: HERITAGE INVENTORY SYSTEM FOR CONSERVATION OF GROTTO RESOURCES ON THE GANSU SECTION OF THE SILK ROAD IN CHINA

Abstract. The cultural heritage of the Buddhist grottoes on the Gansu section of the Silk Road in China has important historical, cultural and artistic values. In order to better protect these precious cultural sites including through facilitating ongoing assessment of their condition, the Dunhuang Academy has created a pilot inventory based on the Arches open-source software platform. This paper first briefly introduces the most important grottoes and the representative caves in Gansu province. It then, in view of the diversity of the grotto architecture layout, describes the Arches semantic graph data modelling technology and how the flexibility of Arches Designer was used to design the resource models, which not only meet data management needs, but also ensure data interoperability and longevity through incorporation of the CIDOC-CRM ontology. In addition, through design of the grottoes map plugin and resource components within Arches, the simple interaction and intuitive visualization of grottoes information was achieved. The Dunhuang Academy’s deployment of Arches will serve the need for grottoes data management and provide support for the establishment of a more scientific and effective protection and management system for the grottoes on the Silk Road in Gansu province. It is hoped that this project will demonstrate the suitability of the Arches platform to support the management of other grotto sites outside of Gansu province, as well as for heritage management elsewhere in China.
Deep learning based large vocabulary continuous speech recognition of an under-resourced language Bangladeshi Bangla

Research in corpus-driven Automatic Speech Recognition (ASR) is advancing rapidly towards building a robust Large Vocabulary Continuous Speech Recognition (LVCSR) system. Under-resourced languages like Bangla require benchmarking large corpora for more research on LVCSR to tackle their limitations and avoid the biased results. In this paper, a publicly published large-scale Bangladeshi Bangla speech corpus is used to implement deep Convolutional Neural Network (CNN) based model and Recurrent Neural Network (RNN) based model with Connectionist Temporal Classification (CTC) loss function for Bangla LVCSR. In experimental evaluations, we find that CNN-based architecture yields superior results over the RNN-based approach. This study also emphasizes assessing the quality of an open-source large-scale Bangladeshi Bangla speech corpus and investigating the effect of the various high-order N-gram Language Models (LM) on a morphologically rich language Bangla. We achieve 36.12% word error rate (WER) using CNN-based acoustic model and 13.93% WER using beam search decoding with 5-gram LM. The findings demonstrate by far the state-of-the-art performance of any Bangla LVCSR system on a specific benchmarked large corpus.
Move, eat, sleep, repeat: Living by rhythm with proactive self-tracking technologies

Abstract Proactive self-tracking is a proliferating digital media practice that involves gathering data about the body and the self outside a clinical healthcare setting. Various studies have noted that self-tracking technologies affect people's everyday modes of thought and action and stick to their lifeworlds because these technologies seek to promote “improved” modes of behaviour. We investigate how the specific devices and interfaces involved in self-tracking attract and prescribe rhythmicity into everyday lives and elaborate on how human bodies and technical systems of self-tracking interact rhythmically. We draw from new materialist ontology, combining it with Henri Lefebvre's method of rhythmanalysis and his notion of dressage. We employ a collaborative autoethnographical approach and engage with both of our personal fieldwork experiences in living with self-tracking devices. We argue that rhythmicity and dressage are fruitful analytical tools to use in understanding human–technology attachments as well as a variety of everyday struggles inherent in self-tracking practices.
Dirac quantum walks with conserved angular momentum

A quantum walk (QW) simulating the flat $$(1 + 2)$$ D Dirac equation on a spatial polar grid is constructed. Because fermions are represented by spinors, which do not constitute a representation of the rotation group $${\mathrm{SO}}(3)$$ , but rather of its double cover $${\mathrm{SU}}(2)$$ , the QW can only be defined globally on an extended spacetime where the polar angle extends from 0 to $$4 \pi $$ . The coupling of the QW with arbitrary electromagnetic fields is also presented. Finally, the cylindrical relativistic Landau levels of the Dirac equation are computed explicitly and simulated by the QW.
Augmenting BERT-style Models with Predictive Coding to Improve Discourse-level Representations

Current language models are usually trained using a self-supervised scheme, where the main focus is learning representations at the word or sentence level.However, there has been limited progress in generating useful discourse-level representations.In this work, we propose to use ideas from predictive coding theory to augment BERT-style language models with a mechanism that allows them to learn suitable discourse-level representations.As a result, our proposed approach is able to predict future sentences using explicit top-down connections that operate at the intermediate layers of the network.By experimenting with benchmarks designed to evaluate discourse-related knowledge using pre-trained sentence representations, we demonstrate that our approach improves performance in 6 out of 11 tasks by excelling in discourse relationship detection.
Beyond the BEST Theorem: Fast Assessment of Eulerian Trails

Given a directed multigraph \(G=(V,E)\), with \(|V|=n\) nodes and \(|E|=m\) edges, and an integer z, we are asked to assess whether the number \(\#ET(G)\) of node-distinct Eulerian trails of G is at least z; two trails are called node-distinct if their node sequences are different. This problem has been formalized by Bernardini et al. [ALENEX 2020] as it is the core computational problem in several string processing applications. It can be solved in \(\mathcal {O}(n^{\omega })\) arithmetic operations by applying the well-known BEST theorem, where \(\omega <2.373\) denotes the matrix multiplication exponent. The algorithmic challenge is: Can we solve this problem faster for certain values of m and z? Namely, we want to design a combinatorial algorithm for assessing whether \(\#ET(G) \ge z\), which does not resort to the BEST theorem and has a predictably bounded cost as a function of m and z. We address this challenge here by providing a combinatorial algorithm requiring \(\mathcal {O}(m\cdot \min \{z, \#ET(G)\})\) time.
Generating insights in uncharted territories: real-time learning from data in critically ill patients–an implementer report

Introduction In the current situation, clinical patient data are often siloed in multiple hospital information systems. Especially in the intensive care unit (ICU), large volumes of clinical data are routinely collected through continuous patient monitoring. Although these data often contain useful information for clinical decision making, they are not frequently used to improve quality of care. During, but also after, pressing times, data-driven methods can be used to mine treatment patterns from clinical data to determine the best treatment options from a hospitals own clinical data. Methods In this implementer report, we describe how we implemented a data infrastructure that enabled us to learn in real time from consecutive COVID-19 ICU admissions. In addition, we explain our step-by-step multidisciplinary approach to establish such a data infrastructure. Conclusion By sharing our steps and approach, we aim to inspire others, in and outside ICU walls, to make more efficient use of data at hand, now and in the future.
Semantically Adversarial Learnable Filters

We present an adversarial framework to craft perturbations that mislead classifiers by accounting for the image content and the semantics of the labels. The proposed framework combines a structure loss and a semantic adversarial loss in a multi-task objective function to train a fully convolutional neural network. The structure loss helps generate perturbations whose type and magnitude are defined by a target image processing filter. The semantic adversarial loss considers groups of (semantic) labels to craft perturbations that prevent the filtered image {from} being classified with a label in the same group. We validate our framework with three different target filters, namely detail enhancement, log transformation and gamma correction filters; and evaluate the adversarially filtered images against three classifiers, ResNet50, ResNet18 and AlexNet, pre-trained on ImageNet. We show that the proposed framework generates filtered images with a high success rate, robustness, and transferability to unseen classifiers. We also discuss objective and subjective evaluations of the adversarial perturbations.
Typing Pattern Analysis for Fake Profile Detection in Social Media

Nowadays, interaction with fake profiles of a genuine user in social media is a common problem. General users may not easily identify profiles created by fake users. Although various research works are going on all over the world to detect fake profiles in social media, focus of this paper is to remove additional efforts in detection procedure. Behavioral biometrics like typing pattern of users can be considered to classify genuine profile and fake profile without disrupting normal activities of the users. In this paper, DEEP_ID model is designed to detect fake profiles in Facebook like social media considering typing patterns like keystroke, mouse-click, and touch stroke. Proposed model can silently detect the profiles created by fake users when they type or click in social media from desktop, laptop, or touch devices. DEEP_ID model can also identify whether genuine profiles have been hacked by fake users or not in the middle of the session. The objective of proposed work is to demonstrate the hypothesis that user recognition algorithms applied to raw data can perform better if requirement for feature extraction can be avoided, which in turn can remove the problem of inappropriate attribute selection. Proposed DEEP_ID model is based on multi-view deep neural network, where network layers can learn data representation for user recognition based on raw data of typing pattern without feature selection and extraction. Proposed DEEP_ID model has achieved better results compared to traditional machine learning classifiers. It provides strong evidence that the stated hypothesis is valid. Evaluation results indicate that Deep_ID model is highly accurate in profile detection and efficient enough to perform fast detection.
Incoherent and Robust Projection Matrix Design Based on Equiangular Tight Frame

Designing a projection matrix to optimally select the informative samples from high-dimensional data is a challenging task. Several approaches have been proposed for this task, however conventional methods obtain the projection matrix from the corresponding Gram matrix without considering the underlying structure of the equiangular frame. The study propose a framework to optimize the projections based on the equivalent tight frame, which is in turn constructed from the target Gram matrix. The proposed work optimizes the projection matrix by restricting the eigenvalues of the corresponding Gram matrix to ensure reduced pairwise correlation and tightness of the frame. Additionally, an ℓ2,1-norm based regularization term and a projection matrix energy constraint are incorporated to reduce the effect of outliers and noisy data. This unified optimization problem results in an incoherent and robust projection matrix. Experiments are performed on synthetic data as well as real images. The performance evaluation is carried out in terms of mutual coherence, signal reconstruction accuracy, and peak signal-to-noise ratio (PSNR). The results show that the sensing error constraint enables the design of optimized projections especially when the signals are noisy and not exactly sparse which is the case in real-world scenarios.
Is “moby dick” a Whale or a Bird? Named Entities and Terminology in Speech Translation

Automatic translation systems are known to struggle with rare words.Among these, named entities (NEs) and domain-specific terms are crucial, since errors in their translation can lead to severe meaning distortions.Despite their importance, previous speech translation (ST) studies have neglected them, also due to the dearth of publicly available resources tailored to their specific evaluation.To fill this gap, we i) present the first systematic analysis of the behavior of state-of-the-art ST systems in translating NEs and terminology, and ii) release NEuRoparl-ST, a novel benchmark built from European Parliament speeches annotated with NEs and terminology.Our experiments on the three language directions covered by our benchmark (en→es/fr/it) show that ST systems correctly translate 75-80% of terms and 65-70% of NEs, with very low performance (37-40%) on person names.
Deep Active Learning with Relative Label Feedback: An Application to Facial Age Estimation

Deep learning has emerged as an effective machine learning algorithm to automatically learn a representative set of features and has revolutionized multimedia computing research. However, training a reliable deep model necessitates a large amount of labeled training data, which is time-consuming and labor-intensive to acquire. Active Learning (AL) algorithms address this challenge by automatically identifying the salient and exemplar samples from large amounts of unlabeled data; this drastically reduces human annotation effort, as only a handful of samples, that are identified by the algorithm, need to be labeled manually. However, in applications like vision-based facial age estimation, providing the exact labels (age of a person) may be challenging even for human annotators, as it maybe difficult to accurately estimate the age of a person merely from a facial image; it maybe much easier to provide relative label feedback, such as whether a particular subject is older than another subject. In this paper, we propose a novel deep active learning algorithm (DALRel) which requires only relative label feedback in response to the queried samples. We formulate a loss function relevant to the research task and exploit the gradient descent algorithm to optimize the loss and train the deep network. To the best of our knowledge, this is the first research effort to develop an active learning framework to train a deep neural network, which poses only relative label queries to the labeling oracles. Our extensive empirical studies demonstrate the promise and potential of this method for real-world active learning applications, where providing the exact labels to queried instances can be challenging.