Reviewerly/exHarmony huggingface dataset:
Digital Museums as Pedagogical Mediators in the Pandemic Crisis

As a result of social isolation due to the COVID-19 pandemic, museums around the world have closed their physical doors, but some of them have opened digital windows to be able to visit them through virtual tours. Through them, interactive and sensory experiences are created developing educational and training spaces, which constitute pedagogical mediators that help teachers make their virtual classes more innovative, creating the interest of their students. This article describes the interactive museums as pedagogical mediators that propose a fusion of knowledge and new experiences, which transport the student to a cultural environment fostering the interest of interacting, knowing and visiting museums; in this way they can renew and create new interactive and innovative experiences. Through interactive museums, it is intended to promote a different learning dynamic, and at the same time improve the student’s disposition to receive new knowledge. This proposal contributes to the teaching-learning process with the use of innovative, interactive teaching resources that are at the same time consistent with the current environment in which children grow up. It is relevant to be able to encourage teachers to apply new educational strategies supported by technology, and thereby enrich their teaching management, achieve better teaching results with their students and seek other interrelationships. The study is also consistent and timely with current times, as teachers and students continue to interact in a virtual mode. The study delves into interactive museums and investigates the type of digital resources that can support the teaching-learning process in the area of Sciences. These ideas are intended to contribute to the design of innovative teaching resources that are based on the experiences of virtual visits to interactive museums, through which it is possible to take advantage of all the advantages and benefits they offer.
Conservative Stochastic Optimization: $\mathcal{O}(T^{-1/2})$ Optimality Gap with Zero Constraint Violation

This paper considers stochastic convex optimization problems where the objective and constraint functions involve expectations with respect to the data indices or environmental variables, in addition to deterministic convex constraints on the domain of the variables. Although the setting is generic and arises in different machine learning applications, online and efficient approaches for solving such problems has not been widely studied. Since the underlying data distribution is unknown a priori, closed-form solution is generally not available, and classical deterministic optimization paradigms are not applicable. Existing approaches towards solving these problems make use of stochastic gradients of the objective and constraints that arrive sequentially over iterations. State-of-the-art approaches, such as those using the saddle point framework, are able to ensure that the optimality gap as well as the constraint violation decay as <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1/2</sup> ) where T is the number of stochastic gradients. In this work, we propose a novel conservative stochastic optimization algorithm (CSOA) that achieves zero average constraint violation and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1/2</sup> ) optimality gap. The efficacy of the proposed algorithm is tested on a relevant problem of fair classification.
Certain Strategic Study on Machine Learning-Based Graph Anomaly Detection

“A rotten apple spoils the whole bunch” deciphers the research problem domain. Taking a broader perspective, the existence of anomaly in a graphical community would degrade the global network performance. The anomaly is a hindrance to insight for a better data quality analytics. Though the majority of multidisciplinary contributions in machine learning prevail, a surge towards graph-based learning is gaining a significant importance. Most of the statistical machine learning methodology adopted for outlier identification is inherited in most of the graph-based anomaly detection (GBAD) techniques. This survey aims to establish a broad overview on the state-of-the-art methods for GBAD in a static environment, specifically GBAD techniques by utilizing structural orientation, community-based discovery to seek the quality advancement. To achieve better graph storage on query handling, some graph summary techniques are studied. Intuitive comparative analysis of diverse GBAD algorithm helps to clarify novice researcher in problem solving. Moreover, the survey opens up new research ideas and practical challenges in the realm for a robust futuristic contribution.
Negative binomial graphical model with excess zeros

Abstract Markov random field or undirected graphical models (GM) are a popular class of GM useful in various fields because they provide an intuitive and interpretable graph expressing the complex relationship between random variables. The zero‐inflated local Poisson graphical model has been proposed as a graphical model for count data with excess zeros. However, as count data are often characterized by over‐dispersion, the local Poisson graphical model may suffer from a poor fit to data. In this paper, we propose a zero‐inflated local negative binomial (NB) graphical model. Due to the dependencies of parameters in our models, a direct optimization of the objective function is difficult. Instead, we devise expectation‐minimization algorithms based on two different parametrizations for the NB distribution. Through a simulation study, we illustrate the effectiveness of our method for learning network structure from over‐dispersed count data with excess zeros. We further apply our method to real data to estimate its network structure.
Polarization-spatial Gaussian entanglement in partially coherent light fields

The problem of bipartite entanglement in partially coherent paraxial vector light fields is addressed. A generalized uncertainty principle suited for the polarization-spatial degrees of freedom is introduced. Partial transpose is implemented through the obtained generalized uncertainty principle. Partial transpose is shown to be necessary and sufficient in detecting entanglement for a class of partially coherent vector light fields which have a spatial part to be Gaussian. An experimental realization of the studied entangled states using classical optical interferometry is outlined.
Do Auto-Regressive Models Protect Privacy? Inferring Fine-Grained Energy Consumption From Aggregated Model Parameters

We investigate the extent to which statistical predictive models leak information about their training data. More specifically, based on the use case of household (electrical) energy consumption, we evaluate whether white-box access to auto-regressive (AR) models trained on such data together with background information, such as household energy data aggregates (e.g., monthly billing information) and publicly-available weather data, can lead to inferring fine-grained energy data of any particular household. We construct two adversarial models aiming to infer fine-grained energy consumption patterns. Both threat models use monthly billing information of target households. The second adversary has access to the AR model for a cluster of households containing the target household. Using two real-world energy datasets, we demonstrate that this adversary can apply maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> estimation to reconstruct daily consumption of target households with significantly lower error than the first adversary, which serves as a baseline. Such fine-grained data can essentially expose private information, such as occupancy levels. Finally, we use differential privacy (DP) to alleviate the privacy concerns of the adversary in dis-aggregating energy data. Our evaluations show that differentially private model parameters offer strong privacy protection against the adversary with moderate utility, captured in terms of model fitness to the cluster.
An Autoencoder with a Memory Module for Video Anomaly Detection

With the rise of deep convolutional neural networks (CNNs), considerable attention has been paid to video anomaly detection (VAD). Autoencoders are a popular type of framework for VAD, and many existing VAD methods are based on it. However, these methods take an assumption of closed-world VAD, i.e., do not comprehensively consider the diversity of normal patterns. Besides, a competent CNN allows the autoencoder to reconstruct or predict abnormal video frames proficiently, resulting in missing anomalies. To mitigate these drawbacks, we propose an Autoencoder with a Memory Module (AMM) to realize video anomaly detection by predicting video frames. AMM consists of three modules: an encoder, a decoder, and a memory module. First, the consecutive frames are fed into the encoder to yield latent spatial features. Then, the features are utilized to retrieve corresponding memory items in the memory module to generate memory mapping features. Finally, the memory mapping features are adopted in the decoder for predicting the next frame. To match the queries against memory items accurately, we propose a memory triplet loss, which takes into account both size and angle discrepancies between the queries and memory items. At the training stage, AMM utilizes the memory triplet loss, a prediction loss, and multi-scale structure similarity measure. Moreover, the modes of retrieving and updating memory items are ameliorated by a scaled dot product model, which can alleviate vanishing gradient problems to a certain extent. Extensive experiments are conducted on three benchmark public datasets, and the results demonstrate the superior performance of AMM.
Image Denoising Using an Artificial Neural Network and Genetic Optimization Algorithm Based Dual-Tree Complex Wavelet Transform

The main objective of de-noising in images is to remove the unnecessary noise from the image while preserving the essential signal features as much as possible. Different methods are used for eliminating noises from digital images. The major role in de-noising is choosing a suitable method for the denoising process. Thus, various methods are suggested for image de-noising and all methods have its merits and demerits. This approach explores a Neural Network and Nature-Inspired Optimization Algorithm based denoising method implemented using Dual-Tree Complex wavelet Transform (DT-CWT). During this approach, initially the noisy image is converted into various sub-bands by applying DT-CWT. Then, to acquire noise-free wavelet coefficients, a trained Neural Network is applied on every sub-band. On these output wavelet coefficients Thresholding operation is performed with optimized threshold value by Genetic Algorithm. Now denoised image is generated through inverse DT-CWT. Relating to other existed methods this method gives improved results.
Model-Free Reinforcement Learning for Branching Markov Decision Processes

Abstract We study reinforcement learning for the optimal control of Branching Markov Decision Processes (BMDPs), a natural extension of (multitype) Branching Markov Chains (BMCs). The state of a (discrete-time) BMCs is a collection of entities of various types that, while spawning other entities, generate a payoff. In comparison with BMCs, where the evolution of a each entity of the same type follows the same probabilistic pattern, BMDPs allow an external controller to pick from a range of options. This permits us to study the best/worst behaviour of the system. We generalise model-free reinforcement learning techniques to compute an optimal control strategy of an unknown BMDP in the limit. We present results of an implementation that demonstrate the practicality of the approach.
QBFFam: A Tool for Generating QBF Families from Proof Complexity

We present QBFFam, a tool for the generation of formula families originating from the field of proof complexity. Such formula families are used to investigate the strength of proof systems and to show how they relate to each other in terms of simulations and separations. Furthermore, these proof systems underlie the reasoning power of QBF solvers. With our tool, it is possible to generate informative and scalable benchmarks that help to analyse the behavior of solvers. As we will see in this paper, runtime behavior predicted by proof complexity is indeed reflected by recent solver implementations.
A Transaction Classification Model of Federated Learning

In this paper, we first propose a federated learning-based embedding model for transaction classification. The model takes the transaction data as a set of frequent item-sets. After that model is able to learn low dimensional continuous vector by preserving the frequent item-sets contextual relationship. Results then indicated that the designed model can help and improve the decision boundary by reducing the global loss function.
Pointedness of an Image: Measuring How Pointy an Image is Perceived

For computers to understand human perception, metrics that can capture human perception well are important. However, there are few metrics that characterize the visual perception of humans towards images. Therefore, in this paper, we propose a novel concept and a metric of pointedness of an image, which describes how pointy an image is perceived. The algorithm is inspired by the Features from Accelerated Segment Test (FAST) algorithm for corner detection which looks on the number of continuous neighboring darker pixels surrounding each pixel. We assume that this number would be proportional to the perceived pointedness in the region around the pixel. We evaluated our method towards how well it could capture the human perception of images. To compare the method with similar metrics that describe shapes, we prepared silhouette images of both artificial shapes and natural objects. The results showed that the proposed method gave nearly equivalent perceptual performance to other metrics and also worked in a larger variety of images.
Implementation of Adaptive Real-Time Camouflage System in Visible-Light Band

This paper presents an adaptive camouflage system in visible band, featuring a dominant color feature-matching algorithm and pulse width modulation (PWM)-based display driving circuit. The control system consists of three parts, namely, the background sensing part, the central processing part, and the physical driving waveform generation part. Images of the local environment are sampled by the background sensing part, and then the dominant color feature matching algorithm is conducted to select a proper camouflage image that matches the local environment. Consequently, the cholesteric liquid crystals (CLCs) display using amplitude adjustable AC voltage, which is modulated by the physical driving waveform generation unit. The experimental results show that the matching degree of the proposed algorithm was 2.47 times that of the conventional hue (H), saturation (S), and value (V) histogram camouflage evaluation method, while the output peak wavelength of the reflective band can be adjusted from 604 to 544 nm according to the ambient color profile.
Partial Discharge Testing Platform for PWM Voltage Source Converters in Electric Aircraft

A pulse-width modulation (PWM) voltage source converter is a vital asset in aircraft electrification. These converters operate using fast (10-100kHz) switching power semiconductors. Partial discharge (PD) that occurs in insulation due to this PWM voltage is difficult to detect due to the transient noise of the voltage switching which obscures the smaller partial discharge signal. The PWM voltage also features high dv/dt and along with the high switching frequency increases the stress on the insulation system. Partial discharge is more likely to occur in greater quantity due to PWM voltage, but more difficult to detect. This paper studies the effect that PWM voltage has on insulation due to the high switching frequency and dv/dt. In order to achieve this goal, PWM Voltage testing platform is designed and electrically isolated detection methods are examined for effectiveness in the presence of PWM voltage noise.
RETRACTED ARTICLE: Constant water circulating ecological influence and remote English education system development based on sequence matching

This article discusses the development status of sequence matching and closed-loop detection, and focuses on the inherent problems in closed-loop analysis methods. By analyzing the basic theory of sequence matching, the closed-loop detection implementation method of each sequence matching is summarized, and detailed closed-loop components corresponding to sequence matching are summarized, and the existing methods for achieving different modules are summarized and their disadvantages are reviewed. In line with this, this paper focuses on the focus on the central Z Basin area of the H River domain. Combined with systematic analysis of surface water and groundwater conversion characteristics, the Z Basin annual water balance model is proposed, and the relationship between surface water and groundwater is further explained. The annual water balance model contains a nonlinear model of water circulating ecology, which fully considers the runoff of rivers under river water abundance changes and irrigation on water cycle ecology. Finally, this paper proposes a remote English education system based on JavaBean components and software architectures. After comprehensive, extensive testing and research, it reduces software development costs under this field, not only improves system reliability, but also shortens the development cycle, simplifies system maintenance measures, and ultimately makes the maximum extension and stability. With regard to system testing, a complete test will be performed through web test methods and cookie laws, performance, feasibility, compatibility, and security. This article uses a variety of test methods, a variety of test content, through comprehensive testing, which fully meets the expected requirements, promoting the system to fully meet the ability of daily learning and work requirements.
On Complexity of GLAMs’ Digital Ecosystem: APIs as Change Makers for Opening up Knowledge

This paper explores the role of Application Programming Interface, (API), in Galleries, Libraries, Archives and Museums (GLAM) sector, and reflects on its role for opening up cultural digital heritage for inclusive future memory making. It also discusses possibilities of a participatory API ecosystem, going beyond high quality metadata and data interoperability. APIs are often seen as facilitators for opening up knowledge, but when it comes to future memory making, little is known with regards to the power relations that are constructed within the digital ecosystem and how does this influence the interplay. The paper proposes a contribution towards this domain, critically exploring aspects that are facilitating or hindering people's participation for enabling future collective memory making.
The role of foreign capital and economic freedom in sustainable food production: Evidence from DLD countries

In many developing countries, the deficiency in public and private investment has resulted in lower growth rates and stagnation in productivity. The need for a new paradigm of foreign investment and aid in agricultural production is becoming exigent in developing countries. Given the decline in per capita arable land, the rise in production costs, and the increase in population and urbanization, major changes in agriculture have been proposed to boost agricultural production. This present study endeavours to contribute to the existing literature by proving whether foreign capital and economic freedom could catapult food production into the much-anticipated growth. In that regard, we performed GLS with correlated disturbances, system GMM dynamic panel data estimators and D-H causality test. The study found that foreign capital as a whole plays a positive role in food production in developing and least developed countries, but FDI is insignificant in least-developed countries. Moreover, economic freedom plays a positive role in food production in least-developed countries but negative in developing countries. Policymakers and governments should create an enabling environment for sustainable food production.
PRNet

Perceptual encryption is an efficient way of protecting image content by only selectively encrypting a portion of significant data in plain images. Existing security analysis of perceptual encryption usually resorts to traditional cryptanalysis techniques, which require heavy manual work and strict prior knowledge of encryption schemes. In this paper, we introduce a new end-to-end method of analyzing the visual security of perceptually encrypted images, without any manual work or knowing any prior knowledge of the encryption scheme. Specifically, by leveraging convolutional neural networks (CNNs), we propose a progressive recovery network (PRNet) to recover visual content from perceptually encrypted images. Our PRNet is stacked with several dense attention recovery blocks (DARBs), where each DARB contains two branches: feature extraction branch and image recovery branch. These two branches cooperate to rehabilitate more detailed visual information and generate efficient feature representation via densely connected structure and dual-saliency mechanism. We conduct extensive experiments to demonstrate that PRNet works on different perceptual encryption schemes with different settings, and the results show that PRNet significantly outperforms the state-of-the-art CNN-based image restoration methods.
Body-Part Attention Probability for Measuring Gaze During Impression Word Evaluation

We investigate how to probabilistically describe the distribution of gaze with respect to body parts when an observer evaluates impression words for an individual in an image. In the field of cognitive science, analytical studies have reported how observers view a person in an image and form impressions about him or her. However, a probabilistic representation of their gaze distributions has not yet been discussed. Here, we represent the gaze distribution as a conditional probability according to each body part. To do this, we measured the gaze distribution of observers performing a task that consists of assessing an impression word. We then evaluated whether these distributions change with respect to the impression word and body part specified in the task. Experimental results show that the divergences between the conditional probabilities of gaze distributions are large when the impression words or body parts of the task are changed.
VibWriter: Handwriting Recognition System based on Vibration Signal

The efficiency of human-computer interaction is greatly hindered by the small size of the touchscreens on mobile devices, such as smart phones and watches. This has prompted widespread interest in handwriting recognition systems, which can be divided into active and passive systems. Active systems require additional hardware devices to perceive movements of handwriting or the tracking accuracy is not adequate for hand-writing recognition. Passive methods use the acoustic signal of pen rubbing and are susceptible to environmental noise (above 60dB). This paper presents a novel handwriting recognition system based on vibration signals detected by the built-in accelerometer of smart phones. VibWriter is highly resistant to interference since the normal environmental noise will not cause the vibration of the accelerometer. Extensive experiments demonstrated the efficacy of the system in terms of accuracy in letter recognition (76.15%) and word recognition (88.14%) when dealing with words of various lengths written by various users in a variety of writing positions under a variety of environmental conditions.