Reviewerly/exHarmony huggingface dataset:
Deep Autoencoder for Anomaly Detection in Terminal Airspace Operations

In recent years, the aviation industry has seen a large increase in the volume of operations. The maintenance and improvement of safety at acceptable levels is one of the most important concerns in civil aviation operations. Reactive methods to aviation safety improvement are being augmented with proactive and predictive approaches that leverage large amounts of routinely collected aviation data. Due to the increased availability of airborne sensor data and improvements in computing power, application of machine learning methods to various aviation safety problems for identifying, isolating, and reducing risk has gained momentum. Previous work in this domain has focused on identifying anomalies or abnormal operations as a first step towards identification of potentially risky situations using aircraft sensor data. However, most existing methods rely only on the aircraft data and do not take into consideration the environment and context in which it is operating. In this paper, a novel framework based on deep learning methods using autoencoders is proposed to identify anomalies in terminal airspace operations. Data from multiple sources (aircraft trajectory, weather, traffic/congestion) is fused and utilized in the model development process which has not been attempted in prior work. The framework is proposed with the central idea of using historical aircraft trajectory data fused with weather and traffic metrics to build an anomaly detection model to identify trajectories that deviation from the norm, given a specific context. The framework is demonstrated on six months of arriving flight data collected for San Francisco International Airport as a case study. The developed framework has the potential to aid air traffic controllers in identifying high risk situations from a holistic perspective and applying appropriate mitigation strategies.
Placing AI in the Creative Industries: The Case for Intelligent Music Production

The digital transformation of the recording industry is one of the most well-known in recent times, with the Digital Audio Workstation (DAW) making new methods of music production available to amateurs and professionals in almost any setting beyond the traditional recording studio. However, the uptake of AI in this domain is seemingly impeded by DAW software architectures and a need for designers to better understand production practices and workflows. Our research addresses the latter of these challenges and in this extended abstract we present an overview of an ethnographic study that "gets inside" the work of the recording studio and exposes opportunities for the design of automated production tools. From a summary report of this work, we then aim to motivate or provoke novel conversations and future work in this problem space.
Analysis of PCA Usage to Detect and Correct Skew in Document Images

In recent years, all the documents are digitized by using different tools like camera and scanner. During the digitization, many mistakes are taken place, such as blur image, noise, and skew formation on entire document. These mistakes are directly influence on the consistency and productivity of the document image segmentation and analysis. In these mistakes, skew detection and correction are important part, and for this many approaches are introduced by different researchers. In this paper, principal component analysis (PCA) procedure is used to find the skew angle on full page. First convert the input document into binary form image, then use Sobel and Gaussian filters to find edges and suppressing the noise, respectively. Then apply the PCA to find the skew angle exists in a document. The PCA procedure first finds the covariance matrix, then generates the eigen values and eigen vectors, and from this calculate the unit vector for principal component. Later using principal component, identify relevant skew angle of the document. To correct the skew, rotate the test sample by preferred angle, and it can remove the skew occurred in document while acquiring the image. The evaluation of system methodology is carried out for different documents; the experimental results are significantly better.
Building Common Ground: Applying Mutual Learning in the UI/UX Education

The spreading of information and communication technology and mobile applications in our everyday contexts retrieve attention to UI/UX design. The education field attempted to train qualified UI/UX professionals to address the job market. However, there is a visible gap between the requirements of education and of professionalization in the job market. The interdisciplinary collaboration and communication with other team members from different fields play a cortical role in UI/UX education. To address the needs, we apply the concept of mutual learning in participatory design practices to design a UI/UX curriculum to enhance students' capacity in interdisciplinary collaboration. We conduct a pilot study to test the mutual learning course design in a joint course of graduate students from industrial design and computer science fields. Students were teamed up for a design project. In the class, they helped their teammates from different fields learn their domain knowledge and the thinking model of problem-solving. At the end of the course, we conducted interviews of twelve students in the course and collected their feedback on the mutual learning course design. The results showed that mutual learning benefited interdisciplinary communication and enabled the students to empathize with their team members of professions.
Efficient Reversible Data Hiding Scheme for AMBTC-Compressed Images

Reversible data hiding has attracted significant attention from researchers because it can extract an embedded secret message correctly and recover a cover image without distortion. In this paper, a novel, efficient reversible data hiding scheme is proposed for absolute moment block truncation code (AMBTC) compressed images. The proposed scheme is based on the high correlation of neighboring values in two mean tables of AMBTC-compressed images to further losslessly encode these values and create free space for containing a secret message. Experimental results demonstrated that the proposed scheme obtained a high embedding capacity and guaranteed the same PSNRs as the traditional AMBTC algorithm. In addition, the proposed scheme achieved a higher embedding capacity and higher efficiency rate than those of some previous schemes while maintaining an acceptable bit rate.
Multiscale Brain-Like Neural Network for Saliency Prediction on Omnidirectional Images

Current top-performing saliency prediction methods of omnidirectional images (ODIs) depend on deep feedforward convolutional neural networks (CNNs), benefiting from their powerful multiscale representation ability. Although these methods adopt deep feedforward CNNs to achieve superb performance in saliency prediction task, they have the following limitations: 1) these deep feedforward CNNs are difficult to map to ventral stream structure of the brain visual system due to their vast number of layers and missing biologically important connections, such as recurrence and 2) most deep feedforward CNNs represent the multiscale features in a layerwise manner. To tackle these issues, models that could learn multiscale features yet share the similarities with human brain are needed. In this article, we propose a novel multiscale brain-like network (MBN) model to predict saliency of head fixations on ODIs. Specifically, our proposed model consists of two major modules: 1) a brain-like CORnet-S module and 2) a multiscale feature extraction module. The CORnet-S module is a lightweight backbone network with four anatomically mapped areas (V1, V2, V4, and IT) and it can simulate the visual processing mechanism of ventral visual stream in the human brain. The multiscale feature extraction module is inspired by the multiscale brain structure, which represents multiscale features at a granular level and increases the range of receptive fields for each network layer. Extensive experiments and ablation studies conducted on two major benchmarks demonstrate the superiority of the proposed MBN model over the state-of-the-art methods.
Directions for modernization of innovative youth startup design in Belarus

Technology and change have a great impact on every aspect of life, including education. Educational institutions are undoubtedly an important part of this whole process. Educational institutions are facing major cultural, demographic and, above all, technological changes. Today, teachers are facing new ways of teaching in the digital age that effectively incorporate technology into the educational environment. The authors of this article discuss the issue of change management, learning in the digital age and its impact on educational practices and experiences. In this context, the method of content analysis of relevant documents is applied, as well as the results of previous theoretical and empirical research of many scientists and researchers in this field. The conclusions reached are an appropriate starting point for future action in the framework of educational activities, policies and perspectives. Higher education in its system has always developed in different areas of science and has made changes that are aimed at training professionals in relevant areas of our economy. With different structures, but subject to foundations that provide sustainability and conditions for innovations that update it to be useful to society. Education has always required a lot - to provide specialists for various positions in industry and all economic fields, to be a good basis for research and research, to provide personal development mainly to young people, but also without age restrictions, especially in the last years of his career. To a greater or lesser degree, the correspondence between supply and needs is ensured both by the quality of the process itself and by the quantity of the offered educational service.
An Advanced PBFT-based Consensus Algorithm for a Bidding Consortium Blockchain

The rapid development of blockchain technology has given rise to many applications like digital currency. Building a consortium blockchain for a bidding system is a promising way to benefit the bidding businesses, by reducing cost and sharing data among tenderers and bidders. Generally, Practical Byzantine Fault Tolerance (PBFT) algorithm is the wide-ly-used consensus algorithm in a consortium blockchain. However, this algorithm meets its bottleneck, when there are a large number of nodes in the consortium blockchain net-work. In order to enhance the scalability of the consensus algorithm, this paper puts for-ward an advanced PBFT-based consensus algorithm, named ANPBFT, by taking ad-vantages of the message aggregation technology and tree topology technology. Specially, the consensus nodes in our proposed algorithm are divided into active nodes and passive nodes. Then, the consensus of block information is equivalent to the confirmation of iden-tity aggregation signature and aggregation promise of information key. The experimental results show that our proposed algorithm has better performance than the original PBFT algorithm. It also shows good scalability and can be used in a large-scale license-chain system.
Achieving task allocation in swarm intelligence with bi-objective embodied evolution

Abstract In this paper, we seek to achieve task allocation in swarm intelligence using an embodied evolutionary framework, which aims to generate divergent and specialized behaviors among a swarm of agents in an online and self-organized manner. In our considered scenario, specialization is encouraged through a bi-objective composite fitness function for the genomes, which is the weighted sum of a local and a global fitness function. The former depends only on the behavior of an agent itself, while the latter depends on the effectiveness of cooperation among all nearby agents. We have tested two existing variants of embodied evolution on this scenario and compared their performances against those of an individual random walk baseline algorithm. We have found out that those two embodied evolutionary algorithms have good performances at the extreme cases of weight configurations, but are not adequate when the two objective functions interact. We thus propose a novel bi-objective embodied evolutionary algorithm, which handles the aforementioned scenario by controlling the proportion of specialized behaviors via a dynamic reproductive isolation mechanism. Its performances are compared against those of other considered algorithms, as well as the theoretical Pareto frontier produced by NSGA-II.
MPEG NBMP testbed for evaluation of real-time distributed media processing workflows at scale

Real-time Distributed Media Processing Workflows (DMPW) are popular for online media delivery. Combining distributed media sources and processing can reduce storage costs and increase flexibility. However, high request rates may result in unacceptable latency or even failures in incorrect configurations. Thus, testing DMPW deployments at scale is key, particularly for real-time cases. We propose the new MPEG Network Based Media Processing (NBMP) standard for this and present a testbed implementation that includes all the reference components. In addition, the testbed includes a set of configurable functions for load generation, monitoring, data-collection and visualization. The testbed is used to test Dynamic Adaptive HTTP streaming functions under different workloads in a standardized and reproducible manner. A total of 327 tests with different loads and Real-Time DMPW configurations were completed. The results provide insights in the performance, reliability and time-consistency of each configuration. Based on these tests, we selected the preferred cloud instance type, considering hypervisor options and different function implementation configurations. Further, we analyzed different processing tasks and options for distributed deployments on edge and centralized clouds. Last, a classifier was developed to detect if failures happen under a certain workload. Results also show that, normalized inter-experiment standard deviation of the metric means can be an indicator for unstable or incorrect configurations.
Effect of Input-output Randomness on Gameplay Satisfaction in Collectable Card Games

Randomness is an important factor in games, so much so that some games rely almost purely on it for its outcomes and increase players' engagement with them. However, randomness can affect the game experience depending on when it occurs in a game, altering the chances of planning for a player. In this paper, we refer to it as “input-output randomness”. Input-output randomness is a cornerstone of collectable card games like Hearthstone, in which cards are drawn randomly (input randomness) and have random effects when played (output randomness). While the topic might have been commonly discussed by game designers and be present in many games, few empirical studies have been performed to evaluate the effects of these different kinds of randomness on the players' satisfaction. This research investigates the effects of input-output randomness on collectable card games across four input-output randomness conditions. We have developed our own collectable card game and experimented with the different kinds of randomness with the game. Our results suggest that input randomness can significantly impact game satisfaction negatively. Overall, our results present helpful considerations on how and when to apply randomness in game design when aiming for players' satisfaction.
Frequency Domain for Color Image Authentication Proofing

Abstract Digital watermarking is the process of embedding data known as a watermark into a multimedia object so that the watermark can later be identified or recovered to claim its object. The use of an embedded watermark would enable the owner of the work hardware to be identified. In the proposed method the Particle-Swarm-Optimization (PSO) algorithm has been applied to embed a binary watermark bit in the optimal color sub-bands, resulting in a less extreme value after the PSO point. The embedding level, which uses Discrete Wavelet Transform (DWT) to embed binary bits in the resulted image in the low-frequency domain, must be able to withstand intended attacks while maintaining high quality, as well as assess image authentication and localize tampering areas (s). Various consistency metrics and watermark removal attacks are used to evaluate the algorithm in an experimental environment. The results show that the model can support imperceptible watermarking as well as high attack resistance.
Risk-Aware Trajectory Planning Using Energy-based Analysis for Aerial Vehicles

Unmanned aerial systems increasingly operate in the national airspace and crowded urban environments. Therefore, there is a need for a risk aware trajectory planning method for safe flight. This work builds on the well-known rapidly exploding random tree (RRT) algorithm by considering the energy of the system and the environment to relate to the safety of a flight. The result is an energy aware RRT algorithm that can generate safe flight paths using the energy indicators from the system and the environment. The proposed algorithm also considers temporal effects to generate paths that are safe from dynamic obstacles. Two simulated experiments are completed to evaluate the capability and limitation of the technique. The result shows that the framework is capable of generating safe flight paths that are collision free for aerial vehicles in a crowded environment. The framework is also capable of identifying alternate safe landing points in terms of energy cost in case the target landing point is inaccessible. The quantified overall risk of a mission may be used to evaluate the safety of a similar scenario.
Operation Efficiency Study on a New Cooperative VR Whiteboard System

Due to the COVID-19 pandemic, remote collaboration becomes widely needed. To this end, this research proposes a VR whiteboard system that facilitates multiple participants collaborate remotely using natural handwriting. Moreover, a controlled experiment is conducted to compare the operation efficiency of the VR whiteboard with two other whiteboards, including the physical whiteboard and electronic whiteboard. It shows that the VR whiteboard improves collaboration efficiency significantly compared to the mouse-based electronic whiteboard and even compatible with the traditional whiteboard.
Advancing Trajectory Optimization with Approximate Inference: Exploration, Covariance Control and Adaptive Risk

Discrete-time stochastic optimal control remains a challenging problem for general, nonlinear systems under significant uncertainty, with practical solvers typically relying on the certainty equivalence assumption, replanning and/or extensive regularization. Control-as-inference is an approach that frames stochastic control as an equivalent inference problem, and has demonstrated desirable qualities over existing methods, namely in exploration and regularization. We look specifically at the input inference for control (I2C) algorithm, and derive three key characteristics that enable advanced trajectory optimization: An `expert' linear Gaussian controller that combines the benefits of open-loop optima and closed-loop variance reduction when optimizing for nonlinear systems, adaptive risk sensitivity for regularized exploration, and performing covariance control through specifying the terminal state distribution.
Investigation of Single Image Depth Prediction Under Different Lighting Conditions

This article investigates the limitations of single image depth prediction (SIDP) under different lighting conditions. Besides that, it also offers a new approach to obtain the ideal condition for SIDP. To satisfy the data requirement, we exploit a photometric stereo dataset consisting of several images of an object under different light properties. In this work, we used a dataset of ancient Roman coins captured under 54 different lighting conditions to illustrate how the approach is affected by them. This dataset emulates many lighting variances with a different state of shading and reflectance common in the natural environment. The ground truth depth data in the dataset was obtained using the stereo photometric method and used as training data. We investigated the capabilities of three different state-of-the-art methods to reconstruct ancient Roman coins with different lighting scenarios. The first investigation compares the performance of a given network using previously trained data to check cross-domains performance. Second, the model is fine-tuned from pre-trained data and trained using 70% of the ancient Roman coin dataset. Both models are tested on the remaining 30% of the data. As evaluation metrics, root mean square error and visual inspection are used. As a result, the methods show different characteristic results based on the lighting condition of the test data. Overall, they perform better at 51° and 71° angles of light, so-called ideal condition afterward. However, they perform worse at 13° and 32° because of the high density of shadows. They also cannot reach the best performance at 82° caused by the reflection that appears on the image. Based on these findings, we propose a new approach to reduce the shadows and reflections on the image using intrinsic image decomposition to achieve a synthetic ideal condition. Based on the results of synthetic images, this approach can enhance the performance of SIDP. For some state-of-the-art methods, it also achieves better results than previous original RGB images.
Uncover quantumness in the crossover from coherent to quantum-correlated phases via photon statistics and entanglement in the Tavis–Cummings model

Collective phenomena in the Tavis–Cummings model has been widely studied, focusing on the phase transition features. On many occasions, it has been used variational approaches that consider separated radiation-matter systems. This paper examines the role of the quantum entanglement of an assembly of two-level emitters coupled to a single-mode cavity; this allows us to characterise the quantum correlated state for each regime. Statistical properties of the system, e.g., the first four statistical moments, show clearly the structure of the light and matter distributions even though the second-order correlation function goes to one in some regimes; the statistical analysis evidences a sharp departure from coherent behaviour, contrary to the common understanding.
The Attraction Indian Buffet Distribution

We propose the attraction Indian buffet distribution (AIBD), a distribution for binary feature matrices influenced by pairwise similarity information. Binary feature matrices are used in Bayesian models to uncover latent variables (i.e., features) that explain observed data. The Indian buffet process (IBP) is a popular exchangeable prior distribution for latent feature matrices. In the presence of additional information, however, the exchangeability assumption is not reasonable or desirable. The AIBD can incorporate pairwise similarity information, yet it preserves many properties of the IBP, including the distribution of the total number of features. Thus, much of the interpretation and intuition that one has for the IBP directly carries over to the AIBD. A temperature parameter controls the degree to which the similarity information affects feature-sharing between observations. Unlike other nonexchangeable distributions for feature allocations, the probability mass function of the AIBD has a tractable normalizing constant, making posterior inference on hyperparameters straight-forward using standard MCMC methods. A novel posterior sampling algorithm is proposed for the IBP and the AIBD. We demonstrate the feasibility of the AIBD as a prior distribution in feature allocation models and compare the performance of competing methods in simulations and an application.