Reviewerly/exHarmony huggingface dataset:
Stochastic Simulation for Assessing DLT Application Design Quantitatively

The distributed ledger technology (DL T) secures data and the execution of program code in a decentral network. This enables new forms of business cooperation without the need of central intermediaries. However, the design process of DLT applications is not a simple task. The engineering process is burdened with the responsibility of choosing a suitable distributed ledger implementation, that will cope with the environmental limitations and transaction loads. The nature of the distributed ledger technology makes an exchange of the technology base at a later point expensive and complicated, since every network participant needs to agree to the change. That makes prototyping and applied field tests difficult. Stochastic simulation is a means that allows to simulate complex systems that are affected by randomly occurring events. In this paper, we contribute to the advance of using stochastic simulation to support DLT application engineering processes by 1) presenting a structured approach in applying stochastic simulation, that is also configurable depending on individual evaluation goals, 2) use hypothesis tests to assess the model quality of the Ethereum block generation process, when using homogeneous Poisson processes and 3) discuss concrete measures that help assessing the feasibility of implementing an application on the Ethereum blockchain, which is demonstrated for German waterway transportation processes.
Towards the Synthesis of Parent-Infant Facial Interactions

This work is motivated by the need to automate the analysis of parent-infant interactions to better understand the existence of any potential behavioral patterns useful for the early diagnosis of autism spectrum disorder (ASD). It presents an approach for synthesizing the facial expression exchanges that occur during parent-infant interactions. This is accomplished by developing a novel approach that uses landmarks when synthesizing changing facial expressions. The proposed model consists of two components: (i) The first is a landmark converter that receives a set of facial landmarks and the target emotion as input and outputs a set of new landmarks transformed to match the emotion. (ii) The second component involves an image converter that takes in an input image, a target landmark and a target emotion and outputs a face transformed to match the input emotion. The inclusion of landmarks in the generation process proves useful in the generation of baby facial expressions; babies have somewhat different facial musculature and facial dynamics than adults. This paper presents a realistic-looking matrix of changing facial expressions sampled from a 2-D emotion continuum (valence and arousal) and displays successfully transferred facial expressions from real-life mother-infant dyads to novel ones.
The “Reliable History” of Geohazards

Abstract Estimating rates of occurrence over time of hazards such as volcanic eruptions and earthquakes is crucial for risk assessment and planning. But it can be difficult. Human records only go back so far, and older ones may be patchy, making it hard to pinpoint the start of the “reliable history” for a particular type of event. Fear not, though, says Jonathan Rougier: a simple event-rate estimator might still hold up, even if we “overshoot” the reliable history by a century or more
Determining the Significance of Lexical Features as Indicative of CBH and LBH: Insights from the Tiberias Stylistic Classifier for the Hebrew Bible

Since the time of Gesenius, scholars have rightly grounded their determinations of linguistic dating of the biblical texts on the comparison of pairs of features that can be contrasted as early and late. However, at times Hebraists also identify terms as indicative of either Classical Biblical Hebrew (CBH) or Late Biblical Hebrew (LBH) on account of their relatively exclusive occurrence in either one corpus or the other. In this study I demonstrate the propensity of scholars to identify such terms in an impressionistic manner and unwittingly fall victim to a probability illusion long known by cognitive psychologists as the small sample fallacy. Conversely, I will show that in seeking lexical terms that are indicative of CBH and LBH respectively, they overlook other terms that are far more indicative and significant from a statistical perspective. To arrive at these conclusions, I employ data generated by the recently launched Tiberias Stylistic Classifier for the Hebrew Bible. Tiberias marshals cutting edge advances in the field of machine learning and computational linguistics to empower users to easily conduct their own experiments analysing and classifying the texts of the Hebrew Bible through the measurable features of linguistic data, and providing them with verifiable results. As an illustration of what is at stake, I reference the debate surrounding the linguistic profile of Genesis 24.
Influence of Training Data on the Invertability of Neural Networks for Handwritten Digit Recognition

Model inversion attacks aim to extract details of training data from a trained model, potentially revealing sensitive information about a person’s identity. To abide with protection of personal privacy requirements, it is important to understand the mechanisms that increase the privacy of training data. In this work, we systematically investigated the impact of the training data on a model’s susceptibility to model inversion attacks for models trained at the task of hand-written digit recognition with the openly available MNIST dataset. Using an optimization-based inversion approach, we studied the impacts of the quantity and diversity of training data, and the number and selection of classes on the susceptibility of models to inversion. Our model inversion attack strategy was less successful for models with a larger number of training data and greater training data diversity. Moreover, atypical training records provided additional protection against model inversion. We discovered that not every class was equally susceptible to model inversion attacks and that the inversion results of one class were changed when models were trained with a different selection of classes. However, we did not detect a clear relationship between the number of classes and a model’s susceptibility to inversion. Our study shows that the inversion susceptibility of a model depends on the training data-not only the data used to train the class that is inverted, but also the data used to train the other classes.
A New Hyperchaotic System and Its Point Multiplication Combination Synchronization

This paper constructs a new 7-dimensional hyperchaotic system. The hyperchaotic system has two positive Lyapunov exponents and rich chaotic dynamics. Based on the Matlab simulation software, the dynamic behavior of the hyperchaotic system is analyzed, such as phase diagram, dissipativity, equilibrium point, Lyapunov exponent, etc. The simulation results show that the system can produce periodic orbits, chaotic attractors, hyperchaotic attractors and other rich dynamic behaviors with changes of parameters. According to the vector dot product operation, a new point multiplication combination synchronization method is defined. Based on the Lyapunov stability theorem, a nonlinear controller is designed to realize point multiplication combination synchronization between two drive systems and one response system. Numerical simulations of the new hyperchaotic system verify the correctness of the theoretical analysis.
Human Action Recognition Based on STDMI-HOG and STjoint Feature

More and more attention has been focused on the human action recognition domain. The existing methods are mostly based on single-mode data. However, single-mode data lacks adequate information. So, it is necessary to propose methods based on multimode data. In this paper, we extract two kinds of features from depth videos and skeleton sequences, named STDMI-HOG and STjoint feature respectively. STDMI-HOG is extracted from a new depth feature map Spatial-Temporal Depth Motion Image by Histogram of Oriented Gradient. STjoint feature is extracted from skeleton sequences by ST-GCN extractor. Then two kinds of features are connected to make up a one-dimensional vector. Finally, SVM classifies the actions according to the feature vector. To evaluate the performance, several experiments are conducted on two public datasets: the MSR Action3D dataset and the UTD-MHAD dataset. The accuracy of our method on two datasets is compared with the existing methods, and the experiments prove the outperformance of our method.
A Structure to Effectively Prepare the Data for Sliding Window in Deep Learning

Convolution operation consists of about 90% of the computation of a Deep Neural Network, so convolution computation acceleration can boost the application of Deep Learning in engineering applications. In this paper, we propose a method for convolution computation implemented on FPGA. Utilizing shift registers of FPGA, the proposed method can reduce the time needed to obtain data during sliding the kernel window. Furthermore, it needs no extra memory space, making this method superior to the classic sliding window method and the widely used img2col method.
Protometrics

We introduce the concept of protometric and present some properties of protometrics.
Efficient Face Verification Under Makeup Changes Using Few Salient Regions

Face recognition has attracted the attention of many researchers during the last years due to its many applications in various fields. However, this task faces several challenges related to many changes that can affect the human face. In particular, make-up faces represent a major challenge for facial verification. To deal with this issue, we propose in this work an efficient salient patch-based method for verifying faces under makeup variation. Firstly, we use Mutli-Task Cascaded Convolutional Neural Networks (MTCNN) in order to jointly detect and align the face. Then, we have adapted the O-NET network in order to robustly detect five landmarks by training it on makeup faces. The Histogram of Oriented Gradients (HOG) descriptor and the Local Binary Patterns (LBP) are then used to represent the face by concatenating their histogram features in few salient regions around the detected landmarks. Finally, we estimate the similarity measure between the extracted features in order to compare the two faces while determining whether they are for the same person or not. The performance of the proposed method has been validated on the challenging YMU (YouTube Makeup dataset ) and MIFS (Makeup Induced Face Spoofing) datasets, and the obtained results proved the superiority of the proposed method against relevant multi-patch-based methods from the state of the art.
Neural Networks Application for Modelling of RES production

The article is focused on modeling and analyses of electricity production from renewable energy sources in a single-family house using artificial intelligence. The neural networks are one of the main instruments for modeling and forecasting of dynamic and stochastic processes. In the current research, they are implemented to modeling of the electricity production of a photovoltaic station of a single-family house with the aim to be able to analyze and predict the PV production in short and long term periods. This is very important in the current electricity systems in respect to ensure optimal energy utilization in the house and optimal facilities exploitation. In the article real data from an existing SCADA are used which makes the results close to real exploitation.
Introduction

Networks (or graphs) are ubiquitous in the real world, such as social networks, academic networks, biological networks, and so on. Heterogeneous information network (HIN), a.k.a., heterogeneous graph (HG), is an important type of network, which contains multiple types of nodes and edges. To date, the research of HG has attracted extensive attention, the most important of which is the heterogeneous graph representation (HGR), a.k.a., heterogeneous network embedding. In this chapter, we first introduce some basic concepts and definitions in HG and emphasize the importance of graph representation learning in the field of data mining. Then, we analyze the unique challenges of HGR compared with homogeneous network. In the end, we briefly introduce the organization of this book.
A Conceptual Anonymity Model to Ensure Privacy for Sensitive Network Data

In today’s world, a great amount of people, devices, and sensors are well connected through various online platforms, and the interactions between these entities produce massive amounts of useful information. This process of data production and sharing appears to be on the rise. The growing popularity of this industry, as well as the required development of data sharing tools and technology, pose major threats to an individual’s sensitive information privacy. These privacy-related issues may elicit a regularly strong negative reaction and restrain further organizational invention. Researchers have identified the privacy implications of large data collections and contributed to the preservation of data from unauthorised exposure to solve the challenge of information privacy. However, the majority of privacy strategies concentrate solely on traditional data models, such as micro-data. The academe and industry are paying more attention to network data privacy challenges. In this paper, we offer (ℓ, k)-anonymity, a novel privacy paradigm for network data that focuses on maintaining the privacy of both node and link information. Here, original network data will turn to attribute generalization nodes through a complex process, where several algorithms, clustering, node generalization, link generalization and ℓ-diversification will be applied. As a result, (ℓ, k)-anonymous network will be generated and will filter original network data to ensure publishable (ℓ, k)-anonymize data. Hopefully, this anonymity model will have a stronger role against homogeneity attacks of intruders, which will prevent the unauthorized disclosure of sensitive network data for several areas, such as - health sector. This model will also be cost effective and data loss will be controlled using two different ways.
The Framework of Cross-Domain and Model Adversarial Attack against Deepfake

To protect images from the tampering of deepfake, adversarial examples can be made to replace the original images by distorting the output of the deepfake model and disrupting its work. Current studies lack generalizability in that they simply focus on the adversarial examples generated by a model in a domain. To improve the generalization of adversarial examples and produce better attack effects on each domain of multiple deepfake models, this paper proposes a framework of Cross-Domain and Model Adversarial Attack (CDMAA). Firstly, CDMAA uniformly weights the loss function of each domain and calculates the cross-domain gradient. Then, inspired by the multiple gradient descent algorithm (MGDA), CDMAA integrates the cross-domain gradients of each model to obtain the cross-domain perturbation vector, which is used to optimize the adversarial example. Finally, we propose a penalty-based gradient regularization method to pre-process the cross-domain gradients to improve the success rate of attacks. CDMAA experiments on four mainstream deepfake models showed that the adversarial examples generated from CDMAA have the generalizability of attacking multiple models and multiple domains simultaneously. Ablation experiments were conducted to compare the CDMAA components with the methods used in existing studies and verify the superiority of CDMAA.
Severity Level Assessment from Semantically Fused Video Content Analysis for Physical Threat Detection in Ground Segments of Space Systems

Disaster risks related to natural hazards are evolving gradually, albeit accelerating over time, the human-made and cyber threats are changing rapidly exploiting the increasing progress in technologies and the complex, highly interlinked, modern environment of critical infrastructures. Therefore, as these threats have been intensifying, the actions to strengthen the resilience of critical infrastructures should be step up, by understanding their complex systems as well as the multi-risks nature. In this landscape, the aim of this work focuses to propose a framework enables to identify potential human-made threats, generated by using physical means, and captured by heterogeneous sources (CCTVs, UAVs etc.). Advanced machine learning techniques provide analysis of events and useful information, which are fused semantically and estimate the severity level of the potential attack, serving the needs for real-time monitoring and mitigating the risk.
Software Engineering Education for the Next Generation SEENG 2021 Workshop Report

The 3rd International Workshop on Software Engineering Education for the Next Generation was held remotely on May 24, 2021. The workshop was an integral component of the Joint Track on Software Engineering Education and Training at the 43rd International Conference on Software Engineering. It specifically supported the general theme of "Educating the Next Generation of Software Engineers". Building on its predecessors, the workshop used a highly interactive format, structured around eight short paper presentations to generate discussion topics, an activity to select the most interesting topics, and structured breakout sessions to allow participants to discuss those topics in detail. Participants presented the results of the breakout sessions using mind maps.
General Pacman AI: Game Agent With Tree Search, Adversarial Search And Model-Based RL Algorithms

Tree search algorithms like DFS, BFS and A* with plain logic can be easily implemented and works well in single-agent games, performing the property of completeness and optimality. Adversarial search algorithms like Minimax and Alpha-Beta Pruning perform an optimal result in zero-sum games like chess. Reinforcement Learning receives feedback in rewards, and the reward function defines the agent’s utility. All the learning is based on observed samples of outcomes to maximize expected rewards. This paper examines all the algorithms mentioned above to compare the correctness and optimality of the Pacman game. In the experiment, the paper illustrates why model-based reinforcement learning is optimal for the Pacman and meanwhile analyzes the correctness and complexity of all algorithms examined.
Variants and Applications of Generative Adversarial Networks

Generative adversarial network (GAN) has been extensively applied in many fields since it was first proposed in 2014. There have been ongoing attempts at improving the model and developing its extensions. The basic idea behind GAN stems from the two-player zero-sum game. Composed of a generator and a discriminator, GAN is trained through a “battle” between the two networks, where the generator tries to fool the discriminator. In contrast, the discriminator tries not to be deceived. The purpose is to estimate the potential distribution of data samples and generate new data samples. GAN is currently being studied in computer vision, language processing, and information security and is still far from reaching its potential. Since there are limited existing resources on GAN reviews, this paper provides a comprehensive review of a few of the most important GAN variants. We divided the variants into three categories: foundation, extension, and advanced, gave an overview of each architecture, and analyzed their advantages and disadvantages. We also summarized some of the most popular applications of GAN, and finally, we brought up the problems of GAN-based architectures that have yet to be solved and pointed out the future direction of GAN research and development.
Citrix-based digital design cloud platform for coal mine drilling equipment

In order to solve the problems of waste of hardware resources, high maintenance cost and data security in the design of coal mine drilling equipment, a digital cloud platform for drilling design is built, which integrates VMware virtualization of back-end server and Citrix desktop virtualization of front-end. It is expounded that the software and hardware components of the cloud platform for coal mine drilling equipment design. The following four parts are introduced in detail for the platform: VMware server virtualization, Citrix desktop virtualization, secondary development for actual R and D environment application and data protection. The practical results of the digital design cloud platform for coal drilling equipment show that the virtualized cloud platform solution has significant advantages in terms of information security, ease of maintenance, frequency of hardware replacement, rapid deployment and convenience of out-of-home office.
The Private and Public Lives of a Lost Concerto: Milton Babbitt’s <i>Concerti for Violin, Orchestra, and Synthesized Sound</i>

Milton Babbitt’s violin concerto (1975–1976), entitled Concerti for Violin, Orchestra, and Synthesized Sound, was premiered at Juilliard in January of 2016, an impressive feat given that it was only unearthed in the spring of 2015 and that the electronic part was incomplete. After discussing Concerti’s history and discovery, a pedagogical and philosophical perspective is offered for future soloists. Concerti demands the highest levels of technical preparation, intellectual engagement, and expressive commitment. The performer is encouraged to seek and live with two simultaneous lives of the piece: a private life, characterised by a ceaseless drive for betterment and deeper understanding, and a public life, in which organic utterances of expressive communication are the top priority.
Local Geometry Preserving Deep Networks For Featurizing High Dimensional Datasets

The quality of the features engineered to represent data during dimension reduction greatly impacts the performance of machine learning. Autoencoders are a popular deep-learning approach for generating lower-dimensional representations of complex, high-dimensional data. Unfortunately, features derived from Autoencoders do not preserve the local geometry of data. Thus, relationships between data may be lost during the featurization process, leading to lower-quality representations. To address this, we propose Deep Maximum Variance Unfolding (DMVU), a deep learning architecture for manifold-preserving dimension reduction. DMVU is a deep-learning extension of Maximum Variance Unfolding (MVU), a classic machine learning approach for dimension reduction. DMVU retains the local geometry preserving characteristics of MVU, while leveraging deep learning’s representational capacity and ability to manage large, high-dimensional datasets. This is achieved by DMVU’s novel loss function, which encourages the distance between neighboring points to be preserved while encouraging overall variance between data points. Our experimental study demonstrates that DMVU consistently outperforms state-of-the-art methods using the standard metrics of continuity and trustworthiness on 4 benchmark datasets.