Reviewerly/exHarmony huggingface dataset:
An Empirical Investigation on Evolutionary Algorithm Evolving Developmental Timings

Recently, evolutionary algorithms that can efficiently solve decomposable binary optimization problems have been developed. They are so-called model-based evolutionary algorithms, which build a model for generating solution candidates by applying a machine learning technique to a population. Their central procedure is linkage detection that reveals a problem structure, that is, how the entire problem consists of sub-problems. However, the model-based evolutionary algorithms have been shown to be ineffective for problems that do not have relevant structures or those whose structures are hard to identify. Therefore, evolutionary algorithms that can solve both types of problems quickly, reliably, and accurately are required. The objective of the paper is to investigate whether the evolutionary algorithm evolving developmental timings (EDT) that we previously proposed can be the desired one. The EDT makes some variables values more quickly converge than the remains for any problems, and then, decides values of the remains to obtain a higher fitness value under the fixation of the variables values. In addition, factors to decide which variable values converge more quickly, that is, developmental timings are evolution targets. Simulation results reveal that the EDT has worse performance than the linkage tree genetic algorithm (LTGA), which is one of the state-of-the-art model-based evolutionary algorithms, for decomposable problems and also that the difference in the performance between them becomes smaller for problems with overlaps among linkages and also that the EDT has better performance than the LTGA for problems whose structures are hard to identify. Those results suggest that an appropriate search strategy is different between decomposable problems and those hard to decompose.
Step Change in Efficiency and Economies of Scale in OBN Seismic

Summary Technology developments have driven the seismic industry since its inception. 50 years ago, surveys consisted of a maximum of 500,000 traces. The Utsira OBN survey, acquired over two seasons in 2018 and 2019, using the latest multi-source, multi-vessel, high node count acquisition techniques and employing a multiclient business model, achieved a step change in seismic efficiency, generating 57.6 billion seismic traces and almost 1.5Pb of field data. Further improvements in efficiency in 2019 were achieved by applying lessons learned from 2018. Recognising the attributes of a good survey and the best approaches to field acquisition was important to understanding how these efficiencies could be delivered through the optimisation of scheduling, design, and technological advances. Costs per trace of OBN have fallen rapidly as a consequence.
The Current Situation, Hot Spot, and Frontier of the Research on the Employment Pressure of College Students in China (Quantitative Visualization Analysis Based on CNKI Core Literature Library in 2002-2019)

In order to understand the development of the research on College Students' employment pressure and provide a reference for the research field of College Students' employment pressure, this paper analyzes the current situation, hot spots, and frontier of the research on College Students' employment pressure by using the bibliometric method. The results showed that: the total amount of research on the employment pressure of college students in China showed a significant fluctuation trend; the authors and institutions of the research are distributed in colleges and universities; the research on the employment pressure, social support, mental health education and coping style of college students is the research hotspot and frontier in this field at present.
About Two-dimensional Mathematical Model of Contaminant Migration in Unsaturated Catalytic Porous Media with Traps

This paper proposes an approach for the computer simulation of complex physical problem of contaminant migration through unsaturated catalytic porous media to the filter-trap. The corresponding mathematical model in the two-dimensional nonlinear case is presented. The model includes detailed considerations of the moisture transfer of saline solutions under the generalized Darcy’s and Cluta’s laws in different subregions of soil. The numerical solution of the boundary value problem was found by the finite difference method and proposed the algorithm for computer implementation. The proposed algorithm may be used for creating software with effective risk assessment strategies and predicting the cleaning and further useful use of the soil massifs.
Image Classification of Pandawa Figures Using Convolutional Neural Network on Raspberry Pi 4

Abstract Pandawa is one of the stories in wayang show consisting of five figures: Yudhistira, Bima, Arjuna, Nakula and Sadewa. This research is using Convolutional Neural Network (CNN) and apply it on the Raspberry Pi 4 to classify the puppet figures. CNN is one of the methods that can be used for classification of image data that has more than two classes. The network architecture used can classify Pandawa figures, using 1000 dataset with a size of 100 × 100 consisting of 80% training data and 20% test data. The network architecture has 3 convolution layers and 3 hidden layers, as well as 1 layer output. The classification results on the training data have an accuracy rate of 97.88% while test data has an accuracy rate of 96.5%.
Blockchain Technology Testing Challenges and Approaches

For achieving ‘perfect’ production quality for software in functional testing enterprises face unique challenges since the applications and systems become more complex. With technology experience and robust testing processes, corporations need strong domain skills in order to perform effective functional testing. Blockchain technology provides a very innovative and secure way of managing online transactions. Blockchain is set to pioneer changes in the way businesses are conducted today through its promise of tamper-proof, secure, peer-to-peer decentralized networks with distributed consensus. These basic features built into the very fabric bring in a host of additional dimensions and provide greater challenges in their testing. This paper highlights the challenges currently faced in testing such applications. It also acknowledges the need to devise specialized tools and techniques for blockchain oriented software testing in order to ensure high standards of quality.
Towards a Common Framework for Mediated Embodiment

Mediated embodiment (ME) is the technologically generated illusion of substituting a person’s body with an avatar body. Virtual reality is the most representative technology of ME. However, other forms of embodiment are emerging and need to be examined. The inclusion of all ME technologies under a common paradigm would largely facilitate their study. Here, I develop a unified conceptual framework of ME and integrate robot embodiment as part of the phenomenon. I first describe the central component of ME: avatars. I extend the concept of avatar used in virtual reality to other forms of ME. I then outline the minimum conditions necessary to induce the embodiment illusion as well as the technical principles used to create this illusion. Next, I suggest that ME technologies can be regarded as tools that increase human capabilities in four directions: embodiment of a new self, expansion of traveling capabilities, expansion of body capabilities, and the reach of immortality. I connect the principal research conducted in the field to these categories and suggest potential research directions to promote the positive applied uses of ME. I conclude with the importance to also address ethical issues related to this phenomenon.
Rethinking Breiman's Dilemma in Neural Networks: Phase Transitions of Margin Dynamics

Margin enlargement over training data has been an important strategy since perceptrons in machine learning for the purpose of boosting the confidence of training toward a good generalization ability. Yet Breiman (1999) shows a dilemma that a uniform improvement on margin distribution does not necessarily reduces generalization errors. In this paper, we revisit Breiman's dilemma in deep neural networks with recently proposed spectrally normalized margins, from a novel perspective based on phase transitions of normalized margin distributions in training dynamics. Normalized margin distribution of a classifier over the data, can be divided into two parts: low/small margins such as some negative margins for misclassified samples vs. high/large margins for high confident correctly classified samples, that often behave differently during the training process. Low margins for training and test datasets are often effectively reduced in training, along with reductions of training and test errors; while high margins may exhibit different dynamics, reflecting the trade-off between expressive power of models and complexity of data. When data complexity is comparable to the model expressiveness, high margin distributions for both training and test data undergo similar decrease-increase phase transitions during training. In such cases, one can predict the trend of generalization or test error by margin-based generalization bounds with restricted Rademacher complexities, shown in two ways in this paper with early stopping time exploiting such phase transitions. On the other hand, over-expressive models may have both low and high training margins undergoing uniform improvements, with a distinct phase transition in test margin dynamics. This reconfirms the Breiman's dilemma associated with overparameterized neural networks where margins fail to predict overfitting. Experiments are conducted with some basic convolutional networks, AlexNet, VGG-16, and ResNet-18, on several datasets including Cifar10/100 and mini-ImageNet.
Smart Education — The Necessity And Prospect Of Big Data Mining And Artificial Intelligence Technology In Art Education

Abstract smart education refers to a new education form and education mode relying on Internet technology, big data mining, artificial intelligence (AI) technology and mobile communication technology to realize online, platform, convenient, modular, accurate, and even mobile and fragmented learning knowledge and skills. This is a big trend. The education form of traditional art colleges is old-fashioned, conservative and lack of innovative means to adapt to it. It is imperative to improve the guidance, systematicness and convenience of art education curriculum by using new technology and new means. However, it is not very beneficial to simply transfer the curriculum and teaching materials under the offline learning mode to the Internet for all art students. Therefore, we put forward the concept and Prospect of "smart education". In short, smart education is the comprehensive development and utilization of online education platform, knowledge base, artificial intelligence (AI) and big data mining technology.
Logistical flows as an element of library collection management

Library collection development is today being accomplished in the highly competitive environment. Therefore new approaches to this process have to be found. The author attempts to apply several logistical instruments. The author identifies information; documentary and financial flows in the process of collection development technology and substantiates their applicability theoretically. Managing these flows would enhance the processes of library collection development. Despite the fact that the “documentary flow’’ category has been used both by Russian and Belarusian librarians and library scientists; this is the first attempt made in Belarusian library science and in the collection studies; in particular; to apply logistical approach to library collection development; while the author takes precedence of specifying information and financial flows as science categories. The parameters of the flows are identified; i. e.: document class; type; genre; form; language and country; number of document titles and copies; financial characteristics (price); and dimensions and weights (document format). The financial flow; in its turn; is characterized by volume; time; cost and vector; while the information flow’s characteristics are: information generation source; information target addressee, and purpose of generated information.
A Framework for Learning Predator-prey Agents from Simulation to Real World

In this paper, we propose an evolutionary predatorprey robot system which can be generally implemented from simulation to the real world. We design the closed-loop robot system with camera and infrared sensors as inputs of controller. Both the predators and prey are co-evolved by NeuroEvolution of Augmenting Topologies (NEAT) to learn the expected behaviours. We design a framework that integrate Gym of OpenAI, Robot Operating System (ROS), Gazebo. In such a framework, users only need to focus on algorithms without being worried about the detail of manipulating robots in both simulation and the real world. Combining simulations, real-world evolution, and robustness analysis, it can be applied to develop the solutions for the predator-prey tasks. For the convenience of users, the source code and videos of the simulated and real world are published on Github.
Runtime modeling and analysis of IoT systems

Internet-of-things systems are difficult to understand and debug due to their distributed nature and weak connectivity. We address this problem by using relational reference attribute grammars to model and analyze IoT systems with unreachable parts. A transitive device-dependency analysis is given as an example.
Scene Flow from Point Clouds with or without Learning

Scene flow is the three-dimensional (3D) motion field of a scene. It provides information about the spatial arrangement and rate of change of objects in dynamic environments. Current learning-based approaches seek to estimate the scene flow directly from point clouds and have achieved state-of-the-art performance. However, supervised learning methods are inherently domain specific and require a large amount of labeled data. Annotation of scene flow on real-world point clouds is expensive and challenging, and the lack of such datasets has recently sparked interest in self-supervised learning methods. How to accurately and robustly learn scene flow representations without labeled real-world data is still an open problem. Here we present a simple and interpretable objective function to recover the scene flow from point clouds. We use the graph Laplacian of a point cloud to regularize the scene flow to be as-rigid-as-possible. Our proposed objective function can be used with or without learning---as a self-supervisory signal to learn scene flow representations, or as a non-learning-based method in which the scene flow is optimized during runtime. Our approach outperforms related works in many datasets. We also show the immediate applications of our proposed method for two applications: motion segmentation and point cloud densification.
Computer Network Security Analysis Modeling Based on Deep Learning Algorithm

Abstract With the continuous development of human civilization and the continuous progress of computer technology, computer network has been widely used in our life, which has brought great convenience to people’s production and life. As the computer network plays an increasingly important role in daily life, the security of computer network has been paid more and more attention. Therefore, this paper studies the modeling of computer network security analysis based on deep learning algorithm. Starting from the types, levels and main problems of security needs, this paper analyzes the threats faced by the system equipment, access rights, and the main connection relationship, and makes a visual description of the network threats from the perspective of the purpose of the attack is to enhance higher permissions. The security analysis and modeling work is applied to the security fault tree method of computer system and the attack method of network information system. Through research and analysis, starting from the current state of computer network security, this paper deeply studies the analysis and modeling of computer network security, and realizes the evaluation of current computer network security. The accuracy rate of the proposed method for intrusion detection reaches 91.6%.
Exploring Secret Keys in Searching Integral Distinguishers Based on Division Property

Division property proposed by Todo at EUROCRYPT 2015 is a generalized integral property. Then, conventional bit-based division property (CBDP) and bitbased division property using three subsets (BDPT) were proposed by Todo and Morii at FSE 2016. At ASIACRYPT 2016, Xiang et al. extended Mixed Integer Linear Programming (MILP) method to search integral distinguishers based on CBDP. And at ASIACRYPT 2019, Wang et al. proposed an MILP-aided method of searching integral distinguishers based on BDPT. Although BDPT is powerful in searching integral distinguishers, the accuracy is not perfect.For block cipher SPECK32, as the block size is only 32 bits, we can experimentally observe the behaviors of all the plaintexts under a fixed key. By testing 210 random secret keys, we experimentally find a better integral distinguisher of 6-round SPECK32 with 30 active bits. But this experimental integral distinguisher cannot be proved by existing methods. So there still exists a gap between the proved distinguisher and the experimental one.To fill the gap, we explore secret keys in searching integral distinguishers based on BDPT. We put forward a situation where “Xor with The Secret Key” operation can be bypassed. Based on the new BDPT propagation rule, an improved automatic algorithm of searching integral distinguishers is proposed. For SPECK32, our improved algorithm can find the 6-round integral distinguisher with 230 chosen plaintexts. The gap between the proved distinguisher and the experimental one is filled. Moreover, we apply this improved method to search the integral distinguishers of SPECK, KATAN/KTANTAN, SIMON, SIMECK, SIMON(102), PRESENT and RECTANGLE block ciphers. The integral distinguishers found by our improved method are better than or consistent with the previous longest distinguishers.
Modern Development Strategy of Russian Education

In this paper, it was tried to consider the current state of normative support for the development of strategic planning documents in the field of Russian education. The strategy for the development of education (DoE), as an industry document of strategic planning, has not been adopted in the Russian Federation (RF) to date. The article attempts to generalize the developments in this area, considers several international documents and projects of strategies for the development of Russian education (DRE), national strategies focused on them. The analysis of regional strategy for the DoE, which is either component of strategies for socioeconomic development of the region, or separate concepts that are focused on municipal development strategies. Due to the lack of a strategic document at the Federal level, regional development concepts are mostly aimed at solving regional problems in the field of education, at a process approach, and, in fact, are not focused on solving breakthrough problems and taking into account all-Russian and global challenges. It is concluded that the Russian educational system cannot develop effectively in the course of planning only national projects that have their own time horizon; to solve this problem, a strategy for the DRE space is necessary, which can provide guidelines for the development of each educational organization.
THE ANALYSIS OF COOPERATIVE PRINCIPLE IN CORBUZIER PODCAST OF SITI FADILAH, SEBUAH KONSPIRASI – SAYA DIKORBANKAN EPISODE

Discussing the misleading of information in interaction in Podcast is rare in language research. Considering the concept of cooperative principle and the failure of observing maxims in an interaction by Grice (1989[1967]), this research investigated the flouting and violating of maxims appears within the podcast. The data source of study is a podcast of Deddy Corbuzier with Siti Fadilah Supari, the Former of Indonesian Health Minister which entitled Siti Fadilah, Sebuah Konspirasi – Saya dikorbankan. This study discovers the participant’s failing to observe the maxims, in form of flouting and violating. There are 57 exchanges flouted as the most common participant’s failure in observing the maxims, which consists of 29 quantity maxims (50%), 14 manner maxims (25%), 9 quality maxims (16%), and 5 relation maxims (9%). Then it is followed by the violation in 7 exchanges, which occurs in 3 quality maxims (43%), 2 quantity maxims (29%), 1 relation maxim (14%) and 1 manner maxim (14%). The study finds the podcast provides the various information, since the participant provides the more contribution rather than is required as the domination of flouting in quantity maxim. This study implies the need of reconfirming of the utterance that is presumable to lead failure of information understanding.
Speech Chain VC: Linking Linguistic and Acoustic Levels via Latent Distinctive Features for RBM-Based Voice Conversion

This paper proposes a voice conversion (VC) method based on a model that links linguistic and acoustic representations via latent phonological distinctive features. Our method, called speech chain VC, is inspired by the concept of the speech chain, where speech communication consists of a chain of events linking the speaker's brain with the listener's brain. We assume that speaker identity information, which appears in the acoustic level, is embedded in two steps — where phonological information is encoded into articulatory movements (linguistic to physiological) and where articulatory movements generate sound waves (physiological to acoustic). Speech chain VC represents these event links by using an adaptive restricted Boltzmann machine (ARBM) introducing phoneme labels and acoustic features as two classes of visible units and latent phonological distinctive features associated with articulatory movements as hidden units. Subjective evaluation experiments showed that intelligibility of the converted speech significantly improved compared with the conventional ARBM-based method. The speaker-identity conversion quality of the proposed method was comparable to that of a Gaussian mixture model (GMM)-based method. Analyses on the representations of the hidden layer of the speech chain VC model supported that some of the hidden units actually correspond to phonological distinctive features. Final part of this paper proposes approaches to achieve one-shot VC by using the speech chain VC model. Subjective evaluation experiments showed that when a target speaker is the same gender as a source speaker, the proposed methods can achieve one-shot VC based on each single source and target speaker's utterance.