Reviewerly/exHarmony huggingface dataset:
Features of Professional Individuality of Chinese Students in Russian Art Universities

The article provides a comparative analysis of the role of the professional personality of the future artist-teacher in the professional education of China and Russia in order to build an effective process of training Chinese specialists in Russian universities. In accordance with this , the Russian and Chinese interpretation of the concepts is given: "professional individuality" and "individual educational route". The subject of the study is the study of the role of the development of an educational route in the process of training artistic personnel. The hypothesis of the study is that in order to successfully achieve pedagogical goals in working with Chinese students, it is necessary to take into account the options for building individual educational routes in Chinese universities. It also substantiates the need for drawing up educational routes in art universities, taking into account the ethnocultural and ethnopsychological characteristics of Chinese-speaking students, which is the key to the development of individuality in the profession and subsequent adaptation to the realities of their native country. Similar studies have not been undertaken in Russian before in relation to students from China studying in a modern Russian university specializing in artistic specialties. These materials can be used in the preparation of curricula and educational programs in art and pedagogical universities of Russia, taken into account in further studies of the adaptation of foreign students.
Efficient ECC-Based CP-ABE Scheme With Constant-Sized Key and Scalable User Revocation

A lightweight encryption technique with low storage overheads is essential to secure data access from Resrouce-Constrained Devices (RCD). It must protect against unsolicited users with scalable user revocation with uninterrupted access to valid users. A user must be able to access RCD over low-energy wireless communication interfaces through a user reader device (mobile). Attribute-based encryption is an efficient technique for securely sharing sensitive data with multiple stakeholders without the requirement of sharing multiple security keys. This paper proposes a novel ECC-based scalable user revocation (EASER) ciphertext policy attribute-based encryption (CP-ABE) encryption scheme to secure and selectively share data from RCD with multiple stakeholders. It is based on ECC, with lower decryption time and constant-sized secret keys with low storage overhead. EASER extends Odelu et al.'s ECC-based CP-ABE scheme for scalable user revocation and mitigates a proven key-collusion attack. It is suitable for securing RCD and provides secure, selective access from a user reader device.
Research on Bitcoin Anti-anonymity Technology Based on Behavior Vectors Mapping and Aligning Model

Abstract Traditional anti-anonymity technologies for Bitcoin transactions include two types. One is network-layer anti-anonymity technology, which achieves the purpose of locating the initial IP of specific transaction information by speculating on the IP propagation path of transaction; the other is the anti-anonymity technology of the transaction layer. By analyzing the data of the Bitcoin ledger, it realizes the on-chain behavior portrait of a specific wallet address attributable to the user. In this work, we propose a new anti-anonymity technology, by constructing transaction behavior vectors and social behavior vectors based on Bitcoin ledger data and off-chain social data respectively, and build a model for mapping and aligning the two vectors. Experimental test shows that the proposed anti-anonymity technology is more accurate and has better practical effects. Furthermore, the technology suits for the anti-anonymity of other virtual currencies as well.
Financial Integration, Productive Development and Fiscal Policy Space in Developing Countries

This paper presents a simple post-Keynesian model, which highlights the importance of productive development in defining the dynamics of the Real Exchange Rate (RER) and foreign debt in a small open developing economy. In developing countries that are integrated in international financial markets, capital flows and fluctuations in the RER endogenously feed back into each other and give rise to cyclical macroeconomic volatility. Fiscal austerity supposedly taming external imbalances exacerbates such instability. More diversified productive structures open more space for expansionary fiscal policies, make the economy more resilient to finance-led macroeconomic cycles, and make external debt more sustainable. Capital controls together with stronger price sensitivity of net exports can further stabilize the economy. The paper provides support to the combination of industrial and macroprudential policies in peripheral economies to foster productive diversification, which is turn critical to sustain macroeconomic stability in the short and the long run.
Embedded Software Architecture Design

If I could describe my first few years as an embedded software engineer, I would use the words "code first, architect second." I could simplify this statement further to "There is only code!". Creating a software architecture was honestly a foreign concept, and it was an industry-wide issue, not just for me. When a task needed to be done, we would jump into our IDE and start coding. For simple projects, this wasn't an issue. Everything that needed to be done could be kept straight in our minds; however, as projects became more complex, the rework required to add features ballooned out of control.
Convolutional Neural Network based Classification for automatic segregation of distorted digital images

In this digital era, passion for photography has seen a tremendous rise owing to the desire to upload photos on social media with the intention of increasing likes or followers. Pre-event and post event shoots for various occasions is a new trend which has increased the demand for professional photographers. With the advent of digital and mobile cameras, a massive number of digital photos can be produced which is only limited by the camera or mobile storage capacity. All the images produced by these cameras necessarily need not be of good quality. Some of the photos may include excessive amounts of blurring which in turn degrades the photo clarity. Manual separation of such low-quality photos can be a difficult and time-consuming task for both professional as well as amateur photographers. In such a situation, automatic detection and classification of blurred images and moving them to a separate folder would save the user from the strenuous job manual separation or deletion of blurred images. This paper proposes a convolution neural network (CNN) based classifier model to identify and classify digital photos based on their clarity. The proposed CNN model was trained on a dataset of 20k digital images (both mobile and digital camera photos) collected from multiple sources and annotated with two classes as blurred and unblurred. The proposed CNN model achieved a validation accuracy of 95% and could efficiently identify the blurred images and classify them.
Penyuluhan Budaya Siaga Bencana Siswa SDN 3 Kabila Bone

Disasters in Indonesia can be detrimental every time they occur, Gorontalo, which is on the Pacific plate route, has the potential for disasters to occur, therefore it is necessary to be prepared for disasters, most victims of school-age children, the importance of disaster knowledge from an early age in order to reduce threats to children. among others (before, during, and after a disaster) conducted using the lecture method which is expected to provide an overview of disaster preparedness. from this counseling explains the culture of disaster preparedness where this explanation is carried out for six disasters that often occur in Indonesia. To reduce disaster risk, a culture of preparedness must be instilled from an early age on a regular basis so that disaster preparedness is a step to deal with disasters. disaster preparedness can be improved from a low level of knowledge to be better by implementing a daily disaster preparedness culture
Purification at the ultimate limit using quantum superpositions of causal order

Pure states are essential to quantum technologies. We show how generate an unlimited number of pure states by applying a pair of operations in a superposition of their causal order.
Studi Litelatur: Peningkatan Kinerja Digital Forensik Dan Pencegahan Cyber Crime

The more complex a problem that arises due to an ease in information technology makes cyberattacks increasingly dangerous and dangerous for systems within a country or entity. Crimes thatarise must be detected and a preventive action taken immediately so that no party is harmed for atechnological crime. This study aims to find various methods to review and investigate cyberattacks for security in digital forensics and can provide input and additional insight to all partiesincluding investigators or practitioners and users in the field of digital forensics about variousmethods that can be used to detect and prevent cyber attacks. This research was conducted byconducting a literature study of various studies on cyber attacks. The research method used isdescriptive qualitative method by taking data from literacy studies on research on cyber attacks.The results of the literature study show that the factors that can improve the performance of digitalforensics are the Digital Forensics Framework For Reviewing And Investigating Cyber Attacks(D4I), holistic digital organization, biometric authentication with image forensics schemes, largescale interconnection systems, LSS control architecture, system network control (AE-Safe), DigitalForensic Readiness Index (DiFRI), LEChain, Verification Of Digital Evidence (VODE)Framework, Capsule of Digital Evidence (CODE), The Phase-oriented Advice and ReviewStructure (PARS), IEEE-system 14 and IEEE-39 stability control, Cyber-Physical System (CPS),STPA-SafeSec, Digital Evidence Reporting and Decision Support (DERDS), ExperDF-CM modeland Argumentation-Based Reasoner (ABR)
Asymob

Chatbots have become a popular way to access all sorts of services via natural language. Many platforms and tools have been proposed for their construction, like Google's Dialogflow, Amazon's Lex or Rasa. However, most of them still miss integrated quality assurance methods like metrics. Moreover, there is currently a lack of mechanisms to compare and classify chatbots possibly developed with heterogeneous technologies.
Cluster application in a virtual CAVE computing environment

The main goal of this work is to create a software solution to add 3D caves to the game engine Unity. VR technologies, 3D caves, graphics engines, and computer clusters were analyzed. Based on the analysis, the solution in the form of a library that can be used in Unity projects was designed and subsequently implemented. The library ensures synchronization of Unity application instances in the cluster, which display the virtual space of the 3D cave on individual walls. It also calculates the perspective of the displayed scene based on tracking the position of the user's head. The solution was then verified by integration into sample projects from the Unity store as well as by performance tests. This solution can be the basis for creating a virtual environment, e.g. therapist - patient interaction.
Obstacle Aware Sampling for Path Planning

Many path planning algorithms are based on sampling the state space. While this approach is very simple, it can become costly when the obstacles are unknown, since samples hitting these obstacles are wasted. The goal of this paper is to efficiently identify obstacles in a map and remove them from the sampling space. To this end, we propose a pre-processing algorithm for space exploration that enables more efficient sampling. We show that it can boost the performance of other space sampling methods and path planners. Our approach is based on the fact that a convex obstacle can be approximated provably well by its minimum volume enclosing ellipsoid (MVEE), and a non-convex obstacle may be partitioned into convex shapes. Our main contribution is an al-gorithm that strategically finds a small sample, called the active-coreset, that adaptively samples the space via membership-oracle such that the MVEE of the coreset approximates the MVEE of the obstacle. Experimental results confirm the ef-fectiveness of our approach across multiple planners based on rapidly-exploring random trees, showing significant improve-ment in terms of time and path length.
Advanced Shaders

So far, we have primarily discussed vertex and fragment shaders that take meshes, transform their vertices onto the screen, and color the pixels. Most shaders take this form. We’ve already seen the power of these types of shaders and the broad range of capabilities they have, but they are not the only types of shaders. In the shader pipeline, there are two optional stages that we have not yet encountered: the tessellation shader and the geometry shader. On top of that, there are compute shaders, which operate outside the usual mesh shading pipeline and can be used for arbitrary calculations on the GPU. In this chapter, we will explore some of these strange and exotic new types of shaders and add ever-powerful new tools to our box of tricks.
Checkpointing and deterministic training for deep learning

Checkpointing and faithful replay are important for the training process of a Deep Learning (DL) model. It may improve productivity, model performance, robustness, and help security auditing. However, the inherent nondeterminism in training poses prominent challenges. Even with fixed random seeds, multiple runs of a same training pipeline may yield models whose performance varies by 20% percent. With existing infrastructural checkpointing support, developers cannot faithfully replay a training process. In this paper, we propose DETrain, a new solution to checkpointing and faithful execution/replay for long running DL training programs. We introduce a novel random number generation mechanism that can generate consistent random numbers in the presence of data parallelism. In addition, we devise a novel analysis that can determine a set of state variables that are necessary for faithful replay. These variables are either saved in a checkpoint or re-generated by fast forwarding, a selective execution technique. DETrain is evaluated on 13 PyTorch models and 16 Tensorflow models. It can deterministically execute these programs and replay from checkpoints with reasonable overhead. It also helps developers in diagnosing problems in training.
Dynamic Multi-Scale Network for Remote Sensing Image Super-Resolution

Although single image super-resolution(SISR) method with deep neural network has already been explored in depth in natural images, further research on SISR in remote sensing image is still desired as aerial imagery has distinctive characteristics such as varied scenes across wide areas. In this paper, considering the scarcity of remote sensing images that may restrict the performance of data-driven neural network, we first propose a new data augmentation method, RotBlur, to promote sample diversity dramatically with a rotated cropped block at random angle. And we also design a Dynamic Multi-Scale Network(DMSN) to enhance details of low-resolution(LR) remote sensing images adaptively according to current scene of various images. Experiments performed on the UC Merced Land-Use dataset demonstrate that our DMSN outperforms several state-of-the-art methods in terms of PSNR of reconstructed images.
Explicitly Constrained Black-Box Optimization With Disconnected Feasible Domains Using Deep Generative Models

We tackle explicitly constrained black-box continuous optimization problems in which the feasible domain forms a union of disconnected feasible subdomains. The decoder-based constraint-handling technique is a promising approach when the feasible domain is disconnected. However, the design of a reasonable decoder requires deep prior knowledge of the optimization problem to be solved and, hence, human effort. In this study, we investigated the usefulness of a deep neural network as a decoder and developed a training scheme for a deep neural network without prior information, such as a training dataset consisting of feasible and infeasible solutions required by existing decoder approaches. To stabilize the training of the deep generative model as the decoder, we propose decomposing the decoder into sub-models, introducing skip connections to each sub-model, and training the sub-models sequentially with separate loss functions. Numerical experiments using a test problem and a topology optimization problem show that the proposed method can find feasible domains with better objective function values and higher probability than both conventional decoder-based constraint-handling methods and non-decoder-based constraint-handling methods.
Using an Algorithm Portfolio to Solve Sokoban

The game of Sokoban is an interesting platform for algorithm research. It is hard for humans and computers alike. Even small levels can take a lot of computation for all known algorithms. In this paper we will describe how a search based Sokoban solver can be structured and which algorithms can be used to realize each critical part. We implement a variety of those, construct a number of different solvers and combine them into an algorithm portfolio. The solver we construct this way can outperform existing solvers when run in parallel, that is, our solver with 16 processors outperforms the previous sequential solvers.
Best-First Width Search for Lifted Classical Planning

Lifted planners are useful to solve tasks that are too hard to ground. Still, computing informative lifted heuristics is difficult: directly adapting ground heuristics to the lifted setting is often too expensive, and extracting heuristics from the lifted representation can be uninformative. A natural alternative for lifted planners is to use width-based search. These algorithms are among the strongest for ground planning, even the variants that do not access the action model. In this work, we adapt best-first width search to the lifted setting and show that this yields state-of-the-art performance for hard-to-ground planning tasks.
Geochemical classification for bottled natural waters in China: using unsupervised and supervised machine leaching algorithm

Major elements in still bottled natural water were analyzed in China.An unsupervised machine learning algorithm, principal component analysis (PCA), was used to classify the samples.The PCA result suggested four groups to discriminant the chemical composition of the samples.By using the labelled data, supervised machine learning methods, random forest, and support vector classification, were used to train models.The models were then applied on the data obtained from literature.To analyze the water-rock interactions of the samples from different groups, reverse modeling in the software PHREEQC was implemented.
A Structural Expansion Method of Fuzzy Cognitive Map Under Cognitive Limitations

With the diversification of social development, the difficulty and complexity of prediction problems have increased significantly. However, most prediction models are not interpretative, can only deal with limited and relatively simple problems, and lack solutions to complex problems. As a data-driven discrete nonlinear system, Fuzzy Cognitive Map combines the advantages of neural network and fuzzy logic, has strong knowledge representation and logical reasoning ability. However, the nodes selected in complex systems cannot be comprehensive and practical due to the limitations of human cognition, and the lack of essential nodes will affect the accuracy of the prediction model. This paper will focus on the structure of FCM and analyze the FCM prediction model under cognitive limitations: The results show that cognitive limitations reduce the prediction accuracy; We propose a complete cognitive structure fuzzy cognitive map method (CC-FCM) to improve the prediction accuracy and solve the modeling and prediction problems of complex systems.
Detecting Bitcoin Nodes by the Cyberspace Search Engines

Abstract Nowadays, the cyberspace search engines have showed great power to find entities and services in the network, which provide new ideas and methods to detect the Bitcoin nodes. This paper introduces the Bitcoin’s P2P network and nodes including the reachable nodes and the unreachable nodes. Then, the results of detecting reachable nodes by the cyberspace search engines are showed. Next, the author proposes a new approach to find and verify the unreachable nodes by the cyberspace search engines. Finally, this paper illustrates the de-anonymization of some Bitcoin nodes by the cyberspace search engines, which map some node’s IP addresses to real Bitcoin entities, such as Zeblockchain (a browser website), Microwallet (a wallet website) and Laurentia Pool (a non-profit pool website).
Analyzing Image Denoising Using Generative Adversarial Network

Motion images they have great use in television and film making and virtual reality technology.This paper gives the solution of reconstruction the dynamic blur images.itconverts from blur images to actual images by the mapping, it can be happened by using an image reconstruction algorithm using the GAN convolution model.The model runs on the input sets and the output will be an actual image that came from the blur images.The restoration effect will be good and quality is high in terms of stability, phase manipulation, conversion efficiency, noise performance.
Organic Molecules for Quantum Communication

We demonstrate a quantum key distribution testbed exploiting a molecule-based single-photon source operating at room-temperature. With an estimated secret key rate of 0.5Mbps, we set the ground for room-temperature deterministic emitters for quantum communication protocols.
Fearless steps Apollo: Advancements in robust speech technologies and naturalistic corpus development from Earth to the Moon

Recent developments in deep learning strategies have revolutionized Speech and Language Technologies(SLT). Deep learning models often rely on massive naturalistic datasets to produce the necessary complexity required for generating superior performance. However, most massive SLT datasets are not publicly available, limiting the potential for academic research. Through this work, we showcase the CRSS-UTDallas led efforts to recover, digitize, and openly distribute over 50,000 hrs of speech data recorded during the 12 NASA Apollo manned missions, and outline our continuing efforts to digitize and create meta-data through diarization of the remaining 100,000hrs. We present novel deep learning-based speech processing solutions developed to extract high-level information from this massive dataset. Fearless-Steps APOLLO resource is a 50,000 hrs audio collection from 30-track analog tapes originally used to document Apollo missions 1,7,8,10,11,&amp;13. A customized tape read-head developed to digitize all 30 channels simultaneously has been deployed to expedite digitization of remaining mission tapes. Diarized transcripts for these unlabeled audio communications have also been generated to facilitate open research from speech sciences, historical archives, education, and speech technology communities. Robust technologies developed to generate human-readable transcripts include: (i) speaker diarization, (ii) speaker tracking, and (iii) text output from speech recognition systems.