Reviewerly/exHarmony huggingface dataset:
Reed Solomon Coding-Based Medical Image Data Hiding Method against Salt and Pepper Noise

Medical data hiding is used to hide patient information inside medical images to protect patient privacy. Patient information in the image should be protected when sending medical images to other specialists or hospitals over the communication network. However, the images are exposed to various unwanted disruptive signals in the communication channel. One of these signals is salt and pepper noise. A pixel exposed to salt and pepper noise becomes completely black or completely white. In pixel-based data hiding methods, it is not possible to extract the secret message in the pixel exposed to this kind of noise. While current data hiding methods are good for many disruptive effects, they are weak against salt and pepper noise. For this reason, the proposed study especially focuses on the accurate extraction of patient information in the salt and pepper noisy medical images. This study was proposed for the most accurate extraction of secret message despite salt and pepper noise, by use of a Reed Solomon error control coding-based data hiding method. The most important feature of Reed Solomon codes is that they can correct errors in non-binary (decimal) numbers directly. Therefore, the Reed Solomon coding-based data hiding method that proposed in this study increases the resistance against salt and pepper noise. Experimental studies show that secret data is accurately extracted from stego images with various densities of salt and pepper noise. Stego medical images created by the proposed method have superior quality values compared to similar literature studies. Additionally, compared to similar methods, the secret message is extracted from the noisy stego image with higher accuracy.
The TAWOCK conceptual model at content knowledge for professional teaching in vocational education

Now days, the integration of technology in the concept of learning is the trend in global education. The conceptual framework model is a general reference, and the only concept is Technology, Pedagogy, and Content Knowledge (TPACK). However, vocational education has knowledge of specific content, so adjusting conceptual models in professional learning is important to learn and offer. The purpose of this study is to evaluate the concept of TPACK into the concept of vocational education to improve the professionalism of vocational teachers in content knowledge. The author discusses the knowledge structure of vocational fields that are built based on work, content, technology, and the suitability of the learning approach. Based on the results of the article found a new construction in building knowledge in the field of vocational education with special expertise characteristics and shifting pedagogical concepts towards andragogy in learning concept. The results of the analysis recommend the TPACK concept transformed into the Technology, Andragogy, Work, and Content Knowledge (TAWOCK) concept in vocational learning.
GO: Group Optimization

This article introduces a modern optimization algorithm to solve optimization problems. Group Optimization (GO) is based on concept that uses all agents to update population of algorithm. Every agent of population could to be used for population updating. For these purpose two groups is specified for any agent. One group for good agents and another group for bad agents. These groups is used for updating position of each agent. twenty-three standard benchmark test functions are evaluated using GO and then results are compared with eight other optimization method.
R-ELMNet: Regularized extreme learning machine network

Principal component analysis network (PCANet), as an unsupervised shallow network, demonstrates noticeable effectiveness on datasets of various volumes. It carries a two-layer convolution with PCA as filter learning method, followed by a block-wise histogram post-processing stage. Following the structure of PCANet, extreme learning machine auto-encoder (ELM-AE) variants are employed to replace the PCA’s role, which come from extreme learning machine network (ELMNet) and hierarchical ELMNet. ELMNet emphasizes the importance of orthogonal projection while overlooking non-linearity. The latter introduces complex pre-processing to overcome drawback of non-linear ELM-AE. In this paper, we analyze intrinsic characteristics of ELM-AE variants and accordingly propose a regularized ELM-AE, which combines non-linearity learning capability and approximately orthogonal projection. Experiments on image classification show the effectiveness compared to supervised convolutional neural networks and related shallow networks on unsupervised feature learning.
A Frame-based Sentence Representation for Machine Reading Comprehension

Sentence representation (SR) is the most crucial and challenging task in Machine Reading Comprehension (MRC).MRC systems typically only utilize the information contained in the sentence itself, while human beings can leverage their semantic knowledge.To bridge the gap, we proposed a novel Frame-based Sentence Representation (FSR) method, which employs frame semantic knowledge to facilitate sentence modelling.Specifically, different from existing methods that only model lexical units (LUs), Frame Representation Models, which utilize both LUs in frame and Frame-to-Frame (F-to-F) relations, are designed to model frames and sentences with attention schema.Our proposed FSR method is able to integrate multiple-frame semantic information to get much better sentence representations.Our extensive experimental results show that it performs better than state-of-the-art technologies on machine reading comprehension task.
Off-Policy Actor-Critic with Shared Experience Replay

We investigate the combination of actor-critic reinforcement learning algorithms with uniform large-scale experience replay and propose solutions for two challenges: (a) efficient actor-critic learning with experience replay (b) stability of off-policy learning where agents learn from other agents behaviour. We employ those insights to accelerate hyper-parameter sweeps in which all participating agents run concurrently and share their experience via a common replay module. To this end we analyze the bias-variance tradeoffs in V-trace, a form of importance sampling for actor-critic methods. Based on our analysis, we then argue for mixing experience sampled from replay with on-policy experience, and propose a new trust region scheme that scales effectively to data distributions where V-trace becomes unstable. We provide extensive empirical validation of the proposed solution. We further show the benefits of this setup by demonstrating state-of-the-art data efficiency on Atari among agents trained up until 200M environment frames.
Haar Graph Pooling

Author(s): Wang, Yu Guang; Li, Ming; Ma, Zheng; Montufar, Guido; Zhuang, Xiaosheng; Fan, Yanan | Abstract: Deep Graph Neural Networks (GNNs) are useful models for graph classification and graph-based regression tasks. In these tasks, graph pooling is a critical ingredient by which GNNs adapt to input graphs of varying size and structure. We propose a new graph pooling operation based on compressive Haar transforms -- HaarPooling. HaarPooling implements a cascade of pooling operations; it is computed by following a sequence of clusterings of the input graph. A HaarPooling layer transforms a given input graph to an output graph with a smaller node number and the same feature dimension; the compressive Haar transform filters out fine detail information in the Haar wavelet domain. In this way, all the HaarPooling layers together synthesize the features of any given input graph into a feature vector of uniform size. Such transforms provide a sparse characterization of the data and preserve the structure information of the input graph. GNNs implemented with standard graph convolution layers and HaarPooling layers achieve state of the art performance on diverse graph classification and regression problems.
Thermal Entanglement and Correlated Coherence in Two Coupled Double Quantum Dots Systems

Abstract In this work, the thermal quantum correlations in two coupled double semiconductor charge qubits are investigated. This is carried out by deriving analytical expressions for both the thermal concurrence and the correlated coherence. The effects of the tunneling parameters, the Coulomb interaction, and the temperature on the thermal entanglement and on the correlated coherence are studied in detail. It is found that the Coulomb potential plays an important role in the thermal entanglement and in the correlated coherence of the system. The results also indicate that the Coulomb potential can be used for significant enhancement of the thermal entanglement and quantum coherence. One interesting aspect is that the correlated coherence capture all the thermal entanglement at low temperatures, that is, the local coherences are totally transferred to the thermal entanglement. Finally, the role played by thermal entanglement and the correlated coherence responsible for quantum correlations are focused on. It is shown that in all cases, the correlated coherence is more robust than the thermal entanglement so that quantum algorithms based only on correlated coherence may be more robust than those based on entanglement.
A Multi-player Game for Studying Federated Learning Incentive Schemes

Federated Learning (FL) enables participants to "share'' their sensitive local data in a privacy preserving manner and collaboratively build machine learning models. In order to sustain long-term participation by high quality data owners (especially if they are businesses), FL systems need to provide suitable incentives. To design an effective incentive scheme, it is important to understand how FL participants respond under such schemes. This paper proposes FedGame, a multi-player game to study how FL participants make action selection decisions under different incentive schemes. It allows human players to role-play under various conditions. The decision-making processes can be analyzed and visualized to inform FL incentive mechanism design in the future.
The Scikit HEP Project overview and prospects

Scikit-HEP is a community-driven and community-oriented project with the goal of providing an ecosystem for particle physics data analysis in Python. Scikit-HEP is a toolset of approximately twenty packages and a few "affiliated" packages. It expands the typical Python data analysis tools for particle physicists. Each package focuses on a particular topic, and interacts with other packages in the toolset, where appropriate. Most of the packages are easy to install in many environments; much work has been done this year to provide binary "wheels" on PyPI and conda-forge packages. The Scikit-HEP project has been gaining interest and momentum, by building a user and developer community engaging collaboration across experiments. Some of the packages are being used by other communities, including the astroparticle physics community. An overview of the overall project and toolset will be presented, as well as a vision for development and sustainability.
Promoting Students’ Autonomy through Online Learning Media in EFL Class

The trend of teaching English as foreign language in 21st century has changed from teacher centered to students centered paradigm which results the need of students’ autonomy in learning process. Then, this research was aimed to depict how the schoology as e-media to promote 25 VII A class students’ autonomy in learning English as foreign language (EFL) in discourse analysis course. This was a case study qualitative research by applying triangulation of the data. The results of this research mentioned that schoology successfully promoted students’ autonomy by considering some facts such as students active participations through logging in and commenting others’ idea; students’ control on deciding learning modes, setting, and materials; and students’ enthusiasm to finish the lecturer’s challenges. One unique thing was that shy students were more active in Schoology. Finally, this research suggests to present schoology in teaching content course.
A model-driven framework for data-driven applications in serverless cloud computing

In a serverless cloud computing environment, the cloud provider dynamically manages the allocation of resources whereas the developers purely focus on their applications. The data-driven applications in serverless cloud computing mainly address the web as well as other distributed scenarios, and therefore, it is essential to offer a consistent user experience across different connection types. In order to address the issues of data-driven application in a real-time distributed environment, the use of GraphQL (Graph Query Language) is getting more and more popularity in state-of-the-art cloud computing approaches. However, the existing solutions target the low level implementation of GraphQL, for the development of a complex data-driven application, which may lead to several errors and involve a significant amount of development efforts due to various users' requirements in real-time. Therefore, it is critical to simplify the development process of data-driven applications in a serverless cloud computing environment. Consequently, this research introduces UMLPDA (Unified Modeling Language Profile for Data-driven Applications), which adopts the concepts of UML-based Model-driven Architectures to model the frontend as well as the backend requirements for data-driven applications developed at a higher abstraction level. Particularly, a modeling approach is proposed to resolve the development complexities such as data communication and synchronization. Subsequently, a complete open source transformation engine is developed using a Model-to-Text approach to automatically generate the frontend as well as backend low level implementations of Angular2 and GraphQL respectively. The validation of proposed work is performed with three different case studies, deployed on Amazon Web Services platform. The results show that the proposed framework enables to develop the data-driven applications with simplicity.
Capturing associations in graphs

This paper proposes a class of graph association rules, denoted by GARs, to specify regularities between entities in graphs. A GAR is a combination of a graph pattern and a dependency; it may take as predicates ML (machine learning) classifiers for link prediction. We show that GARs help us catch incomplete information in schemaless graphs, predict links in social graphs, identify potential customers in digital marketing, and extend graph functional dependencies (GFDs) to capture both missing links and inconsistencies. We formalize association deduction with GARs in terms of the chase, and prove its Church-Rosser property. We show that the satisfiability, implication and association deduction problems for GARs are coNP-complete, NP-complete and NP-complete, respectively, retaining the same complexity bounds as their GFD counterparts, despite the increased expressive power of GARs. The incremental deduction problem is DP-complete for GARs versus coNP-complete for GFDs. In addition, we provide parallel algorithms for association deduction and incremental deduction. Using real-life and synthetic graphs, we experimentally verify the effectiveness, scalability and efficiency of the parallel algorithms.
How Social Cues in Virtual Assistants Influence Concerns and Persuasion: The Role of Voice and a Human Name

The aim of this study was to test how two important types of social cues used by virtual assistants today can affect consumer concerns and persuasion. These two cues are modality (voice-based via smart speaker, voice-based via a smartphone, or text-based on a smartphone screen) and the adoption of a human name rather than no name. An online scenario-based experiment (n = 180) has shown that participants who were exposed to a voice-based recommendation via a smart speaker were the most concerned about security and found text-based recommendations on a screen to be the most persuasive. Participants who were exposed to a virtual assistant with a human name were less concerned about their autonomy and were more strongly persuaded than those exposed to an assistant without a human name.
Evaluation model of art internal auxiliary teaching quality based on artificial intelligence under the influence of COVID-19

At present, the evaluation of normal teaching order and teaching quality has been seriously interfered by the impact of COVID-19. In order to ensure the quality of art classroom teaching, this article uses BP neural network technology to build a model for art teaching quality evaluation during the epidemic. Based on the introduction of the BP neural network model and the problems of art teaching quality evaluation, the article focuses on the art teaching quality evaluation indicators and the BP neural network algorithm and process. In addition, the article also uses an empirical method to verify the effect of the BP network model training method, and obtains the expected effect. Finally, it discusses the problem of information processing in art teaching evaluation.
A Systematic Review on Reinforcement Learning-Based Robotics Within the Last Decade

Robotics is one of the many tools that is making a substantial difference as the world is experiencing the fourth industrial revolution. To ease control over this engineering marvel substantially, Reinforcement Learning (RL) has paved its way in recent years quite remarkably. RL enables robots to become self-aware towards carrying out a specific task followed by user operations. For decades of rigorous endeavor, this research field has gone through numerous groundbreaking developments and it will be the same for the coming days. Therefore, this paper steps in to enlighten the scientific community with a systemic review of the published research papers within the past decade. The bibliographic data that is extracted from the papers are analyzed using an automated tool named Vosviewer with respect to some parameters. Substantial excerpts from the most influential papers are highlighted in this work. Furthermore, this paper points out the global research practice in this field. The paper also generates some intriguing questions and answers them in regards to the research topic. After reading this paper, future researchers will have a firm idea in the RL-based robotics and will be able to incorporate in their own research.
Efficient Scheduling of Irregular Network Structures on CNN Accelerators

The state-of-the-art convolutional neural network (CNN) structures present growing irregularity in the sense of layer connections, which derives from the innovative manual designs and the recently proposed neural architecture searching approaches. Such irregular structures improve recognition accuracy, but also bring challenges for hardware deployment, especially on CNN accelerators with regular architectures: 1) the complicated data dependency makes it nontrivial to decide the data reuse strategy between layers and 2) since the execution order of each network is not unique, the choice of layer scheduling, memory allocating, and loop tiling strategies greatly impact the hardware performance. These challenges cannot be solved by the existing CNN schedulers, which mainly focuses on the dataflow of a single layer. In this work, we propose a comprehensive framework to analyze and solve the mapping of an arbitrarily connected CNN network to specific hardware accelerators. We propose: 1) a dynamic programming and node-clustering-based DAG partitioning approach to efficiently exploit interlayer data reuse and 2) a subgraph scheduling and on-chip memory allocating strategy to find the optimal execution order. With the modeling of CNN accelerators, we also propose a loop tiling approach for fused layers. An automated framework is established to generate binary machine codes from original CNN models produced by mainstream deep learning frameworks, which can process large-scale CNNs with more than 1000 layers in only a few minutes. Experiments based on state-of-the-art accelerators (e.g., NVDLA) show that our techniques greatly reduce the external data transfer of interlayer dependencies and bring significant performance improvement over existing approaches.
Security Challenges and Cyber Forensic Ecosystem in IoT Driven BYOD Environment

The usage of Internet of Things (IoT)/ Bring Your Own Devices (BYOD) has grown up exponentially, as expected 50 Billion IoT devices will get connected by the end of 2020 in the world in smart city environment. Advancement of Human Driven Edge Computing (HEC) with 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> Generation Internet services also makes this more feasible. Use of IoT and increased demand of BYOD becomes one of the fundamental needs to increase the organization employee productivity and business agility. But it increased the significant risk of the cyber-attack which is leading a major reason for business disruption and becomes a leading question about how to get a cyber secured citizenship in the smart city environment as well as in the organization where BYOD is used in large numbers. In order to conduct forensic investigation post incident detection of malicious activities from IoT/BYOD end point is most challenging task. A strategic practical approach in this research is presented to detect malicious activities so that organization can adopt to protect the critical infrastructure and smart city critical infrastructure. In order to achieve the goal of detecting of malicious activities in BYOD environment, simulation performed in 3 phases. The 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> phase of the simulation performed while BYOD endpoint was outside the organization over the internet without VPN. The 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> phase of the simulation was performed where BYOD endpoint was securely onboarded using a corporate wireless network with a secured onboarding process. The 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> phase of the test done where IoT/BYOD was outside the organization with a VPN. A unique robust scalable model puts forward with significant result from this research and concluded for creating a cyber forensic ecosystem in IoT/BYOD environment to enable cyber secured citizenship in era of HEC with 5G and IoT.
Shedding light on manganese cycling in the early oceans

Humans become increasingly fragile as they age. We show that something similar may happen to states, although for states, the risk of termination levels off as they grow older, allowing some to persist for millennia. Proximate causes of their ...How states and great powers rise and fall is an intriguing enigma of human history. Are there any patterns? Do polities become more vulnerable over time as they age? We analyze longevity in hundreds of premodern states using survival analysis to help ...