Reviewerly/exHarmony huggingface dataset:
Graph Neural Networks with Haar Transform-Based Convolution and Pooling: A Complete Guide.

Graph Neural Networks (GNNs) have recently caught great attention and achieved significant progress in graph-level applications. In order to handle graphs with different features and sizes, we propose a novel graph neural network, which we call HaarNet, to predict graph labels with interrelated convolution and pooling strategies. Similar to some existing routines, the model assembles unified graph-level representations from samples by first adopting graph convolutional layers to extract mutual information followed by graph pooling layers to downsample graph resolution. By a sequence of clusterings, we embed the intrinsic topological information of each graph into the GNN. Through the fast Haar transformation, we made our contribution to forming a smooth workflow that learns multi-scale graph representation with redundancy removed. As a result, our proposed framework obtains notable accuracy gains without sacrificing performance stability. Extensive experiments validate the superiority on graph classification and regression tasks, where our proposed HaarNet outperforms various existing GNN models, especially on big data sets.
Proposal of a Multi-standard Model for Measuring Maturity Business Levels with Reference to Information Security Standards and Controls

The continuous security information risks force organizations to constantly update their security protocols. This implies, among other aspects, to base their monitoring mainly on their own maturity status in the SGSI (Information Security Managing System). When a Chief Information Security Officer elaborates a protection plan of IT assets, a wide and varied range of threats must be considered. These tasks are executed using conceptual models, which do not usually work in an integrated and systematic way. Thus, these models seek to increase maturity levels for protecting and safeguarding information security. Among the most common [1], we find COBIT 5, CSE-CMM, NIST-CRST to which we add the security standards like OWASP, ISO 27000-1, SANS. From here then, it is possible to see the lack of a multi-standard model that integrates systematically the individual actions with the expected results. The present project proposes an integrated model that links and blends, on the one hand, the security standards and, on the other hand, the measurements of the organization’s maturity levels. By doing this, it is possible to count with a set of relevant actions, classified by evaluation categories, which provide conditions for crossing regulations and standardized controls. This finally allows to explore how efficient these acquired measures are, and, when needed, the corrections that should be introduced ahead.
Security of Chip Bank Card in Remote Payment Based on Risk Feature

Payment is a necessary thing in people’s daily life, and the development of the Internet makes it possible that people can shop at home. As for chip bank card, it is an important payment method that has been developed in recent years and plays a key role in remote payment. In this study, firstly, the risk features of chip bank cards were analyzed from the general remote payment scheme. Then, based on the security technology theory, a chip bank card remote payment model using elliptic curve hybrid encryption algorithm and identity authentication technology was constructed. In terms of security testing, the National Institute of Standards and Technology (NIST) randomness test was used to illustrate the high randomness of the key, and cryptographic security formal verification method based on Hoare logic was used to illustrate the convergence of the key to the defect, which verified the high security of the chip bank card in the remote payment process.
Divide and conquer

The multi-cloud composite applications deployment problem (MCADP) aims to select proper cloud resources from multiple cloud providers at different locations to deploy applications with shared constituent services in order to optimize performance and cost. In this paper, we propose divide-and-conquer seeding strategies to find diverse and high-quality deployment plans as seeds. By using the real-world datasets, the experimental results demonstrate that the proposed seeding strategies can outperform recently proposed approaches, i.e. AO-Seed and SO-Seed, for MCADP.
Evaluative research on teaching innovation with simulators in the area of Technology in Compulsory Secondary Education.

The meaning of knowledge through simulation implies knowing how to interpret everyday processes and situations. The knowing of mechanisms and technological operators involved, being competences that are worked with special attention in the Technology classroom and help to consolidate knowledge, awaken the interest of students and motivate them towards research in the field of science and technology, as well as their capacity for analysis and reasoning.&#x0D; The research methodology is an action research, common to evaluative studies of educational innovation in training processes and improvement of professional practice. The selection of this teaching experience is due, on the one hand, to its innovation proposal integrating the methodological strategy of "Problem-Based Learning (ABP) being the backbone of the Technology area. On the other hand, the use of simulators, playing a fundamental role in the implementation of those processes embodied in projects that solve simple day-to-day problems. The emerging critical explanatory categories - selected for this work - have been; interest in solving technical problems, in robotics sessions, and programming simple mechanisms.&#x0D; As a result, the design of educational experiences based on the simulation of mechanisms or processes through digital media awakens the interests of the students as well as other sensations and emotions linked to the great diversity of content developed in the computer room, highlight the themes current technological booming as programming, simulation of structures, mechanical movements or electrical and automatic circuits.
Fixing the Fixes: Assessing the Solutions of SAST Tools for Securing Password Storage

Text passwords are one of the most widely used authentication mechanisms on the internet. While users are responsible for creating secure passwords, application developers are responsible for writing code to store passwords securely. Despite continued reports of password database breaches, recent research studies reveal that developers continue to employ insecure password storage practices and have several misconceptions regarding secure password storage. Therefore, it is important to detect security issues relating to password storage and fix them in a timely manner before the application is deployed. In this paper, we survey several open-source (SpotBugs, SonarQube, CryptoGuard, CogniCrypt) Static Application Security Testing (SAST) tools to understand their detection capabilities with respect to password storage vulnerabilities and determine if the remediation fixes suggested by these tools are consistent with the OWASP or NIST recommended password storage guidelines. We found that none of the surveyed tools covers all potential vulnerabilities related to password storage. Further, we found that solutions suggested by the tools are either imprecise or they are not in accordance with the latest password storage guidelines. We conduct a study with 8 developers where each of them attempted to replace insecure SHA-1 based password storage implementation with PBKDF2 solution recommended by the surveyed tools. The study results show that, in the absence of specific examples, developers choose insecure values for PBKDF2 parameters (salt, iteration count, key length). Thus, although the use of PBKDF2 is in adherence with the tool requirements, the resulting password storage code may not be secure in practice.
The Shared View Paradigm in Asymmetric Virtual Reality Setups

Abstract Asymmetric Virtual Reality (VR) applications are a substantial subclass of multi-user VR that offers not all participants the same interaction possibilities with the virtual scene. While one user might be immersed using a VR head-mounted display (HMD), another user might experience the VR through a common desktop PC. In an educational scenario, for example, learners can use immersive VR technology to inform themselves at different exhibits within a virtual scene. Educators can use a desktop PC setup for following and guiding learners through virtual exhibits and still being able to pay attention to safety aspects in the real world (e. g., avoid learners bumping against a wall). In such scenarios, educators must ensure that learners have explored the entire scene and have been informed about all virtual exhibits in it. According visualization techniques can support educators and facilitate conducting such VR-enhanced lessons. One common technique is to render the view of the learners on the 2D screen available to the educators. We refer to this solution as the shared view paradigm . However, this straightforward visualization involves challenges. For example, educators have no control over the scene and the collaboration of the learning scenario can be tedious. In this paper, we differentiate between two classes of visualizations that can help educators in asymmetric VR setups. First, we investigate five techniques that visualize the view direction or field of view of users ( view visualizations ) within virtual environments. Second, we propose three techniques that can support educators to understand what parts of the scene learners already have explored ( exploration visualization ). In a user study, we show that our participants preferred a volume-based rendering and a view-in-view overlay solution for view visualizations. Furthermore, we show that our participants tended to use combinations of different view visualizations.
Simulated business effectiveness: learning and performance outcomes

Purpose This study investigates simulated business learning and performance effectiveness during a simulation task. The learning and performance outcomes of two groups of postgraduate student participants are investigated namely: (a) participants who do not struggle with the task and (b) participants who do struggle with completing the task. Design/methodology/approach An experiment was conducted using a simulated business, which was manipulated into two initial commencement formats: positive initial format (PIF) and negative initial format (NIF). Individual performance on the task was measured via achievement of performance targets whilst individual learning was measured via causal cognitive maps. Findings Participants using PIF did not struggle with completing the task and achieved higher performance outcomes compared to participants using NIF, who struggled with completing the task. In addition, the positive association between learning and performance was significantly reduced for participants using NIF who struggled with completing the task Research limitations/implications This study’s findings are tentative as the sample size is small and several moderator/mediator variables, which may influence the findings (i.e. student learning style/instructor style/cognitive factors), are outside the scope of the study and thus not included. Practical implications Causal cognitive mapping results and students’ self-assessment of learning during simulated business debriefing, may further help instructors/students identify the differences in individual learning outcomes between those who have and have not struggled with increasing simulated business performance. Originality/value By using an experiment and causal cognitive mapping to measure individual learning, this study contributes further empirical evidence to the literature.
Explaining Misclassification and Attacks in Deep Learning via Random Forests

Artificial intelligence, and machine learning (ML) in particular, is being used for different purposes that are critical for human life. To avoid an algorithm-based authoritarian society, AI-based decisions should generate trust by being explainable. Explainability is not only a moral requirement, but also a legal requirement stated in the European General Data Protection Regulation (GDPR). Additionally, it is also beneficial for researchers and practitioners relying on AI methods, who need to know whether the decisions made by the algorithms they use are rational, lack bias and have not been subjected to learning attacks. To achieve AI explainability, it must be possible to derive explanations in a systematic and automatic way. A common approach is to use a simpler, more understandable decision algorithm to build a surrogate model of the unexplainable, a.k.a. black-box model (typically a deep learning algorithm). To this end, we should avoid surrogate models that are too large to be understood by humans. In this work we focus on explaining the behavior of black-box models by using random forests containing a fixed number of decision trees of limited depth as surrogates. In particular, our aim is to determine the causes underlying misclassification by the black box model. Our approach is to leverage partial decision trees in the forest to calculate the importance of the features involved in the wrong decisions. We achieve great accuracy in detecting and explaining misclassification by deep learning models constructed via federated learning that have suffered attacks.
Robots and the Complexity of Everyday Worlds

Now that robots are leaving their cages in factory shop floors and laboratories, they are confronted with human everyday worlds. With this transfer from being exclusively concerned with technical systems to building socio-technical systems, everyday worlds become a wicked problem of robotics: They are interpretative, highly context dependent and products of constant interactive negotiation. The key to understanding and modelling this wicked problem is acknowledging the complexity of a human interaction. We stress three basic factors, which are constitutive for this complexity: indexicality, reciprocity of expectations, and double contingency. Although it is in the nature of these factors that they cannot be formalized, roboticists are forced to translate them into complicated, rather than complex, formalizations.
Anomaly detection of Vessel Main Engine Big Data using Gaussian Mixture Model

선박 메인 엔진은 선박의 추진 동력을 제공하는 핵심 장비로써 고장 예방을 위한 주기적인 정비와 모니터링이 요구된다. 최근 선박에 부착된 센서를 이용하여 선박 메인 엔진의 상태를 실시간으로 측정할 수 있게 되면서, 수집된 빅데이터를 바탕으로 엔진의 이상 징후를 조기에 발견할 수 있는 체계가 마련되었다. 본 연구는, 7개월 이상 수집된 선박 메인 엔진 데이터를 학습하여 선박 메인 엔진의 이상 징후를 조기에 파악할 수 있는 방법론을 제안한다. 학습을 위해 전문가 인터뷰 및 상관계수 분석을 통해 중요 변수를 추출하였고, 데이터 전처리 기법을 통해 학습에 필요한 변수만을 추출하였다. 다수의 정상적인 메인 엔진 상태 데이터로부터 이상치를 분리하기 위하여 비지도 학습(unsupervised learning) 기반의 이상치 탐지 기법인 Gaussian Mixture Model을 적용하였다. 또한, 이상치 값들에 대한 사후적인 분석을 하여 선박 메인 엔진 이상치의 특성과 관련된 정보들을 파악했다. 이를 통해 선박 빅데이터를 활용하여 실시간으로 이상치를 탐지할 수 있는 의사결정 체계를 구축하고, 선박 유지 보수의 효율성과 경제성을 높이는데 이바지할 수 있을 것으로 예상한다.The ship s main engine is a key equipment that provides power for propulsion of the ship, and requires regular maintenance and monitoring to prevent failure. Recently, the health state of the ship s main engine can be measured in real time using a sensor attached to the ship, enabling an user to detect abnormal signs of the engine early on the basis of the collected big data. This study proposes a methodology for early identification of abnormal signs of the ship main engine by learning the ship main engine data collected for more than 7 months. For learning, important variables were extracted through expert interviews and correlation coefficient analysis, and only important variables were extracted through data pre-processing techniques. To separate outliers from a number of normal main engine state data, the Gaussian Mixture Model, an unsupervised learning-based outlier detection technique, was applied. In addition, by comparing the distribution of the outliers and the normal distribution, the characteristics of the ship s main engine outlier was analyzed. Through this, it is expected to build a decision-making system that can detect outliers in real time using ship big data and contribute to improving the efficiency and economics of ship maintenance.
Far Distance Estimation in Mixed Reality

Mixed reality is increasingly being used for training, especially for tasks that are difficult for humans to perform such as far distance perception. Although there has been much prior work on the perception of distance in mixed reality, the majority of the work has focused on distances ranging up to about 30 meters. The little work that has investigated distance perception beyond 30 meters has been mixed in terms of whether people tend to over- or underestimate distances in this range. In the current study, we investigated the perception of distances ranging from 25-500 meters in a fully mediated outdoor environment using the ZED Mini and HTC Vive Pro. To our knowledge, no work has examined distance perception to this range in mixed reality. Participants in the study verbally estimated the distances to a variety of objects. Distances were overestimated from 25-200 meters but underestimated from 300-500 meters. Overall, far distance perception in mixed reality may be biased in different directions depending on the range of distances, suggesting a need for future research in this area to support training of far distance perception.
Preprocessing Techniques for Colon Histopathology Images

The glandular morphology analysis done within the colon histopathological images is an imperative step for grade determination of colon cancer. But the manual segmentation is quite laborious as well as time-consuming. It also suffers from the subjectivity among pathologists. Thus, the rising computational pathology has escorted to the development of various automated methods for the gland segmentation task. However, automated gland segmentation remains an exigent task due to numerous factors like the need for high-level resolution for precise delineation of glandular boundaries, etc. Thus, in order to alleviate the development of automated gland segmentation techniques, various image enhancement techniques are applied on colon cancer images for preprocessing them in order to get an enhanced image in which all the critical elements are easily detectable. The enhancement results are analyzed based on both objective qualitative assessment as well as subjective assessment given in the form of scores by the pathologists. And thus based on the qualitative analysis, a new combined technique, i.e., colormap-enhanced image sharpening, is proposed in order to get an enhanced image in which all the critical elements are easily detectable. These techniques’ results will thus help pathologists in better colon histopathology image analysis.
Efficient Transfer Learning for Quality Estimation with Bottleneck Adapter Layer

The Predictor-Estimator framework for quality estimation (QE) is commonly used for its strong performance. Where the predictor and estimator works on feature extraction and quality evaluation, respectively. However, training the predictor from scratch is computationally expensive. In this paper, we propose an efficient transfer learning framework to transfer knowledge from NMT dataset into QE models. A Predictor-Estimator alike model named BAL-QE is also proposed, aiming to extract high quality features with pre-trained NMT model, and make classification with a fine-tuned Bottleneck Adapter Layer (BAL). The experiment shows that BAL-QE achieves 97% of the SOTA performance in WMT19 En-De and En-Ru QE tasks by only training 3% of parameters within 4 hours on 4 Titan XP GPUs. Compared with the commonly used NuQE baseline, BAL-QE achieves 47% (En-Ru) and 75% (En-De) of performance promotions.
Abstraction of linked data’s world

Linked Data, as a strategy of the Semantic Web, is based on application of some basic principles that contribute to the growth of the Web, thus allowing the transit of the Web of Documents to the Web of Data. Developed process by Linked Data is supported in different scenarios, which interact in order to carry out the linking of resources on the Web. Some of these scenarios present a solid technological background, while others propose challenges when they are implemented. This paper aims to identify and expose a generic abstraction of Linked Data, in order to identify problem situations that restrict Linked Data process.
Resemblance of Biological Particle Swarm Optimization and Particle Swarm Optimization for CBFR by using NN

In this paper, we propose Biogeography Particle Swarm Optimization and Partical Swarm Optimization for CBFR which is based on Neural Network (NN) i.e. BPSONN approach for content based face retrieval. This learning based on biogeography of swarm migration is used for classical PSO to avoid trapping of local optima. BPSO is used to initialize the weights and threshold of neural network. CBFR measure function is based on color and texture features in which facial features is to be detected. Color histogram is used to find out color feature of an image and Filters are used to extract the texture features. Proposed method is tested on FEI dataset, to show illustrative and effective performance
A community-based intervention study to assess the effectiveness of awareness imparted on earthquake preparedness among the residents of South Delhi, India

New Delhi is located in seismic zone IV, and the occurrence of earthquake is fairly common. Previous disasters have shown that only sustained and timely action to reduce risk can prevent or mitigate the impact of a disaster.The objective was to assess the knowledge and preparedness regarding earthquake among the residents of South Delhi and to impart the awareness and assess the effectiveness of the awareness imparted.One group pretest-posttest quasi-experimental research was done among 300 respondents on earthquake preparedness.As per the study, 89% of participants felt that earthquake is a concern for Delhi. It was observed that majority of the population are not prepared to handle large scale disaster. Significant association was found between pretest and posttest knowledge assessment scores of the study population.Disasters due to their unpredictable occurrence are difficult to tackle, but awareness and preparedness to face the calamity will help to reduce the burden of disaster.
Optimized image watermarking with artificial neural networks and histogram shape

In today’s era, Digital watermarking is a well-known application in direction of proving the authenticity of digital content. In this concern, a new optimized image watermarking method has been suggested by incorporating the concepts of artificial neural networks and histogram together. In this paper, the histogram shape concept has been kept into practice to implant the watermark information inside the host image with a view to maintain imperceptibility and resistance. In this suggested method, optimization of extraction process has been holded as a substantial problem that helps in enhancing the resistance in front of distinct attacks. So, the problem of strengthening resistance in attacked environment is solved by implementing the artificial neural networks in the proposed method. The optimization of extraction process is accomplished through two distinct neural networks called backpropagation neural networks and autoencoder neural networks. As an outcome, the experimental outcome of suggested image watermarking method is assessed on a set of three images in form of PSNR and NC. The resistance of the suggested method has been checked under different attacks alike rotation, histogram equalization, gaussian noise, cropping, JPEG compression, poisson noise, average filter, speckle noise, median filter and salt and pepper noise validates that proposed watermarking has accomplished its objective. The experimental results in the form of PSNR and NC verify that proposed scheme has accomplished its objective.