Reviewerly/exHarmony huggingface dataset:
Enhanced social engineering framework mitigating against social engineering attacks in higher education

Abstract The purpose of this paper was to develop and validate an enhanced social engineering framework to mitigate against social engineering attacks. The study formulated a theoretical framework which was informed by the strengths and weaknesses of existing social engineering frameworks, the framework was also guided by the Dhillon's balanced control theory. The theoretical framework was validated by experts using the Delphi technique which comprised of three rounds. A sample of 25 experts from three higher education institutions which met the inclusion criteria were selected. The study was guided by the interpretivism philosophy to get a deep understanding of the phenomenon under study. The findings reveal that social engineering awareness, organizational security policy and Internet of Things (IOT) security succor in reducing social engineering attacks. The findings from this study will be utilized by decision makers in higher education sector to come up with engaging social engineering training programs, set up an organizational security policy and preclude IOT attacks to mitigate social engineering attacks in higher education. The study contributes to the field of social engineering with an enhanced social engineering framework that mitigate against social engineering attacks. The study adds to under‐represented social engineering framework in higher education.
The Discussion of the Adaptation Communication under the Paris Agreement and Korea’s Strategic Approach to the Communication

Under the Paris Agreement, each Party should submit and update periodically an adaptation communication (ADCOM), independently, as a component of, or in conjunction with other communications or documents, including a national adaptation plan, a nationally determined contribution (NDC), a national communication, or biennial transparency report. Submitted adaptation communications shall be recorded in a public registry. The Glasgow Climate Pact adopted at the 26th Conference of the Parties to the United Nations Framework Convention on Climate Change (COP26) also requested Parties to submit their adaptation communications ahead of the COP27 which will be held in November 2022 so as to provide timely input to the global stocktake. Under this context, however, Korea has not yet submitted its adaptation communication. Surely, the Carbon Neutrality and Green Growth Act for the Climate Change of the Republic of Korea which has just entered into force on 25th March 2022 stipulates that the government can prepare and update an adaptation communication in accordance with the Paris Agreement. Also, the NDC that Korea submitted includes the adaptation component. However the Korean government has not submitted nor recorded it as the adaptation communication. Therefore, the Korean government should make a timely decision on and prepare its submission of the adaptation communication. There has been hardly any studies regarding the preparation, submission, or evaluation of adaptation communications in consideration of Korean context. Hence, this paper discusses the implication of communicating adaptation information and compare possible adaptation reporting instruments that exist under the UNFCCC and the Paris Agreement, for a strategic approach to the preparation of adaptation communication. The paper concludes that it is important to participate in the global efforts for adaptation to climate change and thus recommends for the Korean government to submit and record its adaptation communication in a timely manner. The paper recognizes that both NDC and independent ADCOM are the possible instruments for Korea, but the independent ADCOM will be the best option in case that the government wants to increase the visibility of Korea’s adaptation actions.
A Feasibility Study on Hand Gesture Intention Interpretation Based on Gesture Detection and Speech Recognition

In this paper, considering the characteristics of hand gestures that occur during a natural, unscripted human conversation, we report the results of experiments involving the data analysis of the video recordings of experiments. The recorded conversations between two subjects and the assistant in the experiments (the first author) are analyzed for findings that might be useful to future human-robot interaction (HRI), findings regarding the recognition of hand gestures in natural conversations. In the experiment, hand gestures that appeared naturally during the conversations were manually selected, and their elapsed times and number of appearances were analyzed. In addition, these unscripted conversations were analyzed with a voice recognition system, and the meanings of the hand gestures that appear in the recorded video were interpreted. It was observed that some gestures were made consciously and others unconsciously. Some gestures appeared multiple times with similar meanings and the same intention, especially gestures involving pointing at something to emphasize a sentence or word. Other observations included that there were times when (i) each subject made different hand gestures to convey the same meaning, (ii) both subjects made the same hand gesture but intended different meanings, and (iii) a specific gesture was made by the same subject but with different intended meanings. After considering the results of these experiments, we conclude that it is better to combine gestures with the utterances at the time in order to interpret their intended meanings correctly.
Control algorithm of path planning and path tracking for automated valet parking

Abstract Automated Valet Parking (AVP) can relieve drivers from having to park. It enables vehicles to drive, park and charge fully automatically. This paper focuses on the first working step of the motion planning module, the global path planner. Therefore, a global path planner will be developed and evaluated based on actual research methods. A route will be computed by using an A^* algorithm based on the occupancy grid map. Subsequently, a smoothing operation will follow to obtain a drivable path. By implementing a single polar polynomial function each vertex is smoothened. The resulting global path serves as an input for path tracking. For a reasonable simulation, a vehicle controller with a lock-ahead distance and a node-check will be designed in order to keep the vehicle on the desired path. A simulation of the designed components in CarSim will be carried out to analyze the path tracking and vehicle behavior in detail.
Software Updates Strategies: A Quantitative Evaluation Against Advanced Persistent Threats

Software updates reduce the opportunity for exploitation. However, since updates can also introduce breaking changes, enterprises face the problem of balancing the need to secure software with updates with the need to support operations. We propose a methodology to quantitatively investigate the effectiveness of software updates strategies against attacks of Advanced Persistent Threats (APTs). We consider strategies where the vendor updates are the only limiting factors to cases in which enterprises delay updates from 1 to 7 months based on SANS data. Our manually curated dataset of APT attacks covers 86 APTs and 350 campaigns from 2008 to 2020. It includes information about attack vectors, exploited vulnerabilities (e.g. 0-days vs public vulnerabilities), and affected software and versions. Contrary to common belief, most APT campaigns employed publicly known vulnerabilities. If an enterprise could theoretically update as soon as an update is released, it would face lower odds of being compromised than those waiting one (4.9x) or three (9.1x) months. However, if attacked, it could still be compromised from 14% to 33% of the times. As in practice enterprises must do regression testing before applying an update, our major finding is that one could perform 12% of all possible updates restricting oneself only to versions fixing publicly known vulnerabilities without significant changes to the odds of being compromised compared to a company that updates for all versions.
Single-image reconstruction using novel super-resolution technique for large-scaled images

A fast and novel method for single-image reconstruction using the super-resolution (SR) technique has been proposed in this paper. The working principle of the proposed scheme has been divided into three components. A low-resolution image is divided into several homogeneous or non-homogeneous regions in the first component. This partition is based on the analysis of texture patterns within that region. Only the non-homogeneous regions undergo the sparse representation for SR image reconstruction in the second component. The obtained reconstructed region from the second component undergoes a statistical-based prediction model to generate its more enhanced version in the third component. The remaining homogeneous regions are bicubic interpolated and reflect the required high-resolution image. The proposed technique is applied to some Large-scale electrical, machine and civil architectural design images. The purpose of using these images is that these images are huge in size, and processing such large images for any application is time-consuming. The proposed SR technique results in a better reconstructed SR image from its lower version with low time complexity. The performance of the proposed system on the electrical, machine and civil architectural design images is compared with the state-of-the-art methods, and it is shown that the proposed scheme outperforms the other competing methods.
HRM Risk Early Warning Based on a Hybrid Solution of Decision Tree and Support Vector Machine

Human resource management is the core support and strong guarantee for the survival and innovative development of enterprises, which is directly related to the realization of the strategic development goals of enterprises. However, in today’s rapidly changing internal and external environment, HRM (Human Resource Management) activities of enterprises may face various risks at any time. Therefore, this paper designs a HRM risk warning scheme based on decision tree and support vector machine, which combines the features of support vector machine binary classification and recombines each category of multicategory classification according to decision tree multi-sub-category classification, and can identify all categories more accurately. Simulation experiments show that the convergence of the network designed in this way tends to be stable, and the performance of the early warning for enterprises meets the standard.
VisualCommunity: a platform for archiving and studying communities

VisualCommunity is a platform designed to support community or neighborhood scale research. The platform integrates mobile, AI, visualization techniques, along with tools to help domain researchers, practitioners, and students collecting and working with spatialized video and geo-narratives. These data, which provide granular spatialized imagery and associated context gained through expert commentary have previously provided value in understanding various community-scale challenges. This paper further enhances this work AI-based image processing and speech transcription tools available in VisualCommunity, allowing for the easy exploration of the acquired semantic and visual information about the area under investigation. In this paper we describe the specific advances through use case examples including COVID-19 related scenarios.
StreamGCN: Accelerating Graph Convolutional Networks with Streaming Processing

While there have been many studies on hardware acceleration for deep learning on images, there has been a rather limited focus on accelerating deep learning applications involving graphs. The unique characteristics of graphs, such as the irregular memory access and dynamic parallelism, impose several challenges when the algorithm is mapped to a CPU or GPU. To address these challenges while exploiting all the available sparsity, we propose a flexible architecture called StreamGCN for accelerating Graph Convolutional Networks (GCN), the core computation unit in deep learning algorithms on graphs. The architecture is specialized for streaming processing of many small graphs for graph search and similarity computation. The experimental results demonstrate that StreamGCN can deliver a high speedup compared to a multi-core CPU and a GPU implementation, showing the efficiency of our design.
Context-Aware Recommendation Systems Using Consensus-Clustering

Recommendation Systems (RSs) have proved a compelling performance to overcome the data overload problem. Context-aware recommenders guide users/clients to more personalized recommendations. Incorporating contextual features in recommendation systems improves the systems’ accuracy; however, they still suffer from sparsity and scalability problems which impact the quality of recommendations. In this paper, to overcome these limitations, we propose a context-aware recommendation system using the notion of consensus clustering, named CARS-CC. The proposed recommendation system is experimentally evaluated using contextual Pre-filtering and Post-filtering approaches. Experimental results show that the concept of consensus learning using clustering analysis can significantly improve the recommender systems’ accuracy. The proposed method surpasses the other recommendation algorithms in terms of accuracy, precision and recall, particularly using the Hybrid Bipartite Graph Formulation (HBGF) method. In addition, CARS-CC(hgpa) has outperformed all other clustering techniques in terms of MAE and RMSE with 23.73% and 7.54%, respectively. The MAE and RMSE results show that consensus clustering leads to better accuracy measures and a more stable resilient recommendation system. The response time taken to generate recommendations using post-filtering is less than that of the pre-filtering approach. The CARS-CC(HGPA) in the post-filtering approach; generates recommendations 58.4% faster than pre-filtering, which speeds up the recommendation process and facilitates real-time response.
Enhancing Tourism Experiences Via Mobile Augmented Reality by Superimposing Virtual Information on Artefacts

These interactive, attentive, and adaptable technologies that give visitors relevant services and information at any time and from any location in the actual world were something that can only be regarded as a view of the future, some place unattainable but present. The introduction of new technology has resulted in a paradigm change in display. It is the result of the convergence of modern smart mobile devices, contextual awareness, and augmented reality, which may enhance visitors' experiences and give them the sense of having accomplished something more. The introduction of new technology has resulted in a paradigm change in display. It is the result of the convergence of modern smart mobile devices, contextual awareness, and augmented reality, which may enhance visitors' experiences and give them the sense of having accomplished something more. The study on Enhancing Tourism Experiences via Mobile Augmented Reality by Superimposing Virtual Information on Artefacts (MARVIA) aims to enhance the user's experience aside from giving the user additional value when visiting the museum. A tracker or a marker that will be the trigger is the artefacts on the museum itself. The application will use the superimposition-based technique in augmented reality. It will superimpose several parts of the artefacts and overlay them with augmented objects, creating an augmented reality environment. This study focuses on augmented reality as it can be used without additional hardware that might incur more cost. It only needs a piece of software technology and a device that can be used alongside the software. Whilst virtual reality requires additional items and hardware such as goggles, and not only that, with the usage of different hardware, it will limit the movement of the tourists and thus will decrease the value of the tourist that comes to the museum.
3D Browsing of Wide‐Angle Fisheye Images Under View‐Dependent Perspective Correction

Abstract This paper presents a novel technique for 3D browsing of wide‐angle fisheye images using view‐dependent perspective correction (VDPC). First, the fisheye imaging model with interior orientation parameters (IOPs) is established. Thereafter, a VDPC model for wide‐angle fisheye images is proposed that adaptively selects correction planes for different areas of the image format. Finally, the wide‐angle fisheye image is re‐projected to obtain the visual effect of browsing in hemispherical space, using the VDPC model and IOPs of the fisheye camera calibrated using the ideal projection ellipse constraint. The proposed technique is tested on several downloaded internet images with unknown IOPs. Results show that the proposed VDPC model achieves a more uniform perspective correction of fisheye images in different areas, and preserves the detailed information with greater flexibility compared with the traditional perspective projection conversion (PPC) technique. The proposed algorithm generates a corrected image of 512 × 512 pixels resolution at a speed of 58 fps when run on a pure central processing unit (CPU) processor. With an ordinary graphics processing unit (GPU) processor, a corrected image of 1024 × 1024 pixels resolution can be generated at 60 fps. Therefore, smooth 3D visualisation of a fisheye image can be realised on a computer using the proposed algorithm, which may benefit applications such as panorama surveillance, robot navigation, etc.
Technology of digital ecosystems

The phenomenon of technology is considered in the context of its presence in natural and artificial ecosystems. Accordingly, the features of the evolution of technology under the influence of the co-evolutionary model of human life, developed under the impression of the growing risks of destruction of the dynamic balance of the biosphere, are revealed. It also describes the features of the evolution of technology in artificial ecosystems based on convergent technologies. The possibilities of pairing the priorities of minimizing the anthropogenic pressure of the technosphere on the biosphere with smart technologies are analyzed.
Explicit Instruction of Scientific Uncertainty in an Undergraduate Geoscience Field-Based Course

Understanding and communicating uncertainty is a key skill needed in the practice of science. However, there has been little research on the instruction of uncertainty in undergraduate science education. Our team designed a module within an online geoscience field course which focused on explicit instruction around uncertainty and provided students with an uncertainty rating scale to record and communicate their uncertainty with a common language. Students then explored a complex, real-world geological problem about which expert scientists had previously made competing claims through geologic maps. Provided with data, expert uncertainty ratings, and the previous claims, students made new geologic maps of their own and presented arguments about their claims in written form. We analyzed these reports along with assessments of uncertainty. Most students explicitly requested geologists' uncertainty judgments in a post-course assessment when asked why scientists might differ in their conclusions and/or utilized the rating scale unprompted in their written arguments. Through the examination of both pre- and post-course assessments of uncertainty and students' course-based assessments, we argue that explicit instruction around uncertainty can be introduced during undergraduate coursework and could facilitate geoscience novices developing into practicing geoscientists.
Dynamic probability modeling of photovoltaic strings and its application in fault diagnosis

China’s installed photovoltaic (PV) capacity has surged in recent years, and the intelligent operation of PV power generation is of great significance to improve the generating of PV power stations. As the core of the PV power generation system, PV strings are exposed outdoors all the year round, which is easy to bring safety hazards affecting the safe operation of power stations. Affected by weather conditions, the output of PV strings is fluctuating, random and time-varying, which brings great challenges to fault diagnosis. Therefore, taking the uncertainty of the PV power generation system during its operation into consideration, this paper used the nonparametric kernel density estimation method to fit the probability density curve of the output for PV strings, updated the model with the dynamic time window technology, and eventually established a fault diagnosis method based on the dynamic modeling results of PV strings. Different from the typical real-time diagnosis, by setting a dynamic time window for statistical analysis, the PV strings with degraded performance that fail to be detected in real-time monitoring can be identified, which is conducive to fault diagnosis for PV power stations.
A method for finding the background potential of quantum devices from scanning gate microscopy data using machine learning

Abstract The inverse problem of estimating the background potential from measurements of the local density of states is a challenging issue in quantum mechanics. Even more difficult is to do this estimation using approximate methods such as scanning gate microscopy (SGM). Here, we propose a machine-learning-based solution by exploiting adaptive cellular neural networks (CNNs). In the paradigmatic setting of a quantum point contact, the training data consist of potential-SGM functional relations represented by image pairs. These are generated by the recursive Green’s function method. We demonstrate that the CNN-based machine learning framework can predict the background potential corresponding to the experimental image data. This is confirmed by analyzing the estimated potential with image processing techniques based on the comparison between the charge densities and those obtained using different techniques. Correlation analysis of the images suggests the possibility of estimating different contributions to the background potential. In particular, our results indicate that both charge puddles and fixed impurities contribute to the spatial patterns found in the SGM data. Our work represents a timely contribution to the rapidly evolving field of exploiting machine learning to solve difficult problems in physics.
The Dawn of Mineralogy

The history of mineralogy can be traced back to the Greco-Roman period, from Theophrastos to Agricola. Linné, Cronstedt, and Werner established classical mineralogy. Crystallography developed with mineralogy, and classical crystallography was established by Haüy through Steno. In the 19th century, the discovery of X-rays by Röntgen and the discovery of X-ray diffraction by Laue gave birth to modern mineralogy and crystallography. A little later, electron diffraction was discovered, leading to the invention of the electron microscope. In Japan, Wada laid the foundations for mineralogy and crystallography.
Artificial Neural Network-Based Leukaemia Identification and Prediction using Ensemble Deep Learning Model

Leukemia (ALL) is a type of blood cancer that causes a huge number of deaths throughout the Globe. Technology is advancing in decision making like moving from manual inspection to automatic (using deep learning) detection. As flaws persist in manual identification, detection through deep ensemble learning on enhanced augmented images or datasets led to flawless identification. Here two different datasets were used from Kaggle. The proposed artificial neural network on classified data by ensemble classifier led to the generation of best accuracy. The proposed method can provide 100% accuracy with a good quality dataset. Whereas with poor quality data set also proposed method can provide 96.3% of accuracy. Elapsed time for the best-case dataset is 0.366137 whereas 0.38861 for the worst-case dataset. The mean square error is 0.00911. It is analyzed with both types of datasets, producing efficient results.