Reviewerly/exHarmony huggingface dataset:
A Model for and Inventory of Cybersecurity Values: Metrics and Best Practices

The practice of threat modeling to identify and mitigate, or eliminate, vulnerabilities, has a long history in military operations research. More recently, these practices are applied to the cyber domain to give a defender an increased advantage. Most cybersecurity research, however, is performed in the computer science and network infrastructure fields where the focus is on theory and infrastructure management. Consequently, the work is lacking traditional operations research perspectives in risk analysis, assessment, and decision support that may lead to a greater overall defensive posture. This chapter outlines several operations research techniques used in modeling cybersecurity threats and proposes a value model framework for security metrics and best practices that is supported by data and interviews with subject matter experts. The value model, which includes a framework that can be customized for any organization, is illustrated using notional data. Finally, through a survey of cyber professionals with both military and corporate industry experience, an inventory of valued attributes for a secure cyber system is provided, along with potential differences in values based on an organization’s history of attacks and/or breaches. This model, grounded in the principles of military operations research, will enable organizations to assess the performance of their respective cyber systems, manage risk, and continuously improve their processes.
Self-explainability as an alternative to interpretability for judging the trustworthiness of artificial intelligences.

The ability to explain decisions made by AI systems is highly sought after, especially in domains where human lives are at stake such as medicine or autonomous vehicles. While it is often possible to approximate the input-output relations of deep neural networks with a few human-understandable rules, the discovery of the double descent phenomena suggests that such approximations do not accurately capture the mechanism by which deep neural networks work. Double descent indicates that deep neural networks typically operate by smoothly interpolating between data points rather than by extracting a few high level rules. As a result, neural networks trained on complex real world data are inherently hard to interpret and prone to failure if asked to extrapolate. To show how we might be able to trust AI despite these problems we introduce the concept of self-explaining AI. Self-explaining AIs are capable of providing a human-understandable explanation of each decision along with confidence levels for both the decision and explanation. For this approach to work, it is important that the explanation actually be related to the decision, ideally capturing the mechanism used to arrive at the explanation. Finally, we argue it is important that deep learning based systems include a warning light based on techniques from applicability domain analysis to warn the user if a model is asked to extrapolate outside its training distribution. For a video presentation of this talk see this https URL .
Crowd Behavior Classification based on Generic Descriptors

Crowd behavior analysis plays an important role in high security interests in public areas such as railway stations, shopping centres, and airports, where large populations gather. The crowded scenes vary in various densities, structures and occlusion. It brings enormous challenges in identifying generic descriptors to describe motion dynamics caused by pedestrians walk in different directions with extremely diverse behaviors. Therefore, this research is proposal an approach for crowd behavior analysis to recognize the common properties across different crowded scenes. The recognized common properties are then used to identify generic descriptors from group-level for crowd behavior classification.
Integrating Internet-based applications in English language teaching: Teacher practices in a Thai university

This study was conducted to identify various Internet-based applications integrated in an English as a foreign language (EFL) classroom. It explored how seven English language teachers utilised different applications in their English language teaching (ELT) in a university in Thailand. The methods used were classroom observations and follow-up individual interviews. Findings revealed that 'Kahoot, Socrative, Google Form, QR code, Facebook, YouTube, Quizizz' and 'Quizlet' were among the Internet-based applications the teachers used in their classroom teaching. Moreover, the teachers responded positively to changes in the ELT landscape as they integrated these different applications in their classrooms. They seemed to be confident about the advantages these applications can bring into their teaching practices. Furthermore, their common responses as regards their reason for utilising Internet-based applications point to one evident perspective: Internet-based applications make their ELT classroom more convenient, exciting, and fluid. Implications for ELT are discussed in light of the findings, and recommendations for future research are offered.
Submodule Clustering based on Latent Representation Tensor

This paper focuses on clustering of image dataset based on multilinear algebra. The proposed method is designed to work in two scenarios: 1. when the samples available are sufficient and 2. when the samples are limited. The proposed method is based on tensor-based approach which models clusters as a union of free submodules rather than union of subspaces. The proposed method is inspired by the self-expressiveness property which says that the image belongs to a particular submodule can be expressed as t-linear combination of data points in its own submodule whereas, it is difficult to express it as at-linear combination of data points that belongs to a different submodule. The key steps involved in the process of clustering are:- the submodule identification and grouping of images based on the identified submodule membership. Firstly, a tensor is constructed by arranging images in the dataset as the lateral slice of it for the effective utilization of structural information. In the next step, submodule identification is done on this tensor by capturing the self-expressiveness property through a representation tensor. When the datasamples are limited, the conventional method fails to cluster accurately. A latent representation tensor is incorporated into the model to maintain the clustering accuracy. Experimental results reveals that the proposed method yields better results as comnared with the state-of-art methods.
Reconfirm gestalt principles from scan-path analysis on viewing photos

The advances in communication technology not only ushered in an era of visual image explosion, but also led to the emergence of the “visual generation” who are heavily image-reliant in their quotidian existence. Photography is a form of visual arts that sets great store by human visual perception. It brings aesthetic delight by connecting human vision with psychological experiences. Every captivating or heart-touching photograph has its sui generis context or story, and gestalt theory may be the key to this kind of mystery. However, people have simply a limited knowledge of their viewing mechanism, and compelling evidence for the links between viewers' inner feelings and visual cognition remains wanting. To remedy this deficiency, this study takes the angle of positive aesthetics and employs the eye-tracking method to investigate the influence of photographic compositions' gestalt on the subjects' visual cognition, so as to grasp how they look at, interpret and appreciate photographic works. By manipulating the gestalt elements, calculating the dispersion degree of the subjects' scan paths, and collecting the subjects' opinions on the aesthetic quality of each photographic work, we found that the works embodying the gestalt principles tend to confine the subjects' scan paths to a unified context, while those violating the principles do not. Enhancing the foundation of photography and developing a new eye-tracking method with positive aesthetics, this study will stimulate photographers' artistic creativity, promote arts education, improve students' aesthetic literacy, and produce useful teaching material on the pedagogy and theories of photography.
Current research on theory and practice of digital libraries: best papers from TPDL 2017

This volume presents a special issue on the 2017 edition of the theory and practice of digital libraries (TPDL) conference, held in Thessaloniki, Greece. We provide a brief overview of TPDL 2017 and introduce the selected papers that make up the rest of this volume. The papers cover different aspects of current digital library research, highlighting the important and multidisciplinary nature of the field.
Automated Menu Recommendation System Focused on Clinical Nutrition

The nutritional status of a person describes its situation with respect to the intake and physiological adaptation after the consumption of nutrients. Having a good nutritional status is crucial in both prevention and treatment of diseases, in order to reach a good quality of life. The process of elaborating and providing nutritional minutes (menus) to patients, is a task that a nutritionist usually performs manually for each one of his patients. Currently, the use of computer platforms has taken increasing prominence in the area of health. The case of Chile is not an exception, since information systems have been designed to support the correct and fair administration of health of the population. The adoption of programs and guidelines in this regard are parts of government policy as evidenced by programs such as “Healthy Living Choose” and “Healthy Life” of the Ministry of Health, among others. This paper proposes the implementation of a web application with the integration of knowledge on nutrition modelled from a knowledge base being the basis of the recommendation system. This application serves as a tool to support and aid to nutritionists making effectively their work when generating the nutritional minutes considering multiple factors in decision making. Methodologies of systems development and knowledge engineering were used. The system is implemented and tests of components, integration and acceptance have been successfully executed.
Pengaruh Model Pembelajaran Cooperative Integrated Reading And Composition (Circ) Terhadap Hasil Belajar Siswa Sekolah Menengah Atas

This research aims to determine the differences geography with use Cooperative Integrated Reading and Composition (CIRC) with konvensional model in subject matter hidrosphere in Senior High School 3 Makassar. The study was a quasy experimental design involving two groups were taught using Cooperative Integrated Reading and Composition (CIRC) model and the group that wastaught using conventional learning models. The population in this study were all students of class X Senior High School 3 Makassar and sample are students of class X3 and X4 with purposive sampling. Technic data collection is done by giving a pre – test and post- test. The data were analyzed with descriptive and inferential techniques. Descriptive analysis showed that: (1) the study of students taught with conventional learning model gained an average score 65,25 Value obtained the percentage of students in the category of very less 0 % Category less 2.77 %, category 36.11 % enough , 61.12 % good category and the category of very good 0 %. (2) the study of students taught with Cooperative Integrated Reading and Composition (CIRC) learning model gained an average score 79,05 Value obtained the percentage of students in the category of very less 0% Category less 0 %, category 0 % enough , 88,57 % good category and the category of very good11,43 %. (3) According to the result is Thitung = 10.410 with Ttabel = 1,671 with the value α use the 0,05 (5 %) with df 69. Jadi Thitung &lt; Ttabel, point to that P-value for t-test is 0,000. Hal ini berarti 0,000 &lt; 0,05, maka H0 ditolak. Berdasarkan nilai signifikasnsi tersebut, maka dapat disimpulkan bahwa ada pengaruh yang signifikan terhadap hasil belajar geografi antara siswa yang belajar dengan model CIRC.
A Selection Method Based on Equipment Classification in Nuclear Power Plant for I&amp;C EMC Test Standard

Because the numbers and the types of EMC qualification standards are very large, it may take many confuse to the instrument and control (I&C) engineers when they want to select some standard to guide equipment qualification. This paper provides a selection method based on the equipment classification and help the I&C engineer of Nuclear Power Plant (NNP) to select the EMC qualification standards to guide equipment qualification. At last, combining with the selection approach it provides a suit of general instrument and control EMC qualification tables including qualification items and levels for safety class and none safety class I&C equipment.
Searching ROI for Object Detection based on CNN

Several studies have explored the structural design of CNN to improve the network's performance since a well-designed feature extractor can benefit convolution-based tasks. Although CNNs are able to learn important patterns on raw images, images may contain unpredictable noise that can negatively influence the convolutional stage. Feature extraction cannot always accurately capture the desired features based solely on the input image, but including extra information could improve the result. This paper proposes a fusion input design to generate an additional feature that a CNN can use to provide extra ROI information. Whether a model can utilize the additional information is a determining factor that affects the performance improvement. The proposed method is tested on two public datasets with different structural designs. Overall, the results indicate that additional ROI information can deliver benefits to specific tasks.
Leveraging Ontologies for Natural Language Processing in Enterprise Applications

The recent advances in Artificial Intelligence and Deep Learning are widely used in real-world applications. Enterprises create multiple corpora and use them to train machine learning models for various applications. As the adoption becomes more widespread, it raises further concerns in areas such as maintenance, governance and reusability. This paper will explore the ways to leverage ontologies for these tasks in Natural Language Processing. Specifically, we explore the usage of ontologies as a schema, configuration and output format. The approach described in the paper are based on our experience in a number of projects for medical, enterprise and national security domains.
Quantitative Comparison of Generative Shape Models for Medical Images

Generative shape models play an important role in medical image analysis. Conventional methods like PCA-based statistical shape models (SSMs) and their various extensions have shown great success modeling natural shape variations in medical images, despite their limitations. Corresponding deep learning-based methods like (variational) autoencoders are well known to overcome many of those limitations. In this work, we compare two conventional and two deep learning-based generative shape modeling approaches to shed light on their limitations and advantages. Experiments on a publicly available 2D chest X-ray data set show that the deep learning methods achieve better specificity and generalization abilities for large training set sizes. However, for smaller training sets, the conventional SSMs are more robust and their latent space is more compact and easier to interpret.
A Conceptual Model of the Blockchain

Hyperledger Fabric is a very large project under the umbrella of the Linux Foundation, with hundreds of developers involved. In this paper we will illustrate how the application of fact-based modeling will help us in understanding some basic features of the blockchain concept as is used in Hyperledger Fabric (HLF) and that it can serve as a conceptual blueprint of HLF for all involved to use.
Why Jakarta EE?

As a Java developer, you have many choices when it comes to frameworks and platforms for software development. A question you might be asking yourself is why you should choose Jakarta EE as your primary software development platform. What makes it a better choice?
Real Estate Transactions using Blockchain Technology

Banking is considered only one of many industries that could benefit from using blockchain technology. A promising area in blockchain technology is so-called smart contracts, which is automated, de ...
A Preliminary Method to Support the Semantic Interoperability in Models of Manufacturing (MfM) Based on an Ontological Approach

The product design and manufacturing complexity have been increased in the last few years. This has challenged the manufacturing industry to rationalise different ways of bringing to the market novel products in a short lead-time with competitive prices while ensuring higher quality levels and customisation. Design and Simulation systems bring to the product developer experts an abstraction required for the design of complex products. However, for a complex product manufacturing process has required simultaneously collaborations with multiple groups, producing and exchanging information from multi-perspectives within and across institutional boundaries. Thousands of different information must be exchanged across heterogeneous systems. Semantic interoperability obstacles have been identified in view of the information heterogeneity from multiple perspectives and their relationships across different phases of product manufacturing. In this context, this paper presents a preliminary method for Models for Manufacturing (MfM) to support the semantic interoperability across the manufacturing system based on reference ontologies, application ontologies and semantic rules. The MfM has been modelled in reference ontologies and specialised to perform multiple specific applications according to the product to be manufactured. Semantic rules are used to share, convert or translate information from multiple perspectives in order to infer the relation between multiple manufacturing levels. The main research contributions are: (i) the intelligence structuring information in elementary concepts responsible for representing the MfM, modelled in the core ontologies (Reference Ontologies) and (ii) the improvement of information exchanging (translation, conversion and sharing) from heterogeneous domain across different phases of manufacturing process based on the semantic rules.
Aggregating Motion and Attention for Video Object Detection

Video object detection plays a vital role in a wide variety of computer vision applications. To deal with challenges such as motion blur, varying view-points/poses, and occlusions, we need to solve the temporal association across frames. One of the most typical solutions to maintain frame association is exploiting optical flow between consecutive frames. However, using optical flow alone may lead to poor alignment across frames due to the gap between optical flow and high-level features. In this paper, we propose an Attention-Based Temporal Context module (ABTC) for more accurate frame alignments. We first extract two kinds of features for each frame using the ABTC module and a Flow-Guided Temporal Coherence module (FGTC). Then, the features are integrated and fed to the detection network for the final result. The ABTC and FGTC are complementary to each other and can work together to obtain a higher detection quality. Experiments on the ImageNet VID dataset show that the proposed framework performs favorable against the state-of-the-art methods.
Finding Logo and Seal in Historical Document Images - An Object Detection Based Approach

Logo and Seal serves the purpose of authenticating and referring to the source of a document. This strategy was also prevalent in the medieval period. Different algorithm exists for detection of logo and seal in document images. A close look into the present state-of-the-art methods reveals that those methods were focused toward detection of logo and seal in contemporary document images. However, such methods are likely to underperform while dealing with historical documents. This is due to the fact that historical documents are attributed with additional challenges like extra noise, bleed-through effect, blurred foreground elements and low contrast. The proposed method frames the problem of the logo and seals detection in an object detection framework. Using a deep-learning technique it counters earlier mentioned problems and evades the need for any pre-processing stage like layout analysis and/or binarization in the system pipeline. The experiments were conducted on historical images from 12th to the 16th century and the results obtained were very encouraging for detecting logo in historical document images. To the best of our knowledge, this is the first attempt on logo detection in historical document images using an object-detection based approach.
Optimization of Cultural Heritage Virtual Environments for Gaming Applications

Serious games are games with a purpose beyond entertainment and are widely acknowledged as fruitful tools for learning and developing skills across multiple domains, including educational enhancement. In the last few years, the world of serious games has widely increased. The use of these types of games can aid in classrooms to not only help the students learn concepts but also to improve their motivation to do so. However, designing games necessitates very specialized personnel and the process can often be costly and slow. The adaptions of the design to the implantation phase are also difficult and the process needs more focus. The challenge of this study was to create a game within the confines of cultural heritage and preservation that was an adventure game as well, available on any platform with a friendly user interface and free to download on the web. To achieve this great goal, it was necessary to overcome the limits and problems due to the virtual environment starting from 3D acquisition and finishing with image stability. In this paper, we show the flowchart and methodology for the optimization of a cultural heritage virtual environment for gaming applications.
Transfer Learning for Abstractive Summarization at Controllable Budgets.

Summarizing a document within an allocated budget while maintaining its major concepts is a challenging task. If the budget can take any arbitrary value and not known beforehand, it becomes even more difficult. Most of the existing methods for abstractive summarization, including state-of-the-art neural networks are data intensive. If the number of available training samples becomes limited, they fail to construct high-quality summaries. We propose MLS, an end-to-end framework to generate abstractive summaries with limited training data at arbitrary compression budgets. MLS employs a pair of supervised sequence-to-sequence networks. The first network called the \textit{MFS-Net} constructs a minimal feasible summary by identifying the key concepts of the input document. The second network called the Pointer-Magnifier then generates the final summary from the minimal feasible summary by leveraging an interpretable multi-headed attention model. Experiments on two cross-domain datasets show that MLS outperforms baseline methods over a range of success metrics including ROUGE and METEOR. We observed an improvement of approximately 4% in both metrics over the state-of-art convolutional network at lower budgets. Results from a human evaluation study also establish the effectiveness of MLS in generating complete coherent summaries at arbitrary compression budgets.