Reviewerly/exHarmony huggingface dataset:
No Resilience without Security

In this paper we discuss the core capabilities that a system needs to provide in order to ensure resilience. In short such a system needs to be capable of detecting its own state in the sense of recognizing errors and then reacting appropriate in order to restore its full functionality. In addition we argue why security is the essential prerequisite to achieve resilience. If a system can be compromised the attacker is in control of what the system does i.e. he can for example switch it off or simply disable error recovery mechanisms. This is the reason why we argue for proper design and assessment of security means used in any system that aims at achieving resilience.
Modeling Dynamic Patterns Adapted Joint Multidimension Resource Scheduling via Graph Sequence in Optical Data Center Network

Traffic patterns in data center network (DCN) may have distinguished features in a graph view. The traffic patterns may vary by time in DCN, and network topology reconstruction may help to adapt these dynamic traffic patterns. Traditional network scheduling may ignore the feature of patterns, which results in performance deteriorations. Optical DCN with multidimension resources may provide topology flexibility. If the multidimension resource scheduling problem is modeled as topology reconstruction by time, it may benefit the dynamic traffic patterns. To achieve this goal, this paper used a graph sequence to represent the time-varying network topology, and then a graph sequence based scheduling (GSS) algorithm has been proposed to optimize the traffic patterns. By simulation on our network modeling tool, the effectiveness on lowering latency and maximizing the link utilization of GSS has been verified.
An Approach on Efficient Clustering Technique of High Dimensional Records

Nowadays very large amounts of data can be created in various domain systems, specifically healthcare systems. It is difficult to analyze the complex large amount of data. Clustering is the concept which is used to put together a collection of similar data in a group. There are many techniques which can handle the large amount of data set to cluster such as K-Means, hierarchical and density-based approaches to analyze the data to improve the decision making capability of data sets. In specifically healthcare domain is the extraction of comprehensible knowledge from medical diagnosis records, which is one of the major challenges. This paper is to highlight the various clustering techniques used in the various domains and the comparisons of the different clustering techniques and discuss about the review of ensemble approach for the clustering of high dimensional records. Finally, Enhanced Design Weighted Mean Shift Ensemble Clustering approach was proposed for clustering the high dimensional records, which is improve the accuracy rate of the clustering results.
A Comparative Study of Famous Image Compression Methods Based on Bits per Pixel: A Survey

Image processing techniques are one of the most popular research directions for the concentration by the researchers for many years. The popularity of image based data representation has increased and the demand for faster transmission and low cost storage of the image has also increased. Thus, the major focus of the researchers is to increase the transmission speed and reduce the storage cost. The only way to obtain these goals is to reduce the size of the image without losing much of the information. The reduction of image size is a popular technique and understood as image compression techniques. The compression on image will certainly reduce the size. Nevertheless it will also reduce the information from the image. Thus the loss of the image data will strive to be maintained. Thus, the compression techniques are bound by the trade-off between the compression ratios and lose of image information. Therefore, this paper analyses and compares the image compression techniques based on compression ratio, lossy or lossless nature of the method and bits per pixel density. The purpose of this study is to understand the scope for improvements in the image processing capabilities by computations.
Estimation of discrete data using binomial mixture

Data analysis is an important method for obtaining information which is useful in many projects such as e.g. Smart Cities. Common data sources are questionnaires, their output mainly purveys discrete data. The most common description of discrete data is through categorical models. These models have several advantages such as flexibility but there are also disadvantages such as a huge dimension of the table expressing this distribution for more variables and values and their overparametrization. The aim of this paper is to replace the categorical distribution by another discrete distribution with a lower number of parameters while maintaining the model quality. Binomial distribution was chosen a suitable one because it is determined only by one parameter and this parameter allows to shape the probability function of binomial distribution well. The output of the paper is the presented model of mixture with binomial components. The suggested estimation algorithms are tested on real traffic data.
An interview with Barbra Kingsley from Kleimann Communication Group on information design

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Design and Development of Heuristic Utility Management Algorithm for Chinese Library Management System

Utility Management in a library is the programmatic tool with the synthetic mental program ability, along with Artificial Intelligence capacities, headed to manage a high volume of books, articles, and assignments, which help to ease the manual significance of librarians. This computerized machine code helps librarians to deal with various databases of the library management system. This framework keeps the records of all the resource details in an optimized manner. It uses a utility management software code with an optimized search classifier that helps to deal with the resource of the library. In this work, the Heuristic Utility Management Algorithm (HUMA) has been used to keep track of resources in the library using mathematical modeling and standardized programmatic computation on tags, which relates the decode scanner to parse the input information. HUMA helps to reduce the manual routine work done by the librarians, and it has been analyzed in this research with prominent survey outcomes based on experimental validation.
Effect of Window's Shape on Median Filtering

Impulse noise filtering plays important role in image processing applications and median filtering is widely applied to perform such task. The median filter's ability to preserve edges and simplicity with implementation has made it popular for the suppression of impulse noise. However, the challenge that most users of the median filtering have to deal with is the nature of the shape and size of a window to be used for the filtering. Various works on effect of window sizes have been done, but that of the shapes have not been extensively explored. This paper therefore assessed median filter's performance based on window shapes and sizes. Nine (9) different shapes of windows, five (5) different window sizes (3×3, 5×5, 7×7, 9×9, 11×11) and five (5) different noise density injection were used to evaluate the performance of the median filter. The experimental results show that the amount of noise in an image and selected window size strongly affect the output of a particular shape and that, a window's shape may not necessary produce excellent results at all times. However, the Square, Cross, Horizontal, Z-Shape and re-Z-Shape exhibited stronger performance as compared with the rest of the shapes.
Using pagerank to uncover patterns in search behavior induced by the bit flip operator

Understanding how the complex interactions of the problem-algorithm combination lead to an algorithm's search performance is arguably one of the most important open questions in metaheuristic algorithm theory. Examination of the fitness landscape does not provide all of the information needed to understand a metaheuristic algorithm's search behavior. We introduce an extension to the fitness landscape, which we call a search behavior diagram, that models a metaheuristic algorithm's expected search behavior across an entire fitness landscape. We then show that analyzing a search behavior diagram can produce insights into the nature of metaheuristic algorithm search behavior on problems from binary optimization, including one interesting insight about the relationship between the distribution of optima and adaptive search behavior.
The probabilistic reasoning of hierarchical diagnosis decision-making

Abstract Aiming at the problems of information incompleteness, model complexity and conclusion inaccuracy existing in diagnosis decision-making reasoning under uncertain conditions, probability information and its unique attributes are introduced on the basis of ontology reasoning to realize the formal expression of uncertainty in various elements of diagnosis decision-making ontology, and the probability ontology is obtained. On this basis, Probabilistic Graphical Model (PGM)—Dynamical Uncertain Causality Graph (DUCG) is selected to implement complex uncertain causality in hierarchical diagnosis decision-making ontology and compact expression of effective probability reasoning. And the hierarchical diagnosis decision-making ontology based dynamic uncertain diagnosis decision-making causality diagram model is put forward, and its system structure is built. Combining with the ontology concepts, relations, attributes to build, simplify, split, and delete the causality diagram, the chain of reasoning, probability calculation and sorting under the uncertain conditions are realized.
An Exploratory Study on the Perception of Optical Illusions in Real World and Virtual Environments

Virtual environments offers the possibility to simulate the real world as well as the effects like optical illusions. They are images with special arranged content to pretend the human visual system to observe non-existent content for instance. In this study we use several well known optical illusions implemented in real world and in a virtual environment. They were present to subjects to explore their properties and their perception within the different environments.
Spatio-Temporal Change Detection Using Granger Sequence Pattern

This paper proposed a method to detect changes in causal relations over a multi-dimensional sequence of events. Cluster Sequence Mining algorithm was modified to extract causal relations in the form of g-patterns: a pair of clusters of events that have their occurrence time determined by Granger causality. This paper also proposed the pattern time signature, a probabilistic density function of the cluster sequence occurring at any given time. Synthetic data were used for validation. The result shows that the proposed algorithm can correctly identify the changes in causal relations even under noisy data.
ReEx: An integrated architecture for preference model representation and explanation

Recommender systems based on collaborative filtering suggest items to users according to the similarity of items or similarity of preferences of other users. Latent factor models produce rather accurate predictions of user preferences, but the latency of the features extracted make it difficult to substantiate a recommendation to a user. The realisation that many aspects tend to exist in rating and social connections data, such as social influence or bias, led to the development of a component-based matrix factorisation approach in earlier work. The ability to quantify the contributions of a component to a recommendation opens up possibilities to identify reasons for recommendations which can be presented to the consumer receiving them. Reviews that accompany rating data can be analysed and correlated with latent factors to provide more detailed reasons for recommendations. This paper introduces a general comprehensive framework which supplements earlier work that models a users’ preferences and makes recommendations by extracting reasons from the recommendation framework as well as the accompanying reviews. Both rating-based and review-based reasons for recommending an item are presented in a visual manner. These visualisations help the user understand the origins of the recommendations but also assist businesses in identifying properties in users and communities that open up opportunities for targeted marketing and customer management. The usefulness of the explanation tool is demonstrated with an example recommendation of four items for a user in the Yelp restaurants dataset.
Different Approaches for Semantic Segmentation

Semantic image segmentation is an emerging task in the field of automation. Its application varies from autonomous driving to medical diagnosis. Semantic segmentation of an image means to label each pixel in that image to a particular class. As an example consider an outdoor street image where there are different objects like car, road, sky, trees, pedestrians etc. After applying semantic segmentation each pixel in the image belonging to the car will have the label car and road will have label road and so on. A recent trend in performing semantic segmentation is by using Convolutional Neural Networks, (CNN), which acted as a catalyst for segmentation. In this paper, a detailed discussion of various approaches for segmentation using CNN has been presented. Also, various datasets and their format and evaluations metrics are discussed. All the approaches discussed are diverse and has its pros and cons. Finally, an application-specific semantic segmentation method using a genetic CNN algorithm for classification task has been proposed. The proposed method has shown improvement in the M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iou</sub> score when tested on the CamVid dataset and on a dataset created by combining two small object classification datasets, MNIST and CIFAR10.
Affective Communication Enhancement System for Locked-In Syndrome Patients

Patients with Locked-In Syndrome such as people with Amyotrophic Lateral Sclerosis (ALS) rely on technology for basic communication. However, available Augmentative and Alternative Communication (AAC) tools such as gaze-controlled keyboards have limited abilities. In particular, they do not allow for expression of emotions in addition to words. In this paper we propose a novel gaze-based speaking tool that enable locked-in syndrome patients to express emotions as well as sentences. It also features patient-controlled emotionally modulated speech synthesis. Additionally, an emotional 3D avatar can be controlled by the patient to represent emotional facial-expressions. The systems were tested with 36 people without disabilities separated into an affective group - full control of emotional voice, avatar facial expressions and laugh - and a control group - no emotional tools. The study proved the system's capacity to enhance communication for both the patient and the interlocutor. The emotions embedded in the synthesized voices were found recognizable at 80% on the first trial and 90% on the second trial. The conversation was perceived as more natural when using the affective tool. The subjects felt it was easier to express and identify emotions using this system. The emotional voice and the emotional avatar were found to help the conversation. This highlights the needs for more affective-driven communicative solutions for locked-in patients.
Building Human-Autonomy Teaming Aids for Real-Time Strategy Games

StarCraft II (SC2) is a real-time strategy science-fiction video game developed by Blizzard Entertainment. Known for its complex state space and open-source environment [8], SC2 has become a popular domain for Artificial Intelligence (AI) research. This paper leverages the advances in AI research from SC2 to build human-autonomy teaming (HAT) aids, AI-driven software tools for human interactivity, for players to improve their skills. The human-machine interface (HMI) that houses these tools breaks the game into different components and visually represents each HAT aid to increase situational awareness and decrease response time.
A Study on Training Story Generation Models Based on Event Representations

For story generation tasks, the event representation, a semantic abstraction of sentences, improves neural network based approaches. That is, to expect better generated results, we can train generative models with the extracted event representations as input, instead of the original sentences. However, the process for training such event-based generative models has yet been adequately studied. In this work, we found that proposed n-gram based approaches outperformed the prior all-to-all approach; among n-gram based approaches, 2-gram obtained best training and validation perplexity of generated results. Moreover, we found that an event reversing strategy with a single directional recurrent neural network (RNN) outperformed more advanced architectures such as bidirectional RNNs (biRNNs) and transformers, in terms of training perplexity.
The Implementation of Credit Risk Scorecard Using Ontology Design Patterns and BCBS 239

Abstract Nowadays information and communication technologies are playing a decisive role in helping the financial institutions to deal with the management of credit risk. There have been significant advances in scorecard model for credit risk management. Practitioners and policy makers have invested in implementing and exploring a variety of new models individually. Coordinating and sharing information groups, however, achieved less progress. One of several causes of the 2008 financial crisis was in data architecture and information technology infrastructure. To remedy this problem the Basel Committee on Banking Supervision (BCBS) outlined a set of principles called BCBS 239. Using Ontology Design Patterns (ODPs) and BCBS 239, credit risk scorecard and applicant ontologies are proposed to improve the decision making process in credit loan. Both ontologies were validated, distributed in Ontology Web Language (OWL) files and checked in the test cases using SPARQL. Thus, making their (re)usability and expandability easier in financial institutions. These ontologies will also make sharing data more effective and less costly.
A Relevance Index-Based Method for Improved Detection of Malicious Users in Social Networks

The phenomenon of “trolling” in social networks is becoming a very serious threat to the online presence of people and companies, since it may affect ordinary people, public profiles of brands, as well as popular characters. In this paper, we present a novel method to preprocess the temporal data describing the activity of possible troll profiles on Twitter, with the aim of improving automatic troll detection. The method is based on the zI, a Relevance Index metric usually employed in the identification of relevant variable subsets in complex systems. In this case, the zI is used to group different user profiles, detecting different behavioral patterns for standard users and trolls. The comparison of the results, obtained on data preprocessed using this novel method and on the original dataset, demonstrates that the technique generally improves the classification performance of troll detection, virtually independently of the classifier that is used.
Trends on Design Tools Under Futurology

This paper proposes a structured research for design tools from the perspective of futurology, especially the tool research that can help future scenarios, and aims to comprehensively understand the current research situation. This paper displays a framework to help summarize the current research focus, using design, futurology and tool research as the three keywords for discussion, and taking the “worldview-scenario-product” of future design as the three levels of content, carrying design process with four dimensions of “look back-insight-construction-reflection”. On this basis, this article discusses views of design future and its corresponding design methods and tools, bringing new perspectives and content to design tools.
Explore the Usability of the Cultural Museum Website – An Example of Pazeh Cultural Museum

The aim of this research is to Construct the Pazeh Cultural museum website and explore the usability of the Pazeh Cultural museum website. The method of this research is explained as following. 1) Field investigation and data collection. 2) Construction of information structure of Pazeh Cultural Mobile Museum website. 3) Transform the information structure into an interface prototype. 4) Recruit users for testing. 5) Apply the system usability scale (SUS) and open questionnaire to evaluate usability. 6) Statistics and Analysis the system usability scale (SUS) Finally, according to the research results, suggestions are proposed for the future design and development of Pazeh cultural museum website.
Improved Heuristic Data Management and Protection Algorithm for Digital China Cultural Datasets

In the present scenario sustainable management and protection of digital cultural datasets are considered as a significant area of research. In the recent past, the protection and management of cultural data are facing several new challenges and opportunities. Though several researchers explored their work on managing and protecting cultural data, efficiently and reliability of the present data management algorithm seems to be more complicated due to its incompetence in managing data in an optimized manner. This work presents an improved heuristic big data management algorithm for cultural datasets which is considered as a new discipline of digital cultural heritage specially established for strengthening strategic and interdisciplinary research. The scientific operation and management mechanism of digital protection of cultural heritage is experimentally validated and results show promising outcomes.
디지털 시대 청소년 학습자의 서사 경험 변모 양상 및 서사 교육 내용 연구

This study explores the changes in the patterns of narrative experiences among adolescent learners during the digital era and the direction in which it will develop. Additionally, it looks further into how narrative education should respond to these changes.A deeper understanding of the changing patterns was obtained through literary and qualitative research which included questionnaires and interviews with adolescent learners. Results showed that adolescent learners have increasingly distanced themselves from the traditional novel genre and now turn to normalized digital media entertainment instead. Majority of the subjects were biased towards accepting and distributing narratives and rarely perceived themselves as critics or creative agents. In addition, learners showed a preference for “differentiation” when it came to their entertainment.The study recommends the active exploration and development of Media Narrative Literacy. Learners should recognize that it is essential to fully enjoy and appreciate the narrative of the digital media age. They should not only be nurtured as acceptance and distribution players but as critical and creative producers as well. Lastly, learners should be able to recognize and reflect on the concept of “differentiation” in the age of digital media.