Reviewerly/exHarmony huggingface dataset:
An Improved Color Attenuation Priori Dehazing Algorithm and Its Hardware Implementation

An improved color attenuation priori dehazing algorithm is proposed in this paper. The performance of the improved algorithm is tested with MATALAB program. The hardware architecture for the improved algorithm is designed, the RTL codes of the architecture are programed, simulated, and synthesized to Altera's FPGA. Experiment results show that the improved algorithm is efficient for image dehazing and the hardware architecture designed in this paper is correct.
The New Thinking in Emotional User Experience: From Visual Metaphor to Interactive Affordance

In our daily lives, products always catch our attention with form, color, texture, sound, meaning, and environment. Recently, products have become more intelligent and adaptive that they change people’s emotional expression and experience perception due to the more complex human-product interaction. This paper aims to develop a theoretical framework, which establishes a general understanding of the experience emerged from the interactive activities between people and technology, and the confusion within specific critical dimensions, which include subjective feelings, behavior, social practice, and knowledge. This study reveals the usability of products mainly depends on the designers’ understanding of the users’ need and perception. By setting a visual metaphor which promotes interactive affordance and enables users to be well acquainted with how to use at a glance, this study suggests that better design need to focus on better creation of user experience, which combines utilitarian and hedonic aspects. Therefore, the comprehensive consideration from users’ psychological and behavioral perspectives is essential. This study makes a relatively original contribution to exploring the importance of affordance in interaction design which invents products acceptance and adorable.
Online Performance Prediction and Profiling of Human Activities by Observation

The capacity of a system to automatically analyze and predict the performance of a human in a particular task can provide important information in Human-Robot Interaction. Despite its usefulness, the above topic has received rather limited attention in the literature. In the current work, we introduce a method for performance prediction and profiling of human activities. Using little information about a task, our method is able to extract the characteristic motion patterns of an agent, analyze them and predict his/her performance in a given activity. We demonstrate the robustness of the method in several different activities, that involve both periodic and oscillatory primitive motions. In addition, we evaluate it thoroughly on data obtained from public datasets and discuss its usefulness for contemporary robotic applications.
Joint detection and tracking algorithm for cognitive radar based on parallel structure of EKF and particle filter

In order to reduce the uncertainty of radar manoeuvring target tracking (RMTT) in cluttered background, a joint detection and tracking algorithm based on cognitive radar is proposed. First, a prism structure resolution cell of time-delay, Doppler and azimuth is designed. Then, an approximate expression of measurement error covariance including waveform and detection threshold parameters is given. Then, based on the idea of human brain perception-action cycle, a joint waveform and detection threshold adaptive tracking algorithm based on minimum information entropy criterion is proposed. Finally, a cognitive structure adaptive particle filter (CSAPF) algorithm, based on parallel structure of extended Kalman filter (EKF) and particle filter, are used with Probabilistic Data Association (PDA) algorithm for RMTT. During the process, CSAPF-PDA can always obtain the best tracking performance with the minimum number of particle samples, thus effectively taking into account the tracking accuracy and efficiency. The effectiveness of the proposed algorithm is verified by simulation experiments.
Information Processing in Research Paper Recommender System Classes

Research-related publications and articles have flooded the internet, and researchers are in the quest of getting better tools and technologies to improve the recommendation of relevant research papers. Ever since the introduction of research paper recommender systems, more than 400 research paper recommendation related articles have been so far published. These articles describe the numerous tools, methodologies, and technologies used in recommending research papers, further highlighting issues that need the attention of the research community. Few operational research paper recommender systems have been developed though. The main objective of this review paper is to summaries the state-of-the-art research paper recommender systems classification categories. Findings and concepts on data access and manipulations in the field of research paper recommendation will be highlighted, summarized, and disseminated. This chapter will be centered on reviewing articles in the field of research paper recommender systems published from the early 1990s until 2017.
Developing the Museum-Monumental Experience from Linear to Interactive Using Chatbots

Under the general hypothesis that a museum or a monument can create qualitative learning experiences for their visitors by using the latest technology of Artificial Intelligence, this paper presents a proposal on the use of chatbots (e.g., digital co-speech emulators) in order to enhance a museum visitor’s experience. Two examples, making use of real use cases for the Museum of Greek Folk Art in Athens, are presented where two chatbots are proposed, covering the needs of prior and during the visit, together with a reference implementation on a mobile device. A methodology for evaluating the results of application of the proposal, based on semi-structured interviews for experts is also provided, in order to assist the application of the proposed idea in different cases.
A Meta-Language Approach for Machine Learning

In the last decade, machine learning has increasingly been utilized for solving various types of problems in different domains, such as, manufacturing finance, and healthcare. However, designing and fine-tuning algorithms require extensive expertise in artificial intelligence. Although many software packages wrap the complexity of machine learning and simplify their use, programming skills are still needed for operating algorithms and interpreting their results. Additionally, as machine learning experts and non-technical users have different backgrounds and skills, they experience issues in exchanging information about requirements, features, and structure of input and output data. This paper introduces a meta-language based on the Goal-Question-Metric paradigm to facilitate the design of machine learning algorithms and promote end-user development. The proposed methodology was initially developed to formalize the relationship between conceptual goals, operational questions, and quantitative metrics, so that measurable items can help quantify qualitative goals. Conversely, in our work, we apply it to machine learning with a two-fold objective: (1) empower non-technical users to operate artificial intelligence systems, and (2) provide all the stakeholders, such as, programmers and domain experts, with a modeling language.
Comparison of Thai Sentence Sentiment Tagging Methods Using Thai Sentiment Resource

Opinion mining aims to determine text sentiment from social network. Thai sentiment resource [3] can be utilized to tag sentiment of Thai text. Thai sentiment resource consists of Thai terms, for each term a number of English synsets and a set of sentic values and a polarity. However, most terms have many synsets. In order to decide on a sentence emotion, six methods for Thai sentence sentiment tagging were proposed and evaluated. The dataset was obtained from social media comments of an airline service. For the six methods, tagging accuracies ranged from 64.7 to 73.6%. The best method used only adverb and adjective synsets to determine a sentence emotion; while the worst method used only verb synsets.
A Vision for Helping Developers Use APIs by Leveraging Temporal Patterns

To achieve any meaningful task of a certain complexity, developers need to use APIs. Learning to use them is both time consuming and cognitively demanding. We propose to leverage a formal description of API usage as temporal patterns to help developers make sense of the complexities of working with APIs. To achieve this, we propose to deploy recommender systems at various points of the development process that make these patterns useful when most needed. In this paper, we illustrate the approach on a non trivial, real world running example from Android development. The example allows us to articulate a research agenda for leveraging API usage patterns during: (a) testing and compilation times by recommending potential violations of the patterns; (b) coding time by recommending API method calls; and (c) API delivery by recommending improvements to documentation.
Visual Servoing of Aerial Manipulators

This chapter describes the classical techniques to control an aerial manipulator by means of visual information and presents an uncalibrated image-based visual servo method to drive the aerial vehicle. The proposed technique has the advantage that it contains mild assumptions about the principal point and skew values of the camera, and it does not require prior knowledge of the focal length, in contrast to traditional image-based approaches.
Optimal Sampling Sets in Cographs

In this paper, we calculate the optimal sampling sets for bandlimited signals on cographs. We take into account the tree structure of the cograph to derive closed form results for the uniqueness sets of signals with a given bandwidth. These results do not require expensive spectral decompositions and represent a promising tool for the analysis of signals on graphs that can be approximated by cographs.
The Neural Network for Online Learning Task Without Manual Feature Extraction

The article is devoted to the problem of feature extraction in online learning tasks. In many cases, the proper feature extraction is very time-consuming. Currently, in some cases, this problem is successfully solved by deep neural networks. However, deep models are computationally expensive and so hardly applicable for online learning tasks which require frequent updating of the model. This paper proposes the lightweight neural net architecture that can be learned in online mode and doesn’t require complex handcrafted features. The small sample processing time distinguishes the proposed model from more complex deep neural networks. The architecture and learning process of the proposed model are discussed in detail. The special attention is paid to fast software implementation. On benchmarks, we show that developed implementation processes one sample several times faster than implementations on the base of deep learning frameworks. The conducted experiments on CTR prediction task show that the proposed neural net with raw features gives the same performance as the logistic regression model with handcrafted features. For a clear description of the proposed architecture, we use the metagraph approach.
New models and methods for programming cyber-physical systems (keynote)

Emerging cyber-physical systems are distributed systems in constant interaction with their physical environments through sensing and actuation at network edges. Over the past decade, the embedded and control systems community have vigorously pursued a vision of coupled feedback-controlled systems with a broad range of real-life applications from transportation, smart buildings to human health. These efforts have continued to push intelligent processing to edge and near-edge devices, provide new capabilities for improved sensing with high quality timing information, establish limits on the quality of time and its impact on the stability of control algorithms etc.
A Security Framework for Service-Oriented Architecture Based on Kerberos

Service-Oriented Architecture is a new paradigm in Software computing that focused on web-based Service-Oriented computing. This paradigm based on client services architecture and involves communication among three different entities: Service Provider, Service Consumer and Service Broker or Registry. These three entities are loosely coupled. Security is an important aspect for any application. In SOA, managing and organizing security is a challenging issue, because services are deployed in distributed environment. In SOA, security is defined in terms of services which are secure service registration and deregistration, secure service discovery, secure service delivery and availability of trustworthy services. This paper proposed a Kerberos based security framework that fulfills the security requirement needed for secure SOA. The implementation of the proposed security framework is also provided.
Using Model-Based Reasoning for Enhanced Chatbot Communication

Chatbots as conversational recommender have gained increasing importance for research and practice with a lot of applications available today. In this paper, we present the methods to support conversational defaults within a human-chatbot conversation that simplifies communication with the purpose of improving the overall recommendation process. In particular, we discuss our model-based reasoning approach for easing user experience during a chat, e.g., in cases where user preferences are mentioned indirectly causing inconsistencies. As a consequence of inconsistencies, it would not be possible for the chatbot to provide answers and recommendations. The presented approach allows for removing inconsistencies during the interactions with the chatbot. Besides the basic foundations, we provide use cases from the intended tourism domain to show the simplification of the conversation process. In particular, we consider recommendations for booking hotels and planning trips.
KANDINSKY

Many social media sites allow users to upload text, images, and videos (collectively referred to asanchor post) for public consumption. These posts may attract hundreds of comments from many social users leading to social conversations (ie discussions). Tools that can facilitate user-friendly and effective understanding and analysis of large volumes of comments associated with anchor posts can be of great benefit to individuals and organizations. In this demonstration, we present a novel end-to-end visualization system called Kandinsky to supportmulti-faceted visualization of social discussions associated with an anchor post. In Kandinsky, the social discussion landscape is visualized using a collection of colorfulcircles andconcentric circles, which are inspired from the famous abstract arts called"Squares with Concentric Circles" and"Several Circles" by Russian painter Wassily Kandinsky (1866-1944). Intuitively, a circle and a concentric circle represent a social comment and a collection of comments in a discussion thread, respectively. We discuss various innovative features of Kandinsky and demonstrate its effectiveness.
Improving Event Monitoring in IoT Network Using an Integrated Blockchain-Distributed Pattern Recognition Scheme

The application of blockchain technology for data storage and verification has been expanding from financial applications to other fields such as asset management and event monitoring in Internet-of-Things (IoT). This expansion consequently intensifies the problem of an increasing size of data stored in the blockchain, especially in event monitoring application where streams of data need to be stored and verified accordingly. In this paper, we propose an IoT-blockchain event monitoring framework that utilizes a distributed pattern recognition scheme for event data processing. Event data are treated as patterns comprising individual data retrieved from interconnected IoT sensors within a network composition. Preliminary results obtained indicate that the proposed scheme is capable of reducing the number of data blocks generated in the blockchain network, hence minimizing the needs for intensive storage and verification.
Cascaded network with deep intensity manipulation for scene understanding

Abstract Scene understanding is essential to robotic navigation and autonomous driving as it provides semantic information to their controlling system. However, it will fail when processing low‐light images/videos captured under adverse weather or at night use state‐of‐the‐art scene understanding methods. A naive way to directly infer semantics from low‐light images is ill posed because the low‐light condition distorts pixel intensities and buries details. In order to address this problem, we propose the Deep Intensity Manipulation Network (DIMNet), which could relight the input images and recover the details, and combine the DIMNet with a scene understanding network to get a cascaded network to learn the semantics from low‐light images. Through learning pixel intensity manipulation, our method can generate images not only visually pleasing but also practical for scene understanding. Qualitative and quantitative experiments demonstrate that the proposed method is effective and robust for both synthetic and real‐world images.
The spirals of the Slope Chain Code

Generally speaking, a spiral is a 2D curve which winds about a fixed point. Now, we present a new, alternative, and easy way to describe and generate spirals by means of the use of the Slope Chain Code (SCC) [E. Bribiesca, A measure of tortuosity based on chain coding, Pattern Recognition 46 (2013) 716–724]. Thus, each spiral is represented by only one chain. The chain elements produce a finite alphabet which allows us to use grammatical techniques for spiral classification. Spirals are composed of constant straight-line segments and their chain elements are obtained by calculating the slope changes between contiguous straight-line segments (angle of contingence) scaled to a continuous range from −1 (−180∘) to 1 (180∘). The SCC notation is invariant under translation, rotation, optionally under scaling, and it does not use a grid. Other interesting properties can be derived from this notation, such as: the mirror symmetry and inverse spirals, the accumulated slope, the slope change mean, and tortuosity for spirals. We introduce new concepts of projective polygonal paths and osculating polygons. We present a new spiral called the SCC polygonal spiral and its chain which is described by the numerical sequence 2n for n ≥ 3, to the best of our knowledge this is the first time that this spiral and its chain are presented. The importance of this spiral and its chain is that this chain is covering all the slope changes of all the regular polygons composed of n edges (n-gons). Also, we describe the chain which generates the spiral of Archimedes. Finally, we present some results of different kind of spirals from the real world, including spiral patterns in shells.
Norms, institutions and freedom of speech in the US, the UK and Australia

Abstract While it has become accepted that norms can act in institution-like ways, a highly valued norm that has not been examined is free speech. Can free speech be conceptualised as acting in institution-like ways? If it can, what does this illuminate about processes of policy change? I analyse policy change between 2001 and 2011 in the United States, the United Kingdom and Australia, a period during which significant new limits were introduced on free speech in relation to national security. In addition to showing how free speech acted in institution-like ways, the analysis suggests three implications: norms can both act in institution-like ways and be subject to change in interaction with other institutions; a broad, cultural level institution can mask policy change at the narrow, rule-based level even where the latter contradicts the former; and complexity and variation in speech regulation can be understood as consequences of the to-be-expected variability in the institutionalisation of a norm.
Enhanced Automated-Scripting Method for Improved Management of SQL Injection Penetration Tests on a Large Scale

Typically, in an assessment project for a web application or database with a large scale and scope, tasks required to be performed by a security analyst are such as SQL injection and penetration testing. To carry out these large-scale tasks, the analyst will have to perform 100 or more SQLi penetration tests on one or more target. This makes the process much more complex and much harder to implement. This paper attempts to compare large-scale SQL injections performed with Manual Methods, which is the benchmark, and the proposed SQLiAutoScript Method. The SQLiAutoScript method uses sqlmap as a tool, in combination with sqlmap scripting and logging features, to facilitate a more effective and manageable approach within a large scale of hundreds or thousands of SQL injection penetration tests. Comparison of the test results for both Manual and SQLiAutoScript approaches and their benefits is included in the comparative analysis. The tests were performed over a scope of 24 SQL injection (SQLi) tests that comprises over 100,000 HTTP requests and injections, and within a total testing run-time period of about 50 hours. The scope of testing also covers both SQLiAutoScript and Manual methods. In the SQLiAutoScript method, each SQL injection test has its own sub-folder and files for data such as results (output), progress (traffic logs) and logging. In this way across all SQLi tests, the results, data and details related to SQLi tests are logged, available, traceable, accurate and not missed out. Available and traceable data also facilitates traceability of failed SQLi tests, and higher recovery and reruns of failed SQLi tests to maximize increased attack surface upon the target.
Factors Affecting the Integration of ICT in Education

Abstract This study aims to find the factors that influence the integration of ICT in learning by high school teachers throughout Makassar. This study is the result of preliminary research conducted on five schools as a sample. The method is surveyed. Sampling was conducted on 134 subject teachers. The results of this survey show several factors that influence, among others; factors of vision and personal perception, the competence of teachers, lack of time, lack of resources, and lack of electronic-based learning resources.
Analogy-Based Approaches to Improve Software Project Effort Estimation Accuracy

Abstract In the discipline of software development, effort estimation renders a pivotal role. For the successful development of the project, an unambiguous estimation is necessitated. But there is the inadequacy of standard methods for estimating an effort which is applicable to all projects. Hence, to procure the best way of estimating the effort becomes an indispensable need of the project manager. Mathematical models are only mediocre in performing accurate estimation. On that account, we opt for analogy-based effort estimation by means of some soft computing techniques which rely on historical effort estimation data of the successfully completed projects to estimate the effort. So in a thorough study to improve the accuracy, models are generated for the clusters of the datasets with the confidence that data within the cluster have similar properties. This paper aims mainly on the analysis of some of the techniques to improve the effort prediction accuracy. Here the research starts with analyzing the correlation coefficient of the selected datasets. Then the process moves through the analysis of classification accuracy, clustering accuracy, mean magnitude of relative error and prediction accuracy based on some machine learning methods. Finally, a bio-inspired firefly algorithm with fuzzy analogy is applied on the datasets to produce good estimation accuracy.