Reviewerly/exHarmony huggingface dataset:
Experimentation for Development for Ark�a Modulam Foundation as an Alternative to Create Amphibious Architecture in the Urban Floodplain in Kalimantan

Objective: This study describes the development study on amphibious foundation called Ark’a Modulam. To describe the development study of this Ark’a Modulam foundation. Methods/Findings: This study is arranged based on the first year of 3 years consecutively-planned research. The method used was engineering design to formulate the alternatives. The experimenta description and discussion supports the concept of amphibious architecture of living houses. Application: This study is hopefully able to benefit in developing flood resilient houses especially for urban floodplain settlement in Kalimantan. Keywords: Amphibious, Architecture, Ark’a Modulam, Floodplain, Foundation
Characterization of the Geophysical Telemetry System Based on the Gunn Diode Transmitter

In recent years, the without-cable system for the decline-directed and horizontal drilling has attracted of significant research interest. Advanced telemetry system of transfer measurement information for Measurement-while-drilling is designing on the system level in this study. Evaluation of the use of video pulse signal in this system is carried out. The structure of transfer microwave pulse transmitter based on Gunn diode is considered. Characteristics of transmitter are resulted.
Bar-Hillel Theorem Mechanization in Coq

Formal language theory has a deep connection with such areas as static code analysis, graph database querying, formal verification, and compressed data processing. Many application problems can be formulated in terms of languages intersection. The Bar-Hillel theorem states that context-free languages are closed under intersection with a regular set. This theorem has a constructive proof and thus provides a formal justification of correctness of the algorithms for applications mentioned above. Mechanization of the Bar-Hillel theorem, therefore, is both a fundamental result of formal language theory and a basis for the certified implementation of the algorithms for applications. In this work, we present the mechanized proof of the Bar-Hillel theorem in Coq.
A unified formulation of a class of graph matching techniques

In this paper, we show that graph matching methods based on relaxation labeling, spectral graph theory and tensor theory have the same mathematical form by employing power iteration technique. Besides, the differences among these methods are also fully discussed and can be proven that distinctions have little impact on the final matching result. Moreover, we propose a fast compatibility building procedure to accelerate the preprocessing speed which is considered to be the main time consuming part of graph matching. Finally, several experiments are conducted to verify our findings.
Modified Backward Chaining Algorithm Using Artificial Intelligence Planning IoT Applications

In this chapter, an automated planning algorithm has been proposed for IoT-based applications. A plan is a sequence of activities that leads to a goal or sub-goals. The sequence of sub-goals leads to a particular goal. The plans can be formulated using forward chaining where actions lead to goals or by backward chaining where goals lead to actions. Another method of planning is called partial order planning where all actions and sub-goals are not illustrated in the plan and left incomplete. When many IoT devices are interconnected, based on the tasks and activities involved resource allocation has to be optimized. An optimal plan is one where the total plan length is minimum, and all actions consume similar quantum of resources to achieve a goal. The scheduling cost incurred by way of resource allocation would be minimum. Compared to the existing algorithms L2-Plan (Learn to Plan) and API, the algorithm developed in this work improves optimality of resources by 14% and 36%, respectively.
Socio-cognitive ACO in Multi-criteria Optimization

In this paper a socio-cognitive ACO-type algorithm is proposed for multi-criteria TSP problem optimization. This algorithm is rooted in psychological inspirations and follows other socio-cognitive swarm intelligence methods proposed up to now. This paper presents the idea and shows the applicability of the proposed algorithm based on selected benchmark functions from the scope of well-known TSPLIB library.
Television analyzer of aerosol particles images

In the paper an aerosol image analyzer is described, which allows to obtain in three projections the main geometric and optical parameters of the aerosol particles. It is necessary for the calibration of simple optical-electronic sensors. We describe a recognition algorithm that allows real-time obtaining of data on the shape, size, and color of studying aerosols.
Reminisce: Transparent and Contextually-Relevant Retrospection

Many envisioned applications of pervasive computing, from smart personal assistants to interaction in the IoT, assume the ability to assess the context and provide data and services relevant to that context. As a demonstration of needed practical support for seamless context-based interaction, we present Reminisce, an application that allows users to look back on their memories by prioritizing photographs that were taken in contexts that are similar to the current context. While we focus on the use of context to draw up photographic history, the vision behind Reminisce is more general. We describe how Reminisce is built on a contextual database middleware that provides context storage and context-sensitive queries that determine the similarity between pairs of contexts. Reminisce uses the contextual database to store the contexts of a user's photos, which include location, time, neighboring devices, and weather conditions. Later, the app identifies the photos whose stored contexts are most similar to the user's current context by querying the contextual database; this query uses a context similarity function to compare entries in the database to the user's current context. During the demonstration, visitors will be able to interact with the app on the spot with auto-generated contexts and an on-device context simulation.
Research and Design of Block Cipher Special Instruction Set

Abstract The existing instructions are used to implement the block cipher algorithm with low efficient. So this paper classifies the operations in the cryptographic algorithm, and analyses the characteristics of bit width, combination and parallel mode in the cipher algorithm of different operations. Further develop the parallelism of S-box and shift instructions, improve the hybrid computing ability of arithmetic operation instructions, research and design new types of instructions for data position transformation, such as extract shift, shift insertion, data grouping and data mixing. At the last, this paper implements the cipher algorithm with the new instruction, such as DES, IDEA, BEST and other block cipher algorithms. The number of instructions is 14% ∼ 35% of Haswell processor, and 40% to 68% of the COBRA cipher processor.
Mimir - Streaming Operators Classification with Artificial Neural Networks

Streaming applications are used for analysing large volumes of continuous data. Achieving efficiency and effectiveness in data streaming imply challenges that gen all the more important when different parties (i) define applications' semantics, (ii) choose the stream Processing Engine (SPE) to use, and (iii) provide the processing infrastructure (e.g., cloud or fog), and when one party's decisions (e.g., how to deploy applications or when to trigger adaptive reconfigurations) depend on information held by a distinct one (and possibly hard to retrieve). In this context, machine learning can bridge the involved parties (e.g., SPEs and cloud providers) by offering tools that learn from the behavior of streaming applications and help take decisions.
Pedestrian Detection Based on Deep Neural Network in Video Surveillance

Pedestrian detection is an essential and challenging problem in machine vision and video surveillance signal processing. To handle the high cost of training-specific discriminative classifier for pedestrian detection, we focus on the learning of suitable features for pedestrian detection representation. A deep neural network is presented in this paper to resolve the above issue. Our pedestrian detection method has several appealing properties. First, the learning of features is much more efficient under the configuration of the proposed framework due to the reduction of training classifier. Second, a K-Nearest Neighbor (KNN) method is adopted to solve the comparison between the regions of interest and the templates. Third, due to the less dependency of the classifier, the performance across different datasets overcomes most traditional ones. Finally, we perform extensive comparison across different public datasets and compared with corresponding benchmarks.
A Policy Based Framework for Privacy-Respecting Deep Packet Inspection of High Velocity Network Traffic

Deep Packet Inspection (DPI) is instrumental in investigating the presence of malicious activity in network traffic and most existing DPI tools work on unencrypted payloads. As the internet is moving towards fully encrypted data-transfer, there is a critical requirement for privacy-aware techniques to efficiently decrypt network payloads. Until recently, passive proxying using certain aspects of TLS 1.2 were used to perform decryption and further DPI analysis. With the introduction of TLS 1.3 standard that only supports protocols with Perfect Forward Secrecy (PFS), many such techniques will become ineffective. Several security solutions will be forced to adopt active proxying that will become a big-data problem considering the velocity and veracity of network traffic involved. We have developed an ABAC (Attribute Based Access Control) framework that efficiently supports existing DPI tools while respecting user's privacy requirements and organizational policies. It gives the user the ability to accept or decline access decision based on his privileges. Our solution evaluates various observed and derived attributes of network connections against user access privileges using policies described with semantic technologies. In this paper, we describe our framework and demonstrate the efficacy of our technique with the help of use-case scenarios to identify network connections that are candidates for Deep Packet Inspection. Since our technique makes selective identification of connections based on policies, both processing and memory load at the gateway will be reduced significantly.
Prospects for Protecting Business Microdata when Releasing Population Totals via a Remote Server

Abstract Many statistical agencies face the challenge of maintaining the confidentiality of respondents while providing as much analytical value as possible from their data. Datasets relating to businesses present particular difficulties because they are likely to contain information about large enterprises that dominate industries and may be more easily identified. Agencies therefore tend to take a cautious approach to releasing business data (e.g., trusted access, remote access and synthetic data). The Australian Bureau of Statistics has developed a remote server, called TableBuilder, which has the capability to allow users to specify and request tables created from business microdata. The tables are confidentialised automatically by perturbing cell values, and the results are returned quickly to the users. The perturbation method is designed to protect against attacks, which are attempts to undo the confidentialisation, such as the well-known differencing attack. This paper considers the risk and utility trade-off when releasing three Australian Bureau of Statistics business collections via its TableBuilder product.
Cooperative Principle’s Violation and Politeness Strategies in A Movie

The present study was aimed to find out the type of politeness strategies which was frequently used by the casts in “Mr. Bean” movie and to find out the type of cooperative principle’s maxims which was frequently violated by the casts in “Mr. Bean” movie. This research was a descriptive qualitative method. All data were taken from the utterances of all casts in “Mr. Bean” movie which contained the politeness strategies and the violation of cooperative principle’s maxims. The researcher himself was the instrument of the research. The results show that bald on-record strategy is often used by the casts in “Mr. Bean” movie and the violation of relevance maxim is often used by them in “Mr. Bean” movie. It indicates that the casts of “Mr. Bean” movie tend to use bald on-record strategy and to violate the relevance maxim than other categories.
Evaluating the Accuracy and Efficiency of Sentiment Analysis Pipelines with UIMA

Sentiment analysis methods co-ordinate text mining components, such as sentence splitters, tokenisers and classifiers, into pipelined applications to automatically analyse the emotions or sentiment expressed in textual content. However, the performance of sentiment analysis pipelines is known to be substantially affected by the constituent components. In this paper, we leverage the Unstructured Information Management Architecture (UIMA) to seamlessly co-ordinate components into sentiment analysis pipelines. We then evaluate a wide range of different combinations of text mining components to identify optimal settings. More specifically, we evaluate different pre-processing components, e.g. tokenisers and stemmers, feature weighting schemes, e.g. TF and TFIDF, feature types, e.g. bigrams, trigrams and bigrams+trigrams, and classification algorithms, e.g. Support Vector Machines, Random Forest and Naive Bayes, against 6 publicly available datasets. The results demonstrate that optimal configurations are consistent across the 6 datasets while our UIMA-based pipeline yields a robust performance when compared to baseline methods.
OTS 2019 Advanced Information Technology and Services

Conference proceedings of the Twenty-fourth Conference, Maribor, June 18th & 19th, 2019 The papers in the proceedings of the 24th Professional Conference on Advanced Information Technology and Services address the extremely topical challenges that we (IS/IT experts, Software Engineers, Computer Scientists, Data Scientists, software and solution architects, developers,  IT managers, etc.) encounter in our daily work. The authors present the innovative solutions and rich experience gained by conducting real-life projects: Applying Augmented Intelligence and Machine Learning technologies, Using Blockchain technologies and platforms, Developing loosely coupled micro-services, Providing full virtualization and exploitation of distributed infrastructures, Ensuring cybersecurity, confidentiality and privacy, Applying large-scale agile methods in distributed teams, Introducing agile approaches that enable the rapid and efficient software development via automated processes, and continuous testing, integration, delivery & deployment Updating and upgrading existing Information Systems, Incorporating advanced programming languages and development environments, Developing user-friendly web and mobile solutions & services.
A new use of the ant system algorithm for the max-mean dispersion problem

This study addresses an NP-hard problem known as the max-mean dispersion problem (MaxMeanDP), which belongs to a well-known class of diversity problems. In view of the increasing importance of parallel computing, this study proposes an ant system (AS) approach to the MaxMeanDP in which ants execute the search process in parallel. Moreover, by proposing a rather different use of the pheromone trail and visibility compared to the original AS, a new AS algorithm is introduced that runs in parallel. Our analysis indicates that the new use of the pheromone trail and visibility significantly contributes to the quality of the solution. The results show that the proposed method is effective, efficient and comparable to other approaches.
Automatic image annotation base on Naïve Bayes and Decision Tree classifiers using MPEG-7

recently It has become essential to search for and retrieve high-resolution and efficient images easily due to swift development of digital images, many present annotation algorithms facing a big challenge which is the variance for represent the image where high level represent image semantic and low level illustrate the features, this issue is known as "semantic gab". This work has been used MPEG-7 standard to extract the features from the images, where the color feature was extracted by using Scalable Color Descriptor (SCD) and Color Layout Descriptor (CLD), whereas the texture feature was extracted by employing Edge Histogram Descriptor (EHD), the CLD produced high dimensionality feature vector therefor it is reduced by Principal Component Analysis (PCA). The features that have extracted by these three descriptors could be passing to the classifiers (Naïve Bayes and Decision Tree) for training. Finally, they annotated the query image. In this study TUDarmstadt image bank had been used. The results of tests and comparative performance evaluation indicated better precision and executing time of Naïve Bayes classification in comparison with Decision Tree classification.
Light field three-dimensional measurement

This letter reports a novel method for light field three-dimensional measurement by using unfocused plenoptic cameras. A light field metric model, based on which nonmetric depths in the image space can be mapped to metric dimensions in the object space, was established. Furthermore, with the aid of a three-dimensional measurement system, light field metric calibration was carried out by determining light rays with spatio-angular parameters and set up the depth relationship between the object and image spaces in accordance with the light field image properties.
Modeling the survivorship and the hazard functions of lognormal distribution used to predict risk factors for stroke

Model of data distribution used for predict by the general of the events. Survival times are data that measure by the time to a certain period. The survivorship function is used to predict probability that an individual survives, while the hazard function is used to predict the probability of individual sick during a time. The data are assumed lognormal distribution. This matter took a random sample of participants from a study of the incidence and risk factors for stroke. The covariates are body mass index, smoking, alcoholic, diabetes mellitus, and albuminuria. This paper discusses comparing between models of the survivorship and the hazard functions by without covariate and with covariate. Determining the survivorship and the hazard functions with covariate use Accelerated Failure Time (AFT) model. The result of this research is body mass index, smoking, and diabetes mellitus cause stroke. Body mass index is positive effect, while smoking and diabetes mellitus are negative effect. Alcoholic and albuminuria are not significant. Measure of prediction accuracy uses the Mean Absolute Percentage Error (MAPE). The MAPE is 0.8729%, this value means that 99.1271% of models are good for prediction.
Enhanced Performance of Cryptographic Mechanism using Randomized Generation Technique for Secure Transmission of Data

The rapid growth of electronic devices depending on the internet has solved many problems that the humans were facing. The major challenge is “To transmit secure data over the network and to assure its confidentiality, integrity and availability”. The solution is to transfer encrypted data over the network to prevent its unauthorized identification, modification, destruction etc. A new proposed encryption and decryption algorithm has been designed in this paper, in which the data is mathematically scrambled to a form such that the original data is surrounded by a set of randomly generated alphabets. The encrypted format is based on the collection of ASCII codes. This form is used to transfer the data over the insecure network channels.
PFCO-based Automatic Annotation on Cultural Relic Images

Aiming at the problem of automatic image annotation of Chinese ancient pottery figurines, this paper proposes a novel automatic annotation method based on Pottery Figurine Classification Ontology (PFCO), in which we use the Jseg algorithm to segment the pottery figurine images into regions, and adopt the multi-SVM to establish the relationship between low-level features and PFCO attribute concepts, finally implement inference rules of semantic concepts of the PFCO to capture the high-level semantic concepts of pottery figurines. Experimental results on cultural relic images and ECCV datasets demonstrate the utility and efficiency of the proposed approach as compared to the state-of-the-art algorithms.
T–S Fuzzy Systems

This chapter presents an overview of ideas and techniques of Takagi–Sugeno (T–S) fuzzy systemsTakagi–Sugeno (T–S) fuzzy system. In Chap. 1 , we have introduced the basic concepts of fuzzy sets, fuzzy logic, and fuzzy inference mechanism. This chapter aims to present a necessarily selective review on T–S fuzzy systems including their architectures, important properties, and applicability in the field of nonlinear system identification and control with particular emphasis on the issues which are directly related to the main topics addressed in the following chapters.
Developing Computerised Health Translation Readability Evaluation Tools

Health translation readability assessment represents an important yet largely underexplored research area in translation studies. This chapter introduces an integrated analytical system developed for the computer-aided assessment of the readability of Chinese health translations. The system comprises two components which are a computerised Chinese text lexical profile analyser; and a data-driven statistical instrument that can be used to diagnose and label the readability level of Chinese translations and non-translated health education materials. The online Chinese lexical profile analyser was informed by recent research in corpus linguistics and Chinese educational literacy. It includes thirty-nine individual and compound lexical features to enable in-depth and systematic analyses of the lexical complexity and textual coherence of Chinese health education and promotion materials. The statistical instrument was developed using a large Australian Chinese Health Translation Corpus. The statistical instrument built contains two measurement scales which are related to the information load and the lexical technicality as two important indicators of the readability of Chinese health education resources. The study demonstrated the viability and effectiveness of developing digital analytical tools and instruments for the objective assessment of the readability of health materials, especially health translations which hold the key to the success and sustainability of health promotion and communication in multicultural societies with diverse population groups.
Ryukyu-substrate Japanese

This chapter provides a description of the various dialects of Ryukyu-substrate Japanese, that is, informal varieties of Japanese with substratal influence from Ryukyuan languages. Ryukyu-substrate Japanese has developed along with the language shift process over the last century as each generation adopts or discards certain features of their local language. Since younger generations have not acquired their local language, they assign new functions to linguistic items heard in the speech of their elders, or otherwise create new ones based on Ryukyuan word formation rules. The text summarizes the most important studies of geographical and generational variation in phonological, grammatical and lexical contact effects on the Japanese spoken in Amami, Okinawa, Miyako, Yaeyama and Yonaguni.