Reviewerly/exHarmony huggingface dataset:
Generative adversarial networks based super resolution of satellite aircraft imagery

Generative Adversarial Networks (GANs) are one of the most popular Machine Learning algorithms developed in recent times, and are a class of neural networks that are used in unsupervised machine learning. The advantage of unsupervised machine learning approaches such as GANs is that they do not need a large amount of labeled data, which is costly and time consuming. GANs may be used in a variety of applications, including image synthesis, semantic image editing, style transfer, image super-resolution and classification. In this work, GANs are utilized to solve the single image super-resolution problem. This approach in literature is referred to as super resolution GANs (SRGAN), and employs a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes the solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and the original photo-realistic images, and the content loss is motivated by the perceptual similarity and not the similarity in the pixel space. This paper presents implementation of SRGAN using Deep convolution network applied to both the aerial and satellite imagery of the aircrafts. The results thus obtained are compared with traditional super resolution methods. The resulting estimates of SRGAN are compared against the traditional methods using peak signal to noise ratio (PSNR) and structure similarity index metric (SSIM). The PSNR and SSIM of SRGAN estimates are similar to traditional method such as Bicubic interpolation but traditional methods are often lacking high-frequency details and are perceptually unsatisfying in the sense that they fail to match the fidelity expected at the higher resolution.
PredICT

In today's competitive economy, a lot of graduating students from different programs find it hard to look for the appropriate job after graduation that is related to their undergraduate programs. The proposed PredICT mobile application using the Naïve Bayes algorithm is designed to help the students achieve their career goals by predicting the jobs depending on their skills. With the predicted job, the students can have more understanding of the required skills of the career they would most likely pursue. The result of the tests and evaluation shows that most users strongly agreed on the application's software quality.
Prediction of water pollution in Kali Surabaya river segment Karangpilang-Ngagel using stella model

Abstract Kali Surabaya River is one of the river in Surabaya that use as the water source of PDAM Surabaya. Moreover, Kali Surabaya River takes an important role of society, industry, agriculture and economic commerce in its surrounding area. The increasing of industry and domestic waste that dispose to Kali Surabaya River, causing the degradation of its water quality. From parameters DO, BOD, and COD of Kali Surabaya River, we can look for relation and mutual attachment to determine the class of water body of Kali Surabaya. The method that use in this case is system dynamics using Stella Model. The purpose of this modelling is to identify and determine the relation of DO, BOD and COD value that contains in Kali Surabaya river segment Karangpilang-Ngagel Surabaya, and to determine the class of water body of Kali Surabaya River segment Karangpilang-Ngagel using dynamic system method. The result of this modelling is prediction of DO parameter value that decreasing in 2018 at all three segments. In December 2018, concentration of DO in segment 1 is 3.79 mg/L; in segment 2 is 4.34 mg/L and in segment 3 is 3.72 mg/L. According to the prediction result from this model, DO value at all segments in November and December is still exceed the standard value of class II river standard, which is 4 mg/L. In the other hand, in January until October DO value at all segments is under the standard of class II river standard. The average DO value of Kali Surabaya River in 2018 is in the range of 3.72-4.9 mg/L.
Towards the Processes Discovery in the Medical Treatment of Mexican-Origin Women Diagnosed with Breast Cancer

In the last decades, the society went from being predominantly analog to digital. This has had a significant impact on the way of doing new analyzes for business, communications, education, health, safety, among other areas of interest. This technological advance, in turn, represents one of the main challenges in today’s organizations. This is due to the need to extract information and value from the stored data, for example, to discover the real behavior of the operating processes registered in the information systems, and to define the way in which the resources interact with the process, that is, activities, roles, assignment rules, and priorities. This paper presents the proposal for the design of a method for the discovery of processes in the medical treatment of Mexican-origin women diagnosed with breast cancer. Disease caused by the uncontrolled growth of cells. These cells that are not necessary for the body could eventually form a cancerous tumor.
Collaborative Filtering Algorithm Based on User Characteristic and Time Weight

This paper proposes a collaborative filtering recommendation algorithm based on user characteristics and time weight which focuses on the data sparseness and cold start problems of collaborative filtering algorithms. First, digitize user's characteristics in the dataset and calculate the similarity degree of the user's feature, then weight the similarity calculation formula with the integration time function to obtain the comprehensive similarity so that a more accurate prediction score is obtained. The comparison experiments showed that the algorithm can reduce the sparseness of the data set effectively when the data is extremely sparse, and to some extent, it alleviates the cold start problem and improves the prediction accuracy of the recommendation algorithm.
Combining Statistical and Structural Approaches for Arabic Handwriting Recognition

A novel technique to recognize full range of shapes of Arabic handwritten characters is presented. This is done by combining statistical and structural approaches for character recognition. The statistical method first recognizes the main body of a character using modified direction features and Support Vector Machines. Then in structural classification, dot-descriptors are used to recognize the exact shape of an Arabic character. On IfN/ENIT benchmarking database, we achieved 96.71% accuracy for character main-bodies and 94.52% accuracy on the complete characters. Our results compare favorably with the state of the art.
Noncontact Bending and Torsional Stiffness Estimation Model for Automobile Frames Based on 3D Displacements

In this study, a noncontact bending and torsional stiffness estimation model for automobile frames is proposed. The deformed shapes of an automobile frame during bending and torsional loading tests were determined based on the 3D displacements from motion capture system (MCS). The measurement results from the system and existing sensors, such as linear variable differential transformers (LVDTs) and an inclinometer, were compared. The vertical displacements from a total of 30 markers measured by the MCS can be used to define the 3D bending and torsional deformed shapes of the automobile frame. The test results confirmed that the stiffness estimation model based on MCS facilitates more detailed and accurate estimation of stiffness of the automobile.
Overview of machine learning (ML) based perception algorithms for unstructured and degraded visual environments

Machine learning based perception algorithms are increasingly being used for the development of autonomous navigation systems of self-driving vehicles. These vehicles are mainly designed to operate on structured roads or lanes and the ML algorithms are primarily used for functionalities such as object tracking, lane detection and semantic understanding. On the other hand, Autonomous/ Unmanned Ground Vehicles (UGV) being developed for military applications need to operate in unstructured, combat environment including diverse off-road terrain, inclement weather conditions, water hazards, GPS denied environment, smoke etc. Therefore, the perception algorithm requirements are different and have to be robust enough to account for several diverse terrain conditions and degradations in visual environment. In this paper, we present military-relevant requirements and challenges for scene perception that are not met by current state-of-the-art algorithms, and discuss potential strategies to address these capability gaps. We also present a survey of ML algorithms and datasets that could be employed to support maneuver of autonomous systems in complex terrains, focusing on techniques for (1) distributed scene perception using heterogeneous platforms, (2) computation in resource constrained environment (3) object detection in degraded visual imagery.
Mini-ME Swift: The First Mobile OWL Reasoner for iOS

Mobile reasoners play a pivotal role in the so-called Semantic Web of Things. While several tools exist for the Android platform, iOS has been neglected so far. This is due to architectural differences and unavailability of OWL manipulation libraries, which make porting existing engines harder. This paper presents Mini-ME Swift, the first Description Logics reasoner for iOS. It implements standard (Subsumption, Satisfiability, Classification, Consistency) and non-standard (Abduction, Contraction, Covering, Difference) inferences in an OWL 2 fragment. Peculiarities are discussed and performance results are presented, comparing Mini-ME Swift with other state-of-the-art OWL reasoners.
Flexible Signatures: Making Authentication Suitable for Real-Time Environments

This work introduces the concept of flexible signatures. In a flexible signature scheme, the verification algorithm quantifies the validity of a signature based on the number of computations performed, such that the signature’s validation (or confidence) level in [0, 1] improves as the algorithm performs more computations. Importantly, the definition of flexible signatures does not assume the resource restriction to be known in advance, a significant advantage when the verification process is hard stopped by a system interrupt. Prominent traditional signature schemes such as RSA, (EC)DSA seem unsuitable towards building flexible signatures because rigid all-or-nothing guarantees offered by the traditional cryptographic primitives have been particularly unattractive in these unpredictably resource-constrained environments. In this work, we find the use of the Lamport-Diffie one-time signature and Merkle authentication tree to be suitable for building flexible signatures. We present a flexible signature construction based on these hash-based primitives and prove its security with concrete security analysis. We also perform a thorough validity-level analysis demonstrating an attractive computation-vs-validity trade-off offered by our construction: a security level of 80 bits can be ensured by performing only around $$\frac{2}{3}$$ rd of the total hash computations for our flexible signature construction with a Merkle tree of height 20. Finally, we have implemented our constructions in a resource-constrained environment on a Raspberry Pi. Our analysis demonstrates that the proposed flexible signature design is comparable to other standard signature schemes in terms of running time while offering a quantified level of security at each step of the verification algorithm. We see this work as the first step towards realizing the flexible-security cryptographic primitives. Beyond flexible signatures, our flexible-security conceptualization offers an interesting opportunity to build similar primitives in the asymmetric as well as symmetric cryptographic domains.
Feature Based Data Anonymization with Slicing Method for Data Publishing

Information technology has enabled the collection and sharing of large amounts of data. This data is highly dimensional and contains sensitive information which needs to be protected. When the dimensionality of data increases, a feature selection mechanism can be used to determine a subset of the attributes that have high relevance. The information contained in features with high relevance should be preserved as much as possible. Anonymization techniques have been used to protect sensitive information in published datasets. However anonymization approaches may cause a data distortion that affects attributes with high relevance and thus affect classification accuracy. This work proposes information gain based anonymization with slicing method. We conduct experiments on real life datasets. Our results show that by reducing the amount of data distortion for features with high relevance in a dataset the privacy and quality of data can be enhanced.
Refactoring Decision Support for Developers and Architects Based on Architectural Impact

Refactorings are key activities for achieving sustainable software systems. However, refactorings demand high effort and features are often deemed more important. The time pressure to deliver is always high and to select important refactorings, one has to consider manifold criteria. Current approaches only relate to code smells and design flaws, and do not take architectural impact into consideration. The project aims at developing a decision framework integrating architecture smell detection, selection of appropriate refactorings and impact analysis to prioritize refactorings and support not only developers but also software architects. It shall be evaluated using control groups by measuring and comparing the required time and the resulting software quality.
Formal Interoperability Models of Sensor Networks Based on Logical Workflow Nets

With the advent of Internet of things and cyber physical system, sensor networks become more and more important. To better accomplish a task, multiple sensors or even multiple sensor networks need to interoperate. Logical workflow nets and cooperative logical workflow nets are introduced to formally model and analyze interoperability of sensor networks. Independent feasibility and Interoperable feasibility are important properties for ensuring correct execution and interoperability of sensor networks. Complete path nets, possible path nets, cooperative complete path nets and cooperative possible path nets are presented to decide independent feasibility and interoperable feasibility of logical workflow nets and cooperative logical workflow nets denoting interoperability of sensor networks.
Color Feature Extraction

This chapter focuses on one of the three major types of image features; colors. It first gives a brief introduction to color science, followed by the introduction of four color spaces commonly used in image feature extractionFeature extraction . Readers are demonstrated with pros and cons of each color spaceColor space . Two segmentationSegmentation techniques are also shown to divide an image into regions. In the next, different types of histogram features are introduced to give readers ideas on how simple features can be extracted from a color image. Finally, a number of most commonly used color features are described and discussed in details including four color descriptors standardised by MPEG-7MPEG-7 such as CSDColor Structure Descriptor (CSD) , DCDDominant Color Descriptor (DCD) , CLDColor Layout Descriptor (CLD) , and SCDScalable Color Descriptor (SCD) . This chapter is also a shortcut to color science, which is a complex theory.
A Topic Matching based CNN for Sentence Classification

Convolutional Neural Network (CNNs) are widely used in NLP tasks for their powerful ability to capture n-gram features. An effective way to improve CNNs performance is to incorporate prior knowledge or external resources like topic distribution. This paper considers sentence classification problem as semantic matching problem and proposes a topic matching CNN model, referred to as TM-CNN. Firstly, topic words are extracted directly from training data by a novel Naïve Bayes weighting technique. Then a convolutional neural network is applied on a topic matching matrix whose entries represent the soft similarity interaction between words in sentence and those topic words, which allows model to extract both word level and n-gram level matching signals. Experiments demonstrate the effectiveness of TM-CNN on seven text classification tasks, including sentiment analysis and topic classification.
Exposure correction using deep learning

In the case of poor lighting conditions, it is easy to capture the underexposed images with low contrast and low quality. Traditional single-image enhancement methods often fail in revealing image details because of the limited information in a single-source image. A single underexposed image enhancement method based on adaptive decomposition and convolutional neural network (CNN) is proposed. The CNN training models only need images with different brightness rather than a strict ground-truth image. First, a simple effective synchronous decomposition method is proposed to solve the synergy problem in multisource image decomposition. Then, two CNN models are designed for the high-frequency part and low-frequency part, respectively. They process the high-frequency and low-frequency sub-bands, instead of the entire source images. The weight map obtained from the CNN model represents the contrast distribution. The exposure map generated by gradient-based visibility assessment indicates the exposure distribution. Finally, the weight map and the exposure map are multiplied to generate the final decision map. Experimental results demonstrate that the proposed method outperforms competing methods.
The pragmatic use of vocatives in private one-to-one digital communication

Abstract This article examines a corpus of private text messages collected in Birmingham and surrounding towns in 2015. We look specifically at pragmatic roles played by the vocatives we find in the corpus. Since text messages are sent to targeted recipients, vocatives are structurally redundant, and we review literature concerning vocatives in spoken and written data to first see what categories others have proposed for these functions. We then challenge some of these categorisations, identifying a new category of ‘focuser’ which we argue is akin to a summons in spoken language. We also examine the pragmatic value of vocatives in performing identity work for both sender and receiver. We finish by looking at gendered performance of identity as a case study in our corpus of how text producers construct themselves and others.
Detection of Texture Objects on Multichannel Images

A method for detecting extended texture objects on multi-channel images is proposed. The method is based on the representation of multi-digit digital images by a set of bit images and their approximation by three-dimensional Markov chains with two states. It is proposed to use transition probabilities for three-dimensional Markov chains and brightness characteristics averaged within a sliding window as textural features. To reduce computational resources, determination of signs is carried out by the bit planes of the senior, most informative digits of the digital image. The detection of texture objects is based on the analysis of the histogram of the texture feature and the brightness of the pixels. The simulation results confirm the effectiveness of the proposed method.
Cloud Computing Adoption in the Government Sector in Brazil: An Exploratory Study with Recommendations from IT Managers

Cloud Computing constitutes an alternative for organizations that want to optimize the use of computing resources and rationalize costs with IT infrastructure. However, the adoption and implementation in the government sector pose several challenges, regarding IT control, data protection in the Internet, efficient use of computing resources and cost rationalization. Within the government sector, the lack of knowledge about issues involving adoption and migration to cloud computing may negatively impact IT. This study aims to identify the factors influencing the adoption of cloud computing in the government sector in Brazil. We carried out the study interviewing IT professionals that successfully migrated to the cloud with three organizations, two large and public universities and one data processing agency. Using the approach Value-Focused Thinking, we analyzed the data collected from the interviews. Our Findings revealed a set of twenty-one recommendations to take into account when implementing cloud computing in the government sector, such as using a pilot project, training, and consulting, among others. These recommendations are useful to guide the IT decision makers in the process of cloud computing adoption and implementation as an efficient and reliable approach in the government sector.
Comparing the performance of different types of distributed fuzzy-based traffic signal controllers

This paper compares the performance of three different types of traffic signal timing controllers that all of them apply fuzzy logic systems in their controlling structure. The comparisons are done between an interval type-2 adaptive neuro-fuzzy inference system (IT2-ANFIS) traffic signal controlle r, a type-1 ANFIS (T1-ANFIS), and a fixed-fuzzy logic, and three different fixed-time controllers as benchmarks. Obtained results demonstrate the superiority of the fuzzy controller over traditional controllers. The traffic network used in experiments is a network of nine intersections and all controllers are designed in distributed form. Each intersection has its own controller while it considers its neighbor intersections' traffic jam in determining signal time for each traffic phase. The average performance of IT2-ANFIS, T1-ANFIS, and fixed-fuzzy controllers against the fixed-time controller are approximately 35%, 30%, and 8%, respectively.
Efficient CP-ABE Scheme for IoT CCN Based on ROBDD

A content centric network (CCN) is a promising network proposed for fifth generation (5G) network paradigm. This will improve the efficient distribution of the future Internet of Things (IoT) media content and allow nodes communication based on content names. End users can obtain content from any intermediate caches which possess difficulties in securing data cached. Therefore, the ability for self-contained protection is paramount. A ciphertext-policy attribute base encryption (CP-ABE) scheme had been identified as a preferable solution. To achieve an efficient performance of CP-ABE scheme, we exploited access tree representation of access structure which defines access policy and modified it into a unique CP-ABE scheme based on the reduced ordered binary decision diagram (ROBDD), and proposed an efficient non-monotonic ROBDD CP-ABE scheme for IoT CCN. The size of the key is not affected by the number of users. The ROBDD legitimate path is used to determine the size of the ciphertext instead of the number of the nodes. This scheme has a high decryption stage and offers resistance to collision attack. The efficiency of the proposed scheme is based on the efficiency of the ROBDD. In accordance with performance analysis, ROBDD CP-ABE scheme achieves a high efficiency as compared to the existing CP-ABE schemes.