Reviewerly/exHarmony huggingface dataset:
No-reference quality assessment for synthesized images based on local geometric distortions

Depth-image-based-rendering (DIBR) techniques are significant for view synthesis. However, such technique may introduce challenging distortions. Unlike traditional uniform artifacts, the distortions of the synthesized could be local and non-uniform, thus are challenging for traditional image quality assessment metrics. To tackle this problem, aiming at the geometric distortions, a no reference quality assessment for DIBR-synthesized images is proposed in this paper. First, considering that the hue distribution of disoccluded regions is different from that of the natural image, the disoccluded regions are extracted from the hue difference map. The disoccluded regions with different sizes are calculated adaptively by overlapping the hue difference map according to the distortion intensity based on the progressive layer partitioning principle. Second, the artifacts of edges are measured as the distance between the patches at critical regions and their down-sampled versions based on the property of scale invariance. Finally, the perceptual quality is estimate by linearly pooling the scores of two geometric distortions together. The experimental results show that the PLCC, SRCC, RMSE of the proposed model are 0.7613, 0.6965, and 0.4244, respectively. In summary, the proposed metric achieves higher performance, but lower computational complexity than other models.
Video-Based Distance Estimation for a Stream of Vehicles

A novel measuring method of a distance between vehicles in a vehicle stream is presented in the paper. The presented method utilizes vision data for vehicle detection. Input images are converted into binary images, Conversion is performed with the use of analysis of small gradient magnitudes in the input images. Two detection fields, initial and final, are defined. Vehicle detection is carried out by analysis of the state of detection fields. The changes of the state of both detection fields, caused by passing vehicles, are indicate by appropriate image ordinal numbers from the sequence of input images. Application of two detection fields allows determination of a time period between successive vehicles and vehicle speed. The distance between two consecutive vehicles is calculated as a relation of ordinal image numbers. Processing quantities and experimental results are provided.
Improved Saliency-Enhanced Multi-cue Correlation-Filter-Based Visual Tracking

A discrete correlation-filter-based multi-cue-analysis framework is constructed by fusing different feature types to form potential candidate trackers that track the target independently. The selection of corresponding cues and the exploitation of their individual or combined strengths is a less researched topic especially in the context of ensemble tracking. Every candidate tracker from the ensemble is chosen according to the degree of its robustness per frame. We argue that, if each of the candidate trackers is guided by higher-level semantic information (i.e. pixel-wise saliency maps in ensemble-based tracker), this will make tracking better to cope with appearance or view point changes. Recently, saliency prediction using deep architectures have made this process accurate and fast. The formation of multiple candidate trackers by saliency-guided features along with other different handcrafted and hierarchical feature types enhances the robustness score for that specific tracker. It improved multiple tracker-based DCF frameworks in efficiency and accuracy as reported in our experimental evaluation, compared to state-of-the-art ensemble trackers.
Weighted Nearest Centroid Neighbourhood

A novel binary classifier based on nearest centroid neighbours is presented. The proposed method uses the well known idea behind the classic k-Nearest Neighbours (k-NN) algorithm: one point is similar to others that are close to it. The new proposal relies on an alternative way of computing neighbourhoods that is better suited to the distribution of data by considering that a more distant neighbour must have less influence than a closer one. The relative importance of any neighbour in a neighbourhood is estimated using the SoftMax function on the implicit distance. Experiments are carried out on both simulated and real data sets. The proposed method outperforms alternatives, providing a promising new research line.
Char-RNN and Active Learning for Hashtag Segmentation

We explore the abilities of character recurrent neural network (char-RNN) for hashtag segmentation. Our approach to the task is the following: we generate synthetic training dataset according to frequent n-grams that satisfy predefined morpho-syntactic patterns to avoid any manual annotation. The active learning strategy limits the training dataset and selects informative training subset. The approach does not require any language-specific settings and is compared for two languages, which differ in inflection degree.
A Cost-Efficient Multi-cloud Orchestrator for Benchmarking Containerized Web-Applications

Benchmarking the containerized web-applications across multiple cloud gives web-application owners more chance to deploy their applications on cheaper host while meeting their performance requirements. However, benchmarking a large number of cloud hosts (about 267 cloud providers in the world) to find a flexible deployment option becomes a grand challenge. Users need to evaluate as many hosts as possible to find an option which offers expected performance at the lowest price. It is also necessary to benchmark the hosts for longer duration so that it can capture the uncertainty of cloud environment. In this paper, we present Smart Docker Benchmarking Orchestrator (SDBO), a general orchestrator that automatically benchmarks containerized web-applications in multi-cloud environment. At the same time, SDBO is able to maximize the numbers of evaluated cloud providers and type of hosts without exceeding users' budgets. Moreover, we propose a flexible execution module which enhances SDBO 's ability to capture the performance variation of benchmark web-application for longer period of time in the defined users' budgets.
Preliminary design of mobile-asynchronous language laboratory on learning writing paragraph

Abstract The problem of a post-modern language laboratory is to formulate the concept of a mobile laboratory which is the device for laboratory users connected to the internet. This research attempts to formulate a preliminary design of mobile-asynchronous language laboratory. The concept of mobile-asynchronous language laboratory formulated through a trial of paragraph writing learning. The result of this research is that the mobile-asynchronous language laboratory uses social media WhatsApp is being developed. Further progress research will analyse data about learning instruction, learning activity, and learning result. This preliminary research only report limited progress of the research.
Image Encryption using DNA rules &amp; Transmission of an encrypted digital image using USRP-2901 and MATLAB

Securing the originality of a document or an image is very important in this world of technology. The transmission and reception of an image in any domain is to be done with care, because an image reveals more information in just a view of it, when compared to a document. Encryption is the simplest method to secure any data while transmitting it. Deoxyribonucleic Acid (DNA) is the genetic material in any living organism. It is fundamentally formed by nucleotide and phosphate group. The nucleotide is made up of either Cytosine (C), Guanine (G), Adenine (A) or Thymine (T), which are called as nucleobases. There are possibly eight combinations of these DNA nucleobases, and according to the eight combinations eight rules are used for encryption. But before encryption part, shuffling of the pixels will provide outrageous security, and accordingly three levels of shuffling is done here to enhance the twist. The shuffling involves wavelet transform and chaotic maps. The second part of the paper consists of transmission and reception of the encrypted image using Universal Software Radio Peripheral (USRP) by interfacing with MATLAB 9.5. Performance Analysis and comparative analysis is performed to prove that the methodology is satisfactory with the critical values of Next Pixel Change Ratio (99%), Unified Average Change in Intensity (33%) and entropy (nearly 8).
A Study on the Attitude of Students in Higher Education Towards Information Communication Technology

Learning today is different from traditional ways due to the development in Information Communication technology (ICT). The extensive Internet accessibility of personal computers, laptops, smart phones and tablets and numerous literature recovery applications have altered the education and the training surroundings in entire disciplines. Several teachers identify the essential to exploit the abilities of ICT to improve their learning packages. Clarifications on student's aptitude with ICT are little, and are approved in countries where informatics is well established. Data collection is done through questionnaire from nearly 250 students who are exhausting computers for their theoretical purposes. This process is done by using the Apriori algorithm of Association Rule mining, Bayesian Classification algorithms and compared in Data mining using the WEKA tool. BayesNet Classification model provides the maximum accuracy of the students' approach on Information Technology making them to select a job.
Aster 20<sup>th</sup> Anniversary: Achievements and Geologic Contributions to Mineral and Lithologic Mapping

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a 15-channel imaging instrument operating since1999 on NASA's Earth Observing morning orbital platform Terra ( Yamaguchi et al., 1998 ). The ASTER instrument was built and provided by Japan's Ministry of Economy, Trade, and Industry (METI). U.S. National Aeronautics and Space Administration (NASA) provided the platform and launch vehicle. ASTER is jointly scheduled and operated by NASA and METI. ASTER has three separate optical subsystems: the visible and near-infrared (VNIR) radiometer, acquiring images in 3 bands with a 15 m instantaneous field of view (IFOV), and an additional backward-looking band for stereo; the shortwave infrared (SWIR) radiometer, acquiring images in 6 bands with a 30 m IFOV; and the thermal infrared (TIR) radiometer, acquiring images in 5 bands with a 90 m IFOV. ASTER acquires images in all bands with a swath width of 60 km. It orbits the earth in a sun-synchronous near-polar orbit with an equator crossing time of 10:30 am, and the daytime repeat visit interval is 16 days at the equator. On average, 550 scenes are acquired every day; over 3.5 million scenes are currently in the archive.
Development of the Twikle CRM system

This article presents an analysis of trends in the development of the information technology market. The development of the content management systems (CMS) is traced from its origin. The tools designed to create such systems are considered. First of all, these are programming languages and technology companies working in this field. It is no secret that the development of the Internet is primarily associated with the development of the sources of information, that is, with the websites. Currently, there constantly appear much easier ways of website creation and the procedure of information uploading is getting significantly simplified year by year. A brief analysis of programming languages for creating websites is given. The possibilities and tools for creating CMS augmented with a number of frameworks to speed up the development exponentially and having other advantages as well are dwelt upon. The functional capabilities of CRM-systems both in general and for the development of the whole business, especially the online segment are presented. The article substantiates the necessity for the implementation of CRM system and describes the main features of the system.
Configuring the information environment of microcomputers with the Microsoft Windows 10 operating system

The article summarizes the 9-month experience in configuring a distributed information environment of six microcomputers with the Microsoft Windows 10 operating system in accordance with the BYOD (Bring Your Own Device) paradigm.It has been established that for scientific and educational presentations, office and trading activities it is currently advisable to use fanless microcomputers with a perforated case and an internal WiFi antenna, at least 4 GB of RAM and 64 GB of permanent memory, a microSD memory card slot (TF, at least 128 GB, NTFS file system), Intel HD Graphics, USB3.0 and HDMI.Based on comparative experiments, methodological recommendations were created on optimizing the configuration of the hardware-software environment of microcomputers in stationary and mobile conditions.Common problems were noted when connecting peripheral devices via Bluetooth due to their disengagement by the energy optimization system.The problems of major updates of Windows 10, as well as the compatibility of Microsoft Store software and third-party manufacturers, are analyzed.It is recommended to specialize individual microcomputers for working with 32-bit applications; accounting and cryptographic programs; as well as conducting presentations with their videorecording.Options for optimal configuration of the Start menu of the Windows 10 desktop are suggested.It is concluded that specialization of the hardware-software configuration of modern microcomputers can increase the work efficiency with the use of single devices and their systems in accordance with BYOD standards.
Workshop Summary

The second Workshop on Emerging Trends in Software Engineering for Blockchain (WETSEB 2019) intends to pursue the experience of WETSEB 2018 which inherited from the past eight editions of WETSoM (Workshop on Emerging Trends on Software Metrics) the challenges of gathering together researchers interested in emerging topics and trends in software engineering. WETSEB 2018 marked a transition in the workshop interest from the software metrics to the newly emerging fields of research in a software technology whose powerful wide-spreading is disruptively invading academy, industry, finance and media: the Blockchain software technology. Motivations for this workshop are augmented since last edition given the clear outstanding interest in software communities, in the academy, in the industry, in the finance and in the media on the new emerging software technology of the Blockchain witnessed by the larger and larger amount of publications, of start-ups and companies which last year exploited this new technology. We first outline the aims of the workshop, followed by a discussion of its keynote speech and technical program.
ewrapper

In this paper, we report findings from our pilot study using semi-structured interviews to assess users' perceived receptivity to an adaptive system of engagement strategies, ewrapper, that "wraps" around decision points in a dietary self-monitoring mobile application. Study participants were asked to perform a "think-aloud" usability exercise with paper prototypes of the intervention's interface design. This was followed by a needs and preferences assessment of the operationalization of various engagement strategies used in the design of ewrapper. We provide future design guidelines for such systems to improve user engagement.
The Analysis of Modern Data Storage Systems

Today, Storage Area Network and Cloud Storage are the common Storage System. Storage Area Network includes NAS, SAN, DAS systems. Cloud Storage includes object storage, file storage, block storage. Storage Area Network is an important technology because it may give a lot of data volume with a high recovery chance and secure access, work and central management with data. Cloud Storage has many advantages: data mobility, teamwork, stability, scalability, quick start. The main threats include destruction, theft, corruption, unauthentication, replacement, blocking. Storage Area Network components (architecture elements, protocols, interfaces, hardware, system software, exploitation) have a lot of vulnerabilities. Cloud Storage may be attacked by software, functional elements, clients, hypervisor, management systems. A lot of companies design storage solutions: DropBox, QNAP, WD, DELL, SEAGATE.
WEBLORS – A Personalized Web-Based Recommender System

Nowadays, personalization and adaptivity becomes more and more important in most systems. When it comes to education and learning, personalization can provide learners with better learning experiences by considering their needs and characteristics when presenting them with learning materials within courses in learning management systems. One way to provide students with more personal learning materials is to deliver personalized content from the web. However, due to information overload, finding relevant and personalized materials from the web remains a challenging task. This paper presents an adaptive recommender system called WEBLORS that aims at helping learners to overcome the information overload by providing them with additional personalized learning materials from the web to increase their learning and performance. This paper also presents the evaluation of WEBLORS based on its recommender system acceptance using data from 36 participants. The evaluation showed that overall, participants had a positive experience interacting with WEBLORS. They trusted the recommendations and found them helpful to improve learning and performance, and they agreed that they would like to use the system again.
Management of planting system based on water balance patterns on corn plants using Cropwat 8.0 model

Abstract Corn plants are the second most important food crop after rice; however, climate anomalies will affect the amount of production that is related to the water requirements available during the process of growing corn plants. This study aims to determine the amount of effective rainfall in one year and the need for irrigation water in one planting season, the water requirements for maize in a particular planting period and the comparison of the rate of evapotranspiration and the level of water that must be available to plants. This research was conducted from May to September 2014 at the Agricultural Technology Research Institute (BPTP) of South Sulawesi, Gowa Regency. The data obtained in the field was then processed using the simulation method of a Windows-based application, Cropwat 8.0. The results show that the water needs of corn plants in each growth phase are: 107 mm/decade in the early development phase, 132.9 mm / decade in the mid-phase, and 107.4 mm / decade in the late phase. The amount of rain data for a year in 2014 obtained using Cropwat was 395.6 mm, and the effectiveness of the rain that occurs was 355.9 mm. These figures then used to determine the irrigation water needs for one growing season.
Hierarchical Segmentation of Entities in a Handwritten Document

The increasing deceptive ways of counterfeiting handwritings have given rise to the field of study of handwriting. In this digital age, considering the amount of data generated and to be processed, there is a need of computer-based verification for handwritings. The system designed for such purpose works on the feature extraction at various levels of an input document. Most of the feature extraction methodologies and algorithms deliver a lower efficiency due to improper segmentation of the handwriting samples. Hence, there arises a need of a robust image segmentation preprocessing block. A handwritten document has three main key elements forming it i.e. the sentences forming the document, the words forming the sentences and the characters forming the words. These elements are an ocean of opportunities for feature extraction, which is possible by extracting them efficiently and with minimum misidentifications. Hence, the paper presents a novel idea of segmentation into various levels of the elements forming the handwritten document. The proposed method exploits the behavior of watershed algorithm in combination with a linearly programmed approach for reliable segmentation of the handwritten documents.
A planar-pattern-based calibration method for high-precision structured laser triangulation measurement

A flexible calibration method with high precision was proposed for calibration of structured laser triangulation in the applications of 3D reconstruction and point cloud processing. The camera coordinate system was chosen as the ultimate reference coordinate. In this calibration process, be required only a planar pattern placed at different orientations and positions for obtainment of the camera intrinsic parameters. And then two or more non-coplanar sets of three collinear points were calculated for the laser projection plane coefficients. A line laser triangulation measurement system is established, consisted of laser diode, cylindrical lens module and camera imaging system. Experimental results based on the constructed structured laser triangulation system revealed that accuracy of the profile measurement is less than ±0.005mm, which can meet the majority of industrial inspection applications.
A Knowledge Representation for Planning-Based Story Generation Applied to the Manual and Automatic Encoding of Plot

There have been a range of coding schemes to code story structure. However, few of these coding schemes map directly to expressive formal models of story that also characterize character beliefs or the complexities that arise when mistaken beliefs lead to action failure. We describe HeadCode, a coding scheme motivated by recent work in plan-based story generation.
Breast Cancer and Mammographic Anomalies

Breast cancer is one of the common globally detected cancers, affecting each year an average of 1.4 million women. It has caused a toll of 1 in 8 out of all cancer-related deaths especially among women under the age of 50. It scores to be the second leading cause of deaths in country as well as other parts of the world (lungs and skin cancer being the major cause of mortality).
A Framework For Human Behaviour Detection Using Combined Analysis of Facial Expression and Eye Gaze

Facial appearances like a happy, sad, disgust, surprise, angry, fear and Eye Gaze Estimations are the fastest means of communication while conveying any type of information. Expressions not only expose the sensitivity or feelings of any person but can also be used to judge his/her mental states. Therefore, it has become a wide interest area of research due to its applications to the fields like Human Computer Interface (HCI), Man Machine Interface (MMI) etc. In order to get more effective human behavior identification, we have presented a combination of facial expression recognition and eye gaze estimation technique where we have to recognize the real time expressions by using Convolutional Neural Network (CNN) model with transfer learning method. After that, we determine the eye gaze by using Viola Jones, Circular Hough Transform (CHT) and a geometric method. With the combination of both processes, we able to predict the human behavior like drivers concentration levels. In this way experiments are carried out on MUG database for our own trained CNN model based on VGG16 (Visual Geometry Group) pre trained model and gives better performance with accuracy of 93% for training and 94% for overall process.