Reviewerly/exHarmony huggingface dataset:
A Novel 3-D Image Retargeting by using Stereo Seam Carving with Disparity Map Acquisition (DMA) Algorithm

Gaining popularity in digital world imposes the challenge to preserve the semantic significances in the original image while display on any arbitrary device irrespective of its size or aspect ratio is the well-known `Image Retargeting'. When the image is to be tailored by focusing on its objectives tenacity, then the insignificant portions of the images are identified and wipe out. The historical method envisages the due respect to the pixels by considering its bottom to top style. On the contrary, the projected method builds by using top-down tactic. This appraisal is fused by `Classification guided Fusion Network (CFN). The feature is widely applied on 3D images which fuses left as well as right eye images which are having different viewpoints and differently designed. The disparity map acquisition algorithm fuses the images with semantic collage of the images.
Using self-organizing maps as unsupervised learning models for meteorological data mining

One of the most challenging topics within the meteorological domain is weather forecasting, with a particular focus on predicting severe weather. Weather forecasting for short-time periods (0 to 6 hours) is called weather nowcasting and is of major interest both for researchers and for operational meteorologists. For operational meteorologists, the task of issuing nowcasting warnings is not an easy one as it is highly dependent on their experience in analysing and correlating various meteorological observations. A study on using self-organizing maps for analyzing radar data is conducted in this paper, with the aim of detecting meaningful patterns which are relevant for short-term weather prediction. The Romanian National Meteorological Administration has provided actual radar data which is used in our experiments and highlight that self-organizing maps have the potential to uncover meteorological relevant relationships.
Blockchain Research and Applications: A Systematic Mapping Study

Sagar Bharadwaj, K. S. Dharanikota, Samvid Honawad, Adarsh Chandrasekaran, K.Brought to the limelight by the famous Bitcoin, blockchain has since evolved and now sees a lot of use cases apart from cryptocurrencies, such as in distributed storage systems, finance, health care, and so on. It is, therefore, an area of scrutiny by a lot of researchers and application developers. Significant amount of research on blockchain involves the application of blockchain technology to solve problems from various domains or improve the existing architecture of blockchain itself. The recent trend toward the decentralization of the Internet has given rise to many decentralized applications which also rely fundamentally on blockchain. In this paper, we conducted a systematic mapping study on blockchain technologies. The objective of the study is to identify and map various domains of research related to blockchain and recognize possible directions for future research. We do so by formulating a set of well-defined research questions and providing answers to them.
Comparison of Data Mining Algorithms for Predicting the Cancer Disease Using Python

Fundamentally, machine learning is the part of data science which is nothing but AI. We use machine learning algorithms for predicting the future results after analyzing the past data. This technique of data processing is called data analytics. Machine Learning algorithms are divided in three sections: Supervised, Unsupervised and Reinforcement. These algorithms are further subdivided in other sections. In this paper we are comparing these algorithms by which in future we could easily update the accuracy level of the ML algorithms. For doing this we used the healthcare data which has been uploaded on the kaggle. We implemented the machine learning algorithm using python programming language and calculated the accuracy level of each algorithm.
Emerging Truths in Mandarin Mobile-Assisted Language Learning

Abstract Mobile devices and technology integration in teaching and learning are becoming common tools in education. However, not all technology-enhanced instruction is structured in the same manner. At the same time, mobile learning is a comparatively new occurrence and the theoretical fundamental is presently under development. Thus, this paper presents a comprehensive pedagogical view among the current Mandarin mobile learning practice to the existing MALL framework and principles. The researcher cum instructor conducted an individual Mandarin mobile instruction via smartphone applications. The results from the implementation were qualitatively studied and analysed to evaluate as to what extent the Mandarin practice has kept up with the existing MALL pedagogical framework and principles. The emerging truths found in the results that might be impactful for Mandarin mobile learning were pedagogical skill, situation task content, continuous reflection, readiness, and native speaker.
AUGMENTED REALITY TO ELEVATE THE CHENTA KOPI

Abstract Augmented Reality (AR) to elevate the Chenta Kopi is an application which uses ‘augmented reality’ as a platform to well-known by students and also staff among university. It developed for all student and also staff Universiti Teknikal Malaysia Melaka. This project contain various type of interactivity and element of multimedia like graphic, video, audio and ‘augmented reality’. Element multimedia like this can afford for attract the user also know much further Chenta Kopi. This paper attached with ‘augmented reality’ which enable users fully use their smart phone function. This project develops by using a few software applications like Adobe Illustrator, Adobe Photoshop, Adobe Premiere Pro, and Entiti. The entire graphical element available in this project is design in Adobe Photoshop and Adobe Illustrator. Adobe Premiere Pro is for editing video. Entiti is the platform where all the multimedia element ware integrating to develop an augmented reality. This is reform way for student and staffs acquaint Member of Chenta Kopi closer.
Programming Real-Time Sound in Python

For its versatility, Python has become one of the most popular programming languages. In spite of its possibility to straightforwardly link native code with powerful libraries for scientific computing, the use of Python for real-time sound applications development is often neglected in favor of alternative programming languages, which are tailored to the digital music domain. This article introduces Python as a real-time software programming tool to interested readers, including Python developers who are new to the real time or, conversely, sound programmers who have not yet taken this language into consideration. Cython and Numba are proposed as libraries supporting agile development of efficient software running at machine level. Moreover, it is shown that refactoring few critical parts of the program under these libraries can dramatically improve the performances of a sound algorithm. Such improvements can be directly benchmarked within Python, thanks to the existence of appropriate code parsing resources. After introducing a simple sound processing example, two algorithms that are known from the literature are coded to show how Python can be effectively employed to program sound software. Finally, issues of efficiency are mainly discussed in terms of latency of the resulting applications. Overall, such issues suggest that the use of real-time Python should be limited to the prototyping phase, where the benefits of language flexibility prevail on low latency requirements, for instance, needed during computer music live performances.
Design-flow Methodology for Secure Group Anonymous Authentication

In heterogeneous distributed systems, computing devices and software components often come from different providers and have different security, trust, and privacy levels. In many of these systems, the need frequently arises to (i) control the access to services and resources granted to individual devices or components in a context-aware manner and (ii) establish and enforce data sharing policies that preserve the privacy of the critical information on end users. In essence, the need is to authenticate and anonymize an entity or device simultaneously, two seemingly contradictory goals. The design challenge is further complicated by potential security problems, such as man-in-the-middle attacks, hijacked devices, and counterfeits. In this work, we present a system design flow for a trustworthy group anonymous authentication protocol (GAAP), which not only fulfills the desired functionality for authentication and privacy, but also provides strong security guarantees.
An ICT System Fault Analysis Technology Based on Text Classification and Image Recognition

Due to the increasing complexity of power grid, SGCC hopes to have a computer-aided decision-making scheme for ICT system fault identification. This paper proposes an ICT fault analysis technology which integrates text classification and image recognition. It aims to solve the problem that only relying on the knowledge reserve and personal experience of a single staff member is often unable to analyze and judge the fault of power grid system. In this paper, the main work is as follows: first, preprocess the ICT fault report data to get more easily recognized structured data by computer; second, build a text recognition model to classify the text content; then build an image recognition model to classify the image content; finally, integrating the text and image classification based on the above content, linear regression is applied to the weight parameters to improve the accuracy and reliability of the classification results. According to the test results of comprehensive test data, the accuracy of the method is good and it can be used as a reliable scheme test.
A Fault Analysis Method Based on Text Clustering

A large number of typical fault cases accumulated in the informatization work of State Grid Corporation of China are mostly descriptive text data, which is difficult to understand and analyze by means of automation. In view of this problem, text mining technology is used to extract fault problems and causes from fault cases to form the causal relationship of faults, so as to provide necessary conditions for the next step of fault text mining. This system uses the method of text clustering for fault location and auxiliary research. First of all,do the segmentation of fault information and processing scheme, in this step, the Chinese word segmentation is carried out by using the Jieba word segmentation tool. Secondly, it is necessary to clean the segmentation results and build a corpus. Thirdly, in order to represent the corpus as the type that the computer can calculate the similarity, we need to transform the corpus into frequency matrix. And then instead of using traditional k-means clustering algorithm to cluster, we use the calinski_harabaz score to evaluate the best value of K. Finally, we put this model into application in actual production, build the fault information and solution mapping table.
Logical Approach to the Research of Properties of Software Engineering Ecosystem

A formal model of an ecosystem is proposed. The proposed model consists of an external environment, a server to connect with external environment and internal communication, finite set of working servers, a finite set of operations that can be performed by servers. The working servers are divided into input servers, where initial data are generated, production servers, where tasks are performed, and output servers, where the results of work execution by working servers are stored. The first of the reflections determines a subset of the operations for each production server, that this server can perform, and the second reflection determines the time of each operation execution on the corresponding server. Since it is assumed that the same operation can be performed on different servers, the execution time of such an operation by different servers can also be different. The main problem is to satisfy logical specifications for correct execution of tasks in ecosystem. The proposed formalism is illustrated by the example of a system with two input servers, three production servers and represented as multi-agent system with description in temporal, propositional epistemic logic and propositional dynamic logic. This specification is checked by using corresponding checkers.
Affects of Perceived-Actions within Virtual Environments on User Behavior on the Outside

3D visualization has witnessed exponential growth driven by advances in computer-generated imagery that now include immersive technologies enabling first-person experiences with depth perception and spatial awareness. We investigated two popular uses of VR in 3D architectural visualization: a “passive walkthrough” vs. an “interactive walkthrough”. We designed a within-subject experiment to measure the user-perceived quality and conduct a behavior analysis of users in both experiences. All participants (N=34) were exposed to both conditions and afterwards responded to a post-experience questionnaire. We recorded the physical activity of all participants while they were immersed within the virtual environments and each session was logged in a time diary. So far QoE measurements have relied on subjective and objective evaluations. In this paper, we discuss the behavioral analysis of the effects of immersion and interaction on the simple active behaviors (movements + gestures) of the users. We apply quantitative behavioral observation to cross-examine user behavior against their self-reported responses to a “presence” questionnaire. We conclude that there is significant potential for applying cross-disciplinary behavior analysis tools to overall Quality of Experience within virtual environments.
Prenc — Predict Number of Video Encoding Passes with Machine Learning

Video streaming providers spend huge amounts of processing time to get a quality-optimized encoding. While the quality-related impact may be known to the service provider, the impact on video quality is hard to assess, when no reference is available. Here, bitstream-based video quality models may be applicable, delivering estimates that include encoding-specific settings. Such models typically use several input parameters, e.g. bitrate, framerate, resolution, video codec, QP values and more. However, for a given bitstream, to determine which encoding parameters were selected, e.g., the number of encoding passes, is not a trivial task. This leads to our following research question: Given an unknown video bitstream, which encoding settings have been used? To tackle this reverse engineering problem, we introduce a system called prenc. Besides the use in video-quality estimation, such algorithms may also be used in other applications such as video forensics. We prove our concept by applying prenc to distinguish between one- and two-pass encoding. Starting from modeling the problem as a classification task, estimating bitstream-based features, we further describe a machine learning approach with feature selection to automatically predict the number of encoding passes for a given video bitstream. Our large-scale evaluation consists of 16 short movie type 4K videos that were segmented and encoded with different settings (resolutions, codecs, bitrates), so that we in total analyzed 131.976 DASH video segments. We further show that our system is robust, based on a 50% train and 50% validation approach without source video overlapping, where we get a classification performance of 65% F1 score. Moreover, we also describe the used bitstream-based features in detail, the feature pooling strategy and include other machine learning algorithms in our evaluation.
Algorithm for Tracing Train Delays to Incident Causes

Knowledge of the root cause(s) of delays in transit networks has obvious value; it can be used to direct resources toward mitigation efforts and measure the effectiveness of those efforts. However, delays with indirect causes can be difficult to attribute, and may be assigned to broad categories that indicate “overcrowding,” incorrectly naming heavy ridership, train congestion, or both, as the cause. This paper describes a methodology to improve such incident assignments using historical train movement and incident data to determine if there is a root-cause incident responsible for the delay. It is intended as first step toward improved, data-driven delay recording to help time-strapped dispatchers investigate incident impacts. This methodology considers a train’s previous trip and when it arrived at the terminal to begin its next trip, as well as en route running times and dwell times. If the largest source of delay can be traced to a specific incident, that incident is suggested as the cause. For New York City Transit (NYCT), this methodology reassigns about 7% of trains originally without a root cause identified by dispatchers. Its results are provided to NYCT’s Rail Control Center staff via automated daily reports which, along with other improvements to delay recording procedures, has reduced these “overcrowding” categories from making up 38% of all delays in early 2018 to only 28% in 2019. The results confirm both that it is possible to improve delay cause diagnoses with algorithms and that there are delays for which both humans and algorithms find it difficult to determine a cause.
Trustworthy Human-Centered Automation Through Explainable AI and High-Fidelity Simulation

As we become more competent developers of artificially intelligent systems, the level of deployment and associated implicit trust in these systems will increase in kind. While this is an attractive concept, with an already-demonstrated capability to positively disrupt industries around the world, it remains a dangerous premise that demands attention and intentional resource allocation to ensure that these systems’ behaviors match our expectations. Until we can develop explainable AI techniques or high-fidelity simulators to enable us to examine the models’ underlying logic for the situations we intend to utilize them in, it will be irresponsible to place our trust in their ability to act on our behalf. In this work we describe and provide guidelines for ongoing efforts in using novel explainable AI techniques and high-fidelity simulation to help establish shared expectations between autonomous systems and the humans who interact with them, discussing collaborative robotics and cybersecurity domains.
Modeling of spatio-temporally clustered survival HIV/AIDS data in the presence of competing risks setting

In some applications, clustered survival data are arranged spatio-temporally such as geographical regions over multiple time periods. Incorporating spatio-temporal variation in these data not only can improve the accuracy and efficiency of parameter estimation, but it also investigates spatial pattern of survivorship over the study period for identifying high-risk areas. Competing risks in survival data concern a situation where there is more than one cause of failure, but only the occurrence of the first one is observable. In this paper, we considered several Bayesian hierarchical survival models in the setting of competing risks for the spatio-temporally clustered HIV/AIDS data. An intrinsic conditional autoregressive (ICAR) distribution and a multivariate intrinsic conditional autoregressive (MICAR) distribution were employed to model random effect terms. The comparison between competing models was performed using the deviance information criterion and log pseudo-marginal likelihood. We illustrated the gains of final model through simulation study and application to the HIV/AIDS data.
Affinity Propagation Initialisation Based Proximity Clustering For Labeling in Natural Language Based Big Data Systems

A key challenge for natural language based large text data is automatically extracting knowledge, in terms of entities and relations, embedded in it. State of the art relation extraction systems requires large amounts of labeled data, which is costly and very difficult, especially in industrial settings, due to time constraints of subject matter experts. Techniques like distant supervision require the availability of a related knowledge base, which is rarely possible. We have developed a novel model for automatically clustering textual Big Data, based on techniques inspired from Active Learning and Clustering, that can derive powerful insights and make the data ready for machine learning with minimal manual effort. Our approach differs from Active Learning as we operate under weak supervision, where all the instances provided for training are not manually labeled. Secondly, This differs from any prevailing clustering algorithms as we adopt a whole new approach of proximity clustering based on affinity propagation. Due to the extrapolation of the labeling efforts, our model makes it easier to adopt deep learning approaches with minimal manual effort. In this paper, we describe our algorithm in detail, along with the experimental results obtained for them.
The Multi-Primes Lanstra's Elliptic Curve Factorization Algorithm

The Lenstra's elliptic curve factorization (LEF) algorithm is a problem to factor a composite number N that is a modulus to define an elliptic curve over it, namely E mod N. The LEF method considered as the third fastest algorithm comparing to multiple polynomial quadratic and general number field sieves algorithms. On the LEF, the number N factors into two primes p and q based on the elliptic curve arithmetic. This arithmetic consists of two operations, doubling and addition points on E mod N. Using these operations, the computations 2 P, 3P, ..., mp = ∞, where p is a point on E, are done. Two curves E mod p, and E mod q are determined. On one of these curves an efficient calculating is occurred. In this work, the previous version of the LEF algorithm can be extended to factor N into multi-primes p, q, r, ..., n. The points 2P, 3P, ..., mP=∞ can be computed on n curves, namely E mod p, E mod q, E mod r, ..., E mod n to determine which curve is an efficient for computing. Any algorithm for factoring N through applying the proposed multi-primes Lenstra's elliptic curve factorization (M-PLEF) algorithm is much more difficult. So, the M-PLEF algorithm is more secure in compare to LEF algorithm.
Generalized Net Model of the Deep Convolutional Neural Network

Generalized Nets (GNs) are constructed in a series of papers, representing the functioning and the results of the work of different types of Neural Networks (NNs). In the present research, we show the functioning and the results of the structure of a Convolutional Neural Networks.
Agile method and implementation of gamification in an engineering course

The main objective of this document is to describe the students' behavior in the implementation of a gamification tool. The academic performance of students has increased significantly in the 2019-1 semester compared to the 2017-1 period; one of the factors could be the introduction of a gamification tool accessed by a smartphone in the learning process. In the investigation two groups of students were analyzed: in the first case, the results show the good connection between academic performance and gamification; in the second case, students express mixed feelings after an evaluation using Quizizz. The relevance of this research is that the questions are reused, and the elements, mechanics and dynamics of gamification are combined.