Reviewerly/exHarmony huggingface dataset:
The Improved Equilibrium Optimization Algorithm with Tent Map

We all know that the chaos could be introduced to the bionic algorithms and improve their capabilities. It can be used to spread the populations at the initializing phase, it can be also used to map the best candidate for another iteration choice. In this paper, we introduce the Tent map to the equilibrium optimization algorithm (EOA) and results show that the first mapping would fail and the second mapping increase the performance of the chaotic EOA dramatically compared to the original one.
Hazard Identification and Detection using Machine Learning Approach

Internet surfing has become a vital part of our daily life. So to catch the attention of the users' different browser vendors compete to set up the new functionality and advanced features that become the source of attacks for the intruder and the websites are put at hazard. However, the existing approaches are not adequate to protect the surfers which require an expeditious and precise model that can be able to distinguish between the begnign or malicious webpages. In this research article, we design a new classification system to analyze and detect the malicious web pages using machine learning classifiers such as, random forest, support vector machine. naïve Bayes, logistic regression and Some special URL (Uniform Resource Locator) based on extricated features the classifiers are trained to predict the malicious web pages. The experimental results have shown that the performance of the random forest classifier achieves better accuracy of 95% in comparison to other machine learning classifiers.
Shared Low-Rank Correlation Embedding for Multiple Feature Fusion

The diversity of multimedia data in the real world usually forms heterogeneous types of feature sets. How to explore the structure information and the relationships among multiple features is still an open problem. In this paper, we propose an unsupervised subspace learning method, named the shared low-rank correlation embedding (SLRCE) for multiple feature fusion. First, in the learned subspace, we implement the low-rank representation on each feature set and enforce a shared low-rank constraint to uncover the common structure information of multiple features. Second, we develop an enhanced correlation analysis in the learned subspace for simultaneously removing the redundancy of each feature set and exploring the correlation of multiple features. Finally, we incorporate the shared low-rank representation and the correlation analysis into a unified framework. The shared low-rank constraint not only depicts the data distribution consistency among multiple features, but also assists robust subspace learning. Our method is robust to noise in practice and can be extended to the kernel case to handle the nonlinear feature fusion. Experimental results on several typical datasets demonstrate the superior performance of the proposed methods.
Multi-camera Motion Estimation with Affine Correspondences

We present a study of minimal-case motion estimation with affine correspondences and introduce a new solution for multi-camera motion estimation with affine correspondences. Ego-motion estimation using one or more cameras is a well-studied topic with applications in 3D reconstruction and mobile robotics. Most feature-based motion estimation techniques use point correspondences. Recently, several researchers have developed novel epipolar constraints using affine correspondences. In this paper, we extend the epipolar constraint on affine correspondences to the multi-camera setting and develop and evaluate a novel minimal solver using this new constraint. Our solver uses six affine correspondences in the minimal case, which is a significant improvement over the point-based version that requires seventeen point correspondences. Experiments on synthetic and real data show that, in comparison to the point-based solver, our affine solver effectively reduces the number of RANSAC iterations needed for motion estimation while maintaining comparable accuracy.
Performance Measurement of Multiple Supervised Learning Algorithms for Bengali News Headline Sentiment Classification

The reading newspaper is a common habit in today's life. Before reading news article all are focused on the news headline. Understanding the meaning of news headline everybody can easily identify the news types. That means the containing news article provides positive or negative news. Analysis of the sentiment of the news headline is a good solution for this kind of problem. Sentiment Analysis is a chief part of Natural Language Processing. It mines any kinds of opinion and set the sentiment of any text. We proposed a method for Bengali news headline sentiment measurement with different kinds of the supervised learning algorithm and their performance. Firstly, we set sentiment of each news headline then used the classification method to predicting the news headline which was containing a positive or negative headline. After all, Bengali is one of the most used languages in this world. A lot of research work done previously in a different language but very few in the Bengali language. So, increasing the Bengali language research resource need to develop different kinds of tools and technology.
An Improved Model for Power Prediction of PV System Based on Elman Neural Networks

High penetration of Photovoltaic (PV) systems is variable resource as challenges to the stability and power quality of electrical grids. Accurate prediction of PV power has been recognized as a way to solve this problem. An improved model for power prediction of PV system based on Elman neural networks is proposed in this paper. Comparing with traditional BP network, the context layer is added in Elman, which make it fewer iterations and less computation. Except from irradiance and temperature, four physical quantities, wind speed, the direction of the wind, humidity, air pressure, are u] sed as inputs in the proposed model. The influence of different weather factors on the accuracy of the proposed model is discussed. The proposed model is applied to experimental data of PV station and compared with BP neural networks. The results indicate that the Elman model with 6 inputs has best accuracy in performance prediction. Moreover, the accuracy is improved especially in the region of low solar irradiance by adding more weather factors. The proposed model can effectively and accurately predict the output power of PV system, which is useful to enhance the safety and stability of the electrical grid.
Gesture Controlled Home Automation using CNN

As technology is rising, more advancements are made in making the life of people easier, by providing methods for easy monitoring and managing. In this paper, a Home Automation model is designed to provide ease of control of home appliances, using an android application. The elderly and physically challenged people can perform their day-to-day activities efficiently. In previous methods, accelerometers are used to monitor the activity, which are accurate but are not flexible and portable. The proposed system detects the gestures given as input by the user and controls the home appliance. The client interface is responsible for capturing the input gesture from the user, using an android application and uploading it on the raspberry pi server. Raspberry pi acts as an important preprocessor. Backend Processing involves image preprocessing, training the CNN model, and prediction of image class category of input gesture image. Based on this predicted class of image, the respectively assigned action takes place at the home interface.
Visual Quality Assessment for Interpolated Slow-Motion Videos Based on a Novel Database

Professional video editing tools can generate slow-motion video by interpolating frames from video recorded at a standard frame rate. Thereby the perceptual quality of such interpolated slow-motion videos strongly depends on the underlying interpolation techniques. We built a novel benchmark database that is specifically tailored for interpolated slow-motion videos (KoSMo-1k). It consists of 1,350 interpolated video sequences, from 30 different content sources, along with their subjective quality ratings from up to ten subjective comparisons per video pair. Moreover, we evaluated the performance of twelve existing full-reference (FR) image/video quality assessment (I/VQA) methods on the benchmark. In this way, we are able to show that specifically tailored quality assessment methods for interpolated slow-motion videos are needed, since the evaluated methods — despite their good performance on real-time video databases — do not give satisfying results when it comes to frame interpolation.
Searching Towards Class-Aware Generators for Conditional Generative Adversarial Networks

Conditional generative adversarial networks (cGANs) are designed to generate images based on the provided conditions, <i>e.g.</i>, class-level distributions, semantic label maps, <i>etc</i>. Existing methods have used the same generator architecture for all classes. This paper presents an idea that adopts neural architecture search (NAS) to find a class-aware architecture for each class. The search space contains regular and class-modulated convolutions, where the latter is designed to introduce class-specific information while avoiding the reduction of training data for each class generator. The search algorithm follows a weight-sharing pipeline with mixed-architecture optimization so that the search cost does not grow with the number of classes. To learn the sampling policy, a Markov decision process is embedded into the search algorithm, and a moving average is applied for better stability. Class-aware generators show advantages over class-agnostic architectures experimentally. Moreover, we discover two intriguing phenomena that are inspirational to craft cGANs by hand.
Analysis of time-to-event data using a flexible mixture model under a constraint of proportional hazards

Guanghan Frank Liu* & Jason J. Z. LiaoBiostatistics and Research Decision Sciences, Merck & Co., Inc., North Wales, Pennsylvania, USA
Architecting Intelligent Digital Systems and Services

Our paper gives first answers on a fundamental question: How can the design of architectures of intelligent digital systems and services be accomplished methodologically? Intelligent systems and services are the goals of many current digitalization efforts today and part of massive digital transformation efforts based on digital technologies. Digital systems and services are the foundation of digital platforms and ecosystems. Digitalization disrupts existing businesses, technologies, and economies and promotes the architecture of open environments. This has a strong impact on new value-added opportunities and the development of intelligent digital systems and services. Digital technologies such as artificial intelligence, the Internet of Things, services computing, cloud computing, big data with analytics, mobile systems, and social enterprise networks systems are important enablers of digitalization. The current publication presents our research on the architecture of intelligent digital ecosystems of products and services influenced by the service-dominant logic. We present original methodological extensions and a new reference model for digital architectures with an integral service and value perspective to model intelligent systems and services that effectively align digital strategies and architectures with artificial intelligence as main elements to support intelligent digitalization.
Teaching Software Engineering Methods with Agile Games

With agile methodology and international collaboration having become industry standards not only in software engineering, how would we enable students in higher education to gather these experiences? One possible approach presents our intensive course on agile software development, applying games in engineering education, within the framework of a one-week pan-European exchange program. First, we briefly introduce the program that’s held twice a year, offering about 60 scientific intensive courses in each session. Topics come from the research fields at the partner institutions and focus on engineering, natural sciences, and mathematics. Roughly 4,000 students from 24 universities in 15 European countries participate in the program annually. Second, we report on the concept and design of our intensive course on agile software development. We apply a flipped classroom setting to acquire the basic knowledge; we use game-based learning for in-class exercises; we foster project-based and collaborative learning for practical implementation. Finally, we outline the project work in our course and share the most inspiring student projects diving into data-driven visualizations and learning analytics. Thus, the participants gain insights into the real-life application of agile project management methods and international collaboration in heterogeneous teams despite the limited course length.
A Multiswitch Open-Circuit Fault Diagnosis of Microgrid Inverter Based on Slidable Triangularization Processing

In this article, a robust inverter fault diagnosis algorithm is proposed under microgrid environment considering unbalanced state and overcurrent component interference. First, the detected signals are transformed into different data triangles by the proposed slidable triangularization processing which can conveniently and effectively extract the multiscale trend features and data jitter components. Further, the signed features are obtained by the proposed jitter signing processing method, which can reduce the influence of amplitude, and facilitate the logical operation and mixed operation. Then, a quantitative trend fault degree is calculated by the signed information to reflect the fault degree and location information, which has the characteristics of quantitative change. It can reduce the fluctuation and quantity of fault data features. Finally, echo state network is used to implement the intelligent classification. Because the design of hidden layer and the network training speed is simple and fast, which makes the design, implementation, and debugging of fault diagnosis more convenient. Compared with the existing fault diagnosis algorithms, it is robust to unbalanced state and overcurrent component interference of microgrid. These features make it more suitable for inverter fault diagnosis of microgrid environment. The effectiveness of fault diagnosis algorithm is verified by the experiments and comparisons.
Dual dense context-aware network for hippocampal segmentation

The morphological analysis of hippocampus based on magnetic resonance imaging is significant for computer-aided neurological disease diagnosis. The segmentation of the hippocampus is a prerequisite for calculating its volume and measuring its shape. In this study, we propose a dual dense context-aware network (DDCNet) for automatic hippocampal segmentation, mainly including a multi-scale input approach and multi-resolution feature fusion. First, considering that a single-scale network cannot accurately segment the hippocampus and background with large difference in volume, we design a multi-scale input module (MSIM) to extract the hippocampus features of different receptive fields. Specifically, large scales are used to learn spatial dependencies, while smaller scales are used to learn more hippocampal details. Taking advantage of the fact that the features from different resolution layers offer semantically different information for segmentation, we develop a multi-resolution feature fusion module (MRFFM) to effectively aggregate complementary features and to simultaneously utilize the high-resolution features from the encoder to guide the low-resolution hippocampus edge segmentation in the decoder phase. In addition, many dense connections are introduced to further extend the feature fusion and contextual information of various resolutions between the encoder and decoder. The proposed approach achieves state-of-the-art performance, outperforming existing hippocampal segmentation approaches on the ADNI dataset.
Dynamic Backlight Scaling Considering Ambient Luminance for Mobile Videos on LCD Displays

The power consumption of mobile devices is always a concern for users due to the constraints on the size and weight of mobile devices. Up to now, mobile video traffic has accounted for the majority of total traffic, which implies that viewing mobile videos has been the major activity when using mobile devices. Among all the subsystems involving in mobile video playback, the display is the most power consuming subsystem. To reduce the power consumption, dynamic backlight scaling (DBS) technique is developed by adjusting the backlight magnitude when playing the mobile video. However, the convenience of mobile devices makes lots of people watch mobile videos in various luminance environments, which makes the existing DBS methods ineffective since ambient luminance varies greatly. In this paper, we propose a novel DBS strategy to maximally enhance the battery power performance under various ambient luminance conditions through backlight magnitude adjusting, while without negatively impacting users’ quality of experience (QoE). In particular, we conduct a series of subject quality assessment experiments to uncover the quantitative relationship among QoE, ambient luminance, video content luminance, and backlight luminance. We then investigate whether the continuous playback of backlight-scaled videos using the proposed scaling magnitude under various luminance environments would cause flicker effect or not. Motivated by the findings of these studies, we implement a novel DBS strategy for mobile energy saving which is suitable for various ambient luminance conditions. The experimental results demonstrate that the proposed DBS strategy can save more than 40 percent power at most and can save 10 percent power even at a very high ambient luminance condition. We also show that the proposed DBS strategy can be easily adapted to different user preferences and different devices, and can be conveniently integrated into practical applications.
Subjective Assessment of Stereoscopic Image Quality: The Impact of Visually Lossless Compression

In stereoscopic displays different images are presented separately to the left and right eyes. This requirement may increase the bandwidth demand as well as increase the occurrence of visible compression-related artefacts. Here we report the results of a large-scale subjective assessment of high dynamic range (HDR) stereoscopic image compression. The ISO/IEC 29170-2 flicker paradigm was adapted for stereoscopic images and used to evaluate two VESA (Video Electronics Standards Association) image compression codecs: DSC 1.2a and VDCM 1.2.2. We compared the performance on stereoscopic images versus 2D images for both codecs.
Using Gaussian Mixtures on Triphone Acoustic Modelling-Based Punjabi Continuous Speech Recognition

Continuous speech recognition for a particular language is always an area which relies, for its performance, on these major aspects: acoustic modelling and language modelling. Gaussian mixture model-hidden Markov model (GMM–HMM) is a part of acoustic modelling. These components are applied at the back end of ASR design to accurately and efficiently convert continuous speech signal to corresponding text. Triphone-based acoustic modelling makes use of two different context-dependent triphone models: word-internal and cross-word models. In spite of active research in the field of automatic speech recognition for a number of Indian and foreign languages, only few attempts have been made for Punjabi language, specially, in the area of continuous speech recognition. This research paper is aimed at analysing the impact of GMM–HMM-based acoustic model on the Punjabi speaker-independent continuous speech recognition. Recognition accuracy has been determined at word and sentence levels, respectively, with PLP and MFCC features by varying Gaussian mixtures from 2 to 32.
Improving Teaching and Learning Experience in Engineering Education Using Sentiment Analysis Techniques

Abstract Students are the golden commodity when it comes to ringing in revenue for academic institution. Therefore, it is vital to ensure the opinions and feedback of students is taken seriously to ensure continuous improvement in the teaching and learning experience. In an era of digital information and rich opinionated text being easily available, it is crucial to look into different forms of data analysis from which vital information can be extracted. Sentiment and emotion analysis are one such area of research that looks to extract implicit information from written text and analyse data that would be able to provide a deeper insight compared to conventional measures. This paper is an extension of a previously conducted study of analysing emotion as well as sentiment of students’ feedback taking Thermal Engineering (MEC551) from Universiti Teknologi Mara (UiTM). Supervised learning technique was adopted and data analysed revealed students were biased towards assignment and quizzes as these would help improve their carry forwards for the subject and the preference of chapters to the exam was more for conduction and convection compared to others which had more mathematical related calculation.
Role of project and research activities in developing cognitive abilities in students with hearing impairments in their study of natural sciences at a technical university

This work is devoted to developing new technologies in the engineering education of persons with hearing impairments of varying severity – deaf and hard of hearing (hereinafter – d/hh). Solving the problem of content accessibility of STEM disciplines, including “Chemistry”, for those d/hh students who are subjects of Bauman Moscow State Technical University (BMSTU) inclusive major professional academic programs is being carried out taking into account their individual cognitive features identified during their comprehensive expert diagnostic testing at the stage of admission to the university. Based on test results, rehabilitation potential of an entrant and a student with hearing disability is being determined and an individual package of support services and technologies is being formed. The technology of project and research activities with the focus on creating a collection of content accessible multimedia educational materials on “Chemistry” discipline has been recognized as especially important for developing cognitive abilities in hearing impaired students while mastering chemistry. Such materials facilitate students' perception and understanding meaning of discipline content and thus are being an element of barrier-free didactics. A methodological approach to designing special conditions for mastering STEM disciplines by d/hh students, taking into account their cognitive characteristics is offered. Actual types and stages of project and research activities, as well as strategies of their successful implementing by such a students, are considered. Special role of project and research activity in creating actively developing information and educational environment at a general type university is shown. Project and research activity is recognized as an innovative vocational and rehabilitation service that increase the rehabilitation potential of students with hearing disability at technical university.