Reviewerly/exHarmony huggingface dataset:
Comparison of incremental linear dimension reduction methods for streaming data

Traditional linear dimension reduction methods such as Principal component analysis (PCA) and linear discriminant analysis (LDA) have been used in many application areas due to simplicity and high performance. However, in data streams where data instances are generated continuously over time, it is difficult to apply traditional PCA or LDA. Moreover, data streams can have drifting concepts over time. In this paper, we compared several incremental linear dimension reduction algorithms which can be applied for classification in streaming data. Also, the performance comparison for prediction accuracy and time complexity was conducted in various streaming environments such as low dimensional data streams, high dimensional data streams, and data streams with concept drifts. Experimental results showed that incremental least squares formulation (ILS) combined with incremental PCA can be used effectively for classification in streaming data.
A Framework for Enhancing the Agriculture yield using Cloud Clusters

Even today fifty percent of India’s working force is employed in agriculture and agro related industries. Which contributes 17 to 18% of India’s GDP as of 2018. With that context, many farmers face problems in getting good agriculture yield and price for the products they produce and there are no proper guidance with respect to upcoming techniques and technology platforms where they can easily discuss and clarify their issues regarding the agriculture related topics. Hence, here is a new strategy to find solutions for all such issues and provide a platform to better knowledge sharing and enhancing the opportunities. A Web app for updating about the crop growth and monitoring in real time so as to keep the yield in better possible way. Also, an online discussion forum that would help farmers at a different location to remotely communicate and get the suggestion instantly. Unlike the normal web app, this web app model is hosted in the Customized cloud server (servers) which act as a reliable and efficient environment. The Innovative cloud servers are not only reliable with service but also provides many advantages like scalability, availability and cost-effectiveness. The server will be very robust in running the process in multiple operating systems without any delay or lag for the farmers. In this paper we propose a framework for increasing the agriculture yield using distributed computing with low cost approach.
A Novel Two-Pathway Encoder-Decoder Network for 3D Face Reconstruction

3D Morphable Model (3DMM) is a statistical tool widely employed in reconstructing 3D face shape. Existing methods are aimed at predicting 3DMM shape parameters with a single encoder but suffer from unclear distinction of different attributes. To address this problem, Two-Pathway Encoder-Decoder Network (2PEDN) is proposed to regress the identity and expression components via global and local pathways. Specifically, each 2D face image is cropped into global face and local details as the inputs for the corresponding pathways. 2PEDN is trained to predict 3D face shape components with two sets of loss functions designed to supervise 3D face reconstruction error and face identification error. To reduce the conflict between abundant facial details and saving computer storage space, a magnitudes converter is devised. Experiments demonstrate that the proposed method outperforms several 3D face recontruction methods.
Synthesis of Factors Model of Data Visualization in the Infographics

This article describes the factors of presentation of the data visualization in infographics. We constructed a graph of interconnections among these factors. Using the methods of system analysis and graph theory, we developed a graphical model, which contains hierarchically ordered by priority factors presentation of the data visualization in the infographics. Experimental studies have found that the greatest influence on the quality of perception of data visualization in the infographics has the form of representation of numerical information. We also established that the scale of the structural elements of data visualization occupies a key place in the development of the presentation of infographics.
Automatic and Simultaneous Adjustment of Learning Rate and Momentum for Stochastic Gradient-based Optimization Methods

Stochastic gradient-based methods are prominent for training machine learning and deep learning models. The performance of these techniques depends on their hyperparameter tuning over time and varies for different models and problems. Manual adjustment of hyperparameters is very costly and time-consuming, and even if done correctly, it lacks theoretical justification which inevitably leads to "rule of thumb" settings. In this paper, we propose a generic approach that utilizes the statistics of an unbiased gradient estimator to automatically and simultaneously adjust two paramount hyperparameters: the learning rate and momentum. We deploy the proposed general technique for various stochastic gradient-based methods to train Convolutional Neural Networks (CNN’s). The results match the performance of the best settings obtained through an exhaustive search and therefore, removes the need for a tedious manual tuning.
Visual computing resources distribution and balancing by multimodal cat swarm optimization

According to the fast development of Internet technologies, nowadays more and more web applications require accessing and processing massive-scale visual data, such as content image search and automatic navigation. Practically, the computing power of a single personal PC is insufficient for handling such massive-scale visual elements. Owing to the advancement of cloud platforms, this problem can be well handled. In this work, we study the problem of how to optimally distribute the P visual processing tasks to Q computing resources. Specifically, we proposed an enhanced multimodal cat swarm optimization (MCSO) algorithm to fulfill this task. Given a huge number of images/videos, we first extract color, texture, and semantic channel to represent the visual content of each image/video. Afterward, we develop a multi-view feature learning algorithm to intelligently combine the multiple features into a descriptive one, wherein the weights of different feature channels are adjusted automatically. Subsequently, we use the CSO algorithm to assign each image/video to different remote servers. The CSO algorithm mimics the conventional cat hunting process, that is, a set of cats is divided into two groups, one for searching and the other for tracking. The two patterns are interacted by the mixture ratio, which indicates how many cats will conduct tracking in the next round. Based on the output of CSO, each image/video will be assigned to the most optimal remote sensor. Finally, based on the optimal assignment, the image/video processing can be conducted with the minimal time consumption. Comprehensive experimental comparisons have demonstrated the advantages of our method, that is, our MCSO can achieve a visual processing time three times faster than the second best performer.
Forensic Artifacts of Network Traffic on WeChat Calls

Voice over Internet Protocol (VoIP) applications, such as WeChat, Whatsapp, or LINE, have gained increasing popularity during the last few years. Packets analysis of VoIP is one of the critical criminal investigation strategies for law enforcement agencies (LEAs). Due to its convenience and multi-functionality, the WeChat application is accessible by a massive amount of people in Asia, especially in Taiwan and China. Therefore, this study uses Wireshark to reveal the tool marks behind the WeChat messages and voice calls. It will be an excellent help for LEAs to identify cybercriminals and bring them to justice.
Cognification of Program Synthesis—A Systematic Feature-Oriented Analysis and Future Direction

Program synthesis is defined as a software development step aims at achieving an automatic process of code generation that is satisfactory given high-level specifications. There are various program synthesis applications built on Machine Learning (ML) and Natural Language Processing (NLP) based approaches. Recently, there have been remarkable advancements in the Artificial Intelligent (AI) domain. The rise in advanced ML techniques has been remarkable. Deep Learning (DL), for instance, is considered an example of a currently attractive research field that has led to advances in the areas of ML and NLP. With this advancement, there is a need to gain greater benefits from these approaches to cognify synthesis processes for next-generation model-driven engineering (MDE) framework. In this work, a systematic domain analysis is conducted to explore the extent to the automatic generation of code can be enabled via the next generation of cognified MDE frameworks that support recent DL and NLP techniques. After identifying critical features that might be considered when distinguishing synthesis systems, it will be possible to introduce a conceptual design for the future involving program synthesis/MDE frameworks. By searching different research database sources, 182 articles related to program synthesis approaches and their applications were identified. After defining research questions, structuring the domain analysis, and applying inclusion and exclusion criteria on the classification scheme, 170 out of 182 articles were considered in a three-phase systematic analysis, guided by some research questions. The analysis is introduced as a key contribution. The results are documented using feature diagrams as a comprehensive feature model of program synthesis showing alternative techniques and architectures. The achieved outcomes serve as motivation for introducing a conceptual architectural design of the next generation of cognified MDE frameworks.
Learning Multi-Scale Attentive Features for Series Photo Selection

People used to take a series of nearly identical photos about the same subject, but it is usually a tedious chore to select the reversed ones from them. Despite the remarkable progress, most existing studies on image aesthetics assessment fail to fulfill the task of series photo selection. In this paper, we develop a novel deep CNN architecture that aggregates multi-scale features from different network layers, in order to capture the subtle differences between series photos. To reduce the risk of redundant or even interfering features, we introduce the spatial-channel self-attention mechanism to adaptively recalibrate the features at each layer, so that informative features can be selectively emphasized and less useful ones suppressed. Extensive experiments on a benchmark dataset well demonstrate the potential of our approach for series photo selection.
Development of a Quantitative Instrument to Measure Mobile Collaborative Learning (MCL) Using WhatsApp: The Conceptual Steps

It has been reported that WhatsApp, a social media application, had approximately 1.6 billion active users globally as of July 2019, almost one-fifth of the total world's population. Thus, research about WhatsApp's influence in general and especially its influence in education was relevant and significant. While there was much research involving WhatsApp and learning, it was not conclusive about the effects of WhatsApp on student learning. Specifically, research focusing on collaborative learning using WhatsApp was lacking, including research instruments for measuring collaboration on WhatsApp. Consequently, the paper's research problem was the lack of research instruments for measuring collaboration on WhatsApp in relation to academic achievement. To address the research problem, the study followed the important initial and conceptual steps of the instrument development process to develop a research instrument to measure collaboration on WhatsApp in relation to academic achievement. The result of the paper was a developed instrument that provides researchers with a basis to measure the explanatory constructs involved in mobile collaborative learning (MCL) processes on WhatsApp and potentially other social media platforms. Therefore, the paper made an appropriately theoretical contribution, which was grounded in the scientific literature. The study facilitated positivistic research and epistemology for acquiring objective and precise scientific knowledge. Such deductive research promotes theory testing and development and presents educators and students with scientific evidence about learning with MCL applications such as WhatsApp from which both curriculum and learning design can be informed and benefited. In the age of connected mobility this is a necessity.
Automatic regime detection for Rotor Track and Balance using vibration only sensor data

ABSTRACT Rotor Track and Balance (RTB) is an important part of regular helicopter maintenance. The ability to perform this service assessment during normal operations, rather than with a series of explicit RTB flights, would greatly reduce the time the vehicle is non-operational and the maintenance costs associated with these flights and adjustments. This paper presents a novel methodology for identifying the RTB-related flight regimes, using a minimal number of vibration signals and comparing these to repeatable and stable characteristic vibration profiles. The technique is stable, with an 81% success in correct identification of the flight regime, when applied to a whole flight with a number of unknown regime events. The method can be run in real time, making it an effective way of identifying periods of flight that are suitable for RTB measurements. A new technique for visually representing any real-time flight signal, such as vibration, is also presented.
Scalable Self-Adaptive Synchronous Triggering System in Superconducting Quantum Computing

Superconducting quantum computers (SQCs) can solve some specific problems which are deeply believed to be intractable for classical computers. One of the key components of SQCs is a high-precision synchronous control system which consists of arbitrary waveform generators (AWGs) and an array of digitizers. Here, we propose a scalable, self-adaptive, synchronous triggering system which allows the synchronization of dozens of qubits with jitters of around 3 ps. The system achieves better phase noise performance than the commercial instruments we tested. Using this scheme, we have successfully implemented the synchronous control and measurement of all the qubits on a 23-qubit quantum processor.
Single Image Dehazing Using Neural Network

The need for Single Image Dehazing came up as a result of hazy input images captured during foggy or hazy weather. This occurs due to the fact that certain dust particles and smog can easily scatter light, especially during morning haze, some firework or at the dawn time. Therefore, a hazy image gets piled over the original image. And hence, it becomes a challenging task to retrieve the original image from the input hazy image. Generally for single image dehazing, a massive dataset of input hazy image is required, the reason being Deep Learning is the backbone of the entire functionality of this concept. Deep Neural Networks require multiple hidden layers between the input hazy image and the output layer. Though Single Image Dehazing employs methods like polarization, prior based approach, extra information method, prior based method, learning based method have shown the greatest level of accuracy in recovering a clear image. Amongst the existing methods, polarization method and contrast based methods weren't applicable in real time scenarios. Although, Dark Channel Prior based method was one of the most successful amongst the prior based strategies, it's drawback was that it overestimates the thickness of the haze. In this paper, the main focus will be at comparing different Deep Learning methods, stressing upon various Convolutional Neural Networks, thereby giving a deep insight of various CNN strategies for retrieving the original dehazed image.
Persuade Me!: A User-Based Recommendation System Approach

Recommendation systems are gaining their momentum with popular Internet platforms such as Amazon, Netflix or Spotify. As more users are joining these online consumer and entertainment sectors, the profile-based data for providing accurate just-in-time recommendations is rising thanks to strategies based on collaborative filtering or content-based metrics. However, these systems merely focus on providing the right item for the users without taking into account what would be the best strategy to suggest the movie, the product or the song (i.e. the strategy to increase the success or impact of the recommendation). Taking this research gap into consideration, this paper proposes a profile-based recommendation system that outputs a set of potential persuasive strategies that can be used with users with similar characteristics. The scope of the tailored persuasive strategies is to make office-based employees of tertiary buildings increase their pro-environmental awareness and enhance the energy efficiency at work (the dataset used on this research is specific of this sector). Throughout the paper, shreds of evidence are reported assessing the validity of the proposed system by not only providing effective mechanisms to increase the success of the recommendations but also alleviating the cold-start-problem when newcomers arrive.
Unsupervised Key Hand Shape Discovery of Sign Language Videos with Correspondence Sparse Autoencoders

Recognition of sign language is a difficult task which often requires tedious annotations by sign language experts. End-to-end learning attempts that bypass frame level annotations have achieved some success in limited datasets, but it has been shown that high quality annotations improve performance drastically. Recent unsupervised learning methods using deep neural networks have achieved successes in learning feature extraction. Yet a technique for high quality frame level classification using unsupervised techniques does not exist. In this paper, we assign labels of an isolated Sign Language(SL) dataset using end-to-end neural network architectures that have proven success in unsupervised discovery of sub-word acoustic units in speech processing. We observe that key-hand-shape s(KHS), which are meaningful visual basic parts of signs in a SL dataset can be detected using unsupervised clustering techniques. Sparse autoencoders can successfully retrieve and cluster KHS s used in isolated signs. In addition, using correspondent frames in an autoencoder scheme has the power to continue the learning process.
Self Attentive Context dependent Speaker Embedding for Speaker Verification

In the recent past, Deep neural networks became the most successful approach to extract the speaker embeddings. Among the existing methods, the x-vector system, that extracts a fixed dimensional representation from varying length speech signal, became the most successful approach. Later the performance of the x-vector system improved by explicitly modeling the phonological variations in it i.e, c-vector. Although the c-vector framework utilizes the phonological variations in the speaker embedding extraction process, it is giving equal attention to all the frames using the stats pooling layer. Motivated by the subjective analysis of the importance of nasals, vowels, and semivowels for speaker recognition, we extend the work of the c-vector system by including a multi-head self-attention mechanism. In comparison with the earlier subjective analysis on the importance of different phonetic units for speaker recognition, we also analyzed the attentions learnt by the network using TIMIT data. To examine the effectiveness of the proposed approach, we evaluate the performance of the proposed system on the NIST SRE10 database and get a relative improvement of 18.19 % with respect to the c-vector system on the short-duration case.
Multimedia Service Management with Virtualized Cache Migration

The demand for playing the video with the highest quality possible leads video streaming companies to utilize network-based solutions and server assistance. As being one of the popular technology in video streaming area, HTTP adaptive video streaming systems can benefit from the emerging network technologies such as SDN and NFV. Such multimedia systems require the management of network resources efficiently to be able to provide acceptable service. In this paper, we propose a multimedia service management architecture that focuses on migration one of the most crucial components of such systems, virtualized caches. SDN controller gathers underlay network statistics and based on real-time network condition determines the optimal location for virtual cache and/or migrate it when network conditions change. We focus on hand-off affects the perceptual quality when clients migrate from one cache to another. The proposed approach was evaluated using the Mininet Emulator and its performance was given comparatively. The performance results show that the combination of SAND (Server and Network Assisted DASH) features with the advantage of the NFV-SDN technology can provide good service to its users.
Fine-grained action plausibility rating

An essential capability of humans is the effortless identification of useful tasks based on visual cues in everyday situations. Objects and their surroundings are integrated and processed to differentiate plausible from implausible actions. In this work, we study how to teach this ability to robots. In contrast to many tasks in computer vision where the goal is an accurate description (object labels, caption, scene class) of the present situation here the challenge is to make reasonable guesses about which forms of plausible and implausible actions can be conducted. To this end, we collect a dataset that associates images with probabilities over a set of actions. A convolutional neural network is trained to match these ground truth plausibility scores using this dataset. We compare the performance of state-of-the-art encoder architectures and specifically analyze the role of contextual cues quantitatively. While the object recognition capabilities of the encoder have a strong impact on performance, using context did not lead to substantial improvements. We show qualitatively the utility of such a system for robotic action selection in a household setting.