Reviewerly/exHarmony huggingface dataset:
Prediction based Dynamic Load Balancing System for Text to Speech Conversion

Dynamic load balancing becomes a prominent stakeholder in a distributed system for execution of massive computational load. Based on the dynamic state of underlying resources dynamic load balancing disseminates load efficiently to underlying networked resources to achieve efficient and unfair load balancing. It's very difficult task to choose scheduling mechanism in these cases. To predict the load of incoming jobs accurately and matchmaking of these jobs to computational resources dynamically is very crucial task. In proposed research we have used linear regression mechanism for achieving dynamic load balancing in text to speech conversion process. Now a day's shrewd users prefers listening the text literature than reading it. The considerable amount of load is generated on the system as number of input text files increases. Proposed capacitive dynamic load balancing technique considers the computational capacity of CPU which is taken as a key factor for load dissemination. In this technique load given to a CPU is proportionate to its computational capacity.
Breast Cancer Diagnostic Tool Using Deep Feedforward Neural Network and Mother Tree Optimization

Automatic diagnostic tools have been extensively implemented in medical diagnosis processes of different diseases. In this regard, breast cancer diagnosis is particularly important as it becomes one of the most dangerous diseases for women. Consequently, regular and preemptive screening for breast cancer could help initiate treatment earlier and more effectively. In this regard, hospitals and clinics are in need to a robust diagnostic tool that could provide reliable results. The accuracy of diagnostic tools is an important factor that should be taken into consideration when designing a new system. This has motivated us to develop an automatic diagnostic system combining two methodologies: Deep Feedforward Neural Networks (DFNNs) and swarm intelligence algorithms. Swarm intelligence techniques are based on Particle Swarm Optimization (PSO) as well as the Mother Tree Optimization (MTO) algorithm we proposed in the past. In order to asses the performance, in terms of accuracy, of the proposed system, we have conducted several experiments using the Wisconsin Breast Cancer Dataset (WBCD). The results show that the DFNN combined with a variant of our MTO attains a high classification performance, reaching 100% precision.
Short-Spoken Language Intent Classification with Conditional Sequence Generative Adversarial Network

As one of the most thrilling tasks of natural language understanding (NLU), intent classification in a dialogue system has received a great deal of attention in both industry and academia. The major limiting factor on intent classification is the lack of tagged data. To solve it, in this paper, we propose a conditional sequence generative adversarial network (cSeq-GAN) for intent classification of short-spoken language, in which we simultaneously train a generative model and a discriminative model for two tasks, one to distinguish the generated text from the real spoken one, while the other to predict its intent category. More reliable tagged data obtained by the generator greatly improves the performance of the intent classification task. Extensive experiments on both Air Travel Information System (ATIS) and our selling robot dialogue system for insurance industries demonstrate that our cSeq-GAN achieves competitive classification accuracy with other state-of-art methods of text classification.
A Large-Scale Evaluation Of Shape-Aware Neighborhood Weights And Neighborhood Sizes

In this paper, we define and evaluate a weighting scheme for neighborhoods in point sets. Our weighting takes the shape of the geometry, i.e., the normal information, into account. This causes the obtained neighborhoods to be more reliable in the sense that connectivity also depends on the orientation of the point set. We utilize a sigmoid to define the weights based on the normal variation. For an evaluation of the weighting scheme, we turn to a Shannon entropy model for feature classification that can be proven to be non-degenerate for our family of weights. Based on this model, we evaluate our weighting terms on a large scale of both clean and real-world models. This evaluation provides results regarding the choice of optimal parameters within our weighting scheme. Furthermore, the large-scale evaluation also reveals that neighborhood sizes should not be fixed globally when processing models. Finally, we highlight the applicability of our weighting scheme withing the application context of denoising.
A Noniterative Simultaneous Rigid Registration Method for Serial Sections of Biological Tissues

In this paper, we propose a novel noniterative algorithm to simultaneously estimate optimal rigid transformations for serial section images, which is a key component in performing volume reconstructions of serial sections of biological tissue. To avoid the error accumulation and propagation caused by current algorithms, we add an extra condition: that the positions of the first and last section images should remain unchanged. This constrained simultaneous registration problem has not previously been solved. Our solution is noniterative; thus, it can simultaneously compute rigid transformations for a large number of serial section images in a short time. We demonstrate that our algorithm obtains optimal solutions under ideal conditions and shows great robustness under nonideal circumstances. Further, we experimentally show that our algorithm outperforms state-of-the-art methods in terms of speed and accuracy.
Integration of digital library server with Service Oriented Architecture (SOA) based on cloud computing using proxmox server

Abstract With the advancement of computer virtualization technology, the use of cloud computing technology began to be scrutinized by researchers. With the existence of cloud computing technology can make computer virtualization on a server that is running. This can save the cost of providing information technology and operational infrastructure independently for each service that will be served. With this technology one physical machine is made into a shared resource that can be shared and used by several services at once. In this study made 4 servers using SLIMS7, digital libraries are virtual machines implemented in cloud computing servers with Proxmox servers. Database server digital library integration uses Service Oriented Architecture (SOA), by connecting to 4 virtual machine servers. Integration is carried out on the book data search system by entering keywords as book data searches that exist in each digital library server, with the results of displaying all book data on each digital library server according to the keywords. The conclusions of this study obtained after testing the system include the acquisition of a new model in the use of Service Oriented Architecture (SOA) to integrate the search for data books that exist in 4 digital library servers.
A novel fault diagnosis method based on improved AdaBoost and Kemelized Extreme Learning Machine for industrial process

For the industrial process, fault diagnosis technology is an effective and important way to ensure the process safety and prevent the accidents. With the increasing complexity of modern industrial process systems, some tiny faults in the system should be detected early and eliminated in time. Otherwise, it may cause the failure and paralysis of the whole system. At the background, higher requirements are put forward for the accuracy and self-adaptive capacity of fault diagnosis. To solve the problem, a novel method based on improved AdaBoost and Kernelized Extreme Learning Machine (KELM) is proposed. Firstly, the error feedback mechanism is introduced to traditional Extreme Learning Machine (ELM) network. Though dynamically adjusting the hidden layer output, this neural network error can substantially decrease. Secondly, it is considered that AdaBoost algorithm can raise the data classification ability by adjusting the sample weights and weaken the classifier weights, then the established ELM with error feedback is used as weak classifier to increase the ability of characteristic extraction for AdaBoost. Thirdly, KELM carries out effective optimizing training on the data of the improved AdaBoost with extracted data features. Then, it performs fault diagnosis for optimizing data. The performance is tested and verified by standard UCI data sets and TE simulation process. The experimental results show that the proposed method achieves better performances in fault diagnosis than the traditional approaches.
On timeline-based games and their complexity

In timeline-based planning, domains are described as sets of independent, but interacting, components, whose behaviour over time (the set of timelines) is governed by a set of temporal constraints. A distinguishing feature of timeline-based planning systems is the ability to integrate planning with execution by synthesising control strategies for flexible plans. However, flexible plans can only represent temporal uncertainty, while more complex forms of nondeterminism are needed to deal with a wider range of real-world domains. In this paper, we propose a novel game-theoretic approach to timeline-based planning problems, generalising the state of the art while uniformly handling temporal uncertainty and nondeterminism. We define a general concept of timeline-based game and we show that the notion of winning strategy for these games is strictly more general than that of control strategy for dynamically controllable flexible plans. Moreover, we show that the problem of establishing the existence of such winning strategies is 2EXPTIME-complete.
Text Style Transfer Using Partly-Shared Decoder

This work expands upon existing approaches to text style transfer, the task of changing the style of a sentence without changing its content. This task could have many applications, from creating headlines for a general audience based on scientific paper headlines to making offensive language more suitable for children. We focus specifically on sentiment transfer, due to the availability of large sets of data. Text style transfer is uniquely difficult since it is hard to find parallel data, sentence-for-sentence translations, to train on. In this work, we begin by training an encoder neural network to produce representations of text that encode only content, not style, using an adversarial approach. We then introduce a new partly-shared decoder architecture to turn these representations back into sentences, attempting to achieve a better mix of content preservation and style modification. We find that a partly-shared decoder results in content preservation and style modification scores in between different baselines, suggesting a potential utility depending on the desired application.
MOPNAR-II: An Improved Multi-Objective Evolutionary Algorithm for Mining Positive and Negative Association Rules

This paper designs a multi-objective evolutionary algorithm called MOPNAR-II to mine positive and negative association rules. The main idea of MOPNAR-II is to improve the related operations of multi-objective optimization approach using reference-point based non-dominated sorting approach to guarantee the diversity of rules obtained. In MOPNAR-II, a gene-oriented crossover strategy is utilized to perform extended crossover operation between two intervals, so as to improve the ability of exploitation, while the mutation of rules and the limitation of boundary are also improved. In the experiments, MOPNAR-II is evaluated over two algorithms that are for mining positive and negative association rules and a classical algorithm Apriori on real-world datasets. Results show that MOPNAR-II performs better in terms of quality measures, which prove the validity of our algorithm.
Research on Correction Method of Local Feature Descriptor Mismatch

In this paper, the local feature description algorithm increases the number of mismatches in the case of illumination mutation, and the image deformation often does not appear in a single way. Therefore, there is a high requirement for the stability of the image matching algorithm. If a similar structure appears in the local region of the image, the mismatch rate is higher, especially when the feature vector has no feature semantics or position information. The one-to-many, many-to-many mismatched pair has a higher frequency. In general, most of the methods used to circumvent such errors are commonly used to adjust the distance threshold, but the distance threshold is not targeted and the correct matching point pair may be eliminated. Therefore, Based on the KNN and RANSAC methods, this paper further uses geometric constraints to eliminate mismatches. This method can greatly improve the matching accuracy, so that the image descriptor can maintain high accuracy under the local light mutation environment and even the local similar content. Experiments have shown that our method can achieve very high matching accuracy.
Face Detection via HOG and GA Feature Selection with Support Vector Machines

This work employs a new method of feature selection and classification of image objects by combining previous studies in literature of feature selection and classification of images. In the new algorithm, instead of sliding a window on the image and scaling the window by applying normalized cuts and image segmentation algorithms, information related to the position of objects is considered. Accordingly, the scaling of searching image window process is been exceeded. For this purpose, the obtained segments of image features extracted by employing Histogram Oriented Gradient (HOG) descriptor, which clears the images from the curse of dimensions. These extracted features optimized by applying Genetic Algorithm (GA) method, to detect faces and compared with HOG feature results. The main objective of this work is to improve the accuracy of Support Vector Machine (SVM) classifier in detail description applications.
Text-Independent Speaker ID Employing 2D-CNN for Automatic Video Lecture Categorization in a MOOC Setting

A new form of distance and blended education has hit the market in recent years with the advent of massive open online courses (MOOCs) which have brought many opportunities to the educational sector. Consequently, the availability of learning content to vast demographics of people and across locations has opened up a plethora of possibilities for everyone to gain new knowledge through MOOCs. This poses an immense issue to the content providers as the amount of manual effort required to structure properly and to organize the content automatically for millions of video lectures daily become incredibly challenging. This paper, therefore, addresses this issue as a small part of our proposed personalized content management system by exploiting the voice pattern of the lecturer for identification and for classifying video lectures to the right speaker category. The use of Mel frequency Cepstral coefficients (MFCC) as 2D input features maps to 2D-CNN has shown promising results in contrast to machine learning and deep learning classifiers - making text-independent speaker identification plausible in MOOC setting for automatic video lecture categorization. It will not only help categorize educational videos efficiently for easy search and retrieval but will also promote effective utilization of micro-lectures and multimedia video learning objects (MLO).
Improved Edge Detection Model Based on HED

Influenced by the HED network structure, how to further solve the image multi-scale multi-level representation problem and optimize the detection of the overall image. We propose an end-to-end network structure. While enhancing the robustness of backbone network, the BN layer is used to solve the problem of network gradient dispersion, the attention mechanism is added to strengthen the role of deep supervision and final fusion module. The network can learn related semantic features autonomously, obtain richer image information, and solve the problem that HED cannot fully extract features, and high-level and low-level information are simply fused together. Considering the modification on the basis of the HED network structure, the BN layer and the SE structure are added, and the manner of downsampling is modified. The experimental results show that the edge effect from the BSDS300 data set is good, and the running speed is better than the HED model.
A new Quantum Processor Architecture

This paper analyzes the current state of the two leading Quantum Computing architectures, namely Ion Trap and Super Conducting, outlines the challenges facing both methods and proposes a new architecture that incorporates the best aspect of both techniques along with a novel method to isolate the Qubits. This new architecture retains the Ion Trapped architecture ability to operate its Qubits at near room temperature while eliminating its scalability issue by utilizing a novel shielding technique and by adopting the surface code layout of the superconducting architecture. By eliminating the extremely low-temperature requirement of the superconducting architecture, this new architecture is poised to be cost-effective, scalable, and practical for mass production.
The Extraction of the Future-Oriented Sentences from Annual Reports

In annual securities report, various information such as results of diverse business performances, point of view about causation of these outcomes, and issues and challenges to be addressed in the near future are included. Most of previous researches proposed the extraction methods of important sentences containing causal information of past company's performances but not effort to address future company's issues from text materials. In this paper, we propose our original method to extract future-oriented sentences by the combination of two SVM identification models, one of which captures features of future and the other aims for purposes and means in sentences of Japanese annual reports. All mean evaluations of our models, which were precision, recall and F-score, showed more than almost 0.9 and indicated that by using our model, we can effectively collect future information about business activities from annual reports as well as other relevant sources, which would allow us to make unique investment decisions and to develop unprecedented investment methods.
Architectural Survey on Internet-of-Things

The basic premise of Internet of Things (IoT) is to have intelligent devices that can communicate and cooperate directly without human involvement and to deliver a new class of applications such as Bio stamps, Wearable things, Smart City, Smart Toll Plaza etc. For the purpose, the key focus of IoT is on four key components, namely, enabling technologies, standard protocols, technological barriers and application areas. Some IoT architectures like SOA, ROA, NEBULA FIA, NDN, CASAGRAS and AKARI have been proposed by different researchers and are discussed in this article. The paper not only narrow down the promising research domains like identification technology, energy optimization, hardware and software solutions with their major research issues, but also discusses the technological tradeoffs of applications.
Facial Analysis Using Jacobians and Gradient Boosting

Security and identity have become one of the primary concerns of the people in this digital world. Person authentication and identification is transforming the way these services are provided. Earlier it was mainly achieved through passwords and patterns but with significant advancements in face recognition technologies, it has been exploited in providing authentication in smart phones and computers. Face Recognition (FR) extends its use in applications like face tagging in social media, surveillance system at theaters, airports and so on. The proposed mathematical model employs linear algebra and mathematical simulations for the task of person identification. Kernel singular value decomposition is used to denoise the facial image which is then passed to a feature detector and descriptor based on nonlinear diffusion filtering. The obtained descriptors are quantized into a vector using an encoding model called VLAD which uses k-means++ for clustering. Further, classification is done using Gradient boosting decision trees. The pipeline proposed aims at reducing the average computational power and also enhancing the efficiency of the system. The proposed system has been tested on the three benchmark datasets namely Face 95, Face 96 and Grimace.
An Efficient Palette Reordering For Lossless Compression of Color Indexed Images

Image compression with color image re-indexing is a key focus in this research. Improving the efficiency of the current state-of-art approach which is TSP based Ant colony optimization by increasing the compression gain is a prime focus. However, obtaining an optimal re-ordering for a palette is a crucial task to choose from by any brute-force way. We have successfully outperformed the existing method using PNG as codec and applying a circular shift as our heuristic method.
Sentiment Analysis System of Indonesian Tweets using Lexicon and Naïve Bayes Approach

Nowadays, social media become the most popular user-generated content on the internet. In Indonesia, there are 150 million active social media users or 56% of the total population in 2019 [1]. It shows that social media produced big data, which is potential to give us meaningful information. Twitter is one of the popular social media platforms in Indonesia, where 50% of internet users use this platform [1]. Therefore, we conducted a sentiment analysis of Indonesian tweets with two different approaches: lexicon-based and machine learning. To achieve this objective, we developed a system that can identify and categorize Indonesian tweets into specific polarity (positive, neutral, negative). This system consists of main processes: environment preparation, text preprocessing, read dataset bag of words, data processing, result, and conclusion. In the testing phase, we used several keywords as inputs to this sentiment analysis system. The results show naïve Bayes obtained an accuracy of 84% and lexicon-based was 72%. It concludes that the machine learning approach gives better accuracy than lexicon-based in our system.
Earthquake Investigation and Visual Cognizance of Multivariate Temporal Tabular Data Using Machine Learning

This paper presents our tool for the Vast Challenge 2019 Mini Challenge 1 (MC1). It will give an overview of the approach of data preprocessing techniques used for the given dataset and it will introduce our application which is built considering the requirements and questions to be answered for the MC1. This application consists of Machine Learning techniques and Information Visualization techniques such as Integrated Spatial Uncertainty Visualization as shown in this paper [1] to convey the needed information to the end users. To show the usefulness of this application we give examples of analysis.