Reviewerly/exHarmony huggingface dataset:
Task scheduling using artificial bee foraging optimization for load balancing in cloud data centers

Abstract Load balancing in cloud data centers is a process of distributing the incoming service requests or the incoming tasks to the available virtual machines (VMs). This can be achieved by proper scheduling mechanisms through which the tasks will be allocated to suitable VMs. Scheduling in distributed systems, such as cloud data centers, is considered to be an NP‐complete problem. An efficient method of scheduling will result in balancing the load on the VMs, thereby achieving effective resource utilization. Hence, there is a need for a new scheduling framework to perform load balancing amid considering multiple quality of service (QoS) metrics, such as makespan, response time, execution time, and task priority. Therefore, considering the above metrics, task scheduling using artificial bee foraging (TSABF) optimization is proposed to obtain an optimal schedule of tasks to VMs. The resulting optimal schedule consists of a set of VMs to which the tasks are scheduled in a preemptive manner. Task preemption is done to reduce the response and the execution time of the tasks pertaining to different priorities. The experimental results are compared to the existing honey bee behavior‐inspired load balancing (HBB‐LB) algorithm. The results show that TSABF acts as an alternative scheduling strategy to perform load balancing and also improves the QoS metrics when compared to HBB‐LB.
An Image Registration Method for Multisource High-Resolution Remote Sensing Images for Earthquake Disaster Assessment

For earthquake disaster assessment using remote sensing (RS), multisource image registration is an important step. However, severe earthquakes will increase the deformation between the remote sensing images acquired before and after the earthquakes on different platforms. Traditional image registration methods can hardly meet the requirements of accuracy and efficiency of image registration of post-earthquake RS images used for disaster assessment. Therefore, an improved image registration method was proposed for the registration of multisource high-resolution remote sensing images. The proposed method used the combination of the Shi_Tomasi corner detection algorithm and scale-invariant feature transform (SIFT) to detect tie points from image patches obtained by an image partition strategy considering geographic information constraints. Then, the random sample consensus (RANSAC) and greedy algorithms were employed to remove outliers and redundant matched tie points. Additionally, a pre-earthquake RS image database was constructed using pre-earthquake high-resolution RS images and used as the references for image registration. The performance of the proposed method was evaluated using three image pairs covering regions affected by severe earthquakes. It was shown that the proposed method provided higher accuracy, less running time, and more tie points with a more even distribution than the classic SIFT method and the SIFT method using the same image partitioning strategy.
An Approach for Transforming CIM to PIM up To PSM in MDA

Models transformation is the most important principle in MDA (model driven architecture).Indeed, two transformation kinds are possible in MDA: CIM (computing independent model) to PIM (platform independent model) and PIM to PSM (platform specific model). Most researchers deal with the transformation from PIM to PSM, since several elements are shared between these two levels. However, CIM to PIM transformation is rarely highlighted in research topics since they are two radically different levels. However an approach that ensures a transformation from CIM to PIM up to PSM almost extinct. Indeed, this paper represents a methodology to master model transformation from CIM to PIM up to PSM into MDA. This methodology is instituted on creating a rich CIM, through well-chosen rules, to facilitate transformation to the PIM. However, we establish a good PIM, taking into consideration the three classic modeling views: functional, dynamic and static to expedite transformation to PSM. Our methodology conforms to MDA by respecting the business dimension in CIM, by using BPMN (Business Process Model and Notation) which is the OMG (Object Management Group) standard for modeling business process. Thereby, we used UML (Unified Modeling Language) models in PIM level, because UML is recommended by MDA in this level. Indeed, we use IFML (Interaction Flow Modeling Language) that is OMG standard for representing web interface model in PSM.
Named Entity Recognition and Classification for Punjabi Shahmukhi

Named entity recognition (NER) refers to the identification of proper nouns from natural language text and classifying them into named entity types, such as person, location, and organization. Due to the widespread applications of NER, numerous NER techniques and benchmark datasets have been developed for both Western and Asian languages. Even though Shahmukhi script of the Punjabi language has been used by nearly three fourths of the Punjabi speakers worldwide, Gurmukhi has been the main focus of research activities. Specifically, a benchmark NER corpus for Shahmukhi is non-existent, which has thwarted the commencement of NER research for the Shahmukhi script. To this end, this article presents the development and specifications of the first-ever NER corpus for Shahmukhi. The newly developed corpus is composed of 318,275 tokens and 16,300 named entities, including 11,147 persons, 3,140 locations, and 2,013 organizations. To establish the strength of our corpus, we have compared the specifications of our corpus with its Gurmukhi counterparts. Furthermore, we have demonstrated the usability of our corpus using five supervised learning techniques, including two state-of-the-art deep learning techniques. The results are compared, and valuable insights about the behaviors of the most effective technique are discussed.
Food Image Recognition Based on Densely Connected Convolutional Neural Networks

Convolutional neural networks have been widely used for image recognition as they are capable of extracting features with high accuracy. In this paper, we propose a DenseFood model based on densely connected convolutional neural network architecture, which consists of multiple layers. A combination of softmax loss and center loss is used during the training process to minimize the variation within the same category and maximize the variation across different ones. For performance comparison, three models, namely, DenseFood, DenseNet121, and ResNet50 are trained using VIREO-172 dataset. In addition, we fine tune pre-trained DenseNet121 and ResNet50 models to extract features from the dataset. Experimental results show that the proposed DenseFood model achieves an accuracy of 81.23% and outperforms the other models in comparison.
Content based Fine-Grained Image Retrieval using Convolutional Neural Network

Content based image retrieval (CBIR) is the problem of retrieving similar images from a database to a given query by use of its visual information only. It has been a hot topic for years. Current CBIR methods rely on the fact that the database consists of large inter-class variance but in real scenario a user for example wants to retrieve same sub-category images to the query, in that case inter-class variance is quite small. Retrieving similar images from database of small inter-class variance is quite difficult from that of large inter-class variance. Convolutional neural networks (CNN) has shown tremendous results in image tasks such as classification, detection, retrieval, segmentation and more. In this paper we proposed a framework for content based fine-grained image retrieval (CB-FGIR) by using CNN. Oxford flower-17 dataset is used to test the proposed framework. Five splits of the dataset is used to evaluate the CB-FGIR framework and achieves superior retrieval results than other handcrafted and state of art methods Keywords— CNN, Retrieval, CB-FGIR, Deep learning.
Incorporating Sememes into Chinese Definition Modeling

Chinese definition modeling is a challenging task that generates a dictionary definition in Chinese for a given Chinese word. To accomplish this task, we built two novel datasets based on Chinese Concept Dictionary (CCD) and Chinese WordNet (CWN) respectively. Each dataset contains triples of a word, sememes, and a corresponding definition. We present two novel models to improve Chinese definition modeling: the Adaptive-Attention model (AAM) and the Self- and Adaptive-Attention Model (SAAM). AAM successfully incorporates sememes for generating the definition with an adaptive attention mechanism. It has the capability to decide which sememes to focus on and when to pay attention to sememes. SAAM further replaces recurrent connections in AAM with self-attention and relies entirely on the attention mechanism, reducing the path length between word, sememes and definition. Experiments on both datasets demonstrate that by incorporating sememes, our model can generate definitions with more concrete information. And the best model that we proposed outperforms the state-of-the-art method by a large margin on both datasets.
Online Fuzzy Cognitive Map Learning

The fuzzy cognitive map (FCM) is an effective tool for modeling and simulating complex dynamic systems. The research on the problem of learning FCM from the available time series is outstanding. Many batch FCM learning methods have been proposed to address this issue and the performance of these methods is satisfactory. However, these batch-learning methods are difficult to cope with large-scale data sets (for example, the memory in computers is not enough to store all instances) and real-time streaming data, leading to the failure of real-time and online analysis of complex systems. In this article, unlike the existing batch learning methods, such as evolutionary and regression-based methods, we first extend the FCM learning to an online setting, and then develop an effective algorithm based on a follow-the-regularized-leader (FTRL)-proximal style learning algorithm to address the online FCM learning problem, termed as OFCM. The performance of OFCM is validated on constructed benchmark data sets, including synthetic data sets, and gene regulatory network reconstruction data sets. The experimental results demonstrate the merits of OFCM, which can effectively solve online FCM learning problems.
Social Link Inference via Multiview Matching Network From Spatiotemporal Trajectories

In this article, we investigate the problem of social link inference in a target location-aware social network (LSN), which aims at predicting the unobserved links between users within the network. This problem is critical for downstream applications, including network completion and friend recommendation. In addition to the network structures commonly used in general link prediction, the studies tailored for social link inference in an LSN leverage user trajectories from the spatial aspect. However, the temporal factor lying in user trajectories is largely overlooked by most of the prior studies, limiting the capabilities of capturing the temporal relevance between users. Moreover, effective user matching by fusing different views, i.e., social, spatial, and temporal factors, remains unresolved, which hinders the potential improvement of link inference. To this end, this article devises a novel multiview matching network (MVMN) by regarding each of the three factors as one view of any target user pair. MVMN enjoys the flexibility and completeness of modeling each factor by developing its suitable matching module: 1) location matching module; 2) time-series matching module; and 3) relation matching module. Each module learns a view-specific representation for matching, and MVMN fuses them for final link inference. Extensive experiments on two real-world data sets demonstrate the superiority of our approach against several competitive baselines for link prediction and sequence matching, validating the contribution of its key components.
Analisis Komparasi Metode Klasifikasi Data Mining dan Reduksi Atribut Pada Data Set Penyakit Jantung

Heart disease is a disease with a high mortality rate, there are 12 million deaths each year worldwide. This is what causes the need for early diagnosis to find out the heart disease. But the process of diagnosis is quite challenging because of the complex relationship between the attributes of heart disease. So it is important to know the main attributes that are used as a decision making process or the classification process in heart disease. In this study the dataset used has 57 types of attributes in it. So that reduction is needed to shorten the diagnostic process, the reduction process can be carried out using the Principal Component Analysis (PCA) method. The PCA method itself can be combined with data mining calcification techniques to measure the accuracy of the dataset. This study compares the accuracy rate using the C5.0 algorithm and the Naïve Bayes Classifier (NBC) algorithm, the results obtained both after and before the reduction are Naïve Bayes Classifier (NBC) algorithms that have better performance than the C5.0 algorithm
QoS Guaranteed Resource Allocation for Live Virtual Machine Migration in Edge Clouds

Live Virtual Machine (VM) migration among geographically distributed edge clouds is an important strategy for providing low latency and reliable services for mobile end users. VM migration among edge clouds is more challenging than that in cloud computing, because the network bandwidth among edge clouds is more constrained than the cloud data center networks. In this paper, we study the bandwidth allocation among multiple concurrent live VM migrations in edge clouds. This problem is novel in that existing works aim to reduce the migration time for a single live VM migration among the edge clouds, and also ignores the QoS requirement for the service running on the VM in migration. However, our problem considers multiple VM migration tasks, and aims to maximize the average QoS while meeting the migration time constraint for each VM migration task. We formulate the problem as a Non-Linear Programming (NLP) problem which is also shown to be NP-Hard. We develop a new method to solve this problem. In our approach, we first transfer the problem into a Linear Programming (LP) problem by reducing the solution space. Taking the output from the LP solver as an initial solution, we then develop a heuristic to adjust it in order to find a better one to the original NLP problem. Finally, we design a set of evolutionary algorithms to select the optimal initial solution from the LP solver. Extensive simulations show that our proposed method can achieve good QoS and also has a fast convergence speed.
Design of an E-Voucher System for Supporting Social Welfare Using Blockchain Technology

Issuing vouchers is a means of implementing social welfare. There are some security concerns about paper-based vouchers, such as counterfeiting, reproducing, their low operating efficiency and so on. This study explores how to apply blockchain technology and cryptography to build a secure e-voucher system in order to solve various shortcomings of paper vouchers. A feasible application model is proposed that integrates blockchain technology in the context of vouchers to support the field of social welfare. In this study, we conducted an analysis to prove that the security requirements of the e-voucher system are met when applying this model. Subsequently, we used the Hyperledger Fabric blockchain platform and Kafka ordering services to implement the campus welfare meal voucher system. A large number of experimental data were obtained to show that the system has a satisfactory performance. In the conclusion section, we discussed the theoretical and management implications of this study, and how this study meets UN Sustainable Development Goals.
Active contours driven by Gaussian function and adaptive-scale local correntropy-based K-means clustering for fast image segmentation

In this paper, we proposed a novel active contour model based on Gaussian function and adaptive-scale local correntropy-based K-means clustering (Gaussian-ASLCK) that is applicable to images with severe intensity inhomogeneity. Firstly, Gaussian function is used to capture the global intensity of images, which boosts the robustness to the initialization. Subsequently, by employing the adaptive scale operator, the scale of the proposed model can be adjusted automatically according to the degree of intensity inhomogeneity. In addition, the regularized term is approximated by a non-local energy, which is constructed based on characteristic functions in the field of heat kernel convolution. Finally, the iterative convolution-thresholding method is designed for minimizing the energy function. This method is simple, efficient and has the optimal complexity of O(Nlog N) per iteration. The experimental results comprehensively validate that the proposed Gaussian-ASLCK model enjoys greater success in images with severe intensity inhomogeneity and complex noise over other state-of-the-art models.
Software Product Quality Models, Developments, Trends, and Evaluation

Software product quality models have evolved in their abilities to capture and describe the abstract notion of software quality since the 1970s. Many models constructed deal with a specific part of software quality only which makes them ineligible to assess the quality of software products as a whole. Former publications failed to thoroughly examine and list all the available models which attempt to describe each known property of software product quality. This paper discovers such complete software product quality models published since 2000; moreover, it endeavours to measure the relevance of each model quantitatively by introducing indicators with regard to the scientific and industrial community. The identified 23 software product quality model classes differ significantly in terms of publication intensity, publication range, quality score average, relevance score and the 12-month average of the Google Relative Search Index. The results offer a foundation for selecting the appropriate software product quality model for use or for extension if newly identified quality properties need to be connected to a general context. Furthermore, the experiences accumulated on the field of software product quality modelling motivated researchers to successfully transfer the concepts to other areas where abstract entities need to be compared or assessed including the quality of higher educational teaching and business processes, which is also briefly highlighted in the paper.
Secured and multi optimized image watermarking using SVD and entropy and prearranged embedding locations in transform domain

Digital watermarking is a branch of steganography, which is used to secure the copyright ownership of owner of the content available in digital form. It refers to an infrastructure where any digital content is added into a cover data while maintaining its quality. A technique using concepts of DWT, SVD, entropy and pixel position shuffling is proposed to achieve the robustness, imperceptibility and security characteristics of watermarking. To provide security feature, embedding is performed on particular pixel positions rather than performing it linearly. Two performance measures PSNR and NC are calculated to measure the performance of the scheme. A PSNR value greater than 40 dB is achieved by the scheme. The results of experiments show that this scheme is robust against various attacks like rotation, gaussian noise, JPEG compression, weiner filtering etc.
Super Resolution of B-Mode Ultrasound Images With Deep Learning

Ultrasound offers a safe, non-invasive, and inexpensive way of imaging.However, due to its natural intrinsic imaging characteristics, it produces poor quality images with low resolution (LR) compared to other medical imaging modalities.Various image enhancement techniques have been extensively studied to overcome these shortcomings.Super-resolution (SR) is one of these methods, which endeavor to obtain high resolution (HR) images from LR images while enlarging them.Numerous studies have already utilized different SR techniques in various stages of ultrasound imaging (USI).Unlike other studies, which aimed at obtaining SR in the pre-processing phase or early stages of the post-processing phase of USI, we achieved SR on B-mode ultrasound images, which is the last stage of USI.We constructed a deep convolutional neural network (CNN) and trained it with a very large dataset of B-mode ultrasound images for the scale factors 2, 3, 4, and 8.We evaluated the performance of our proposed model quantitatively with eight image quality measures.The quantitative results revealed that our algorithm is much more successful than other methods at each magnification factor.Furthermore, we also verified that there is a statistically significant difference between our approach and others.Besides, qualitative analysis of the reconstructed images also confirms that it produces much better quality HR images than other methods in terms of the human visual system.
Cross-modal guidance based auto-encoder for multi-video summarization

Multi-Video Summarization (MVS) aims at condensing a great number of videos with the same search query to obtain a compact storyboard, which improves user’s browsing experience. Recently, a line of research resorts to a mass of extra web images to specify the search intent for a query. However, these web images are indirect and noisy. In contrast, the tag information for queried videos is more accurate and concise than those web images, which can also be directly employed as side information. To this end, we proposed to employ tag information, i.e., titles and descriptions, as the side information for the generation of summarization. Specifically, we employed sparse auto-encoder as the main model to generate the final summarization, where the input was the multiple videos, the output was the keyframes set. Meanwhile, we fused the visual and tag information to guide visual features, which constrained the sparse auto-encoder to select the important candidate keyframes. The proposed approach is called Cross-modal guidance based Auto-Encoder for MVS approach (CAE-MVS). Extensive experimental results demonstrate that the CAE-MVS clearly outperforms the state-of-the-art methods.
FALKON: Large-Scale Content-Based Video Retrieval Utilizing Deep-Features and Distributed In-memory Computing

In the boom of the big-data era, social media, 5g, High-speed Internet, and High-Tech Vision equipment have resulted in an enormous amount of video data production at an alarming speed. A fast and efficient content-based video retrieval system is not only desirable but essential in many domains with widespread applications. Conventional video retrieval techniques are inadequate to fulfill the needs of the day and keep pace with the rate of video production due to three core challenges: the sheer volume of the videos being produced, the complexity of video data, and redundancy in the video data. Due to these challenges, video operations & processing demand an enormous computing power and resources to process effectively. In this paper, we propose FALKON, a content-based video retrieval system harnessing the power of big-data technologies, deep-learning and distributed in-memory computation. First, we perform structural analysis on the videos, then spatial and temporal features are computed and indexed. We apply various optimization techniques to accelerate video processing as well as accuracy. We introduce VidRDD as a basic unit for distributed in-memory video computation. Furthermore, we introduce Video Query Maps as a relevance feedback mechanism to make the proposed system more reliable, user-friendly, and to improve the retrieval results. We implement FALKON on Hadoop, Hbase, Spark, and OpenCV. We achieve an average accuracy of 97.3%. Our evaluation results show that FALKON performs very well in terms of efficiency, scalability, computation time, and precision.