Reviewerly/exHarmony huggingface dataset:
Optimization of SLA aware live migration of multiple virtual machines using Lagrange multiplier

Cloud computing has attracted customers who want computing as a utility for reducing the overall business cost. For the success of the cloud, it is desirable to have a high degree of trust between the cloud service providers and customers. Service level agreements (SLAs) are major tools to build this trust. Cloud service provider often offers infrastructure-as-a-service, predominantly in the form of virtual machines (VM). These VMs allow customers to run their services in an isolated environment. While provisioning VMs on the cloud, it is often required to migrate multiple virtual machines from one server to another for load balancing, maintenance, or other reasons. This requires allocation of transfer rate to these VMs, which is usually done such that either the total migration time and/or total downtime is minimized without considering the penalty imposed for the service downtime during migration. In this paper, we propose a model for allocation of transfer rate to multiple migrating VMs using Lagrange multiplier such that the SLA violation and consequently the provider’s penalty is minimized during the migration process. To evaluate the model, extensive simulation has been done for various test cases. The results show that the penalty the provider has to pay for the violation of SLA during migration can be reduced significantly with the proposed approach.
Embodied design using augmented reality: the case of the gradient

Abstract We study the augmented reality sandbox (ARSB) as an embodied learning environment to foster meaning making in the context of bivariable calculus. We present the case of Tiago, a first-year bachelor chemistry student, performing a series of tasks based on embodied design, including perception-based, action-based and incorporation-based tasks. Tiago’s work demonstrates the affordances of the ARSB, e.g. to trace a height line and to manipulate plastic planes either with or without feedback from projected height lines. Tiago’s reasoning about mathematical concepts, e.g. the parameters in a plane equation and the gradient vector, is supported by perceptual structures that he discovers during these embodied tasks. We distinguished two ways in which ARSB affordances were used in the learning sequence. In perception-based and action-based tasks, the affordances of the ARSB were immediately available and intensively involved in the interaction. In incorporation tasks, on the contrary, a critical affordance was deliberately removed and the student was able to reproduce its functionality without technology.
Optical image encryption scheme with extended visual cryptography and non-mechanical ptychographic encoding

Abstract Non-mechanical ptychographic encoding (NPE) transforms the secret information into series of diffractive patterns through a spatial light modulator (SLM), saving the need to fabricate the secret objects. Conventionally, the shares in extended visual cryptography (EVC) are printed on transparent sheets or fabricated with diffractive optical elements and metasurface, but these methods are expensive and disposable. To solve these problems, we proposed an optical image encryption scheme that combines EVC and NPE. In the encryption process, the secret image is decomposed into multiple shares that are digitally loaded on the SLM, and the ciphertexts are generated according to the ptychographic encoding scheme. The decryption is performed by superimposing the shares reconstructed from the ciphertexts. We present optical experiments to demonstrate the feasibility and effectiveness of the proposed method.
A Robust and Adaptive Watermarking Technique for Relational Database

Abstract A watermarking technology is a kind of marker covertly embedded to identify ownership of the copyright. The existing database watermarking techniques cannot automatically adapt to different types of data and have poor robustness. In this paper, we present a new robust database watermarking scheme. The scheme can automatically adapt the watermarking algorithm and parameters according to the data characteristics for numerical and text-based data. We have theoretically established and verified experimentally the performance of our method in terms of robustness and less data distortion. This makes it suitable for copyright protection, owner identification, or traitor tracing purposes.
Improvements of bat algorithm for optimal feature selection: A systematic literature review

Bat Algorithm (BA) has been extensively applied as an optimal Feature Selection (FS) technique for solving a wide variety of optimization problems due to its impressive characteristics compared to other swarm intelligence methods. Nevertheless, BA still suffers from several problems such as poor exploration search, falling into local optima, and has many parameters that need to be controlled appropriately. Consequently, many researchers have proposed different techniques to handle such problems. However, there is a lack of systematic review on BA which could shed light on its variants. In the literature, several review papers have been reported, however, such studies were neither systematic nor comprehensive enough. Most studies did not report specifically which components of BA was modified. The range of improvements made to the BA varies, which often difficult for any enhancement to be accomplished if not properly addressed. Given such limitations, this study aims to review and analyse the recent variants of latest improvements in BA for optimal feature selection. The study has employed a standard systematic literature review method on four scientific databases namely, IEEE Xplore, ACM, Springer, and Science Direct. As a result, 147 research publications over the last ten years have been collected, investigated, and summarized. Several critical and significant findings based on the literature reviewed were reported in this paper which can be used as a guideline for the scientists in the future to do further research.
Accurate Brain Age Prediction Model for Healthy Children and Adolescents using 3D-CNN and Dimensional Attention

The brain age, estimated from the brain MRI data, is found to be a promising biomarker for human brain development and neuroanatomical aging processes. A well-performed brain age predicting model is in great demand for many applications like healthcare and disease diagnosis. In this paper, we proposed a dimensional-attention-based 3D convolutional neural network (DACNN) to estimate the biological age for developing normal brain from T1-weighted MRI, in which a dimensional attention module was designed and applied to restrain noises and increase the weights of effective voxels for feature maps. Experimental results indicated that our model significantly outperformed the best reported methods up to now. In particular, with the dilated convolution, the proposed DACNN achieved the state-of-the-art result of 1.01 MAE on a combined dataset consisted of 880 healthy children and adolescents.
Path Planning for Mobile Robots using Deep Learning Architectures

In recent years robots are replacing the human workforce in hospitals, defense, agricultural fields, laboratories, etc,. Majority of the robots working in such areas should possess the ability of path planning to quickly navigate without striking on the obstacles. There are many conventional algorithms for path generation for a given grid map such as A*, Dijikstra, RRT, etc., but they demand more memory resources and take a lot of time. These disadvantages can be overcome by using Deep Learning architectures. In this paper different deep learning architectures like Multi Layer Perceptron, CNN, LSTM, CNN-LSTM for path planning on a grid map are designed and their performances are compared. Among all the deep learning architectures implemented for path planning, CNN-LSTM model performed better with minimum validation Mean Absolute Error(MAE) of 0.0366, and with test MAE of 0.0352, it performed well for dynamic obstacle and moving goal point environments too.
Multiple perspectives integration for virtual reality-aided assemblability assessment in narrow assembly spaces

Abstract Virtual reality (VR) has been proved as a promising tool for industrial design, but the traditional VR interface of first-person perspective (1PP) is not efficient to support assemblability assessment in narrow assembly spaces. In this paper, we proposed the multi-perspectives interface (MPI) which integrates the 1PP and the third-person perspective (3PP) using the handheld world-in-miniature (WIM). The MPI allows users to simulate the assembly operations in a natural manner similar to 1PP, while providing users with an overview of the assembly status through the WIM to assess the assemblability with superior spatial awareness. Two studies were conducted to test the performance of the proposed MPI. The first study tested user’s interaction performance in MPI using a common interaction task, which reveals stronger spatial awareness in MPI than in 1PP without the cost of losing natural interaction. Based on the results of the first study, the second study tested the performance, usability, and workload of MPI in an assemblability assessment task. The results show the advantages of MPI in the reachability evaluation in the narrow spaces. The main contribution of this paper is improving the interface and user-interface interaction in VR-aided assembly assessment system to improve user’s interaction performance and assessment ability in narrow assembly spaces.
Artificial intelligence techniques in Cancer research: Opportunities and challenges

Cancer is a leading cause of mortality and morbidity on a global scale. Cancer research has gradually improved in the past three decades with the advent of automated learning techniques. Artificial Intelligence (AI) practices have emerged as valuable tools in predictive modeling. AI-based prediction models can serve as clinical decision support systems and aid in improving cancer mortality rates. Prominent research works have been conducted to predict cancer at an early stage. AI practices extending from machine learning to deep learning architectures have been employed in cancer prediction. Although the validation of AI prediction models in clinical settings is missing, many studies have still achieved better prediction outcomes than physicians, which advocate integrating AI in real-world settings. The review paper aims to highlight the potential of AI in cancer detection. This study also provides an outline of the automated prediction framework used for the diagnosis of cancer.
Personalized Education Based on Hybrid Intelligent Recommendation System

Differentiated pedagogy is a flexible and organized adaptation of teaching and learning as it argues that students, even those of the same age, have differences in learning readiness, interests, learning style, experiences, and living circumstances. These differences are important in the determination of requirements of their learning and the way of effective learning. In addition, the foundation for effective learning is the sense of community within the classroom, the authentic learning opportunities of using educational equipment, and the connection of the lesson with the experiences and interests of the students. In essence, the support of a teacher guides the pupils to learn to work on their own during a declining guidance policy, to improve their abilities and skills. Thus, the teachers are asked to modify their teaching methods instead of applying a similar way of teaching for all students. The modified teaching style should meet the different levels of readiness of students, the different ways they learn, and their different interests. In support of this specific task for teachers, the current work presents a personalized education system based on hybrid intelligent recommendations. Specifically, a hybrid framework of artificial intelligence is proposed, which focuses on the way to provide targeted recommendations for the implementation of integrated standard lesson plans, which will be the main tool for creating flexible differentiated pedagogical programs that will perfectly meet the personal needs and particularities of each student.
Analysis of Ciliwung river flood debit and city flood anticipation using floods early detection system (FEDS)

Abstract This research is based on flood conditions that often occur in lowland areas such as Jakarta and Semarang. The problem faced is that the notification and early detection of floods is often late, done manually so that it cannot be anticipated by areas downstream of the river. Therefore, it is very important to be able to develop an IoT-based early warning tool so that floods can be detected early in a fast, real time, and immediately anticipated in the upstream area of the river. This research method uses design methods and experiments carried out in the field and laboratory. This research will present a prototype of the FEDS (Floods Early Detection System), based on the Blynk application. The results showed that the calculation of planned flood discharge with a return period of 2, 5, 10, 25 and 50 years can provide an overview of the ability of an area to face the maximum possible rainfall. The FEDS prototype tool, with the Blynk application, can work well using a microcontroller, ultra sonic sensor, and a rainfall sensor. This system is suitable for use in the community to determine rain conditions and water level conditions used at river water level conditions, for early notification of floods.
Invasive Ductal Carcinoma Grade Classification in Histopathological Images using Transfer Learning Approach

Cancer is the greatest cause of mortality in the world wherein; Breast Invasive Ductal Carcinoma (IDC) is the second leading cause of death among women. Computer-assisted diagnostic (CAD) systems embedded with advanced artificial intelligence techniques have demonstrated a significant performance in early detection on a variety of malignancies. Digital histopathological images and deep learning architectures are the preferred approaches in the analysis of biomedical images. In this conjunction, pre-existing deeper networks are trained on large image datasets and adapted for the classification of grades using the transfer learning approach. In this study, a transfer learning approach is proposed for the classification of IDC grade breast. Herein, we have utilized deeper pre-trained models, VGG16, Inception_V3, ResNet152V2, DenseNet201, and NASNetMobile for the feature extraction tasks, and Random Forest (RF) classifier is applied for the final decision on IDC grade classification. A new histopathological microscopy databiox image dataset, containing 922 images from 124 IDC patients that are publicly available is used in this work. The proposed transfer learning approach attained the best performance using DensNet201 (AUC=98%) and VGG16 (AUC=83%) for grade class 0 (Grade-I), DensNet201 (AUC=75%) and NASNetMobile (AUC=72%) for grade class 1 (Grade-II) and Inception_V3 (AUC=65%) and NASNetMobile (AUC=69%) for grade class 2 (Grade-III) among the different pre-trained models. The designed model achieved the highest performance as compared to the existing state-of-the-art techniques.
GLOW : Global Weighted Self-Attention Network for Web Search

Deep matching models aim to facilitate search engines retrieving more relevant documents by mapping queries and documents into semantic vectors in the first-stage retrieval. When leveraging BERT as the deep matching model, the attention score across two words are solely built upon local contextualized word embeddings. It lacks prior global knowledge to distinguish the importance of different words, which has been proved to play a critical role in information retrieval tasks. In addition to this, BERT only performs attention across sub-words tokens which weakens whole word attention representation. We propose a novel Global Weighted Self-Attention (GLOW) network for web document search. GLOW fuses global corpus statistics into the deep matching model. By adding prior weights into attention generation from global information, like BM25, GLOW successfully learns weighted attention scores jointly with query matrix Q and key matrix K. We also present an efficient whole word weight sharing solution to bring prior whole word knowledge into sub-words level attention. It aids Transformer to learn whole word level attention. To make our models applicable to complicated web search scenarios, we introduce combined fields representation to accommodate documents with multiple fields even with variable number of instances. We demonstrate GLOW is more efficient to capture the topical and semantic representation both in queries and documents. Intrinsic evaluation and experiments conducted on public data sets reveal GLOW to be a general framework for document retrieve task. It significantly outperforms BERT and other competitive baselines by a large margin while retaining the same model complexity with BERT. The source code is available at https://github.com/GLOW-deep/GLOW.
Limb Pose Aware Networks for Monocular 3D Pose Estimation

In the task of monocular 3D pose estimation, the estimation errors of limb joints (i.e., wrist, ankle, etc) with a higher degree of freedom(DOF) are larger than that of others (i.e., hip, thorax, etc). Specifically, errors may accumulate along the physiological structure of human body parts, and trajectories of joints with higher DOF bring in higher complexity. To address this problem, we propose a limb pose aware framework, involving a kinematic constraint aware network as well as a trajectory aware temporal module, to improve the 3D prediction accuracy of limb joint positions. Two kinematic constraints named relative bone angles and absolute bone angles are introduced in this paper, the former being used for building the angular relation between adjacent bones and the latter for building the angular relation between bones and the camera plane. As a joint result of two constraints, our work suppresses errors accumulated along limbs. Furthermore, we propose a trajectory-aware network, named as Hierarchical Transformer, which takes temporal trajectories of joints as input and generates fused trajectory estimation as a result. The Hierarchical Transformer consists of Transformer Encoder blocks and aims at improving the performance of fusing temporal features. Under the effect of kinematic constraints and trajectory network, we alleviate the problem of errors accumulated along limbs and achieve promising results. Most of the off-the-shelf 2D pose estimators can be easily integrated into our framework. We perform extensive experiments on public datasets and validate the effectiveness of the framework. The ablation studies show the strength of each individual sub-module.
Learning the Morphological and Syntactic Grammars for Named Entity Recognition

In some languages, Named Entity Recognition (NER) is severely hindered by complex linguistic structures, such as inflection, that will confuse the data-driven models when perceiving the word’s actual meaning. This work tries to alleviate these problems by introducing a novel neural network based on morphological and syntactic grammars. The experiments were performed in four Nordic languages, which have many grammar rules. The model was named the NorG network (Nor: Nordic Languages, G: Grammar). In addition to learning from the text content, the NorG network also learns from the word writing form, the POS tag, and dependency. The proposed neural network consists of a bidirectional Long Short-Term Memory (Bi-LSTM) layer to capture word-level grammars, while a bidirectional Graph Attention (Bi-GAT) layer is used to capture sentence-level grammars. Experimental results from four languages show that the grammar-assisted network significantly improves the results against baselines. We also investigate how the NorG network works on each grammar component by some exploratory experiments.
A Time-series Analysis of How Google Trends Searches Affect Cryptocurrency Prices for Decentralized Finance and Non-Fungible Tokens

Boosted by blockchain technology, the sphere of Decentralized Finance (DeFi) and Non-Fungible Tokens (NFTs) is expanding globally. Although valuations of major cryptocurrencies Bitcoin and Ethereum likely will set the future of crypto-assets, the post-2020 expansion of cryptocurrency markets prompted explosive growth of DeFi- and NFT-branded altcoins. This study constructs time-series models to examine DeFi- and NFT-related cryptocurrencies and to clarify how their weekly prices fluctuated over a one-year period. Using Google Trends data, we measure how weekly Internet searches into crypto markets generally and specific branded cryptocurrencies affected price fluctuations for each coin. Results show that Bitcoin prices modeled as an exogenous variable have a positive effect on Ethereum prices. On average, brand-specific and market-level Google searches were estimated to be negative but not statistically convincing for DeFi- and NFT-branded altcoins.
AVS-YOLO: Object Detection in Aerial Visual Scene

Difficult object detection and class imbalance in object detection are the two main challenges faced by aerial image object detection. Difficult objects include small objects, objects of scale variation and objects with serious background interference. Class imbalances come from the number of different classes of objects and sampling of positive and negative samples. Due to these challenges, conventional object detection models usually cannot effectively detect objects in aerial images, especially in the balance between network speed and accuracy. In this paper, the YOLOv3 network structure was improved and an object detection method under the aerial visual scene (AVS-YOLO) was proposed. By introducing a type of densely connected feature pyramid strategy, a scale-aware attention module was constructed, considering both residual dense network blocks and the median-frequency-balancing mechanism. On this basis, an algorithm with ideal speed and accuracy for object detection is obtained. To verify the effectiveness of the algorithm, AVS-YOLO and YOLOv3 were both used to test the VisDrone-DET2019 and UAVDT. The experimental results show that the AP of AVS-YOLO increases by 6.22% and 5.09% on the VisDrone2019 and UAVDT datasets, respectively, compared with YOLOv3. In addition, the AP of AVS-YOLO is 1.82% higher than that of YOLOv4 on the VisDrone2019 dataset. In terms of detection speed, AVS-YOLO can process 31.8 frames per second on a single Nvidia GTX 2080Ti GPU, compared with 44.1 frames per second for YOLOv3. Compared with the other one-stage network in the field of object detection, AVS-YOLO currently achieves the state-of-the-art performance with similar calculation amount on this dataset.