Reviewerly/exHarmony huggingface dataset:
PSO-GA-Based Resource Allocation Strategy for Cloud-Based Software Services With Workload-Time Windows

Cloud-based software services necessitate adaptive resource allocation with the promise of dynamic resource adjustment for guaranteeing the Quality-of-Service (QoS) and reducing resource costs. However, it is challenging to achieve adaptive resource allocation for software services in complex cloud environments with dynamic workloads. To address this essential problem, we propose an adaptive resource allocation strategy for cloud-based software services with workload-time windows. Based on the QoS prediction, the proposed strategy first brings the current and future workloads into the process of calculating resource allocation plans. Next, the particle swarm optimization and genetic algorithm (PSO-GA) is proposed to make runtime decisions for exploring the objective resource allocation plan. Using the RUBiS benchmark, the extensive simulation experiments are conducted to validate the effectiveness of the proposed strategy on improving the performance of resource allocation for cloud-based software services. The simulation results show that the proposed strategy can obtain a better trade-off between the QoS and resource costs than two classic resource allocation methods.
Implementation of Data Mining for Drop-Out Prediction using Random Forest Method

Accreditation is one of the quality measurements for a University. Some elements of these measurements are students and graduate students. Prevention of students to drop out is a problem that is considered very important for the university itself. High levels of drop out students will have a bad impact on the university, such as bad reputation or low-grade accreditation. This research presenting the results of a case study analysis in educational data, by analyzing the data using the data mining technique. The author using the classification method, that focuses on drop-out prediction of undergraduate and diploma students at the ABC Faculty at XYZ University. To predict drop-out classification, academic data are needed. The raw data are student's academic data that enroll in university from 2008 to 2012. The raw data preprocessing then carried out to handle imbalanced data. This research uses synthetic minority oversampling technique (SMOTE) to handle imbalance dataset and random forest algorithm to predict drop-out within 2492 data. As a research result, the random forest algorithm accompanied by SMOTE can provide the best accuracy results by 93.43%. The main results of this research can be used to reduce drop-out levels by predicting potential drop out students and identifying potential factors related to drop out students.
Digital forensics as a service: Stepping up the game

After providing Digital Forensics as a Service (DFaaS) implementations to law enforcement agencies for close to a decade, we present our view from an inside-out perspective. We share the lessons learned from an organizational, operational and development perspective in a forensic and legal context. We conclude with our vision on how to bring the DFaaS concept to the next level for both investigative and innovative purposes.
Evolving multi-label classification rules by exploiting high-order label correlations

In multi-label classification tasks, each problem instance is associated with multiple classes simultaneously. In such settings, the correlation between labels contain valuable information that can be used to obtain more accurate classification models. The correlation between labels can be exploited in different levels such as capturing the pair-wise correlation or exploiting the higher-order correlations. Even though the high-order approach is more capable of modeling the correlation, it is computationally more demanding and has scalability issues. This paper aims at exploiting the high-order label correlations locally using supervised learning classifier systems (UCS). For this purpose, the label powerset (LP) strategy is employed and a prediction aggregation is utilized that improves the prediction capability of the LP method in the presence of unseen labelsets. Exact match ratio and Hamming loss measures are considered to evaluate the rule performance and the expected fitness value of individual classification rules is investigated using both metrics. Also, a computational complexity analysis is provided for training the proposed algorithm. The experimental results of the proposed method are compared with other well-known LP-based methods on multiple benchmark datasets and confirm the competitive performance of this method.
RECIPTOR: An Effective Pretrained Model for Recipe Representation Learning

Recipe representation plays an important role in food computing for perception, recognition, recommendation and other applications. Learning pretrained recipe embeddings is a challenging task, as there is a lack of high quality annotated food datasets. In this paper, we provide a joint approach for learning effective pretrained recipe embeddings using both the ingredients and cooking instructions. We present RECIPTOR, a novel set transformer-based joint model to learn recipe representations, that preserves permutation-invariance for the ingredient set and uses a novel knowledge graph (KG) derived triplet sampling approach to optimize the learned embeddings so that related recipes are closer in the latent semantic space. The embeddings are further jointly optimized by combining similarity among cooking instructions with a KG based triplet loss. We experimentally show that RECIPTOR's recipe embeddings outperform state-of-the-art baselines on two newly designed downstream classification tasks by a wide margin.
Evaluation of an immersive virtual learning environment for operator training in mechanical and plant engineering using video analysis

Abstract A structural evaluation is imperative for developing an effective virtual learning environment. Understanding the extent to which content that has been learned virtually can be applied practically holds particular importance. A group of persons from the technical field of mechanical and plant engineering ( N = 13) participated in a virtual operator training for a case application of additive manufacturing. To evaluate the virtual learning environment the participants answered quantitative questionnaires and were asked to apply what they had learned virtually to the real machine. Both the virtual training and testing phase on the real machine were recorded by video (800 minutes in total). The category system resulting from a structured qualitative video analysis with a total of 568 codes contains design‐, instruction‐ and interaction‐related optimisation potentials for further development of the virtual learning sequence. Mistakes, difficulties and other anomalies during the application on the real machine provide further revision options. The study uses video data for the first time to derive optimisation potentials and to investigate the learning transfer of virtually learned action knowledge to the real‐world activity. Practitioner Notes What is already known about the topic? A combination of quantitative and qualitative data for formative evaluation is feasible to improve the quality of virtual learning environments and maintain critical constructive feedback. Relevant aspects for evaluating virtual learning environments include usability, technology acceptance, motivation and learning efficiency. Different methods for measuring the learning efficiency of VR training exist, eg, subjective measures or pretests/posttests. What this paper adds: An immersive virtual environment with its specific methodical‐didactic concept is introduced. Video data are used for the first time to derive optimisation potentials in a formative evaluation. The procedure of extracting and analysing the data in a structured video analysis is introduced in the paper. Learning transfer of the virtually learned content to the real‐world context of action is assessed and applied in the field of mechanical and plant engineering on a complex real machine for the first time. Implications for practice and/or policy: The described methodical procedure using video analysis enables deriving design‐, instruction‐ and interaction‐related optimisation potentials. The results show that spatial information was transferred to the real machine without any issues, whereby the main difficulties appeared during steps that require haptic feedback. The use of IVR should be critically assessed due to the effort required in creation.
Constrained Clustering With Dissimilarity Propagation-Guided Graph-Laplacian PCA

In this article, we propose a novel model for constrained clustering, namely, the dissimilarity propagation-guided graph-Laplacian principal component analysis (DP-GLPCA). By fully utilizing a limited number of weakly supervisory information in the form of pairwise constraints, the proposed DP-GLPCA is capable of capturing both the local and global structures of input samples to exploit their characteristics for excellent clustering. More specifically, we first formulate a convex semisupervised low-dimensional embedding model by incorporating a new dissimilarity regularizer into GLPCA (i.e., an unsupervised dimensionality reduction model), in which both the similarity and dissimilarity between low-dimensional representations are enforced with the constraints to improve their discriminability. An efficient iterative algorithm based on the inexact augmented Lagrange multiplier is designed to solve it with the global convergence guaranteed. Furthermore, we innovatively propose to propagate the cannot-link constraints (i.e., dissimilarity) to refine the dissimilarity regularizer to be more informative. The resulting DP model is iteratively solved, and we also prove that it can converge to a Karush-Kuhn-Tucker point. Extensive experimental results over nine commonly used benchmark data sets show that the proposed DP-GLPCA can produce much higher clustering accuracy than state-of-the-art constrained clustering methods. Besides, the effectiveness and advantage of the proposed DP model are experimentally verified. To the best of our knowledge, it is the first time to investigate DP, which is contrast to existing pairwise constraint propagation that propagates similarity. The code is publicly available at https://github.com/jyh-learning/DP-GLPCA.
Immutable DNA Sequence Data Transmission for Next Generation Bioinformatics Using Blockchain Technology

In recent years, there is fast growth in the high throughput DNA sequencing technology, and also there is a reduction in the cost of genome-sequencing, that has led to a advances in the genetic industries. However, the reduction in cost and time required for DNA sequencing there is still an issue of managing such large amount of data. Also, the security and transmission of such huge amount of DNA sequence data is still an issue. The idea is to provide a secure storage platform for future generation bioinformatics systems for both researchers and healthcare user. Secure data sharing strategies, that can permit the healthcare providers along with their secured substances for verifying the accuracy of data, are crucial for ensuring proper medical services. In this paper, it has been surveyed about the applications of blockchain technology for securing healthcare data, where the recorded information is encrypted so that it becomes difficult to penetrate or being removed, as the primary goals of block-chaining technology is to make data immutable.
Structural Properties of Nonanticipatory Epsilon Entropy of Multivariate Gaussian Sources

The complete characterization of the Gorbunov and Pinsker [1], [2] nonanticipatory epsilon entropy of multivariate Gauss-Markov sources with square-error fidelity is derived, which remained an open problem since 1974. Specifically, it is shown that the optimal matrices of the stochastic realization of the optimal test channel or reproduction distribution, admit spectral representations with respect to the same unitary matrices, and that the optimal reproduction process is generated, subject to pre-processing and post-processing by memoryless parallel additive Gaussian noise channels. The derivations and analyses are new and bring out several properties of such optimization problems over the space of conditional distributions and their realizations.
COVID-19 Apps: Privacy and Security Concerns

Today, with the rapid spread of COVID-19, many governments and start-ups are coming forward to develop smartphone apps that trace where we all are, whom we met and for how long, with a goal of interrupting new chains by informing potentially exposed people. These new platforms make use of anonymous use of Bluetooth technology and GPS, enabled either on smartphones or armbands in order to prepare maps corresponding to quarantine monitoring, contact tracing, movement tracking, social distancing and density reports. With different apps for different countries, one thing most of the apps facilitate is tracking. To save lives during an extraordinary crisis, many governments are willing to overlook privacy implications. Keeping in view that the sensitive data being collected is not exclusive to public health organizations and governments, this chapter explores different apps that were developed aiming to combat COVID-19, and the related personal data privacy concerns that arise in the post-coronavirus era.
Keyword Search over RDF: Is a Single Perspective Enough?

Since the task of accessing RDF datasets through structured query languages like SPARQL is rather demanding for ordinary users, there are various approaches that attempt to exploit the simpler and widely used keyword-based search paradigm. However this task is challenging since there is no clear unit of retrieval and presentation, the user information needs are in most cases not clearly formulated, the underlying RDF datasets are in most cases incomplete, and there is not a single presentation method appropriate for all kinds of information needs. As a means to alleviate these problems, in this paper we investigate an interaction approach that offers multiple presentation methods of the search results (multiple-perspectives), allowing the user to easily switch between these perspectives and thus exploit the added value that each such perspective offers. We focus on a set of fundamental perspectives, we discuss the benefits from each one, we compare this approach with related existing systems and report the results of a task-based evaluation with users. The key finding of the task-based evaluation is that users not familiar with RDF (a) managed to complete the information-seeking tasks (with performance very close to that of the experienced users), and (b) they rated positively the approach.
Knowledge-Biased Sampling-Based Path Planning for Automated Vehicles Parking

We consider automated vehicles operation in constrained environments, i.e. the automated parking (AP).The core of AP is formulated as a path planning problem, and Rapidly-exploring Randomized Tree (RRT) algorithm is adopted.To improve the baseline RRT, we propose several algorithmic tweaks, i.e. reversed RRT tree growth, direct tree branch connection using Reeds-Shepp curves, and RRT seeds biasing via regulated parking space/vehicle knowledge.We prove that under these tweaks the algorithm is complete and feasible.We then examine its performance (time, success rate, convergence to the optimal path) and scalability (to different parking spaces/vehicles) via batched simulations.We also test it using a real vehicle in a realistic parking environment.The proposed solution presents itself more applicable when compared with other baseline algorithms.
AutoGrow: Automatic Layer Growing in Deep Convolutional Networks

Depth is a key component of Deep Neural Networks (DNNs), however, designing depth is heuristic and requires many human efforts. We proposeAutoGrow to automate depth discovery in DNNs: starting from a shallow seed architecture,AutoGrow grows new layers if the growth improves the accuracy; otherwise, stops growing and thus discovers the depth. We propose robust growing and stopping policies to generalize to different network architectures and datasets. Our experiments show that by applying the same policy to different network architectures,AutoGrow can always discover near-optimal depth on various datasets of MNIST, FashionMNIST, SVHN, CIFAR10, CIFAR100 and ImageNet. For example, in terms of accuracy-computation trade-off,AutoGrow discovers a better depth combination in \resnets than human experts. OurAutoGrow is efficient. It discovers depth within similar time of training a single DNN. Our code is available at \urlhttps://github.com/wenwei202/autogrow.
Design and Validation of a Spinal Surgical Navigation System Based on Spatial Augmented Reality

Study Design. Prospective observational study. Objective. This article aims to develop a spatial augmented reality-based surgical navigation system to assist in the placement of pedicle screws in minimally invasive spine surgery and to verify the accuracy of this method. Summary of Background Data. Due to their high accuracy and good visualization ability, augmented reality surgical navigation systems have been used in minimally invasive surgeries. However, augmented reality does not allow information to be shared and restricts doctors. Methods. A surgical navigation system that implements augmented reality based on a projector can be used to realize the external visualization of virtual organs and surgical information through an improved multiple information fusion method. Using fiducial markers and imaging technology, the patient's spatial position is tracked and registered in real time. All the information is accurately fused with the patient's back skin, and the surgeon can see surgical information such as the preoperative plan and bones. Phantom experiments were used to verify the accuracy and effectiveness of the system. Results. In the phantom experiments, the accuracy of the pedicle screw insertion point on the dummy's skin was 0.441 ± 0.214 mm, the average location error into the dummy's body was 1.645 ± 0.355 mm, and the average axial and sagittal angulation errors were &lt;0.9°. Conclusion. This article introduces and verifies the design of a new surgical navigation system based on spatial augmented reality for lumbar pedicle screw implantation. The system passed a series of phantom accuracy experiments. Compared with the traditional augmented reality navigation system, this system avoids the use of glasses and truly realizes the effect of naked-eye 3D, which is more convenient for doctors to use for communication during an operation. Level of Evidence: N/A
Visual fixation prediction with incomplete attention map based on brain storm optimization

We cannot see everything around us. Instead, the visual attention mechanism will select some fixations from extensive visual information for further processing. Many computational attention models have been proposed by imitating this mechanism. However, almost all of the state-of-the-art computational models output a complete saliency map, which means one needs to go through all the regions in a scene before figuring out which part is more salient. According to the findings of neuroscience researches, it is unnecessary to evaluate every part at the glance stage of visual perception. Many researchers believe that the attention maps in different parts of our brain should be an incomplete one. What illustrated in this paper is a visual fixation prediction model that calculates the attention regions in a partially random manner. The output saliency map indeed is incomplete. We first translate the fixation prediction problem to a 2-D searching problem, then apply a newly proposed swarm intelligence algorithm known as Brain Storm Optimization (BSO) to search the fixation in different scenes. The proposed method can guide the searching process, converging to relatively more salient positions during iterations without going through all parts of an image. We evaluate the proposed method on a large scale CAT2000 dataset and the Extended Complex Scene Saliency Dataset (ECSSD). By comparatively studying with the other eight fixation prediction models and 24 salient region detection models, results indicate that the proposed method is effective in predicting the fixation rapidly, which makes it a good candidate for computational visual attention modeling.
Quantum locally linear embedding for nonlinear dimensionality reduction

Reducing the dimension of nonlinear data is crucial in data processing and visualization. The locally linear embedding algorithm (LLE) is specifically a representative nonlinear dimensionality reduction method with maintaining well the original manifold structure. In this paper, we present two implementations of the quantum locally linear embedding (QLLE) algorithm to perform the nonlinear dimensionality reduction on quantum devices. One implementation, the linear-algebra-based QLLE algorithm, utilizes quantum linear algebra subroutines to reduce the dimension of the given data. The other implementation, the variational quantum locally linear embedding (VQLLE) algorithm, utilizes a variational hybrid quantum-classical procedure to acquire the low-dimensional data. The classical LLE algorithm requires polynomial time complexity of N, where N is the global number of the original high-dimensional data. Compared with the classical LLE, the linear-algebra-based QLLE achieves quadratic speedup in the number and dimension of the given data. The VQLLE can be implemented on the near-term quantum devices in two different designs. In addition, the numerical experiments are presented to demonstrate that the two implementations in our work can achieve the procedure of locally linear embedding.
Malaria Disease Prediction Based on Machine Learning

Malaria detection is a stressful job for most doctors and it requires experiences and expertise. The machine learing (ML) method can be used to releave this issue. This paper try to find suitable model to help detect malaria with accuracy. The used dataset was released by National Institute of Health in USA and contained a total number of 27,560 red blood cell (RBC) images with equivalent instances of parasitized and uninfected RBCs images. A single hidden layer feedforward neural networks methodology namely Extreme Learning Machine (ELM) model was applied to classify and predict whether a patient has been affected by malaria or not. ELM has been compared with other machine learning techniques like SVM, KNN, CART, RF, CNN, VGG16, RESNET, and DENSENET, and it has outperformed all the other with 99% of accuracy, 28seconds cost time, 0.0095 Misclassification Error, and 98% precision which showed the effectiveness of ELM in the application of malaria cell detection scenario and it can also be referred by other researchers in the related field.
SOK

Cryptocurrencies such as Bitcoin are realized using distributed systems and hence critically rely on the performance and security of the interconnecting network. The requirements on these networks and their usage, however can differ significantly from traditional communication networks, with implications on all layers of the protocol stack. This paper is motivated by these differences, and in particular by the observation that many fundamental design aspects of these networks are not well-understood today. In order to support the networking community to contribute to this emerging application domain, we present a structured overview of the field, from topology and neighbor discovery to block and transaction propagation. In particular, we provide the context, highlighting differences and commonalities with traditional networks, review the state-of-the-art, and identify open research challenges. Our paper can hence also be seen as a call-to-arms to improve the foundation on top of which cryptocurrencies are built.