Reviewerly/exHarmony huggingface dataset:
Study on the major minerals potential in China

China is located at the convergence zone of the Paleo-Asian metallogenic domain, East Pacific margin metallogenic domain, and Tethyan metallogenic domain. Theoretically, this feature favor metallogenic conditions, which suggest an abundance of mineral resources. Today, the rapid development of urbanization and industrialization has triggered a continuously increasing demand for and a large scale consumption of mineral resources. Therefore, in order to satisfy this demand, China has furthered its evaluation of mineral resources potential to help guide future mineral explorations. Based on the innovation of geological theory, prediction and prospecting models, as well as GIS-based comprehensive assessment methods, 25 important mineral resources and key metals have been assessed in China. Herein, we evaluate the national mineral resources assessment study by combining it with metallogenic areas (belts) research. Through our analyses, we identify the potential characteristics of important mineral resources in different metallogenic areas (belts) to enhance the scientific decision-making process for the exploration and deployment of mineral resources in China. We found that the exploration and deployment of bulk minerals such as iron, manganese, and copper are continuously changing. Currently, the eastern region should be the primary focus of deep prospecting, while exploration for copper, lead, and zinc should occur in the central and western regions.
A System for Prevention of SQLi Attacks

The internet and web application plays a very important role in our modern life style today. Nowadays many large organizations has huge database storage and important information stored. “Existing vulnerabilities of web system threaten the regular work of the information system. The most common Web vulnerability is SQLi” [9]. SQLi attacks pose a huge security threat to these organizations. This includes confidential data or information is deleted, lost or stolen, unauthorized access to accounts or the entire system. Many tools are created along the past years to prevent these kinds of attacks. The SQLi is an engine dependent database. Unintentionally user is giving access to that input directly into the database, when the user is asked for the input such as username and password on the web page. To have a safe online shopping this application is created. It will detect any SQLi attack and prevent it from accessing the personal data or any debit/credit card information.
Cloud Workflow Task and Virtualized Resource Collaborative Adaptive Scheduling Algorithm Based on Distributed Deep Learning

Cloud workflow task scheduling and resource allocation are core issues in cloud computing environment. Balancing the quality of user service and the revenue of service providers is a challenge in cloud workflow task scheduling and resource allocation. Therefore, we propose a cloud workflow task and virtualized resource collaborative adaptive scheduling algorithm based on heterogeneous distributed deep learning. This solves the multi-queue multi-cluster workflow task scheduling and resource allocation problem by combining multiple heterogeneous deep neural networks as the scheduling model of the cloud system. An optimal scheduling strategy is generated by minimizing the workflow task delay and energy consumption. Results from extensive experiments show that the proposed framework can effectively solve the multi-objective optimization problem of cloud Workflow task scheduling and resource allocation, and provide a nearly optimal scheduling strategy.
Benchmarking Deep Neural Network Inference Performance on Serverless Environments With MLPerf

We provide a novel decomposition methodology from the current MLPerf benchmark to the serverless function execution model. We have tested our approach in Amazon Lambda to benchmark the processing capabilities of OpenCV and OpenVINO inference engines.
Simulation of variational Gaussian process NARX models with GPGPU

Gaussian processes (GP) regression is a powerful probabilistic tool for modeling nonlinear dynamical systems. The downside of the method is its cubic computational complexity with respect to the training data that can be partially reduced using pseudo-inputs. The dynamics can be represented with an autoregressive model, which simplifies the training to that of the static case. When simulating an autoregressive model, the uncertainty is propagated through a nonlinear function and the simulation cannot be evaluated in closed-form. This paper combines the variational methods of GP approximations with a nonlinear autoregressive model with exogenous inputs (NARX) to form variational GP (VGP-NARX) models. We show how VGP-NARX models, on average, better approximate a full GP-NARX model than more commonly used GP-NARX (FITC) model on 10 chaotic time-series. The modeling capabilities of VGP-NARX models are compared with the existing approaches on two benchmarks for modeling nonlinear dynamical systems. The advantage of general-purpose computing on graphics processing units (GPGPU) for Monte Carlo simulation on large validation data sets is addressed.
A Weakly Supervised WordNet-Guided Deep Learning Approach to Extracting Aspect Terms from Online Reviews

The unstructured nature of online reviews makes it inefficient and inconvenient for prospective consumers to research and use in support of purchase decision making. The aspects of products provide a fine-grained meaningful perspective for understanding and organizing review texts. Traditional aspect term extraction approaches rely on discrete language models that treat words in isolation. Despite that continuous-space language models have demonstrated promise in addressing a wide range of problems, their application in aspect term extraction faces significant challenges. For instance, existing continuous-space language models typically require large collections of labeled data, which remain difficult to obtain in many domains. More importantly, previous methods are largely data driven but overlook the role of human knowledge in guiding model development. To address these limitations, this study designs and develops weakly supervised WordNet-guided deep learning to aspect term extraction. The approach draws on deep-level semantic information from WordNet to guide not only the selection representative seed terms but also the pruning of aspect candidate terms. The weak supervision is provided by a very small set of labeled data. We conduct a comprehensive evaluation of the proposed method using both direct and indirect methods. The evaluation results with Yelp restaurant reviews demonstrate that our proposed method consistently outperforms all baseline methods including discrete models and the state-of-the-art continuous-space language models for aspect term extraction across both direct and indirect evaluations. The research findings have broad research, technical, and practical implications for various stakeholders of online reviews.
Nonce: Life Cycle, Issues and Challenges in Cryptography

We all are living in the era of online processing, where the maximum of the information is available online. As the facilities of computer technology have increased, threats of losing personal and sensitive information have also increased. Cryptographic software and algorithms are good at some extent but as we all are seeing several attacks like Plaintext attack, Replay attack on Apply pay, Interleaving attack on PKMv2, etc. show us that our cryptographic software is less likely to be broken due to the weakness in the underlying deterministic cryptographic algorithms.
Partial Classifier Chains with Feature Selection by Exploiting Label Correlation in Multi-Label Classification

Multi-label classification (MLC) is a supervised learning problem where an object is naturally associated with multiple concepts because it can be described from various dimensions. How to exploit the resulting label correlations is the key issue in MLC problems. The classifier chain (CC) is a well-known MLC approach that can learn complex coupling relationships between labels. CC suffers from two obvious drawbacks: (1) label ordering is decided at random although it usually has a strong effect on predictive performance; (2) all the labels are inserted into the chain, although some of them may carry irrelevant information that discriminates against the others. In this work, we propose a partial classifier chain method with feature selection (PCC-FS) that exploits the label correlation between label and feature spaces and thus solves the two previously mentioned problems simultaneously. In the PCC-FS algorithm, feature selection is performed by learning the covariance between feature set and label set, thus eliminating the irrelevant features that can diminish classification performance. Couplings in the label set are extracted, and the coupled labels of each label are inserted simultaneously into the chain structure to execute the training and prediction activities. The experimental results from five metrics demonstrate that, in comparison to eight state-of-the-art MLC algorithms, the proposed method is a significant improvement on existing multi-label classification.
Learning the Relation Between Interested Objects and Aesthetic Region for Image Cropping

As one of the fundamental techniques for image editing, image cropping discards irrelevant contents and remains the pleasing portions of the image to enhance the overall composition and achieve better visual/aesthetic perception. In this paper, we primarily focus on improving the efficiency of automatic image cropping, and on further exploring its potential in public datasets with high accuracy. From this perspective, we propose a deep learning based framework to learn the objects composition from photos with high aesthetic qualities, where an interested object region is detected through a convolutional neural network (CNN) based on the saliency map. The features of the detected interested objects are then fed into a regression network to obtain the final cropping result. Unlike the conventional methods that multiple candidates are proposed and evaluated iteratively, only a single interested object region is produced in our model, which is mapped to the final output directly. Thus, low computational resources are required for the proposed approach. The experimental results on the public datasets show that as a weakly supervised method, the proposed network outperforms the other weakly supervised methods on FLMS and FCD datasets and achieves comparable results to the existing methods on CUHK dataset. Furthermore, the proposed method is more efficient than these methods, where the processing speed is as fast as 20 ms per image.
Angle of Arrival and Time of Flight Estimation as an Ising Energy Minimization Problem

This paper presents a ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm minimization based angle-of-arrival (AoA) estimation approach using an Ising model (AIM). The proposed method takes advantage of the fact that incoming signals are spatially sparse and that the angular domain can be discretized. To solve the NP-hard problem, we formulate the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm minimization as an Ising energy minimization problem. The minimization of the Ising energy results in the maximization of the Gibbs distribution. The performance of the proposed method in identifying and resolving the angle of arrival of incoming signals is studied for different scenarios, in all of which the proposed method outperforms the state-of-the-art methods.
Using Work System Design, User Stories and Emotional Goal Modeling for an mHealth System

In Malaysia, Heart Failure (HF) is one of the commonest reasons for hospitalization, with a quarter of HF patients readmitted within 30 days from the onset of acute HF. Reducing frequent hospitalization from HF would significantly reduce the burden on the health care system and resources. The opportunity exists to create digital health system to provide continuous patient care after the diagnosis of HF and subsequent discharge from inpatient care to reduce hospital readmission. This paper reports our ongoing project in Malaysia with the Ministry of Health Malaysia cardiac centers to address some of the barriers to continuous care for patients with chronic HF using digital health systems' interventions. We have applied a combination of existing requirements approaches - work system design, User Stories, personas, and emotional goal modeling - for the elicitation, modeling, and analysis of requirements. In this paper we report the results of our research including key contributions, lessons learned and the strengths and limitations of our approaches.
Server-aided searchable encryption in multi-user setting

Searchable encryption schemes allow users to search encrypted data containing some keyword without decrypting the data. It protects the data privacy of data owners, meanwhile facilitates efficient data access in secure cloud storage service. Most of the existing schemes only considered the single-user setting, in which a user uploads his encrypted data and later performs searches on it. However, the majority of databases in practice do not only serve one user; instead, they support write operations by multiple data owners and search operations by some authorized users. There exists the following issues in existing multi-user searchable encryption schemes. First, some schemes allow only one data owner to write to the encrypted database; second, a number of schemes may require the data owner to interact with the authorized users to distribute secret keys; third, some other schemes cannot efficiently support the search authorization revocation on users, and do not achieve the trapdoor unlinkability. In this paper, we address these issues by proposing a server-aided searchable encryption scheme in multi-user setting. In our scheme, the data owners only need to know the public key of an administration server to generate the searchable ciphertext, regardless of the number of authorized users. Furthermore, our scheme is shown to be semantically secure against outside keyword guessing attacks. Finally, the performance analyses and comparisons with some existing schemes demonstrate that our scheme achieves better performance.
RankViz: A visualization framework to assist interpretation of Learning to Rank algorithms

Although Machine Learning (ML) tools and techniques are widespread and quite popular, in some cases, the patterns found by a model are either not accessible or not easily understandable from a human perspective. Therefore, not only model fitness given a quality metric is important, but also understanding how it makes decisions has become critical. One example of an ML approach growing in relevance that still lacks support to interpretation is the Learning to Rank (LtR). LtR models are typically used to rank elements, and, as in most ML areas, much effort has been put into creating more accurate models, but little or no effort has been devoted to understanding how elements are ranked. In this paper, we propose RankViz, a novel visualization framework that aims to fill this gap by supporting LtR model analysis and interpretation through a set of coordinated visualizations. RankViz provides information about the most important data features to a specific ranking result, supports a detailed comparative analysis of elements’ positions, and enables the investigation of iterative models evolution. Our results and study cases show the usefulness of the proposed framework to investigate a model and to aid users in creating and understanding a ranking, supporting tasks that are difficult, if not impossible, to be executed without proper visual representations.
Preventing Spread of Spam Transactions in Blockchain by Reputation

As one of the fastest-growing applications in the Peer-to-Peer (P2P) network, the development of blockchain technology is accompanied by different attacks. Those include whitewashing, free-riding, and distributed denial of service (DDoS) attacks, particularly because of features such as anonymity, distributed, permissionless in the blockchain network. One popular of them is spam transactions. Although the blockchain protocol requires each node to verify all received transactions, many nodes choose to forward transactions without verification to conserve their computational power, as there is no punishment for such a shirking. And it makes the blockchain vulnerable to the spreading of spam transactions over the network and creates extra burdens for all nodes in the network. We propose a reputation mechanism for the blockchain system to tackle this problem: Each node will locally compute reputations of its neighbors, and decide the probability to verify a received transaction based on the reputation value of the transaction sender. In turn, its neighbors will have an incentive to conduct verification to keep its reputation high. Subsequently, spam transactions can be blocked before reaching the miners. We have conducted a series of simulations, which clearly demonstrate the advantage of our reputation mechanism.
A Hybrid Feature Selection Approach Using Artificial Bee Colony and Genetic Algorithm

When data is produced in large quantities, classification requires proper techniques for data analysis and representation. Successful optimization techniques can be used to find the most informative features from the data. Feature selection enhances classification accuracy as it eliminates irrelevant and redundant data from the dataset. Swarm Intelligent algorithms, which are inspired by the social behaviour of living organisms are found to have good performance in feature selection. This paper investigates the possibility of enhancing the artificial bee colony algorithm, one of the efficient swarm intelligent techniques, by hybridizing it with the genetic algorithm. Experiments on various datasets prove that the proposed approach has better results than the existing techniques.
LineSmooth: An Analytical Framework for Evaluating the Effectiveness of Smoothing Techniques on Line Charts

We present a comprehensive framework for evaluating line chart smoothing methods under a variety of visual analytics tasks. Line charts are commonly used to visualize a series of data samples. When the number of samples is large, or the data are noisy, smoothing can be applied to make the signal more apparent. However, there are a wide variety of smoothing techniques available, and the effectiveness of each depends upon both nature of the data and the visual analytics task at hand. To date, the visualization community lacks a summary work for analyzing and classifying the various smoothing methods available. In this paper, we establish a framework, based on 8 measures of the line smoothing effectiveness tied to 8 low-level visual analytics tasks. We then analyze 12 methods coming from 4 commonly used classes of line chart smoothing-rank filters, convolutional filters, frequency domain filters, and subsampling. The results show that while no method is ideal for all situations, certain methods, such as Gaussian filters and TOPOLOGY-based subsampling, perform well in general. Other methods, such as low-pass CUTOFF filters and Douglas-peucker subsampling, perform well for specific visual analytics tasks. Almost as importantly, our framework demonstrates that several methods, including the commonly used UNIFORM subsampling, produce low-quality results, and should, therefore, be avoided, if possible.
Join cardinality estimation by combining operator-level deep neural networks

Join cardinality estimation is fundamental to cost-based query optimizers. The state-of-the-art deep learning based join cardinality estimation methods do not fully take the structure of a query plan into account and lack of data information about joins, causing significant performance degradation when the number of joins in a query increases. In this paper, we propose CAPE, a join cardinality estimation method combining operator-level deep neural networks. CAPE introduces two operator-level deep neural networks for selection operators and join operators, as well as an output deep neural network that maps the intermediate representations to join cardinality estimates. Given the query plan rooted at a join operator, CAPE generates a plan-level deep neural network by combining operator-level deep neural networks. In this way, CAPE can handle arbitrary query plans with simple operator-level deep neural networks rather than a single complicated model. In addition, we introduce join-crossing sampling information to detect join-crossing correlations. Experiments are conducted on a dataset constructed from the IMDb dataset, and the experimental results show that CAPE is significantly better than the state-of-the-art methods.
Real-time Motion Planning of Curvature Continuous Trajectories for Urban UAV Operations in Wind

A key challenge in enabling autonomous Un-manned Aerial Vehicles (UAVs) to operate in cluttered urban environments is to plan collision-free, smooth, dynamically feasible trajectories between two locations with the wind in real-time. This paper presents a novel path planning strategy using sampling-based planning that uses a two-point boundary value problem (BVP) to connect states in the presence of wind. Unlike most approaches that use a curvature discontinuous solution, the proposed BVP is formulated as a nonlinear constrained optimization problem with curvature and curvature-rate continuous profile to generate smoother trajectories. To achieve real-time performance, our method uses surrogate solutions from a pre-calculated library while solving the planning problem and then runs a repair routine to generate the final trajectory. To validate the feasibility of the offline-online strategy, simulation results on a 3D model of an actual city block with a realistic wind-field are presented. Results with a trochoid-based BVP solver are also presented for comparison. For the given simulation scenario, we could demonstrate a 93% success rate for the algorithm in finding a valid trajectory.