Reviewerly/exHarmony huggingface dataset:
Registration of point cloud data for matching crushed sand particles

Identifying the fracture surface of fragments that are resulted from the mechanical crushing of a single sand particle and matching individual fragments to the mother particle remains a challenge in the geomechanics research. This paper presents a novel particle tracking method that can track fractured fragments from the original sand particle through the application of the point cloud registration technique based on the X-ray tomography data. Firstly, the external morphologies of the mother particle and the child particles were acquired from the X-ray tomography data from the single particle crushing test. A series of imaging process techniques were applied to treat the raw images and obtain the particle fracture pattern. Secondly, after the image processing, the contour-based mesh segmentation algorithm was utilised to divide a child particle into multiple faces. Then the iterative closest point algorithm was used to try matching each face of the child particle to the mother particle and evaluate the matching results through the distance error. Finally, upon a successful match, the region with a higher point density on the child particle can be extracted and marked as a part of the original particle surface, and the rest part marked as a part of the fracture surface. The effectiveness and efficiency of the tracking method were demonstrated using the tomography data of 9 crushed Leighton Buzzard sand particles.
Multimodal Learning of Social Image Representation by Exploiting Social Relations

Learning the representation for social images has recently made remarkable achievements for many tasks, such as cross-modal retrieval and multilabel classification. However, since social images contain both multimodal contents (e.g., visual images and textual descriptions) and social relations among images, simply modeling the content information may lead to suboptimal embedding. In this paper, we propose a novel multimodal representation learning model for social images, that is, correlational multimodal variational autoencoder (CMVAE) via triplet network. Specifically, in order to mine the highly nonlinear correlation between the visual content and the textual content, a CMVAE is proposed to learn a unified representation for the multiple modalities of social images. Both common information in all modalities and private information in each modality are encoded for the representation learning. To incorporate the social relations among images, we employ the triplet network to embed multiple types of social links in the representation learning. Then, a joint embedding model is proposed to combine the social relations for representation learning of the multimodal contents. Comprehensive experiment results on four datasets confirm the effectiveness of our method in two tasks, namely, multilabel classification and cross-modal retrieval. Our method outperforms the state-of-the-art multimodal representation learning methods with significant improvement of performance.
Profile-Guided Three-Phase Virtual Resource Management for Energy Efficiency of Data Centers

Energy efficiency is a critical issue in the management of data centers, which form the backbone of cloud computing. Virtual resource management has a significant impact on improving the energy efficiency of data centers. Despite the progress in this area, virtual resource management has been considered mainly at two separate levels: application assignment and virtual machine placement. It has not been well-investigated in a unified framework for both levels, limiting further improvement in the energy efficiency of data centers. To address this issue, this paper proposes the virtual resource management problem for energy efficiency as a constrained optimization problem. Then, this paper simplifies the problem through profile-guided task classification and problem decomposition for complexity reduction and improved energy efficiency. After that, a three-phase framework and algorithms are presented for profiling and profile updating, task classification and application assignment, and successive virtual machine placement. Experimental studies show energy savings of 8-12% by the three-phase framework compared to the existing technique.
Using predictive analytics to identify drug-resistant epilepsy patients

Epilepsy is one of the most common brain disorders that greatly affects patients’ quality of life and poses serious risks to their health. While the majority of the patients positively respond to the existing anti-epilepsy drugs, others who developed the refractory type of epilepsy show resistance against drug therapy and need to undergo advance treatments such as surgery. Given that identifying such patients is not a straightforward process and requires long courses of trial and error with anti-epilepsy drugs, this study aims at predicting those at-risk patients using clinical and demographic data obtained from electronic medical records. Specifically, the study employs several predictive analytics machine-learning methods, equipped with a novel approach for data balancing, to identify drug-resistant patients using their comorbidities and demographic information along with the initial epilepsy-related diagnosis made by their physician. The promising results we obtained highlight the potential use of machine-learning techniques in facilitating medical decisions and suggest the possibility of extending the proposed approach for developing a clinical decision support system for medical professionals.
Twin-field quantum key distribution with modified coherent states

The twin-field quantum key distribution (TF-QKD) protocol is designed to beat the rate-distance limit of quantum key distributions without employing quantum repeaters; meanwhile, it can offer the measurement-device-independent secure level. In this Letter, we propose to improve the performance of TF-QKD protocols by employing modified coherent states. Based on the Wang et al. sending-or-not scheme [Phys. Rev. A98, 062323 (2018)PLRAAN1050-294710.1103/PhysRevA.98.062323], we study the key rate with the modified coherent states in finite data size and do comparisons with the one using weak coherent states. Through numerical simulations, we demonstrate that modified coherent states can substantially increase the performance of QKD more than the latter.
A Transaction on the Blockchain: An AIS Perspective, Intro Case to Explain Transactions on the ERP and the Role of the Internal and External Auditor

ABSTRACT This case promotes discussions about blockchain technology. It shows a transaction execution scenario and its tax implications. The case provides (1) an overview of the steps required to install a Digital Wallet; (2) an explanation of the need to add cryptocurrencies to the wallet in order to transact on the blockchain; (3) an overview of executing a transaction, in which an accounting consulting service is provided in the exchange of a cryptocurrency; (4) methods to review the existing transaction, from the service provider side, the service receiver side, and from the blockchain side, with a highlight of the role of both internal and external auditors; (5) clarifies the importance of dealing with cryptocurrencies, given that the IRS identifies cryptocurrencies as properties; and (6) a reflection on the link between a company ERP system and the transaction information on the blockchain.
Automated language‐independent authorship verification (for Indo‐European languages)

In this article we examine automated language‐independent authorship verification using text examples in several representative Indo‐European languages, in cases when the examined texts belong to an open set of authors, that is, the author is unknown. We showcase the set of developed language‐dependent and language‐independent features, the model of training examples, consisting of pairs of equal features for known and unknown texts, and the appropriate method of authorship verification. An authorship verification accuracy greater than 90% was accomplished via the application of stylometric methods on four different languages (English, Greek, Spanish, and Dutch, while the verification for Dutch is slightly lower). For the multilingual case, the highest authorship verification accuracy using basic machine‐learning methods, over 90%, was achieved by the application of the kNN and SVM‐SMO methods, using the feature selection method SVM‐RFE. The improvement in authorship verification accuracy in multilingual cases, over 94%, was accomplished via ensemble learning methods, with the MultiboostAB method being a bit more accurate, but Random Forest is generally more appropriate.
Decision tree classification: Ranking journals using IGIDI

Selection of an attribute for placement of the decision tree at an appropriate position (e.g. root of the tree) is an important decision. Many attribute selection measures such as Information Gain, Gini Index and Entropy have been developed for this purpose. The suitability of an attribute generally depends on the diversity of its values, relevance and dependency. Different attribute selection measures have different criteria for measuring the suitability of an attribute. Diversity Index is a classical statistical measure for determining the diversity of values, and according to our knowledge, it has never been used as an attribute selection method. In this article, we propose a novel attribute selection method for decision tree classification. In the proposed scheme, the average of Information Gain, Gini Index and Diversity Index are taken into account for assigning a weight to the attributes. The attribute with the highest average value is selected for the classification. We have empirically tested our proposed algorithm for classification of different data sets of scientific journals and conferences. We have developed a web-based application named JC-Rank that makes use of our proposed algorithm. We have also compared the results of our proposed technique with some existing decision tree classification algorithms.
Machine learning in quantum spaces

Ordinary computers can perform machine learning by comparing mathematical representations of data. An experiment demonstrates how quantum computing could use quantum-mechanical representations instead. How quantum computers could be used to learn from data.
Automatic fault detection in seismic data using Gaussian process regression

Compartmentalization of hydrocarbon reservoirs, change in fluid contacts, effects of high permeable fractures around faults, and hydrocarbon traps created by seal faults make fault detection and extraction as a necessity in the analysis of seismic data. Faulting disrupts the smoothness trend of geological layers (reflections in seismic sections) and displaces layers along its plane. Thus, a fault could be considered as an abnormal phenomenon that globally deviates normal behavior of layers around its plane. In the present study, faults are considered as sparse global anomalies in a seismic section that can be extracted using Gaussian process regression. The Gaussian process regression is a nonparametric probabilistic model based on Bayesian statistics that can be used to model spatial properties as a regression problem. The Gaussian process usually is used to extract and describe normal interactions from the data set using smooth functions. The main idea of this study is to detect the global anomaly using Gaussian process regression. For this purpose, we considered geological layers as smooth normal events in seismic sections. Therefore, the location of the fault plane is where the Gaussian process gets an error during describing the layers. Abnormalities such as faults cause the Gaussian process to suffer an error near the anomaly. We used these errors and analyzed them to detect probable locations of fault edge. Finally, we used a consistent connection algorithm to separate most probable fault points and to connect them to an edge using morphological reconstruction algorithm. The proposed algorithm was evaluated based on the receiver operating characteristics analysis. Several synthetic seismic sections with different levels of signal to noise ratios were used to evaluate the algorithm in the presence of random noise. The results showed that all points predicted by a diagnostic test fell into the area above the diagonal of the receiver operating characteristics space, which represents a good diagnostic classification.
Revisiting the refactoring mechanics

Refactoring is a key practice in agile methodologies used by a number of developers, and available in popular IDEs. However, it is unclear whether the refactoring mechanics have the same meaning for developers. In this article, we revisit the refactoring mechanics. We conduct a survey with 107 developers of popular Java projects on GitHub. We asked them about the output of seven refactoring types applied to small programs. Developers do not expect the same outputs in all questions. The refactoring mechanics is based on developers' experience for a number of them (71.02%). Some developers (75.70%) use IDEs to apply refactorings. However, the output yielded by the preferred IDE is different from what they want. Developers and IDE developers use different mechanics for most refactoring types considered in our survey, and this may impact developers' communication.
Improving author verification based on topic modeling

Authorship analysis attempts to reveal information about authors of digital documents enabling applications in digital humanities, text forensics, and cyber‐security. Author verification is a fundamental task where, given a set of texts written by a certain author, we should decide whether another text is also by that author. In this article we systematically study the usefulness of topic modeling in author verification. We examine several author verification methods that cover the main paradigms, namely, intrinsic (attempt to solve a one‐class classification task) and extrinsic (attempt to solve a binary classification task) methods as well as profile‐based (all documents of known authorship are treated cumulatively) and instance‐based (each document of known authorship is treated separately) approaches combined with well‐known topic modeling methods such as Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA). We use benchmark data sets and demonstrate that LDA is better combined with extrinsic methods, while the most effective intrinsic method is based on LSI. Moreover, topic modeling seems to be particularly effective for profile‐based approaches and the performance is enhanced when latent topics are extracted by an enriched set of documents. The comparison to state‐of‐the‐art methods demonstrates the great potential of the approaches presented in this study. It is also demonstrates that even when genre‐agnostic external documents are used, the proposed extrinsic models are very competitive.
Multiscale deep desmoking for laparoscopic surgery

In minimally invasive surgery, smoke generated by such as electrocautery and laser ablation deteriorates image quality severely. This creates discomfortable view for the surgeon which may increase surgical risk and degrade the performance of computer assisted surgery algorithms such as segmentation, reconstruction, tracking, etc. Therefore, real-time smoke removal is required to keep a clear field of view. In this paper, we propose a real-time smoke removal approach based on Convolutional Neural Network (CNN). An encoder-decoder architecture with Laplacian image pyramid decomposition input strategy is proposed. This is an end-to-end network which takes the smoke image and its Laplacian image pyramid decomposition as inputs, and outputs a smoke free image directly without relying on any physical models or estimation of intermediate parameters. This design can be further embedded to deep learning based follow-up image guided surgery processes such as segmentation and tracking tasks easily. A dataset with synthetic smoke images generated from Blender and Adobe Photoshop is employed for training the network. The result is evaluated quantitatively on synthetic images and qualitatively on a laparoscopic dataset degraded with real smoke. Our proposed method can eliminate smoke effectively while preserving the original colors and reaches 26 fps for a video of size 512 &times; 512 on our training machine. The obtained results not only demonstrate the efficiency and effectiveness of the proposed CNN structure, but also prove the potency of training the network on synthetic dataset.
Business process outsourcing to cloud containers: How to find the optimal deployment?

Containers are a new service model that empowers cloud computing by offering horizontally scalable systems while bypassing high-performance challenges of traditional hypervisors. In the business process management context, Containers-as-a-Service can be used to outsource business processes to the cloud and allow an enterprise to bundle its processes and data in a simpler and more performance-oriented manner. To profit from containers, an enterprise must however have a means to identify the optimal resource allocation. Towards this end, we propose a system architecture for optimal containers-based deployment of business processes. The proposed system architecture relies on our extension of ContainerCloudSim simulator to estimate the execution time of business processes deployed according to the CaaS model. In addition, it encloses a business process deployment optimizer. To develop this latter, we examine a linear program and a genetic algorithm to find out the optimal deployment of a business process on cloud containers. We show experimentally the effective performance of containers-based versus VM-based deployment, and linear program versus the First-Fit container strategy and the genetic algorithm.
Query optimization mechanisms in the cloud environments: A systematic study

Summary The goal of a query optimizer is to provide an optimal Query Execution Plan (QEP) by comparing alternative query plans. In a distributed database system over cloud environment, the relations required by a query plan may be stored at multiple sites. This leads to an exponential increase in the number of possible equivalent plan alternatives to find an optimal QEP. Although it is not computationally reasonable to explore exhaustively all possible plans in such large search space. Although query optimization mechanisms are important in the cloud environments, to the best of our knowledge, there exists no complete and systematic review on investigating these issues. Therefore, in this paper, four categories to study these mechanisms are considered which are search‐based, machine learning‐based, schema‐based, and security‐based mechanisms. Also, this paper represents the advantages and disadvantages of the selected query optimization techniques and investigates the metrics of their techniques. Finally, the important challenges of these techniques are reviewed to develop more efficient query optimization techniques in the future.
Obstacle Avoidance Using Multiobjective Optimization and a Dynamic Obstacle Manager

This paper describes a set of software modules and algorithms for obstacle avoidance. The approach described here is designed to work in conjunction with any path planning method that generates a viable sequence of waypoints, with obstacle avoidance regarded as a local deviation of the planned path of points. The helm architecture used here will temporarily spawn a new obstacle avoidance behavior for each near detected obstacle, and generate a maneuvering objective function to avoid the obstacle. Once the vehicle passes the obstacle, the helm deletes the behavior and frees its memory space. The helm, obstacle manager, and obstacle avoidance behavior are described here. This software arrangement was used by the MIT 2014 RobotX team, and will be included in the next software release of the open source MOOS-IvP marine autonomy project.
Industrial Applications of Blockchain

Blockchain technologies have recently come to the forefront of the scientific and industrial communities. This is due to their practical capabilities in solving many issues in various industrial domains. Issues like securely sharing transactional data, establishing efficient supply chain processes, and enhancing transparency are some examples. Blockchain offers an effective way to tackle these issues using distributed, shared, secure, and permissioned transactional ledgers. The employment of blockchain technologies and the possibility of applying them in different situations enables many industrial applications through increased efficiency and security; enhanced traceability and transparency and reduced costs. In this paper, different industrial application domains where the use of blockchain technologies have been proposed are surveyed.
Digitalization of Education in the Global Context

The paper addresses the advanced topics of engineering education, including digitalization, which were discussed at the 21st International Conference on Interactive Collaborative Learning (ICL) and 47th IGIP International Conference on Engineering Pedagogy (IGIP) held on the Kos Island, Greece in September, 2018. The conference set out a wide range of problems relating to a new interactive educational technology, to developing online-, digital, and e-learning, to the visualization and gamification of education, to developing in engineers academic and applied competences, to implementing new education environments, etc. The Russian researchers’ contribution to the development of engineering pedagogy was emphasized, including improving training of engineering university professors and holding the SYNERGY Engineering Education Research and Practice Network Conference in the leading technical universities of Russia.
Two innovative modelling approaches in order to forecast consumption of blast furnace gas by hot blast stoves

The online optimization of the use of process off gases in integrated steelworks can greatly contribute to increase the sustainability of the steel production. A correct management of these gases could allow both the reduction of natural resources exploitation (e.g. natural gas) and of the facility's environmental impact. However, in order to achieve an almost complete use of these gases, it is fundamental to forecast their production and consumption according to the production plan and to use such forecasting to optimize the gases distribution inside the network by considering possible interactions. According to these needs, this paper presents two models, which allow forecasting the consumption of blast furnace gas by some major consumers: the hot blast stoves. Due to the almost regular operation of these plants, two kinds of models can be applied: an Echo State Network-based model, which is more complex and sensitive to the variations of the operating practices and a simpler switch model, which does not require training and is very easy to use. Both models provide good results and the user can interchangeably exploit them.