bluuebunny/crossref_metadata_2025 huggingface dataset:
The effect of dark matter on stars at the Galactic center: The paradox of youth problem
Authors: Hassani Ebrahim , Pazhouhesh Reza , Ebadi Hossein
Published: July 2020

Stars that evolve near the Galactic massive black hole show strange behaviors. The spectroscopic features of these stars show that they must be old. But their luminosities are much higher than the amounts that are predicted by the current stellar evolutionary models, which means that they must be active and young stars. In fact, this group of stars shows signatures of old and young stars, simultaneously. This is a paradox known as the “paradox of youth problem” (PYP). Some people tried to solve the PYP without supposing dark matter (DM) effects on stars. But, in this work, we implemented Weakly Interacting Massive Particles (WIMPs) annihilation as a new source of energy inside such stars. This implementation is logical for stars that evolve at high DM density environments. The new source of energy causes stars to follow different evolutionary paths on the H-R diagram in comparison with classical stellar evolutionary models. Increasing DM density in stellar evolutionary simulations causes the deviations from the standard H-R diagrams becomes more pronounced. By investigating the effects of WIMPs density on stellar structures and evolutions, we concluded that by considering DM effects on stars at the Galactic center, it is possible to solve the PYP. In addition to DM effect, complete solutions to PYP must consider all extreme and unique physical conditions that are present near the Galactic massive black hole.
Nonminimal dyons with regular gravitational, electric and axion fields
Authors: Balakin Alexander B. , Groshev Dmitry E.
Published: July 2020

We consider the distribution of the axionic dark matter and the profiles of the axionically induced electric field on the background of a regular magnetic monopole. The background static spherically symmetric spacetime is considered to be regular at the center of the object; it has no event horizons, the nonminimal interaction of strong gravitational and monopole type magnetic fields being the source of this regularity. We assume the pseudoscalar (axion) field to be nonminimally coupled to the electromagnetic and gravitational fields, and we search for solutions to the extended equations of nonminimal axion electrostatics, which satisfy two requirements: first, the axionically induced radial electric field vanishes at the center; second, the pseudoscalar field is regular at the center of the formed axionic dyon. We have found the set of parameters, describing the nonminimal axion-photon coupling, for which both requirements are satisfied. Properties of the two-parameter family of regular solutions to the equations of nonminimal axion electrostatics are analyzed numerically. The regular axionically induced electric field is shown to have the structure, which is typical for a simple electric layer.
Constraining bag constant for hybrid neutron stars
Authors: Rather Ishfaq A. , Kumar Ankit , Das H. C. , Imran M. , Usmani A. A. , Patra S. K.
Published: July 2020

We study the star matter properties for Hybrid equation of state (EoS) by varying the bag constant. We use the effective field theory motivated relativistic mean field model (E-RMF) for hadron phase with recently reported FSUGarnet, G3 and IOPB-I parameter sets. The results of NL3 and NL3[Formula: see text] sets are also shown for comparison. The simple MIT bag model is applied for the quark phase to construct the hybrid EoS. The hybrid neutron star mass and radius are calculated by varying with [Formula: see text] to constrain the [Formula: see text] values. It is found that [Formula: see text]–160[Formula: see text]MeV is suitable for explaining the quark matter in neutron stars.
A stochastic Prékopa–Leindler inequality for log-concave functions
Authors: Pivovarov Peter , Rebollo Bueno Jesus
Published: July 2020

The Brunn–Minkowski and Prékopa–Leindler inequalities admit a variety of proofs that are inspired by convexity. Nevertheless, the former holds for compact sets and the latter for integrable functions so it seems that convexity has no special signficance. On the other hand, it was recently shown that the Brunn–Minkowski inequality, specialized to convex sets, follows from a local stochastic dominance for naturally associated random polytopes. We show that for the subclass of [Formula: see text]-concave functions and associated stochastic approximations, a similar stochastic dominance underlies the Prékopa–Leindler inequality.
Calculation of immune cell proportion from batch tumor gene expression profile based on support vector regression
Authors: Ai Dongmei , Liu Gang , Li Xiaoxin , Wang Yuduo , Guo Man
Published: July 2020

In addition to tumor cells, a large number of immune cells are found in the tumor microenvironment (TME) of cancer patients. Tumor-infiltrating immune cells play an important role in tumor progression and patient outcome. We improved the relative proportion estimation algorithm of immune cells based on RNA-seq gene expression profiling and solved the multiple linear regression model by support vector regression ([Formula: see text]-SVR). These steps resulted in increased robustness of the algorithm and more accurate calculation of the relative proportion of different immune cells in cancer tissues. This method was applied to the analysis of infiltrating immune cells based on 41 pairs of colorectal cancer tissues and normal solid tissues. Specifically, we compared the relative fractions of six types of immune cells in colorectal cancer tissues to those found in normal solid tissue samples. We found that tumor tissues contained a higher proportion of CD8 T cells and neutrophils, while B cells and monocytes were relatively low. Our pipeline for calculating immune cell proportion using gene expression profile data can be freely accessed from GitHub at https://github.com/gutmicrobes/EICS.git.
Bis(4′-tert-butylbiphenyl-4-yl)aniline (BBA)-substituted A3B zinc porphyrin as light harvesting material for conversion of light energy to electricity
Authors: Duvva Naresh , Gangada Suneel , Chitta Raghu , Giribabu Lingamallu
Published: July 2020

Limited synthetic steps via low-cost starting materials are needed to develop large-scale light-active materials for efficient solar cells. Here, novel bis(4[Formula: see text]-tert-butylbiphenyl-4-yl)aniline (BBA) based A3B zinc porphyrin (GB) is synthesized and applied as a light harvesting/electron injection material in dye-sensitized solar cells. The GB sensitizer was characterized by various spectroscopic techniques and the optimized device shows [Formula: see text] of 10.98 ± 0.37 mA/cm2 and power conversion efficiency (PCE) of 3.34 ± 0.26%. In addition, performance is enhanced up to ∼3.9% by the addition of co-adsorbent 3a,7a-dihydroxy-5b-cholic acid (chenodeoxycholic acid, CDCA) to minimize [Formula: see text]-[Formula: see text] staking of the planar porphyrin macrocycles. These results demonstrate that novel broad-absorbing light-active material (GB) could be used for indoor solar panels.
Radiosynthesis, molecular modeling and biodistribution of 99mTc-Protoporphyrin as a preclinical model for tumor diagnosis
Authors: Gizawy Mohamed A. , El-Tahawy Mohsen M. T. , Motaleb Mohamed A.
Published: July 2020

Porphyrins are among the most important and widely used compounds involved in a variety of chemical and biochemical applications. These molecules exhibit very special properties that encourage researchers to label many derivatives with diagnostic or therapeutic radionuclides for medical applications. This study reports the radiolabeling and biodistribution of [Formula: see text]Tc-protoporphyrin IX ([Formula: see text]Tc-PPIX) as a novel potential solid-tumor imaging agent. The factors affecting the radiolabeling process were varied to achieve maximum radiochemical yield. [Formula: see text]Tc-PPIX was obtained in high yield of 97.34 ± 0.21% and high stability in serum up to 24 h. The radiochemical yield of [Formula: see text]Tc-PPIX was assessed by a combination of a paper chromatographic technique and HPLC. A computational analysis for all the potential structures that may be formed due to the interaction between protoporphyrin IX and technetium was performed via the DFT method of calculations in gas phase to predict the most likely structure. Molecular docking was further employed to shed light on the nature of the interaction between the most stable complexes with the target protein. Finally, the in-vivo biodistribution of [Formula: see text]Tc-PPIX complex was evaluated in solid-tumor-bearing mice and high tumor/tissue ratio of 5.17 ± 0.34 at 60 min post injection was obtained. Our finding clearly suggests [Formula: see text]Tc-PPIX as a potential SPECT agent for tumor imaging.
A combinatorial proof of a recurrence relation for the sum of divisors function
Authors: Kim Sun
Published: July 2020

We give a combinatorial proof of a generalization of an identity involving the sum of divisors function [Formula: see text] and the partition function [Formula: see text] which is a companion of Euler’s recurrence formula for [Formula: see text]
Closed knight’s tour problem on some (m,n,k,1)-rectangular tubes
Authors: Singhun Sirirat , Loykaew Nathaphat , Boonklurb Ratinan , Srichote Wasupol
Published: July 2020

A closed knight’s tour of a normal two-dimensional chessboard by using legal moves of the knight has been generalized in several ways. One way is to consider a closed knight’s tour on a ringboard of width [Formula: see text], which is the [Formula: see text] chessboard with the middle part missing and the rim contains [Formula: see text] rows and [Formula: see text] columns. Another way is to stack [Formula: see text] copies of the [Formula: see text] chessboard to construct an [Formula: see text] rectangular chessboard and the closed knight’s tour can be on the surface or within the [Formula: see text] rectangular chessboard. This paper combines these two ideas by stacking [Formula: see text] copies of [Formula: see text] ringboard of width [Formula: see text], which we call the [Formula: see text]-rectangular tube. We explore the existence and the nonexistence of closed knight’s tours for [Formula: see text]-tube and [Formula: see text]-tube.
Chinese Foreign Policy in 2019: Coping with Hard Times
Authors: GORE Lance L P
Published: July 2020

China had a tough year in 2019. Amid an economic slowdown and a worsening trade war, China and the United States are testing each other’s strengths and limitations to redefine their relationship. China was determined to preserve its state-led development model and step up strategic cooperation with Russia. The game on the Korean peninsula has also changed. China’s policy emphasis in the South China Sea has seemingly shifted from security to resources. The cross-strait situation also became increasingly precarious.
South Korea in 2019: Unfulfilled Ambitions, Debilitating Troubles
Authors: RYU Yongwook
Published: July 2020

This article provides a comprehensive review of important events in South Korea in 2019. What began as an audacious year for the Moon Jae-in administration had seen its foreign policy goals unfulfilled and domestic situation increasingly mired in political scandals and socio-economic problems. The Moon administration needs to re-examine its policies, face up to its failures and reassess its policy options for the second half of Moon’s presidency to produce concrete results.
“FINANCE AND GROWTH” RE-VISITED
Authors: MÉNDEZ-HERAS LIZETHE , ONGENA STEVEN
Published: July 2020

We test whether the relationship between finance and growth is present in 48 countries over 20 different periods of an equal length of 15 years, starting in 1980 (to 1995) and ending in 1999 (to 2014). We estimate growth regressions using an IV approach and we find that (1) overall financial development had a positive effect on economic growth for almost all our studied periods, (2) the legal system is the primary determinant of the effectiveness of the overall financial system, and (3) financial services were relevant for economic growth even during the financial crisis of 2008. This research is part of a research agenda revisiting the finance–growth nexus using up-to-date empirical methodologies.
Cracking Channel Hopping Sequences and Graph Routes in Industrial TSCH Networks
Authors: Cheng Xia , Shi Junyang , Sha Mo
Published: July 2020

Industrial networks typically connect hundreds or thousands of sensors and actuators in industrial facilities, such as manufacturing plants, steel mills, and oil refineries. Although the typical industrial Internet of Things (IoT) applications operate at low data rates, they pose unique challenges because of their critical demands for reliable and real-time communication in harsh industrial environments. IEEE 802.15.4-based wireless sensor-actuator networks (WSANs) technology is appealing for use to construct industrial networks because it does not require wired infrastructure and can be manufactured inexpensively. Battery-powered wireless modules easily and inexpensively retrofit existing sensors and actuators in industrial facilities without running cables for communication and power. To address the stringent real-time and reliability requirements, WSANs made a set of unique design choices such as employing the Time-Synchronized Channel Hopping (TSCH) technology. These designs distinguish WSANs from traditional wireless sensor networks (WSNs) that require only best effort services. The function-based channel hopping used in TSCH simplifies the network operations at the cost of security. Our study shows that an attacker can reverse engineer the channel hopping sequences and graph routes by silently observing the transmission activities and put the network in danger of selective jamming attacks. The cracked knowledge on the channel hopping sequences and graph routes is an important prerequisite for launching selective jamming attacks to TSCH networks. To our knowledge, this article represents the first systematic study that investigates the security vulnerability of TSCH channel hopping and graph routing under realistic settings. In this article, we demonstrate the cracking process, present two case studies using publicly accessible implementations (developed for Orchestra and WirelessHART), and provide a set of insights.
Proxy Painting
Authors: Lange Vanessa , Kurth Philipp , Keinert Benjamin , Boss Martin , Stamminger Marc , Bauer Frank
Published: July 2020

For archaeologists, it is often desirable to present statues in their original material and coloration. With projection mapping, real-world surfaces are augmented by digital content to create compelling alterations of the scene’s visual appearance without actually altering or even damaging the object. While there are frequent advances in projection quality, content creation is still a challenging and often unintuitive task, especially for non-experts. In our system, we combine the advantages of digital content creation, such as rapid prototyping, with the convenience of an analog workflow. Users paint on smaller versions of the projection mapping target, employing real-world brushes and pencils, while the results are presented live on its large counterpart. We further demonstrate the integration of our system into a state-of-art game engine. By leveraging a powerful rendering and material workflow, we make creating compelling materials and lighting situations an intuitive experience.
Optimal Receiver Placement for
K
-barrier Coverage in Passive Bistatic Radar Sensor Networks
Authors: Chen Jiaoyan , Yang Laurence T. , Deng Xianjun , Hong Xianggong , Yi Lingzhi
Published: July 2020

The improvement of coverage quality in the construction of multiple-barrier coverage is a critical problem in a wireless sensor network. In this article, we investigate the
K
-barrier coverage construction problem in passive bistatic radar sensor networks. In contrast to traditional bistatic radar networks, the transmitters in a passive bistatic radar network are predeployed and noncooperative. To construct
K
barriers, we need to deploy receivers that couple with predeployed transmitters to build continuous barriers. In this work, we focus on the minimum number of receivers problem of constructing
K
-barrier coverage, where the minimum number of receivers is based on the predeployed transmitters. To handle this problem, we first investigate the optimal placement of receivers between adjacent transmitters for a sub-barrier formation and then determine the optimal placement of receivers for the one-barrier construction. For multiple-barrier coverage construction, we introduce a weighted transmitter graph (WTG) to describe the relation among different transmitters, where the weight in the graph is the minimum number of receivers needed for these two transmitters for a sub-barrier formation. Based on WTG, the minimum receivers problem changes to a problem of how to find
K
-disjoint paths with the minimum total weight in the graph. For large-scale networks, we also propose two efficient heuristic algorithms to solve the corresponding problem. Finally, we conduct extensive experiments to validate the correctness and the efficiency of the proposed algorithms.
S
EADS
Authors: Fu Xiaoqin , Cai Haipeng , Li Wen , Li Li
Published: July 2020

Distributed software systems are increasingly developed and deployed today. Many of these systems are supposed to run continuously. Given their critical roles in our society and daily lives, assuring the quality of distributed systems is crucial. Analyzing runtime program dependencies has long been a fundamental technique underlying numerous tool support for software quality assurance. Yet conventional approaches to dynamic dependence analysis face severe scalability barriers when they are applied to real-world distributed systems, due to the unbounded executions to be analyzed in addition to common efficiency challenges suffered by dynamic analysis in general.

In this article, we present S
EADS
, a
distributed
,
online
, and
cost-effective
dynamic dependence analysis framework that aims at scaling the analysis to real-world distributed systems. The analysis itself is distributed to exploit the distributed computing resources (e.g., a cluster) of the system under analysis; it works online to overcome the problem with unbounded execution traces while running continuously with the system being analyzed to provide timely querying of analysis results (i.e., runtime dependence set of any given query). Most importantly, given a user-specified time budget, the analysis automatically adjusts itself to better cost-effectiveness tradeoffs (than otherwise) while respecting the budget by changing various analysis parameters according to the time being spent by the dependence analysis. At the core of the automatic adjustment is our application of a reinforcement learning method for the decision making—deciding which configuration to adjust to according to the current configuration and its associated analysis cost with respect to the user budget. We have implemented S
EADS
for Java and applied it to eight real-world distributed systems with continuous executions. Our empirical results revealed the efficiency and scalability advantages of our framework over a conventional dynamic analysis, at least for dynamic dependence computation at method level. While we demonstrate it in the context of dynamic dependence analysis in this article, the
methodology
for achieving and maintaining scalability and greater cost-effectiveness against continuously running systems is more broadly applicable to other dynamic analyses.
Intelligent Control and Security of Fog Resources in Healthcare Systems via a Cognitive Fog Model
Authors: Al-Khafajiy Mohammed , Otoum Safa , Baker Thar , Asim Muhammad , Maamar Zakaria , Aloqaily Moayad , Taylor Mark , Randles Martin
Published: July 2020

There have been significant advances in the field of Internet of Things (IoT) recently, which have not always considered security or data security concerns: A high degree of security is required when considering the sharing of medical data over networks. In most IoT-based systems, especially those within smart-homes and smart-cities, there is a bridging point (fog computing) between a sensor network and the Internet which often just performs basic functions such as translating between the protocols used in the Internet and sensor networks, as well as small amounts of data processing. The fog nodes can have useful knowledge and potential for constructive security and control over both the sensor network and the data transmitted over the Internet. Smart healthcare services utilise such networks of IoT systems. It is therefore vital that medical data emanating from IoT systems is highly secure, to prevent fraudulent use, whilst maintaining quality of service providing assured, verified and complete data. In this article, we examine the development of a Cognitive Fog (CF) model, for secure, smart healthcare services, that is able to make decisions such as opting-in and opting-out from running processes and invoking new processes when required, and providing security for the operational processes within the fog system. Overall, the proposed ensemble security model performed better in terms of Accuracy Rate, Detection Rate, and a lower False Positive Rate (standard intrusion detection measurements) than three base classifiers (K-NN, DBSCAN, and DT) using a standard security dataset (NSL-KDD).
An Interactive Narrative to Improve Cultural Heritage Experience in Elementary School Children
Authors: Torsi Silvia , Ardito Carmelo , Rebek Cristina
Published: July 2020

Cultural Heritage can use ICT and game design to provide compelling experiences of visiting ancient ruins. In fact, archaeological sites benefit from being enriched with additional meanings that help visitors to contextualize what they see. For example, young tourists, when accessing cities of the past, could miss the chance of really appreciating the history and the culture of extinguished civilizations. We propose an interactive story for them through which they can explore an archaeological site. We carefully planned the structure of this game according to four main dimensions: magic circle, narrative, immersion, and agency. Afterward, the prototype was presented as a scenario to five tourist guides, who positively evaluated the game and also gave us some valuable feedback.
Fine-grained Adaptive Testing Based on Quality Prediction
Authors: Liu Mengyun , Pan Renjian , Ye Fangming , Li Xin , Chakrabarty Krishnendu , Gu Xinli
Published: July 2020

The ever-increasing complexity of integrated circuits inevitably leads to high test cost. Adaptive testing provides an effective solution for test-cost reduction; this testing framework selects the important test items for each set of chips. However, adaptive testing methods designed for digital circuits are coarse-grained, and they are targeted only at systematic defects. To incorporate fabrication variations and random defects in the testing framework, we propose a fine-grained adaptive testing method based on machine learning. We use the parametric test results from the previous stages of test to train a quality-prediction model for use in subsequent test stages. Next, we partition a given lot of chips into two groups based on their predicted quality. A test-selection method based on statistical learning is applied to the chips with high predicted quality. An ad hoc test-selection method is proposed and applied to the chips with low predicted quality. Experimental results using a large number of fabricated chips and the associated test data show that to achieve the same defect level as in prior work on adaptive testing, the fine-grained adaptive testing method reduces test cost by 90% for low-quality chips and up to 7% for all the chips in a lot.
Synthesis of Incremental Linear Algebra Programs
Authors: Shaikhha Amir , Elseidy Mohammed , Mihaila Stephan , Espino Daniel , Koch Christoph
Published: July 2020

This article targets the Incremental View Maintenance (IVM) of sophisticated analytics (such as statistical models, machine learning programs, and graph algorithms) expressed as linear algebra programs. We present LAGO, a unified framework for linear algebra that automatically synthesizes efficient incremental trigger programs, thereby freeing the user from error-prone manual derivations, performance tuning, and low-level implementation details. The key technique underlying our framework is abstract interpretation, which is used to infer various properties of analytical programs. These properties give the reasoning power required for the automatic synthesis of efficient incremental triggers. We evaluate the effectiveness of our framework on a wide range of applications from regression models to graph computations.
USER LOCATION PREDICTION USING HYPERGRAPH IMPACT FACTOR IN TWITTER WITH GLOBAL DATA COMMUNICATION
Authors: S Pradeepa , K R Manjula
Published: July 2020

Twitter is one of the most prominent online media that acts as a global network for sharing sensitive real-time information like earthquake alerts, political news, product review, personality identification, criminal detection etc. along with regular usage, which is why knowing the location of a user in twitter gets at most important even though they do not tend to disclose it. In this paper, we propose a technique to detect the name of the locations for the twitter users. This technique involves a hypergraph-based map-reduce concept to represent the user tweets with their locations. The Helly property of the hypergraph was used to remove less potential words and the Impact Factor measure (IF) was introduced to calculate the score of each location for a particular user. The algorithm (HIF) was implemented in a big data environment provided by Hadoop and found to give an average accuracy of 78% which is well ahead of the existing methodologies. This method gives appreciable results, with high values of precision and recall for all locations.
FinPrivacy: A Privacy-preserving Mechanism for Fingerprint Identification
Authors: Wang Tao , Zheng Zhigao , Bashir Ali Kashif , Jolfaei Alireza , Xu Yanyan
Published: July 2020

Fingerprint provides an extremely convenient way of identification for a wide range of real-life applications owing to its universality, uniqueness, collectability, and invariance. However, digitized fingerprints may reveal the privacy of individuals. Differential privacy is a promising privacy-preserving solution that is enforced by injecting random noise into preserved objects, such that an adversary with arbitrary background knowledge cannot infer private input from the noisy results. This study proposes FinPrivacy, a privacy-preserving mechanism for fingerprint identification. This mechanism utilizes the low-rank matrix approximation to reduce the dimensionality of fingerprint and the exponential mechanism to carefully determine the value of the optimal rank. Thereafter, FinPrivacy injects Laplace noise to the singular values of the approximated singular matrix, thereby trading off between privacy and utility. Analytic proofs and results of the comparative experiments demonstrate that FinPrivacy can simultaneously enforce ɛ-differential privacy and maintain an efficient fingerprint recognition.
An Efficient Angle-based Universum Least Squares Twin Support Vector Machine for Classification
Authors: Richhariya B. , Tanveer M. ,   Alzheimer’s Disease Neuroimaging Initiative Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India Program
Published: July 2020

Universum-based support vector machine incorporates prior information about the distribution of data in training of the classifier. This leads to better generalization performance but with increased computation cost. Various twin hyperplane-based models are proposed to reduce the computation cost of universum-based algorithms. In this work, we present an efficient angle-based universum least squares twin support vector machine (AULSTSVM) for classification. This is a novel approach of incorporating universum in the formulation of least squares-based twin SVM model. First, the proposed AULSTSVM constructs a universum hyperplane, which is proximal to universum data points. Then, the classifying hyperplane is constructed by minimizing the angle with the universum hyperplane. This gives prior information about data distribution to the classifier. In addition to the quadratic loss, we introduce linear loss in the optimization problem of the proposed AULSTSVM, which leads to lesser computation cost of the model. Numerical experiments are performed on several benchmark synthetic, real-world, and large-scale datasets. The results show that proposed AULSTSVM performs better than existing algorithms w.r.t. generalization performance as well as computation time. Moreover, an application to Alzheimer’s disease is presented, where AULSTSVM obtains accuracy of 95% for classification of healthy and Alzheimers subjects. The results imply that the proposed AULSTSVM is a better alternative for classification of large-scale datasets and biomedical applications.
Deep Triplet Neural Networks with Cluster-CCA for Audio-Visual Cross-Modal Retrieval
Authors: Zeng Donghuo , Yu Yi , Oyama Keizo
Published: July 2020

Cross-modal retrieval aims to retrieve data in one modality by a query in another modality, which has been a very interesting research issue in the field of multimedia, information retrieval, and computer vision, and database. Most existing works focus on cross-modal retrieval between text-image, text-video, and lyrics-audio. Little research addresses cross-modal retrieval between audio and video due to limited audio-video paired datasets and semantic information. The main challenge of the audio-visual cross-modal retrieval task focuses on learning joint embeddings from a shared subspace for computing the similarity across different modalities, where generating new representations is to maximize the correlation between audio and visual modalities space. In this work, we propose TNN-C-CCA, a novel deep triplet neural network with cluster canonical correlation analysis, which is an end-to-end supervised learning architecture with an audio branch and a video branch. We not only consider the matching pairs in the common space but also compute the mismatching pairs when maximizing the correlation. In particular, two significant contributions are made. First, a better representation by constructing a deep triplet neural network with triplet loss for optimal projections can be generated to maximize correlation in the shared subspace. Second, positive examples and negative examples are used in the learning stage to improve the capability of embedding learning between audio and video. Our experiment is run over fivefold cross validation, where average performance is applied to demonstrate the performance of audio-video cross-modal retrieval. The experimental results achieved on two different audio-visual datasets show that the proposed learning architecture with two branches outperforms existing six canonical correlation analysis–based methods and four state-of-the-art-based cross-modal retrieval methods.
A Blockchain-based Iterative Double Auction Protocol Using Multiparty State Channels
Authors: Nguyen Truc D. T. , Thai My T.
Published: July 2020

Although the iterative double auction has been widely used in many different applications, one of the major problems in its current implementations is that they rely on a trusted third party to handle the auction process. This imposes the risk of single point of failures, monopoly, and bribery. In this article, we aim to tackle this problem by proposing a novel decentralized and trustless framework for iterative double auction based on blockchain. Our design adopts the smart contract and state channel technologies to enable a double auction process among parties that do not need to trust each other, while minimizing the blockchain transactions. In specific, we propose an extension to the original concept of state channels that can support multiparty computation. Then, we provide a formal development of the proposed framework and prove the security of our design against adversaries. Finally, we develop a proof-of-concept implementation of our framework using Elixir and Solidity, on which we conduct various experiments to demonstrate its feasibility and practicality.
Big Data Analysis of Internet of Things System
Authors: Lv Zhihan , Singh Amit Kumar
Published: July 2020

The study aims at exploring the Internet of things (IoT) system from the perspective of data and further improving the performance of the IoT system. The IoT data energy collection and information transmission system model is constructed by combining IoT and wireless relay cooperative transmission technology. Moreover, the energy efficiency, outage probability (OP), and accuracy of the model are evaluated by simulation experiments. The results show that, in the energy efficiency analysis, with the increase of power split factor ρ, the information transmission ability of the system increases. Whereas, the energy collection ability decreases, so the energy efficiency is reduced. Thus, choosing a more suitable power split factor for the energy efficiency of IoT is important. By analyzing OP and bit error rate (BER), as the values of
m
(Nakagami, the fading index of the fading distribution) and multi-hop paths increase, the OP and BER are reduced while the system performance is increased. Therefore, this article uses wireless relay cooperative transmission technology to integrate big data analysis into the IoT system. Finally, by adding multi-hop path and other methods to reduce the OP and BER of system, the system performance is improved. It provides experimental basis for the development of IoT systems.
CGPOPS
Authors: Agamawi Yunus M. , Rao Anil V.
Published: July 2020

A general-purpose C++ software program called CGPOPS is described for solving multiple-phase optimal control problems using adaptive direct orthogonal collocation methods. The software employs a Legendre-Gauss-Radau direct orthogonal collocation method to transcribe the continuous optimal control problem into a large sparse nonlinear programming problem (NLP). A class of
hp
mesh refinement methods are implemented that determine the number of mesh intervals and the degree of the approximating polynomial within each mesh interval to achieve a specified accuracy tolerance. The software is interfaced with the open source Newton NLP solver IPOPT. All derivatives required by the NLP solver are computed via central finite differencing, bicomplex-step derivative approximations, hyper-dual derivative approximations, or automatic differentiation. The key components of the software are described in detail, and the utility of the software is demonstrated on five optimal control problems of varying complexity. The software described in this article provides researchers a transitional platform to solve a wide variety of complex constrained optimal control problems.
Privacy-preserving Crowd-sensed Trust Aggregation in the User-centeric Internet of People Networks
Authors: Azad Muhammad Ajmal , Perera Charith , Bag Samiran , Barhamgi Mahmoud , Hao Feng
Published: July 2020

Today we are relying on Internet technologies for numerous services, for example, personal communication, online businesses, recruitment, and entertainment. Over these networks, people usually create content, a skillful worker profile, and provide services that are normally watched and used by other users, thus developing a social network among people termed as the Internet of People. Malicious users could also utilize such platforms for spreading unwanted content that could bring catastrophic consequences to a social network provider and the society, if not identified on time. The use of trust management over these networks plays a vital role in the success of these services. Crowd-sensing people or network users for their views about certain content or content creators could be a potential solution to assess the trustworthiness of content creators and their content. However, the human involvement in crowd-sensing would have challenges of privacy preservation and preventing intentional assignment of the fake high score given to certain user/content. To address these challenges, in this article, we propose a novel trust model that evaluates the aggregate trustworthiness of the content creator and the content without compromising the privacy of the participating people in a crowdsource group. The proposed system has inherent properties of privacy protection of participants, performs operations in the decentralized setup, and considers the trust weights of participants in a private and secure way. The system ensures privacy of participants under the malicious and honest-but-curious adversarial models. We evaluated the performance of the system by developing a prototype and applying it to different real data from different online social networks.
Knowledge Discovery in Simulation Data
Authors: Feldkamp Niclas , Bergmann Soeren , Strassburger Steffen
Published: July 2020

This article provides a comprehensive and in-depth overview of our work on knowledge discovery in simulations. Application-wise, we focus on manufacturing simulations. Specifically, we propose and discuss a methodology for designing, executing, and analyzing large-scale simulation experiments with a broad coverage of possible system behavior targeted at generating knowledge about the system. Based on the concept of data farming, we suggest a two-phase process which starts with a data generation phase, in which a smart experiment design is used to set up and efficiently execute a large number of simulation experiments. In the second phase, the knowledge discovery phase, data mining and visually aided analysis methods are applied on the gathered simulation input and output data. This article gives insights into this knowledge discovery phase by discussing different machine learning approaches and their suitability for different manufacturing simulation problems. With this, we provide guidelines on how to conduct knowledge discovery studies within the manufacturing simulation context. We also introduce different case studies, both academic and applied, and use them to validate our methodology.
Computation Offloading and Retrieval for Vehicular Edge Computing
Authors: Boukerche Azzedine , Soto Victor
Published: July 2020

The rapid evolution of mobile devices, their applications, and the amount of data generated by them causes a significant increase in bandwidth consumption and congestions in the network core. Edge Computing offers a solution to these performance drawbacks by extending the cloud paradigm to the edge of the network using capable nodes of processing compute-intensive tasks. In the recent years, vehicular edge computing has emerged for supporting mobile applications. Such paradigm relies on vehicles as edge node devices for providing storage, computation, and bandwidth resources for resource-constrained mobile applications. In this article, we study the challenges of computation offloading for vehicular edge computing. We propose a new classification for the better understanding of the literature designing vehicular edge computing. We propose a taxonomy to classify partitioning solutions in filter-based and automatic techniques; scheduling is separated in adaptive, social-based, and deadline-sensitive methods, and finally data retrieval is organized in secure, distance, mobility prediction, and social-based procedures. By reviewing and analyzing literature, we found that vehicular edge computing is feasible and a viable option to address the increasing volume of data traffic. Moreover, we discuss the open challenges and future directions that must be addressed towards efficient and effective computation offloading and retrieval from mobile users to vehicular edge computing.
Analysis and Performance Evaluation of Adjoint-guided Adaptive Mesh Refinement for Linear Hyperbolic PDEs Using Clawpack
Authors: Davis Brisa N. , LeVeque Randall J.
Published: July 2020

Adaptive mesh refinement (AMR) is often used when solving time-dependent partial differential equations using numerical methods. It enables time-varying regions of much higher resolution, which can selectively refine areas to track discontinuities in the solution. The open source Clawpack software implements block-structured AMR to refine around propagating waves in the AMRClaw package. For problems where the solution must be computed over a large domain but is only of interest in a small area, this approach often refines waves that will not impact the target area. We seek a method that enables the identification and refinement of only the waves that will influence the target area.

Here we show that solving the time-dependent adjoint equation and using a suitable inner product allows for a more precise refinement of the relevant waves. We present the adjoint methodology in general and give details on the implementation of this method in AMRClaw. Examples and a computational performance analysis for linear acoustics equations are presented. The adjoint method is compared to AMR methods already available in AMRClaw, and the advantages and disadvantages are discussed. The approach presented here is implemented in Clawpack, in Version 5.6.1, and code for all examples presented is archived on Github.
Automating the Formulation and Resolution of Convex Variational Problems
Authors: Bleyer Jeremy
Published: July 2020

Convex variational problems arise in many fields ranging from image processing to fluid and solid mechanics communities. Interesting applications usually involve non-smooth terms, which require well-designed optimization algorithms for their resolution. The present manuscript presents the Python package called fenics_optim built on top of the FEniCS finite element software, which enables one to automate the formulation and resolution of various convex variational problems. Formulating such a problem relies on FEniCS domain-specific language and the representation of convex functions, in particular, non-smooth ones, in the conic programming framework. The discrete formulation of the corresponding optimization problems hinges on the finite element discretization capabilities offered by FEniCS, while their numerical resolution is carried out by the interior-point solver Mosek. Through various illustrative examples, we show that convex optimization problems can be formulated using only a few lines of code, discretized in a very simple manner, and solved extremely efficiently.
Shortcut Gestures for Mobile Text Editing on Fully Touch Sensitive Smartphones
Authors: Le Huy Viet , Mayer Sven , Weiß Maximilian , Vogelsang Jonas , Weingärtner Henrike , Henze Niels
Published: July 2020

While advances in mobile text entry enable smartphone users to type almost as fast as on hardware keyboards, text-heavy activities are still not widely adopted. One reason is the lack of shortcut mechanisms. In this article, we determine shortcuts for text-heavy activities, elicit shortcut gestures, implement them for a fully touch-sensitive smartphone, and conduct an evaluation with potential users. We found that experts perform around 800 keyboard shortcuts per day, which are not available on smartphones. Interviews revealed the lack of shortcuts as a major limitation that prevents mobile text editing. Therefore, we elicited gestures for the 22 most important shortcuts for smartphones that are touch-sensitive on the whole device surface. We implemented the gestures for a fully touch-sensitive smartphone using deep learning and evaluated them in realistic scenarios to gather feedback. We show that the developed prototype is perceived as intuitive and faster than recent commercial approaches.
Dealing with Uncertainty in pWCET Estimations
Authors: Reghenzani Federico , Santinelli Luca , Fornaciari William
Published: July 2020

The problem of estimating a tight and safe Worst-Case Execution Time (WCET), needed for certification in safety-critical environment, is a challenging problem for modern embedded systems. A possible solution proposed in past years is to exploit statistical tools to obtain a probability distribution of the WCET. These probabilistic real-time analyses for WCET are, however, subject to errors, even when all the applicability hypotheses are satisfied and verified. This is caused by the uncertainties of the probabilistic-WCET distribution estimator. This article aims at improving the measurement-based probabilistic timing analysis approach providing some techniques to analyze and deal with such uncertainties. The so-called region of acceptance model based on state-of-the-art statistical test procedures is defined over the distribution space parameters. From this model, a set of strategies is derived and discussed to provide the methodology to deal with the trade-off safety/tightness of the WCET estimation. These techniques are then tested over real datasets, including industrial safety-critical applications, to show the increased value of using the proposed approach in probabilistic WCET analyses.
Paving the Way for NFV Acceleration
Authors: Fei Xincai , Liu Fangming , Zhang Qixia , Jin Hai , Hu Hongxin
Published: July 2020

As a recent innovation, network functions virtualization (NFV)—with its core concept of replacing hardware middleboxes with software network functions (NFs) implemented in commodity servers—promises cost savings and flexibility benefits. However, transitioning NFs from special-purpose hardware to commodity servers has turned out to be more challenging than expected, as it inevitably incurs performance penalties due to bottlenecks in both software and hardware. To achieve performance comparable to hardware middleboxes, there is a strong demand for a speedup in NF processing, which plays a crucial role in the success of NFV. In this article, we study the performance challenges that exist in general-purpose servers and simultaneously summarize the typical performance bottlenecks in NFV. Through reviewing the progress in the field of
NFV acceleration
, we present a new taxonomy of the state-of-the-art efforts according to various acceleration approaches. We discuss the surveyed works and identify the respective advantages and disadvantages in each category. We then discuss the products, solutions, and projects emerged in industry. We also present a gap analysis to improve current solutions and highlight promising research trends that can be explored in the future.
Large-Alphabet Semi-Static Entropy Coding Via Asymmetric Numeral Systems
Authors: Moffat Alistair , Petri Matthias
Published: July 2020

An entropy coder takes as input a sequence of symbol identifiers over some specified alphabet and represents that sequence as a bitstring using as few bits as possible, typically assuming that the elements of the sequence are independent of each other. Previous entropy coding methods include the well-known Huffman and arithmetic approaches. Here we examine the newer asymmetric numeral systems (ANS) technique for entropy coding and develop mechanisms that allow it to be efficiently used when the size of the source alphabet is large—thousands or millions of symbols. In particular, we examine different ways in which probability distributions over large alphabets can be approximated and in doing so infer techniques that allow the ANS mechanism to be extended to support large-alphabet entropy coding. As well as providing a full description of ANS, we also present detailed experiments using several different types of input, including data streams arising as typical output from the modeling stages of text compression software, and compare theproposed ANS variants with Huffman and arithmetic coding baselines, measuring both compression effectiveness and also encoding and decoding throughput. We demonstrate that in applications in which semi-static compression is appropriate, ANS-based coders can provide an excellent balance between compression effectiveness and speed, even when the alphabet is large.