Miracle-dz/UniFAR-FTUs huggingface dataset:
A Pricing Model for Sharing Cloudlets in Mobile Cloud Computing

Driven by pervasive mobile devices and ubiquitous wireless communication networks, mobile cloud computing emerges as an appealing paradigm to accommodate demands for running power-hungry or computation-intensive applications over resource-constrained mobile devices. Cloudlets that move available resources closer to the network edge offer a promising architecture to support real-time applications, such as online gaming and speech recognition. To stimulate service provisioning by cloudlets, it is essential to design an incentive mechanism that charges mobile devices and rewards cloudlets. Although auction has been considered as a promising form for incentive, it is challenging to design an auction mechanism that holds certain desirable properties for the cloudlet scenario. In the paper, we propose an appropriate auction mechanism for the allocation of resource between mobile devices as service users (buyers) and cloudlets as service providers (sellers). The mechanism can effectively distribute cloudlets to meet the requirement of mobile devices. By analyzing both theory and experimental results, we find that the proposed mechanism achieves individual rationality, budget balance, and near-optimal economic efficiency.
Signatures of Microevolutionary Processes in Phylogenetic Patterns

&NA; Phylogenetic trees are representations of evolutionary relationships among species and contain signatures of the processes responsible for the speciation events they display. Inferring processes from tree properties, however, is challenging. To address this problem, we analyzed a spatially‐explicit model of speciation where genome size and mating range can be controlled. We simulated parapatric and sympatric (narrow and wide mating range, respectively) radiations and constructed their phylogenetic trees, computing structural properties such as tree balance and speed of diversification. We showed that parapatric and sympatric speciation are well separated by these structural tree properties. Balanced trees with constant rates of diversification only originate in sympatry and genome size affected both the balance and the speed of diversification of the simulated trees. Comparison with empirical data showed that most of the evolutionary radiations considered to have developed in parapatry or sympatry are in good agreement with model predictions. Even though additional forces other than spatial restriction of gene flow, genome size, and genetic incompatibilities, do play a role in the evolution of species formation, the microevolutionary processes modeled here capture signatures of the diversification pattern of evolutionary radiations, regarding the symmetry and speed of diversification of lineages.
Secure Comparison Under Ideal/Real Simulation Paradigm

Secure comparison problem, also known as Yao’s Millionaires’ problem, was introduced by Andrew Yao in 1982. It is a fundamental problem in secure multi-party computation. In this problem, two millionaires are interested in determining the richer one between them without revealing their actual wealth. Yao’s millionaires’ problem is a classic and fundamental problem in cryptography. The design of secure and efficient solutions to this problem provides effective building blocks for secure multi-party computation. However, only a few of the solutions in the literature have succeeded in resisting attacks of malicious adversaries, and none of these solutions has been proven secure in malicious model under ideal/real simulation paradigm. In this paper, we propose two secure solutions to Yao’s millionaires’ problem in the malicious model. One solution has full simulation security, and the other solution achieves one-sided simulation security. Both protocols are only based on symmetric cryptography. Experimental results indicate that our protocols can securely solve Yao’s millionaires’ problem with high efficiency and scalability. Furthermore, our solutions show better performance than the state-of-the-art solutions in terms of complexity and security. Specifically, our solutions only require <inline-formula> <tex-math notation="LaTeX">$O(|U|)$ </tex-math></inline-formula> symmetric operations at most to achieve simulation-based security against malicious adversaries, where <inline-formula> <tex-math notation="LaTeX">$U$ </tex-math></inline-formula> denotes the universal set and <inline-formula> <tex-math notation="LaTeX">$|U|$ </tex-math></inline-formula> denotes the size of <inline-formula> <tex-math notation="LaTeX">$U$ </tex-math></inline-formula>.
Layout Error Correction Using Deep Neural Networks

Layout analysis, mainly including binarization and text-line extraction, is one of the most important performance determining steps of an OCR system for complex medieval historical document images, which contain noise, distortions and irregular layouts. In this paper, we present a novel text-line error correction technique which include a VGG Net to classify non-text-line and adversarial network approach to obtain the layout bounding mask. The presented text-line error correction technique are applied to a collection of 15th century Latin documents, which achieved more than 75% accuracy for segmentation techniques.
Deeply learned electrocardiogram representations are robust

This paper proposes a discriminative feature representation for electrocardiogram (ECG) identification. A new architecture based on deep Convolutional Neural Networks (CNNs), auto-feature extractor, is designed for one-dimensional ECG signals to extract deep features efficiently. The deep feature performs well for ECG identification without restriction on classifier and ECG status. The method achieves 98.49% accuracy for different state ECG signals from four public ECG databases and a private ECG database. The experiments demonstrate that the deep features extracted using the auto-feature extractor are discriminative and robust in real applications.
A Virtual Reality System for Treatment of Phantom Limb Pain using Game Training and Tactile Feedback

Phantom Limb Pain (PLP) affects many amputees and many conventional treatment methods have been proven unreliable. This paper describes the implementation of a PLP treatment system for upper-limb amputees, using Virtual Reality (VR) to create an illusion that they regain and can control both hands again, which may suppress PLP. The system uses motion capture sensors to track the movement of the intact hand and arm, and mirrors the hand movement to the amputated side in the Virtual Environment (VE). Electrical stimulation was used to provide haptic feedback. The system consists of three games: (1) a bending game where the user has to grab, move, bend and release a 'nunchuck' to encourage hand rotations, (2) a frequency discrimination game, where the user has to distinguish between haptic frequencies, by pressing different buttons, and (3) a location discrimination game, where the user has to distinguish between stimulation locations. The system was tested on three amputees with PLP over the span of three weeks. For two participants, the games encouraged the use of both intact and amputated hand while playing. These two participants also felt an increased control of the amputated limb.
Avaliação neuropsicológica das funções executivas: considerações metodológicas Neuropsychological assessment of executive functions: methodological questions

The article aims to review methodological and conceptual issues related to neuropsychological assessment of executive functions. The main limitations and dichotomies of current studies related to executive functions are presented and the importance of theoretical support to validate the instruments and their clinical interpretation are discussed. There is still the need of additional evidences on executive functions that allow a complete understanding of all the processes involved. Finally, it is suggested the adoption of a specific model that may guide the study of the executive functions.
Scale-Down Methods for Optimizing Resource Allocation In Providing Virtual Laboratory Environment by Cloud Computing

Nowadays, Cloud Computing plays a vital role in providing virtual infrastructure for computing demands and services such as Amazon EC2, Google Cloud, Azure. In addition, Cloud Computing has potential for supporting experimental environment as Virtual Laboratory in terms of education. Instead of the real laboratory, Virtual Laboratory can save deploying time and cost. With the model that we are implementing on our High Performance Computing system, lecturers are considered as users who request the virtual resources for experimental environment. Hence, the problem that we pose is how to optimize the mechanism of allocating resources efficiently for both the user side and the server side. This paper contributes two scale-down algorithms which serve Virtual Laboratory users and optimize the scheduler of resource allocation on server side for multiple courses. The results illustrate that our aforementioned algorithms not only save costs by scaling down virtual resources but also satisfy students experiment in using the Virtual Laboratory.
Framework for efficient SCA resistance verification of IoT devices

Side-channel attacks (SCA) are capable of bypassing the theoretical protection of cryptographic schemes and revealing secret information in hardware devices. They have become a serious threat to the hardware security of the cryptographic modules. Entering the Internet of Things (IoT) era, it is crucial to verify the IoT devices' ability to thwart potential attacks from stealing confidential data. In this work, we set out to design and implement a framework for efficient SCA resistance verification. By integrating trace measurement equipment and GPU-accelerated analyzing engines, we have successfully built an automatic system which greatly simplifies the test procedure and enables on-the-fly SCA tests. Experiment results on common IoT development boards show both promising strength and flexibility of our framework to meet different testing requirements smoothly.
Topical delivery of tetrahydrocurcumin lipid nanoparticles effectively inhibits skin inflammation: in vitro and in vivo study

Abstract Tetrahydrocurcumin (THC) also referred to as ‘white curcumin’, is a stable colorless hydrogenated product of curcumin with superior antioxidant and anti-inflammatory properties. The present study is an attempt to elevate the topical bioavailability of THC, post-incorporation into a nano-carrier system with its final dosage as a hydrogel. Lipid nanoparticles of THC (THC-SLNs) prepared by microemulsification technique were ellipsoidal in shape (revealed in transmission electron microscopy) with a mean particle size of 96.6 nm and zeta potential of −22 mV. Total drug content and entrapment efficiency of THC-SLNs was 94.51% ± 2.15% and 69.56% ± 1.35%, respectively. Differential scanning calorimetry and X-ray diffraction studies confirmed the formation of THC-SLNs. In vitro drug release studies showed the drug release from THC-SLNs gel to follow Higuchi’s equation revealing a Fickian diffusion. Ex vivo permeation studies indicated a 17 times (approximately) higher skin permeation of THC-SLNs gel as compared with the free THC gel. Skin irritation, occlusion, and stability studies indicated the formulation to be nonirritating, and stable with a desired occlusivity. Pharmacodynamic evaluation in an excision wound mice model clearly revealed the enhanced anti-inflammatory activity of THC-SLNs gel and was further confirmed using biochemical and histopathological studies. It is noteworthy to report here that THC-SLNs gel showed significantly better (p ≤ 0.001) activity than free THC in gel. As inflammation is innate to all the skin disorders, the developed product opens up new therapeutic avenues for several skin diseases. To the best of our knowledge, this is the first paper elaborating the therapeutic usefulness of white curcumin-loaded lipidic nanoparticles for skin inflammation.
Mathematical modeling of intracrine androgen metabolism in prostate cancer: Methodological aspects

Progression of castration‐recurrent/resistant prostate cancer (CRPC) relies in part on dihydrotestosterone derived from intratumoral androgen metabolism. Mathematical modeling provides a valuable tool for studies of androgen metabolism in CRPC. This modeling approach integrates existing knowledge about complex biologic systems and provides a means of interrogating the effects of various interventions. We sought to model a single reaction in the androgen biosynthesis network, namely the oxidation of androsterone (AND) to androstanedione (5α‐dione) by four 3α‐oxidoreductase enzymes, as an initial effort to establish the feasibility of our modeling approach.
An Experimental Investigation into the Effect of State-Anxiety on State-Paranoia in People Experiencing Psychosis

Background: There is strong evidence to suggest that anxiety is associated with paranoia in clinical and non-clinical samples. However, no research to date has directly manipulated anxiety to investigate if state-anxiety has a causal role in state-paranoia in clinical populations. Aims: To investigate whether an anxious-mood induction leads to greater paranoia than a neutral-mood induction in people experiencing psychosis and paranoia and, if so, whether this is predicted by anxiety over and above other variables. Method: 22 participants with a psychosis-spectrum diagnosis took part in a two condition cross-over experimental design. Participants underwent a neutral-mood and an anxious-mood induction with levels of state-anxiety, state-affect and state-paranoia being measured before and after each condition. Results: State-paranoia was significantly higher after the anxious condition compared with the neutral condition. State-anxiety and negative-affect were significant predictors of levels of state-paranoia after the anxious condition. When both predictors were included in a regression model, only negative-affect was a significant predictor of state-paranoia after the anxious condition. There were a number of methodological limitations. Conclusions: State-anxiety and negative-affect may both be involved in the maintenance of paranoia in clinical populations, as predicted by cognitive models. Negative-affect may be the strongest predictor of state-paranoia in clinical populations. Reasons for this are discussed, as well as the implications. Interventions that seek to reduce negative state-affect may be beneficial in managing state-paranoia. Further research is warranted to explore the suggested clinical and theoretical implications of these findings.
Ceci n'est pas la data: Towards a Notion of Interaction Literacy for Data Visualization

The VisBIA 2018 Workshop on Visual Interfaces for Big Data Environments in Industrial Applications invited Benjamin Bach to talk about interaction literacy in data science and visualization. He has been involved in numerous visualization projects over the last years, also with many international collaborations. This paper introduces Benjamin Bach and his work and briefly outlines the contents of the talk.
A face-recognition approach based on secret sharing for user authentication in public-transportation security

High-risk security concerns are receiving increasing attention in public-transportation systems. Accordingly, this study proposed a secret-sharing-based face recognition model in which the distributed authentication of an identity is determined by collecting and comparing user RFID tags and their facial features in public-transportation systems. Specifically, the wavelet transforms incorporating a secret sharing scheme are used to distribute a secret in two parts. The similarity of face recognition can be calculated by the nearest-neighbor (NN) approach. Experimental results showed that our system provides a more secure technique by evading disclosure of personal information.
Gender differences in the value of using Facebook: A pilot study

Social Networking Sites have become a part of our daily lifestyle as well as a universal aspect of internet usage, where 98% of internet users are on social networks. Several statistics highlight that the adoption rate of Social Networking Sites (SNS), and more specifically Facebook, is increasing continuously and especially among youth. However, little attention has been paid in literature to measure the value gratified by these young users when using Facebook as an example of SNS in terms of perceived benefits and perceived sacrifices. Grounded in the value literature, this paper develops a conceptual model that consists of a set of perceived benefits and perceived sacrifices related to the use of SNS, particularly the Facebook. In addition, the majority of prior research on SNS usage assumes that females and males evaluate SNS in a similar manner. Therefore, it is also important to examine the differences in term of perceived value (i.e. perceived benefits minus perceived sacrifices) between young males and young females when using Facebook. This is significant in framing future strategies and plans for Facebook and other similar SNS. As part of a larger research project, the paper also reports the survey instrument development and validation in the field. We administered the survey instrument to a sample of 60 students randomly chosen from University students in Jordan. Although the sample was small, it indicates that the reliability of the scales is within an acceptable range and it can be used to test the main study hypotheses.
Analysis of the impacts of ionospheric scintillation on geosynchronous SAR based on spherical wave correction

In this paper, geosynchronous synthetic aperture radar (GEO SAR) signal influenced by iononspheric scintillation is modeled and analyzed quantitatively based on spherical-wave corrected phase screen simulation. Firstly, the signal traversing and propagting within ionospheric scintillation are modeled and the influences are deduced for point target and area target based on plane wave correction. We find that these models are only suitable for small scene and will surely induce errors in case of larger scenes, especially for GEO SAR. Secondly, the influenced GEO SAR signal model is modified based on the spherical wave corrected phase screen. The impacts of different propagation distance are analyzed. Finally, simulation of area target in large scale is realized based on the proposed method.
Based local density trajectory outlier detection with partition-and-detect framework

In this paper, with the application of partition-and-detect framework[1], we propose a trajectory outlier algorithm based in terms of local density on the basis of the traditional TRAOD algorithm. First of all, we preprocess the trajectory data, which is to divide trajectory data into a series of t-partitions with approximate trajectory partitioning algorithm, which can improve the algorithm expansibility and the running efficiency. Afterwards, we propose a local trajectory outlier algorithm based on local density to combine the two parameters of distance between t-partitions and t-partitions with a given range and get the concept of local density. Then on basis of the local outlier factor, we are able to judge whether the trajectory is an outlier. This method is of great realistic significance in that it avoids the traditional TRAOD algorithm which cannot detect outliers from the local dense trajectory. Besides, it improves the quality of outlier detection on the basis of ensuring the efficiency of the algorithm. In the meantime, we also adopt the grid index structure (the method of obtaining the trajectory in the neighborhood of the trajectory) and improve the efficiency of the algorithm. Finally, through a lot of experiments we demonstrated our algorithm in partition-and-detect framework, using the neighborhood of t-partitions with grid search and a local trajectory outlier algorithm based on density so that the efficiency and the accuracy of the results have been greatly improved.
Smart metasurface with non-reciprocity for Fog layer in IoT environment

Recent rapid advancement of wireless communication prevails wireless user terminals by high density in space. This spread of wireless equipments leads to high congestion of electromagnetic environment, therefore innovative metasurface working as “electromagnetic field coordinator” is immensely desirable in order to support highly reliable communication. In this paper, a smart metasurface with non-reciprocity for fog layer in IoT environment is proposed and prototype implementation by slot-ring resonator with switchable FET isolator is examined as a proof of concept. The presented structure exhibits full transmission control feature by external applied bias voltage to FET. This type of smart metasurface implementation can provide stable and reliable IoT communication environment.
Talbot displacement lithography with soft elastomeric conformal phase masks

We report a novel method to create 2D hierarchical nanopatterns with a simple PDMS scaffold made of a replica of a grating and a spacer. This technique takes advantage of the Talbot effect, creating an interference pattern a given distance from a diffraction grating. By combining a conformal soft mask made of PDMS and a spacer to separate the phase shift mask from the photoresist, we can exploit the interference pattern to imprint smaller structures than the initial grating.
A Method to Calculate Elevation-Change Rate of Jakobshavn Isbrae Using Operation IceBridge Airborne Topographic Mapper Data

To bridge the data gap between Ice, Cloud, and land Elevation Satellite (ICESat) and the delayed forthcoming ICESat-2, the National Aeronautics and Space Administration has launched the Operation IceBridge (OI) campaign in 2009, which has provided variable data set to reveal snow and ice changes in the Arctic and Antarctic. In this letter, we employ the Airborne Topographic Mapper (ATM) data from OI to detect a five-year (between 2010 and 2015) elevation-change rate of Jakobshavn Isbrae (JI), the largest and fastest flowing outlet glacier in Greenland. Grid elevation of OI ATM is calculated and a method to calculate elevation-change rate using these grid data is implemented. The comparison of repeat grid elevation indicates that our method can generate unbiased elevation data. The uncertainty of grid elevation data using our method is approximately 1.0 m (standard deviation). The five-year average elevation rate of JI over regions with ice velocity ≥300 m/a between 2010 and 2015 was about −5.0 m/a (root mean square uncertainty: 0.7 m/a), with a maximum thinning rate of −18.2 m/a near the terminus of the main trunk. Compared with the previously reported elevation-change rates from earlier periods, JI has now reached a new high thinning record.
An Analysis of Language Impact on Augmented Reality Order Picking Training

Order picking is a difficult and cognitively demanding task. Traditionally textual instructions are helping new workers to learn different picking routines. However, the textual instructions are sometimes not written in the workers ' native languages. In the area of Industry 4.0, where digital functions are finding their way into manufacturing processes, language-independent instructions are possible. Through a user study with 15 participants, we compare textual feedback in the workers' native language, textual feedback that is written in an unknown foreign language, and visual Augmented Reality (AR) feedback. We found that AR feedback is significantly faster and leads to a lower perceived cognitive load.