Miracle-dz/UniFAR-FTUs huggingface dataset:
Sit Here: Placing Virtual Machines Securely in Cloud Environments

: A Cloud Computing Environment (CCE) leverages the advantages offered by virtualisation to enable virtual machines (VMs) within the same physical machine (PM) to share physical resources. Cloud service providers (CSPs) accommodate the ﬂuctuating resource demands of cloud users dynamically, through elastic resource provisioning. CSPs use VM allocation techniques such as VM placement and VM migration to optimise the use of shared physical resources in the CCE. However, these techniques are exposed to potential security threats that can lead to the problem of malicious co-residency between VMs. This threat happens when a malicious VM is co-located with a critical (or target) VM on the same PM. Hence, the VM allocation techniques need to be made secure. While earlier works propose speciﬁc solutions to address this malicious co-residency problem, our work here proposes to investigate the allocation patterns that are more likely to lead to a secure allocation. Furthermore, we introduce a security-aware VM allocation algorithm (SRS) that aims to allocate the VMs securely, to reduce the potential for co-residency between malicious and target VMs. Our study shows: (i) our SRS algorithm outperforms all state-of-the-art allocation algorithms and (ii) algorithms that adopt stacking-based behaviours are more likely to return secure allocations than those with spreading or random behaviours.
Characterization of Network Management Traffic in OpenStack based on Virtual Machine State Changes

OpenStack is a popular and versatile solution for creating IaaS clouds. OpenStack has several private cloud issues implementations regarding its network infrastructure with which organizations are not always familiar. In this context, this work characterizes the administrative network traffic of OpenStack clouds. Administrative traffic has a separate network, which can affect the performance of the cloud as a whole. We set up an induced lifecycle for virtual machines (VMs) and measured network traffic and Application Programming Interface (API) calls for each task with different operating system (OS) images. Moreover, we also provide an analysis and characterization of the measured network traffic in the management security domain of OpenStack, as well as verify the possibility of using a linear regression model for predicting the traffic volume produced for
A Hybrid Approach to MVC Architectural Layers Analysis

: Mobile applications have become one of the most important means of interacting with businesses, getting information, or accessing entertainment and news for the vast majority of the people, especially for the young generations. How those applications are being built, heavily inﬂuences their lifecycle, costs, and product roadmap, that is why software architecture plays a very important role as it affects the maintainability and extensibility of those products. We are presenting a novel automatic approach for detecting MVC architectural layers from mobile codebases that combines an unsupervised Machine Learning algorithm and a classic static analysis. Our proposal does not require any prior training stage or datasets since it does not rely on apriori annotated codebases. As another key of novelty, it uses the information obtained from the mobile SDKs for enhancing the detection process. The validation of our proposal is done in eight different sized codebases that operate in various domains and come from either open-source projects as well as closed-source ones. The performance of the detection quality is measured by the accuracy of the system, as we compared to a manually constructed ground truth, achieving an average accuracy of 85% on all the analysed codebases. Our proposal provides a viable hybrid approach for detecting architectural layers from mobile codebases achieving good results by providing the accurate detection of the layers using a deterministic step and great ﬂexibility for being used on other architectural patterns via the non-deterministic step. Furthermore, we con-sider our approach as being valuable to students or beginners because it could provide insightful information on how the code should be structured and help them to respect architectural guidelines in real-world projects.
Defamation 2.0: New Threats in Digital Media Era - An Overview on Forensics Approaches in the Social Network Ecosystem

Recently, social networks have become the largest and fastest growing websites on the Internet. These platforms contain sensitive and personal data of hundreds of millions of people, and are integrated also into millions of other websites so it is more and more important to focus on security and privacy issues. In this work, we expose the defamation issue in the social network context and apply some known methods to recover data of person who offends reputation of others over 250 different social media frameworks. The datasets, that it is possible to exploit, can contain various profile information (user data, photos, etc.) and associated meta-data (internal timestamps and unique identifiers). These data are significant in the field of digital forensics to be properly used as evidences in front of Court.
Academic Network for Strengthening Accounting and Fiscal Research in Post-pandemic Times

Academic collaboration networks are organizations that strengthen knowledge in different disciplines, generating spaces for global interaction between university institutions through exchanges that contribute to the research process. During the post-pandemic period, they are a key factor in the generation of new knowledge and teamwork, building scenarios of participation. This research seeks to promote the study of fiscal and accounting issues, through processes of academic and research articulation, among the different Higher Education Institutions (HEI), at the Colombian level with extension to Latin America, contributing to the generation of new knowledge, based on a mixed type of applied research, with a triangular approach and descriptive scope, where 242 institutions are identified, taking a sample of 149 institutions to which a survey is applied, having a 95% reliability with a margin of error of 5%, which will lead to obtain as a result the creation of an academic cooperation network.
BarChartAnalyzer: Digitizing Images of Bar Charts

Charts or scientific plots are widely used visualizations for efficient knowledge dissemination from datasets. However, these charts are predominantly available in image format. There are various scenarios where these images are interpreted in the absence of datasets used initially to generate the charts. This leads to a pertinent need for data extraction from an available chart image. We narrow down our scope to bar charts and propose a semi-automated workflow, BarChartAnalyzer, for data extraction from chart images. Our workflow integrates the following tasks in sequence: chart type classification, image annotation, object detection, text detection and recognition, data table extraction, text summarization, and optionally, chart redesign. Our data extraction uses second-order tensor fields from tensor voting used in computer vision. Our results show that our workflow can effectively and accurately extract data from images of different resolutions and of different subtypes of bar charts. We also discuss specific test cases where BarChartAnalyzer fails. We conclude that our work is an effective and special image processing application for interpreting charts.
A Two-stage Learning Approach for Traffic Sign Detection and Recognition

With the progress of advanced driver assistance systems (ADAS), the development of assisted driving technologies is becoming more and more important for vehicle subsystems. The traffic signs are designed to remind the drivers of possible situations and road conditions to avoid traffic accidents. This paper presents a two-stage network to detect and recognize the traffic sign images captured by the vehicle on-board camera. In the detection network, we adopt Faster R-CNN to detect the location of the traffic signs. For the classification network, we use SVM, VGG, and ResNet for validation and testing. We compare the results and integrate the detection and classification systems. The datasets used in this work include TT100K and our own collected Taiwan road scene images. Our technique is tested using the videos acquired from the highway, suburb and urban scenarios. The results using Faster R-CNN for detection combined with VGG17 for classification have demonstrated superior performance compared to YOLOv3 and Mask R-CNN.
A Machine Learning based Context-aware Prediction Framework for Edge Computing Environments

: A Context-aware Prediction Framework (CAPF) can be provided through a Self-adaptive System (SAS) resource manager to support the autoscaling decision in Edge Computing (EC) environments. However, EC dynamicity and workload fluctuation represent the main challenges to design a robust prediction framework. Machine Learning (ML) algorithms show a promising accuracy in workload forecasting problems which may vary according to the workload pattern. Therefore, the accuracy of such algorithms needs to be evaluated and compared in order to select the most suitable algorithm for EC workload prediction. In this paper, a thorough comparison is conducted focusing on the most popular ML algorithms which are Linear Regression (LR), Support Vector Regression (SVR), and Neural Networks (NN) using real EC dataset. The experimental results show that a robust prediction framework can be supported by more than one algorithm considering the EC contextual behavior. The results also reveal that the NN outperforms LR and SVR in most cases.
DEVELOPMENT OF TELEMETRY SYSTEM FOR ELECTRIC POWERED VEHICLE

Wireless Telemetry system is crucial for electric vehicles to monitor its data, remotely. It is important to measure electrical parameters in real time for safe operation. hardware and software which can broadcast information by using wireless connection over serial communication protocol. Wireless connection range is up to 3 km which is sufficient for using at orchards. The designed da interface is also written by using C# and Visual Basic programming languages for monitoring device features easily from groun station. Vehicle speed, voltage levels and temperature of drawing current, total power usage and remained battery power can be monitored by this system.
Exploring Bilingualism through the Lens of Teachers

In the light of qualitative phenomenological inquiry, this study explored the underlying knowledge on bilingualism through the lens of teachers’ perspectives. Five teachers in a certain public high school were interviewed then transcription, translation, intensive review of related literature, and thematic analysis were used to gather and validate data. Persons involved in the language events were teacher and principal, teacher and co-teacher, teacher and students, teacher and family members, and teacher and market vendor. Pair of languages found were English and Bisaya, English and Filipino, and Filipino and Bisaya with various dominant language found. Language events where bilingualism exists were meeting, casual conversation, class discussion, dinner, and product buying. Reasons of pairing a language used are “being used to”, type of people, and language events. The concept of bilingualism is a very important phenomenon to be understood.
Physiological and biochemical responses of thyme plants to some antioxidants

Orabi SA, Talaat IM, Balbaa LK. 2014. Physiological and biochemical responses of thyme plants to some antioxidants. Nusantara Bioscience 6: 118-125. Two pot experiments were conducted to investigate the effect of tryptophan, nicotinamide and αtocopherol (each at 50 and 100 mg/L) on plant growth, essential oil yield and its main constituents. All treatments significantly promoted plant height, and increased fresh and dry mass (g/plant) of thyme (Thymus vulgaris L.). The treatment with 100 mg/L nicotinamide showed increasing in total potassium mainly in the first cut. Total soluble sugars, oil percentage and oil yield and protein recorded increments with tryptophan treatments. Treatment of Thymus plants with 100 mg/L nicotinamide observed the highest percentage of thymol (67.61%). Oxygenated compounds recorded the highest value with 50 mg/L α-tocopherol treatment, while the maximum non-oxygenated ones resulted from the application of 100 mg/L nicotinamide. All treatments under study significantly affected the activity of oxidoreductase enzymes (POX and PPO). Nicotinamide at the concentration of 100 mg/L recorded the highest increments in APX and GR and the lowest values in oxidoreductase enzyme activities added to the lowest values of lipid peroxidation to enhance the best protection of thyme plants.
Voluntary appointment of independent directors: evidence from Taiwan

Purpose Using a data set of Taiwanese listed firms from 2002 to 2015, this paper aims to examine the determinants to voluntarily appoint independent directors. Design/methodology/approach This study uses panel estimation to exploit both the cross-section and time-series nature of the data. Further, this paper uses Tobit regression, generalized linear model (GLM) in the additional analysis and the two-stage least squares to mitigate for a possible endogeneity issue. Findings The main findings show that Taiwanese firms with large board sizes tend to voluntarily appoint independent directors and firms that already have independent supervisors more willingly to accept additional independent directors onto the board. Furthermore, ownership concentration and institutional ownership are positively associated with the voluntary appointment of independent directors. On the contrary, firms controlled by family members are generally reluctant to voluntarily appoint independent directors. Research limitations/implications The findings are important for managers, shareholders, creditors and policymakers. In particular, when considering the determinants of the voluntary appointment of independent directors, the results indicate that independent supervisors, outside shareholders and institutional investors are significant factors in influencing effective internal and external corporate governance mechanisms. This research work focuses on the voluntary appointment of independent directors. It would be interesting to compare the effectiveness of voluntary appointments with a mandatory appointment within Taiwan and with other jurisdictions. Originality/value This study incrementally contributes to the corporate governance literature in several ways. First, this study extends the earlier research by using a more comprehensive data set of non-financial Taiwanese firms and using alternative methodologies to investigate the determinants of voluntary appointment of independent directors. Second, prior studies tend to neglect the possible issue of using a censored and fractional dependent variable, the proportion of independent directors, which might yield biased and inconsistent parameter estimates when using ordinary least squares regression estimation. Finally, this study addresses the relevant econometric issues by using the Tobit, GLM and the two-stage least squares for a possible endogeneity concern.
Using TerraSAR X-Band and Sentinel-1 C-Band SAR Interferometry for Deformation Along Beijing-Tianjin Intercity Railway Analysis

The Beijing-Tianjin Intercity Railway (BTIR) is an important transportation hub connecting Beijing and Tianjin. It is the China's first high-speed railway reaching a speed of 350 km/h and has been in operation for ten years. Since the foundation of the BTIR is made up of soft and loose soil, hence suffers greatly from potential deformation. In order to investigate the impact of ground subsidence on the railway line, 26 TerraSAR-X SpotLight images, 23 TerraSAR-X StripMap images, and 31 Sentinel-1 Interferometric Wide images were processed to obtain the deformation of the Yizhuang areas along the BTIR from January 2015 to March 2017 by permanent scatterers interferometric synthetic aperture radar technology. The deformation results of these three datasets are self-consistent. Through the vertical and horizontal deformation of the railway using fusion of the railway deformation results from different line of sight direction, we found that the deformation of the railway line is mainly in the vertical direction. Furthermore, the high precision spatial-temporal evolution of the deformation along the BTIR is revealed. Finally, the advantages and limitations of different SAR resolution are analyzed and summarized.
Silicon Add-Drop Multiplexer Based on π Phase-Shifted Antisymmetric Bragg Grating

We proposed a new kind of add-drop multiplexer based on <inline-formula> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula> phase-shifted antisymmetric Bragg grating (<inline-formula> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula>-ASBG). Light resonance is formed in the <inline-formula> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula>-ASBG with mode conversion between backward fundamental transverse electric (TE<sub>0</sub>) mode and the forward first order transverse electric (T<sub>1</sub>) mode. With the aid of the TE<sub>1</sub> mode generated in the <inline-formula> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula> -ASBG, add-drop multiplexer can be designed. As examples, two multiplexer structures based on single- and dual-bus resonator, on silicon-on-insulator (SOI) platform have been studied in detail. Besides, no light is reflected to the input port, if the structure is carefully designed with critical coupling condition satisfied. For the fabricated add-drop multiplexer based on single-bus resonator, the extinction ratio is 23.90 dB and full width at half maximum (FWHM) is 0.33 nm at the through port. For the fabricated add-drop multiplexer based on dual-bus resonator, the extinction ratio is 17.22 dB and FWHM is 0.29 nm at through port. The results show that proposed structures provide a new method to realize add-drop multiplexer and other integrated optical filters for optical communications and sensors.
Personalized and Quality-Aware Task Recommendation in Collaborative Crowdsourcing

Crowdsourcing is a promising solution of collaborative computing aiming at solving settling problems within distributed environment. A collaborative crowdsourcing system(CCS) usually consists of a platform, users performing tasks and thousands or even millions of tasks, each of which is usually simple such as choice making or item rating. However, existing works always match users and tasks without addressing users' interest, which could lead to a reduction of users' enthusiasm involved in the subsequent tasks. In addition, researchers often ignore the truth inference while completing task matching, which should be both significant in CCS. To this end, we jointly investigate the task matching and truth inference in CCS. We propose an integrated framework that can motivate users' participates while guaranteeing the quality of tasks. Finally, extensive simulations illustrate that our algorithm has an outstanding performance on both task matching and truth inferring.
Saliency Maps-Based Convolutional Neural Networks for Facial Expression Recognition

Facial expression recognition (FER) is one of the important research contents in affective computing. It plays a key role in many application fields of human life. As a most common expression feature extraction method, the convolutional neural network (CNN) has the following main limitation. Due to the fact that the CNN network lacks the visual attention guidance, when it gets expression information it brings background noises, resulting in the lower recognition accuracy. In order to simulate the attention mechanism in human visual system, a salient feature extraction model is proposed, including the dilated inception module, the Difference of Gaussian (DOG) module, and the multi-indicator saliency prediction module. This model can effectively reflect the key facial information through the increase of the receptive field, the acquisition of multiscale features, and the simulation of human vision. In addition, a novel FER method for one single person is proposed. With the prior knowledge of saliency maps and the multilayer deep features in the CNN network, the recognition accuracy is improved by obtaining more targeted and more complete deep expression information. The experimental results of saliency prediction, action unit (AU) detection, and smile intensity estimation on the CAT2000, the CK+, and the BP4D databases prove that the proposed method improves the FER performance and is more effective than the existing approaches.
Exploring Sybil and Double-Spending Risks in Blockchain Systems

The first step to realise the true potential of blockchain systems is to explain the associated security risks and vulnerabilities. These risks and vulnerabilities, exploited by the threat agent to affect the valuable assets and services. In this work, we use a security risk management (SRM) domain model and develop a framework to explore two security risks – Sybil and Double-spending – that are observed and considered most concerning security risks within blockchain systems. The framework illustrates the protected assets or assets to secure, the classification of threats that the attacker can trigger using Sybil attack, the identification of threats that cause Double-spending, the vulnerabilities of identified threats, and their countermeasures. We evaluated a newly built framework by exploring Sybil and Double-spending risks in Ethereum-based healthcare applications. We also recognise the various other security and implementation challenges of blockchain that hinder the acceptance of blockchain-enabled solutions. Furthermore, we discuss the permissioned blockchain systems making an appearance in industry-level enterprises and how permissioned blockchain systems control these challenges. We conclude the paper and outline the future work that aims to build an ontology-based blockchain security reference model. The results of this work could help blockchain developers, practitioners, and other associated stakeholders to communicate about Sybil and Double-spending risks, what security countermeasures should be introduced, and what security and implementation challenges are emerging in blockchain systems.
A Compact 2 Element MIMO Antenna for UWB Applications

A novel compact 2 element MIMO micro-strip patch antenna for UWB applications is presented in this paper, designed in CST Microwave Studio. The geometry is symmetrical having a dimension of $\mathbf{25}\times \mathbf{40}\times \mathbf{1}\ \pmb{mm}^{\mathbf{3}}$ with FR-4 dielectric material used as substrate. It is composed of two round shaped radiators with four slots making hexagonal shape and one plus-shaped slot, fed by a micro-strip feed line with $\mathbf{50}\ \Omega$ impedance. The analysis of a design shows that the achieved $\pmb{S}_{\mathbf{11}}$ and $\pmb{S}_{\mathbf{12}} < -\mathbf{10\ dB}$ from 3.39 GHz to 9.1 GHz reflecting UWB characteristics. The diversity performance for the MIMO antenna has also been studied with the envelope correlation coefficient, which is less than 0.003 and the diversity gain is ~10dB. The observed realized gain is $>\mathbf{2.5\ dBi}$ and the simulated total efficiency of the system is approximately 93%.