Miracle-dz/UniFAR-FTUs huggingface dataset:
Workload and Capacity Optimization for Cloud-Edge Computing Systems with Vertical and Horizontal Offloading

A collaborative integration between cloud and edge computing is proposed to be able to exploit the advantages of both technologies. However, most of the existing studies have only considered two-tier cloud-edge computing systems which merely support vertical offloading between local edge nodes and remote cloud servers. This paper thus proposes a generic architecture of cloud-edge computing with the aim of providing both vertical and horizontal offloading between service nodes. To investigate the effectiveness of the design for different operational scenarios, we formulate it as a workload and capacity optimization problem with the objective of minimizing the system computation and communication costs. Because such a mixed-integer nonlinear programming (MINLP) problem is NP-hard, we further develop an approximation algorithm which applies a branch-and-bound method to obtain optimal solutions iteratively. Experimental results show that such a cloud-edge computing architecture can significantly reduce total system costs by about 34%, compared to traditional designs which only support vertical offloading. Our results also indicate that, to accommodate the same number of input workloads, a heterogeneous service allocation scenario requires about a 23% higher system costs than a homogeneous scenario.
Impact of communication delay on distributed load frequency control (dis-LFC) in multi-area power system (MAPS)

AIn this paper, impact of communication delay on distributed load frequency control (dis-LFC) of multi-area interconnected power system (MAIPS) is investigated. Load frequency control (LFC), as one of ancillary services, is aimed at maintaining system frequency and inter-area tie-line power close to the scheduled values, by load reference set-point manipulation and consideration of the system constraints. Centralized LFC (cen-LFC) requires inherent communication bandwidth limitations, stability and computational complexity, as such, it is not a good technique for the control of large-scale and geographically wide power systems. To decrease the system dimensionality and increase performance efficiency, distributed and decentralized control techniques are adopted. In distributed LFC (dis-LFC) of MAIPS, each control area (CA) is equipped with a local controller and are made to exchange their control actions by communication with controllers in the neighboring areas. The delay in this communication can affect the performance of the LFC scheme and in a worst case deteriorates power system stability. To investigate the impact of this delay, model predictive controller (MPC) is employed in the presence of constraints and external disturbances to serve as LFC tracking control. The scheme discretizes the system and solves an on-line optimization at each time sample. The system is subjected to communication delay between the CAs, and the response to the step load perturbation with and without the delay. Time-based simulations were used on a three-area MAIPS in MATLAB/SIMULINK environment to verify the investigations. The overshoot and settling time in the results reveals deterioration of the control performance with delay. Also, the dis-LFC led to zero steady states errors for frequency deviations and enhanced the MAIPS’ performance. With this achievement, MPC proved its constraints handling capability, online rolling optimization and ability to predict future behavior of systems.
Dual-stream Shallow Networks for Facial Micro-expression Recognition

Micro-expressions are spontaneous, brief and subtle facial muscle movements that exposes underlying emotions. Motivated by recent exploits into deep learning for micro-expression analysis, we propose a lightweight dual-stream shallow network in the form of a pair of truncated CNNs with heterogeneous input features. The merging of the convolutional features allows for discriminative learning of micro-expression classes stemming from both streams. Using activation heatmaps, we further demonstrate that salient facial areas are well emphasized, and correspond closely to relevant action units belonging to emotion classes. We empirically validate the proposed network on three benchmark databases, obtaining state-of-the-art performance on the CASME II and SAMM while remaining competitive on the SMIC. Further observations point towards the sufficiency of utilizing shallower deep networks for micro-expression recognition.
Efficient and Flexible Multi-Factor Authentication Protocol Based on Fuzzy Extractor of Administrator's Fingerprint and Smart Mobile Device

In an era of tremendous development in information technology and the Internet of Things (IoT), security plays a key role in safety devices connected with the Internet. Authentication is vital in the security field, and to achieve a strong authentication scheme, there are several systems using a Multi-Factor Authentication (MFA) scheme based on a smart card, token, and biometric. However, these schemes have suffered from the extra cost; lost, stolen or broken factor, and malicious attacks. In this paper, we design an MFA protocol to be the authenticated administrator of IoT’s devices. The main components of our protocol are a smart mobile device and the fuzzy extractor of the administrator’s fingerprint. The information of the authenticated user is stored in an anomalous manner in mobile devices and servers to resist well-known attacks, and, as a result, the attacker fails to authenticate the system when they obtain a mobile device or password. Our work overcomes the above-mentioned issues and does not require extra cost for a fingerprint device. By using the AVISPA tool to analysis protocol security, the results are good and safe against known attacks.
A Convolutional Neural Network for Pavement Surface Crack Segmentation Using Residual Connections and Attention Gating

Conventional surface crack segmentation requires images manually labelled by a trained expert. It is a challenging task as cracks can vary in orientation and size, with some parts of cracks only being one pixel wide. Further, available training data for crack segmentation is sparse. In this work we propose to automate this annotation task, by introducing a fully convolutional U-Net based architecture for semantic segmentation of surface cracks which allows for the use of small datasets through a patch based training process. Our proposed configuration makes use of residual connections inside the convolutional blocks as well as including an attention based gating mechanism between the encoder and decoder section of this architecture, which only propagates relevant activations further. Using our proposed architecture we achieve new state of the art results in two different crack datasets, outperforming the previous best results in two metrics each.
Efficient Privacy-Preserving Access Control of Mobile Multimedia Data in Cloud Computing

With the nature of allowing the encryptor to define the access policy before encrypting a message, Ciphertext-policy attribute-based encryption (CP-ABE) has been widely adopted as a primitive to design cloud-assisted mobile multimedia data sharing system. However, in most previous works, the access policy sent along with ciphertext remains in the palintext form, which would expose the user’s privacy to anyone who can get the ciphertext even if he is not authorized to decrypt. In addition, the resource-constrained mobile devices can not carry out frequent encryption and decryption task caused by CP-ABE. Such drawbacks may reduce the enthusiasm of consumers to share the multimedia data via their mobile devices. In this paper, we proposed PPCMM, a privacy-preserving cloud-assisted mobile multimedia data sharing scheme, where each attribute is described by an attribute name and an attribute value. The attribute values are embedded in the chphertext and only the attribute names are revealed in the access policy. The encryption is divided to two phase: online and offline. In the offline stage, the data owner can prepare the intermediate ciphertext components. Once receiving the encryption requirement of a specific access policy and the multimedia data, the data owner can quickly form the final legal ciphertext in the online stage. By employing the decryption outsourcing technique, most computation overhead of matching test and decryption is offload to the cloud server. The security proof showed that PPCMM is adaptively secure in the standard model. The performance analysis indicated that PPCMM greatly reduces the computation cost in both online encryption and user decryption.
Calibrated Learning for Online Distributed Power Allocation in Small-Cell Networks

This paper introduces a combined calibrated learning and bandit approach to online distributed power control in small cell networks operated under the same frequency bandwidth. Each small base station (SBS) is modelled as an intelligent agent who autonomously decides on its instantaneous transmit power level by predicting the transmitting policies of the other SBSs, namely the opponent SBSs, in the network, in real-time. The decision making process is based jointly on the past observations and the calibrated forecasts of the upcoming power allocation decisions of the opponent SBSs who inflict the dominant interferences on the agent. Furthermore, we integrate the proposed calibrated forecast process with a bandit policy to account for the wireless channel conditions unknown a priori, and develop an autonomous power allocation algorithm that is executable at individual SBSs to enhance the accuracy of the autonomous decision making. We evaluate the performance of the proposed algorithm in cases of maximizing the long-term sum-rate, the overall energy efficiency and the average minimum achievable data rate. Numerical simulation results demonstrate that the proposed design outperforms the benchmark scheme with limited amount of information exchange and rapidly approaches towards the optimal centralized solution for all case studies.
Stellar Parameterisation using KPCA and SVM

Abstract With so many spectroscopic surveys, both past and upcoming, such as SDSS and LAMOST, the number of accessible stellar spectra is continuously increasing. There is therefore a great need for automated procedures that will derive estimates of stellar parameters. Working with spectra from SDSS and LAMOST, we put forward a hybrid approach of Kernel Principal Component Analysis (KPCA) and Support Vector Machine (SVM) to determine the stellar atmospheric parameters effective temperature, surface gravity and metallicity. For stars with both APOGEE and LAMOST spectra, we adopt the LAMOST spectra and APOGEE parameters, and then use KPCA to reduce dimensionality and SVM to measure parameters. Our method provides reliable and precise results; for example, the standard deviation of effective temperature, surface gravity and metallicity for the test sample come to approximately 47–75 K, 0.11–0.15 dex and 0.06–0.075 dex, respectively. The impact of the signal:noise ratio of the observations upon the accuracy of the results is also investigated.
A Real-Time Multi-Label Classification System for Short Videos

Efficient classification of short videos is of a great challenge in industry due to their large amounts and diverse semantics. In this paper, we present a real-time multi-label classification system to attain it. Specifically, a frame-level preprocessing strategy is first proposed to efficiently decode the videos for useful information. Then an image-based model is developed to achieve the final video-level classification. For the multi-label classification case, three modules are proposed to enhance the model’s classification capability, relieve the label-imbalance issue and exploit the label correlation respectively. Our system achieves an accuracy of 86% on both private test sets of the challenge "Short Video Real-time Classification" in AI Challenger 2018, and on its open validation set it achieves an accuracy of 89.2% with a speed of 28ms per video on a workstation with an Intel Xeon CPU E5-2650 and a NVIDIA GPU TITAN Xp.
Smart City Service Monitoring Using Twitter Analytics

Smart City domain has multiple verticals like Water Management, Garbage Management, Power Management, Transportation Management etc. Such services will have to find problems in their respective domain and react to it in less time. An application is proposed in this paper to collect the relevant information from social network data, to process and filter the received relevant information and to communicate about it to the relevant department to handle the issues. The proposed application is scalable and fault tolerant. KeywordsSmart City; Twitter Analytics
A formação e o apoio da gestão no sucesso dos ERP

Bento, F. , Costa, C. J., & Aparicio, M. (2019). A formacao e o apoio da gestao no sucesso dos ERP. In R. Goncalves, I. Pedrosa, M. P. Cota, & A. Rocha (Eds.), 2019 14th Iberian Conference on Information Systems and Technologies (CISTI) [8760794] (Iberian Conference on Information Systems and Technologies, CISTI; Vol. 2019-June). IEEE Computer Society. https://doi.org/10.23919/CISTI.2019. 8760794
A Stylometry Toolkit for Latin Literature

Computational stylometry has become an increasingly important aspect of literary criticism, but many humanists lack the technical expertise or language-specific NLP resources required to exploit computational methods. We demonstrate a stylometry toolkit for analysis of Latin literary texts, which is freely available at www.qcrit. org/stylometry. Our toolkit generates data for a diverse range of literary features and has an intuitive point-and-click interface. The features included have proven effective for multiple literary studies and are calculated using custom heuristics without the need for syntactic parsing. As such, the toolkit models one approach to the user-friendly generation of stylometric data, which could be extended to other premodern and non-English languages underserved by standard NLP resources.
Nonblocking Supervisory Control of State-Tree Structures With Conditional-Preemption Matrices

State-tree structure (STS) is a powerful tool for the modeling and control of large-scale discrete-event systems (DES) whose structure is organized in a top–down hierarchy. The notorious state-explosion problem can be managed effectively in the framework of STS. Priority is an important concept and exists in a wide range of DES, such as manufacturing systems, traffic systems, and logistic (service) systems. In this article, a conditional-preemption matrix is used to describe the preemption relations among events, which can represent the priority intuitively. In order to augment STS with priority, we propose a novel STS framework: STS with conditional-preemption matrices (STSM), which aims to solve different kinds of issues related to priority. The optimal nonblocking supervisory control is deployed in the framework of STSM. Symbolic synthesis algorithms are implemented and integrated in a software tool, STSLib, which exploits binary decision diagrams as a basis for efficient computation. The developed approach is demonstrated by (a) manufacturing systems using automatic guided vehicles and (b) real-time systems.
Dynamic Path Planning for Autonomous Driving on Branch Streets With Crossing Pedestrian Avoidance Guidance

This paper presents a real-time dynamic path planning method for autonomous driving to avoid collision with crossing pedestrian on branch streets. The velocity obstacle algorithms are introduced to pick up the collision-free velocities for vehicles. In this method, the curvilinear lane edges are considered as static obstacle while crossing pedestrians and approaching vehicles are considered as velocity obstacles. The paths planning of vehicles are optimized by considering the delay minimum and comfort of drivers under the constraints of appropriate parameters for veer, throttle, or brake systems. A single vehicle’s path planning and multi-vehicles ’coordinated or uncoordinated paths planning with crossing pedestrian collision avoidance are experimentally simulated including the longitudinal and lateral motions planning of vehicles. The simulation results demonstrate the effectiveness of the proposed method and indicate its wide practical application on autonomous driving to improve the traffic safety of branch streets.
Consideration of space debris in the life cycle assessment framework

Several actors of or related to the European space industry, such as ArianeGroup and the European Space Agency (ESA), have identified life cycle assessment (according to ISO14040/44) as the most appropriate methodology to measure and minimise their environmental impact. Nevertheless, space systems deal with a strong particularity which adds new aspects considering the scope of the LCA framework. Space missions are the only human activity that crosses all segments of the atmosphere and stays “out” of the natural environment and ecosystems. Regarding space systems with a holistic approach, environmental impacts could occur not only in the conventional ecosphere but also in the outer space (i.e. the orbital environment). Consequently, the current scope of LCA studies should be broadened to take into account the on-orbit lifetime as well as the end-of-life disposal of the spacecraft. Yet, it is becoming a crucial point of the space mission design due to the future increase of the orbital population composed in a major part by space debris. In this way, LCA studies of space missions could indicate trade-offs not only between typical impact categories (toxicity and climate change for example) but also with regard to impacts generated in the orbital environment with a particular focus on space debris related impacts.Hence, the priority has been given to the integration of a new impact category called orbital space use in the life cycle impact assessment framework. To address the environmental burdens comprehensively in this new category, impact pathways linking elementary flows to environmental mechanisms (midpoint) and damages (endpoint) are developed within the Area-of-Protection ‘Natural resources’. Space debris is identified as the main stressor of the orbital environment. Thus, ‘characterisation factors’ are defined and calculated at midpoint level to assess the potential impact of a space mission on the orbital environment. The methodology is applied to a case study to prove its applicability: the potential impact of a theoretical space mission is addressed through the comparison of three disposal scenarios. Also, a first attempt regarding the characterisation of the endpoint damage is provided taking into account the economic externalities caused by space debris. Finally, remaining methodological challenges and perspectives for future work are provided.
Relating the small world coefficient to the entropy of 2D networks and applications in neuromorphic engineering

The study of networks pervades science. The techniques of networks are recently being applied to biomedical disciplines, where the complexity of biomedical systems requires new schemes that can process elevated volumes of data with high efficiency. Artificial neural networks, on which much of artificial intelligence relies, are statistical models partially modeled on biological neural networks. They are capable of modeling and processing nonlinear relationships between inputs and outputs in parallel—in opposition to deterministic models and classical computation schemes, which perform tasks in linear sequences of calculations and may fail to keep up with the challenges of complex biological systems. For these biological or bio-inspired systems, the performance of the networks depends on their topological characteristics. Here, we generated a large number of configurations of points in a plane, in which the entropy s of the configurations was varied over large intervals. Then, we connected points using the Waxman model to obtain the corresponding networks. In correlating the entropy (s) to the small-world coefficient ( SW ) of those networks, we found that SW varies hyperbolically with s as SW = 0.88 + 0.28/s, where s is expressed in millibits per node. Since the entropy of a distribution of points depends in turn on the density of those points in the plane, such a relationship suggests that the distribution of mass ( s ) in a complex system determines the topological characteristics ( SW ) of that system. The small-world-ness of the system, in turn, determines its information efficiency. These findings may have implications in neuromorphic engineering, where chips modeled on biological brains may lead to machines that are able, as for some examples, to diagnose diseases, develop drugs and drug delivery systems faster, design personalized treatments targeted to patient’s needs.
Robust Sparse Learning Based on Kernel Non-Second Order Minimization

Partial occlusions in face images pose a great problem for most face recognition algorithms due to the fact that most of these algorithms mainly focus on solving a second order loss function, e.g., mean square error (MSE), which will magnify the effect from occlusion parts. In this paper, we proposed a kernel non-second order loss function for sparse representation (KNS-SR) to recognize or restore partially occluded facial images, which both take the advantages of the correntropy and the non-second order statistics measurement. The resulted framework is more accurate than the MSE-based ones in locating and eliminating outliers information. Experimental results from image reconstruction and recognition tasks on publicly available databases show that the proposed method achieves better performances compared with existing methods.
GSM and Arduino based power theft detection and protection

Theft of electricity is the criminal practice of stealing electrical power. It is a crime and is punishable by fines and/or incarceration. It belongs to the non-technical losses. According to the annual Emerging Markets Smart Grid: Outlook 2015 study by the Northeast Group, LLC, the world loses US$89.3 billion annually to electricity theft. The highest losses were in India ($16.2 billion), followed by Brazil ($10.5 billion) and Russia ($5.1 billion). Electrical power theft detection and control system is used to detect the consumer when they try to use the power illegally. Electricity theft happens when the customer uses energy meter tampering such as placing a magnet near to energy meter, disconnecting the neutral line and shorting the phase coil of the current transformer. To identify and control power theft here an intelligent system is introduced. It consists of two current transformers that are used to measure the actual load current and the other is to measure the turning back or neutral current. Those two current signals are fed to the microcontroller. The microcontroller will compare these two current signals. Depending on the comparison made by the microcontroller it concludes whether power is theft through bypassing the energy meter or not and a message will be sent to notify the authorized power vendor using GSM Hence the power vendor can easily identify the customer who is illegally consuming the power for that energy meter. Moreover, when there is an occurrence of theft the relay will disconnect the load from the supply. The GSM based power theft control system is developed. We used Proteus software to analyze and estimate what the hardware will look like. Mainly this system consists of microcontroller, energy meter, current transformers, LDR, relay, LCD and GSM module.