Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-Objective Optimized Service Level Agreement Approach Applied on a Cloud Computing Ecosystem

The cloud ecosystem provides transformative advantages that allow elastically offering on-demand services. However, it is not always possible to provide adequate services to all customers and thus to fulfill service level agreements (SLA). To enable compliance with these agreements, service providers leave the customer responsible for determining the service settings and expect that the client knows what to do. Some studies address SLA compliance, but the existing works do not adequately address the problem of resource allocation according to clients’ needs since they consider a limited set of objectives to be analyzed and fulfilled. In previous work, we have already addressed the problem considering a single-objective approach. In that work, we identified that the problem has a multi-objective characteristic since several attributes simultaneously influence the SLA agreement, which can lead to conflicts. This paper proposes a multi-objective combinatorial optimization approach for computational resources provisioning, seeking to optimize the efficient use of the infrastructure and provide the client with greater flexibility in contract closure.
Case Report: Outbreak of Bumper Car Disease Caused by Anophryoides haemophila in a Lobster Holding Facility in Nova Scotia, Canada

Abstract This case report provides pathologic and confirmatory molecular characterization of an outbreak of bumper car disease caused by the scuticociliate Anophryoides haemophila in American lobster Homarus americanus, from a commercial holding facility in Nova Scotia, Canada. Although sporadically and anecdotally reported to be present in Atlantic Canada, this is the first report detailing observations from a natural outbreak of bumper car disease by gross and microscopic pathology and its positive identification by small subunit rRNA gene sequencing. Randomly amplified polymorphic DNA analysis confirmed that the genotype of the present outbreak isolate is identical to the original isolate of A. haemophila reported from Maine lobsters in 1993.
Autonomous Robotic Platform Training on Behavioral Cloning Neural Networks using ROS and VESC Project Resources

The endeavor on this paper aims to present a physical robotic platform with the main purpose of addressing the subject of autonomous mobility for both people and goods. The platform, which runs on ROS (Robot Operating System) and VESC Project resources, contains a basic implementation of a behavioral cloning deep neural network in order to achieve a certain level of autonomy, which will be further developed in a series of subsequent papers. Use cases for the autonomous platform are warehouse commodity management, medical material transportation in hospitals, airport luggage logistics and personal mobility for handicapped people. The progress landed in this regard goes in hand with self-driving cars, another key target use case for the proposed platform as test bench. Mobility tests are carried out to assert adequate physical operation, resulting in effective performance at up to 17km/h and secure current, voltage and temperature values for the brushless DC motors, battery and controllers. As for artificial intelligence testing, training accuracy for the neural network presents a value of 0.9536, whereas validation settles at 0.9481, which provides a confident trained model for later implementation.
ConMidbox: Consolidated Middleboxes Selection and Routing in SDN/NFV-Enabled Networks

Software defined networking (SDN) and network function virtualization (NFV) can flexibly manage software middlebox based services, and the consolidated middlebox model is able to simplify traffic routing and reduce the number of routing rules in the SDN-enabled switches. However, different network functions in middleboxes may change the volume of processed traffics, thus high congestion may occur in specific bottleneck links if middlebox selection and traffic routing are not well jointly planned. Besides, in a statically switch-controller configured SDN, traffic dynamics will not only affect the link load in data plane, but also pose a challenge to controller load balancing. Therefore, it’s necessary to achieve better quality-of-service (QoS) performance in both control and data plane. This paper first formulates it as a joint traffic-aware consolidated middleboxes selection and routing (JTMSR) problem and proves its NP-hardness. Then, a two-phase RL_RFRD algorithm is designed to achieve the controller and link load balancing where the first phase is to redirect selected flows by applying wildcard rules and the second phase is to find fine-grained routing path by a rounding-based algorithm with bounded approximation factor. Finally, the extensive simulation results demonstrate that the proposed algorithm has near-optimal controller load balancing and link load balancing performance and reduces response time by about 2x-5x compared with other algorithms.
A Comparative Performance Study of Hybrid Firefly Algorithms for Automatic Data Clustering

In cluster analysis, the goal has always been to extemporize the best possible means of automatically determining the number of clusters. However, because of lack of prior domain knowledge and uncertainty associated with data objects characteristics, it is challenging to choose an appropriate number of clusters, especially when dealing with data objects of high dimensions, varying data sizes, and density. In the last few decades, different researchers have proposed and developed several nature-inspired metaheuristic algorithms to solve data clustering problems. Many studies have shown that the firefly algorithm is a very robust, efficient and effective nature-inspired swarm intelligence global search technique, which has been successfully applied to solve diverse NP-hard optimization problems. However, the diversification search process employed by the firefly algorithm can lead to reduced speed and convergence rate for large-scale optimization problems. Thus this study investigates the application of four hybrid firefly algorithms to the task of automatic clustering of high density and large-scaled unlabelled datasets. In contrast to most of the existing classical heuristic-based data clustering analyses techniques, the proposed hybrid algorithms do not require any prior knowledge of the data objects to be classified. Instead, the hybrid methods automatically determine the optimal number of clusters empirically and during the program execution. Two well-known clustering validity indices, namely the Compact-Separated and Davis-Bouldin indices, are employed to evaluate the superiority of the implemented firefly hybrid algorithms. Furthermore, twelve standard ground truth clustering datasets from the UCI Machine Learning Repository are used to evaluate the robustness and effectiveness of the algorithms against those of the classical swarm optimization algorithms and other related clustering results from the literature. The experimental results show that the new clustering methods depict high superiority in comparison with existing standalone and other hybrid metaheuristic techniques in terms of clustering validity measures.
Solute fluxes from tobacco to the parasitic angiosperm Orobanche cernua and the influence of infection on host carbon and nitrogen relations

Orobanche species are holoparasites which are very efficient sinks for host-derived solutes. Here, we report the use of direct measurements of xylem sap solute concentrations and water fluxes, together with a modelling procedure to calculate element fluxes within an association between Orobanche cernua and its tobacco host. Infection of tobacco by the parasite markedly influenced carbon acquisition and partitioning; net fixation of carbon was 20% higher in infected tobacco compared with controls. Orobanche cernua caused a 84% increase in net carbon flux moving downward from the tobacco shoot and 73% of this carbon was intercepted by the parasite, almost entirely through the phloem (>99%). Further, the parasite also exerted a large impact on the nitrogen relations of the plant, notably nitrate uptake was stimulated and the amino acid content of xylem sap was lower. The parasite also relied heavily on host phloem for the supply of other resources, with only 5 to 15% of N, and 16% of K, 23% of Na, 63% of Mg and 13% of S being derived from the xylem. Thus, we provide quantitative information on the phloem dependency of the parasite and show that host carbon and nitrogen metabolism is stimulated as a consequence of infection.
SofTEE: Software-Based Trusted Execution Environment for User Applications

Commodity operating systems are considered vulnerable. Therefore, when an application handles security-sensitive data, it is highly recommended to run the application in a trusted execution environment. In response to this demand, hardware-based trusted execution environments such as Intel SGX and ARM TrustZone have been developed in commodity computers. However, hardware-based approaches cannot be quickly upgraded to address design vulnerabilities or to reflect customer feedback. In this paper, we propose SofTEE, a software framework to support a trusted execution environment for user applications. For a trusted execution environment, SofTEE should support memory isolation and attestation. For memory isolation, SofTEE relies on kernel deprivileging which delegates the execution of privileged operations such as memory management, from a kernel to a special module called a security monitor. To reduce the overhead of switching between the deprivileged kernel and the security monitor, SofTEE proposes an efficient management mechanism of the address space identifier. SofTEE supports attestation by assuming minimal hardware functionalities of random entropy and root of trust. The main challenge of SofTEE is to guarantee security properties like confidentiality and integrity of security-sensitive applications. For security analysis, we have identified security invariants that SofTEE should meet for confidentiality and integrity guarantees. Based on the security invariants, we have designed and prototyped each component of SofTEE on a Raspberry Pi 3 board. SofTEE produces about 3% overhead in case of a security-sensitive application with long execution time and 23% overhead in case of a security-sensitive application with short execution time.
Automatized Evaluation of the Left Ventricular Ejection Fraction from Echocardiographic Images Using Graph Cut

In this paper, we present a fast and interactive graph cut method for 3D segmentation of the endocardial wall of the left ventricle (LV) given 3D echocardiographic images. This is a challenging task due to the poor contrast and the low signal-to-noise ratio typical of echocardiographic images. The method is carried out in 3 steps. First, 3D sampling of the LV cavity is made in a spherical-cylindrical coordinate system. Then, a gradient-based energy term is assigned to each voxel, some of which being given an infinite energy to make sure the resulting volume passes through key anatomical points. Then, a graph-cut procedure provides delineation of the endocardial surface. Results obtained on 30 exams from the 2014 CETUS MICCAI challenge dataset reveal that our method takes between 5 and 10 seconds to segment a 3D volume with an overall mean surface distance lower than 2.3 mm and an ejection fraction error of less than 5% compared to a manual tracing by an expert.
ECG Data Mining Using SAS

ECG analysis is an important part of finding devices, especially in the cardiovascular system. Various parameters obtained with the ECG curve, such as P-R, QT, R-R, Q-R-S, and S-T intervals, as well as heart rate variability, serve as safety and efficacy endpoints for many clinical trials. There are different algorithms based on graphs and frequency domains to detect different waveforms and ECG cells [4]. This article discusses the study and determination of ECG using SAS. ECG data, which are used for analysis and articles in clinical, are usually processed. The study of the algorithm of ECG signs using SAS allows you to flexibly and clearly perform a preliminary analysis of the parameters obtained from the ECG. In addition, it allows the programmer or researcher to analyze the same data from a research point of view.
An intelligent driving simulation platform: architecture, implementation and application

with the fast-growing advancements in highly automated driving technologies, cost-effective evaluation and validation of functional modules and full-stack driving system have become great challenges for automakers prior to the release of new intelligent vehicle models. Simulation based on high-fidelity rendering and physics engine has been widely used as a powerful tool to develop self-driving systems. From an OEM perspective, a flexible, extensible, and scalable virtual platform is proposed to integrate individual functional modules, support typical scenario libraries, and evaluate single functional algorithm or an entire domain controller system for rapid algorithm iterations and high-efficiency system testing. The platform demonstrates that a stand-alone machine can support up to 32-channel simulations in parallel depending on system resources, a semi-automatic method is introduced to generate a couple of scenarios based on map data and standard road network format, and typical perception algorithms are visualized and evaluated. In addition, such evaluation system can be deployed to the cloud to support large-scale simulations and testing automation.
New Product Diffusion Models in Marketing: A Review and Directions for Research

Since the publication of the Bass model in 1969, research on the modeling of the diffusion of innovations has resulted in a body of literature consisting of several dozen articles, books, and assorted other publications. Attempts have been made to reexamine the structural and conceptual assumptions and estimation issues underlying the diffusion models of new product acceptance. The authors evaluate these developments for the past two decades. They conclude with a research agenda to make diffusion models theoretically more sound and practically more effective and realistic.
A Feedforward Neural Network based Model to Predict Sub-optimal Path Attack in IoT-LLNs

The Internet of Things achieves its vision to connect all physical devices to the Internet through the Low power and Lossy Networks (LLNs). The LLNs comprise constrained devices like sensors, actuators and RFIDs. Since the IoT environment involves large scale deployment of sensor networks, routing becomes an essential requirement. IPv6 Routing Protocol over Low Power and Lossy Network (RPL) is the most popular routing protocol suggested by the Internet Engineering Task Force (IETF) for the IoT-LLN environment. RPL facilitates communication among the sensor nodes in the IoT-LLNs by organizing them in the form of a Destination Oriented Directed Acyclic Graph (DODAG). The term destination-oriented is derived from the fact that data traffic from all the sensor nodes is destined towards the sink (root) node, which acts as a bridge between the LLNs and the intended IoT application. According to RPL, a node chooses its parent from a set of neighboring nodes based on the rank value advertised by them. The rank of any node is a numeric value which is estimated through an objective function and reflects the path quality offered by a parent node to the sink node. Lesser the rank value, the higher is the path quality in terms of the objective function. In a sub-optimal path attack, a malicious node intentionally chooses a sub-optimal path to the sink node by selecting a parent node with a higher rank value. Since the motive of the attacker node is always to select an inferior parent node, the attack is named as the Worst Parent Attack. In this paper, we analyze the impact of the Worst Parent Attack on the overall performance of the IoT-LLNs. Following the analysis, we propose a mechanism based on the Feedforward Neural Network to predict the worst parent attack in RPL supported IoT-LLNs and to identify the source of the attack.
A strange familiarity? Place perceptions among the globally mobile

How do globally mobile people perceive and make sense of a new place in which they have to create an everyday life for themselves? And how may their place perception be communicated through photographs? These are the questions around which this article revolves. The visual material discussed in the article stems from a participatory research project, in which North Denmark functions as a setting for studying local particularities and global convergences. Analysing part of this material, the article explores the perception of – and affiliation with – places and localities, pointing to how perceptions of strangeness and familiarity occur along unexpected lines of difference and similarity depending on the embodied positionality of the involved participants.
Transaction-Based Core Reliability

Modern microprocessor designs are becoming more vulnerable to transient faults leading to transient errors due to design trends mandating low supply voltage and reduced noise margins, shrinking feature sizes and increased transistor density for fast, low power circuits. Detecting and correcting transient errors in random logic in a processor core has become an important design goal and confronts design challenges such as input replication, error confinement and overheads minimization. Transactional Memory (TM) is a recent paradigm to improve the programmability and performance of parallel programs. TM has appeared in industry, providing hardware mechanisms for conflict detection and resolution, and checkpointing and rollback. In this paper, we leverage the features of Hardware TM (HTM) to provide processor cores with transient error detection and recovery at low hardware cost. We contribute to the current state of the art of dependable systems by proposing a novel microarchitecture.
Planar Feeding Techniques for Wearable Textile Antennas

Feeding techniques for textile antennas are of crucial importance to simultaneously address mechanical requirements of robustness while maintaining high RF performance. To assist in determining which planar feeding mechanism is best suited for textile antennas, a dedicated study of various planar feeding mechanisms is undertaken as one of the main aims of this article. Two of the most prominent techniques, namely the inset and proximity-coupled methods, are investigated on a quarter-wave patch, with particular attention to their suitability for various substrate thicknesses. It is shown that neither technique is suitable beyond a substrate thickness of <inline-formula> <tex-math notation="LaTeX">$0.03\lambda _{0}$ </tex-math></inline-formula>. Consequently, a new textile feeding technique is proposed that blends aspects of the inset and proximity-coupled feeding techniques and represents the main contribution of this article. It is shown that this new feeding mechanism can serve as viable planar textile replacement for a coaxial probe feeding mechanism up to the substrate thicknesses of <inline-formula> <tex-math notation="LaTeX">$0.05\lambda _{0}$ </tex-math></inline-formula>.
Spara: An Energy-Efficient ReRAM-Based Accelerator for Sparse Graph Analytics Applications

Resistive random access memory (ReRAM) addresses the high memory bandwidth requirement challenge of graph analytics by integrating the computing logic in the memory. Due to the matrix-structured crossbar architecture, existing ReRAM-based accelerators, when handling real-world graphs that often have the skewed degree distribution, suffer from the severe sparsity problem arising from zero fillings and activation nondeterminism, incurring substantial ineffectual computations.In this paper, we observe that the sparsity sources lie in the consecutive mapping of source and destination vertex index onto the wordline and bitline of a crossbar. Although exhaustive graph reordering improves the sparsity-induced inefficiency, its totally-random (source and destination) vertex mapping leads to expensive overheads. This work exploits the insight in a mid-point vertex mapping with the random wordlines and consecutive bitlines. A cost-effective preprocessing is proposed to exploit the insight by rapidly exploring the crossbar-fit vertex reorderings but ignores the sparsity arising from activation dynamics. We present a novel ReRAM-based graph analytics accelerator, named Spara, which can maximize the workload density of crossbars dynamically by using a tightly-coupled bank parallel architecture further proposed. Results on real-world and synthesized graphs show that Spara outperforms GraphR and GraphSAR by 8.21 × and 5.01 × in terms of performance, and by 8.97 × and 5.68× in terms of energy savings (on average), while incurring a reasonable (<9.98%) pre-processing overhead.
Blockchain Based Trading Platform for Electric Vehicle Charging in Smart Cities

This paper presents a novel blockchain-based energy trading architecture for electric vehicles (EVs) within smart cities. By allowing local renewable energy providers to supply public charging stations, EV drivers can gain access to affordable energy and optimally plan for their charging operations. For this purpose, we present a smart-contract based trading platform that runs on top of a private Ethereum network. Contrary to existing solutions, we rely on the legacy billing and metering of the existing utility company in order to avoid making major changes to the existing infrastructure. The trading logic, including the auction mechanism, used to exchange energy can be defined in a smart-contract and applied within the platform. We conduct extensive experiments to evaluate the performance of some existing auction mechanisms and the underlying private Ethereum network in supporting the corresponding energy trading transaction load. We develop a virtualization-based simulator for Ethereum and measure both the transaction throughput and latency under different network and workload scenarios. The obtained results have shown that the current Ethereum implementation can support charging requests from EVs during peak hours in very crowded cities, such as Singapore.
Prediction of Attendance at Medical Appointments Based on Machine Learning

A recurring problem in the area of public health is the high rate of patients who do not attend scheduled medical examinations and consultations. Absenteeism from previously scheduled appointments compromises the effectiveness of medical care and generates a series of problems in the public health system, such as long waiting lines for care, lack of resources to meet demand and high financial loss, by paying a professional who will be idle due to the absence of patients. Thus, this study aims to explore different models of machine learning in order to help predict whether the patient will attend the scheduled appointment or not.