Miracle-dz/UniFAR-FTUs huggingface dataset:
Pair-Wise Multipath Routing with Mobile Sink in WMSN

Multipath routing in wireless multimedia sensor network (WMSN) is used to transfer data simultaneously so can reduce delay and congestion of the network. The main problem with the multipath routing strategy is that the node energy near sink becomes obviously higher than other nodes. Multiple paths are used to transfer data from source to the destination that is many-to-one traffic patterns, the traffic of the whole network will be converge to a specific set of sensor nodes (e.g. neighbours of the sink) and results in the hotspot problem, which makes the network stop functioning. So we propose a Pair-Wise Multipath Routing strategy to solve the energy bottle neck problem. It uses all nodes around the sink in 360 degree scope. These nodes are called pair-wise nodes. Therefore, it can reduce the energy problem and can improve the network life. But when sink is at the edge of the network or when the node density is too small to find enough pair-wise nodes, the performance will be affected. However, as long as the sink is static, this issue cannot be fully solved. Therefore, there is a recent approach to exploit mobility of the sink to deal with these problems. Keywords— Pair-Wise nodes, Hot-spot problem, mobile sink, Multipath Routing, WMSN
Demonstrating the Safety of Cellulose Nanomaterials – High Performance Bio-based Materials

Commercialization of cellulose nanomaterials (CN) is rapidly advancing to the benefit of many end use product sectors and disseminating knowledge regarding the safe manufacturing and handling for CN is a priority. There is significant interest in a breadth of application categories from packaging and construction materials to transportation, food, electronics and consumer products. Each application can trigger different regulatory requirements, from safety data sheet development to premarket regulatory reviews. This presentation will discuss the current efforts to establish safety determinations for CN in the U.S. and beyond. Specifically, we report on the data available and an analysis of current gaps for cellulose nanomaterials with regard to the toxicological profile, environmental characteristics, physical and chemical properties, and exposure data, to inform the development of key information to advance safe commercialization.
Feasibility Study of Hollow Fiber Submerged Membrane Bioreactor

Treatability studies for two different wastewaters; one origina ting from a residential colony and another from hospital has been carried out by l aboratory-scale hollow fiber membrane bioreactor (MBR). The aim of the study was to explore the poss ibility of simultaneous removal of organics, nitrog en, phosphorus and suspended solids. Two different start up period were considered with tota l 18 runs by varying SRTs (10, 20 and 30 days) and HRTs (4, 6 an d 8 hours). Results confirmed that removal efficiencies for the MBR varied from 93.15 to 97.98 % for COD, 94.20 to 98.74 % for BOD and 97.21 to 98.74 % for ammonia. The phosphorus removal efficie ncy (54.55 to 58.00 %) was not satisfactory. But the retention of total suspended solids and volatil e suspended solids were almost above 99 %. The resistances due to membrane fouling were found to b e fluctuates from 0.85 x 10 12 /m to 1.25 x 10 12 /m for residential wastewater and for hospital wastewater it fluctuates from 1.05 x 10 12 /m to 1.45 x 10 12 /m. The results prove that the Anoxic- Aerobic MBR system w as highly competent in treating both residential an d hospital wastewater. , and signifies that the system is also capable of treating synthetic pharmaceutic al substances which are likely to be present in hospit al wastewater.
How data brokers endanger privacy

In the last decades, the information trading industry experienced important growth with the advent of Big Data. Information traders such as data brokers keep more and more detailed pro-ﬁles of individuals, thus storing a variety of sensitive information. Those practices raise public concern, as leakage of personal data can harm data subjects and put the individual at risk of frauds or identity thefts. Even more worrisome is the fact that some data brokers, namely person search sites, deliver important amounts of personal data for free, by providing person registries on their public website. While a single data broker doing so may cause limited harm, when multiple person search sites provide different kinds of easily accessible personal information, those data can be linked to produce a complete proﬁle containing a wide range of sensitive information. To provide the readers with an understanding of the current data broker industry, we conducted a survey on 75 data brokers and present the results in this paper. Furthermore, to show how easy it is to link data across different data brokers, we developed a system that automatically collects and links proﬁles from different person search sites. This system, named DROPLET, requires limited human intervention, but can produce linked proﬁles containing large amounts of personal sensitive information.
Web camera automatic object detection with graphical representation of the object total appearance time

Inability to identify objects accurately for a long time has been a challenge to claiming ownership, justice, and securing life and property. Previous methods employed to provide solution to these problems proved unsatisfactory. Many of the algorithms used in the previous methods could no longer handle the challenges confronting today’s identification tasks. This paper is a novel approach to detecting object whether static or on motion in web camera. The algorithmic program used was developed using OpenCV python modules. Initial and subsequent captured object’s images were saved in a frame before converting them to Gaussian blur images. The difference between the initial and subsequent images was calculated and their threshold defined to remove the shadows and other noises. Borders of the objects were defined before a rectangular box was added around the object. Then, the time when the object appeared and exited the camera was also calculated.
A Practical Slack-time Analysis Method for DVS Real-time Scheduling

— This work presents a scheduling algorithm to reduce the energy of hard real-time tasks with fixed priorities assigned in a rate-monotonic policy. Sets of independent tasks running periodically on a processor with dynamic voltage scaling (DVS) are considered as well. The proposed online approach can cooperate with many slack-time analysis methods based on low-power work demand analysis ( lpWDA) without increasing the computational complexity of DVS algorithms. The proposed approach introduces a novel technique called low-power fluid slack analysis ( lpFSA ) that extends the analysis interval produced by its cooperative methods and computes the available slack in the extended interval. The lpFSA regards the additional slack as fluid and computes its length, such that it can be moved to the current job. Therefore, the proposed approach provides cooperative methods with additional slack. Experimental results show that the proposed approach combined with lpWDA -based algorithms achieves more energy reductions than do the initial algorithms alone.
Development and Evaluation of an Educational Program for Community Pharmacists on Cardiovascular Risk Assessment

Purpose: Cardiovascular disease (CVD) risk assessment is an important strategy for the prevention of CVD. Pharmacists play an important role in CVD risk assessment and management (CVDRAM). Our previous study identi ﬁ ed gaps in knowledge among community pharmacists for the provision of CVDRAM services as assessed through patient simulation. Therefore, our objectives were: a) to develop and evaluate an educational program on CVD risk assessment for community pharmacists, b) to assess the knowledge and skills of participating pharmacists in assessing and managing CVD risk before and after enrolling in the educational program and c) to explore pharmacists ’ satisfaction and perceived effectiveness of the educational program. Methods: Using a blended learning instructional approach, the educational program for a subset of 25 community pharmacists recruited from our previous study consisted of two face-to-face workshops, and an online 5-module course on CVD risk factors such as hyperlipidemia, hypertension, diabetes, obesity, and smoking cessation based on principles of adult learning. A repeated measures study design was utilized by measuring participants ’ knowledge on pre- and post-questionnaires and an objective structured clinical examination (OSCE) at the conclusion of the educational program was also used to assess its impact on the knowledge and skills of community pharmacists in the provision of CVD risk assessment and management (CVDRAM) services. The knowledge questionnaire was completed by 23 pharmacists while the OSCE was completed by 8 pharmacists. In addition, a survey assessed the pharmacists ’ level of satisfaction with the educational program. Results: At the conclusion of the educational program, the participating pharmacists achieved knowledge and skills for the provision of CVDRAM services. Knowledge scores in relation to CVDRAM signi ﬁ cantly improved after the educational program [out of a maximum of 20 points, the median (interquartile range) = 9 (7 – 9) at pre- vs 12 (12 – 13) at post-educational program], p<0.001. On the OSCE, the median (interquartile range) scores for Stations 1 and 2 were 66 (63 – 71) and 71 (67 – 76), respectively. Out of the 21 pharmacists that completed the satisfaction survey, 71% were very satis ﬁ ed and 29% were satis ﬁ ed with the educational program. Conclusion: The educational program improved pharmacists ’ knowledge and skills for the provision of CVDRAM services.
on Large-Scale Agile Development

The Large-Scale Agile Development workshop explored the main research challenges in large-scale software development. We considered multi-site organisations with large-scale projects that include a large number of teams adopting agile methods. Such topics include inter-team coordination, knowledge sharing, large project organisation, agile transformation, agile teamwork quality, project models that facilitate several self-organising teams, and practices for scaling agile methods. We accepted ﬁ ve full research papers, which are included in this volume. The accepted papers report empirical research studies using surveys, observations and case studies. Also, an interactive online discussion session was conducted to compare the two approaches, SAFe and Spotify. The workshop participants, which were around a hundred people, joined this discussion to compare the two approaches and suggest some future research questions about the hybridisation of SAFe and Spotify. This workshop summary contributes as a current snapshot of research along with some results from an interactive discussion about SAFe and Spotify.
Federated Learning Empowered Edge Collaborative Content Caching Mechanism for Internet of Vehicles

With the development of smart traffic and assisted driving, the mobile edge computing and artificial intelligence technologies are seen as the key solutions in the internet of vehicles. However, the limited edge network resources and leakage of vehicle private data in assisted driving process are still problems to be solved. Therefore, we design a federated learning (FL) empowered edge collaborative content caching mechanism to provide low latency and high reliable assisted driving services for vehicles. First, we build an edge collaborative cache domain to allow multiple edge nodes to jointly share the service component resources required by vehicles. Next, based on LSTM prediction model obtained by FL, we propose a service component pre-caching and placement strategy according to the predicted and real-time vehicle behavior, to realize fast and accurate content caching services. The simulation results show that the proposed mechanism can improve the performance in terms of caching hit rate, service delay and the resource utilization of edge nodes.
Linearized Model for MIMO-MFSK Systems With Energy Detection

For user terminals, energy-detection based multiple-input-multiple-output (MIMO) has the advantages of low complexity and anti-Doppler capability. However, the energy detector makes it difficult to linearly separate the signals in spatial multiplexing. In this letter, the linearized model is introduced in $2\times 2$ M-ary frequency-shifted keying (MFSK) V-BLAST system with energy detection. To avoid rank deficiency of the model, we propose nonlinear virtual antennas (NVA) to increases both the dimension and receive diversity gain of MIMO system. Then the full-rank condition is discussed. In multiple-antenna systems, NVA is further generalized by spatial index modulation with dual antennas selection. By simulations, linear detectors are verified to be feasible for non-coherent spatial-multiplexing MIMO, and its diversity gain is obviously increased by the proposed NVA.
Analytical Calculation of the User Threshold for the Switching between Unicast and Broadcast in Cellular Networks

The ever increasing demand for high quality multimedia content in mobile networks requires the implementation of techniques for efficient spectrum management. Broadcast transmission is the preferred solution for scenarios in which the same content is transmitted to many users at the same time. The objective of this study is to propose an analytical method to calculate the user threshold to switch from unicast to broadcast in order to reduce radio resource utilization. We compute the probability of coverage of a given location in a Multicast Broadcast Single Frequency Network (MBSFN) area using tools from stochastic geometry. This expression is general and valid for a point not necessarily in the center of the MBSFN area. We use it to develop a method to calculate the user threshold analytically.
Paper on the Issue of Mathematical Modeling of Magnetic Fields of Magnetostrictive Displacement Transducers Containing a Permanent Magnet in the Form of a Rectangular Parallelepiped

The article is devoted to the analysis of existing models of magnetic fields formed by magnetostrictive converters of linear and angular displacements containing permanent magnets in the form of a rectangular parallelepiped. Mathematical models of permanent magnets in the form of a rectangular parallelepiped are considered. It is shown that not all existing mathematical models allow to correctly and accurately calculate the magnetic fields of permanent magnets in the form of a rectangular parallelepiped. Computational experiments were carried out to study the magnetic field of permanent magnets in the form of a rectangular parallelepiped using mathematical models. The results of computational experiments also showed that the difference in the calculations of the magnetic field strength of magnetostrictive displacement transducers containing permanent magnets in the form of a rectangular parallelepiped according to the exact model and the approximate model differ and depend on the ratio of its length or length and width from its thickness. Depending on whether this value exceeds 10 times, it becomes possible or not to use the above formulas and expressions. It is revealed which formulas and expressions are better to use in this or that case. To confirm the results of the computational experiment and theoretical studies, an experimental test was carried out, which showed a coincidence with the results obtained in computational experiments.
Accurate High-Maneuvering Trajectory Tracking for Quadrotors: A Drag Utilization Method

The balanceness between the tracking performance and the aerodynamic drag treatment is of paramount importance especially in the presence of the quadrotor aggressive maneuvers. Different from standard approaches that achieve precise tracking by feedforward compensating the estimated drag, this work presents a scheme to appropriately utilize drag. By means of the proposed drag-utilization scheme, the disturbance absorption can be achieved. In addition to eliminate the adverse effect, the control gains are subtly enlarged with less noise. Moreover, an adaptive law for estimating the drag coefficients onboard is provided. Subsequently, the wind disturbance is explicitly considered at the control design stage. A wind speed observer (WSO) is designed to improve the tracking performance, based on current velocity and attitude. Compared with the traditional disturbance observer (DO), the proposed WSO can not only fully utilize the disturbance characteristic but also contribute to reduce the control conservativeness. In experiments, two types of quadrotors with different thrust-to-weight ratios (TWRs) are employed to evaluate the applicability of the presented scheme. Comparative results show that the proposed scheme outperforms several popular methods.
Automatically Distributing and Updating In-Network Management Rules for Software Defined Networks

Software Defined Networks (SDN) heavily rely on diverse management rules (ACL, traffic control, etc. ) to satisfy security and business requirements of their associated services. As these networks are increasing in size and complexity, their management rules configured in devices are becoming more complex. These rules are constantly growing in size and it is challenging to distribute them across network devices with limited capacities. The most challenging task is to deploy rules updates in a fast and efficient way to avoid a security breach or to meet a service needs. In this paper, we extend our previous work on network management rules distribution by introducing an efficient update strategy. Through extensive experiments on several rule sets with single and multiple path topologies, we evaluate and analyze the performance of our strategy. Our obtained results show a reduction of up to 90% in update time.
Factored Reinforcement Learning for Auto-scaling in Tandem Queues

As today’s networking systems utilise more virtual-isation, efficient auto-scaling of resources becomes increasingly critical for controlling both the performance and energy consumption. In this paper, we study the techniques to learn the optimal auto-scaling policies in a distributed network when parts of the system dynamics are unknown. Reinforcement Learning methods have been applied to solve auto-scaling problems. However they can run into computational and convergence issues as the problem scale grows. On the other hand, distributed networks have relational structures with local dependencies between physical and virtual resources. We can exploit these structures to overcome the convergence issues by using a factored representation of the system.We consider a distributed network in the form of a tandem queue composed of two nodes. The objective of the auto-scaling problem is to find policies that have a good trade-off between quality of service (QoS) and operating costs. We develop a factored Reinforcement Learning algorithm, named FMDP online, to find the optimal auto-scaling policies. We evaluate our algorithm with a simulated environment. We compare it with existing Reinforcement Learning methods and show its relevance in terms of policy efficiency and convergence speed.
Monte Carlo Based Estimation of Weight Functions for Few-View Computed Tomography of Strongly Absorbing Objects

The paper offers a method to simulate weight functions for few-view X-ray computed tomography of strongly absorbing objects. The method is based on a probabilistic interpretation of energy transport through the object from a source to a detector. Photons are tracked with a PRIZMA code package that is developed at RFNC-VNIITF and implements a stochastic Monte Carlo method. The value of the weight function in a discrete cell of the reconstruction region is assumed to be directly proportional to the fraction of photon trajectories which cross the cell from all those recorded by the detector. The efficiency of the method is validated through a numerical experiment aimed to reconstruct a section of a spherical heavy metal phantom with an air cavity and a density difference of 25%. The method proposed for weight function calculation is shown to outperform the method based on projection approximation in case of reconstruction from 9 views.
Acetylene Production Process Control based on Safety Criterion

This paper considers a version of the control system structure for the oxidative pyrolysis process in the production of acetylene with the safety criterion taken into account. In the presented variant of the two-level process control system, the upper level ensures the safe process flow. It determines the settings of the controllers for the local control loops of the lower level with the help of the correction unit. In designing the controllers, the fuzzy logic is used. It is shown that due to this system, the critical values of variables determining technological safety of the process are not exceeded, which reduces the number of the emergency stops during the process.
A testbed for CCAM services supported by edge computing, and use case of computation offloading

Mobile technologies have undergone a great leap forward in a few years, and while 5G networks are already being deployed, there are not yet many proven applications that can fully utilize the advantages of this new technology. Connected and autonomous vehicles are a specific and demanding case, particularly in terms of delay and bandwidth requirements, which can leverage not only 5G but also edge computing technologies. Therefore, the development of testbeds to demonstrate future applications is crucial to enable the full deployment of 5G and edge computing possibilities. In this paper, we present a flexible and modular testbed, targeted towards the evaluation of Cooperative, Connected, and Automated Mobility (CCAM) applications, and we demonstrate a use case (using a 4G system) where an autonomous vehicle offloads processing tasks to an edge server which analyzes images, makes routing decisions, and sends guidance commands back to the vehicle, thus proving the possibilities of edge computing and wireless technologies.
Dynamic Local Community Detection Algorithms

Recent years have witnessed the rapid growth of social network services. Real-world social networks are huge and changing over time. Consequently, the problems in this area have become more complex. Community detection is one of the most important problems in social networks. A good community can be defined as a group of nodes that are highly connected to each other and loosely connected to the nodes outside the community. Regarding the fact that social networks are huge in size, having complete information of the whole network is almost impossible. As a result, the problem of local community detection has become more popular in recent years. The problem of community detection in dynamic networks is well-investigated however, the local community detection is not widely addressed by researchers. In this paper, this problem is investigated by employing a number of existing local community detection algorithms in a dynamic structure. Results are reported in two different experiments. Experimental results show that one of the algorithms (algorithm P) outperforms other algorithms regarding the employed dynamic structure. The results indicate that algorithm P is much faster than other local community detection algorithms in dynamic networks.