Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Handover Scheme for Hybrid LiFi and WiFi Networks

Combining the high transmission speed of light fidelity (LiFi) and the ubiquitous coverage of wireless fidelity (WiFi), hybrid LiFi and WiFi network (HLWNet) has attracted intensive research interest. While such a network can boost the system capacity, it faces a challenging issue in handover since the coverage areas of LiFi and WiFi completely overlap each other. Also, LiFi has a relatively short coverage range from a single access point (AP). As a result, HLWNets are susceptible to frequent handovers, in terms of both handovers between LiFi and WiFi and handovers between two LiFi APs. Therefore handover skipping was introduced, which enables handovers between two non-adjacent APs. Conventional handover skipping schemes rely on knowledge about the user’s trajectory, and are difficult to implement in practice. Alternatively, the rate of change in reference signal received power (RSRP) can be used to reflect the user’s moving direction. Based on this, an adaptive handover scheme is proposed in this paper, which varies the user’s network preference according to the user’s speed. Results show that in comparison with the standard and trajectory-based schemes, the proposed approach can improve user throughput by up to about 120% and 30%, respectively.
In-Depth Optimization with the OpenACC-to-FPGA Framework on an Arria 10 FPGA

The reconfigurable computing paradigm that uses field programmable gate arrays (FPGAs) has received renewed interest in the high-performance computing field due to FPGAs’ unique combination of performance and energy efficiency. However, difficulties in programming and optimizing FPGAs have prevented them from being widely accepted as general-purpose computing devices. In accelerator-based heterogeneous computing, portability across diverse heterogeneous devices is also an important issue, but the unique architectural features in FPGAs make this difficult to achieve. To address these issues, a directive-based, high-level FPGA programming and optimization framework was previously developed. In this work, developed optimizations were combined holistically using the directive-based approach to show that each individual benchmark requires a unique set of optimizations to maximize performance. The relationships between FPGA resource usages and runtime performance were also explored.
A Lightweight And Secure Authentication Protocol for WSN

Wireless sensor networks (WSNs) are gaining traction in day to day applications where data has to be gathered in hostile environments using low computing power sensors. As these sensors are deployed in hostile environments, strong authentication mechanisms are essential to guarantee the safety and security of the data being transferred. But, these strong authentication mechanisms are computationally heavy for the low power sensors. So, there is a need for a lightweight and secure authentication protocol for WSNs. Applications of these sensor ranges from healthcare to oil and exploration. The security of the data being gathered and transmitted is of utmost importance as they can correlate to life and death situations in some instances. So, employment of extremely secure schemes is a necessity but all these schemes are computationally very expensive and require powerful hardware. The sensors used in WSN are designed for low power environments, and longevity and hence do not possess these hardware resources to secure the communication. Hardware-level, as well as lightweight operations, have to employ for securing the communication. This procedure also preserves the integrity of data being gathered and transmitted by the sensor through these WSNs.
NUMA-aware CPU core allocation in cooperating dynamic applications

Building large scientific applications by composing multiple smaller applications is one of current research directions. If the individual component applications are executed together on one compute node, we need to allocate resources to the components. While the operating system is already capable of doing this, it might be possible to get higher efficiency with a more specialized solution. Our goal is to describe opportunities and challenges faced by anyone designing such a system. We look into the specific case where a dynamic runtime system (or multiple such runtime systems) are used by the applications, since we believe the fundamental design of these runtime systems makes them especially suitable for the role. The ideas described in this paper are based on our prior experience in building such a runtime system and our early experiments with cooperating applications. We will focus on CPU core allocation and point out the importance of making non-uniform memory access architectures a prime consideration in such work.
Improving Inference Latency and Energy of Network-on-Chip based Convolutional Neural Networks through Weights Compression

Network-on-Chip (NoC) based Convolutional Neural Network (CNN) accelerators are energy and performance limited by the communication traffic. In fact, to run an inference, the amount of traffic generated both on-chip and off-chip to fetch the parameters of the network, namely, filters and weights, accounts for a large fraction of the energy and latency. This paper presents a technique for compressing the network parameters in such a way to reduce the amount of traffic for fetching the network parameters thus improving the overall performance and energy figures of the accelerator. The lossy nature of the proposed compression technique results in a degradation of the accuracy of the network which we show being, nevertheless, widely justified by the achievable latency and energy consumption improvements. The proposed technique is applied to several widespread CNN models in which the trade-off accuracy vs. inference latency and inference energy is discussed. We show that up to 63% inference latency reduction and 67% inference energy reduction can be achieved with less than 5% top 5 accuracy degradation without the need of retraining the network.
An Image Recognition Algorithm of Soybean Diseases and Insect Pests Based on Migration Learning and Deep Convolution Network

in order to extract the leaf features of soybean diseases and insect pests, reduce the complexity of the image model of soybean diseases and insect pests, and solve the over fitting phenomenon caused by the insufficient number of samples, a classification model of convolution neural network based on transfer learning technology is proposed, and the bottom features are gradually upgraded to abstract high-level features by using the multi-layer structure of convolution network with mature training Good characteristic learning ability. To enhance the data of soybean plant diseases and insect pests, extract the features of the enhanced leaf images, and classify the images by using the deep convolution neural network. The experimental results show that the method has high accuracy and robustness in soybean pest detection.
Reinforcement Learning Based Load Balancing for Hybrid LiFi WiFi Networks

Light fidelity (LiFi) is an emerging communication technology, which utilizes the light-emitting diodes (LEDs) for high-speed wireless communications. Due to its huge unlicensed bandwidth, LiFi is capable of supporting high data rates. The quality of the LiFi channel fluctuates across the room due to interference, reflection from walls or blockage. On the other hand, WiFi is another wireless communication technology that is capable of providing moderate data rates with ubiquitous coverage. As the electromagnetic spectrum of LiFi does not overlap with WiFi, both of them can coexist to form a hybrid LiFi and WiFi network for seamless and high-throughput connectivity. The performance of a hybrid system significantly depends upon the access point (AP) assignment and resource allocation strategies. In this paper, a downlink hybrid system with one WiFi AP and four LiFi APs is considered, and a reinforcement learning (RL) algorithm is implemented in order to determine an optimal AP assignment strategy, which maximizes the long-term system throughput while ensuring the required users fairness and satisfaction. Furthermore, two different scenarios based on the random waypoint model with uniform and non-uniform distribution of users have been studied. The performance of the proposed system is compared against state-of-the-art benchmark approaches e.g., signal strength strategy (SSS), exhaustive search, and an iterative optimization method. The results are reported in terms of the average system throughput, user satisfaction, fairness, and capacity outage probability. It is shown that the proposed RL method performs closer to the exhaustive search scheme at fairly low complexity. The RL method also outperforms the SSS scheme and the iterative algorithm in most scenarios.
Studies on British Laminariaceae. I. Growth in Laminaria Saccharina (L.) Lamour

The production, longevity, growth, regeneration and reproduction of Laminaria saccharina (L.) Lamour. on the Devon and Argyll coasts are described. Bathymetric zone and habitat control the fertility and longevity of the gametophyte and consequently the production of the sporophyte. Sporophytes develop at the higher levels during the winter, early spring, late summer and autumn, and at the lower levels during spring, summer and autumn. Longevity of the sporophyte depends on season of germination, bathymetric zone and habitat. Winter sporophytes rarely persist to maturity. Spring sporophytes form the bulk of all L. saccharina populations except on very sheltered coasts in the sublittoral zone where summer sporophytes may be equally numerous. On the British coast the life-span of a L. saccharina sporophyte does not exceed 3 years.
Correlation analysis between drowsiness and heart rate variability parameters

Electrocardiography (ECG) is an important physiological signal that reflects the autonomic nervous system (ANS) activity. The correlation between ANS measures and sleepiness has been used to assess the drowsiness of a person from spectral analysis of heart rate variability (HRV). Power spectral analysis of HRV derived from RR interval time series gives three frequency bands, namely very low frequency band (0.0030.04 Hz), low frequency band (0.04-0.15 Hz) and high frequency band (0.15-0.4 Hz). Drowsiness has an adverse effect on the reaction time, alertness and concentration level of vehicle driver that can reduce driver performance. Surveys suggest that driver fatigue is one of the main causes of road accidents, contributing up to 20% of all road accidents. Researches have shown that bio-signals like electrocardiography, electroencephalography, blood pressure, etc. are affected by the mental state of the individual, and thus, these physiological signals can be used to assess the mental state of individuals. Hence, it is possible to make a system to monitor the driver drowsiness based on bio-signal. Recording EEG and EOG involves attaching electrodes on the head which is not comfortable, and may cause distraction to the driver. ECG is thus a better choice as it is possible to set up non-intrusively by placing electrically conductive fabric on the steering wheel and driver seat as electrodes. Non-contact electrodes have also been tested. Low frequency band power spectrum is related with sympathetic and parasympathetic activity of ANS, whereas high frequency band power spectrum is related with parasympathetic activity of ANS. The sympathetic nervous system signals the body to be in a more alert state whereas the parasympathetic nervous system signals the body to be in a more relaxed state. The objective of this paper is to analyze the changes in spectral components of HRV when one becomes drowsy and detect drowsiness.
Real-Time Nonlinear Model Predictive Control for the Energy Management of Hybrid Electric Vehicles in a Hierarchical Framework*

In this paper a real-time capable hierarchical nonlinear model predictive control framework for the energy management of a hybrid electric vehicle is presented. As high-level energy management, nonlinear model predictive control is employed. Therefore, a nonlinear optimal control problem is formulated using a control-oriented internal model derived from high-fidelity models and experimental data. The multiple shooting algorithm and an Euler backward scheme are used to discretize the optimal control problem. The resulting nonlinear problem is solved in real-time using Sequential Quadratic Programming. A rule-based gear choice and engine on / off strategy is added. Actuator dynamics and drivability are addressed in a fast low-level linear time-variant model predictive controller. The result is analyzed and compared to the optimal solution obtained by dynamic programming using a simplified model of the vehicle.
Rethinking Segmentation Guidance for Weakly Supervised Object Detection

Weakly supervised object detection aims at learning object detectors with only image-level category labels. Most existing methods tend to solve this problem by using a multiple instance learning detector which is usually trapped to discriminate object parts, rather than the entire object. In order to select high-quality proposals, recent works lever-age objectness scores derived from weakly-supervised segmentation maps to rank the object proposals. Base our observation, this kind of segmentation guided method always fails due to neglect of the fact that objectness of all proposals inside the ground-truth box should be consistent. In this paper, we propose a novel object representation named Ob-jectness Consistent Representation (OCR) to meet the consistency criterion of objectness. Specifically, we project the segmentation confidence scores into two orthogonal directions, namely vertical and horizontal, to get the OCR. With the novel object representation, more high-quality proposals can be mined for learning a much stronger object detector. We obtain 54.6% and 51.1% mAP scores on VOC 2007 and 2012 datasets, significantly outperforming the state-of-the-arts and demonstrating the superiority of OCR for weakly supervised object detection.
Integral Sliding Mode based Model Reference FTC of an Over-Actuated Hybrid UAV using Online Control Allocation

This paper presents a novel concept for active fault-tolerant control (FTC) of dual system hybrid unmanned aerial vehicles (UAVs) based on analytical redundancy to increase the operational safety in the face of primary actuator faults. The proposed scheme exploits the inherent overactuation property of hybrid UAVs when in addition to the aerodynamic surfaces four lift rotors are used to control the aircraft during long range fixed-wing flight mode. Fault tolerance is achieved by utilizing an integral sliding mode based model reference control law combined with control allocation techniques to reallocate control signals among healthy effectors in the face of actuator faults and maintain nominal closedloop performance. After introducing the modelling procedure of the UAV, including the identification of aerodynamical cross-couplings between lift rotors and airframe dynamics, Hardware-in-the-loop (HIL) simulation results are presented to demonstrate the efficiency of the proposed scheme in a realistic hardware setup.
Tensor-based High-Accuracy Position Estimation for 5G mmWave Massive MIMO Systems

Highly accurate localization is important for wire-less communications. In this paper, we propose a new tensor-based positioning method for 5G wideband mmWave massive MIMO systems. We first develop an extended multidimensional interpolation (E-MI)-based method as the preprocessing step to suppress the frequency-dependence of the array steering vectors. By using this method, the data across the whole frequency band can be processed jointly, and the high temporal resolution offered by wideband mmWave signals can be exploited. Then, we propose a parameter decoupling (PD)-based tensor multiparameter estimation algorithm. This algorithm can suppress the noises in all of temporal, spatial and frequency domains, and thus all the parameters can be precisely estimated. A simplified perturbation term (S-PT)-based method is also presented to match the estimated parameters at low complexity. Based on the quasi-optical property of mmWave signals, we propose a novel method to compute the 3D coordinates of the target. Simulation results demonstrate the effectiveness of the proposed positioning method in the end.
PAMOGK: a pathway graph kernel-based multiomics approach for patient clustering

MOTIVATION Accurate classification of patients into molecular subgroups is critical for the development of effective therapeutics and for deciphering what drives these subgroups to cancer. The availability of multi-omics data catalogs for large cohorts of cancer patients provides multiple views into the molecular biology of the tumors with unprecedented resolution. RESULTS We develop PAMOGK (Pathway based Multi Omic Graph Kernel clustering) that integrates multi-omics patient data with existing biological knowledge on pathways. We develop a novel graph kernel that evaluates patient similarities based on a single molecular alteration type in the context of a pathway. To corroborate multiple views of patients evaluated by hundreds of pathways and molecular alteration combinations, we use multi-view kernel clustering. Applying PAMOGK to kidney renal clear cell carcinoma (KIRC) patients results in four clusters with significantly different survival times (p-value = 1.24e-11). When we compare PAMOGK to eight other state-of-the-art multi-omics clustering methods, PAMOGK consistently outperforms these in terms of its ability to partition KIRC patients into groups with different survival distributions. The discovered patient subgroups also differ with respect to other clinical parameters such as tumor stage and grade, and primary tumor and metastasis tumor spreads. The pathways identified as important are highly relevant to KIRC. AVAILABILITY github.com/tastanlab/pamogk. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Outage Analysis of Hop-by-Hop Relay Selection in Multi-Hop Cognitive Relay Networks

We propose a hop-by-hop relay selection strategy for multi-hop decode-and-forward (DF) underlay cognitive relay networks (CRNs). In this strategy, taking both maximum transmit power and maximum interference constraints into account, one relay in each decoding set is selected to transmit to the next hop. Relay selection at each stage only depends on the channel state information (CSI) of the following hop. We analyze the performance of the proposed strategy in terms of end-to-end outage probability. Numerical results are presented from analysis which closely match those obtained from simulation. The results are compared to those from other hop-by-hop strategies, showing improved outage probability. Moreover, the numerical results show that the outage probability of the proposed method is very close to that of exhaustive strategy which provides a lower bound for outage probability of all relay selection methods.
An Intelligent Mechanism for Sybil Attacks Detection in VANETs

Vehicular Ad Hoc Networks (VANETs) have a strategic goal to achieve service delivery in roads and smart cities, considering the integration and communication between vehicles, sensors and fixed road-side components (routers, gateways and services). VANETs have singular characteristics such as fast mobile nodes, self-organization, distributed network and frequently changing topology. Despite the recent evolution of VANETs, security, data integrity and users privacy information are major concerns, since attacks prevention is still open issue. One of the most dangerous attacks in VANETs is the Sybil, which forges false identities in the network to disrupt compromise the communication between the network nodes. Sybil attacks affect the service delivery related to road safety, traffic congestion, multimedia entertainment and others. Thus, VANETs claim for security mechanism to prevent Sybil attacks. Within this context, this paper proposes a mechanism, called SyDVELM, to detect Sybil attacks in VANETs based on artificial intelligence techniques. The SyDVELM mechanism uses Extreme Learning Machine (ELM) with occasional features of vehicular nodes, minimizing the identification time, maximizing the detection accuracy and improving the scalability. The results suggest that the suitability of SyDVELM mechanism to mitigate Sybil attacks and to maintain the service delivery in VANETs.
A Spatial-Motion Assist-as-Needed Controller for the Passive, Active, and Resistive Robot-Aided Rehabilitation of the Wrist

Demand for robot-assisted therapy has increased at every stage of the neurorehabilitation recovery. This paper presents a controller that is suitable for the assist-as-needed (AAN) training of the wrist when performing the spatial motion. A compact wrist exoskeleton robot is presented to realize the AAN controller. This wrist robot includes series elastic actuators with high torque-to-weight ratios to provide accurate force control required for the AAN controller. In addition to assist-as-needed rehabilitation, the parameters of the AAN controller can be adjusted to deliver passive, active, or resistive rehabilitation. Experimental results demonstrate that the proposed AAN controller can provide the total solution to cover each stage of wrist spatial-motion rehabilitation.
Topic Modeling and User Network Analysis on Twitter during World Lupus Awareness Day

Twitter is increasingly used by individuals and organizations to broadcast their feelings and practices, providing access to samples of spontaneously expressed opinions on all sorts of themes. Social media offers an additional source of data to unlock information supporting new insights disclosures, particularly for public health purposes. Systemic lupus erythematosus (SLE) is a complex, systemic autoimmune disease that remains a major challenge in therapeutic diagnostic and treatment management. When supporting patients with such a complex disease, sharing information through social media can play an important role in creating better healthcare services. This study explores the nature of topics posted by users and organizations on Twitter during world Lupus day to extract latent topics that occur in tweet texts and to identify what information is most commonly discussed among users. We identified online influencers and opinion leaders who discussed different topics. During this analysis, we found two different types of influencers that employed different narratives about the communities they belong to. Therefore, this study identifies hidden information for healthcare decision-makers and provides a detailed model of the implications for healthcare organizations to detect, understand, and define hidden content behind large collections of text.
Hepatoprotective and antioxidant effect of Carissa spinarum root extract against CCl4 and paracetamol-induced hepatic damage in rats

Ethanolic extract of the roots of C. spinarum was evaluated for hepatoprotective and anti-oxidant activities in rats. Oral pre-treatment with ethanolic extract (100, 200 and 400 mg/kg) showed significant hepatoprotective activity against CCl 4 and paracetamol-induced hepatotoxicity by decreasing the activities of bilirubin and lipid peroxidation, and significant increase in the levels of uric acid, glutathione, super oxide dismutase, catalase and protein in a dose dependent manner, which was confirmed by the decrease in liver wet weight and histopathological examination. The extract possessed strong anti-oxidant activity. This suggests that the hepatoprotective activity of C. spinarum is possibly attributed to its free radical scavenging properties.