Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the Performance of Deep Neural Networks Using Two Proposed Activation Functions

In artificial neural networks, activation functions play a significant role in the learning process. Choosing the proper activation function is a major factor in achieving a successful learning performance. Many activation functions are sufficient universal approximators, but their performance is lacking. Thus, many efforts have been directed toward activation functions to improve the learning performance of artificial neural networks. However, the learning process involves many challenges, such as saturation, dying, and exploding/vanishing the gradient problems. The contribution of this work resides in several axes. First, we introduce two novel activation functions: absolute linear units and inverse polynomial linear units. Both activation functions are augmented by an adjustable parameter that controls the slope of the gradient. Second, we present a comprehensive study and a taxonomy of various types of activation functions. Third, we conduct a broad range of experiments on several deep neural architecture models with consideration of network type and depth. Fourth, we evaluate the proposed activation functions’ performance in image and text classification tasks. For this purpose, several public benchmark datasets are utilized to evaluate and compare the performance of the proposed functions with that of a group of common activation functions. Finally, we deeply analyze the impact of several common activation functions on deep network architectures. Results reveal that the proposed functions outperform most of the popular activation functions in several benchmarks. The statistical study of the overall experiments on both classification categories indicates that the proposed activation functions are robust and superior among all the competitive activation functions in terms of average accuracy.
A Systematic Analysis on Blockchain Integration With Healthcare Domain: Scope and Challenges

A secure, smart and peer-to-peer transactions framework can be developed by Blockchain. Blockchain has enormous potential to turn health care systems as a horizontal technology, which has changed many fields of industry. The aim of this article is to critically review 50 papers published between 2015 and 2020 on blockchain-based health systems. 36 of these were journal papers; 7 were from conferences, 4 were from various symposiums; 3 were from seminars and 1 chapter was written in the book. Three key questions will be answered in this report. Firstly, what are the emerging trends of blockchain application development in healthcare from a technical perspective? Secondly, in what ways will the systematic analysis presented here contribute to a better understanding of the potential for incorporating blockchain-based technologies into the healthcare domain? Third, what are the important challenges in adopting blockchain as a solution in the healthcare domain? The descriptive analysis contains in this article shows the statistical statistics on the strategies of these 50 papers reveal that many of the blockchain systems proposed are using privately held blockchain and Ethereum platforms. We also address possible emerging trends of blockchain application such as blockchain integration with artificial intelligence, cloud based solutions and parallel block chain architecture.
Collective Perception service for Connected Vehicles and Roadside Infrastructure

Collective Perception Service (CPS) is under standardisation at European Telecommunications Standardisation Institute (ETSI) with the objective of enabling sensor-equipped vehicles and roadside units (RSU) to notify nearby vehicles of the objects detected by their sensors. This service is expected to significantly improve perception of vehicles and, hence, improve road safety. While both vehicles and RSUs can contribute in CPS, they have very different characteristics in terms of mobility, sensor field of view (FOV), communication coverage, processing capacity, and service integration cost. This paper compares CPS provided by vehicles and by RSUs, particularly their impacts on extending vehicles’ perception. We will first develop an analytical model that formulates the number of objects perceived by vehicles and RSUs and the success probability of CPM targeting the IEEE 802.11p technology. The analytical results will then be confirmed by simulation evaluations conducted using the VEINS simulator. The analytical and simulation results reveal that, RSU-assisted CPS significantly outperforms vehicle-assisted CPS, providing up to 8-times higher effective number of perceived objects. The results suggest a great opportunity of optimising radio resource utilisation without degrading collective perception by exploiting roadside infrastructure.
Deep Face Age Progression: A Survey

Face Age Progression (FAP) refers to synthesizing face images while simulating ageing effects, thus enabling predicting the future appearance of an individual. The generation of age-progressed face images brings benefits for various applications, ranging from face recognition systems to forensic investigations and digital entertainment. In particular, the recent success achieved with deep generative networks significantly leveraged the quality of age-synthesized face images in terms of visual fidelity, ageing accuracy and identity preservation. However, the high number of contributions in recent years requires systematically structuring new findings and ideas to identify a common taxonomy, accelerate future research and reduce redundancy. Therefore, we present a comparative analysis of recent deep learning based face age progression methods for both adult and child-based face ageing, broken down into three high-level concepts: translation-based, condition-based, and sequence-based FAP. Further, we offer a comprehensive summary of the most common performance evaluation techniques, cross-age datasets, and open challenges to steer future research in the right direction.
A Collaborative Task Offloading Scheme in Vehicular Edge Computing

The increase of mobile applications in the internet of vehicles (IoVs), necessitates the demand for higher computation capabilities. Vehicles can transfer related applications to another nodes for processing. In this paper, an efficient task offloading scheme for cellular vehicle to everything (C-V2X) is proposed to improve offloading reliability and latency. Vehicles are grouped into clusters, where vehicles in need of assistance can transfer their task to other vehicles for processing through the vehicle to vehicle (V2V) link, or transfer their task to the mobile edge computing (MEC) server via the vehicle to network (V2N) link. Matching theory is exploited for the task assignments. Simulation results reveals that the proposed scheme performs better than the existing schemes.
A Hybrid Personalized Scientific Paper Recommendation Approach Integrating Public Contextual Metadata

Rapid increase in scholarly publications on the web has posed a new challenge to the researchers in finding highly relevant and important research articles associated with a particular area of interest. Even a highly relevant paper is sometimes missed especially for novice researchers due to lack of knowledge and experience in finding and accessing the most suitable articles. Scholarly recommender system is a very appropriate tool for this purpose that can enable researchers to locate relevant publications easily and quickly. However, the main downside of the existing approaches is that their effectiveness is dependent on priori user profiles and thus, they cannot recommend papers to the new users. Furthermore, the system uses both public and non-public metadata and therefore, the system is unable to find similarities between papers efficiently due to copyright restrictions. Considering the above challenges, in this research work, a novel hybrid approach is proposed that separately combines a Content Based Filtering (CBF) recommender module and a Collaborative Filtering (CF) recommender module. Unlike previous CBF and CF approaches, public contextual metadata and paper-citation relationship information are effectively incorporated into these two approaches separately to enhance the recommendation accuracy. In order to verify the effectiveness of the proposed approach, publicly available datasets were employed. Experimental results demonstrate that the proposed approach outperforms the baseline approaches in terms of standard metrics (precision, recall, F1-measure, mean average precision, and mean reciprocal rank), indicating that the proposed approach is more efficient in recommending scholarly publications.
Effect of GroEL-like heat shock protein on epithelial behavior

This research work focused on the effect of heat shock protein 60 (hsp60) from a periodontopathogen Actinobacillus actinomycetemcomitans on keratinocyte behavior and regulation of cell signaling. The research project encompasses four aspects. In the first study, we investigated whether bacterial hsp60 can alter epithelial cell mitogen-activated protein kinase (MAPK) signaling and cell proliferation. The results showed that hsp60 greatly increased viable cell number in culture. Hsp60-induced phosphorylation of ERK, JNK, and p38 was doseand time-dependent. The association between hsp60-induced cell proliferation and activation of E R K and p38 was determined by PD 98059 (a specific inhibitor of M E K ) and SB 203580 (a specific inhibitor of p38). The results indicated that hsp60 was able to induce phosphorylation of ERK1/2 and thereby promote keratinocyte proliferation. In the second study, the effect of bacterial hsp60 on stress-induced cell death was investigated. The results showed that hsp60 significantly protected against U V radiationand heat shockinduced cell death. Hsp60 pretreatment strongly induced activation of ERK1/2 and inhibited U V radiation-induced activation of caspase 3, an excecutioner enzyme of apoptosis. E R K pathway specific inhibitor partially blocks this hsp60 inhibitory effect. The results indicated that U V radiation induces apoptosis through the p38/caspase3 pathway and hsp60 protects keratinocytes from apoptosis by activation of ERK, which in turn inhibits stress-induced caspase 3 activation. In the third study, long-term effects of hsp60 on keratinocytes were studied by measuring total and viable cell number, production of TNFoc, synthesis of cellular hsp60 and hsp70, and M A P K phosphorylation. The results showed that in a prolonged culture, hsp60 increased cell death, reduced the ratio of phosphorylated ERK:p38, increased T N F a production, and inhibited heat shock-induced cellular hsp60 synthesis. In the fourth study, the effect of hsp60s on keratinocyte migration was investigated. The results showed that hsp60 from bacteria and human promoted cell migration by 2-5-fold. Hsp60 specifically reduced a6 integrin subunit expression and cell surface levels that may facilitate tail detachment and migration of keratinocytes. The pretreatment of the cells with SB 203580 and
Computation-efficient Hybrid Offloading for Backscatter-assisted Wirelessly Powered MEC

Computation efficiency (CE) is crucial to mobile edge computing (MEC) for intelligent Internet of Things (IoT) applications, in addition to energy efficiency. To improve CE, this paper designs a backscatter-assisted wireless powered MEC network, where IoT terminals can partially offload their computation tasks via hybrid offloading through harvest-then-transmit protocol and/or backscatter communications. In particular, a CE maximization problem is formulated from a system perspective and an iterative algorithm is developed to tackle the problem, by using the Dinkelbach and Lagrangian duality methods. Simulation results demonstrate the superiority of the proposed scheme over competing ones and the flexibility in achieving trade-offs between different computation and communication modes.
Inductorless Multi-Mode RF-CMOS Low Noise Amplifier Dedicated to Ultra Low Power Applications

This work presents and analyses the design of a multi-mode Low Noise Amplifier (LNA) dedicated to 2.4 GHz Wireless Sensor Network (WSN) applications. The proposed inductorless LNA, implemented in a 28 nm FDSOI CMOS technology, is based on a common-gate configuration imbedded with a common-source stage to boost the overall transconductance of the circuit. The LNA is specifically designed, and optimized, to address three modes of operation. The reconfiguration is performed through current tuning, combined with switching the back gate of the amplification transistors. The proposed implementation allows the figure of merit (FOM) to be maintained constant in the different modes of operation. In the low power mode, the LNA only consumes 350 uW. It achieves a voltage gain ( $\text{G}_{\mathrm {v}}$ ) of 16.8 dB and a noise figure (NF) of 6.6 dB. In the medium performance mode, the gain and the NF are respectively improved to 19.4 dB and 5.4 dB, the power consumption is 0.9 mW. In the high-performance mode, the gain is maximum, 22.9 dB, and the noise figure is minimum, 3.6 dB, for a power consumption of 2 mW. The linearity represented by the input referred third-order intercept point (IIP3) is constant, close to −16 dBm. The reported LNA occupies only 0.0015 mm2.
Energy Efficiency of Multiuser Sparse Massive MIMO System using Orthogonalized Hybrid Beamforming

In this paper, we present an energy efficient two-stage hybrid beamforming for the multiuser massive multiple-input multiple-output (MIMO) downlink system. The millimeter wave (mmWave) technology provides high bandwidth but at the same time suffers from high path-, penetration-, and absorption-losses. In massive MIMO system, the large number of antennas at base-station side compensates the mmWave losses but causes high power consumption in the large number of radio frequency (RF) chains. In this paper, we propose a low complexity orthogonal hybrid beamforming (OHBF) design. We use the Householder reflectors generate the orthogonal analog precoding matrix. It reduces the dimension of the digital precoder as well as the inter-user interference in the beam domain. The proposed Householder based OHBF (HOHBF) scheme provides better energy-efficiency (EE) performance than the Gram-Schmidt based hybrid beamforming design in the real-World ill-conditioned massive MIMO channel. It has been shown that the beam domain orthogonality error remains less than 0.5 with 0.98 probability and the proposed OHBF provides 19.5% improvement in EE at 5 dB SNR as compared to the Gram Schmidt based hybrid beamforming.
Data Provenance in Vehicle Data Chains

With almost every new vehicle being connected, the importance of vehicle data is growing rapidly. Many mobility applications rely on the fusion of data coming from heterogeneous data sources, like vehicle and "smart-city" data or process data generated by systems out of their control. This external data determines much about the behaviour of the relying applications: it impacts the reliability, security and overall quality of the application’s input data and ultimately of the application itself. Hence, knowledge about the provenance of that data is a critical component in any data-driven system. The secure traceability of the data handling along the entire processing chain, which passes through various distinct systems, is critical for the detection and avoidance of misuse and manipulation. In this paper, we introduce a mechanism for establishing secure data provenance in real time, demonstrating an exemplary use-case based on a machine learning model that detects dangerous driving situations. We show with our approach based on W3C decentralized identity standards that data provenance in closed data systems can be effectively achieved using technical standards designed for an open data approach.
A Rectangular Patch Linear 2X2 Array MSPA for IEEE p 802.11 Application

Simulation of the rectangular patch linear 2x2 array antenna by strip line feeding technique is presented for the vehicular communication applications (v2x) under the IEEE p 802.11 & ITU-T X.1372 standards by resonating at 5.94 GHz are discussed in this letter. The Simulation process of the presented array MSPA is carried out in ANSYS Electromagnetic Suite version 20 and analyzed the electromagnetic parameters. This presented array MSPA is simulated using the FR4 dielectric material with the dielectric constant of 4.4 by using this dielectric constant the length & width of the radiating patch is assigned and achieved less signal scattering in transmission with high efficiency in signal radiating with compact dimension of 21 x 25 x 1.6 mm3.
Abnormal Driving Behavior Detection System

The detection of abnormal driving behavior is important for safety. However, driving is a combination of both internal (e.g., skills) and external (e.g., road type, traffic conditions) factors that make abnormal driving behavior detection largely subjective. On the other hand, this subjectivity could be handled through the analysis of driving data at multiple levels including the low-level driving action recognition up to high-level inference of a road section. In this paper, we tackle this problem and propose a Hierarchical Abnormal Driving Behavior Detection System (H-ABDS) that is not only mining individual behavior of vehicles but also runs a statistical learning-based technique to understand human driving data at multiple levels. We design a hierarchical architecture to enable abnormal driving behavior detection at the city-scale and demonstrate its benefits through extensive simulations conducted on simulated traffic data. Our preliminary result has shown that H-ABDS can identify all driving anomalies by about 75% accuracy under a certain degree of connected vehicle penetration rates.
Particle Swarm Optimization Algorithms for Altitude and Transmit Power Adjustments in UAV-Assisted Cellular Networks

After providing ubiquitous and high-speed network connectivity to mobile users, cellular operators are exploring unique domains to extend the reach of cellular networks. In this direction, the use of Unmanned Aerial Vehicles (UAVs) has received significant interest from both industry and academia. UAVs equipped with a transceiver module can act as relays and/or base stations to extend coverage and provide line-of-sight connectivity to mobile users, especially during emergencies such as earthquakes and floods. To reap the gains of UAV-based cellular networks, deployment and operational parameters of UAVs such as altitude and transmit power need to be carefully controlled. In this paper, we propose two algorithms for independently adjusting the altitude and transmit power of UAVs to maximize the system throughput. These algorithms are based on Particle Swarm Optimization and are shown to quickly converge to a better solution when compared to the traditional fixed altitude and fixed transmit power approaches.
Fault-Tolerant Torque Control Based on Common and Differential Mode Modeling for Multi-Three-Phase Induction Machines

Among the multiphase solutions, multi-three-phase drives are experiencing significant industrial development since they can be configured as multiple three-phase units operating in parallel. The literature reports several control approaches to perform the torque regulation of multi-three-phase machines. Most of such solutions use the vector space decomposition (VSD) approach since it allows the control of a multi-three-phase machine using the conventional control schemes of three-phase drives, reducing the complexity of the control algorithm. However, this advantage is practically lost in the case of open-three-phase faults. Indeed, the post-fault operation of the VSD-based drive schemes requires the implementation of additional control modules, often specifically designed for the machine under consideration. Therefore, this paper aims at proposing a novel control approach that allows using any control scheme developed for three-phase motors to perform the torque regulation of a multi-three-phase machine both in healthy and faulty operation. In this way, the previously mentioned drawbacks of the VSD-based control schemes in dealing with the machine's faulty operation are avoided. Moreover, the simplicity of the control algorithm is always preserved regardless of the machine operating condition. The proposed solution has been experimentally validated through a 12-phase induction motor, rated 10 kW at 6000 r/min, which uses a quadruple-three-phase configuration of the stator winding.
Sub-10 nm Nanogap Arrays for the Next-Generation Electronics

Nanogap electrodes (NGEs) with decreasing separations have received growing attention due to their potential as building blocks in the next-generation electronics including nanoelectronics and molecular electronics. Although various NGE-manufacturing methods have been developed and offer promising results, sub-10 nm nanogap arrays connected with macroscopically measurable pad electrodes have not been achieved without compromising high cost, low efficiency, limited materials, mass-fabrication compatibility, or/and failing to connect with macroscopic contacts, which hampers their widespread practical applications. Here, a novel fabricating method was proposed to successfully prepare macroscopically measurable NGE arrays with tunable tip-to-tip separations down to sub-10 nm. The fabrication process is simple, low-cost, lithography-free and CMOS-compatible, which would enable a breakthrough in fabrication of sub-10 nm NGE arrays and promote the development of NGE-related extended applications in various research fields.
A Novel Intelligent SIC Detector for NOMA Systems Based on Deep Learning

In this paper, we propose a novel intelligent successive interference cancellation (SIC) detection algorithm, namely I-SIC, for the uplink non-orthogonal multiple access (NOMA) system. Compared with some traditional SIC detection algorithms based on channel state information (CSI) and quality of service (QoS), the proposed I-SIC can learn the implied characteristics in the received signal, channel state information and power information through deep neural network (DNN), so as to more intelligently provide sorting scheme for SIC detection algorithm and further improve the detection performance of the system. Experimental results show that compared with the traditional SIC detection algorithm based on CSI (CSI-SIC), this algorithm can significantly improve the detection performance of the system(up to 6 dB for three-user scenario with QPSK modulation).
Untargeted Large Volume Hyperthermia Reduces Tumor Drug Uptake From Thermosensitive Liposomes

Goal: The impact of hyperthermia (HT) method on tumor drug uptake with thermosensitive liposomes (TSL) is not well understood. Methods: We created realistic three-dimensional (3-D) computer models that simulate TSL-encapsulated doxorubicin (TSL-DOX) delivery in mouse tumors with three HT methods (thermistor probe (T), laser (L) and water bath (WB), at 15 min and 60 min HT duration), with corroborating in vivo studies. Results: Average computer model-predicted tumor drug concentrations (μg/g) were 8.8(T, 15 min), 21.0(T, 60 min), 14.1(L, 15 min), 25.2(L, 60 min), 9.4(WB, 15 min), and 8.7(WB, 60 min). Tumor fluorescence was increased by 2.6 × (T) and 1.6 × (L) when HT duration was extended from 15 to 60 min (p < 0.05), with no increase for WB HT. Pharmacokinetic analysis confirmed that water bath HT causes rapid depletion of encapsulated TSL-DOX in systemic circulation due to the large heated tissue volume. Conclusions: Untargeted large volume HT causes poor tumor drug uptake from TSL.
Optimal Policies for Age and Distortion in a Discrete-Time Model

We propose a simple model to study the tradeoff between timeliness and distortion, where different pieces of data have a different cost of not being sent. We pose the question of finding the optimal tradeoff as a policy design problem amenable to dynamic programming methods. We study the structural properties of optimal transmission policies, give an algorithmic procedure to find the optimal tradeoff, and numerically evaluate some instances.