Miracle-dz/UniFAR-FTUs huggingface dataset:
Secrecy Performance Analysis of Two-Way Relay Non-Orthogonal Multiple Access Systems

This paper studies physical layer security for two-way relay non-orthogonal multiple access systems. Specifically, by employing a decode-and-forward relay and considering both the maximum-ratio combining and selection combining schemes at the eavesdropper, instantaneous end-to-end signal-to-interference-plus-noise ratios are first formulated and approximated for the interference-limited scenario. Then analytical expressions for the secrecy outage probability and the intercept probability are obtained. Moreover, we analyze the effective secrecy diversity order and demonstrate that the eavesdropper severely degrades the secrecy performance, and even reduces the diversity order to zero. Numerical and simulation results verify our derivations and reveal that, due to information leakage in the first time phase, there exists a secrecy performance floor. It is also demonstrated that the secrecy outage probability performance degrades when the eavesdropper is closer to either of the users. Between the two combining schemes, the results demonstrate that the secrecy outage probability performance is better when the selection combining scheme is employed by the eavesdropper.
Dynamic Hand Gesture Authentication using Electromyography (EMG)

Biometrics has recently been the go-to authentication method in most electronic devices due to its convenience. There are two types of biometrics – static biometrics and dynamic biometrics. Hand gesture can both static and dynamic biometrics. Static hand gesture can be in the form of static hand sign; whereas dynamic hand gesture can be in the form of moving finger, hand, or arm. And due to its continuity, dynamic biometrics is usually correlated with behavioral. Hand gesture can also be a combination of both static and dynamic biometrics, for example continuous hand signs, where the hand sign is the static and the changes from one sign to another is the dynamic. In our research, we will be using the combination of both static and dynamic hand gesture. In order to simulate the worst case scenario, all the participants were asked to perform the same static hand gesture. From the result, it has been shown that the system can discern individuals through their gesture behavior. The accuracy rate from the confusion matrix and equal error rate from receiver operating characteristic (ROC) are above 0.84 and below 0.0701 respectively.
Research on Resolution Limit of Community Detection in Location-Based Social Networks

Location-based social network (LBSN) is a highly used social network at present. Detecting the community structures in LBSN plays a significant role in analyzing the network comprehensively. Modularity optimization is a traditional social network community detection method which has excellent performance. Whereas, resolution limit is an inherent problem of it. Some existing community detection methods for LBSN fail to take account of the full network information. To solve above problems, a network weighting scheme is proposed in this paper. Firstly, we combine a network embedding method that named walk2friends with this paper. In view of the shortcoming of walk2friends, we proposes a collaborative filtering method based on social relationship to improve it. Then, the learned similarity is obtained by improved walk2friends and the structural similarity is obtained by a random walk method. These two similarities are fused and then transformed into edge weight. Finally, the undirected unweighted graph is preprocessed as an undirected weighted graph. Experimental results on real networks show that when using modularity optimization method for optimization optimization the weighting scheme not only can avoid the resolution limit but also applicable to LBSN . The mined community is more cohesive than the existing work on LBSN.
Performance Modelling of Synchronized Predictive Sensing for Clustered Wireless Sensor Networks

The physical constraints in wireless sensor networks can often be a significant compromising factor to their effective performance. To improve energy efficiency, there has been increasing research efforts that investigate employing predictive sensing mechanisms where adaptive clustering schemes and intelligent predictive algorithms are incorporated in data acquisition and transmission. There seems, however, little treatment in the literature on performance modelling of the predictive sensing scenarios. In this paper we examine a few light-weight data prediction algorithms operating in a synchronized cluster setting on some real-world environmental sensing data streams. Besides their capabilities in transmission reduction, we look at the relevant traffic modelling of the sensing scheme. Some empirical results obtained from real-world environment monitoring data are presented and discussed.
Are We Susceptible to Rowhammer? An End-to-End Methodology for Cloud Providers

Cloud providers are concerned that Rowhammer poses a potentially critical threat to their servers, yet today they lack a systematic way to test whether the DRAM used in their servers is vulnerable to Rowhammer attacks. This paper presents an endto-end methodology to determine if cloud servers are susceptible to these attacks. With our methodology, a cloud provider can construct worst-case testing conditions for DRAM.We apply our methodology to three classes of servers from a major cloud provider. Our findings show that none of the CPU instruction sequences used in prior work to mount Rowhammer attacks create worst-case DRAM testing conditions. To address this limitation, we develop an instruction sequence that leverages microarchitectural side-effects to "hammer" DRAM at a near-optimal rate on modern Intel Skylake and Cascade Lake platforms. We also design a DDR4 fault injector that can reverse engineer row adjacency for any DDR4 DIMM. When applied to our cloud provider’s DIMMs, we find that DRAM rows do not always follow a linear map.
High Gain Low Cost Vivaldi Antenna Design Using Double Slits and Triangle Metallic Strip for WiFi Applications

This paper introduces a new configuration of a high gain Vivaldi antenna for WiFi applications. The proposed antenna shows excellent performance from 2.4 GHz to more than 2.5 GHz in bandwidth over 100 MHz. The antenna is structured with a thickness of 1.6 mm on a low cost FR4 substrate material. A conventional exponential tapered slit Vivaldi antenna is presented to cover the proposed bandwidth. In addition, the Vivaldi antenna is reconfigured with Double Slits on the fringes of the exponential metal flaring portion, which increases the antenna gain slightly. Finally, the Vivaldi antenna is loaded by Double Slits and Triangle Metallic Strip on the slit area, which results in improving the antenna peak gain from 7.7 dB to 12.3 dB. The suggested antenna demonstrates high gain, directivity and almost stabilized unidirectional radiation patterns over the proposed range of frequencies. All the design and simulation steps are done by using (CST) Studio Suite. The presented antenna is fabricated, measured, and a good agreement is found between the simulated and measured values.
About Some Methods of Implementing Network Coding based on Polynomial Rings and Polynomial Fields

Network coding is a network technique in which transmitted data are coded and decoded for the purpose of increasing network traffic, reducing latency and making the network more stable. Network coding technique usually uses some mathematical manipulation on the data to minimize the number of transmission sessions between the source nodes and the destination nodes, but it will require more computational processing at intermediate nodes and terminal nodes as well. This paper presents some new methods of implementing network coding that using additive and multiplicative operations on polynomial rings and polynomial fields.
Modified Complementary Joint Sparse Representations: A Novel Post-Filtering to MVDR Beamforming

Post-filtering is a popular technique for multichannel speech enhancement system, in order to further improve the speech quality and intelligibility after beamforming. This paper presents a novel post-filtering to a minimum variance distortionless response (MVDR) beamforming which is a single-channel modified complementary joint sparse representations (M-CJSR) method. First, MVDR beamformer is used to suppress interference and noise. Subsequently, the proposed M-CJSR approach based on joint dictionary learning is applied as a single microphone post-filter to process the beamformer output. Different from the existing post-filtering techniques which rely on the assumptions about the noise field, this algorithm considers a more generalized signal model including the ambient noise, like diffuse noise or white noise, as well as the point-source interference. Moreover, the original CJSR method is extended to jointly learn dictionaries for not only the mappings from mixture to speech and noise, but also the mapping from mixture to interference. In order to take the complementary advantages of different sparse representations, we design the weighting parameters based on the residual components of the estimated signals. An experimental study which consists of objective evaluations under various conditions verifies the superiority of the proposed algorithm compared to other state-of-the-art methods.
Towards the design of ethics aware systems for the Internet of Things

The Internet of Things promises to offer numerous societal benefits by providing a spectrum of user applications. However, ethical ramifications of adopting such pervasive technology on a society-wide scale have not been adequately considered. Smart things endowed with artificial intelligence may carry out decisions that entail ethical consequences. It is assumed that the functioning of a smart device does not involve any ethical responsibility vis-a-vis its application context. Such a perspective may precipitate situations that endanger essential human values or cause physical or emotional harm. Therefore, it is necessary to consider the design of ethics within intelligent systems to safeguard human interests. In order to address these concerns, we propose a novel method based on Boolean algebra that enables a machine to exhibit varying ethical behaviour by employing the concept of ethics categories and ethics modes. Such enhancement of smart things offers a way to design ethically compliant smart devices and paves way for human friendly technology ecosystems.
Transition to professional social work practice: the initial year

This paper presents the findings of the first year of a three-year longitudinal study of new graduate social workers from a Bachelor of Social Work (BSW) program in Aotearoa New Zealand. We compare work outcomes and graduates' perceptions of their readiness for practice against the New Zealand Social Workers Registration Board's (SWRB's) 10 core competencies. This study's impetus came from an increase in the professionally accepted minimum qualification benchmark, recent political commentary on the preparedness of social work graduates, and associated roles of the SWRB and Aotearoa New Zealand Association of Social Work (ANZASW). The aim of this longitudinal research is to track paid and unpaid work outcomes and identify the support needs of social work graduates as they transition from students into professional practitioners. An on-line questionnaire offered graduates the opportunity to comment annually on their professional progress. The respondents all found paid employment as social workers in that first year and identified transitional challenges. Supports to ease this transition included supervision, mentoring, collegiality, coaching, case-load protection (both volume and complexity), continuing professional development, and professional networking. Concluding that the first year of practice is a highly demanding one, we highlight the need for new graduates to have reduced case-loads and additional levels of support. This article is highly relevant for the profession in Aotearoa New Zealand and elsewhere, particularly for countries such as Australia where there is no legislated registration process for social workers.
A Low-Power Capacitance-to-Voltage Converter For MEMS Capacitive Sensors

A new low-power capacitance-to-voltage converter (CVC) for MEMS capacitive sensors is presented. The CVC is implemented using a charge transfer technique, based on a current signal. Its mainly composed of a current mirror and a negative feedback system. The proposed CVC noise performance is less sensitive to parasitic capacitance unlike conventional capacitance-to-voltage converters. The theory of operation of the proposed CVC is presented together with a noise-analysis. The proposed CVC architecture is used to implement an interface circuit in 180nm CMOS technology. Also simulation results are presented to verify the operation of the proposed CVC. The simulation results show that the interface achieves $37\mu \mathbf{W}$ for capacitance range 5.5pF and 1.8V supply CMOS technology.
Detection of R-Peaks Using Single-Scale Wavelet Transform

The wavelet-based algorithm is suitable for the automatic detection of electrocardiogram (ECG). In this paper, we have developed and evaluated an ECG feature extraction system based on a single characteristic scale in the wavelet transform. For validation purposes, we also evaluated the proposed algorithm on MIT-BIH Database (the manually annotated database). Moreover, We have compared our threshold with the Martínez et al. threshold and tested on only a single characteristic scale (a fourth-scale) instead of multiscale. The results of our proposed algorithm and Martínez algorithm for QRS detection in terms of sensitivity and positive predictivity are 92.36%, 96.97% and 87.53%, 96.82% over the validation database respectively. Furthermore, in terms of sensitivity, we have obtained a 5% better than Martínez results.
Average Model based Effective Control Approach for a Grid-Connected PV PUC5 Inverter

This paper presents a novel control approach for a 5-level Packed U Cell (PUC5) inverter based on its average model. The system under study composed of PV arrays connected to the grid through a single-phase PUC5 inverter. The PV system injects an alternative current into the grid with low Total Harmonic Distortion (THD), while balancing power among the inverter cells. A non-linear feedback technique plugged-in with a reduced carriers Pulse Width Modulation (PWM) approach (only 2 PWM carrier signals are employed) is applied to ensure a good tracking quality. Simulation results are presented to prove the effectiveness of the proposed control strategy.
UWB-based System for UAV Localization in GNSS-Denied Environments: Characterization and Dataset

Small unmanned aerial vehicles (UAV) have penetrated multiple domains over the past years. In GNSS-denied or indoor environments, aerial robots require a robust and stable localization system, often with external feedback, in order to fly safely. Motion capture systems are typically utilized indoors when accurate localization is needed. However, these systems are expensive and most require a fixed setup. In this paper, we study and characterize an ultra-wideband (UWB) system for navigation and localization of aerial robots indoors based on Decawave’s DWM1001 UWB node. The system is portable, inexpensive and can be battery powered in its totality. We show the viability of this system for autonomous flight of UAVs, and provide open-source methods and data that enable its widespread application even with movable anchor systems. We characterize the accuracy based on the position of the UAV with respect to the anchors, its altitude and speed, and the distribution of the anchors in space. Finally, we analyze the accuracy of the self-calibration of the anchors’ positions.
Using convolutional neural network to classify convective cloud on radar echoes

Convective weather is one of the main types of weather events causing meteorological disasters. It is significant to distinguishing convective weather for analysis of the subsequent development of the convective region. This paper proposes an automatic classification system to classify convective areas into single and multi-cell convective cloud. Our previous study successfully apply back propagation neural network (BPNN) to divide the cloud reflectivity on the radar echoes into the convective cloud and the stratiform cloud. In this paper, the convective regions are further divide d into single and multi-cell convective cloud by convolutional neural network (CNN). Our model depends on radar reflectivity only, while traditional convection classification methods used a variety of meteorological data, which leads to restricted application. Moreover, in this study, features are automatically learned by CNN. Simulation results show that the proposed algorithm is capable to identify isolated single-cell and multi-cell convective cloud accurately.
New Constructions of Short Length Binary Locally Repairable Codes

In this short paper, our main objective is to construct binary locally repairable codes (LRCs) with good properties. Two constructions of LRCs with short lengthes are proposed. The first one is that define a <inline-formula> <tex-math notation="LaTeX">$u$ </tex-math></inline-formula>-linearly independent set (<inline-formula> <tex-math notation="LaTeX">$u$ </tex-math></inline-formula>-LIS) of an LRC with disjoint repair groups (DRGs) and enlarge it into another one with bigger size to construct new LRCs. The second is puncturing check matrices of known codes to construct new LRCs. As an application, many new binary LRCs are constructed from distance optimal linear codes, which are also locality optimal according to the Cadambe-Mazumdar (C-M) bound.
Disaster-Aware Dynamic Routing for SDN-Based Active-Active Data Center Networks

Data center networks heavily rely on their physical infrastructures. The large-scale disasters may destroy infrastructures and then cause huge data loss or connectivity disruption for data center networks. Software-Defined Networking (SDN) technology can control the network without depending on the physical equipment. Thus SDN facilitates network programmability and opens up new ways to design disaster-resistant networks. Based on the SDN technology, we propose a disaster aware dynamic routing (DADR) scheme. When a disaster signal is received, this triggers the event that the data center router stops advertising via BGP. And the SDN controller recalculates the current optimal routing path of the now-active data center to deal with double traffic. In this paper, we show the design and implementation of our scheme. Our results show that the packet loss rate and delay in the network significantly reduce when a disaster occurs.
Cau Giay: A Dataset for Very Dense Building Extraction from Google Earth Imagery

One of the major topics in photogrammetry is the automated extraction of building from data acquired by airborne sensors. What makes this task challenging is the very heterogeneous appearance and dense distribution of buildings in urban areas. While many dataset has been established, none of them pay attention to developing cities where buildings are not well planned. To complement the development of building extraction algorithms, a dataset of high resolution satellite image is constructed in this paper covering Cau Giay district, Hanoi, Vietnam. The dataset consists of 2100 images of size 1024× 1024 pixels extracted from Google Earth. Shape, size, and construction material differ greatly from building to building, thus make it challenging for state-of-the-art algorithm to accurately extract building location. Some baselines are provided using Convolutional Neural Networks (CNNs). Experimental results show that U-Net model trained with Mean Square Error loss is able to achieve comparable results (OA = 92.04).
Analysis of the effect of sampling strategy: flicking for chemical plume tracing by an autonomous robot

In this paper, we propose an odor sampling strategy with flicking motion for chemical plume tracing(CPT). CPT consists of locating a chemical source according to the chemical particles in the air. The capability of performing CPT by an autonomous robot is highly demanded, yet difficult due to the complex behavior of airflow. From the viewpoint of bio-inspired systems, we focused on the flicking of an insect, by which it swings its antennae (chemical sensors) variably. We expected the flicking provide variable sampling resolutions according to searching phases. We first designed and implemented a variable air sampling mechanism that gave flicking ability to the robot. Then, we introduced the flicking to a common CPT algorithm inspired by the programmed behavior of a silkworm moth, which was composed of three motion phases. For the motion phases, we tested all combinations of the wide or narrow range of the sampling system. Through CPT experiments, results indicated that the flicking could improve CPT performance.
Physical Layer Security in MIMO/NOMA in the multiple UEH relaying network with TAS/MRC

In this paper, we investigate the secrecy outage performance of the Non-orthogonal Multiple Access system (NOMA) in an untrusted relaying energy harvesting (UEH) network with the multiple-input multiple output (MIMO) architecture using the transmit antenna selection/ maximal ratio combining (MRC) techniques. In proposed network, the source communicates with users under the help of multiple UEH network using the power-splitting (PS). In addition, to improve the secrecy outage performance and protect the confidential source signal from the untrusted relaying network, the source works as the jammer with the role generating the artificial noise (AN). For the evaluation of secrecy performance of each user we derive the closed- form expressions of the secrecy outage probability (SOP) over Rayleigh fading. Moreover, comparisons between MIMO/NOMA and MIMO/OMA systems and the effects of various system parameters are examined. The accuracy of analytical results is verified by Monte Carlo simulations.
A Content-Based Method for Sybil Detection in Online Social Networks via Deep Learning

Online social networks (OSNs) are generally susceptible to Sybil attack, which causes a series of cybersecurity problems and privacy violations. Malicious attackers can create massive Sybils and further utilize those fake identities to launch various Sybil attacks. Therefore, Sybil detection in OSNs has become an urgent security research problem for both academia and industries. The existing content-based methods to detect Sybils base on the combination of manual-design features and machine learning algorithms, which requires lots of professional experiences and human effort. These methods divide the Sybil detection problem into two piece-wise sub-problems, which prevents us from getting the optimal solution. In this work, we propose a novel content-based method to detect Sybils. The proposed method is an end-to-end classification model that extracts features directly from the input data, and then output the classification results in a unified framework. The proposed method includes three main parts: first, the self-normalizing convolutional neural network (CNN) is adopted to extract lower features from the multi-dimensional input data; second, the bidirectional self-normalizing LSTM network (bi-SN-LSTM) is developed to extract higher features from the compressed feature map sequence; third, the dense layer and softmax classifier are stacked to output the classification results. Unlike the traditional bidirectional long short-term memory network (bi-LSTM), the proposed bi-SN-LSTM network utilizes SELU as the activation function of its recurrent step, which provides unbounded changes to the state value. Through the case study of the real-world dataset, the comparison experiments demonstrate that our method significantly outperforms other state-of-the-art methods.