Miracle-dz/UniFAR-FTUs huggingface dataset:
An Empirical Study of the Acceptance of IoT-Based Smart Meter in Malaysia: The Effect of Electricity-Saving Knowledge and Environmental Awareness

Today’s increasing demand for electricity requires solutions that better align with energy demand and supply. Innovative technological solutions such as smart metering applications are gaining popularity among electricity providers. Despite numerous benefits, smart meters, a part of the technology on the Internet of Things (IoT), continue to struggle for widespread consumer acceptance due to limited knowledge on electricity savings and environmental awareness. These factors were examined in isolation and have not been theoretically incorporated or examined. Hence, this study investigates the factors that influence residential consumers’ acceptance of smart meters by integrating electricity-saving knowledge and environmental awareness with the second generation of “unified theory of acceptance and use of technology” (UTAUT2). The literature revealed an important link between users’ behavioural intention and users’ use behaviour. Well-established theories of acceptance like “technology acceptance model” (TAM) and UTAUT, incorporate the behavioural intention variable in the nomological network of technology adoption determinants. This study highlighted the impact of users’ behavioural intention on users’ use behaviour, which was not examined previously by any of the smart meter acceptance models. The data were collected from 318 consumers of residential smart meters in Putrajaya and Malacca, the cities in Malaysia, and were statistically tested using SME-PLS The study confirms that adding electricity-saving knowledge and environmental awareness to the UTAUT2 leads to a significant increase in the explained variance in consumer acceptance of smart meter.
HUBUNGAN MOTIVASI PERAWAT DENGAN KINERJA PERAWAT DI RUANG RAWAT INAP RUMAH SAKIT DAERAH PANEMBAHAN SENOPATI BANTUL TAHUN 2008

Background: Hospital was an organization that is found because of the society demands which are more complex and they have realized that health is very important. It makes the hospital must gives the optimal services for the society. Nursing services in the hospital are the main factor to decide the service quality and the image hospital on the society, because the greater society need of the good services the higher nurses’ working motivation will be needed in order to improve their performance optimally. The purpose of the study is to find out the relation between nurses’ motivation and performance in Hospital Ward in Panembahan Senopati Bantul. Method: This was a survey analytical research using crosssectional approach to find out the relation between nurses’ motivation and performance at the same time. The instrument was questioners that consist of the nurse characteristics and questions on nurses’ motivation and performance. Results: The finding analysis using product moment correlation with significance score is 5 % (p < 0.005) showing there was a significance relation between internal motivation and external motivation factors (p = 0,000) with nurses’ performance. There was a relation between the sub-variable responsible (p=0,001), acknowledgment (p=0,000), achievement (p=0,000), development (p=0,000), task (p=0,033), supervise (p=0,001), work condition (p=0,000), organization policy (p=0,000), salary (p=0,000) and nurses’ performance. However, there is no significance relation between colleagues and nurses’ performance. The result of double regression analysis shows the relation between internal and external motivation factor (p=0,000) and nurses’ performance. While the sub-variable achievement (p=0,005), development (p=0,003), work condition (p=0,004), and organization policy (p=0,000) have a not significance relation with nurses’ performance, but the others (responsible (p=0,734), acknowledgment (p=0,320), task (p=0,145), supervise (p=0,268), colleagues (p=0,773) and salary (p=0,118). Conclusion: This research showed that the internal and external motivation factors were good, so it must be maintained to keep the effect on nurses’ motivation. According to the research findings, it also needs to increase the nurses’ motivation so that it can increase the nurses’ performance in order to get the hospital goal. It can be done by developing the nurses’ resource by doing workshop or getting higher education. Keywords: motivation, performance, nurse
Increasing exercise intensity during walking by sound intervention using real-time gait event detection

Brisk walking is efficient for maintaining health and reducing the risk of health problem, yet keeping the pace of fast walking consciously is difficult. A walking assist for increasing the walking intensity in an unconscious manner is necessary such as sound intervention during walking. So far, previous research have not focused on the changes in walking intensity induced by the sound intervention. Thus, we prepare the two types of intervention (static intervention defined before walking and real-time intervention based on the real-time gait measurement) and Figure out the difference in the exercise intensity between them. The real-time intervention is realized by a shoe-attached small gait measurement module composed of an inertial measurement unit (IMU) and custom-made firmware running on a microcomputer, and a smartphone that generates the beep sound based on the real-time gait measurement. We conducted preliminary experiments for three healthy males. Among two interventions, the significant increases in the walking speed were observed in the real-time intervention. This result clearly demonstrates that the real-time intervention is effective to increase the walking speed in an unconscious manner.
Hybrid Convolutional Recurrent Neural Networks Outperform CNN and RNN in Task-state EEG Detection for Parkinson's Disease

In hospitals, brain-related disorders such as Parkinson's disease (PD) could be diagnosed by analyzing electroencephalograms (EEG). However, conventional EEG-based diagnosis for PD relies on handcrafted feature extraction, which is laborious and time-consuming. With the emergence of deep learning, automated analysis of EEG signals can be realized by exploring the inherent information in data, and outputting the results of classification from the hidden layer. In the present study, four deep learning algorithm architectures, including two convention deep learning models (convolutional neural network, CNN; and recurrent neural network, RNN) and two hybrid convolutional recurrent neural networks (2D-CNN-RNN and 3D-CNN-RNN), were designed to detect PD based on task-state EEG signals. Our results showed that the hybrid models outperformed conventional ones (fivefold average accuracy: 3D-CNN-RNN 82.89%, 2D-CNN-RNN 81.13%, CNN 80.89%, and RNN 76.00%) as they combine the strong modeling power of CNN in temporal feature extraction, and the advantage of RNN in processing sequential information. This study represents the an attempt to use hybrid convolutional recurrent neural networks in classifying PD and normal take-state EEG signals, which carries important implications to the clinical practice.
Analysis of Outage Probability and Rate Coverage in Heterogeneous Cellular Network with Joint Uniform and Clustered Users

A heterogeneous cellular network (HCNet) is one of the key enabling technologies to improve performance gain in future cellular networks. In the literature, the location of users is modeled according to homogeneous Poisson point process (HPPP), which is not a suitable assumption in HCNets. In this paper, the users in the network are assumed to be a set of uniformly distributed users and clustered users, which are modeled according to HPPP and Poisson cluster process (PCP), respectively. The small base stations (SBSs) are deployed at the center of user cluster. We evaluate the network performance in terms of outage probability and rate coverage of the proposed HCNet model. The results show that the proposed model surpasses the conventional uniformly distributed users model.
Adversarial Data Augmentation Network for Speech Emotion Recognition

Insufficient data is a common issue in training deep learning models. With the introduction of generative adversarial networks (GANs), data augmentation has become a promising solution to this problem. This paper investigates whether data augmentation can help improve speech emotion recognition. Unlike conventional GANs, we train a GAN with an autoencoder, where the input to the discriminator comes from the bottleneck layer of the autoencoder and the output of the generator. The synthetic samples can be obtained from the decoder, using the output of the generator as the decoder's input. The combined network, namely adversarial data augmentation network (ADAN), can generate samples that share common latent representation with the real data. Evaluations on EmoDB and IEMOCAP show that using OpenSmile features as input, the ADAN can produce augmented data that make an ordinary SVM classifier outperforms an RNN classifier with local attention and make a DNN competitive to some state-of-the art systems.
Reliable and Secure Pre-Coding OFDM-DCSK Design for Practical Cognitive Radio Systems With the Carrier Frequency Offset

In this paper, we present a pre-coding (PC) orthogonal frequency division multiplexing-aided differential chaos shift keying (OFDM-DCSK) scheme over non-contiguous (NC) spectrum bands for cognitive radio (CR) systems. In practical CR systems, the offset of fundamental clocks of transceivers or non-ideal components induce the carrier frequency offset (CFO), which will reduce the spectrum sensing accuracy and degrade reliability performances. Additionally, CR systems probably suffer from eavesdropping or malicious attacks due to the broadcasting property of wireless channels. With the aim to simultaneously combat the CFO and to enhance the security performance, in our OFDM-DCSK design, the information bits are modulated by reference chaotic sequences, then the data-bearing chaotic symbols and reference symbols are delivered to the pre-coding module, wherein we interleave chaotic chips, add the chaotic mask and duplicate chips. After OFDM modulations, the chaotic signals are transmitted over channels. At the receiver, reverse operations are performed. Moreover, we derive the theoretical bit error rate (BER) and information leakage expressions. Simulations are provided to verify the effectiveness of our derivations, and to demonstrate that more reliable and secure performances can be achieved by the presented system compared with counterpart systems.
Factors influencing the intentions of customer with regard to the use of E-WOM behavior to promote the use of E-commerce websites

Information sharing between businesses and consumers on online platforms has attracted a lot of interest especially with regard to electronic word-of-mouth (e-WOM) communication. This study analyzes factors that influence the intentions of customer with regard to the use of electronic word-of-mouth behavior on platforms and e-commerce websites. In this paper we analyze results statistically to confirm a causal relationship between factors using partial least squares structural equation modeling (PLS-SEM). The results show that the developed model can be used to explain the causal relationship of the factors that affect the intention of customer with regard to the use e-WOM behavior to promote the use of e-commerce websites on the part of community enterprises. The perceived security risk and web interactivity plays an important role in creating customer trust in the use of the e-commerce websites of community enterprises. Trust (TRS) and Web Interactivity (WEB) influence the intention of customer with regard to the use of e-WOM behavior to promote the usage of the e-commerce websites of community enterprises.
Shared LTE Network Performance on Smart Grid and Typical Traffic Schemes

This paper investigates the possibility of delivering distinct smart grid (SG) demand response (DR) applications in a highly loaded LTE network. In a shared LTE network, the proportion of SG DR traffic is relatively low when compared to typical traffics such as voice over IP, Skype video call, FTP, Youtube video stream, and HTTP. The quality of service (QoS) requirements for the SG DR traffics have to be fulfilled by maintaining the network delays and the packet delivery ratios within certain limits, while not causing significant hindrance to the typical traffics. The Riverbed Modeler network simulations are performed using detailed physical layer propagation models, detailed LTE functionality, and a suburban topology. In the simulation scenarios, three distinct DR applications generate varying amounts of SG DR traffic to the LTE network while the LTE capacity is exceeded by the typical traffics. The results illustrate that satisfactory performance for the SG DR traffics can be maintained due to the constant traffic characteristics and relatively low traffic amount that facilitates the scheduling of channel resources. Typically, the more a DR application generates traffic the higher hindrance it causes for the typical traffics other than the voice over IP that applies the QoS class of highest priority.
See the Future: A Semantic Segmentation Network Predicting Ego-Vehicle Trajectory With a Single Monocular Camera

Ego-vehicle trajectory prediction is important for autonomous vehicles to detect collisions and accordingly avoid accidents. Recent approaches employ prior-known or on-line acquired road topology or geometries as motion constraints for their predictive models. However, the prior-known information (e.g., pre-built maps) might become unreliable due to, for example, temporal changes caused by road constructions. Whereas on-line perception may require high-cost sensors, such as large filed-of-view laser scanners, to get an overview structure of the local environment, making the prediction difficult to afford, especially for driving assistance systems. So in this letter, we provide a solution without using road topology or geometries for ego-vehicle trajectory prediction. We formulate this problem as a two-class semantic segmentation problem and develop a novel sequence-based deep neural network to predict the trajectory. The only sensor we need during runtime is a single front-view monocular camera. The inputs to our network are several consecutive images, and the output is the predicted trajectory mask that can be directly overlaid on the current front-view image. We create our datasets with different prediction horizons from KITTI. The experimental results confirm the effectiveness of our approach and the superiority over the baselines.
Measuring the Hazard of Malicious Nodes in Information Diffusion over Social Networks

Social networks have become prevalent in our daily life: people learn, discuss, and spread different kinds of information through social networks every day. While bringing a lot of convenience, the prevalence of social networks creates the security challenge. The information released and/or spread by malicious nodes can be wrong, misleading or even virus, which may lead to bad influences and severe consequences. Therefore, understanding the information diffusion process and the hazard impact of malicious nodes' behaviors to the information diffusion is critical. In this paper, we utilize the evolutionary game theory to measure the hazard influence of malicious nodes to the information diffusion over social networks, by investigating the information diffusion dynamics and evolutionary stable strategies. Finally, simulations are conducted to validate the theoretic analysis and illustrate the impact of the malicious nodes.
Enhanced BAN-GZKP: Optimal Zero Knowledge Proof Based Scheme in Body Area Networks

A network of embedded sensors on the human body called Wireless Body Area Network (WBAN) has recently emerged as a healthcare monitoring framework, to provide better medical services. The data collected by these sensors is transmitted via a wireless medium and contains sensitive information of the patients. Therefore, how to provide security schemes for WBAN with resource constraints devices remains a big challenge. Recently, BAN-GZKP, an authentication scheme based on Zero-Knowledge Proof (ZKP) was designed for WBAN as an optimal solution to several attacks suffered by another ZKP based protocol called BANZKP. However, BAN-GZKP is found to be vulnerable to Node Compromise Attack, Node Impersonation, and Denial-of-Service Attacks. To fix the vulnerabilities of BANGZKP, this paper proposes an enhanced BAN-GZKP which exploits a unique physical layer characteristic coming from the surrounding WBAN, i.e., the distinct received signal strength variation among on-body channels and between on-body and off-body channels, to ensure robust authentication. To prove the reliability of our proposal, we conducted real-world experiments on 3 subjects in indoor and outdoor areas. The results showed that our scheme improves the security of the previous scheme with even lesser cost.
Designing Digital Beamformer in Massive MIMO Systems Using Particle Swarm Optimization

This paper presents the application of particle swarm optimization to synthesize the beam pattern of digital beamformer using different type of planar antenna arrays in massive MIMO systems. Particle Swarm Optimization (PSO) is one of the most famous Swarm Intelligence algorithm. There are a number of variants of PSO according to distinct criteria, Comprehensive Learning Particle Swarm Optimization (CLPSO) and Multiple Objective Particle Swarm Optimization (MOPSO) are two suitable algorithms which are implemented to solve some optimization problems of antenna element location. In this paper, the modified MOPSO is proposed to solve the problem of optimizing antenna element location in massive MIMO systems. The desired objectives of optimization process are decreasing the Maximum Side Lobe (MSL) and decreasing the Half Power Beam Width (HPBW) while maximizing Signal to Interference Ratio (SIR), as they are vitally important parameters showing the quality of a digital beamformer.
QR Approximation for Fronthaul Compression in Uplink Massive MIMO

Massive MIMO's immense potential to expand the capacity of base stations also comes with the caveat of requiring tremendous processing power. This favours a centralized radio access network (C-RAN) architecture that concentrates the processing power at a common baseband unit (BBU) connected to multiple remote radio heads (RRH) via fronthaul links. The large bandwidths of 5G make the fronthaul data rate a major bottleneck. Since the number of active users in a massive MIMO system is much smaller than the number of antennas, we propose a dimension reduction scheme based on QR approximation for fronthaul data compression. Link level simulations show that the proposed method achieves more than 17Ã- compression while also improving the error performance of the system through denoising.
F3Hand II: A Flexible Five-Fingered Prosthetic Hand Using Curved Pneumatic Artificial Muscles

Various five-fingered prosthetic hands have been commercialized and studied to support daily living of forearm amputees. However, most of them are more than 900 g because of several actuators and a complicated mechanism. In addition, they need a fitting mechanism because their fingers with high rigidity are difficult to fit various objects to be grasped. In this paper, we reported a five-fingered prosthetic hand termed as ”F3Hand II”. Flexible grasp and natural motions were achieved by using curved pneumatic artificial muscles (PAMs) as a skeleton and an actuator of its finger. Each finger of the hand had double joints to follow the movement of the PAM. Multiple degrees of freedom of the carpometacarpal (CM) joint in the thumb allowed various grasping patterns manually. Its weight including a socket was 245 g. We improved the durability of the finger joints, the interface of the drive unit, the mechanism of the wrist joint, the appearance of the F3Hand. An evaluation using a Southampton Hand Assessment Procedure (SHAP) test and a test on the manipulation of the daily objects demonstrated that an amputee was able to manipulate various objects.
Deep Reinforcement Learning based Intrusion Detection System for Cloud Infrastructure

Intrusion Detection in cloud platform is a challenging problem due to its extensive usage and distributed nature that are constant targets of new and unknown attacks. Intrusion detection system (IDS) is responsible for monitoring and detecting malicious activities in any computing system or a network. However, most of the traditional cloud IDSs are vulnerable to novel attacks. Also, they are incapable of maintaining a balance between high accuracy and less false positive rate (FPR). In this paper, we propose a deep reinforcement learning-based adaptive cloud IDS architecture that addresses the above limitations and performs accurate detection and fine-grained classification of new and complex attacks. We have done extensive experimentation using the benchmark UNSW-NB15 dataset that shows better accuracy and less FPR compared to the state-of-the-art IDSs.
A Highly Reliable and Energy Efficient Radiation Hardened 12T SRAM Cell Design

In this paper, a novel energy efficient 12T memory cell is proposed which is radiation hardened by design (RHD) to tolerate single-event multiple-node upsets (SEMNU) in near threshold voltage regime. The radiation hardness of the proposed memory cell is improved by controlling the cross coupled inverters’ PMOS devices through dummy access transistors. We validated the proposed memory cell in STMicroelectronics 65-nm CMOS technology. The post layout parasitic extracted simulations show that by employing the proposed RHD-12T memory cell, an average improvement of ~ 42%, 17%, 17%/9% and 6%/7% in layout area, power dissipation, read/write access time, and read/write static noise margin, respectively, is obtained over the recently reported 12T memory cell at supply voltage of 0.4V. We also validated the proposed memory cell at 32-nm CMOS technology node using technology computer-aided design (TCAD) mixed-mode simulations. In the 32-nm technology, the proposed RHD-12T memory cell shows an average improvement of ~15%, 10%/56%, and 8%/10% in power dissipation, read/write access time, and read/write static noise margin, respectively, over the 12T memory cell at supply voltage of 0.3V. Combining layout-topology, HSPICE post layout simulations and TCAD mixed mode simulation results clearly show that the proposed memory cell effectively tolerates single event upset as well as SEMNU. In 32nm technology our memory cell can provide SEMNU tolerance up to the value of LET equals to 62 Mev-cm2/mg.
A LoRaWAN based Open Source IOT Solution for Monitoring Rural Electrification Policy

The Government of India has implemented Solar PV based microgrid Decentralized Distributed Generation (DDG) projects in different parts of the country. The monitoring of the performance of DDG projects is a manual process with reliance on the data provided by the system integrators who oversee the DDG projects. With the advent of the Internet of Things (IOT), the monitoring & evaluation can be improved dramatically by making it objective, and also to monitor the performance of the policy progress on a near real-time basis. The characteristics of an LPWAN technology viz., LoRa that makes it an appropriate communication technology for remote monitoring of DDG projects are: Long-range, Low power, Small data requirements, and flexibility to operate in unlicensed spectrum. An open-source Internet of Things (IOT) solution is proposed here to monitor and evaluate the rural electrification projects. The methodology involves the development of a low-cost prototype and an open-source based solution to monitor the performance of a DC-based stand-alone solar photovoltaic system, which was set up in the IIT Madras campus. The integration across the LoRaWAn architecture was demonstrated, and a visualization dashboard was created in the Ubidots IOT platform to monitor basic parameters such as voltage and current. The goal is to monitor the solar DC system in terms of the objectives set in DDG policy.