Miracle-dz/UniFAR-FTUs huggingface dataset:
QoE-Driven Resource Allocation for SUs with Heterogeneous Traffic using Deep Reinforcement Learning

Quality-of-experience (QoE) driven resource allocation will play a huge role in the deployment of cognitive radio networks (CRNs) towards the 5G era. Cognitive approaches need to exhibit a certain degree of intelligence in order to draw optimal decisions when dealing with heterogeneous traffic. However, with current methods such as reinforcement learning (RL), efficient exploration of the state space still remains a major challenge. The epsilon (e)-greedy exploration strategy suffers from poor convergence since it does not carry out deep exploration. This consequently results in more convergence cycles or exponentially large data requirements. The docitive paradigm, which is an extension of the cognitive paradigm, performs well using RL but it becomes unfeasible when the network becomes distributed with a large number of nodes. Thus, it becomes a huge challenge to perform user experience evaluation of multimedia services on-the-fly since this requires the collection and correlation of a mixture of variables on network conditions, the service, as well as the user. As our first step towards addressing this problem, we perform resource allocation using the mean opinion score (MOS) for heterogeneous traffic. We then demonstrate that deep Q-networks (DQNs) can combine deep exploration with deep neural networks (DNNs) for exponentially faster learning and convergence. Our results indicate that docitive approaches have a 55% better convergence rate compared to individual learning at 5 SUs and 70% better at 42 SUs. Also, perfect docition performs 6.67% better than the other approaches when the number of SUs reaches 32.
Automatic Building Extraction on High-Resolution Remote Sensing Imagery Using Deep Convolutional Encoder-Decoder With Spatial Pyramid Pooling

Automatic extraction of buildings from remote sensing imagery plays a significant role in many applications, such as urban planning and monitoring changes to land cover. Various building segmentation methods have been proposed for visible remote sensing images, especially state-of-the-art methods based on convolutional neural networks (CNNs). However, high-accuracy building segmentation from high-resolution remote sensing imagery is still a challenging task due to the potentially complex texture of buildings in general and image background. Repeated pooling and striding operations used in CNNs reduce feature resolution causing a loss of detailed information. To address this issue, we propose a light-weight deep learning model integrating spatial pyramid pooling with an encoder-decoder structure. The proposed model takes advantage of a spatial pyramid pooling module to capture and aggregate multi-scale contextual information and of the ability of encoder-decoder networks to restore losses of information. The proposed model is evaluated on two publicly available datasets; the Massachusetts roads and buildings dataset and the INRIA Aerial Image Labeling Dataset. The experimental results on these datasets show qualitative and quantitative improvement against established image segmentation models, including SegNet, FCN, U-Net, Tiramisu, and FRRN. For instance, compared to the standard U-Net, the overall accuracy gain is 1.0% (0.913 vs. 0.904) and 3.6% (0.909 vs. 0.877) with a maximal increase of 3.6% in model-training time on these two datasets. These results demonstrate that the proposed model has the potential to deliver automatic building segmentation from high-resolution remote sensing images at an accuracy that makes it a useful tool for practical application scenarios.
Push, Pull, and Mooring Evaluation of User Switching Intention from Social Commerce to E-Commerce

Social commerce is widely used for people who like to socialize while shopping online. However, currently shopping on social commerce has some risk such as especially for security since social commerce platform could not cover end-to-end online transaction process in its platform. Ecommerce on the other hand, could still be relied on to ensure the security and ease of transactions. This characteristic might encourage user to switch from social commerce to e-commerce. This study aims to understand complete understanding of factors that influence user to migrate with push-pull-mooring (PPM) model. The model was used the evaluate the factor the drive user to leave social commerce (push factor), the factor that attract user to migrate to e-commerce (pull factor) and factor that can facilitate or hinder the buyer to switch (mooring factor). This study uses a quantitative approach with collecting data using an online survey filled by 1,271 consumers who have shopped online through social commerce and ecommerce. The process of data analysis uses PLS-SEM with SmartPLS (v. 3.2.8). The results of this study indicate that push factors including familiarity have a positive influence in encouraging consumers to leave social commerce, while the pull factor consists of a security concern and ease of use that positively influence consumers in using e-commerce. Meanwhile, mooring factors which consist of hedonic motivations, habit, and facilitating conditions positively influence the intention to switch.
Visual-Guided Robot Arm Using Self-Supervised Deep Convolutional Neural Networks

Perception-based learning approaches to robotic grasping have shown significant promise. This is further reinforced by using supervised deep learning in robotic arm. However, to properly train deep networks and prevent overfitting, massive datasets of labelled samples must be available. Creating such datasets by human labelling is an exhaustive task since most objects can be grasped at multiple points and in several orientations. Accordingly, this work employs a self-supervised learning technique in which the training dataset is labelled by the robot itself. Above all, we propose a cascaded network that reduces the time of the grasping task by eliminating ungraspable samples from the inference process. In addition to grasping task which performs pose estimation, we enlarge the network to perform an auxiliary task, object classification in which data labelling can be done easily by human. Notably, our network is capable of estimating 18 grasping poses and classifying 4 objects simultaneously. The experimental results show that the proposed network achieves an accuracy of 94.8% in estimating the grasping pose and 100% in classifying the object category, in 0.65 seconds.
Assessing the Entrepreneurial Orientation Model and Linking it with Entrepreneurial Inclination among Information Systems and Computer Science Students in Indonesia

Ministry of Communication and Information, Indonesia has stated that Indonesia will become the largest digital economy player in Southeast Asia in 2020. This phenomenon can definitely trigger the digital players in Indonesia to choose online business as their career option. Therefore, there is a need to inculcate the entrepreneurial spirit of students, especially information systems and computer science students. This research work aims at analysing the Entrepreneurial Orientation by exploring its components in line with students majoring in information systems and computer science in Indonesia. It also examines the correlation between Entrepreneurial Orientation and students’ preference towards entrepreneurship. Questionnaires have been distributed to the information systems and computer science students at 5 different universities in Indonesia. The hypotheses were developed after conducting the pilot testing. By using the cluster probabilistic random sampling, the 1.230 questionnaires have been distributed and this data collection process yielded back with 381 usable responses that were examined by utilizing SPSS and structural equation modelling. This study finally reveals that there are three dimensions under the Entrepreneurial Orientation variable that are risk-taking, innovativeness and proactiveness. Further, this study reveals that the EO dimensions are positively correlated with entrepreneurial intention among information systems and computer science students. This research is one of the first studies that try to exert and analyse the Entrepreneurial Orientation among information system and computer science students in Indonesia using AMOS-SEM software. Suggestion for future research are also proposed.
Adaptive Transceiver for Wireless NoC to Enhance Multicast/Unicast Communication Scenarios

Wireless Network-on-Chip (WiNoC) is a viable solution to overcome critical bottlenecks in on-chip communication backbone. However, standard WiNoC approaches are vulnerable to multi-path interference introduced by on-chip physical structures. To overcome such parasitic phenomenon, this paper presents an adaptive digital transceiver, which enhances communication reliability under different wireless channel configurations. Based on a semi-realistic wireless channel model, we investigate the impact of using some channel correction techniques. Experimental results show that our approach significantly improves Bit Error Rate (BER) under different wireless channel configurations. Moreover, our adaptive transceiver allows for wireless communication links to be established in conditions where this would not be possible for standard transceiver architectures. The proposed architecture, designed using a 28-nm FDSOI technology, consumes only 3.27 mW for a data rate of 10 Gbit/s and has a very small area footprint.
Computer aided medical image analysis for capsule endoscopy using conventional machine learning and deep learning

Large population of the world suffers from diseases related to gastrointestinal (GI) tract. The most modern technique available to scan the GI tract is capsule endoscopy (CE). It is a non-sedative, non-invasive and patient-friendly alternative to conventional endoscopy. However, CE generates approximately 55000 to 60000 images which make the diagnosis process time consuming and tiresome for physicians. Also the diagnosis varies from expert to expert. Hence a computer-aided diagnosis system is a must. This study, addresses a multi-class medical image analysis problem using image processing and machine learning techniques. It presents a computer aided diagnosis (CAD) system based on conventional machine learning as well as deep learning for automatic abnormality detection in GI tract. The system performs with an accuracy of 95.11% and precision of 93.9%.
Exploring Students’ Acceptance of E-Learning Through the Development of a Comprehensive Technology Acceptance Model

Extending the Technology Acceptance Model (TAM) for studying the e-learning acceptance is not a new research topic, and it has been tackled by many scholars. However, the development of a comprehensive TAM that could be able to examine the e-learning acceptance under any circumstances is regarded to be an essential research direction. To identify the most widely used external factors of the TAM concerning the e-learning acceptance, a literature review comprising of 120 significant published studies from the last twelve years was conducted. The review analysis indicated that computer self-efficacy, subjective/social norm, perceived enjoyment, system quality, information quality, content quality, accessibility, and computer playfulness were the most common external factors of TAM. Accordingly, the TAM has been extended by the aforementioned factors to examine the students’ acceptance of e-learning in five different universities in the United Arab of Emirates (UAE). A total of 435 students participated in the study. The results indicated that system quality, computer self-efficacy, and computer playfulness have a significant impact on perceived ease of use of e-learning system. Furthermore, information quality, perceived enjoyment, and accessibility were found to have a positive influence on perceived ease of use and perceived usefulness of e-learning system.
All-Optical Spectral Shuffling of Signals Traveling through Different Optical Routes

A recent proposed physical layer encryption technique uses an all-optical setup based on spatial light modulators to split two or more wavelength division multiplexed (WDM) signals in several spectral slices and to shuffle these slices. As a result, eavesdroppers aimed to recover information from a single target signal need to handle all the signals involved in the shuffling process. In this work, computer simulations are used to analyse the case where the shuffled signals propagate through different optical routes. From a security point of view, this is an interesting possibility because it obliges eavesdroppers to tap different optical fibres/ cables. On the other hand, each shuffled signal experiences different physical impairments and the deleterious consequences of these effects must be carefully investigated. Our results indicate that, in a metropolitan area network environment, penalties caused by attenuation and dispersion differences may be easily compensated with digital signal processing algorithms that are presently deployed.
Design of a Multifunctional Reconfigurable Metasurface for Polarization and Propagation Manipulation

In this paper, a multifunctional reconfigurable polarization conversion metasurface (MRPCM) is designed for controlling both the propagation and the polarization of electromagnetic wave. The proposed metasurface contains two-layered structures separated by the air layer. The top layer is composed of two back-to-back C-shaped metallic resonators, which is used to convert the polarization. The bottom layer is consisted of two orthogonal metal gratings, which works as the patterned ground. The PIN diodes are embedded on both the upper and lower layer of the patterned ground and controlled through two independent sets of external DC bias network to achieve the multi-function. The gratings serve as both the resonant structure and the PIN diode biasing lines. Therefore, no additional feeding lines are introduced, which can decrease the impact on the performance of the MRPCM. When the PIN diodes of the two layers are turned on, the MRPCM will reflect and convert the incident wave into cross-polarization. When the PIN diodes of the two layers are turned off, the dual-polarization incident waves are transmitted through the MRPCM without conversion. If the PIN diodes of the two layers are in different state, the $x$ - (OFF-ON) and $y$ -polarized (ON-OFF) incident waves are transmitted and their E-field are rotated by 90°, respectively. Both numerical simulations and experimental results show good reconfigurable performance of the MRPCM
Performance Comparison on NOMA Schemes in High Speed Scenario

To fulfill service requirements of future mobile communication, non-orthogonal multiple access (NOMA) technology is investigated and supported by big companies for 5G network. Many uplink NOMA schemes have been proposed in 3GPP discussion. The performance comparison of these schemes has been investigated in many papers. However few papers have discussed their performance in high speed scenario especially through tapped delay line (TDL) channel. This paper presents our investigation and link-level simulation on the performance of different NOMA schemes through TDL channel at both normal and high UE speed. Results of three simulations are shown. By comparing the performance of these NOMA schemes, we can see that NOMA schemes have different performance with normal and high UE speed. It inspires us that there exists the possibility to design a special NOMA scheme suitable for high speed scenario.
Reset Control for DC–DC Converters: An Experimental Application

Power converters in grid connected systems are required to have fast response to ensure the stability of the system. The standard PI controllers used in most power converters are capable of fast response but with significant overshoot. In this paper a hybrid control technique for power converter using a reset <inline-formula> <tex-math notation="LaTeX">$PI+CI$ </tex-math></inline-formula> controller is proposed. The <inline-formula> <tex-math notation="LaTeX">$PI+CI$ </tex-math></inline-formula> controller can overcome the limitation of its linear counterpart (PI) and ensure a fast flat response for power converter. The design, stability and cost of feedback analysis for a DC-DC boost converter employing a <inline-formula> <tex-math notation="LaTeX">$PI+CI$ </tex-math></inline-formula> controller is explored in this work. The simulation and experimental results which confirm the fast, flat response will be presented and discussed.
Luminescence and Energy Transfer in Fluoroindate Glasses Doped with Rare-Earth

This work reports the analysis of near-infrared luminescence of low phonon energy (510 cm<sup>−1</sup>) fluoroindate glasses co-doped with Er<sup>3+</sup>/Tm<sup>3+</sup> and Er<sup>3+</sup>/Ho<sup>3+</sup> ions pumped by 796 nm, 980 nm laser diode, respectively. Due to donor-acceptor energy transfer and superposition of 1450 nm (Tm<sup>3+</sup>: <sup>3</sup>H<inf>4</inf> → <sup>3</sup>F<inf>4</inf>) and 1550 nm (Er<sup>3+</sup>: <sup>4</sup>I<inf>13/2</inf> → <sup>4</sup>I<inf>15/2</inf>) radiative transitions in glass co-doped with 0.1ErF<inf>3</inf>/0.3TmF<inf>3</inf> a broadband emission in the range of third telecommunication window (FWHM = 152 nm, λ<inf>exc</inf> = 796 nm) was obtained. Analysis of the spectroscopic properties of the 0.8ErF<inf>3</inf>/1.4HoF<inf>3</inf> co-doped glass showed Er<sup>3+</sup> → +R<sup>3+</sup> energy transfer and efficient sensitization of holmium ions leading to the 1.2 μm and 2 μm emission under 980 nm laser diode excitation.
Intelligent Control Strategy for Robotic Arm by Using Adaptive Inertia Weight and Acceleration Coefficients Particle Swarm Optimization

This paper proposes an intelligent control strategy for enabling a robotic arm to grasp and place water-filled bottles without spilling any of the water. First, the system architecture of a five-degree-of-freedom robotic arm and its mechanical design are introduced. Second, both the forward and inverse kinematics of the robotic arm are derived. The study conducted an experiment in which the designed and implemented robotic arm could grasp a bottle of water and move it to another place. However, if the acceleration or the orientation of the robotic arm were inappropriate, the water in the bottle may be spilled during the movement. Therefore, the proposed strategy applies an inertial measurement unit for obtaining relevant information. According to the obtained information, the velocity curves of each joint could be optimized by adaptive inertia weight and acceleration coefficients particle swarm optimization. Finally, the experimental results demonstrated the feasibility and effectiveness of the proposed method.
Study of the Photo-Switching of a Fe(II) Chiral Complex Through Linear and Nonlinear Ultrafast Spectroscopy.

Photo-switching the physical properties of molecular systems open large possibilities for driving materials far from equilibrium towards new states. Moreover, ultra-short pulses of light make it possible to induce and to record photo-switching on very short time-scale opening the way to fascinating new functionalities. Among molecular materials, Fe(II) complexes exhibit an ultrafast spin-state transition during which the spin state of the complex switches from a low spin state (LS, S=0) to a high spin state (HS, S=2). The latter process is remarkable: it takes place within ~100 fs with a quantum efficiency of ~100%. Moreover, the spin state switching induces an important shift of the broad metal to ligand absorption band of the complex and it results in large modifications of the physical and chemical properties of the compounds. But, since most of the Fe(II) complexes crystallize in centrosymmetric space groups, this prevents them exhibiting piezoelectric, ferroelectric as well as second-order nonlinear optical properties such as second harmonic generation (SHG). This considerably limits their potential applications. We have recently synthesized [Fe(phen)3] [Δ-As2(tartrate)2] chiral complexes which crystallize in a noncentrosymmetric 32 space group. Hereafter, upon excitation of a thin film of these complexes by a femtosecond laser pulse and performing simultaneously transient absorption (TRA) and time-resolved SHG (TRSH) measurements, we have recorded the ultrafast LS to HS switching. While a single TRA measurement only gives partial information, we demonstrate that TRSH readily reveals the different mechanisms in play during the HS to LS state relaxation. Moreover, a simple model makes it possible to evaluate the relaxation times as well as the hyperpolarizabilities of the different excited states along which the system travels through during the spin-state transition.
Patient-reported Outcome in Surgically Treated Pelvic Ring Injuries at 5 Years Post-surgery

Background and Aims: Long-term prospective data on patient-reported outcome after surgical treatment of pelvic ring injuries are scarce. This study aimed at describing results at 5 years post-surgery using validated outcome measures. Patients and Methods: Patients admitted for surgical treatment of pelvic ring injuries were prospectively included and asked to report their outcome at 1, 2 and 5 years post-surgery using two patient-reported outcome measures: the generic Short-Form 36 and the condition-specific pelvic discomfort index. Data were evaluated using mixed-effects linear models. Results: There were 108 patients (68 males and 40 females), mean age 38 years. Injury type according to the AO/OTA-classification was B-type in 68 patients and C-type in 40 patients. No domain of the Short-Form 36 reached norm values at 5 years post-surgery. Females reported a worse outcome than males concerning general health (p < 0.01) at 5 years. Recovery of physical function (p < 0.01), mental health (p = 0.04), and pain (p = 0.01) was observed for males at 5 years compared to earlier assessments, while females on the contrary described more pain at this time-point (p = 0.03). Mean pelvic discomfort index at 5 years was 27, indicating moderate residual pelvic discomfort overall. Males reported less pelvic discomfort than females at 5 years (p = 0.02) and improved when compared to results at 2 years (p = 0.02), while females did not. Influence of age, fracture type, and presence of associated injuries on patient-reported outcome was limited. Conclusion: Surgically treated pelvic ring injuries are associated with long-standing negative effects on patient-reported outcome. Males report a better outcome than females at 5 years post-surgery.