Miracle-dz/UniFAR-FTUs huggingface dataset:
Green Coexistence for 5G Waveform Candidates: A Review

There is a growing demand for 5G applications in all fields of knowledge. Current applications, such as the Internet of Things, smart homes, and clean energy, require sophisticated forms of 5G waveforms. Researchers and developers are investigating the requirements of 5G networks for better waveform types, which will result in high spectrum efficiency and lower latency with less complexity in systems. This paper proposes an assessment of various 5G waveform candidates [filtered orthogonal frequency-division multiplexing (OFDM), universal filtered multicarrier (UFMC), filter bank multicarrier (FBMC), and generalized frequency-division multiplexing] under the key performance indicators (KPIs). This paper assesses the main KPI factors (computational complexity, peak-to-average-power ratio, spectral efficiency, filter length, and latency). Moreover, this paper compares and evaluates all KPI factors in various 5G waveforms. Finally, this paper highlights the strengths and weaknesses of each waveform candidate based on the KPI factors for better outcomes in the industry. In conclusion, the current review suggests the use of optimized waveforms (FBMC and UFMC) for better flexibility to overcome the drawbacks encountered by previous works. Regarding coexistence, FBMC and UFMC showed better coexistence with CP-OFDM in 4G networks with a new radio spectrum. The rapprochement between the above-mentioned waveforms has been called green coexistence and is due to the mix between one waveform in 4G networks and two waveforms in 5G networks based on the subcarrier and subband shaping (FBMC and UFMC).
Mobile Visual Programming Apps for Internet of Things Applications based on Raspberry Pi 3 Platform

The competition in this modern world must be followed by novice developers, especially students to improve competitiveness and creativity. But the complexity to make the programs for build IOT applications are difficult for beginner developers. After analyzing the problems and conditions, we need an application that used to simplify text-based programming into visual based programming to make it easy to understand. This paper describes how to control IoT project easily using visual programming with android application over the internet. The application is created by describing the functions of IoT application with Raspberry, then writers made the communication protocol and user interface design based on classification of software construction elements with object modeling in order to be understood by the user. The practical goal of this paper is to continue the previous work from design into usable application, controlling education robot, learn IoT with RaspberryPi, make Bluetooth and internet connection through database, special additional features like conditional programming and monitoring output. The Software User Testing is used to rate the application and the result from the questionnaire with SUS methodology show that the application has a total score of 72,9 out of 100 which means it has an adjective ranking with “Good” scale, Grade scale in “C” category and “Acceptable” in Acceptability scale that conclude this application is accented and easy to use.
Cooperative Data Routing & Scheduling In Software Defined Vehicular Networks

This paper presents the primordial study on cooperative scheduling of diverse vehicular communication channels for a better dissemination of data in multi-hop routing scenarios. The system adopts a software defined vehicular network (SDVN) architecture to realize a logically centralized authority and utilizes vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and infrastructure-to-vehicle (I2V) channels of dedicated short range communication (DSRC), and infrastructure-to-infrastructure (I2I) interface cooperatively to maximize the delivery of data packets under delay tolerance and link connectivity regions. The problem is formulated as an integer linear programming (ILP) problem and it further thrives on the minimization of service delay based on the traffic type. The model avoids possible packet collisions, simultaneous transmissions, routing loops by means of constraints while scrutinizing the wireless nature of links. The effectiveness of the proposed routing and scheduling scheme is comparatively evaluated for multiple network instances.
One-factor Cancellable Scheme for Fingerprint Template Protection: Extended Feature Vector (EFV) Hashing

Cancellable biometrics is a biometric template protection method that generates a noninvertible identifier from the original biometric template by means of a parameterized transformation function and user/application-specific parameters such as token or password. However, the necessity to input additional factor besides biometrics, jeopardizes usability of the biometrics and security. In this paper, a one-factor cancellable biometrics scheme namely Extended Feature Vector (EFV) hashing, which requires sole biometric as input is proposed. The EFV hashing utilizes a permutated key that separated from the biometric data, to serve as identifier for matching. The crux that enables one-factor authentication in this scheme is the permutation seed of the key is derived from the biometric features of the user, but not from second factor as in existing schemes. The evaluations have been carried out with FVC 2002 and 2004 databases and attest the proposed method satisfies cancellable biometrics design criteria. We also analyze four privacy and security attacks that targeted to this scheme.
Authorship Attribution vs. Adversarial Authorship from a LIWC and Sentiment Analysis Perspective

Although Stylometry has been effectively used for Authorship Attribution, there is a growing number of methods being developed that allow authors to mask their identity [2, 13]. In this paper, we investigate the usage of non-traditional feature sets for Authorship Attribution. By using non-traditional feature sets, one may be able to reveal the identity of adversarial authors who are attempting to evade detection from Authorship Attribution systems that are based on more traditional feature sets. In addition, we demonstrate how GEFeS (Genetic & Evolutionary Feature Selection) can be used to evolve high-performance hybrid feature sets composed of two non-traditional feature sets for Authorship Attribution: LIWC (Linguistic Inquiry & Word Count) and Sentiment Analysis. These hybrids were able to reduce the Adversarial Effectiveness on a test set presented in [2] by approximately 33.4%.
Regeneration of Normal Distributions Transform for Target Lattice Based on Fusion of Truncated Gaussian Components

In this letter, we propose a method that can be used to regenerate the 3-D normal distributions transform (NDT) for target lattice. When a pose is updated by simultaneous localization and mapping (SLAM), the lattice at the pose is also transformed. Given that NDT is a Gaussian mixture model generated by regular cells, the fusion of NDTs transformed with updated poses can distort the shapes of the Gaussian components (GCs). Moreover, when robots without information about other robots’ initial poses share and fuse NDT maps, the simple fusion of NDT maps built in different lattices can distort GCs. To overcome this problem, we propose a method by which GCs are subdivided into truncated GCs by the target lattices on each axis iteratively, and the truncated GCs in the same target cell are fused. To determine whether the GC should be subdivided, we define a weight threshold assigned to the weight corresponding to the truncated GC. In an experiment, we evaluated the receiver operating characteristics, the accuracy, the $L_2$ value, the mean error, the mean covariance distance based on Fréchet distance to assess the similarity of the regenerated NDT, and ground truth NDT. Also, we evaluated the computational performance of the proposed method. Moreover, we evaluated the application of map fusion. It was found that the NDT regenerated by the proposed method showed improvement in the $L_2$ value, mean error, and mean covariance distance.
Software Defined Network-Based Edge Cloud Resource Allocation Framework

A growing number of user applications rely on the computing resources of edge cloud data centers (ECDCs). The efficient management of ECDC resources has a positive impact on the cloud service providers’ profitability and on the stringent quality-of-service (QoS) requirements of user applications. This paper proposes a resource allocation framework of interconnected ECDCs using software-defined networking (SDN). SDN technology is used to ensure QoS and to efficiently embed user applications into the ECDCs. The proposed framework, called infrastructure as a service provisioning using SDN (IaaSP-SDN), includes the two-phase coordinated IaaS requests. Provisioning approach and a set of SDN management modules to set up accepted IaaS. The performance of IaaSP-SDN is evaluated in two steps: 1) a prototype is compared with generalized multi-protocol label switching (GMPLS) and 2) the impact of SDN controller physical location on the performance of the IaaSP-SDN framework is evaluated. The results illustrate that the SDN framework is more stable and scalable than GMPLS. We also show that SDN controller location attributes have a significant effect on the acceptance ratio of IaaS requests.
Autonomous Driving in the Curriculum of Computer Architecture

The need for educated personnel in automotive companies rises steadily due to the new trend towards autonomous driving. Therefore, it is necessary to integrate the required topics into the curriculum of the university students. As a chair for computer architecture, we added selected aspects of autonomous vehicles to the lectures preparing skills in embedded hardware and basic algorithm knowledge. Afterwards, the skills can be put into practice by working in a newly created laboratory which offers the possibility to contribute to a project about the realization of an autonomous remote controlled car. Due to the different requirements of the RC car, the laboratory is interdisciplinary in design. Overall, it was a huge success attracting many students who wanted to work on it. A presentation of the finished vehicle to the public was the culmination of this work so far.
An Empirical Study on Unsupervised Pre-training Approaches in Regression Problems

Unsupervised pre-training allows for efficient training of deep architectures. It provides a good set of initialised weights to the deep architecture that can provide better generalisation of the data. In this paper, we aim to empirically analyse the effect of different unsupervised pre-training approaches for the task of regression on different datasets. We have considered two most common pre-training methods namely deep belief network and stacked autoencoder, and compared the results with the standard training algorithm without pretraining. The models with pretraining performed better than the model without pretraining in terms of error, convergence and the prediction of pattern. The results of the experiments also show the importance of hyperparameters tuning, specially learning rate, in providing a better prediction result. This study once again confirmed the effectiveness and potential of pretraining approach in nonlinear regression problem.
Stability of Perovskite Solar Cell with Optimized CuSCN as Hole Transport Layer*

Perovskite material has extensively investigated in several years due to its ability as solar cell active layer. However, stability of perovskite solar cell is still a challenge for mass production and commercialization. Transport layer is one of concern to overcome the stability problem. Inorganic material such as CuSCN can be use as p-type transport layer and more robust from degradation compared to organic transport layer. Furthermore, CuSCN has excellent transparency, low-cost and simple fabrication, and high hole mobility. In this research, we use CuSCN as hole transport layer and deposited it using spin coating method. Optimized CuSCN layer was obtained by varied spin coating rotational speed. Afterwards, the completed devices were studied its stability. The result is, performance of optimized solar cell was degraded about 50% from its initial measurement after 14 days.
Vehicular VLC Frequency Domain Channel Sounding and Characterization

Vehicular visible light communication (V2LC) has recently gained popularity as a complementary technology to radio frequency (RF) based vehicular communication schemes due to its low-cost, secure and RF-interference free nature. In this paper, we propose outdoor vehicular visible light communication (V2LC) frequency domain channel sounding based channel model characterization under night, sunset and sun conditions with the usage of vector network analyzer (VNA) and commercial off-the-shelf (COTS) automotive light emitting diode (LED) light. We further bring forward a new practical system bandwidth criterion named as effective usable bandwidth (EUB) for an end-to-end V2LC system with respect to the real world measurements. We demonstrate outdoor static V2LC channel measurement results, taking into account vehicle light emitting diode (LED) response, road reflections from nearby vehicles and various daylight conditions with respect to varying inter-vehicular distances. Measurement results indicate that, sunlight decreases system effective usable bandwidth due to the limited dynamic range of avalanche photodiode (APD), nearby vehicles cause constructive interference whereas road reflections change time dispersion characteristics of the V2LC channel.
Direct Adaptive Neural Network Control for Dissolved Oxygen Concentration*

In this paper, a direct adaptive neural-network control (DANC) method is proposed to control the dissolved oxygen (DO) concentration in the wastewater treatment process (WWTP), which employs the forward neural network to approximate an ideal control law. The main features of the DANC system include the following three aspects. Firstly, it is not necessary to establish the accurate plant model, which is favorable because it is an arduous task for the WWTP. Secondly, the multi-condition characteristic of WWTP is considered, and the neural network controller is designed into a self-organizing style. Thirdly, the criterions of growing and pruning network are designed considering the characteristic of WWTP. The adaptive laws of DANC parameters are obtained through the Lyapunov method. Simulation experiments show that the control accuracy and dynamic performance of DO concentration under DANC are improved.
Intrusion Detection in secure network for Cybersecurity systems using Machine Learning and Data Mining

The emergent rate of security dangers in the network commands extremely consistent well-being solution. Researchers usually worked on several way out to detect invasions. The security phases of intrusion detection using machine learning approach have been deliberated in our paper. In the meantime, Intrusion Detection (IDSs) played a crucial part in the outline and evolvement of stout linkage frame that basically secure system by distinguishing and intercepting multiplicity of assaults. A lot of techniques have been established that are built on machine learning approaches. Though, they are not exact effective in detecting all kinds of infringements. In our paper, a comprehensive analysis of several machine learning techniques has been supported for discovering the basis of glitches related with various machine learning techniques in perceiving invasive activities. Limitations accompanying with each of them are also discoursed. Several data mining tools for machine learning have also been contained within the paper. Consistent standard datasets happens serious to asses and estimate enactment of a detection structure. There subset many datasets, for example, DARPA98, KDD99, ISC2012, and ADFA13 etc. are used to estimate the performance of intrusion detection tactics but we have used the latest one in our research i.e. CICIDS2017 provided much better accuracy. Within this paper we commenced a broad assessment of the current datasets by means of our own projected standards, and put forward an estimation outline for IDS datasets. We upkeep this privilege by ascertaining challenges specific to network intrusion detection, and offer a set of guiding principle destined to build up future research on anomaly detection.
Learning Indonesian Frequently Used Vocabulary from Large-Scale News

Frequently used vocabulary of a language plays an important role in language learning. In this work, we introduce how we obtain Indonesian frequently used vocabulary along with a vocabulary level scheme. Techniques in natural language processing and statistics are used to process a large scale of Indonesian news. According to our scheme, an Indonesian language learner may need to learn about 11,200 words to gain adequate comprehension when reading an Indonesian article. In addition, we also compare the vocabulary distribution and usage between English and Indonesian. Our work could be helpful to Indonesian language learners and other relevant research such as language teaching.
OFDM Pilot-Based Radar for Joint Vehicular Communication and Radar Systems

With the large-scale deployment of connected and autonomous vehicles, the demand on wireless communication spectrum increases rapidly in vehicular networks. Due to increased demand, the allocated spectrum at the 5.9 GHz band for vehicular communication cannot be used efficiently for larger payloads to improve cooperative sensing, safety, and mobility. To achieve higher data rates, the millimeter-wave (mmWave) automotive radar spectrum at 76–81 GHz band can be exploited for communication. However, instead of employing spectral isolation or interference mitigation schemes between communication and radar, we design a joint system for vehicles to perform both functions using the same waveform. In this paper, we propose radar processing methods that use pilots in the orthogonal frequency-division multiplexing (OFDM) waveform. While the radar receiver exploits pilots for sensing, the communication receiver can leverage pilots to estimate the time-varying channel. The simulation results show that proposed radar processing can be efficiently implemented and meet the automotive radar requirements. We also present joint system design problems to find optimal resource allocation between data and pilot subcarriers based on radar estimation accuracy and effective channel capacity.
Integrated Robotics Architecture with Kansei Computing: Proposal and Initial Prototype

Robots that aim to communicate with humans are becoming increasingly common. However, with only a single robot, the ability and scope of communication are limited. On the other hand, it is difficult to express the same unified mood and emotion by combining multiple devices. Therefore, we propose integrated robotics architecture (IRA). This architecture provides a communication environment that combines various robots and devices. In this architecture, a publish/subscribe model is used in the communication between devices, and a framework of Kansei computing is used for nonverbal expression. Communication is basically done with meta-level messages. This method makes it possible to dynamically express the emotion linked with a robot's utterances throughout the environment. In this paper, we created an initial prototype and verified the feasibility of communication and control.
Time Estimation and Resource Minimization Scheme for Apache Spark and Hadoop Big Data Systems With Failures

Apache Spark and Hadoop are open source frameworks for big data processing, which have been adopted by many companies. In order to implement a reliable big data system that can satisfy processing target completion times, accurate resource provisioning and job execution time estimations are needed. In this paper, time estimation and resource minimization schemes for Spark and Hadoop systems are presented. The proposed models use the probability of failure in the estimations to more accurately formulate the characteristics of real big data operations. The experimental results show that the proposed Spark adaptive failure-compensation and Hadoop adaptive failure-compensation schemes improve the accuracy of resource provisions by considering failure events, which improves the scheduling success rate of big data processing tasks.
Accuracy-Enhanced Variance-Based Time-Skew Calibration Using SAR as Window Detector

This brief presents a time-interleaved (TI) successive-approximation-register (SAR) analog-to-digital converter (ADC) with an improved variance-based time-skew estimation technique, where we introduce a window detector (WD) based on a SAR ADC. It brings low hardware overhead and 104 times faster convergence speed when compared to the prior variance-based time-skew calibration. Postlayout simulation results of a 10-bit, 2-GS/s TI-ADC in 28-nm CMOS process verify the effectiveness of the proposed calibration. The results indicate that the signal noise and distortion ratio/spurious free dynamic range of the ADC improved from 41.9/48.6 to 53.2/63.3 dB after calibration. The total area and power are 0.105 mm2 and 14.9 mW, respectively, where the WD occupies 0.0015 mm2 and 0.55 mW.
Caterpillar-Inspired Crawling Robot Using Both Compression and Bending Deformations

Multimodal deformations of soft materials such as compression and bending are a commonplace event on soft-bodied animals (e.g., worms) and soft-bodied robots. Usually, such deformation modes are separately analyzed or constrained to decrease the degrees of freedom when one design motion of the soft-bodied robots. This letter proposes to use multiple deformation modes and presents a highly deformable crawling robot that employs both compression and bending deformations simultaneously during the locomotion. This locomotion gait is inspired by the motion analysis experiment of a biological caterpillar, i.e., crawling gait of a silkworm Bombyx mori. Based on the biological experiment, we propose a compressive/bendable beam and develop a crawling robot using the proposed beam. The experimental results of the simulation and the prototype demonstrate that the combination of the multiple deformation modes can contribute to the locomotion speed increase. This also indicates that we can design an enormous variety of locomotion gaits by combining multiple deformation modes, which may increase the adaptability of soft-bodied robots for use in our living and natural environments. These results shed new light on how to design behavioral diversity of soft-bodied robots.
The Varying Success of Bayesian Model Averaging: An Empirical Study of Flood Prediction

Bayesian model averaging attempts to find the most probable prediction, in contrast to finding the prediction of the single best-fit, most probable model. Using an example problem of predicting floods in five intermittent rivers in the Australian outback, this paper establishes that the improvement in prediction accuracy from Bayesian model averaging correlates with the size of the difference in the prediction between Bayesian model averaging and the single best-fit model.