Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of a Collaborative Curation Platform from the Perspective of Selective Exposure

Acropolis is a social computing platform especially designed to engage citizens in building and sharing their own narratives about complex or long-term news stories. The first two design cycles of the platform have shown, by positive evidence, that this goal has been achieved, but in the previous papers no analysis was conducted regarding the social behavior of the participants in relation to consuming or being exposed to different viewpoints of their own. Several studies suggest that people crave more for reinforcement of opinion than they reject exposure from different points of view, and that from the standpoint of strengthening democratic discourse, hearing the other side is crucial. A key goal of this paper is to evaluate the extent to which the participants of the latest experiment in the aforementioned platform followed this behavior. Besides that, we aim to investigate whether the design of the platform favored the goal of encouraging the consumption of diverse sources and opinions, with an eye towards the next design cycles.
Prediction of Ejection State for a Pneumatic Valve-Controlled Micro-Droplet Generator by a BP Neural Network

Pneumatic valve-controlled micro-droplet generation is a printing technique that has potential applications in many fields, especially in the field of biomedical printing. The droplet generation is controlled by a solenoid valve being briefly turned on, so that high pressure gas enters the liquid reservoir, forming a gas pressure pulse P(t), forcing the liquid out through a tiny nozzle to form a micro-droplet. Under the typical working conditions, P(t) is not consistent. Since P(t) is highly correlated with the micro-droplet ejection state, the inconsistency of P(t) results in fluctuation of ejection state. For each injection, the P(t) is acquired by a high speed pressure sensor, and the ejection state is obtained by machine vision processing. A machine learning method based on BP neural network is used to establish a prediction model with P(t) as the input and the droplet ejection state as the output. Experiments show that a BP neural network with only a single hidden layer and two neurons can accurately predict the number of droplets with an accuracy higher than 99%. Another experiment shows that a more complex double hidden layer BP neural network can improve the prediction accuracy for the position of droplets after a certain time delay. In summary, through pressure pulse P(t), the predictive model established by the machine learning method can effectively predict the micro-droplet ejection state. This technique may be used for real time monitoring and control of the pneumatic valve-controlled micro-droplet generator.
Bottom-up Multi-agent Reinforcement Learning for Selective Cooperation

Applications of multi-agent system like cooperative transport are found in various domains of real world. Due to the complexity inherent in multi-agent system, however, handling with preprogramming is difficult. Multi-agent reinforcement learning (MARL), which is a framework to make multiple agents in the same environment learn their policies simultaneously using reinforcement learning, is receiving attention. In the conventional MARL, although decentralization is essential for feasible learning, rewards for the agents have been allocated from a centralized system in the environment. Instead of such "top-down" MARL, to achieve the completely distributed autonomous systems, we tackle a new paradigm named "bottom-up" MARL, where the agents get their own rewards. The bottom-up MARL requires to share the respective rewards for emerging orderly group behaviors, which cannot be acquired merely by maximizing the mean of them. We therefore propose the architecture that has three components: estimating rewards of other agents; selecting rewards to reinforce from the correlation, and; promoting the exploration to find unknown correlation. The proposed architecture is verified that every element is essential by numerical simulation performed in stages. A similar task is also accomplished in dynamical simulation under the same conditions as the actual robots.
Spectrum Occupancy Measurements in the Sub-6 GHz Band for Smart Spectrum Applications

Spectrum measurements are a key part in the development of future spectrum management techniques for the forthcoming Smart Spectrum approach, which looks for the realization of a more sustainable usage of the spectrum for the future wireless world. This paper presents the results of a spectrum measurement campaign evaluating the complete sub 6 GHz band in three different points in the City of San Luis Potosi, Mexico. A robust spectrum measurements platform is implemented and the spectrum occupancy is analyzed in the frequency and time domains. Results show that the spectrum utilization in terms of the average duty cycle of the entire sub-6 G Hz is of 4.73 %. Additionally, in this work, a heavy and intense spectrum usage scenario is considered for comparison purposes.
A Catfish Effect Based Team Recommendation System

Motivated by the growing complex connectedness of modern society, which is found in many incarnations, especially in a team or a particular network, there has been a coming-together of multiple disciplines in an effort to understand how highly connected teams or networks operate and how to improve their work efficiency. As for teams, due to structural diversity, network heterogeneity and scope difference, team building is rather complicated. In order to build a universal recommendation system, we apply biological phenomena, known as the catfish effect, to built a team recommendation system. By extracting the factors which have a direct influence on team performance and modelling RBF neural network, we can predict the relationship between team performance and factors. We design a CTI(Catfish Identification) algorithm based on RBF i.e., RBF -CTI algorithm, which aims to calculate catfish index CI i.e., the intensity of cat-fish effect of each factor. According to CI, we can easily identify the skill of catf ish, namely the most potential impact factor for the team, as well as the indicator of team recommendation system. We apply the idea of the combination of multi-disciplines and give a universal framework of team recommendation system.
Formal Verification of Divide and Conquer Key Distribution Protocol Using ProVerif and TLA+

We conduct formal verification of the divide and conquer key distribution scheme (DC DHKE)–a contributory group key agreement that uses a quasilinear amount of exponentiations with respect to the number of communicating parties. The verification is conducted using both ProVerif and TLA+ as tools. ProVerif is used to verify the protocol correctness as well as its security against passive attacker; while TLA+ is utilized to verify whether all participants in the protocol retrieve the mutual key simultaneously. We also verify the ING and GDH.3 protocol for comparative purposes. The verification results show that the ING, GDH.3, and DC DHKE protocols satisfy the pre-meditated correctness, security, and liveness properties. However, the GDH.3 protocol does not satisfy the liveness property stating that all participants obtain the mutual key at the same time.
Finding the Optimal Cross-Subject EEG Data Alignment Method for Analysis and BCI

"Zero-training" classification, or the ability of a Brain-Computer Interface (BCI) model to perform well on a new subject without obtaining any labeled EEG data from the subject, is an active area of BCI research. We define data alignment as a procedure that reduces subject-dependent variability while preserving aspects of data studied in a particular cross-session analysis or utilized in a zero-training BCI application. We investigated the hypothesis that data alignment methods that reduce subject-dependent variability will simultaneously result in more stable, i.e. less variable across recordings, EEG features and better BCI classifiers. Exploring different families of linear transformations of EEG sensor data, derived from statistical measures computed on five minutes of unlabeled data at the start of test sessions, we investigated whether they produced less variable event-related potentials (ERPs), and whether they increased the zero-training performance of a state-of-the-art deep learning based BCI applied to the task of classifying target versus non-target visual stimuli from EEG data. Our results indicate that zero-phase component analysis (ZCA) sphering-based data alignment methods consistently outperform baseline (no data alignment) and purely channel amplitude-based data alignment methods. We also observed statistically significant improvements in BCI performance on visual target detection tasks after sphering-based data alignment.
Monitoring Pilot's Cognitive Fatigue with Engagement Features in Simulated and Actual Flight Conditions Using an Hybrid fNIRS-EEG Passive BCI

There is growing interest for implementing tools to monitor cognitive performance in naturalistic environments. Recent technological progress has allowed the development of new generations of brain imaging systems such as dry electrode electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) to investigate cortical activity in a variety of human tasks out of the laboratory. These highly portable brain imaging devices offer interesting prospects to implement passive brain computer interfaces (pBCI) and neuroadaptive technology. We developed a fNIRS-EEG based pBCI to monitor cognitive fatigue using engagement related features (EEG engagement ratio and wavelet coherence fNIRS based metrics). This mental state is known to impair cognitive performance and can jeopardize flight safety. In this preliminary study, four participants were asked to perform four traffic patterns along with a secondary auditory task in a flight simulator and in an actual light aircraft. The two first traffic patterns were considered as the low cognitive fatigue class, whereas the two last traffic patterns were considered as the high cognitive fatigue class. As expected, the pilots missed more auditory targets in the second part than in the first part of the experiment. Classification accuracy reached 87.2% in the flight simulator condition and 87.6% in the actual flight conditions when combining the two modalities. This study demonstrates that fNIRS and EEG-based pBCIs can monitor mental states in operational and noisy environments.
A Neural Network-based Approach for Public Transportation Prediction with Traffic Density Matrix

In today's modern cities, mobility is of crucial importance, and public transportation is particularly concerned. The main objective is to propose solutions to a given, practical problem, which specifically concerns the bus arrival time at various bus stop stations, by taking to account local traffic conditions. We show that a global prediction approach, under some global macro-parameters (e.g., total number of vehicles or pedestrians) is not feasible. This observation leads us to the introduction of a finer granularity approach, where the traffic conditions are represented in terms of a traffic density matrix. Under this new paradigm, the experimental results obtained with both linear and neural networks (NN) approaches show promising prediction performances. Thus, the NN approach yields 24% more accurate prediction performances than a basic, linear regression.
A Solution of Concurrent Queue on PSTM

Although TM aids easier parallel programming, designing TM based concurrent queues, still poses open research challenges. In this paper, we developed the concurrent queue on Python STM (CQ-PSTM), and verified it using unit and system testing. For system testing we developed the five application workloads, namely: one enqueuer - one dequeuer, nP enqueuers, nP dequeuers, nE enqueuers and nD dequeuers, and nP processes. The CQ-PSTM successfully passed all of the unit and the system tests.
VLIP: Tightly Coupled Visible-Light/Inertial Positioning System to Cope With Intermittent Outage

Recently, it is believed that visible light communication (VLC) will take the lead in the future indoor communication system as the popularization of high power LEDs. By taking advantage of VLC, many vision-based visible light positioning (VLP) algorithms emerge and achieve high accuracy. However, most of them require at least four visible LEDs, and therefore will not work when there are insufficient LEDs. This is what we called intermittent outage for a vision-based VLP system. To solve this problem, we fuse the inertial measurement unit and VLC information in a tightly coupled scheme so that the VLP system is able to continually provide location service under the aforementioned intermittent outage condition. Experimental results show that the proposed method increases the utilization percentage of image frames from 51.4% to 100.0% in the same dataset.
70W X-Band GaN Internally Matched FET with 40% PAE and 800MHz bandwidth

In this paper, the design and the characteristics of the wide bandwidth X-band high power amplifiers are reported. The computer simulated and the experimental data has been compared. To reduce the size of the power amplifier, single layer capacitors with high dielectric constant were used. The impedance matching circuit was made on the alumina ceramic substrate. The measured data of output power, gain and PAE of the amplifier were over 48.27dBm, 8.2 dB, and 40%, respectively for 8. $\pmb{4\sim 9.2}\mathbf{GHz}$ frequency range while the size of the design was $\pmb{9.2}\ \mathbf{mm }\ \mathbf{x}\ \pmb{5.8}\ \mathbf{mm}$.
Analysis of Stability for the Semi Implicit Scheme for SDEs with Polynomial Growth Condition

Numerical stability plays an important role in numerical analysis. The author analysis the numerical stability of the stochastic delay differential equations (SDDEs). Traditional stability theory for numerical methods applied to SDDEs requires a global Lipschitz assumption or one-side linear growth condition on the coefficients. In this paper we want to further relax the condition. Under polynomial growth condition, this paper shows that the semi implicit method can reproduce almost sure exponential stability of the exact solutions to the SDDEs. This improves the existing results considerably.
AssIUT IOT: A Remotely Accessible Testbed for Internet of Things

Internet of Thins is a paradigm in which things (devices) communicate with each other and with a cloud platform to perform a set of tasks includes monitoring and controlling the device or the surrounding environment. In this paper, we introduce the basic architecture of AssIUT IoT a remotely accessible testbed designed for IoT applications. The testbed adopts Experiment as a Service (EaaS) model as it provides a suitable and easy way to remotely implement experiments related to IoT applications. We provide details about the testbed design and architecture from hardware and software point of views. Also, we provide some examples of the experiments which can be implemented using our testbed.
Automated Feature Synthesis from Relational Database for Data Science Related Problems

Feature Synthesis is the most important aspect of Data Science. This process requires domain knowledge of the data to produce meaningful features that support decision-making algorithms. The biggest technical obstacle which a data scientist must subdue is the synthesis of such features that would assist AI algorithms to predict the target variable with the least error. Feature synthesis is a crucial step toward the application of machine learning which consumes time and resources. Automated Feature Synthesis obviates the tedious task of data scientists. Data Science Machine and One Button Machine are pioneering researches that explored the possibility of feature synthesis from raw relational data by applying linear exploratory algorithms which increase the dimensionality of the feature matrix exponentially, and hence, they consume a lot of time and memory. Our research revolves around the process of feature synthesis from the structured and unstructured data types for relational data which will provide an effective automation of a hectic manual task for the non-experts consuming a reasonable amount of resources. We devised a solution to reduce the time complexity of the operation by reducing dimensionality while considering the target variable by using approximate dynamic programming. The model is designed utilizing the concept of sparse reward setting, and it tries to achieve maximum reward as it explores the feature set. The target variable plays its part to compute the reward eventually leading to an optimum feature set. This process reduces data exploration time, cost and effort for both data scientists and non-experts.
Design of 24-GHz Automotive Antenna Array with Wide Fan Beam

In this study, a 24-GHz horizontally-polarized $1\times 8$ patch antenna array was developed for automotive radar applications. The proposed antenna array offers the advantages of wide fan beam and high gain. The driven patch is loaded with parasitic mushroom-like elements (PMLEs). The PMLEs are 180° out of phase to their corresponding driven patch. The cancellation effect can be properly managed to slightly sacrifice the gain of the array in return for a wider beamwidth in the E- plane. The proposed design exhibits a wide beamwidth of 150° and a high gain of 11.1 dBi.
Development of High-Dorsiflexion Assistive Robotic Technology for Gait Rehabilitation

Active ankle dorsiflexion in swing phase to ensure foot clearance, and resistive dorsiflexion for heel rocker function to prevent foot slap are important for a normal human gait. Stroke patients with hemiplegia have motor deficiency in their paralyzed side, and thus could not voluntarily generate enough dorsiflexion force. In this paper, we present our design of a light-weighted high dorsiflexion supportive robotic technology. It supports swing phase dorsiflexion with a McKibben-type artificial muscle, and supports heel rocker function with a tension spring. A pilot clinical trial shows the immediate improvement of dorsiflexion in swing phase by supporting high dorsiflexion angle in intervention, and the other pilot clinical trial shows the effects of heel rocker function support with lessened angular velocity of ankle plantarflexion and knee flexion.
Influence of GaN- and Si3N4-Passivation Layers on the Performance of AlGaN/GaN Diodes With a Gated Edge Termination

This paper analyses the influence of the GaN and Si<sub>3</sub>N<sub>4</sub> passivation (or “cap”) layer on the top of the AlGaN barrier layer on the performance and reliability of Schottky barrier diodes with a gated edge termination (GET-SBDs). Both GaN cap and Si<sub>3</sub>N<sub>4</sub> cap devices show similar dc characteristics but a higher density of traps at the SiO<sub>2</sub>/GaN interface or/and an increase of the total dielectric constant in the access region result in higher <inline-formula> <tex-math notation="LaTeX">$R_{\mathrm{ON}}$ </tex-math></inline-formula>-dispersion in GaN cap devices. The leakage current at medium/low temperatures in both types of devices shows two low-voltage-independent activation energies, suggesting thermionic and field-emission processes to be responsible for the conduction. Furthermore, a voltage-dependent activation energy in the high-temperature range occurs from low voltages in the GaN cap devices and limits their breakdown voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{\mathsf {BD}}$ </tex-math></inline-formula>). Time-dependent dielectric breakdown measurements show a tighter distribution in Si<sub>3</sub>N<sub>4</sub> cap devices (Weibull slope <inline-formula> <tex-math notation="LaTeX">$\beta = {3.3}$ </tex-math></inline-formula>) compared to GaN cap devices (<inline-formula> <tex-math notation="LaTeX">$\beta = 1.8$ </tex-math></inline-formula>). Additional measurements in plasma-enhanced atomic layer deposition (PEALD)-Si<sub>3</sub>N<sub>4</sub> capacitors with different cap layers and TCAD simulations show an electric field distribution with a strong peak within the PEALD-Si<sub>3</sub>N<sub>4</sub> dielectric at the GET corner, which could accelerate the formation of a percolation path and provoke the device breakdown in GaN cap SBDs even at low-stress voltages.
Graph Attention LSTM Network: A New Model for Traffic Flow Forecasting

For the road networks containing multiple intersections and links, the traffic flow forecasting is essentially a time series forecasting problem on graphs. The task is challenging due to (1) complex spatiotemporal dependence among traffic flows of the whole road network and (2) sharp non-linearity and dynamic nature under different conditions. In this paper, by extending the LSTM to have graph attention structure in both the input-to-state and state-to-state transitions, we propose the Graph Attention LSTM Network (GAT-LSTM) and use it to build an end-to-end trainable encoder-forecaster model to solve the multi-link traffic flow forecasting problem. Experiment results show that our GAT-LSTM network could capture spatiotemporal correlations better and has achieved improvement of 15% - 16% over state-of-the-art baseline.
iSCSI and CIFS Network Disks for RAID0/RAID 1 Configurations

In this paper, we have tested the impact of the network disk configuration to the system performances. The performance analysis compares two network disk configurations, practically evaluating two different network protocols using identical physical disk configurations. One network drive is configured to operate as an iSCSI block device, while the other operates as a network share disk using CIFS protocol. Testing procedure is based on the use of DiskSpd tool and encompasses measurement of the flow rate, number of the operations and the average response time. Based on the results, iSCSI shows significantly higher flow rates and lower delay values as the size of the data block grows. In smaller blocks, the differences between iSCSI and CIFS are almost negligible.
Use of Model Predictive Control for Short-Term Operating Reserve Using Commercial Buildings in the United Kingdom Context

Flexibility, particularly in terms of reserve services, is an essential requirement of power systems with high penetration of renewable electrical generation, which can reduce undesirable curtailment and enable higher integration of clean electrical power from renewable generation. Reserve services are related to additional active power sources available to the grid operator in the form of either increased generation or demand reduction. There is increasing evidence that commercial buildings can provide such reserves. In this paper we present a Model Predictive Control approach to optimization of flexibility afforded by commercial buildings for the provision of Short-Term Operating Reserve in the United Kingdom without compromising the comfort of the occupants. In this reserve scheme the flexibility is to be made available only during selected hours of the day and is to be provided for a few hours with a slow response time ($\leq 5$ minutes) if required by the Transmission System Operator, National Grid. Simulation results show that a commercial building can provide Short-Term Operating Reserve and yield an economic benefit in a robust manner, without violating the indoor comfort of occupants.