Miracle-dz/UniFAR-FTUs huggingface dataset:
Changes in Spectral Power and Functional Connectivity of Response-Conflict Task After Neurofeedback Training

Neurofeedback training (NFT) teaches self-control of brain activity to the subjects by measuring brain waves and providing a feedback signal in real time. NFT is considered to be a promising tool for healthy people to enhance cognition, however its specific effects on response-conflict are unknown. This study employed 50 healthy participants into two groups (NFT group = 25, control group = 25), in order to explore the NFT effects towards response-conflict resolution phenomenon. Changes in Absolute power and functional connectivity in different bands of response-conflict EEG, were investigated. The EEG of each participant was recorded while performing a response-conflict task before and after NFT. The results show an increase in theta and alpha band powers and functional connectivity. No training specific modulation is observed in beta band functional connectivity. A significant improvement in theta and alpha band functional connectivity is observed in the NFT group that is not present in the control group. These findings provide a strong rationale for the use of NFT in the resolution of response-conflict phenomenon in healthy human beings.
Gluing resource proof-structures: inhabitation and inverting the Taylor expansion

A Multiplicative-Exponential Linear Logic (MELL) proof-structure can be expanded into a set of resource proof-structures: its Taylor expansion. We introduce a new criterion characterizing those sets of resource proof-structures that are part of the Taylor expansion of some MELL proof-structure, through a rewriting system acting both on resource and MELL proof-structures. As a consequence, we prove also the semi-decidability of the type inhabitation problem for MELL proof-structures.
Distributed OPF Algorithm for System-Level Control of Active Multi-Terminal DC Distribution Grids

This paper presents a distributed optimal power flow (OPF) algorithm for the system-level control of multi-terminal DC (MTDC) distribution grids with distributed energy resources (DER). At each control period, the algorithm updates the nominal voltage and power set-points of the DER-interfacing converters, which operate according to active network management (ANM) concepts. To achieve this, the OPF problem, in its nodal formulation, includes power dispatch strategies for diverse DER according to their technical characteristics, which change during the system operation. This multi-objective OPF-for-ANM problem is solved by distributed control units (DCUs) according to the distributed algorithm for the alternating direction method of multipliers (ADMM). All DCUs have identical roles in the distributed control structure and thus the distributed OPF-for-ANM algorithm is highly modular. Simulation results in different IEEE standard systems and various scenarios demonstrate that the algorithm is fast and scalable, irrespective of the number and location of integrated DER, as well as the operating condition of the system. The convergence speed of the algorithm is analysed considering the computation and communication time needed for its execution. The online application in a computers cluster demonstrates the fast execution of the developed algorithm in a physically-distributed implementation. Through the proposed OPF-for-ANM algorithm, the system-level control can dispatch fast diverse DER in different coordination approaches in a distributed manner.
Rényi Entropy-Based Spectrum Sensing in Mobile Cognitive Radio Networks Using Software Defined Radio

A very important task in Mobile Cognitive Radio Networks (MCRN) is to ensure that the system releases a given frequency when a Primary User (PU) is present, by maintaining the principle to not interfere with its activity within a cognitive radio system. Afterwards, a cognitive protocol must be set in order to change to another frequency channel that is available or shut down the service if there are no free channels to be found. The system must sense the frequency spectrum constantly through the energy detection method which is the most commonly used. However, this analysis takes place in the time domain and signals cannot be easily identified due to changes in modulation, power and distance from mobile users. The proposed system works with Gaussian Minimum Shift Keying (GMSK) and Orthogonal Frequency Division Multiplexing (OFDM) for systems from Global System for Mobile Communication (GSM) to 5G systems, the signals are analyzed in the frequency domain and the Renyi-Entropy method is used as a tool to distinguish the noise and the PU signal without prior knowledge of its features. The main contribution of this research is that uses a Software Defined Radio (SDR) system to implement a MCRN in order to measure the behavior of Primary and Secondary signals in both time and frequency using GNURadio and OpenBTS as software tools to allow a phone call service between two Secondary Users (SU). This allows to extract experimental results that are compared with simulations and theory using Renyi-entropy to detect signals from SU in GMSK and OFDM systems. It is concluded that the Renyi-Entropy detector has a higher performance than the conventional energy detector in the Additive White Gaussian Noise (AWGN) and Rayleigh channels. The system increases the detection probability (PD) to over 96% with a Signal to Noise Ratio (SNR) of 10dB and starting 5 dB below energy sensing levels.
Fusing Knowledge and Aspect Sentiment for Explainable Recommendation

The current research of recommender system mainly focuses on improving the accuracy of the recommendation, but pays less attention to explainability. Explainability is essential to enhance users’ trust and satisfaction, which can even increase the likelihood of buying items. In the existing explainable recommender systems, the mining of explicit and implicit features is not comprehensive, and the interaction among these features is not considered plentifully. In addition, the generated recommended reason text is not personalized and rich enough. It is necessary to improve the quality of recommended reason text because it is difficult to meet the needs of different users by using low-quality text. In this paper, we propose a new method that fuses external knowledge and aspect sentiment to predict rating and generate personalized, content-rich recommended reasons, which applies fine-tuning BERT to solve aspect-based sentiment analysis and extends Transformer to generate recommended reason by using bi-directional attention. The experiment results on real-world datasets demonstrated that our method was effective, and our model was superior to the baseline models on various metrics. For rating prediction task, our model can achieve an improvement of 0.6% on average in terms of RMSE. For recommended reason generation task, our model can achieve an improvement of 9.2% to 11.3% over state-of-the-arts in terms of BLEU.
Resource Management and Admission Control for Tactile Internet in Next Generation of Radio Access Network

Tactile Internet (TI) is one of the main service types in 5G, which requires tight delay preservation for each end-to-end (E2E) transmission link of TI pair of users. Consequently, it is required a highly sophisticated resource allocation (RA) algorithm over cloud radio access networks (C-RANs). In this paper, we aim to address this problem based on admission control (AC) and power allocation in orthogonal frequency division multiple access (OFDMA) mode with considering joint fronthaul and access delay in C-RAN. The objective of our proposed RA problem is to minimize a total transmit power subject to E2E delays and C-RAN limitations, e.g., a capacity limitation of fronthaul and power transmission links. Due to tight E2E constraints, the RA problems suffers from an infeasibility issue, i.e., the requirements of all TI users are not guaranteed, which leads to high degradation of services of TI users. The proposed AC of this paper can overcome this practical challenge while RA can help to use the resources in the best possible extent. Through simulation results, we investigate diverse service satisfaction parameters such as service acceptance ratio (SAR) and transmit power. Simulation results reveal that by dynamic adjustment of the access and fronthaul delays, transmit power can be saved compared to the case in which the delay is fixed over access and fronthaul links. Moreover, the number of rejected users in the network is significantly reduced, and more users are accepted, i.e., the SAR increases. Simulation results demonstrate that our dynamic delay adjustment and RA along with AC can outperform the benchmark algorithm in this context up to 3 dB.
CycleGAN-Based Deblurring for Gaze Tracking in Vehicle Environments

Recently, the need for research on tracking driver gazes is increasing owing to the development of driver convenience systems, such as autonomous driving or intelligent driver monitoring system, to address traffic accidents caused by negligence. A camera is installed in a vehicle to track the driver’s gaze in vehicle environment. The accuracy of estimating driver gazes in vehicle environments reduces if a motion blur of the driver occurs, owing to vehicular vibrations during driving. Most past studies on gaze-tracking of a driver in a vehicle did not consider the motion blurs in their experiments. To address this concern, we propose a method for improving the accuracy of gaze estimation by deblurring the blurred images of a driver from the vehicle. This study is the first attempt to calculate a driver’s gaze by deblurring a motion blurred image with CycleGAN, whereas simultaneously using the image information from the two cameras in the vehicle. In previous studies, multiple deep CNNs were used for obtaining the images of a driver’s eyes and face. In this study, information obtained from the two cameras in the vehicle are integrated into an image with three channels and thereafter deblurred, consequently reducing the time required for training. Whereas in previous studies the gaze position was not calculated for severe blurs by measuring the level of blur from the input image, the gaze position was calculated for all the input images in this study. From the database (Dongguk blurred gaze database (DBGD)) from 26 drivers in actual vehicles and the Columbia gaze dataset (CAVE-DB) that is an open database, the proposed method exhibited greater accuracy than the existing methods.
Unbiased Learning for the Causal Effect of Recommendation

Increasing users’ positive interactions, such as purchases or clicks, is an important objective of recommender systems. Recommenders typically aim to select items that users will interact with. If the recommended items are purchased, an increase in sales is expected. However, the items could have been purchased even without recommendation. Thus, we want to recommend items that results in purchases caused by recommendation. This can be formulated as a ranking problem in terms of the causal effect. Despite its importance, this problem has not been well explored in the related research. It is challenging because the ground truth of causal effect is unobservable, and estimating the causal effect is prone to the bias arising from currently deployed recommenders. This paper proposes an unbiased learning framework for the causal effect of recommendation. Based on the inverse propensity scoring technique, the proposed framework first constructs unbiased estimators for ranking metrics. Then, it conducts empirical risk minimization on the estimators with propensity capping, which reduces variance under finite training samples. Based on the framework, we develop an unbiased learning method for the causal effect extension of a ranking metric. We theoretically analyze the unbiasedness of the proposed method and empirically demonstrate that the proposed method outperforms other biased learning methods in various settings.
Characterization of the Chilean Public Procurement Ecosystem Using Social Network Analysis

“Mercado Público” is a Chilean electronic platform used for purchasing processes by Chilean public organizations for the last two decades. The main aim of this study is to characterize the Chilean public procurement ecosystem by using social network analysis to detect the main communities of suppliers based on who awarded the tenders. To do this, we use a methodology that first represents the bidder-supplier relationship as a bipartite graph using purchase order information. Then we project the bipartite graph onto a monopartite graph of suppliers. We end by detecting the main supplier communities using a modularity algorithm. When we applied this methodology to the large tender segment in the Chilean public procurement market over a period of four years, we successfully detected the five largest communities and the micro and small companies which had the greatest rate of participation over time.
A branch‐and‐cut approach and alternative formulations for the traveling salesman problem with drone

In this paper, we are interested in studying the traveling salesman problem with drone (TSP‐D). Given a set of customers and a truck that is equipped with a single drone, the TSP‐D asks that all customers are served exactly once and minimal delivery time is achieved. We provide two compact mixed integer linear programming formulations that can be used to address instances with up to 10 customer within a few seconds. Notably, we introduce a third formulation for the TSP‐D with an exponential number of constraints. The latter formulation is suitable to be solved by a branch‐and‐cut algorithm. Indeed, this approach can be used to find optimal solutions for several instances with up to 20 customers within 1 hour, thus challenging the current state‐of‐the‐art in solving the TSP‐D. A detailed numerical study provides an in‐depth comparison on the effectiveness of the proposed formulations. Moreover, we reveal further details on the operational characteristics of a drone‐assisted delivery system. By using three different sets of benchmark instances, consideration is given to various assumptions that affect, for example, technological drone parameters and the impact of distance metrics.
Non-Uniform Embedding Scheme and Low-Dimensional Approximation Methods for Causality Detection

Information causality measures have proven to be very effective in uncovering the connectivity patterns of multivariate systems. The non-uniform embedding (NUE) scheme has been developed to address the “curse of dimensionality”, since the estimation relies on high-dimensional conditional mutual information (CMI) terms. Although the NUE scheme is a dimension reduction technique, the estimation of high-dimensional CMIs is still required. A possible solution is the utilization of low-dimensional approximation (LA) methods for the computation of CMIs. In this study, we aim to provide useful insights regarding the effectiveness of causality measures that rely on NUE and/or on LA methods. In a comparative study, three causality detection methods are evaluated, namely partial transfer entropy (PTE) defined using uniform embedding, PTE using the NUE scheme (PTENUE), and PTE utilizing both NUE and an LA method (LATE). Results from simulations on well known coupled systems suggest the superiority of PTENUE over the other two measures in identifying the true causal effects, having also the least computational cost. The effectiveness of PTENUE is also demonstrated in a real application, where insights are presented regarding the leading forces in financial data.
Driver Lane Change Intention Recognition of Intelligent Vehicle Based on Long Short-Term Memory Network

Driving intention prediction is one of the key technologies for the development of advanced assisted driving systems (ADAS), which could greatly reduce traffic accidents caused by lane change and ensure driving safety. In this paper, an advanced predictive method based on Multi-LSTM (Long Short-Term Memory) is proposed to predict lane change intention effectively. First, the training data set and test set based on real road information data set NGSIM (Next Generation SIMulation) are built considering ego vehicle driving state and the influence of surrounding vehicles. Second, the Multi-LSTM-based prediction controller is constructed to learn vehicle behavior characteristics and time series relation of various states in the process of lane change. Then, the influences of prediction model structure change and data structure change on test results are verified. Finally, the verification tests based on HIL (Hardware-in-the-Loop) simulation are constructed. The results show that the proposed prediction model can accurately predict the vehicle lane change intention in highway scenarios and the maximum prediction accuracy can reach 83.75%, which is higher than that of common method SVM (Support Vector Machine).
Natural Time Analysis of Global Navigation Satellite System Surface Deformation: The Case of the 2016 Kumamoto Earthquakes

In order to have further evidence of the atmospheric oscillation channel of the lithosphere-atmosphere-ionosphere coupling (LAIC), we have studied criticality in global navigation satellite system (GNSS) surface deformation as a possible agent for exciting atmospheric gravity waves (AGWs) in the atmosphere and GNSS fluctuations in the frequency range of AGWs with the use of the natural time (NT) method. The target earthquake (EQ) is the 2016 Kumamoto EQ with its main shock on 15 April 2016 (M = 7.3, universal time). As the result of the application of the NT method to GNSS data, we found that for the one-day sampled GNSS deformation data and its fluctuations in two AGW bands of 20–100 and 100–300 min, we could detect a criticality in the period of 1–14 April, which was one day to two weeks before the EQ. These dates of criticalities are likely to overlap with the time periods of previous results on clear AGW activity in the stratosphere and on the lower ionospheric perturbation. Hence, we suggest that the surface deformation could be a possible candidate for exciting those AGWs in the stratosphere, leading to the lower ionospheric perturbation, which lends further support to the AGW hypothesis of the LAIC process.
Construction of Nonlinear Component of Block Cipher by Action of Modular Group PSL(2, Z) on Projective Line PL(GF(28))

Substitution box (S-Box) has a prominent significance being the fundamental nonlinear component of block cipher which fulfils confusion, one of the properties proposed by Claude Shannon in 1949. In this paper, we proposed an S-Box by using the action of modular group PSL<inline-formula> <tex-math notation="LaTeX">$\left ({2,\mathbb {Z} }\right)$ </tex-math></inline-formula> on projective line PL<inline-formula> <tex-math notation="LaTeX">$\left ({F_{257} }\right)$ </tex-math></inline-formula> over Galois field GF<inline-formula> <tex-math notation="LaTeX">$\left ({2^{8} }\right)$ </tex-math></inline-formula>. In the first step we obtained elements of GF<inline-formula> <tex-math notation="LaTeX">$\left ({2^{8} }\right)$ </tex-math></inline-formula> by using powers of <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> is the primitive root of irreducible polynomial <inline-formula> <tex-math notation="LaTeX">$p\left ({x }\right)$ </tex-math></inline-formula> of order 8 over field <inline-formula> <tex-math notation="LaTeX">$\mathbb {Z}_{2}$ </tex-math></inline-formula>, then applied the generators of PSL<inline-formula> <tex-math notation="LaTeX">$\left ({2,\mathbb {Z} }\right)$ </tex-math></inline-formula> and followed steps to get rid of infinity from output. In the final step of proposed scheme, one of the permutations of <inline-formula> <tex-math notation="LaTeX">$S_{16}$ </tex-math></inline-formula> is applied which enhanced the possible number of S-Boxes obtained by any single specific irreducible polynomial <inline-formula> <tex-math notation="LaTeX">$p(x)$ </tex-math></inline-formula> over field <inline-formula> <tex-math notation="LaTeX">$\mathbb {Z}_{2}$ </tex-math></inline-formula> of order 8. We analyzed performance of the proposed <inline-formula> <tex-math notation="LaTeX">$8\times 8$ </tex-math></inline-formula> S-Box under cryptographic properties such as strict avalanche criterion, bit independence criterion, nonlinearity, differential approximation probability, linear approximation probability; and compared obtained results with a number of renowned S-Boxes. Lastly, we performed statistical analysis (which comprises of contrast analysis, homogeneity analysis, energy analysis, correlation analysis, entropy analysis and mean of absolute deviation analysis) on our proposed S-Box and obtained results have been compared with adequate number of S-Boxes.
Exploiting the SoC FPGA Capabilities in the Control Architecture of a Quadruped Robot

The use of FPGAs in the field of robotics has the potential to create interesting results considering the former’s assets and the latter’s necessities. Motion control is highly demanding both in resources and data, while the number of sensors employed in robotic devices can be large. On the other hand, FPGAs have gained a renowned place in the field of digital circuit implementation media. Simultaneously, the vast number of peripherals they are equipped with, renders them as ideal for the management of multiple external signals. In this paper, a highly affordable control architecture has been implemented for the quadruped robot Laelaps II based on a SoC FPGA, in order to examine the use of FPGAs in demanding robotic applications. The proposed system has realtime characteristics and is capable of controlling more than eight actuators at very high frequencies, outperforming most motion control platforms existing in the market, while keeping the total cost low. To achieve this, signals from all the robots peripherals are handled in parallel by the FPGA at a very high frequency, leaving the embedded ARM processor free to allocate all its resources to motion control and user interfacing tasks. The whole hardware/ software architecture is analyzed and evaluated compared with state-of-the-art approaches, while a trotting experiment with the Laelaps II motion system is presented for validation purposes.
Hard Negative Samples Emphasis Tracker without Anchors

Trackers based on Siamese network have shown tremendous success, because of their balance between accuracy and speed. Nevertheless, with tracking scenarios becoming more and more sophisticated, most existing Siamese-based approaches ignore the addressing of the problem that distinguishes the tracking target from hard negative samples in the tracking phase. The features learned by these networks lack of discrimination, which significantly weakens the robustness of Siamese-based trackers and leads to suboptimal performance. To address this issue, we propose a simple yet efficient hard negative samples emphasis method, which constrains Siamese network to learn features that are aware of hard negative samples and enhance the discrimination of embedding features. Through a distance constraint, we force to shorten the distance between exemplar vector and positive vectors, meanwhile, enlarge the distance between exemplar vector and hard negative vectors. Furthermore, we explore a novel anchor-free tracking framework in a per-pixel prediction fashion, which can significantly reduce the number of hyper-parameters and simplify the tracking process by taking full advantage of the representation of convolutional neural network. Extensive experiments on six standard benchmark datasets demonstrate that the proposed method can perform favorable results against state-of-the-art approaches.
Heterosis in maize (Zea mays L.)

High heterosis over better parents was recorded in 90 hybrids derived from 10 x 10 diallel for yield and its components in maize. The hybrids exhibited maximum heterosis for grain yield per plant followed by cob weight and number of grain rows per cob. The highest value of heterosis over better parent (97.45%) was recorded for grain yield per plant in the cross Prabhat - 1 x UMI 492.