Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of Consensus-ADMM Approach for Fast DC-OPF Studies

This paper proposes a novel method for solving the Optimal Power Flow (OPF) problem in conditions close to realtime. The linearized cost function of the generating units is used to this end. Besides, the presented linear model is solved using the Consensus Alternating Direction Method of Multipliers (C-ADMM) approach. This technique would provide the possibility of modeling the problem both in centralized and decentralized manners. The suggested method exploits the power flow results obtained from the previous iteration to considerably improve the rate of convergence. As the C-ADMM method uses an iterative technique, Lagrange multipliers, and the norm function, the rate of convergence highly depends upon assigning the initial conditions and the optimality gap. Thus, using the operating points of the previous instant due to being close to the operating point of the current instant would enhance the results. The proposed model has been implemented on two case studies including the Pennsylvania-New Jersey-Maryland (PJM) network to verify the results and the 9-bus system to evaluate the performance of the model for the daily operation.
An Improved SAR Imaging Method Based on Nonconvex Regularization and Convex Optimization

Sparse signal processing has been applied in synthetic-aperture radar (SAR) imaging. As a typical sparse reconstruction model, $L_{1}$ regularization often underestimates the intensities of the targets. The estimated radar cross section (RCS) is related to the pixel intensity. Thus, the linear relationship between the targets’ intensities cannot kept. The underestimation will also cause radiometric errors and affect the quantitative use of the SAR data. In this letter, we present a SAR imaging method based on generalized minimax concave (GMC) penalty. GMC is a nonconvex penalty and its cost function is convex. GMC can avoid the underestimation of pixel intensity. In the iteration, the azimuth-range decouple operators are used to avoid the huge memory and computational costs. The performance of the proposed method is verified using real data.
How to Modify Polar Codes for List Decoding

Polar codes are constructed based on the reliability of bit-channels. This construction suits the successive cancellation (SC) decoding, where one error in the successive estimation of the bits fails the decoding. However, in SC list (SCL) decoding, the correct path may remain in the list by tolerating multiple penalties. This characteristic of list decoding demands a different approach in code construction.In this work, we modify the conventional construction by a greedy search algorithm in which a bit-swapping approach is employed to re-distribute the low-reliability bits in the subblocks aiming for a reduction in the probability of correct path elimination. The numerical results for polar codes of length 1 kb under CRC-aided SCL decoding show improvements of about 0.4 dB for R=0.8 and over 0.2 dB for R=0.5 at L=32.
Detection of Satiric News on Social Media: Analysis of the Phenomenon with a French Dataset

The topic of deceptive and satiric news has drawn attention from both the public and the academic community, as such misinformation has the potential to have extremely adverse effects on individuals and society. Detecting false and satiric news automatically is a challenging problem in deception detection, and it has tremendous real-word political and social influences. In this paper, we contribute a useful French satiric dataset to the research community and provide a satiric news detection system using machine learning to automate classifications significantly. In addition, we present the preliminary results of our research designed to discriminate real news from satiric stories, and thus ultimately reduce false and satiric news distribution.
Towards Classifying Epileptic Seizures Using Entropy Variants

Epilepsy is the second most common neurological disorder, affecting about 50 Million people of the world's population. Entropy is a nonlinear measure suitable for studying chaotic behavior and randomness, suits well to analyze perceptual time series data such as EEG data. However, the EEG data are non-stationary and prone to numerous noise types that badly affect the classification accuracy of epileptic seizures. To address these issues this paper introduces a novel index for classifying epileptic seizure to extract the hidden information from recorded EEG signal to discriminate seizure states. The experimental results demonstrate that the proposed method shows better anti-noise performance compared to the conventional State of art entropy variants i.e. Approximate Entropy (ApEn), Sample Entropy (SampEn) and Permutation Entropy (PE). Compared to other variants that are quite sensitive to noise, the proposed method maintains its higher accuracy of 95.3% and AUC among the recorded EEG data of 21 subjects. The discriminative abilities of entropy variants were further tested using student t-test. The results on The Freiburg EEG database proved the superiority of the proposed index over the existing state-of-the-art variants. With the excellent classification performance and low computational complexity, PFuzzy entropy can be utilized for practical seizure classification and epilepsy detection in future hardware implementation and also opens future opportunities towards real-time detection and prediction of epileptic seizures.
Robotic Parasitic Array Optimization in Outdoor Environments

Radio communication between ground robots at low VHF frequencies is generally more reliable in complex environments compared to conventional systems operating at higher UHF frequencies. Unfortunately, high-gain directional antennas operating at low frequencies are impractically large for backpack sized robots. However, if a robot carrying a small, low VHF antenna having an omni-directional radiation pattern is positioned at a desired location close to a set of parasitic antenna elements (i.e., not connected to radios and their input is short-circuited), the overall multi-antenna system can form a parasitic array and create a directional radiation pattern with higher gain. In this paper, we consider a two element parasitic array formed by two ground robots and propose a technique by which this array can optimize its gain in a direction of interest online. It does this by finding the optimal spacing between the two antennas and passive antenna length, both of which depend on the electromagnetic characteristics of the ground. We simulate this array using full-wave FDTD and experimentally demonstrate in various outdoor environments and ground conditions that the robotic array can provide gain of over 2dB.
Joint Congestion Control and Resource Allocation for Massive MTC in 5G Networks Based on SCMA

The massive Machine Type Communications (mMTC) usage scenario, also known as massive Internet of Things (mIoT), involves a large number of MTC devices having high requirements on increased battery lifetime, which autonomously transfer small amounts of data with relaxed delay requirements, without human intervention. The current cellular network is unsuitable for this scenario, due to the limited uplink resources allocated to the Physical Random Access Channel (PRACH) and to the Physical Uplink Shared Channel (PUSCH). With this in mind, in this paper we propose a new framework, customized for massive MTC services, that includes a joint control of the dynamic resource allocation between the PRACH and the PUSCH, and a new random access procedure based on an adaptive Access Class Barring (ACB) scheme that appropriately spreads random access re-attempts in time. In addition, to further increase the transmission efficiency, we adopt the Sparse Code Multiple Access (SCMA) technique for PUSCH resources, because SCMA results as the most promising Non-Orthogonal Multiple Access (NOMA) technique to support massive MTC connectivity with small-size data. Simulation results show that the proposed control framework significantly improves the number of succeeded communications and guarantees lower energy consumption in comparison with other proposals available in literature.
Binary MDS Array Codes with Asymptotically Optimal Repair for All Columns

An m x (k+r) binary maximum distance separable (MDS) array code contains k information columns and r parity columns with each entry being a bit such that any k out of k+r columns can retrieve the k information columns. When there is a failed column, it is critical to minimize the repair bandwidth that is the total number of bits downloaded from d out of k+r-1 surviving columns in repairing the failed column. In this paper, we first propose a new construction of binary MDS array codes with any number of parity columns (i.e., r>= 2) that have asymptotically optimal repair bandwidth for any information column, where d=k+r-1. By applying a transformation for the proposed binary MDS array codes, we then can obtain the transformed binary MDS array codes that also have optimal repair bandwidth for any parity column.
Argumentation for Explainable Scheduling (Full Paper with Proofs)

Mathematical optimization offers highly-effective tools for finding solutions for problems with well-defined goals, notably scheduling. However, optimization solvers are often unexplainable black boxes whose solutions are inaccessible to users and which users cannot interact with. We define a novel paradigm using argumentation to empower the interaction between optimization solvers and users, supported by tractable explanations which certify or refute solutions. A solution can be from a solver or of interest to a user (in the context of ‘what-if’ scenarios). Specifically, we define argumentative and natural language explanations for why a schedule is (not) feasible, (not) efficient or (not) satisfying fixed user decisions, based on models of the fundamental makespan scheduling problem in terms of abstract argumentation frameworks (AFs). We define three types of AFs, whose stable extensions are in one-to-one correspondence with schedules that are feasible, efficient and satisfying fixed decisions, respectively. We extract the argumentative explanations from these AFs and the natural language explanations from the argumentative ones.
Exploiting Function Words Feature in Classifying Deceptive and Truthful Reviews

In this paper, we exploit function words as a feature for classifying deceptive and truthful opinions in the online reviews. Initially, we employ function words as a single feature to classify deceptive and truthful reviews. Then, we combine those function words with widely used text features in this task, namely, word and character n-grams, as our proposed combination feature. The experimental results exhibit that our proposed feature could perform well in this deceptive and truthful reviews classification. In particular, the obtained results of our proposed combination feature could outperform those using the baseline feature, i.e., word and character n-grams combinations. In addition, we apply the feature attribute selection, that is, Information Gain in conjunction with Ranker of WEKA, on our proposed combination feature. This treatment is to examine further the robustness of our proposed combination feature when dealing with this task. The latter experiment shows that after applying the feature attribute selection, our proposed combination feature was able to increase the classification results.
Querying Policies Based on Sparse Matrices for Noisy 20 Questions

This paper shows that the error probability of a querying policy for noisy 20 questions is upper bounded by the minimum Hamming distance of its querying matrix. Following this distance principle, sparse querying matrices with the row-column constraint are constructed for the scenarios, where only limited areas can be detected at each querying round. It demonstrates that the row-column constraint promises a large minimum distance to ensure low error probability of queries. Moreover, the proposed sparse matrices with random block coding provide unequal error-protection capability to further improve the querying accuracy under the scenarios of detecting half of the areas. Simulation results verify that the quantized mean squared errors of our proposed policies outperform those of the existing policies under the above scenarios.
Complexity and Similarity for Sequences using LZ77-based conditional information measure

This work concerns the definition of conditional mutual information in the framework of Algorithmic Information Theory (AIT), which is of use when no probabilistic model of the data is available, or hard to devise. We introduce a practical way to construct a conditional mutual information quantity which respects the chain rule and the data processing inequalityThe proposed implementation, named SALZA, allows to accomplish various information-theoretic tasks on sequences. The algorithmic model of the data used in this work is that of the well-known Lempel-Ziv primitive: we assume new data is to be expressed in terms of references to prior data.SALZA enables a flexible specification of prior data and extracts information quantities based on the significance of the references to these prior data. The tool readily implements the computation of an information measure based on LZ77 and a universal classifier based on the Ziv-Merhav relative coder for the universal clustering of sequences.Illustration of the proposed implementation is provided on clustering and causality inference examples.
Demand Response in Energy Communities Considering the Share of Photovoltaic Generation from Public Buildings

This paper has as ambit to promote the importance of the prosumer and the sustainable development of a community's energy systems through the aid of the incorporation of renewable energy sources in the market and the concept demand response. Moreover, it is intended to efficiently use the energy surplus produced by the photovoltaic panels of the prosumers for self-consumption, distributed by the remaining members of the community. It is estimated that participants, through the energy management of the community, will be able to verify reductions in electricity bills, as well as be compensated for their contribution to demand response through remuneration. Thus, the proposed methodology contributes in an efficient and sustainable way to be implemented in a community, promoting the use of renewable energy.
Operational Integration of Electric Bus Fleets, Charging Process Analysis, and Field Test Results

This paper deals with the operational integration of electric buses and charging infrastructure conducted in field tests. Relevant actors and entities for managing the charging processes are introduced and the main interactions described. Further, an overview of available charging infrastructures and acquisition equipment used in field sites is detailed. These findings reveal as input for a simulation framework allowing to analyze diversified charging processes. Finally, measured real-time data from uncontrolled and controlled charging processes of realworld bus operations is analyzed. The results of the simulation and real-world application state significant implications to be addressed in the operational planing of charging processes and for enhanced power system operations.
Effects of advertisements and questionnaire interruptions on the player experience

New online stores and digital distribution methods have led to the development of alternative monetization models for video-games, such as free-to-play games with advertisements. Although there are many games using such models, until now the effect on the player experience from such interruptions has not been studied. In this controlled experiment, we requested that participants (N=236) play one of three different versions of a platformer game with: 1) no interruptions, 2) 30-second video advertisements, and 3) a multiple-choice questionnaire. We then evaluated the effects on the player experience. The study shows differences in their experiences, namely in: competence, immersion, annoyance, affects, and the reliability of the questionnaire answers. The contribution of this work is to identify which player experience variables are affected by interruptions, which can be valuable for selecting the business model and guiding the game design process.
Will Your Classmates and Colleagues Affect Your Development in the Workplace: Predicting Employees' Growth Based on Interpersonal Environment

Learning the growth of employees is important for personnel selection, staff training and talent retention in human resource management (HRM). Currently, the ideas and techniques of HRM tend to be changed in the era of big data. This paper adopts thoughts of data-driven and methods of data analytics to explore employee growth based on interpersonal environment factors, providing insights into employee growth by carrying out quantitative analysis and designing exact prediction model. Related features are extracted from the database of the HRM information system in a state-owned enterprise in China. Statistics analysis is used to discover the correlations between employee growth and interpersonal environment. Moreover, machine learning is utilized to forecast the growth potential of employee. The proposed model is very effective and has good predictive effects. The results of the experiment prove that classmate relation and people quality in interpersonal environment are critical for the growth of employee.
Control Scheme for Phase Balancing of Low-Voltage Distribution Grids

Low-voltage distribution grids face several challenges due to the high penetration of renewable energy sources, the increasing demand (electrification of thermal and transportation sector), and the limited observability (absence of smart metering infrastructure). These challenges impose problems regarding the integrity, stability, power quality and efficiency of distribution grids. Therefore, intelligent solutions are required in order to overcome these problems. This paper proposes a centralized control scheme for phase balancing of low-voltage distribution grids at the substation level. The proposed solution utilizes the advanced capabilities of the grid tied inverters of photovoltaics and storage systems to increase the utilization of distribution lines and to compensate the asymmetric loading conditions of the substation. The solution is based on only one smart meter installed at the distribution substation and utilizes available information and communication technology infrastructure to integrate the solution. The solution achieves significant benefits for the distribution grids, especially regarding their power quality and the effective utilization of their capacity.
A Path Loss Model for Vehicle-to-Vehicle Visible Light Communications

The increasing adoption of LEDs in exterior automotive lighting makes visible light communication (VLC) a natural solution for vehicular networking. In this paper, we consider a vehicle-to-vehicle link and propose a path loss expression as a function of distance and different weather conditions. We conduct ray tracing simulations and verify the accuracy of proposed expression. We further use this expression to derive the achievable transmission distance for a targeted data rate while satisfying a given value of bit error rate. Numerical results are presented to demonstrate the achievable distances for single and dual photodetector deployment cases.
Visualization of Eye-Tracking Patterns in Autism Spectrum Disorder: Method and Dataset

Autism spectrum disorder (ASD) is a lifelong condition generally characterized by social and communication impairments. One of the characteristic hallmarks of ASD is the difficulty of making or maintaining eye contact. In this respect, the eye-tracking technology has come into prominence to support the study and analysis of autism. This paper develops a methodology to visualize the eye-tracking patterns of ASD-diagnosed individuals with particular focus on children at early stages of development. The key idea is to transform the dynamics of eye motion into a visual representation, and hence diagnosis-related tasks could be approached using image-based techniques. The visualizations produced are made publicly available in an image dataset to be used by other studies aiming to experiment the potentials of eye-tracking within the ASD context. It is believed that the dataset can allow for developing further useful applications or discovering interesting insights using Machine Learning or data mining techniques
Coding for Write ℓ-step-up Memories

In this work, we propose and study a new class of non-binary rewriting codes, called write ℓ-step-up memories (WℓM) codes. From an information-theoretic point of view, this coding scheme is a generalization of non-binary write-once memories (WOM) codes. From a practical point of view, this coding scheme can be used not only to increase the lifetime of flash memories but also mitigate their over-shooting problem. We first provide an exact formula for the capacity region and the maximum sum-rate of WℓM codes. Lastly, we present several explicit constructions of high-rate WℓM codes with efficient encoding/decoding algorithms.
Undirected Unicast Network Capacity: A Partition Bound

In this paper, we present a new technique to obtain upper bounds on undirected unicast network information capacity. Using this technique, we characterize an upper bound, called partition bound, on the symmetric rate of information flow in undirected unicast networks and give an algorithm to compute it. Two classes of networks are presented for which the bound is tight and the capacity is achievable by routing thus confirming the undirected unicast conjecture for these classes of networks. We also show that the bound can be loose in general and present an approach to tighten it.
Managing Background Traffic in Cellular Networks

A large variety of traffic — time-sensitive “foreground” traffic (e.g., web browsing) and time-insensitive “background” traffic (e.g., software updates) — compete for the scarce cellular bandwidth, especially on the downlink. While there is limited in-network support for traffic prioritization, existing endto-end, “low priority transport protocols” exhibit sub-optimal performance in cellular networks. We propose Sneaker, which yields to time-sensitive foreground traffic during periods of congestion and enables time-insensitive background traffic to efficiently utilize any spare capacity. Sneaker achieves the desired goal by randomly dropping packets coming into the base station, based on traffic type and network conditions. Our key contribution is the derivation of the optimal dropping rate and a practical dropping rate, which performs close to optimal. Further, Sneaker co-exists and performs well with existing cellular schedulers and transport protocols.
Train-to-Infrastructure Channel Modeling and Simulation in MmWave Band

Wireless communication has been a fundamental part of railway operation, control, and service systems for decades. In the era of 5G and beyond, the railway communication system is evolving from critical signaling to support various functions to improve the convenience, efficiency, and safety of railway services. Millimeter-wave (mmWave) bands and related technologies are the keys to this vision. Therefore, the characterization, modeling, and simulation of mmWave channels are critical for the design and performance evaluation, as well as for the construction of the future railway infrastructures. In this article, a brief overview of the architecture design of a train-to-infrastructure (T2I) mmWave communication system is provided. Based on investigating the existing research and related work on T2I mmWave channels, the challenges and requirements are identified, and the appropriate research paradigm and future directions in this area are discussed.