Miracle-dz/UniFAR-FTUs huggingface dataset:
A Reweighted Symmetric Smoothed Function Approximating L0-Norm Regularized Sparse Reconstruction Method

Sparse-signal recovery in noisy conditions is a problem that can be solved with current compressive-sensing (CS) technology. Although current algorithms based on L 1 regularization can solve this problem, the L 1 regularization mechanism cannot promote signal sparsity under noisy conditions, resulting in low recovery accuracy. Based on this, we propose a regularized reweighted composite trigonometric smoothed L 0 -norm minimization (RRCTSL0) algorithm in this paper. The main contributions of this paper are as follows: (1) a new smoothed symmetric composite trigonometric (CT) function is proposed to fit the L 0 -norm; (2) a new reweighted function is proposed; and (3) a new L 0 regularization objective function framework is constructed based on the idea of T i k h o n o v regularization. In the new objective function framework, Contributions (1) and (2) are combined as sparsity regularization terms, and errors as deviation terms. Furthermore, the conjugate-gradient (CG) method is used to optimize the objective function, so as to achieve accurate recovery of sparse signal and image under noisy conditions. The numerical experiments on both the simulated and real data verify that the proposed algorithm is superior to other state-of-the-art algorithms, and achieves advanced performance under noisy conditions.
Improvement of Fast Simplified Successive-Cancellation Decoder for Polar Codes

This paper presents a new latency reduction method for successive-cancellation (SC) decoding of polar codes that performs a frozen-bit checking on the rate-other (R-other) nodes of the Fast Simplified SC (Fast-SSC) pruning tree. The proposed method integrates the Fast-SSC algorithm and the Improved SSC method (frozen-bit checking of the R-other nodes). We apply a recognition-based method to search for as many constituent codes as possible in the decoding tree offline. During decoding, the current node can be decoded directly, if it is a special constituent code; otherwise, the frozen-bit check is executed. If the frozen-bit check condition is satisfied, the operation of the R-other node is the same as that of the rate-one node. In this paper, we prove that the frame error rate (FER) performance of the proposed algorithm is consistent with that of the original SC algorithm. Simulation results show that the proportion of R-other nodes that satisfy the frozen-bit check condition increases with the signal-to-noise-ratio (SNR). Importantly, our proposed method yields a significant reduction in latency compared to those given by existing latency reduction methods. The proposed method solves the problem of high latency for the Improved-SSC method at a high code rate and low SNR, simultaneously.
A Coarse-to-Fine Fully Convolutional Neural Network for Fundus Vessel Segmentation

Fundus vessel analysis is a significant tool for evaluating the development of retinal diseases such as diabetic retinopathy and hypertension in clinical practice. Hence, automatic fundus vessel segmentation is essential and valuable for medical diagnosis in ophthalmopathy and will allow identification and extraction of relevant symmetric and asymmetric patterns. Further, due to the uniqueness of fundus vessel, it can be applied in the field of biometric identification. In this paper, we remold fundus vessel segmentation as a task of pixel-wise classification task, and propose a novel coarse-to-fine fully convolutional neural network (CF-FCN) to extract vessels from fundus images. Our CF-FCN is aimed at making full use of the original data information and making up for the coarse output of the neural network by harnessing the space relationship between pixels in fundus images. Accompanying with necessary pre-processing and post-processing operations, the efficacy and efficiency of our CF-FCN is corroborated through our experiments on DRIVE, STARE, HRF and CHASE DB1 datasets. It achieves sensitivity of 0.7941, specificity of 0.9870, accuracy of 0.9634 and Area Under Receiver Operating Characteristic Curve (AUC) of 0.9787 on DRIVE datasets, which surpasses the state-of-the-art approaches.
Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data

With rapid advancement in location-based services (LBS), their acquisition has become a powerful tool to link people with similar interests across long distances, as well as connecting family and friends. To observe human behavior towards using social media, it is essential to understand and measure the check-in behavior towards a location-based social network (LBSN). This check-in phenomenon of sharing location, activities, and time by users has encouraged this research on the frequency of using an LBSN. In this paper, we investigate the check-in behavior of several million individuals, for whom we observe the gender and their frequency of using Chinese microblog Sina Weibo (referred as “Weibo”) over a period in Shanghai, China. To produce a smooth density surface of check-ins, we analyze the overall spatial patterns by using the kernel density estimation (KDE) by using ArcGIS. Furthermore, our results reveal that female users are more inclined towards using social media, and a difference in check-in behavior during weekday and weekend is also observed. From the results, LBSN data seems to be a complement to traditional methods (i.e., survey, census) and is used to study gender-based check-in behavior.
Automatic Expansion of Lexicons for Multilingual Misogyny Detection

English. The automatic misogyny identification (AMI) task proposed at IberEval and EVALITA 2018 is an example of the active involvement of scientific Research to face up the online spread of hate contents against women. Considering the encouraging results obtained for Spanish and English in the precedent edition of AMI, in the EVALITA framework we tested the robustness of a similar approach based on topic and stylistic information on a new collection of Italian and English tweets. Moreover, to deal with the dynamism of the language on social platforms, we also propose an approach based on automatically-enriched lexica. Despite resources like the lexica prove to be useful for a specific domain like misogyny, the analysis of the results reveals the limitations of the proposed approaches. Italiano. Il task AMI circa l’identificatione automatica della misoginia proposto a IberEval e a EVALITA 2018 è un chiaro esempio dell’attivo coinvolgimento della Ricerca per fronteggiare la diffusione online di contenuti di odio contro le donne. Considerando i promettenti risultati ottenuti per spagnolo e inglese nella precedente edizione di AMI, nel contesto di EVALITA abbiamo testato la robustezza di un approccio simile, basato su informationi stilistiche e di dominio, su una nuova collezione di tweet in inglese e in italiano. Tenendo conto dei repentini cambiamenti del linguaggio nei social network, proponiamo anche un approccio basato su lessici automaticamente estesi. Nonostante risorse come i lessici risultano utili per domini specifici come quello della misoginia, analizzando i risultati emergono i limiti degli approcci
A 25-mV input boost converter with 91% MPPT efficiency for energy harvesting application

: In this letter, a low input boost converter with continuous conduction mode is presented for energy harvesting application. The maximum power point tracking (MPPT) e ﬃ ciency at low input voltage is improved by using the proposed input-ripple control technique. Fabricated in a 0.35 µm CMOS process, the boost converter generates an output voltage of 3 V from the input voltage as low as 25 mV. The measurement results show that the boost converter achieves an MPPT e ﬃ ciency of 91% at 25-mV input and a peak power conversion e ﬃ ciency of 82.7% at 250-mV input.
Mean-Square Estimation of Nonlinear Functionals via Kalman Filtering

This paper focuses on estimation of a nonlinear functional of state vector (NFS) in discrete-time linear stochastic systems. The NFS represents a nonlinear multivariate functional of state variables, which can indicate useful information of a target system for control. The optimal mean-square estimator of a general NFS represents a function of the Kalman estimate and its error covariance. The polynomial functional of state vector is studied in detail. In this case an optimal estimation algorithm has a closed-form computational procedure. The novel mean-square quadratic estimator is derived. For a general NFS we propose to use the unscented transformation to calculate an optimal estimate. The obtained results are demonstrated on theoretical and practical examples with different types of NFS. Comparative analysis with suboptimal estimators for NFS is presented. The subsequent application of the proposed estimators to linear discrete-time systems demonstrates their practical effectiveness.
An Assistive Technology for Braille Users to Support Mathematical Learning: A Semantic Retrieval System

Mathematical learning from digital libraries and the web is a challenging problem for people with visual impairments and blindness. In this paper, we focus on developing the mathematical learning skills of braille users with a new assistive technology developed to retrieve semantically mathematical information from the web. This kind of research is still in the study phase. This paper presents an overview of assistive technologies for braille users followed by a description of the proposed system, which works in four phases. In the first phase, we translate a query math formula in braille into MathML code, and then we extract the structural and semantic meaning from the MathML expressions using multilevel presentation. In the classification phase, we choose a multilevel similarity measure based on K-Nearest Neighbors to evaluate the relevance between expressions. Finally, the query result is converted to braille math expressions. Experiments based on our database show that the new system provides more efficient results in responding to user queries.
Logic obfuscation technique using configurable gate diffusion input for improved hardware security

: System-on-Chip (SoC) design using hardware Intellectual Property (IP) cores has become an integral part and pervasive practice in industries to realize error-free complex devices. However, IP vendors face major challenges in protecting hardware IPs against hardware Trojans and preventing revenue loss due to IP piracy. Obfuscation is an exact solution for protecting hardware IP against various attacks such as piracy, overbuilding and tampering. Logic locking technique allows locking outputs by ﬁ xed logic values and generates invalid output of the function if a wrong unlocking key was applied. In this paper, a novel technique called con ﬁ gurable Gate Di ﬀ usion Input (GDI) based logic obfuscation is proposed to enhance the security of hardware IPs. Con ﬁ gurable GDI based obfuscated cell inserts extra gates in the logic path of the circuit with minimum overheads to secure an IC from piracy and overbuilding. The proposed technique is simulated and synthesized using Synopsys software tool. Simulation results on IS-CAS – 89 benchmark circuits show that high levels of security are achieved through a well formulated obfuscation scheme at less than 10% area, power and delay overheads.
Overview of the EVALITA 2018 Spoken Utterances Guiding Chef's Assistant Robots (SUGAR) Task

English. The SUGAR task is intended to develop a baseline to train a voicecontrolled robotic agent to act as a cooking assistant. The starting point will be therefore to provide authentic spoken data collected in a simulated natural context from which semantic predicates will be extracted to classify the actions to perform. Three different approaches were used by the two SUGAR participants to solve the task. The enlightening results show the different elements of criticality underlying the task itself.
Asyncronous late fusion applied to gesture recognition. (Fusion tardive asynchrone appliquée à la reconnaissance des gestes)

Dans cette these, nous nous interessons a la reconnaissance de l'activite humaine. Nous commencons par proposer notre propre definition d'une action : une action est une sequence predefinie de gestes simples et concatenes. Ainsi, des actions similaires sont composees par les memes gestes simples. Chaque realisation d'une action (enregistrement) est unique. Le corps humain et ses articulations vont effectuer les memes mouvements que celles d'un enregistrement de reference, avec des variations d'amplitude et de dynamique ne devant pas depasser certaines limites qui conduiraient a un changement complet d'action. Pour effectuer nos experimentations, nous avons capture un jeu de donnees contenant des variations de base, puis fusionne certains enregistrements avec d'autres actions pour former un second jeu induisant plus de confusion au cours de la classification. Ensuite, nous avons capture trois autres jeux contenant des proprietes interessantes pour nos experimentations avec la Fusion Tardive Asynchrone (ou Asynchronous Late Fusion notee ALF). Nous avons surmonte le probleme des petits jeux non discriminants pour la reconnaissance d'actions en etendant un ensemble d'enregistrements effectues par differentes personnes et captures par une camera RGB-D. Nous avons presente une nouvelle methode pour generer des enregistrements synthetiques pouvant etre utilises pour l'apprentissage d'algorithmes de reconnaissance de l'activite humaine. La methode de simulation a ainsi permis d'ameliorer les performances des differents classifieurs. Un apercu general de la classification des donnees dans un contexte audiovisuel a conduit a l'idee de l'ALF. En effet, la plupart des approches dans ce domaine classifient les flux audio et video separement, avec des outils differents. Chaque sequence temporelle est analysee separement, comme dans l'analyse de flux audiovisuels, ou la classification delivre des decisions a des instants differents. Ainsi, pour deduire la decision finale, il est important de fusionner les decisions prises separement, d'ou l'idee de la fusion asynchrone. Donc, nous avons trouve interessant d'appliquer l'ALF a des sequences temporelles. Nous avons introduit l'ALF afin d'ameliorer la classification temporelle appliquee a des algorithmes de fusion tardive tout en justifiant l'utilisation d'un modele asynchrone lors de la classification des donnees temporelles. Ensuite, nous avons presente l'algorithme de l'ALF et les parametres utilises pour l'optimiser. Enfin, apres avoir mesure les performances de classifications avec differents algorithmes et jeux de donnees, nous avons montre que l'ALF donne de meilleurs resultats qu'une solution synchrone simple. Etant donne qu'il peut etre difficile d'identifier les jeux de donnees compatibles avec l'ALF, nous avons construit des indicateurs permettant d'en extraire des informations statistiques.
Parameter estimation of 2D polynomial phase signals using NU sampling and 2D CPF

The two-dimensional (2D) cubic phase function (CPF) is known as a highly accurate 2D polynomial phase signal estimator, but it has limited applicability due to the requirement for the 3D search for second-order partial phase derivatives. The authors propose an interpolation-based approach simulating non-uniform (NU) signal sampling in order to reduce the 2D CPF calculation complexity. The NU resampling enables the 2D CPF evaluation using the 2D fast Fourier transform and searches over mixed-phase parameter. The computational complexity is reduced from O ( N 5 ) to O ( N 3 log 2 N ) . The additional stage with dechirping, filtering and phase unwrapping is introduced to refine parameter estimates.
Broadband high-efficiency Doherty power amplifier based on novel phase and impedance transform structure

Traditional Doherty power amplifiers (DPAs) have severely limited bandwidth due to the presence of a quarter wavelength (λ/4) compensation microwave line. This paper proposes a novel phase compensation and impedance transform structure that can maintain a 90-degree phase shift over a wide frequency range, which is a good alternative to traditional λ/4 microwave line. To verify the validity of the proposed structure, a DPA has been designed and fabricated based on the proposed structure. The saturated output power reaches 43 dBm, and the output efficiency of the drain stage is more than 65% from 2.6GHz to 3.8GHz. Meanwhile, over 43% drain efficiency is obtained at 6 dB back-off power.
Overview of the EVALITA 2018 Solving Language Games (NLP4FUN) Task

English. This paper describes the first edition of the “Solving language games” (NLP4FUN) task at the EVALITA 2018 campaign. The task consists in designing an artificial player for“The Guillotine” (La Ghigliottina, in Italian), a challenging language game which demands knowledge covering a broad range of topics. The game consists in finding a word which is semantically correlated with a set of 5 words called clues. Artificial players for that game can take advantage from the availability of open repositories on the web, such as Wikipedia, that provide the system with the cultural and linguistic background needed to find the solution. Italiano. Questo lavoro descrive la prima edizione del task “Solving language games” (NLP4FUN) task, proposto durante la campagna di valutazione EVALITA 2018. Il task consiste nella realizzazione di un giocatore artificiale per “La Gigliottina”, un gioco linguistico molto sfidante, la cui soluzione richiede conoscenze in svariati campi. Il gioco consiste nel trovare una parola il cui significatoè correlato a quello di un insieme di 5 parole, chiamate indizi. Un giocatore artificiale per questo task potrebbe sfruttare diverse sorgenti di conoscenza disponibili online, come Wikipedia, che forniscano al sistema le conoscenze linguistiche e culturali necessarie per arrivare alla soluzione.
An accurate analytical memristor model for SPICE simulators

: Memristor holds great potential for memory technology, neuromorphic systems and digital applications. For implementation in complex circuits, an accurate and predictive model is required to make significant progress. This paper introduces a model that is based on the physics of the device. The mathematical modeling helps in understanding the physical properties which determine the behavior of the memristor and also aid in the characterization of the device. Namely, i-v characterization and switching mechanism involved are examined. In our SPICE modeling, Simmons tunnel barrier is incorporated into the port and state equation. The SPICE equivalent circuit for the same is presented and discussed. The presented model satisfies the fingerprints of the memristor.
Transceiver Design and Power Allocation for SWIPT in MIMO Cognitive Radio Systems

In this paper, we consider a symmetric wireless communication network, i.e., each user is equipped with the same number of antennas. Specifically, this paper studies simultaneous wireless information and power transfer (SWIPT) in a K-user multiple-input multiple-output (MIMO) cognitive radio network where the secondary users (SUs) access the same frequency band as the pre-existing primary user (PU) without generating any interference. The transceivers and power splitting ratio are designed and power allocation is considered in our system model. To guarantee the signal-to-interference-plus-noise ratio (SINR) and harvested energy requirement of the PU, its optimal transceiver and minimal transmitted power are obtained by the technique of semi-definite relaxation (SDR). We design the beamformers of the SUs using the distance between the interference subspaces at the PU and the null space of PU’s desired signal to preserve the PU from the interference caused by the SUs. We aim to maximize the sum rate of all the SUs by jointly designing power splitting ratios and allocating transmission power. Furthermore, to consider the performance fairness of SUs, we propose another approach to maximize the minimum SINR of the SUs. Finally, numerical results are given to evaluate the performance of proposed approaches.
g-Good-Neighbor Diagnosability of Arrangement Graphs under the PMC Model and MM* Model

Diagnosability of a multiprocessor system is an important research topic. The system and interconnection network has a underlying topology, which usually presented by a graph G = ( V , E ) . In 2012, a measurement for fault tolerance of the graph was proposed by Peng et al. This measurement is called the g-good-neighbor diagnosability that restrains every fault-free node to contain at least g fault-free neighbors. Under the PMC model, to diagnose the system, two adjacent nodes in G are can perform tests on each other. Under the MM model, to diagnose the system, a node sends the same task to two of its neighbors, and then compares their responses. The MM* is a special case of the MM model and each node must test its any pair of adjacent nodes of the system. As a famous topology structure, the ( n , k ) -arrangement graph A n , k , has many good properties. In this paper, we give the g-good-neighbor diagnosability of A n , k under the PMC model and MM* model.
Cosine Distance Measure between Neutrosophic Hesitant Fuzzy Linguistic Sets and Its Application in Multiple Criteria Decision Making

This paper proposes a neutrosophic hesitant fuzzy linguistic term set (NHFLTS) based on hesitant fuzzy linguistic term set (HFLTS) and neutrosophic set (NS), which can express the inconsistent and uncertainty information flexibly in multiple criteria decision making problems. The basic operational laws of NHFLTS based on linguistic scale function are also discussed. Then we propose the generalized neutrosophic hesitant fuzzy linguistic distance measure and discuss its properties. Furthermore, a new similarity measure of NHFLTS combines the generalized neutrosophic hesitant fuzzy linguistic distance measure and the cosine function is given. A corresponding cosine distance measure between NHFLTSs is proposed according to the relationship between the similarity measure and the distance measure, and we develop the technique for order preference by similarity to an ideal solution (TOPSIS) method to the obtained cosine distance measure. The main advantages of the proposed NHFLTS is defined on linguistic scale function, the decision makers can flexibly convert the linguistic information to semantic values, and the proposed cosine distance measure between NHFLTSs with TOPSIS method can deal with the related decision information not only from the point of view of algebra, but also from the point of view of geometry. Finally, the reasonableness and effectiveness of the proposed method is demonstrated by the illustrative example, which is also compared to the other existing methods.
Reward shaping for knowledge-based multi-objective multi-agent reinforcement learning

Abstract The majority of multi-agent reinforcement learning (MARL) implementations aim to optimize systems with respect to a single objective, despite the fact that many real-world problems are inherently multi-objective in nature. Research into multi-objective MARL is still in its infancy, and few studies to date have dealt with the issue of credit assignment. Reward shaping has been proposed as a means to address the credit assignment problem in single-objective MARL, however it has been shown to alter the intended goals of a domain if misused, leading to unintended behaviour. Two popular shaping methods are potential-based reward shaping and difference rewards, and both have been repeatedly shown to improve learning speed and the quality of joint policies learned by agents in single-objective MARL domains. This work discusses the theoretical implications of applying these shaping approaches to cooperative multi-objective MARL problems, and evaluates their efficacy using two benchmark domains. Our results constitute the first empirical evidence that agents using these shaping methodologies can sample true Pareto optimal solutions in cooperative multi-objective stochastic games.