Miracle-dz/UniFAR-FTUs huggingface dataset:
Competitive Swarm Optimizer with Dynamic Grouping for Large Scale Optimization

The arrival of the era of “big data” has witnessed an urgent need of finding a proper algorithm for large scale optimization. Hence, this paper proposes a competitive swarm optimizer with dynamic grouping (CSO-DG) for large scale optimization. In CSO-DG, particles are separated into many groups to search for different regions of the large landscape. Moreover, the size of the group (the number of particles in each group) changes dynamically to control the swarm competitive strength, so as to balance the convergence and diversity. We also investigate the influences of different group sizes on different problems, and then propose a size range for the dynamic change bounds. By using multiple groups, only the worst particle in each group will be updated by learning from the best particle in the same group and the mean position of the whole swarm. This is helpful to save fitness evaluations. This way, the algorithm is able to work well in high-dimensional optimization. The experimental results show that CSO-DG exhibits a competitive performance than other state-of-the-art algorithms on 20 benchmark functions (CEC‘2010) for large scale optimization.
TextDream: Conditional Text Generation by Searching in the Semantic Space

Conditional text generation is a fundamental task in natural language generation. Traditional conditional generative models build conditional probability distributions over the given labels. However, categorical label information is usually very abstract, e.g., sentiment, and it is difficult to be disentangled from the content. Therefore, instead of generating text by modeling conditional probability distribution, we propose a novel text generation method TextDream through searching in the semantic space. Specifically, in this method, a random text seed is initially given and the new text is generated by local search operation. The generation procedure is guided by a fitness function, typically a classification model. Text with higher fitness will be preserved. This procedure loops until the qualified solution is found. Experimental results show that our method is able to generate more diverse text compared with advanced conditional generative models.
Autocalibrated multiband CAIPIRINHA with through‐time encoding: Proof of principle and application to cardiac tissue phase mapping

In conventional multiband (MB) CAIPIRINHA, additional reference scans are acquired to allow the separation of the excited slices. In this study, an acquisition‐reconstruction technique that makes use of the MB data to calculate these reference data is presented. The method was integrated into a 2D time‐resolved phase‐contrast MR sequence used to assess velocities of the myocardium.
Computational Intelligence for Parameter Estimation of Biochemical Systems

In the field of Systems Biology, simulating the dynamics of biochemical models represents one of the most effective methodologies to understand the functioning of cellular processes in normal or altered conditions. However, the lack of kinetic rates, necessary to perform accurate simulations, strongly limits the scope of these analyses. Parameter Estimation (PE), which consists in identifying a proper model parameterization, is a non-linear, non-convex and multi-modal optimization problem, typically tackled by means of Computational Intelligence techniques, such as Evolutionary Computation and Swarm Intelligence. In this work, we perform a thorough investigation of the most widespread methods for PE-namely, Artificial Bee Colony (ABC), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Differential Evolution (DE), Estimation of Distribution Algorithm (EDA), Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and Fuzzy Self-Tuning PSO (FST-PSO)-comparing their performances on a set of synthetic (yet realistic) biochemical models of increasing size and complexity. Our results show that a variant of the settings-free FST-PSO algorithm can consistently outperform all other methods; ABC and GAs represent the most performing alternatives, while methods based on multivariate normal distributions (e.g., CMA-ES, EDA) struggle to keep pace with the other approaches. Index Terms-Computational Intelligence, Systems Biology, Parameter Estimation, Evolutionary Computation, Swarm Intelligence
A Blockchain Application for Medical Information Sharing

Medical information are private and medical data are valuable for medical research. Medical information sharing is challenging as the data might be manipulated improperly and revealed during the whole operational process. Medical institutions need shared information for scientific research and develop, on the contrary, privacy issue inhibits the sharing process. In this paper, a new business process and a novel architecture for medical information sharing based on blockchain are proposed. Exploiting the advantage of blockchain in recording transactions, information can be shared and verified among parties like a distributed ledger. On the other hand, medical information can be shared securely between users without an intermediary. The main benefit is that both the information and the traces of the transaction can be distributed stored which avoid manipulation and fraud. Consequently, the value of information can be fully utilized.
Planar Antenna Design for Omnidirectional Conical Radiation Through Cylindrical Leaky Waves

An annular periodic leaky-wave antenna (LWA) fed by a simple azimuth-symmetric source is designed to generate a high-gain omnidirectional conical-beam pattern, which scans with frequency over a wide angular range. The proposed structure is defined by a finite metallic radial strip grating printed on a grounded dielectric slab, which supports an $n=0$ cylindrical leaky wave (CLW). The distinctive features of CLWs supported by such a truncated structure are also highlighted and discussed. The directional far-field pattern generated by the proposed LWA, in conjunction with its nondiffracting and wideband behavior in the near field (as previously reported by the authors), defines an original dual-operational LWA. Possible applications include next-generation wireless power transfer systems that provide functionality in both the near and far field, vehicle roof-mounted antennas for base-station data connectivity as well as future short-range near-field communications, and object tracking by ceiling-mounting devices for indoor localization.
Low-Cost Automatic Ambient Assisted Living System

The recent increase in ageing population in countries around the world has brought a lot of attention toward research and development of ambient assisted living (AAL) systems. These systems should be inexpensive to be installed in elderly homes, protecting their privacy and more importantly being non-invasive and smart. In this paper, we introduce an inexpensive system that utilises off-the-shelf sensor to grab RGB-D data. This data is then fed into different learning algorithms for classification different activity types. We achieve a very good success rate (99.9%) for human activity recognition (HAR) with the help of light-weighted and fast random forests (RF).
Quantification of crystallinity using zero‐loss filtered electron diffraction

The quantity of the crystalline phases present in a nanomaterial is an important parameter that governs the correlation between its properties and microstructure. However, quantification of crystallinity in nanoscale‐level applications by conventional methods (Raman spectroscopy and X‐ray diffraction) is difficult because of the spatial limitations of sampling. Therefore, we propose a technique that involves using energy‐filtered electron diffraction in transmission electron microscopy which offers improved spatial resolution. The degree of crystallinity (DOC) was calculated by separating the crystalline and amorphous intensities from the total intensity histogram acquired by the azimuthal averaging of the zero‐loss filtered signals from electron diffraction. In order to validate the method, it was demonstrated that the DOC calculated by zero‐loss filtered electron diffraction was consistent with the DOC measured by the area ratio using an amorphous silicon on crystalline silicon standard sample. In addition, the results obtained from zero‐loss filtered and conventional electron diffractions were compared. The zero‐loss filtered electron diffraction successfully provided the reliable results of the crystallinity quantification. In contrast, the DOC measured using conventional electron diffraction yielded extremely variable results. Therefore, our results provide a crystallinity quantification technique that can extract quantitative information about crystallinity of nanoscale devices by using zero‐loss filtered electron diffraction with better reliability than conventional electron diffraction.
A Fully Integrated Analog Front End for Biopotential Signal Sensing

A low-power fully-integrated analog front end for biopotential sensors is proposed. The signal conditioning circuitry features an integrating sampler and a digital-assisted electrode offset-cancellation loop. The chip is fabricated in a standard 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process. The supply voltage is 1.2 V and the quiescent current is <inline-formula> <tex-math notation="LaTeX">$7.7~\mu \text{A}$ </tex-math></inline-formula>. Measurement results show that the analog front end achieves an in-band gain of 58 dB, a noise spectrum density of 46nV/<inline-formula> <tex-math notation="LaTeX">$\sqrt {\text{Hz}} $ </tex-math></inline-formula> and can tolerate ±60 mV electrode offset. The THD is below 1% with a 2mV<sub>pp</sub> input. The analog signal is converted to digital code at 5kS/s by a 12-bit SAR ADC with 63.2 dB SNDR. The prototype IC is experimented with capturing ECG on human beings.
Planar Wideband Circularly Polarized Cavity-Backed Stacked Patch Antenna Array for Millimeter-Wave Applications

A planar circularly polarized substrate integrated waveguide (SIW) stacked patch antenna array consisting of four sequential-rotation (SR) subarrays is presented for $Ka$ -band applications. Antenna element is realized based on a pair of stacked cavities by the combination of a driven patch layer and a parasitic patch layer, which are formed by a shorted square patch and multiple identical stacked square patches, respectively. Each subarray consisting of two pairs of elements, excited by a coplanar waveguide (CPW) network with four metallic vias, is well matched to a novel SIW SR feeding network. A narrow transverse coupling slot, a semiopening resonant cavity, two pairs of inductive vias, and an inductive window are introduced in the SIW-based feeding network to implement functions of phase shifter, impedance matching, and power divider in the two layers. By employing the proposed SR feeding network, the CPW network, and the four subarrays, the excellent performance of the array is realized. To verify the design, a prototype is fabricated and measured, which demonstrates a −10 dB impedance bandwidth of 29.6%, a 3 dB axial ratio bandwidth of 25.4%, and a peak gain of 20.32 dBic.
Gait Design for a Snake Robot by Connecting Curve Segments and Experimental Demonstration

This paper presents a method for designing the gait of a snake robot that moves in a complicated environment. We propose a method for expressing the target form of a snake robot by connecting curve segments whose curvature and torsion are already known. Because the characteristics of each combined shape are clear, we can design the target form intuitively and approximate a snake robot configuration to this form with low computational cost. In addition, we propose two novel gaits for the snake robot as a design example of the proposed method. The first gait is aimed at moving over a flange on a pipe, while the other is the crawler gait aimed at moving over rough terrain. We demonstrated the effectiveness of the two gaits on a pipe and rough terrain in experiments.
Multi-time Scale Energy Management Strategy for Isolated Microgrid

Because of the randomness, intermittence and volatility of wind and solar resources for a microgrid (MG), its stability, reliability and economy are affected greatly. Based on the microgrid system on a certain island, this paper proposes an energy management strategy which is used for daily or short-term scheduling according to multi-time scale. The daily scheduling is based on the improved adaptive particle swarm optimization (PSO) for multi-object optimization so as to ensure the MG economy and stability. However, due to deviation in the daily prediction, the uses a rolling optimization mode to rectify the deviation of the former to guarantee the effectiveness of the daily prediction. The simulation and experimental results show that the proposed strategy can effectively enhance the economy and reliability of the island MG systems.
Software for Visual Insect Tracking Based on F-transform Pattern Matching

We introduce a problem of tracking small animals, especially insects. To solve this problem, we focus on visual tracking in recorded movies, propose our pattern tracking mechanism based on F -transform, and implement a user-friendly software to handle the movies. The tracking core is compared with five state-of-the-art tracking algorithms: KCF, MIL, TLD, Boosting and MedianFlow from processing time and algorithm failure rate point of views. Based on the results computed from 1000 movie frames, we observed that the proposed F-transform tracking core is the fastest and the most reliable method.
MicroRNA-183-5p Inhibits Aggressiveness of Cervical Cancer Cells by Targeting Integrin Subunit Beta 1 (ITGB1)

Background Accumulating studies demonstrate that microRNAs play crucial roles in multiple processes of cancer progression. Lower levels of miR-183 have ben observed in diverse types of tumors but the mechanism and precise function of miR-183-5p in cervical cancer have largely not been investigated. Material/Methods The level of miR-183-5p in different cervical cancer cell lines and clinical tissues was detected qRT-PCR assays. Transwell and wound-healing migration assays were conducted to assess the functional roles of miR-183-5p in over-expressing cervical cancer cells in vitro. Rescue assays were carried out to confirm the contribution of integrin subunit Beta 1 (ITGB1) to the aggressiveness of cancer cells regulated by miR-183-5p. Results miR-183-5p was reduced in clinical tissues of cervical cancer and cell lines when compared to the normal subjects and normal cervical epithelial cell line, respectively. In addition, over-expression of miR-183-5p markedly inhibited migration and invasion in cervical cancer cells, and increased aggressiveness was observed in miR-183-5p inhibitor transfected cells. Furthermore, the luciferase reporter assays revealed that ITGB1 was the gene directly regulated by miR-183-5p. Notably, a negative association between the ITGB1 and miR-183-5p was found, and the gene expressions of ITGB1 was mediated by miR-183-5p in cervical cancer cells. Conclusions miR-183-5p serves as a latent anti-oncogene by targeting the metastasis-promoter gene, ITGB1.
Representing Multiple Observed Actions in the Motor System

There is now converging evidence that others’ actions are represented in the motor system. However, social cognition requires us to represent not only the actions but also the interactions of others. To do so, it is imperative that the motor system can represent multiple observed actions. The current fMRI study investigated whether this is possible by measuring brain activity from 29 participants while they observed two right hands performing sign language gestures. Three key results were obtained. First, brain activity in the premotor and parietal motor cortex was stronger when two hands performed two different gestures than when one hand performed a single gesture. Second, both individual observed gestures could be decoded from brain activity in the same two regions. Third, observing two different gestures compared with two identical gestures activated brain areas related to motor conflict, and this activity was correlated with parietal motor activity. Together, these results show that the motor system is able to represent multiple observed actions, and as such reveal a potential mechanism by which third-party social encounters could be processed in the brain.
Irony Detection Using Neural Network Language Model, Psycholinguistic Features and Text Mining

Irony is a form of figurative language, which is often used to make fun of an entity. We employ paragraph vector to capture syntactic and semantic features of the figurative language to subsequently detect satiric and ironic content in a given text using data mining. We extract psycholinguistic features with the help of a tool called Linguistic Inquiry Word Count. In order to extract useful insights of what constitutes figurative language, we combined syntactic, semantic, and psycholinguistic features together. We demonstrated the effectiveness of the proposed methodology on two satiric news datasets and one ironic customer review dataset. The proposed approach outperformed on the state of the art results.
Distributed Adaptive Finite-Time Formation Control of Multiple UAV Helicopter System

This paper investigates distributed finite-time formation control (FFDFC) with local information change for multiple unmanned aerial vehicle (UAV) helicopter system with disturbances. The UAV helicopter system is composed by position outer-loop and attitude inner-loop. We introduce an adaptive finite-time formation controller that takes into account motions of the information states. Unlike the existing distributed protocols, we show that the finite-time convergence properties is proved by Lyapunov theory. Moreover, the control algorithm removes the global information that needs to be known by each follower in advance in many references. Simulation results are presented to demonstrate the efficiency of the designed strategy.
Co-design of Model-Reference Scheduling and Control

There have been growing concerns in networked control system with communication constraints. In the paper, a model-reference scheduling scheme is proposed for optimizing the distribution of the network resource. The states are determined to gain the access to the network by computing the difference between the real system and the ideal system. The stability analysis and the joint design of the controller and scheduling are given by formulating the problem as a linear equality equation that can be solved efficiently. The proposed strategy is evaluated through a simple simulation.
Japanese Animation Style Transfer Using Deep Neural Networks

This paper tries to find a deep-learning approach to Japanese Animation style transfer that creates animation background images more easily and efficiently so that we can reduce the cost and workload for Japanese animation industry. Our approach now builds is based on a deep-learning approach using Convolutional Neural Networks(CNN) which is most popular in image processing tasks. Moreover, we limit the number of the color used in the output images to get close to an ideal color.
A Flexible Authorization Mechanism for Enterprise Networks Using Smart-Phone Devices

The authentication and authorization process is a critical factor in Wi-Fi enterprise networks, being the mechanism used to give access to the network resources. The general Wi-Fi enterprise network security frameworks have a static structure, being based on user-name/password credentials and without offering a flexible granular access. Nowadays, smart-phones devices are becoming ubiquitous and these can be used as security elements due to their hardware/software capabilities. Smart-phones have the capability to execute complex cryptographic operations and have the hardware peripherals, which allow authenticating the user. In this paper, we propose a smart-phone centric authentication and authorization solution which employs a bootstrap security protocols to provision temporary credentials for a WPA2 Enterprise protected Wi-Fi network.
Resource-Aware Large-Scale Cooperative Three-Dimensional Mapping Using Multiple Mobile Devices

In this paper, we address the problem of cooperative mapping (CM) using datasets collected by multiple users at different times, when the transformation between the users’ starting poses is unknown. Specifically, we formulate CM as a constrained optimization problem, in which each user's independently estimated trajectory and map are merged together by imposing geometric constraints between commonly observed point and line features. Additionally, we provide an algorithm for efficiently solving the CM problem, by taking advantage of its structure. The proposed solution is proven to be batch-least-squares (BLS) optimal over all users’ datasets, while it is less memory demanding and lends itself to parallel implementations. In particular, our solution is shown to be faster than the standard BLS solution, when the overlap between the users’ data is small. Furthermore, our algorithm is resource-aware as it is able to consistently trade accuracy for lower processing cost, by retaining only an informative subset of the common-feature constraints. Experimental results based on visual and inertial measurements collected from multiple users within large buildings are used to assess the performance of the proposed CM algorithm.
Managing nonuniformities and uncertainties in vehicle-oriented sensor data over next generation networks

Detailed and accurate vehicle-oriented sensor data is considered fundamental for efficient vehicle-to-everything V2X communication applications, especially in the upcoming highly heterogeneous, brisk and agile 5G networking era. Information retrieval, transfer and manipulation in real-time offers a small margin for erratic behavior, regardless of its root cause. This paper presents a method for managing nonuniformities and uncertainties found on datasets, based on an elaborate Matrix Completion technique, with superior performance in three distinct cases of vehicle-related sensor data, collected under real driving conditions. Our approach appears capable of handling sensing and communication irregularities, minimizing at the same time the storage and transmission requirements of Multi-access Edge Computing applications.
Validating the predictors of outcomes after radical cystectomy for bladder cancer

An assessment of surgical risk is essential for patient counseling and decision making, and it can provide rationale adjustment for patient populations as health care moves from a fee‐for‐service to a value‐based reimbursement model. The modified Frailty Index (mFI) has been proposed as a risk‐stratification tool for radical cystectomy (RC), and the objective of the current study was to validate this potential use of the mFI using an institutional cohort.