Miracle-dz/UniFAR-FTUs huggingface dataset:
Reduction with Application to Pattern Recognition in Large Databases

Two distinguished properties of the F-transform: the best approximation in a local sense and the reduction in dimension imply the fact that the F-transform has many successful applications. In the first part, we propose another way of computing the F-transform components of a functional data. This way is based on the particular dimensionality reduction algorithm named Laplacian eigenmaps. In the second part, we strengthen the effect of F-transform-based dimensionality reduction by applying the PCA reduction method over the $F^{0}-$ or $F^{1}-$ transform results. We demonstrate the efficiency of the proposed combinations $F^{0}zT+PCA$ and $F^{1}zT+PCA$ on the problem of patter recognition in a large database. We compare both combinations with other relevant techniques (besides other, LENET-like CNN) and show that they outperform them from the computation time and success rate points of view.
Overlay Community detection using Community Networks

Community detection is useful in understanding the structure of a social network. One of the most commonly used algorithms for community detection is the Louvain algorithm, which is based on the Newman-Girman (NG) modularity optimization technique. It is argued that the close spatial proximity of nodes may increase their chance of being in the same community. Variants of the NG modularity measure such as the dist-modularity attempt to normalize the effect of spatial proximity in extracting communities, causing loss of information about the spatially proximate communities. Other variants of NG modularity such as Spatially-near modularity, try to exploit the spatial proximity of nodes to extract communities, causing loss of information on spatially dispersed communities. We propose that ‘overlay communities’ on existing ‘community networks’ can be used to identify spatially dispersed communities, while preserving the information of spatial proximate communities. The community network is formed by reducing a community into a node using a proximity dimension, which are connected by intercommunity links. The overlay communities are the community pairs that have relatively high normalized link strengths, while being relatively apart in selected proximity dimension. We apply this method to the Gowalla and soc-Pokec online social networks and extract the spatially dispersed overlay communities in them. We select the geographical space and the age of the nodes as the proximity dimension of these two networks, respectively. Detecting spatially dispersed overlay communities may be useful in application domains such as indirect marketing, social engineering, counter terrorism and defense.
Reward based Single Network Adaptive Critic for Optimal Kinematic Control of a Redundant Robot Manipulator

This paper proposes a novel reward based Single Network Adaptive Critic (SNAC) architecture for optimal kinematic control of a redundant robot manipulator. At first, the closed-loop positioning task is formulated as a discrete-time input affine system. The optimal controller is then derived for the reward based cost function with the adaptive critic. The notion of goal-directed optimal behavior is captured by defining a performance measure or cost function in terms of discounted reward function. The reward function is chosen to deliberately speed up convergence of the closed loop positioning error. The performance of the proposed reward based SNAC architecture is compared with standard quadratic cost based SNAC for kinematic control of the redundant robot manipulator. The results show faster convergence of the overall trajectory cost. The proposed kinematic solution has been validated in simulations and experimentally executed using 6 degrees of freedom (DOF) Universal Robot (UR) 10 robot manipulator for both regulation and tracking
Joint Computing Resource, Power, and Channel Allocations for D2D-Assisted and NOMA-Based Mobile Edge Computing

Mobile edge computing (MEC) and non-orthogonal multiple access (NOMA) have been considered as the promising techniques to address the explosively growing computation-intensive applications and accomplish the requirement of massive connectivity in the fifth-generation networks. Moreover, since the computing resources of the edge server are limited, the computing load of the edge server needs to be effectively alleviated. In this paper, by exploiting device-to-device (D2D) communication for enabling user collaboration and reducing the edge server’s load, we investigate the D2D-assisted and NOMA-based MEC system. In order to minimize the weighted sum of the energy consumption and delay of all users, we jointly optimize the computing resource, power, and channel allocations. Regarding the computing resource allocation, we propose an adaptive algorithm to find the optimal solution. Regarding the power allocation, we present a novel power allocation algorithm based on the particle swarm optimization (PSO) for the single NOMA group comprised of multiple cellular users. Then, for the matching group comprised of a NOMA group and D2D pairs, we theoretically derive the interval of optimal power allocation and propose a PSO-based algorithm to solve it. Regarding the channel allocation, we propose a one-to-one matching algorithm based on the Pareto improvement and swapping operations and extend the one-to-one matching algorithm to a many-to-one matching scenario. Finally, we propose a scheduling-based joint computing resource, power, and channel allocations algorithm to achieve the joint optimization. The simulation results show that the proposed solution can effectively reduce the weighted sum of the energy consumption and delay of all users.
Online Computing Quantile Summaries Over Uncertain Data Streams

Quantile summarization is a useful tool in data streams management and mining that can efficiently capture the distribution of the data. A quantile of a sequence of points is the point with a given rank in the sequence. Given a sequence of uncertain points <inline-formula> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula> on the real line, each represented by one-dimensional probability density function (pdf), we study the problem of incrementally maintaining quantile summaries on <inline-formula> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula> such that for any query with a given rank, the summaries can provide a point as the quantile within a given error. We define quantile on uncertain data with discrete or continuous pdf in terms of two error metrics under possible worlds semantics. For an answer to a quantile query on uncertain data, we give the methods for calculating the value of the error and thereby discussing the high-level features of the summaries that can answer approximate quantile query under the two error metrics. We propose an online, space efficient algorithm to compute such summary data on uncertain data streams. The experimental results show that our algorithm substantially outperforms other techniques, such as Monte Carlo and averaging methods, in terms of query error and space for storing the summary data.
Design and Control of a Soft Bending Pneumatic Actuator Based on Visual Feedback

The soft pneumatic actuator has shown great potentials in the prosthetics, rehabilitation and surgery applications, due to its advantages of being safe, small, lightweight and compliant. Whereas, it suffers from the imprecise control and uncertain dynamics. This paper presents the fabrication of a bending pneumatic actuator (BPA) for power assistance and rehabilitation, and the control strategy was designed based on visual feedback. Both the proportion integration differentiation (PID) control and proxy-based sliding mode control (PSMC) methods were applied in the BPA system. Experiments were conducted to verify the reliability of visual recognition and the performance of position control of the bending actuator.
Optimization of the Load Balancing Policy for Tiled Many-Core Processors

Tiled many-core processors (i.e., KNL and the TILE-Gx72 processor), on which processing cores are fitted onto a single chip and cores are interconnected via mesh-based networks, are different from the traditional many-core systems. Their operating system (OS) should be optimized to take into account the unique characteristics (for instance, cores are integrated into a single chip) of tiled many-core processors. This is because these characteristics were not taken into consideration when OSes designed for the traditional multicore (many-core) systems were deployed on tiled many-core processors. In this paper, we propose an optimized load balancing policy to improve the performance of multi-threaded applications. Making a thread select an appropriate idle (lightweight) tile (processing core) across all tiles on the single chip rather than a portion of tiles is able to reduce the overhead triggered by the load balancing policy, the penalty of cache misses because of the scheduling and more threads sharing the same tile (processing core), and the contention for memory controllers due to cache misses. The experimental results demonstrate that the optimized load balancing policy can provide up to $2.7\times $ performance improvement on KNL and mitigate the performance degradation to separate extents on the TILE-Gx72 processor.
Prediction of Protein Complexes Using an Evolutionary Approach

A significant role is played by protein complexes in understanding the cellular life. The understanding of the principles behind cellular organization and function will be enhanced if we can predict the protein complexes still undiscovered. Many computational methods have been developed to predict protein complexes and are mostly applied to static protein-protein interaction (PPI) network. The focus of most of the existing methods is on the topological structure of PPI networks and they are based on the assumption that protein complexes are dense subgraphs of PPI network. In this paper, we have used the characteristic features of the proteins such as functional similarity and domain-domain interaction to identify the clusters of protein interaction. Here, a novel method using differential evolution and fuzzy membership induced ranking technique is proposed to identify protein complexes by utilizing functional similarity and domain-domain interaction feature of proteins. Experimental results show that the proposed method outperforms the existing methods with respect to the standard performance metrics.
Effects of a Psychological Preparation Intervention on Anxiety Associated with Pediatric Anorectal Manometry

Background and Aims High-resolution anorectal manometry (HRM) is associated with significant patient and parent anxiety, which can impact the success and efficiency of the procedure. The nature of HRM necessitates cooperation of an alert child. This study examined effects of psychoeducation intervention on decreasing procedural distress in both pediatric patients and their parents. Methods A prospective randomized study of children aged 3–12 years, undergoing HRM, was performed utilizing child-centric educational video. Patients received either psychological preparation intervention or treatment-as-usual. Distress was assessed through self-reported and parent-reported anxiety measures (STAIC-S; STAI-S), physiological arousal measurements, and an observational scale of procedural distress (PBCL). Results A total of 63 children, aged 3–12 yrs (6.7 ± 2.5), completed the study. Measures of observed and reported distress and anxiety (PBCL; STAIC-S) were significantly less in children receiving intervention. Parents of children in the intervention group also reported significantly less preprocedural anxiety (STAI-S). Effects on physiological arousal were mixed, with significant preprocedural decrease in systolic blood pressure but no difference in heart rate from baseline. Conclusions Preprocedural psychological preparation was effective in decreasing pediatric patient and parental self-reported anxiety associated with HRM. Intervention decreased physician time necessary to successfully complete the study and significantly decreased the number of times patients had to endure balloon inflation.
Measurement Of Latency On Visual Feedback In an Immersive Telepresence Robotic System

This paper addresses the latency of visual feedback in an immersive telepresence robotic system. The latency is defined as the delay between the real event occurring in the robot‘s environment and the event displayed to the user. While it has been a widely recognized problem in teleoperation and telepresence, the existing research mainly focuses on mitigating it and there are relatively few works that conducted quantitative measurements. As opposed to most existing works, we propose to quantify and compare the latency caused by each internal connection in telepresence robotic systems. Our proposed method is able to identify key contributors to the overall latency, and the experimental results can be used to design effective mitigating strategies.
Internet of Things based Context Aware Remote Health Care Services

There are significant advances in the field of Internet of Things in the past years. Correspondingly, there exists a necessity of providing health care services to remote inhabitants to improve their health and well being. Context-aware computing is one of the challenging research issues in IoT paradigm and it is evident that it is successful in reasoning and decision making of real world problems. The deployed sensors provides the data which is to be captured and inferred to meaningful information. This paper presents a rich and unique classification of context information from the perspective of Vector-Borne Diseases. The sensed context information from sensors and other sources is inherently imperfect or imprecise since the physical environment is dynamic. We propose Context Information, Observation and Belief model based on the concept of Bayesian Networks and also the probability estimation procedure to deal with uncertainty. The proposed model identifies and prioritizes the most critical events and provides relevant service.
Simultaneous Recognition of Species, Quality Grades, and Multivariate Calibration of Antioxidant Activities for 12 Famous Green Teas Using Mid- and Near-Infrared Spectroscopy Coupled with Chemometrics

In this paper, mid- and near-infrared spectroscopy fingerprints were combined to simultaneously discriminate 12 famous green teas and quantitatively characterize their antioxidant activities using chemometrics. A supervised pattern recognition method based on partial least square discriminant analysis (PLSDA) was adopted to classify the 12 famous green teas with different species and quality grades, and then optimized sample-weighted least-squares support vector machine (OSWLS-SVM) based on particle swarm optimization was employed to investigate the quantitative relationship between their antioxidant activities and the spectral fingerprints. As a result, 12 famous green teas can be discriminated with a recognition rate of 100% by MIR or NIR data. However, compared with individual instrumental data, data fusion was more adequate for modeling the antioxidant activities of samples with RMSEP of 0.0065. Finally, the performance of the proposed method was evaluated and validated by some statistical parameters and the elliptical joint confidence region (EJCR) test. The results indicate that fusion of mid- and near-infrared spectroscopy suggests a new avenue to discriminate the species and grades of green teas. Moreover, the proposed method also implies other promising applications with more accurate multivariate calibration of antioxidant activities.
Indonesian Vehicle License Plate Number Detection Using Deep Convolutional Neural Network

In Indonesia, the license plate can be the identity of each vehicle, absolutely in every traffic, often occur traffic violations by the driver, the easiest way to identify them is by using their license plate number. Conventional plate detection is proved not solving the problem and not effective. Some problem that usually occurs when using conventional detection is unable to crack down the violator simultaneously, fraud, etc. Therefore to overcome those problems, automatic camera detection becomes an important thing. license plate detection only using camera visualization is an interesting challenge, by applying the latest artificial intelligence that is deep learning, one of them is Convolutional Neural Network (CNN) which is very good on image classification recently. The system will automatically detect the license plate number each vehicle, when the riders entering the zebra cross area, then his license plate will be detected and stored. Besides the system can also detect the plate on the vehicle that has excessive speed, by counting on the counter timer, and when there are vehicles that exceeding the speed limit it will detect the license plate.
An Adjacent-Line-Merging Writeback Scheme for STT-RAM-Based Last-Level Caches

Spin-Transfer Torque RAM (STT-RAM) has attracted attention as a key element for the Last-Level Cache (LLC) of a future microprocessor. Since STT-RAM has a higher density than SRAM and non-volatility, STT-RAM can contribute to building the cache memory with a larger capacity and a less static energy. However, since STT-RAM changes its magnetization state in the case when storing data, the energy cost of write access requests for an STT-RAM LLC is more expensive than that of an SRAM LLC. As a result, the total energy consumption of the STT-RAM LLC for write-intensive applications may increase. To solve this problem, this paper proposes an Adjacent-Line-Merging Writeback Scheme. Since a larger cache line of an STT-RAM cache can contribute to the reduction in the write energy cost per byte, the upper-level cache merges two adjacent small lines to one large line, and then writes the merged line back to the STT-RAM LLC. Moreover, the larger line size for the LLC leads to a reduction in the static energy cost. The evaluation results show that the proposed scheme can reduce the energy consumption of the STT-RAM LLC by up to 26, and 9.3 percent on average.
An Analysis of Japanese Named Entity Recognizer Specialized for Person and Organization Entities

The aim of document masking system is to remove information that can identify individuals and companies in text data. This system can be realized by using named entity recognition as a core technology. In this paper, we construct, analyze and investigate Japanese named entity recognizer specialized for entities related to people and orga-nizations(EPO): (1) evaluating the recognition performance by constructing NER models specialized for EPO, (2) investigating the relationship between recognition performance and the features used in EPO recognition models. From our investigation, it can be suggested that it is sufficient for EPOs with low degree of difficulty to apply models with less syntactic features, while models with more syntactic features need to apply to improve recognition performance.
Acute Hyperglycemia Due to Topical Corticosteroid Administration

We present the case of a 71-year-old man with longstanding, previously well-controlled type 1 diabetes who developed acute hyperglycemia. His insulin requirements, via his insulin pump, increased to nearly five times his typical daily dose. The patient was admitted for evaluation and treatment and started on an insulin infusion. He had minimal insulin requirements with the insulin infusion. History revealed recent use of a super potent topical corticosteroid for a psoriasis flare. The patient was transitioned back to his insulin pump, using his prior to admission settings. He was advised to discontinue using his topical corticosteroid. He had no further hyperglycemic episodes. The clinical presentation is suggestive of corticosteroid-induced hyperglycemia, suggesting that clinically significant changes can occur even with short duration use, particularly with high potency steroids used. This is to our knowledge the first case reported in which the patient required a very significant amount of extra insulin (nearly five times his typical total daily dose) after using high potency topical steroid cream. This case highlights the potentially detrimental effect of topical corticosteroid use in patients with diabetes.
Brain Tumor Detection With Tumor Region Analysis Using Adaptive Thresholding And Morphological Operation

Tumor in brain is life threatening but proper detection of tumor at early stage may save many lives. In our research, we have proposed an adaptive threshold value selection technique with morphological operation for the detection of brain tumor which is very much promising. Our proposed method can adapt with different kinds of intensity values of the pixels of MRI FLAIR image and can detect tumor efficiently. Initially we have detected the highest intensity value of the brightest region assuming as tumorous cell and also specify an intensity value which is covering maximum pixels assuming as healthy cells and their difference is also being calculated. If the difference between the maximum intensity value of the brightest region and the intensity value of any random pixel is in the range of the previous difference, then the pixel is detected as a member of tumor cell otherwise not. In this research, we have used BRATS 2013 and 2015 datasets with accuracy 95% and 89.78% respectively. As our datasets have ground truth value, we have examined our detected images with the ground truth images through the parameters centroid and area.
Identifying Species for Particle Swarm Optimization under Dynamic Environments

The species techniques have been widely used in multimodal optimization, but not paid enough attention in dynamic optimization. In this paper, a novel Nearest-Better Clustering (NBC) method, called psfNBC, is proposed, which is used to identify the species for Particle Swarmoptimization (PSO) under dynamic environments. The proposed psfNBC includes three novel properties. First, we propose the strategy p to cope with the effects of outliers. Second, the strategy s is introduced to deal with the “long-tail phenomenon Third, a random scale factor, which is named as the strategy f, is adopted. In order to evaluate the performance of the psfNBC, a framework of species-based particle swarm optimization for Dynamic optimization Problems (DOPs) is given. Within this framework, the proposed psfNBC is compared with the basic NBC as well as different combinations of the proposed strategies. The experimental results on the moving peak benchmark problems show that psfNBC has better performance.
Sentence Similarity Computation by Integrating Shallow and Deep Information

The calculation of sentence similarity has always been a hot topic in the field of natural language processing. The existing methods of sentence similarity computation usually consider either shallow or deep information of sentences. In this paper, we propose a method to integrating the two types of information. In the model, the shallow information is acquired based on word similarity and sentence length. The deep information is extracted via a parallel convolutional neural network. Then we integrate the two parts linearly. Experiments on SemEval 2007 task 5, similarity evaluation between Chinese and English sentences, show that the performance of our integration model is better than the models which treat the two types of information separately. For parallel convolutional neural network, a performance improvement is evident when shared parameters are assigned.