Miracle-dz/UniFAR-FTUs huggingface dataset:
Hierarchical Temporal Memory Introducing Time Axis in Connection Segments

In this paper, we propose an improved Hierarchical Temporal Memory (HTM) that can consider long-term dependence. HTM is a temporal sequence prediction model imitating the cerebral cortex structure and learning algorithm. This model is composed of cells of a two-dimensional map representing neurons of the brain, and expresses data by a set of cells in an activated state. Further, the data of the next time is predicted from the set of the cells in the predicted state. HTM learns the time series data by updating synapses connecting each cell according to Hebb's rule and keeps the proper relationship of data. In the conventional model, only the connection with the previous data is learned, but in the proposed model the connection with several former data can be learned. The proposed HTM is modified in terms of structure and learning algorithm. In the structure, we introduced a time axis for the segment which is a collection of synapses. About learning algorithm, the connection with several times ago leads the predicted state. As a result of evaluation experiments, it was confirmed that the proposed model can consider longer-term dependency than the conventional model on temporal sequence prediction.
An endophytic Nodulisporium sp. from Central America producing volatile organic compounds with both biological and fuel potential.

A Nodulisporium sp. (Hypoxylon sp.) has been isolated as an endophyte of Thelypteris angustifolia (Broadleaf Leaf Maiden Fern) in a rainforest region of Central America. It has been identified both on the basis of its morphological characteristics and by scanning electron microscopy as well as ITS sequence analysis. The endophyte produces volatile organic compounds (VOCs) that have both fuel (mycodiesel) and use for biological control of plant disease. When grown on potato dextrose agar, the organism uniquely produces a series of ketones, including acetone; 2-pentanone; 3-hexanone, 4-methyl; 3-hexanone, 2,4- dimethyl; 2-hexanone, 4-methyl, and 5-hepten, 2-one and these account for about 25% of the total VOCs. The most abundant identified VOC was 1,8 cineole, which is commonly detected in this group of organisms. Other prominent VOCs produced by this endophyte include 1-butanol, 2- methyl, and phenylethanol alcohol. Moreover, of interest was the presence of cyclohexane, propyl, which is a common ingredient of diesel fuel. Furthermore, the VOCs of this isolate of Nodulisporium sp. were selectively active against a number of plant pathogens, and upon a 24 h exposure caused death to Phytophthora palmivora, Rhizoctonia solani, and Sclerotinia sclerotiorum and 100% inhibition to Phytophthora cinnamomi with only slight to no inhibition of the other pathogens that were tested. From this work, it is becoming increasingly apparent that each isolate of this endophytic Nodulisporium spp. , including the Daldina sp. and Hypoxylon spp. teleomorphs, seems to produce its own unique set of VOCs.
Tree Structure Based Algorithm for Multiobjective Optimization of Switch Allocation in Radial Distribution Networks

Reducing the consequences of outages in power distribution networks can be achieved by the installation of sectionalizing switches, which may be activated to isolate faulty elements. Positions of sectionalizing switches are usually selected to minimize a given reliability factor. In general, optimization of one of the reliability factors is not equivalent to the minimization of other factors. An optimization algorithm to solve the problem of the optimal switch placement to simultaneously minimize several reliability factors is presented. The algorithm is designed for single-feeder distribution networks with a radial topology. The performance of the algorithm is assessed using an existing distribution network of a moderate size.
An Improvement of Channel Estimation for Up-link NOMA Systems

It is predicted that IoT (Internet of Things) will be developed rapidly and then every variety of information will be gathered to central processing systems using wireless systems. In next Generation mobile communications systems, a base station must deal with a huge number of signals transmitted from IoT devices. In addition, some applications require low latency communication for control systems such as auto driving systems. We, therefore, have researched contention based communication methods so that both massive connection and low latency communication can be realized. We have developed the system and evaluated its performance using not only computer simulation but also experiments in real environments. And then, we confirmed that our system could work well in the real environments. In the paper, in order to further enhance the system reliability, we propose the method that improves bit error rate (BER) performance of the system. The paper organized as follows. First, we illustrate our system regarding the sub-frame structure and the signal processing procedure and the basic channel estimation methods. Secondly, we propose how to improve the accuracy of channel estimation. We propose that the Kaiser filter and window function are employed to extract the channel impulse response (CIR). Finally, we evaluated the proposed technique by computer simulation and confirmed that BER performance could be improved. The proposed method could make BER reduce by less than one-tenth even though CIR estimation conditions were severe.
Using Feature Visualisation for Explaining Deep Learning Models in Visual Speech

The use of Deep Neural Network (DNN) models for Visual Speech Recognition (VSR) has recently been gaining traction. The use of more complex DNN models have greatly increased accuracy performance but have the downside of very poor explainability. There is still much room for improvement in using DNN models for VSR in comparison to audio-only speech recognition. Being able to explain the model and its predictions would be beneficial for improving its performance, and the explainability of predictions are important in VSR in order to further improve the model design and handling of real-world data. This paper highlights various deep learning techniques for visual speech recognition and reports on experiments using feature visualisation techniques for these models, successfully demonstrating that CNNs are self-learning features consistent with what we would expect.
Escherichia coli cells bearing mutA, a mutant glyV tRNA gene, express a recA‐dependent error‐prone DNA replication activity

A base substitution mutation (mutA) in the Escherichia coli glyV tRNA gene potentiates asp → gly mistranslation and confers a strong mutator phenotype that is SOS independent, but requires recA, recB and recC genes. Here, we demonstrate that mutA cells express an error‐prone DNA polymerase by using an in vitro experimental system based on the conversion of phage M13 single‐stranded viral DNA bearing a model mutagenic lesion to the double‐stranded replicative form. Amplification of the newly synthesized strand followed by multiplex DNA sequence analysis revealed that mutation fixation at 3,N4‐ethenocytosine (ɛC) was ≈3% when the DNA was replicated by normal cell extracts, ≈48% when replicated by mutA cell extracts and ≈3% when replicated by mutA recA double mutant cell extracts, in complete agreement with previous in vivo results. Mutagenesis at undamaged DNA sites was significantly elevated by mutA cell‐free extracts in the M13 lacZ(α) forward mutagenesis system. Neither polA (DNA polymerase I) nor polB (DNA polymerase II) genes are required for the mutA phenotype, suggesting that the phenotype is mediated through a modification of DNA polymerase III or the activation of a previously unidentified DNA polymerase. These findings define the major features of a novel mutagenic pathway and imply the existence of previously unrecognized links between translation, recombination and replication.
Multi-class Surveillance for Acute Respiratory Distress Syndrome using Belief Functions

The high incidence of pathologies implies the necessity of developing and implementing health surveillance technologies. This paper proposes a multi-class surveillance approach for a particular pathology, which is the acute respiratory distress syndrome. The multi-class model uses parameters extraction and belief functions theory applied on four vital signs. Vital signs are heart rate, respiratory rate, blood oxygen saturation and blood pressure. Thus, different linear and nonlinear parameters are extracted from these vital signs. A modeling of each class according to each parameter is performed in the framework of the belief functions theory. Then, these models are affected by a measure of confidence according to each parameter and combined together to lead finally to one model that distinguishes between the multi classes. This multi-class surveillance approach has shown interesting performances for the prediction of ARDS.
CUBA: Chained Unanimous Byzantine Agreement for Decentralized Platoon Management

Autonomous driving, vehicle platoons and smart traffic management will dramatically improve our transportation systems. In contrast to centralized approaches, which do not scale efficiently with the actual traffic load, a decentralized traffic management based on distributed consensus could provide a robust, fair and well-scaling solution for infrastructures of variable density.In this paper, we propose a distributed platoon management scheme, where platoon operations such as join or merge are decided by consensus over a Vehicular ad hoc network (VANET). Since conventional consensus protocols are not suitable for Cyber-Physical Systems (CPS) such as platoons, we introduce CUBA, a new validated and verifiable consensus protocol especially tailored to platoons, which considers their special communication topology.We demonstrate that CUBA only introduces a small communication overhead compared to the centralized, Leader-based approach and significantly outperforms related distributed approaches.
Data-Glove for Japanese Sign Language Training System with Gyro-Sensor

IT research to support communication between people with normal hearing and the hearing impaired is an emergent and important issue. We propose a data glove for a Japanese sign language training system. Currently, data gloves are used with some video games; however, they are expensive and overly complex for Japanese sign language training. We propose a simple data glove with a gyro sensor that can capture the palm-turning gestures of some Japanese sign language words. In the proposed system, an algorithm determines whether a palm-turning gesture has occurred based on experimentally defined thresholds. We conducted experiments with eight participants without individual enrolling to evaluate the proposed data glove. The results demonstrate that the proposed data glove can capture specific words at greater than 50% accuracy on average. We believe that accuracy can be increased by tuning the thresholds to individuals.
Optimal B-spline joint trajectory generation for collision-free movements of a manipulator under dynamic constraints

A method is proposed which describes the joint trajectories of a manipulator with B-spline curves, and which optimizes the values of these control points using the complex method under collision-free, full dynamic and kinematic constraints. This method utilizes the B-spline curve's uniqueness, continuity, and local controllability to the control points, and incorporates the complex method which does not require evaluation of the gradient in the optimization. This method has the following advantages: (1) It is applicable to manipulators with arbitrary degrees of freedom; (2) It achieves the generation of the trajectory which takes the priority of specified constraints into consideration; (3) Weighting factors of the performance indices corresponding to the constraints are automatically computed. The method has been applied to the collision-free trajectory generation problems of a manipulator with three degrees of freedom and the results show its effectiveness.
Augmenting Surface Reconstructions

The reconstruction of a scene is a fundamental task in many AR applications. Only a dense and complete surface representation enables realistic AR interactions, like occlusions and collisions. The reconstruction of arbitrary scenes is an important issue and many approaches have already been proposed. The results of these methods are mostly dense and accurate, but the reconstructions are still incomplete. This is due to the fact that parts of the scene are invisible during the capturing process (e.g. occluded by another object). We address the problem of filling unknown parts of a surface reconstruction during the capturing process by expanding the well-known KinectFusion pipeline. Our approach diffuses the observed parts to extend the original reconstruction.By showing the results of several scenes, we demonstrate that our algorithm produces visually plausible surfaces. Moreover, we present a method for handling of occlusions and collisions directly on the extended surface.
Deep Learning-Based Relay Selection In D2D Millimeter Wave Communications

This paper proposed a novel deep learning-based relay selection scheme in millimeter wave (mmWave) Device-to-Device (D2D) communication underlying the fifth generation (5G) cellular networks. Relay selection seems to be a promising solution to extend the coverage and solve the blocking problem of mmWave direct communication. In the case of direct path blocking, the base station (BS)/user equipment (UE) has several candidate devices to be selected as a relay. Despite that, conventional scheme when the direct path is blocked, the direct communication link is handover from mmWave to a lower frequency band using a fast session transfer (FST) technique. Such a blocking problem can be solved multi-hop communications by relaying data based on select another device as a relay. Motivated by the importance of selecting the optimal relay which increases reliable connectivity in mmWave communication and expansion of coverage. The proposed deep learning model is developed to overcome the challenges of selected the optimal relay based with low complexity and high efficiency. The proposed scheme considers a deep learning model learns how to predict the best relay for relaying the data in high-reliability communication. The in-depth learning approach is recommended due to its capability in constructing an intelligent model that can take successful decisions and make precise predictions. Simulation results show that the proposed relay selection algorithm outperforms the conventional relay selection in D2D technique in the spectral efficiency and the energy efficiency
Underwater Target Tracking via 3D Convolutional Networks

The task of underwater target tracking is one of the most important challenges in recent upsurge of smart aquaculture, especially in the application of AI-driven cage culture. However, tracking often requires short computational time and draws little attention from researchers in the field of deep learning. A convolutional network tracker (CNT) was proposed [1], which uses 2D features of local structure and internal geometric layout information between the target candidates in adjacent frames to address the tracking tasks without pre-training. In [14], an improved version of CNT (called Fast-CNT) was proposed for performing underwater multi-target tracking. This paper further proposes a 3D version of CNT (called 3D-CNT) characterized by extracting spatiotemporal features between successive frames to make the target (e.g. fish) template more robust in tracking. Experimental results show that with these temporal features, 3D-CNT outperforms the Fast-CNT in tracking moving fish.
Apache Dynamic Update for Feedback Control of Computing Resources

This paper shows an environment for performing data collection in the computational system that provides the response service to “HTTP” requests and makes interventions in the values of the available parameters in order to control the system automatically in the closed loop. The parameters to be obtained as endogenous variables of the computational system are MaxRequestWorks and KeppAliveTimeOut, and the exogenous ones will be the amount of memory consumed and the percentage of processor occupation time. The tool developed modifies neither the source code of the response software to the clients’ “HTTP” requests nor the operating system code of the host computer, being non-intrusive regarding original system configuration. A proportional-integral (PI) controller was designed in order to keep the average perceived time response of “HTTP” messages regulated. The experimental tests carried out on an Apache Web server show the improvement obtained on the system’s time response.
Biotechnological Applications Of Bioinformatics In The Post Genomic ERA

The huge data gleaned from genomes sequencing including (DNA, RNA, protein sequences, structures and interactions, genes, chromosomal maps, pathways, networks, biological signals and images) has led to explosive and diverse growth in biological data that presents the urgent need for intensive computing and big data analysis techniques potential for their storage, organization, analysis and integration. Bioinformatics has come to play this major role and the pursuit for future biological discoveries. It is widely accepted that bioinformatics coupled with the high throughput sequencing technologies has paved the way for the post-genomic era and will become an essential and indispensable part of the future of life sciences and molecular medicine.This review discusses and surveys the concepts and progress of bioinformatics and highlights their recent biotechnological applications in post genomics era, in many fields starting with basic and applied future life sciences where the philosophy and style of both research and knowledge has changed. Furthermore, discusses their related applications in molecular medicine and microbial genome, as well as summarizes all their possible biotechnological applications in agriculture, energy and environment.
Study of Timing Properties on Data Collection and Query Processing Techniques in Wireless Sensor Networks

The real-time aspect is an important factor for Wireless Sensor Network (WSN) applications, which demand a timely data delivery, to reflect the current state of the environment. However, most existing works in the field of sensor networks are only interested in data processing and energy consumption, to improve the lifetime of WSN. Extensive researches have been done on data processing in WSN. Some works have been interested in improving data collection methods, and others have focused on various query processing techniques. In recent years, the query processing using abstract database technology like Cougar and TinyDB have shown efficient results in many researches. This paper presents a study of timing properties through two data processing techniques forWSN. The first is based on a warehousing approach, in which data are collected and stored on a remote database. The second is based on query processing using an abstract database like TinyDB. This study has allowed us to identify some factors which enhance the respect of temporal constraints.
Rao-Test Fusion Rules of Uncensored Decisions Transmitted Over a Collision Channel

We consider composite hypothesis testing problems in distributed detection whose sensors share a collision channel to transmit their uncensored decisions and the fusion center (FC) has a limited time duration to collect local decisions. Rao-test fusion rules for distributed detection whose sensors utilize time division multiple access (TDMA) and frame slotted Aloha to transmit their decisions to the FC are derived. Since using TDMA provides no packet collisions while using frame slotted Aloha induces packet collisions, finding scenarios when one fusion rule achieves a better performance than the other one is our interest.
A Dataglove Hardware Design and Real-Time Sign Gesture Interpretation.

In this work, we attempt to design a dataglove to assist sign language users communicate with non-users. We focus our work on designing a low cost dataglove using open source hardware and software. We run algorithms on the micro controller to interpret basic gestures and phrases. The interpretation results are sent wirelessly to a bluetooth enabled smartphone to be displayed on screen. We benchmark the interpretation accuracy on three different users. Our results show that most basic sign gestures can be interpreted rather accurately using only simple algorithms.
An Optimal LTE-U Access Method for Throughput Maximization and Fairness Assurance

To solve the issue of scarce spectrum resources of the existing cellular network, LTE-U that expands LTE service to the unlicensed 5GHz spectrum is proposed. However, the centralized medium access control protocol of LTE largely decreases the performance of Wi-Fi networks operating in the same unlicensed spectrum. In the paper, to solve the problem, we propose a new mechanism based on the duty-cycle method. It can adaptively adjust the percentage of the airtime used by a LTE Small cell Base Station (SBS) according to the bandwidth of the licensed spectrum of the SBS and downlink data rate demands of the SBS users to maximize throughput of the SBS network on the unlicensed spectrum while ensuring fairness between the Wi-Fi and SBS network. The fairness is based on 3GPP proposed fairness coexistence criterion. To ensure the fairness, we propose a method to construct a W-Fi network offering the same level of the SBS traffic load, and throughput maximization of the SBS is formulated as a constrained non-linear optimization problem solved by an optimal algorithm. We evaluate the proposed mechanism from two aspects. The first is to prove the proposed Wi-Fi network construction method is valid. The second is to evaluate the performance of our proposed method. Simulation results show that our approach is valid and it can maximize the throughput of the SBS network and ensure the fairness criterion.
Improving Blockage Robustness in VLC Networks

Visible Light Communication (VLC) has attracted significant attention over the past decade. Although numerous research studies have been performed to improve the data rate of VLC links, an important fact has been largely neglected: users that host VLC receivers could block their tine-of-sight (LOS) downlinks, and thus, degrade the system performance greatly. In this paper, we propose NutVLC, a system that can significantly improve the blockage robustness for VLC networks. A novel user-in-the-loop mechanism is designed in which users are guided by the network to rotate themselves for the improvement of the system performance as well as the individual user experience. Both our simulation and experimental results demonstrate that NutVLC can improve the system throughput and user fairness on average by 46% and 15%, respectively. For individual users, the average gain in throughput can reach 132%. Further, to make NutVLC more practical, two lightweight heuristics are designed which can achieve similar gains while reducing the computing complexity by 99%.