Miracle-dz/UniFAR-FTUs huggingface dataset:
LTrust: An Adaptive Trust Model Based on LSTM for Underwater Acoustic Sensor Networks

As an effective security mechanism, trust models have been proposed to estimate the reliability of the individual nodes in Underwater Acoustic Sensor Networks (UASNs) during adverse attacks. However, existing trust models neglect the relative importance of the different nodes within the network topology. Further, few trust models study the effects of defective recommendation trust filtering. In this work, we propose an adaptive trust model based on the Long Short-Term Memory (LSTM) network model for UASNs, which we term LTrust. The LTrust is composed of two stages: trust data collection and trust evaluation. In the first stage, the characteristics of the network topology are leveraged towards evaluating direct trust evidence, by aggregating the communication trust and environment trust metrics; a defective recommendation filtering method is designed for broadcasting accurate trust recommendations among the nodes. In the second stage, an adaptive trust model is designed based on the LSTM model, to identify anomalous nodes by evaluating their trust value. The LTrust model has been tested under both hybrid attack and single-mode attack scenarios. Simulation results demonstrate that the LTrust achieves effective performance, as compared to other approaches proposed in the literature, in terms of trust value, accuracy and error rate.
African swine fever vaccine development: current status and challenges ahead

African swine fever (ASF) is a contagious, hemorrhagic and highly lethal viral disease in swine. Following the initial report of ASF in Kenya in 1921 it spread to East Asia through Western Europe and Russia (including Eastern Europe). Many efforts have been dedicated to controlling ASF but the struggle to eradicate this disease continues. Thus far, the efforts to develop an effective vaccine to control ASF have been unsuccessful. A previous inactivated ASF vaccine, developed by traditional methods, failed to protect pigs from ASF virus (ASFV) infection. Neutralizing antibodies were not effective in inducing protective immunity and it appears that cellular immunity is required. To develop an effective ASF vaccine the identification of protective antigens of ASFV has been explored and subunit vaccines that target these potential protective antigens have induced partial protection. DNA vaccines that induce cellular immunity have been effective in inducing protection against ASFV infection. ASFV live attenuated vaccines (LAVs) can be rationally designed and engineered via comparative and functional genomics. LAVs have a major safety concern despite their high protective efficacy. ASF vaccines are urgently needed to control ASF; however, many obstacles remain to be overcome to develop an effective ASF vaccine.
PREPARING FOR ARTEMIS THROUGH LESSONS LEARNED FROM APOLLO 17. HIGHLIGHTING THE PROGRESS OF THE ANGSA INITIATIVE

THE PROGRESS OF THE ANGSA INITIATIVE. C.K. Shearer, F.M. McCubbin, R.A. Zeigler, J. Gross, S.B. Simon, A. Meshik, F. McDonald, R.V. Morris, H.H. Schmitt, M. Neuman, K. Wang, B.L. Jolliff, K. Joy, Z. Sharp, M. Cato, A. Gargano, S. Eckley, E. Cano, R. Para, J. Simon, K.C. Welten, J.J. Barnes, M. Dyar, K. Burgess, N. Petro, N.M. Curran, J.E. Elsila, J. Gillis-Davis, A. Sehlke, B. Cohen, O. Pravdivseva, M.S. Thompson, C.R. Neal, P. Lucey, L. Sun and the ANGSA science team. Dept. of Earth & Planet. Sci., Institute of Meteoritics, University of New Mexico, Albuquerque, NM 87131; Lunar and Planetary Institute, Houston TX 77058; 3 ARES, NASA Johnson Space Center, Houston TX 77058-3696, Washington University in St. Louis, St. Louis, Mo 63130; ESA/ESTEC, Noordwijk, Netherlands; University of Wisconsin-Madison, P.O. Box 90730, Albuquerque NM 87199; University of Manchester, Manchester, UK; Space Sciences Laboratory, University of California, Berkeley, CA 94720; Lunar and Planetary Laboratory, University of Arizona, Tucson, AZ 85721; Department of Astronomy, Mount Holyoke College, South Hadley MA 01075; United States Naval Research Laboratory, Washington DC 20375; NASA Goddard Space Flight Center, Greenbelt, MD 20771; NASA Ames Research Center, Moffett, CA 94035; 14 Purdue University, West Lafayette, IN 47907; University of Notre Dame, Notre Dame IN 46556, 16 University of Hawaii at Manoa, Honolulu, HI 96822; ANGSA Science Team list at https://www. lpi.usra. edu/ ANGSA /teams/. (cshearer@unm.edu).
Reclaiming Racial Authenticity through Tweets, Hashtags, and Images

Black men often navigate society with inconsistent control of their image and ideologies of masculinity. The underpinning of masculinity has established a rigid caste that alienates marginalized gendered performances and presentations within this exclusive club. Till now, with decades of advancements regarding the scope of technology, Black men regained control of their masculine imagery and identity by utilizing social media. This article examines how five Black men utilized Twitter to connect with online communities to reclaim their racial identity by utilizing hashtags, images, and tweets.
On perceptual expertise-July2019-final-web

Expertise is a cognitive achievement that involves experience and learning, and that requires explicit, time-consuming training specific to the relevant domain. It is intuitive that many such experts— radiologists, bird watchers, fingerprint examiners. —are better perceivers in the domain(s) of their expertise. The goal of this paper is to motivate three related claims. Perceptual expertise is genuinely perceptual and genuinely cognitive, and this phenomenon reveals how we can become epistemically better perceivers. These claims are defended against opponents that deny significant cognitive effects on perception, and opponents who maintain that any such effects on perception are epistemically pernicious.
Neural Networks Used in the Prediction of Traffic Flow

-Neural network approaches for trafﬁc ﬂow forecasting are usually even currently based on STL models, which do not take the data from the relative tasks. But instead of using STL and MTL, using Neural Networks can improve on generalization by transferring information in training signals of extra tasks. In our Deep Learning and Neural Networks are used in the prediction of traffic flow. Using DL and NN deep architecture to predict the short-term traffic flow in an urban traffic network have been used and are effective. Comprehensive experiments will be done to show that this method is effective in forecasting traffic.
International Journal of Electrical and Computer Engineering (IJECE)

Received Sep 23, 2019 Revised Nov 8, 2021 Accepted Nov 30, 2021 This paper presents an analysis and design of linear quadratic regulator for reduced order full car suspension model incorporating the dynamics of the actuator to improve system performance, aims at benefiting: Ride comfort, long life of vehicle, and stability of vehicle. Vehicle’s road holding or handling and braking for good active safety and driving pleasure and keeping vehicle occupants comfortable and reasonably well isolated from road noise, bumps, and vibrations are become a key research area conducted by many researchers around the globe. Different researchers were tested effectiveness of different controllers for different vehicle model without considering the actuator dynamics. In this paper full vehicle model was reduced to a minimal order using minimal realization technique. The entire system responses were simulated in MATLAB/Simulink environment. The effectiveness of linear quadratic regulator controller was compared for the system model with and without actuator dynamics for different road profiles. The simulation results were indicated that percentage reduction in the peak value of vertical and horizontal velocity for the linear quadratic regulator with actuator dynamics relative to linear quadratic regulator without actuator dynamics was 28.57%. Overall simulation results were demonstrated that proposed control scheme has able to improve the effectiveness of the car model for both ride comfort and stability.
Building a predictive model for poverty in Palestine using Machine Learning classification tools Prepared by:

This study focuses on comparing competive models in predicting poverty status in Palestine using data provided by The Palestine Expenditure and Consumption Survey, PECS 2017, which is carried by the Palestinian Central Bureau of Statistics (PCBS). It expands on demographic factors by utilizing them in regression models and a machine learning classifier (decision tree) to predict the poverty status of a household. The study finds numerous demographic variables for the head of household, housing, and household conditions that can be used in predicting the poverty status of a household. The study showed that among the four models used, the decision tree provided the highest accuracy and validity indicators.
Fast Pure Exploration via Frank-Wolfe

We study the problem of active pure exploration with ﬁxed conﬁdence in generic stochastic bandit environments. The goal of the learner is to answer a query about the environment with a given level of certainty while minimizing her sampling budget. For this problem, instance-speciﬁc lower bounds on the expected sample complexity reveal the optimal proportions of arm draws an Oracle algorithm would apply. These proportions solve an optimization problem whose tractability strongly depends on the structural properties of the environment, but may be instrumental in the design of efﬁcient learning algorithms. We devise Frank-Wolfe-based Sampling ( FWS ), a simple algorithm whose sample complexity matches the lower bounds for a wide class of pure exploration problems. The algorithm is computationally efﬁcient as, to learn and track the optimal proportion of arm draws, it relies on a single iteration of Frank-Wolfe algorithm applied to the lower-bound optimization problem. We apply FWS to various pure exploration tasks, including best arm identiﬁcation in unstructured, thresholded, linear, and Lipschitz bandits. Despite its simplicity, FWS is competitive compared to state-of-art algorithms.
NeurIPS titled “ A Normative and Biologically Plausible Algorithm for Independent Component Analysis ” , from

We start by restating the setup in which our algorithm operates. The type of ICA considered in our work assumes the following generative model. There are d sources recorded T times forming the columns of S := [s1, . , sT ] ∈ Rd×T whose components st , . , st are assumed non-Gaussian and independent. Without loss of generality, we assume that each source has zero-mean, unit variance, and finite and distinct kurtosis, a common assumption among kurtosis-based ICA methods [12]. The kurtosis of a random variable v is defined as kurt[v] = E [ (v − E(v)) ] / ( E [ (v − E(v)) ])2. Finally, sources are assumed to be mixed through a linear system, i.e., there exists a full rank mixing matrix, A ∈ Rd×d, producing the d-dimensional mixture, xt, expressed as xt = Ast ∀t ∈ {1, . , T} . (S.1)
Short-term Wind Speed Forecasting Based on Support Vector Machine With Similar Data

Wind power prediction is an effective way to decrease the effects for the large-scale wind power connecting to grid.Improving accuracy of short-term wind prediction is key of wind power prediction.In this paper,a support vector machine(SVM) method was proposed based on wavelet analysis and similar data.Similar data were extracted from large amounts of data to create the training samples.The original wind speed data was decomposed into trend signal of low frequency band and random signal of high frequency band by wavelet decomposition.Different SVM speed forecasting models were trained respectively and combined to obtain forecasting results.The simulation experiments show that similar data effectively improve the relevance of data.SVM models better approximate the wind speed characteristics of low and high frequency band by wavelet decomposition and prediction accuracy is improved.By validation with the measured wind speed data of a wind farm,the model has generalization capability and good run time for meeting requirement of engineering application.
Multi-source Unsupervised Domain Adaptation on Corn Yield Prediction

Recently, supervised machine learning methods based on remote sensing observations have achieved satisfactory results in crop yield prediction. However, supervised learning models tend to have poor transferability. Due to domain shifts be-tween observations in different regions, models trained with data from one spatial region (i.e., source domain) often lose their validity when directly applied to another region (i.e., target domain). To address this issue, we proposed a Bayesian Multi-source Maximum Predictor Discrepancy (BMMPD) neural network which is an unsupervised domain adaptation (UDA) approach to improve the model’s transferability for corn yield prediction at the county level. We proposed to maximize the discrepancy between two yield predictors’ outputs to detect unlabeled target samples that are far from the support of the source domain. A feature extractor then learned to align source and target domains by minimizing the predictor discrepancy. Moreover, we applied Bayesian learning to prevent overfitting. A case study was conducted in the U.S. corn belt to evaluate the proposed BMMPD model. Time-series vegetation indices and weather observations were collected and aggregated to the county level and used as the input predictors. Experiment results demonstrated that the proposed BMMPD has effectively reduced domain shifts and outperformed several state-of-art domain adaptation meth-ods.
Maintain High Avidity Memory CTL Vaccines with Enhanced Costimulation

The avidity of Ag-speciﬁc CTL is a critical determinant for clearing viral infection and eliminating tumor. Although previous studies have demonstrated that vaccines using enhanced costimulation will enhance the level and avidity of Ag-speciﬁc T cells from naive mice, there are conﬂicting data about the effects of vaccines using enhanced costimulation (vector or dendritic cell based) on the survival of memory T cells. In this study we have ﬁrst extended previous observations that primary vaccination with a recombinant vaccinia virus (rV-) expressing a model Ag (LacZ) and a triad of T cell costimulatory molecules (B7-1, ICAM-1, and LFA-3 (designated TRICOM)) enhances the level and avidity of T cells from naive vaccinated C57BL/6 (Thy1.2) mice. Adoptive transfer of Thy1.1 memory CD8 (cid:1) T cells into naive Thy1.2 C57BL/6 mice was followed by booster vaccinations with a recombinant fowlpox (rF-)-expressing LacZ (rF-LacZ) or booster vaccinations with rF-LacZ/TRICOM. Analysis of levels of (cid:2) -galac-tosidase tetramer-positive T cells and functional assays (IFN- (cid:3) expression and lytic activity) determined that booster vaccinations with rF-LacZ/TRICOM were superior to booster vaccinations with rF-LacZ in terms of both maintenance and enhanced avidity of memory CD8 (cid:1) T cells. Antitumor experiments using a self-Ag (carcinoembryonic Ag (CEA) vaccines in CEA transgenic mice bearing CEA-expressing tumors) also demonstrated that the use of booster vaccinations with vaccines bearing enhanced costimulatory capacity had superior antitumor effects. These studies thus have implications in the design of more effective vaccine strategies. The Journal of Immunology, 2005, 175: 3715–3723.
Explainable Weakly-Supervised Cell Nuclei Segmentation by Canonical Shape Learning and Transformation

Microscopy images have been increasingly analyzed quantitatively in biomedical research. Segmenting individual cell nucleus is an important step as many research studies involve counting cell nuclei and analysing their shape. We propose a novel weakly supervised instance segmentation method trained with image segmentation masks only. Our system comprises two models: an implicit shape Multi-Layer Perceptron (MLP) that learns the shape of the nuclei in canonical coordinates; and 2) an encoder that predicts the parameters of the affine transformation to deform the canonical shape into the correct location, scale, and orientation in the image. To further improve the performance of the model, we propose a loss that uses the total number of nuclei in an image as supervision. Our system is explainable, as the implicit shape MLP learns that the canonical shape of the cell nuclei is a circle, and interpretable as the output of the encoder are parameters of affine transformations. We obtain image segmentation performance close to DeepLabV3 and, additionally, obtain an F1-score IoU =0 . 5 of 68 . 47% at the instance segmentation task, even though the system was trained with image segmentations.
Structural Equation Model (SEM) Correlation Between Work Breakdown Structure (WBS), Work Method and Risk Towards Cost Of Safety On Low-Cost Apartments Project

Cost of safety is a fundamental aspect that is inevitable to be considered during the construction cost planning phase. The cost of safety estimation is determined based on several factors, including WBS (work breakdown structure), work method, risk and other contributing factors. This estimation is critical in construction, specifically for high rise building constructions. In a case where there is a lack of a suitable methodology in estimating the cost of safety, may incur inaccuracy and uncertainty in determining the actual cost of safety. Besides the mentioned factors, this study will use Structural Equation Model – Partial Least Square (SEM-PLS) in determining the construct model cost of safety, for the case of the build of low-cost apartment building (Rusunawa).
Interactive comment on “ JULES-CN : a coupled terrestrial Carbon-Nitrogen Scheme ( JULES vn 5 . 1 )

Understanding future changes in the terrestrial carbon cycle is important for reliable projections of climate change and impacts on ecosystems. It is known that nitrogen could limit 30 plants’ response to increased atmospheric carbon dioxide and is therefore important to include in Earth System Models. Here we present the implementation of the terrestrial nitrogen cycle in the JULESland surface model (JULES-CN :::: Joint ::: UK ::::: Land ::::::::::: Environment :::::::: Simulator :::::::: (JULES) : ::: the :::: land :::::: surface :::::: scheme :: of ::: the ::: UK ::::: Earth :::::: System :::::: Model ::::::::: (UKESM). Two versions :::::::::::: configurations are discussed the one implemented within the UK Earth System Model (UKESM1) which ::: first :::: one ::::::::::: (JULES-CN) 35 has a bulk soil biogeochemical model and a development version ::: the :::::: second ::: one :: is :: a ::::::::::: development ::::::::::: configuration which resolves the soil biogeochemistry with depth . The nitrogen ::::::::::::::: (JULES-CNlayer). :: In :::::: JULES ::: the :::::::: nitrogen ::: (N) : cycle is based on the existing carbon cycle in the model. It ::: (C) ::::: cycle ::: and represents all the key terrestrial nitrogen processes in an efficient :: N :::::::: processes :: in : a :::::::::::: parsimonious way. Biological :: N fixation is dependent on productivity, with nitrogen : N : deposition as an external 40 input. Loss occurs :::::::: Nitrogen ::::: leaves ::: the ::::::::: vegetation ::: and :::: soil :::::: system : via leaching and a bulk gas loss parameterisation. Nutrient limitation reduces carbon-use efficiency (CUE ratio of net to gross primary productivity) and can slow soil decomposition. We show that ecosystem level : N : limitation of net primary productivity by nitrogen is consistent with observational estimates and that simulated carbon and nitrogen ::::::::: (quantified :: in ::: the ::::: model ::: by ::: the :::: ratio ::: of ::: the ::::::: potential ::::::: amount :: of :: C :::: that ::: can ::: be 45 ::::::: allocated :: to ::::::: growth ::: and ::::::::: spreading :: of ::: the ::::::::: vegetation :::::::: compared ::::: with ::: the ::::: actual ::::::: amount ::::::: achieved ::: in :: its :::::: natural ::::: state) :::: falls :: at ::: the ::::: lower :::: end :: of ::: the :::::::::::: observational :::::::: estimates :: in :::::: forests ::::::::::::: (approximately ::: 1.0 :: in ::: the ::::: model ::::::::: compared :::: with :::: 1.01 :: to :::: 1.38 :: in ::: the :::::::::::: observations). :::: The ::::: model :::::: shows :::: more :: N ::::::::: limitation :: in :::::: tropical ::::::: savanna ::: and :::::: tundra :::::: which :::: falls ::::: within ::: the ::::: range :: of ::: the :::::::: available ::::::::::: observations. ::::::::: Simulated
Experimental Analysis of Flow Boiling in Horizontal Annulus—The Effect of Heat Flux on Bubble Size Distributions

Subcooled flow boiling was experimentally investigated in a horizontal annulus with a temperature-controlled boiling surface and transparent outer pipe facilitating visualization. Boiling occurs on a copper tube with a diameter of 12 mm in an annulus with a 2 mm gap. Refrigerant R245fa is used as a working fluid. The focus of this study is to explore the effect of heat flux variation on the boiling flow patterns at approximately constant inlet flow conditions of the working fluid (fixed mass flux and inlet fluid temperature). Subcooled flow boiling is recorded by a high-speed camera, images are analyzed by a neural network to determine the bubble size distributions and their variation with the heat flux. The experimental setup being a part of the laboratory THELMA (Thermal Hydraulics experimental Laboratory for Multiphase Applications) at the Reactor Engineering Division of Jožef Stefan Institute, analysis methods and measurement results are presented and discussed.
Design and Development of Parallel Biosensing System for Personalized Chemotherapy Treatment

Ahmad Fairuzabadi Mohd Mansor1, Anis Nurashikin Nordin1, Kian Liang Goh2, Soon Hin How2, Yumi Zuhanis Has-Yun Hashim3 and Mardhiah Mohammad4 1Kulliyyah of Engineering, International Islamic University Malaysia (IIUM), Jalan Gombak, Kuala Lumpur, Malaysia 2Kulliyyah of Medicine, IIUM Kuantan, Malaysia 3International Institute for Halal Research and Training (INHART), IIUM Gombak, Malaysia 4Kulliyyah of Allied Health Science, IIUM Kuantan, Malaysia
International Journal of Electrical and Computer Engineering (IJECE)

Received Jul 27, 2021 Revised May 17, 2022 Accepted Jun 12, 2022 The human voice speech essentially includes paralinguistic information used in many real-time applications. Detecting the children’s gender is considered a challenging task compared to the adult’s gender. In this study, a system for human-robot interaction (HRI) is proposed to detect the gender in children’s speech utterances without depending on the text. The robot's perception includes three phases: Feature’s extraction phase where four formants are measured at each glottal pulse and then a median is calculated across these measurements. After that, three types of features are measured which are formant average (AF), formant dispersion (DF), and formant position (PF). Feature’s standardization phase where the measured feature dimensions are standardized using the z-score method. The semantic understanding phase is where the children’s gender is detected accurately using the logistic regression classifier. At the same time, the action of the robot is specified via a speech response using the text to speech (TTS) technique. Experiments are conducted on the Carnegie Mellon University (CMU) Kids dataset to measure the suggested system’s performance. In the suggested system, the overall accuracy is 98%. The results show a relatively clear improvement in terms of accuracy of up to 13% compared to related works that utilized the CMU Kids dataset.