Miracle-dz/UniFAR-FTUs huggingface dataset:
Amblyomma sculptum Salivary PGE2 Modulates the Dendritic Cell-Rickettsia rickettsii Interactions in vitro and in vivo

Amblyomma sculptum is an important vector of Rickettsia rickettsii, causative agent of Rocky Mountain spotted fever and the most lethal tick-borne pathogen affecting humans. To feed on the vertebrate host's blood, A. sculptum secretes a salivary mixture, which may interact with skin resident dendritic cells (DCs) and modulate their function. The present work was aimed at depicting the A. sculptum saliva-host DC network and the biochemical nature of the immunomodulatory component(s) involved in this interface. A. sculptum saliva inhibits the production of inflammatory cytokines by murine DCs stimulated with LPS. The fractionation of the low molecular weight salivary content by reversed-phase chromatography revealed active fractions eluting from 49 to 55% of the acetonitrile gradient. Previous studies suggested that this pattern of elution matches with that observed for prostaglandin E2 (PGE2) and the molecular identity of this lipid mediator was unambiguously confirmed by a new high-resolution mass spectrometry methodology. A productive infection of murine DCs by R. rickettsii was demonstrated for the first time leading to proinflammatory cytokine production that was inhibited by both A. sculptum saliva and PGE2, a result also achieved with human DCs. The adoptive transfer of murine DCs incubated with R. rickettsii followed by treatment with A. sculptum saliva or PGE2 did not change the cytokine profile associated to cellular recall responses while IgG2a-specific antibodies were decreased in the serum of these mice. Together, these findings emphasize the role of PGE2 as a universal immunomodulator of tick saliva. In addition, it contributes to new approaches to explore R. rickettsii-DC interactions both in vitro and in vivo.
Effective Combination of DenseNet and BiLSTM for Keyword Spotting

Keyword spotting (KWS) is a major component of human–computer interaction for smart on-device terminals and service robots, the purpose of which is to maximize the detection accuracy while keeping footprint size small. In this paper, based on the powerful ability of DenseNet on extracting local feature-maps, we propose a new network architecture (DenseNet-BiLSTM) for KWS. In our DenseNet-BiLSTM, the DenseNet is primarily applied to obtain local features, while the BiLSTM is used to grab time series features. In general, the DenseNet is used in computer vision tasks, and it may corrupt contextual information for speech audios. In order to make DenseNet suitable for KWS, we propose a variant DenseNet, called DenseNet-Speech, which removes the pool on the time dimension in transition layers to preserve speech time series information. In addition, our DenseNet-Speech uses less dense blocks and filters to keep the model small, thereby reducing time consumption for mobile devices. The experimental results show that feature-maps from DenseNet-Speech maintain time series information well. Our method outperforms the state-of-the-art methods in terms of accuracy on Google Speech Commands dataset. DenseNet-BiLSTM is able to achieve the accuracy of 96.6% for the 20-commands recognition task with 223K trainable parameters.
Use of Word and Character N-Grams for Low-Resourced Local Languages

Language identification is a text classification task for identifying the language of a given text. Several works use this as a preprocessing technique prior to sentiment analysis, mood analysis, and named entity recognition among others. Thus, building an accurate language identification engine is important given that the Philippines is home to more than 170 languages, and is scarce of language documents and resources. We compare machine learning algorithms such as Naive Bayes, Linear Support Vector Machines (SVM), and Random Forest for classification of Philippine languages. Results show that the Linear SVM model had the best performance with 0.97 Fl-score.
On the Construction of Hierarchical Broad Learning Neural Network: An Alternative Way of Deep Learning

In this paper, we proposed an alternative way of deep learning, named as Hierarchical Broad Learning (HBL) neural network which forms a neural network with three layers. It is based on Broad Learning System (BLS), hence HBL inherits the characteristics of feature and enhancement nodes in a neural network. In HBL, parameters of the hidden layer are trained in a forward manner so that the weights of the current layer can be fixed without fine-tuning once the input layer of feature and enhancement node is established. Due to the multilayer architecture of HBL, it can handle image or video data in the more effective way. Experimentation of the proposed HBL is performed on two benchmark datasets, MNIST and NYU NORB (object recognition dataset). The results show that the training time of proposed HBL is effectively less than the existing state of the art learning methods. It also achieves better accuracy and generalization performance than the Broad Learning System.
Development of Engine Power Capacity Calculation Method for Range Extender and Case Study in Medium-Size Electric Bus

This study was conducted to develop a method for calculating appropriate engine power capacity or engine sizing for range extender application. This method was developed by considering electric vehicle's specification, driving cycle, road's gradient profile, electrical components consumption, and powertrain component's efficiency. The method that had been made is then tested with a case study. Case study for this research was modified a medium-size electric bus with the capacity of 36 people to become range-extended electric bus. Simulation part of this case study was completed by using AVL Cruise software. The operating track used is located in the popular tourist area in Bali. This track was selected because Bali is the leading tourist area in Indonesia and to support Bali as the pioneer in green tourism. Engine power obtained from the calculation is 45,4 kW if the used assumptions were Japan 08 as the driving cycle and road's gradient profile is 0% along the path and 46 kW if assuming road's gradient profile is the average gradient.
Adaptive Channel Estimation in Side Channel Attacks

Side channel attacks exploit physical information that leaks from the target implementations to extract secret information, for example keys. Since the observable physical leakages are dependent on the internal state of the cryptographic implementation, predicting (or profiling) this dependency, which is also called leakage model or leakage function, can significantly improve the performance of attacks. Previous work shows that the leakage function (side channel) can be represented as a noisy channel in communication theory. In this paper, we focus on how to model the side channel as a fading channel. We treat the leakage model coefficients as channel fading (gains), to develop a weighted leakage model. Under this assumption, the profiling problem in side channel attacks becomes a channel estimation problem in communication. This paper also proposes al2-norm based reweighted algorithm for estimating the leakage model. Compared to previous methods, such as the Least Squares and the Ridge-Based method, our algorithm shows significant improvements both in terms of performance of recovery and efficiency of implementation.
A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance

This paper proposes a comparative performance of ten different machine learning algorithms, done on a credit card fraud detection application. The machine learning methods have been classified into two groups namely classification algorithms and ensemble learning group. Each group is comprised of five different algorithms. Besides, the 'Time' feature is introduced in the data set and performances of the algorithms are studied with and without the 'Time' feature. Two algorithms of the ensemble learning group have been found to perform better when the used dataset does not include the 'Time' feature. However, for the classification algorithms group, three classifiers are found to show better predictive accuracies when all attributes are included in the used dataset. The rest of the machine learning models have approximate similar scores between these datasets.
Optimization of Mg Precursor Concentration to Obtain High $J_{c}$ in MgB2 Synthesized With Ag Addition and Carbon Encapsulated Boron

In this work, we report an optimum amount of Mg precursor required for obtaining high critical current density (<inline-formula><tex-math notation="LaTeX">$J_{c}$</tex-math></inline-formula>) of bulk MgB<sub>2</sub> superconductor synthesized via solid state sintering. We add a slight excess of Mg precursor to balance its losses while formation of pinning phases and impurities such as Ag–Mg phases and MgO, respectively. Several amounts of Mg had been varied such as <italic>x</italic> = 1.05, 1.075, 1.1, 1.125, and 1.15 in Mg<sub>x</sub>B<sub>2</sub>. To obtain the best critical current density values, we used carbon encapsulated boron (1.5% carbon) and added 4 wt% Ag. The x-ray diffraction analysis showed the presence of AgMg and AgMg<sub>3</sub> phases and a minute amount of MgO. The SQUID measurements indicated that highest <inline-formula><tex-math notation="LaTeX">$J_{c}$</tex-math></inline-formula> of 350 kA/cm<sup>2</sup> at 20 K and self-field in sample with <italic>x</italic> = 1.075. All results analyzed explain the improved critical current performance based on nanometer-sized Ag–Mg particles in the final product.
What Engineering Admissions Can Learn from Medical School Admissions

Medical school admissions have undergone a drastic change in the past decade with increasing awareness about issues of professionalism and a lack of diversity among physicians. These problems are also arising among engineers, where issues of communication, ethics, and cultural competency are beginning to be identified across the profession. Over the years, the traditional admissions process into medical school has been identified to be particularly problematic, as it places a strong emphasis on the cognitive competencies of incoming students, but frequently neglects their non-cognitive competencies or professionalism. The same parallel can be drawn for admission into engineering programs, where the process strongly focuses on cognitive abilities (GPA, standardized test scores) and less so on the non-cognitive skills. In this paper, we suggest various tools to promote the shift to the assessment of professionalism in engineering admissions to ensure that the profession will keep up with the future needs of the population.
Adaptive Steganography in the Noisy Channel with Dual-Syndrome Trellis Codes

Adaptive steganography aims to reduce distortion in the embedding process, typically using Syndrome Trellis Codes (STCs). However, in the case of non-adversarial noise, these are a bad choice: syndrome codes are fragile by design, amplifying the channel error rate into unacceptably-high payload error rates. In this paper we examine the fragility of STCs in the noisy channel, and consider how this can be mitigated if their use cannot be avoided altogether. We also propose an extension called Dual-Syndrome Trellis Codes, that combines error correction and embedding in the same Viterbi process, which slightly outperforms a straightforward combination of standard forward error correction and STCs.
Exploring Data Reduction Techniques for Time Efficient Support Vector Machine Classifiers

Support Vector Machines [1] (SVMs) are regarded as powerful machine learning tool because of their inherent properties. However, one major challenge for using SVMs in real-world applications with large datasets is its high training time complexity. Over the years, many variants of SVM have been proposed to reduce the training time by either using algorithmic modifications (such as LS-SVM [3], GEP-SVM [4], TWSVM [5]) or training level speed-ups (such as SMO [6], SOR [2] and Stochastic Gradient Descent method [7]). However, these methods deal with the entire data for learning a classifier model, thus the space complexity could be a challenge. A more fitting approach is to use an Instance Selection method (IS) which selects a subset of data which is best representative of the underlying data distribution. Since SVMs by definition use the geometry of patterns for classification, this study explores the effects of different Instance Selection methods on different variants of SVM to check their effectiveness using their comparative performances in terms of training time and generalization ability. Various theoretical and experimental comparisons on standard datasets have been provided to validate the efficacy of different IS methods on SVM based classifiers.
Generalizing Pedestrian Detector to Low Resolution Images by Integrating High and Low Resolution Features

Detecting low resolution (low-res) pedestrians is rather important for automotive driving systems as it can provide enough time for decision making. However, many detectors perform quite well on high resolution (high-res) pedestrians, while perform poorly on low resolution ones. In this paper, to generalize the high performance of high-res pedestrians detectors to low-res pedestrians ones, we propose to integrate the high and low resolution features, the resolution-aware transformations are learned simultaneously to map the high and low resolution pedestrians to a common space and the shared detector in this space can be learned based on the linear SVM model. Meanwhile, a Maximum Mean Discrepancy (MMD) is considered to minimize the distribution mismatch of the transformed high and low resolution features. Then the shared detector and the transformations are learned jointly by minimizing both the distribution mismatch between the mapped high and low resolution samples and structural risk functional. In addition, we show that our proposed learning framework can be converted to a standard Multiple Kernel Learning (MKL) problem, which is convex and hence the global solution can be guaranteed. Experiments on the Caltech Pedestrian Benchmark show that our method is effective for the low resolution pedestrian detection tasks.
Telemedicine via Satellite: Improving Access to Healthcare for Remote Rural Communities in Africa

In this paper, realistic telemedicine implementation scenarios with architecture are proposed to help in extending quality healthcare using satellite and integrated satellite-terrestrial networks (ISTNs). Telemedicine is the use of telecommunications and information technology to extend healthcare service delivery to underserved, remotely isolated communities. Global coverage, broadcast/multicast capability and the high capacity of satellites in Geostationary Earth Orbit (GEO) could potentially serve as a tool to extend quality healthcare to underserved remote rural areas. However, Long End-to-End latency or Round-Trip-Time (RTT) attributed to the GEO satellites could degrade the performance of data communications leading underutilisation of the high available capacity due to high link errors and the long latency, particularly when using Transmission Control Protocol (TCP) over the internet, which accounts for about 90% of the internet traffic today. The actual latency (RTT) of GEO satellites is about 1700ms to 3000ms, which could lead to capacity utilisation as low as 39% of maximum 464kbps available capacity of our testbed service provider. However, TCP Performance could be improved by adopting other transmission protocols which we are currently testing and investigating possible modifications for even more enhance performance over satellite and hybrid (ISTN) channels network environment.
Predicting Personal Mobility Data Disclosure

Personal data has multiple categories, such as demographic, firmographic (organizational), behavioral and psychographic data. Mobility data is one of the behavioral pattern information, for example, location tracking. Disclosing mobility data is a privacy concern of individuals as their location and physical moving pattern can be uncovered and potentially be misused by third parties. Despite the privacy concern, exposing mobility data can be beneficial for individuals to gain advantages of getting customized services from service providers. On the other hand, companies get benefit from mobility data by providing value added services to individuals. Several influential factors of individual’s intention in personal data disclosure have been identified based on existing literature. However, we argue that contextual aspects play an important role in effecting the intention. Therefore, in this research we seek to investigate if the identified factors by prior studies are significantly influential in Malaysian context. Additionally, from a different perspective, our research focuses on mobility data instead of personal data in general using Structural Equation Modelling analysis method. Based on our research finding, we identified an unexplored factor that can be used to predict the intention to disclose mobility data, and our result also confirmed that context aspects such as demographics and different personal data categories (e.g. mobility data) do have impact on the disclosure attention.
Between Vehicle Quality and Driving Satisfaction: Driving Quality as An Intervening Variable

Quality improvement of a product becomes an important program, but if the products quality achieved at the highest level of criteria, will the customers always satisfy? Previous research and experience proved that statement but need deeper elaboration why not all of the users are satisfied by genuine quality of products. A case study of multi-purposes van (MPV) automobile and its user using Structural Equation Modelling with Partial Least Square (PLS-SEM) and Smart-PLS software supported the proposition of this work. In-depth data collecting with questionnaires from 516 car drivers conducted to reveal the role of users related to their post-purchase satisfaction. This research concluded that driving quality of MPV drivers intervened the relationship between variables. Lower quality of driving will reduce driving satisfaction whether the quality of MPV was excellent. Otherwise, high quality of driving will not only enhance the satisfaction but also leverage the effect of other variables to driving satisfaction. The intervening variable role will give a new approach in developing and maintaining driving satisfaction. A new approach to customer satisfaction improvement proposed and valuable programs of MPV producers with a new paradigm will improve manufacturer competitive advantage.
Nowhere to hide? Mix-Zones for Private Pseudonym Change using Chaff Vehicles

In vehicular communication systems, cooperative awareness messages provide contextual information required for transportation safety and efficiency applications. However, without the appropriate design, these messages introduce a new attack vector to compromise passenger privacy. The use of ephemeral credentials – pseudonyms – was therefore proposed, essentially to split a journey into unlinkable segments. To protect segment transitions, encrypted mix-zones provide regions where vehicles can covertly change their pseudonyms. While previous work focused on the placement, shape, and protocols for mix-zones, attacks that correlate vehicles entering and existing these zones still remain a problem. Furthermore, existing schemes have only considered homogeneous traffic, disregarding variations in vehicle density due to differences in driver population, road layout, and time of day. Without realistic experimental results, any conclusion on real-world applicability is precarious. In this paper, we address this challenge and present a novel scheme that works independent of vehicles’ mobility patterns. More precisely, our system generates fictive chaff vehicles when needed and broadcasts their traces, while it remains unobtrusive if sufficiently many vehicles are present. This greatly improves privacy protection in situations with inherently low traffic density, e.g., suburban areas, and during low traffic periods. Our scheme ensure that an external attacker cannot distinguish between real and chaff vehicles, while legitimate vehicles can recognize chaff messages; this is important, because chaff vehicles (and messages) must not affect the operation of safety applications. In our evaluation, we compare our chaff-based approach with an existing cryptographic mix-zone scheme. Our results under realistic traffic conditions show that by introducing fictive vehicles, traffic flow variations can be smoothed and privacy protection can be enhanced up to 76%.
Factors Affecting Secure Software Development Practices Among Developers - An Investigation

An evidently dominant problem in the software development domain is that software security is not consistently addressed from the initial phase of software development which escalates security concerns, results in insecure software development. Several secure software development methodologies were introduced in literature and recommended to the industry but they are usually ignored by the developers and software practitioners. In this research paper, an extensive literature review is performed to find out factors influencing implementation of secure software development practices in industry. Secondly, Based on Unified Theory of Acceptance and Use of Technology model 2 (UTAUT2), this study proposes a model to investigate the factors influencing adoption of secure software development practices among software developers.
Classification of Physiological Data in Affective Exergames

In this work, we present our approach to analyze physiological data in affective exergames by using deep learning algorithms. In previous works, we enhanced a cycling exercise machine to act as a game controller. During a case study, we then collected vision-based and physiological data of 25 participants who rode through a game environment that was designed to provoke emotions. In order to analyze the collected physiological data, we now propose an ensemble learning approach based on three distinct deep learning models: Multilayer Perceptron, Fully Convolutional Networks and Residual Networks. As a result, the proposed algorithms were able to enhance the quality of our event-based emotion analysis method introduced previously.