Miracle-dz/UniFAR-FTUs huggingface dataset:
A Universal Detection Method for Adversarial Examples and Fake Images

Deep-learning technologies have shown impressive performance on many tasks in recent years. However, there are multiple serious security risks when using deep-learning technologies. For examples, state-of-the-art deep-learning technologies are vulnerable to adversarial examples that make the model’s predictions wrong due to some specific subtle perturbation, and these technologies can be abused for the tampering with and forgery of multimedia, i.e., deep forgery. In this paper, we propose a universal detection framework for adversarial examples and fake images. We observe some differences in the distribution of model outputs for normal and adversarial examples (fake images) and train the detector to learn the differences. We perform extensive experiments on the CIFAR10 and CIFAR100 datasets. Experimental results show that the proposed framework has good feasibility and effectiveness in detecting adversarial examples or fake images. Moreover, the proposed framework has good generalizability for the different datasets and model structures.
An Inverted Teaching Model for a Mechanics of Materials Course

mechanical engineering in engineering mechanics S&T. a licensed Professional Engineer. His technical interests are in mechanical characterization, construction, and the inﬂuence of force on biological systems. His artistic interests are in music. Abstract Instructors at Missouri University of Science and Technology have been offering certain sections of a mechanics of materials course in an inverted format for the past two years. In this format, students learn the concepts outside of class, using a textbook, animations and videos developed by the authors, and work on homework either individually or in groups during the optional class time. Students take eight multiple-choice exams and a final exam that is common to both the inverted sections and the more traditional lecture-based sections. Homework in the inverted sections is assigned but not graded. The in-class exams are given in a computer lab, and each student receives an individualized set of questions. Over 1200 students in 18 course sections have participated in either the inverted sections themselves or the other non-traditional sections that preceded the particular format used today. A subset of this group was compared to students in the traditional sections. No statistically significant difference between the two groups was found based on (1) performance on the common final exams or (2) course grade in a structural analysis course. The animations and videos used by students in the inverted sections are available on a class web site. There are 167 animation modules and 230 videos. The animations contain example problems and exercises. The videos are, on average, six minutes in length and cover concepts, demonstrations, problem strategies, problem solutions, and experiments. The authors use Google Analytics to track how much each piece of content is utilized. The website was accessed 46,500 times, and the content, excluding the animations, was used for a total of 12,700 hours during the past 16 months. By how perform on each multiple-choice the authors have developed a concept inventory with numerical rankings from the best to worst understood concepts. Combining this with how much each online resource is utilized, the authors can now target development of future course materials on the least-understood concepts and in the format most preferred by the students. An inverted teaching format would appropriate for every college course, but it the authors begin looking at their mechanics of materials course in a more scientific, data-driven manner.
Active Inference Integrated with Imitation Learning for Autonomous Driving

Classical imitation learning methods suffer substantially from the learning hierarchical policies when the imitative agent faces an unobserved state by the expert agent. To address these drawbacks, we propose an online active learning through active inference approach that encodes the expert’s demonstrations based on observation-action to improve the learner’s future motion prediction. For this purpose, we provide a switching Dynamic Bayesian Network based on the dynamic interaction between the expert agent and another object in its surrounding as a reference model, which we exploit to initialize an incremental probabilistic learning model. This learning model grows and matures based on the free-energy formulation and message passing of active inference dynamically at discrete and continuous levels in an online active learning phase. In this scheme, generalized states of the learning world are represented as distance-vector, where it is the learner’s observation concerning its interaction with a moving object. Considering the distance vector entail intentions, it enables action prediction evaluation in a prospective sense. We illustrate these points using simulations of driving intelligent agents. The learning agent is trained by using long-term predictions from the generative learning model to reproduce the expert’s motion while learning how to select a suitable action through new experiences. Our results affirm that a Dynamic Bayesian optimal approach provides a principled framework and outperforms conventional reinforcement learning methods. Furthermore, it endorses the general formulation of action prediction as active inference.
Resolution Agile Remote Sensing for Detection of Hazardous Material Spills

Traffic carrying flammable, corrosive, poisonous, and radioactive materials continues to increase in proportion with the growth in their production and consumption. The sustained risk of accidental releases of such hazardous materials poses serious threats to public safety. Early detection of spills will potentially save lives, protect the environment, and thwart the need for expensive cleanup campaigns. Ground patrols and terrestrial sensing equipment cannot scale cost-effectively to cover the entire transportation network. Remote sensing with existing airborne and spaceborne platforms has the capacity to monitor vast areas regularly but often lacks the spatial resolution necessary for high accuracy detections. The emergence of unmanned aircraft systems with lightweight hyperspectral image sensors enables a resolution agile approach that can adapt both spatial and spectral resolutions in real time. Equipment operators can exploit such a capability to enhance the resolution of potential target materials detected within a larger field-of-view to verify their identification or to perform further inspections. However, the complexity of algorithms available to classify hyperspectral scenes limits the potential for real-time target detection to support rapid decision making. This research introduces and benchmarks the performance of a low-complexity method of hyperspectral image classification. The hybrid supervised–unsupervised technique approaches the performance of prevailing methods that are at least 30-fold more computationally complex.
Characterization of Interactions between CTX-M-15 and Clavulanic Acid, Desfuroylceftiofur, Ceftiofur, Ampicillin, and Nitrocefin

Cefotaximase-Munich (CTX-M) extended-spectrum beta-lactamases (ESBLs) are commonly associated with Gram-negative, hospital-acquired infections worldwide. Several beta-lactamase inhibitors, such as clavulanate, are used to inhibit the activity of these enzymes. To understand the mechanism of CTX-M-15 activity, we have determined the crystal structures of CTX-M-15 in complex with two specific classes of beta-lactam compounds, desfuroylceftiofur (DFC) and ampicillin, and an inhibitor, clavulanic acid. The crystal structures revealed that Ser70 and five other residues (Lys73, Tyr105, Glu166, Ser130, and Ser237) participate in catalysis and binding of those compounds. Based on analysis of steady-state kinetics, thermodynamic data, and molecular docking to both wild-type and S70A mutant structures, we determined that CTX-M-15 has a similar affinity for all beta-lactam compounds (ceftiofur, nitrocefin, DFC, and ampicillin), but with lower affinity for clavulanic acid. A catalytic mechanism for tested β-lactams and two-step inhibition mechanism of clavulanic acid were proposed. CTX-M-15 showed a higher activity toward DFC and nitrocefin, but significantly lower activity toward ampicillin and ceftiofur. The interaction between CTX-M-15 and both ampicillin and ceftiofur displayed a higher entropic but lower enthalpic effect, compared with DFC and nitrocefin. DFC, a metabolite of ceftiofur, displayed lower entropy and higher enthalpy than ceftiofur. This finding suggests that compounds containing amine moiety (e.g., ampicillin) and the furfural moiety (e.g., ceftiofur) could hinder the hydrolytic activity of CTX-M-15.
Calculating the Apparent Sound Insulation in Concrete Block Buildings

The proposed National Building Code Canada 2015 (NBCC15) is set to replace the traditional design objective - the Sound Transmission Class (STC) - with a requirement including the effect of flanking transmission, namely the Apparent Sound Transmission Class (ASTC). Such a transition requires a supporting set of technical standards for measuring direct and flanking sound transmission for typical assemblies and junctions, plus a credible procedure for calculating system performance from these inputs. As part of this change, the National Research Council Canada has written a guide and construction specific research reports to calculate the ASTC of concrete, concrete block, steel frame, wood frame and cross-laminated timber (CLT) buildings. This paper discusses the calculation method and examples that are contained in the National Research Council Canada’s report, RR-334 “Apparent Sound Insulation in Concrete Block Buildings. ”
A Deep Dive Inside DREBIN: An Explorative Analysis beyond Android Malware Detection Scores

Machine learning advances have been extensively explored for implementing large-scale malware detection. When reported in the literature, performance evaluation of machine learning based detectors generally focuses on highlighting the ratio of samples that are correctly or incorrectly classified, overlooking essential questions on why/how the learned models can be demonstrated as reliable. In the Android ecosystem, several recent studies have highlighted how evaluation setups can carry biases related to datasets or evaluation methodologies. Nevertheless, there is little work attempting to dissect the produced model to provide some understanding of its intrinsic characteristics. In this work, we fill this gap by performing a comprehensive analysis of a state-of-the-art Android malware detector, namely DREBIN, which constitutes today a key reference in the literature. Our study mainly targets an in-depth understanding of the classifier characteristics in terms of (1) which features actually matter among the hundreds of thousands that DREBIN extracts, (2) whether the high scores of the classifier are dependent on the dataset age, and (3) whether DREBIN’s explanations are consistent within malware families, among others. Overall, our tentative analysis provides insights into the discriminatory power of the feature set used by DREBIN to detect malware. We expect our findings to bring about a systematisation of knowledge for the community.
Parameter Estimation of Dynamic Air-conditioning Component Models Using Limited Sensor Data

Parameter Estimation of Dynamic Air-conditioning Component Models Using Limited Sensor Data. (May 2010) Natarajkumar Hariharan, B.E. , Birla Institute of Technology, Ranchi Chair of Advisory Committee: Dr. Bryan Rasmussen This thesis presents an approach for identifying critical model parameters in dynamic air-conditioning systems using limited sensor information. The expansion valve model and the compressor model parameters play a crucial role in the system model‟s accuracy. In the past, these parameters have been estimated using a mass flow meter; however, this is an expensive devise and at times, impractical. In response to these constraints, a novel method to estimate the unknown parameters of the expansion valve model and the compressor model is developed. A gray box model obtained by augmenting the expansion valve model, the evaporator model, and the compressor model is used. Two numerical search algorithms, nonlinear least squares and Simplex search, are used to estimate the parameters of the expansion valve model and the compressor model. This parameter estimation is done by minimizing the error between the model output and the experimental systems output. Results demonstrate that the nonlinear least squares algorithm was more robust for this estimation problem than the Simplex search algorithm.
The Appropriateness of Empiric Treatment of Urinary Tract Infections in a Tertiary Teaching Hospital in Joran: A Cross-Sectional Study

This is a cross-sectional study that was conducted at Jordan University Hospital (JUH) to evaluate the appropriateness of Urinary Tract Infection (UTI) empiric treatment based on microbial culture data and susceptibility testing. All urine cultures requested for adult patients (≥18 years) admitted to JUH within the period from January 2019–July 2021 were reviewed and only those cultures with positive episodes of infection were considered. In this study, 6950 urine culture episodes were screened; among them, 34.5% (n = 2400) revealed positive results. Among those patients with positive culture episodes, 1600 patients (66.7%) were discharged before the availability of culture results and were excluded. Of the remaining eligible 800 patients, 701 (87.6%) received empiric treatment. In 26.8% of the eligible cases (n = 214), the prescribed empiric agents failed to have appropriate coverage of the identified pathogens, and in 14.6% of the cases (n = 117) the identified microorganisms were reported as resistant to the prescribed empiric agents. Furthermore, only 13.4% of the patients (n = 107) were appropriately treated for their UTI with empiric antibacterial agents. We were not able to judge the appropriateness of UTI treatment for one third (n = 263, 32.9%) of the patients, because they did not have susceptibility reports performed. This study revealed an alarmingly high rate of inappropriate treatment of UTIs, which encourages the emergence of bacterial resistance and affects health-related outcomes negatively. Therefore, antimicrobial stewardship programs must be applied to optimize antibiotic consumption in hospital settings.
Emerging Telecommunications Technologies: Cognitive Radio

The goal of this chapter is to introduce the novel concept of cognitive radio (CR) for wireless telecommunications. Cognitive radios are a new type of radio devices that include cognition and reconfigurability features. The raising number of studies in different areas of research shows their potential and the expectation created among the telecommunications community. In this chapter, the authors first introduce the reader to the new paradigm that cognitive radio networks have created; more specifically, they explain in detail the new next generation networks. Given that our intention is to introduce cognitive radio, the authors focus on the challenges in PHY layer and MAC sublayer and the most relevant studies in these fields. Finally, the integration of game theory and cognitive radio creates a new paradigm where the advantages of both technologies merge to solve complex problems.
Green Synthesis and Characterization of Silver Nanoparticles by Using Aloe Vera Leaf Extract to Study the Effects of Synthesis Parameters

In this research silver nanoparticles were synthesized by reducing silver nitrate solution using aloe vera leaf extract as reducing agent. The synthesized nanoparticles had been characterized and the effects of different parameters of synthesis had been evaluated. Silver nanoparticles began to form just after reaction and the whole reduction reaction was completed within 3 hours. The color changed from transparent to dark brown of the aqueous salt solution. Later the nanoparticles were separated out from the mixture by ultra-centrifugation. Here the effects of water bath temperature, the effects of heating time at that certain temperature and the effects of changing concentration of silver nitrate on the size of the synthesized nanoparticles was studied. The particle size and morphology of the synthesized silver nanoparticles were identified by SEM analysis. It was found to be 12-200 nm in different parameters (12-25 nm at the best condition) and spherical in shape. It was also found that the size of silver nanoparticles increased with increasing water bath temperature, increasing heating time and increasing silver nitrate concentration. Energy dispersive X-ray spectroscopy was used to confirm that the nanoparticle suspension contains nothing but metallic silver. It was found that 70% of elemental silver nanoparticles were present.
Predicting students’ academic grade using machine learning algorithms with hybrid feature selection approach

Data plays an important role where any prediction is to be made. Due to the advancement in technology there have been significant ways of collecting students’ data. Educators use lots of supportive digital system to teach and collect data, such as Learning Management System, Student Information system, etc. The data collection through these system is huge and nevertheless it can be called as a big data. Lots of research is being done in how to improve the students’ performance and overall educational performance and to tap key areas that may help in student progress. Traditional analysis of data includes sampling, whether it be a climate prediction or any performance prediction. In this research, the data collected through classroom observation, academic performance or learning management systems is used to predict the performance of the students. Lots of factors were present in the collected data but the main factors that add relevance for appropriate prediction are only few. This paper proposes how more than one feature selection algorithm results can be combined to improve the predictions with machine learning algorithm.
Anterior segment imaging using non-invasive high-resolution optical coherence tomography

One of the most important developments in the ophthalmology and visual sciences field has been the application of new techniques for imaging the anterior and posterior segment of the eye with non-contact devices. These techniques have been developed quickly considering that are non-invasive and provide high-resolution images of the different parts of the human eye. Definitely, the most spread and used technique for non-contact imaging of the human eye is the optical coherence tomography (OCT). OCT technique was first demonstrated in 1991 by Huang et al . (1). Based on low-coherence interferometry, OCT has become a prominent biomedical imaging technique with micrometric resolution and cross-sectional imaging capabilities, being particularly suitable for ophthalmic and other tissue imaging applications requiring millimetre penetration depth. Thus, OCT is especially useful in Ophthalmology, given the ease with which light reaches the ocular structures in the anterior and posterior segments. The advantage in its application is that the light impinges directly on the tissue, without the need to use any transducer, only requiring an optical medium sufficiently transparent to obtain a detectable signal. As a curiosity, OCT has also been used for various art conservation projects, where it is used to analyse, for instance, the different layers of a painting.
Use of Tobacco and Nicotine Products among Young People in Denmark—Status in Single and Dual Use

Lots of new tobacco or nicotine products are being launched, e.g., e-cigarettes and smokeless tobacco, which appeal especially to the youngest part of the population. For example, the use of smokeless tobacco among Danish youth rose from approx. 2% in 2010 to 9% in 2020. Hence, there is an urgent need to follow and intervene against youth tobacco or nicotine product use. This study explored the current use of cigarettes, e-cigarettes, heated tobacco, and smokeless tobacco among Danish 15- to 29-year-olds. Further, we examined the concurrent use of two products or more. We used a nationwide survey conducted among 15- to 29-year-olds in February and March 2020. Overall, approx. 35,700 individuals received the questionnaire of which 35.5% responded (n = 13,315). One out of five (20.1%) smoked cigarettes, half of them daily, the other half occasionally. About one in twenty (3.9%) used e-cigarettes (daily or occasionally), and more than one in three (31.6%) had tried e-cigarettes. The use of heated tobacco among Danish youth is still relatively limited (0.3%). In comparison, about 9% used smokeless tobacco (daily or occasionally). Overall, 27.0% stated that they use at least one type of tobacco or nicotine product, while 5.6% used more than one product. Monitoring tobacco-related behavior in youth provides extremely important information for, e.g., policymakers and health professionals.
REC: REtime convolutional layers in energy harvesting ReRAM-based CNN accelerators

As the Internet of Things (IoTs) increasingly combines AI technology, it is a trend to deploy neural network algorithms at edges and make IoT devices more intelligent than ever. Moreover, the energy harvesting technology-based IoT devices have shown the advantages of green economy, convenient maintenance, and theoretically infinite lifetime, etc. However, the harvested energy is often unstable, resulting in low performance due to the fact that a fixed load can't sufficiently utilize the harvested energy. To address this problem, recent works focusing on ReRAM-based convolutional neural networks (CNN) accelerators under harvested energy have proposed hardware/software optimizations. However, those works have overlooked the mismatch between the power requirement of different CNN layers and the variation of harvested power. Motivated by the above observation, this paper proposes a novel strategy, called REC, that retimes convolutional layers of CNN inferences to improve the performance and energy efficiency of energy harvesting ReRAM-based accelerators. Specifically, at the offline stage, REC defines different power levels to match the power requirements of different convolutional layers. At runtime, instead of sequentially executing the convolutional layers of a reference one by one, REC retimes the execution timeframe of different convolutional layers so as to accommodate different CNN layers to the changing power inputs. What is more, REC provides a parallel strategy to fully utilize very high power income. Our experimental results show that the proposed REC approach achieves an average performance improvement of 6.1x (up to 16.5x) compared to the traditional strategy without the REC idea.
Hybrid Deep Learning Approach for Sentiment Classification of Malayalam Tweets

—Social media content in regional languages is ex- panding from day to day. People use different social media platforms to express their suggestions and thoughts in their native languages. Sentiment Analysis (SA) is the known procedure for identifying the hidden sentiment present in the sentences for categorizing it as positive, negative, or neutral. The SA of Indian languages is challenging due to the unavailability of benchmark datasets and lexical resources. The analysis has been done using lexicon, Machine Learning (ML), and Deep Learning (DL) techniques. In this work, the baseline models and hybrid models of Deep Neural Network (DNN) architecture have been used for the classification of Malayalam tweets as positive, negative and neutral. Since, sentiment-tagged dataset for Malayalam is not readily available, the analysis has been done on the manually created dataset and translated Kaggle dataset. The hybrid models used in this study combine Convolutional Neural Networks (CNN) with variants of Recurrent Neural Net- works (RNN). The RNN models are Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Gated Recurrent Unit (GRU). All these hybrid models improve the performance of Sentiment Classification (SC) compared to baseline models LSTM, Bi-LSTM and GRU.