Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy Leak Identification in Third-Party Android Libraries

Developers of mobile applications rely on the trust of their customers. On the one hand the requirement exists to create feature-rich and secure apps, which adhere to privacy standards to not deliberately disclose user information. On the other hand the development process must be streamlined to reduce costs. Here third-party libraries come into play. Inclusion of many, possibly nested libraries pose security risks, app-creators are often not aware of. This paper presents a way to combine free open-source tools to support developers in checking their application that it does not induce security issues by using third-party libraries. The tools FlowDroid, Frida, and mitm-proxy are used in combination in a simple and viable way to perform checks to identify privacy leaks of third-party apps. Our proposed setup and configuration empowers average app developers to preserve user privacy without being dedicated security experts and without expensive external advice.
CNN based approach for Speech Emotion Recognition Using MFCC, Croma and STFT Hand-crafted features

At present, speech emotion recognition (SER) is a very challenging and demanding research area because of its wide real-life applications. For SER there are two major challenges: first one is to identify the relevant feature vector and the second one is to identify the suitable classifier. To overcome these challenges, we proposed a model for SER. In this approach firstly we extract the hand-crafted features Mel frequency cepstral coefficient (MFCC), Croma and Short-term Fourier Transform (STFT) from the emotional speech signals and these extracted features are considered as input for the considered deep learning classifying technique, Convolutional Neural Networks (CNN). This work investigates the credibility of convolutional neural networks using hand-crafted features MFCC with value 13, Croma with value 13 and STFT. The proposed model comprises 8 CNN layers and output of the last layer is faded to flatten layer and then apply soft-max activation function to identify the emotions. We employed the publicly accessible speech emotional databases RAVDESS, TESS, and SAVEE, as well as their combinations to evaluate the performance of our proposed model. Experiments show that in terms of average accuracy, the suggested model outperforms the current state-of-the-art SER techniques.
Design and Implementation of Scanning Electron Microscope Image Acquisition Software System

Scanning electron microscope image acquisition system is designed to control the scanning generator, collect the real-time image of the scanning electron microscope system, and process the image to help users get high quality and resolution sample images. Electronic microscope image acquisition system software which adapts Client/Server software architecture and develops based on the WPF framework, is aimed at controlling the scanning electron microscope imaging system. After further system testing, the result indicates that this system not only can realize the functions of image display in real-time, image measurement and annotation, image restoration and image storage, but also can attain high quality images by image restoration algorithms, which can meet automated and diversified requirements of the scanning electron microscope operators to capture electron microscope images.
An Organizational Structure for Sustainable Software Development

Over decades, the amount of electronic waste generated, energy consumption, and other environmentally harmful processes in engineering have contributed to the degradation of the environment. It is necessary for companies to keep a track of their power management as the first step towards reducing their carbon footprint. Greening of software plays a key role in power management in companies. This study highlights the importance of green software and how professionals perceive it. It further goes on to discuss and explain existing green software and current methods adopted by organizations for making the software development life cycle energy efficient. A comparative study of these methods is provided. Finally, the paper proposes an organizational structure to incorporate sustainability at every step of the software development process.
Design and Analysis of a Novel Compact Metamaterial Absorber Based on Double-Layer ITO Resistive Film for Improving Signal Integrity

This paper presents a compact metamaterial absorber (MMA) based on double-layer ITO resistive film for suppression of electromagnetic interference (EMI) to improve signal quality and signal integrity (SI). In the design of MMA, based on the classic Jerusalem cross, circular and fan-shaped structures are combined to increase its equivalent capacitance to achieve frequency shift, and an equivalent circuit model is established to analyze the key factors affecting its absorption rate. At the same time, the full-wave simulation results show that more than 90% absorption rate is achieved in the frequency range of 8.9 GHz-14.0 GHz, and the measurement results show that the absorption rate of MMA at different incident angles is consistent with the simulation. Moreover, microstrip antenna and differential microstrip transmission line are also designed as equivalent radiation sources. The co-simulation results show that the 3 m field radiation of the patch antenna is reduced by 10 dBμV at 10 GHz. At the same time, the eye height of the differential microstrip transmission line has increased from 68 mV to 340 mV, indicating that the signal integrity problem has been significantly improved. The size of the unit is only about 0.065 λL ×0.065 λL where λL is the wavelength of the lowest frequency, and the total thickness of the absorber is only 0.047 λL. The proposed MMA has the characteristics of miniaturization, ultra-wideband, high absorption rate, and polarization insensitivity, which provides a new suppression method for the radiation problem of gradually miniaturized electronic equipment.
Beat-to-beat Heart Rate Detection Based on Seismocardiogram Using BiLSTM Network

The detection of consecutive cardiac cycles plays an important role in the daily monitoring of cardiovascular diseases. The Seismocardiogram (SCG) signal measures the cardiac-induced vibrations and is suitable for continuously tracking since it can be measured in a non-invasive fashion. In this paper, a beat-to-beat heart rate detection method based on low-frequency SCG signal using BiLSTM network is proposed. The regression model to predict Electrocardiogram (ECG) from SCG was trained on CEBS dataset and a 5-fold cross-validation method was adopted. The RMSE of the predicted ECG signal and ECG signal was 0.0367. The sensitivity and precision achieved 0.97 and 0.98 separately and the Spearman Correlation was 0.98. This work shows promise for the proposed SCG based methodology to be used as an alternative to ECG for continuous beat-to-beat heart rate monitoring.
Lightning Protection at Petrochemical Facilities - Part 2 Requirements of Standards

Development of lightning protection standards for petrochemical processing and storage facilities has progressed significantly over the past 20 years. Standard requirements have become more stringent and prescriptive. Understanding of development and propagation of lightning has grown with the advent of 3-D detection systems. This is Part 2 of a 3-part primer on lightning protection systems for petrochemical production, storage, and processing facilities. Part 1 - basic science and history. Part 2 - current requirements of national and international standards for protection systems at petrochemical facilities. Part 3 - alternative protection systems. The purpose of this paper, and the primer series, is to update the design and operating engineer’s knowledge of lightning protection at petrochemical facilities, and to increase the safety of these facilities to workers and equipment.
Analysis of Shared Bicycle Usage based on K-Means and GMM Clustering Algorithm

Bicycle sharing in cities facilitated people’s daily travel. The convenience it offers made the sharing model a success in the beginning. However, as the number of users increases, assigning the shared bicycles reasonably to maximize their availability becomes a challenge for suppliers. Classifying the users and predicting their demands are key to solving the problem. The author used historical data to visualize the relationship between travel time and bicycle usage and understand the users’ travel patterns. Principal Component Analysis(PCA) was performed to reduce the dimensionality of the data, and the k-means and Gaussian mixture model (GMM) clustering methods were used to classify the users. In this case, k-means clustering did not effectively classify the data, while GMM clustering divided the data into two categories. With GMM, the users’ travel patterns were associated with weekdays. This article also explores the pros and cons of K-means and GMM are suitable for continuous and discontinuous data through specific cases.
Automated skin lesion segmentation using multi-scale feature extraction scheme and dual-attention mechanism

Segmenting skin lesions from dermoscopic images is essential for diagnosing skin cancer. But the automatic segmentation of these lesions is complicated due to the poor contrast between the background and the lesion, image artifacts, and unclear lesion boundaries. In this work, we present a deep learning model for the segmentation of skin lesions from dermoscopic images. To deal with the challenges of skin lesion characteristics, we designed a multi-scale feature extraction module for extracting the discriminative features. Further in this work, two attention mechanisms are developed to refine the post-upsampled features and the features extracted by the encoder. This model is evaluated using the ISIC2018 and ISBI2017 datasets. The proposed model outperformed all the existing works and the top-ranked models in two competitions.
Voice Doppelgänger Susceptibility among Racial and Gender Groups : IEEE CNS 21 Poster

Voice doppelganger describes a person or thing that sounds like another, which is considered a potential security risk for voice biometrics. Considering that racial or gender groups have different biological vocal structures, it is possible that these subgroups have different vulnerabilities to the voice doppelganger and voice biometrics. This study investigates if racial and gender disparities exist in the security risk of the voice doppelganger towards the voice biometric. We used three different metrics to measure voice similarity: fundamental frequency, MFCCs, and pitch. The result was that persons within gender and racial subgroups do indeed sound more similar to each other, with racial subgroups displaying more similarity than gender.
The CommonLit Ease of Readability (CLEAR) Corpus

In this paper, we introduce the Commonlit Ease of Readability (CLEAR) corpus. The corpus provides researchers within the educational data mining community with a resource from which to develop and test readability metrics and to model text readability. The CLEAR corpus improves on previous readability corpora include size (N = ~5,000 reading excerpts), the breadth of the excerpts available, which cover over 250 years of writing in two different genres, and the readability criterion used (teachers’ ratings of text difficulty for their students). This paper discusses the development of the corpus and presents reliability metrics as well as initial analyses of readability.
Analyzing Student Success and Mistakes in Virtual Microscope Structure Search Tasks

Many modern anatomy curricula teach histology using virtual microscopes, where students inspect tissue slices in a computer program (e.g. a web browser). However, the educational data mining (EDM) potential of these virtual micro-scopes remains under-utilized. In this paper, we use EDM techniques to investigate three research questions on a virtual microscope dataset of N = 1 , 460 students. First, which factors predict the success of students locating structures in a virtual microscope? We answer this question with a generalized item response theory model (with 77% test accuracy and 0 . 82 test AUC in 10-fold cross-validation) and ﬁnd that task diﬃculty is the most predictive parameter, whereas student ability is less predictive, prior success on the same task and exposure to an explanatory slide are moderately predictive, and task duration as well as prior mistakes are not predictive. Second, what are typical locations of student mistakes? And third, what are possible misconceptions explaining these locations? A clustering analysis revealed that student mistakes for a diﬃcult task are mostly located in plausible positions (’near misses’) whereas mistakes in an easy task are more indicative of deeper misconceptions.
Oases of Cooperation: An Empirical Evaluation of Reinforcement Learning in the Iterated Prisoner's Dilemma

In the creation of safe AI systems it is extremely important to ensure cooperative behaviour of these systems, even when there are incentives to act selfishly. In many cases, even when game-theoretic solutions allow for cooperation, actually getting the AI systems to converge on these solutions through training is difficult. In this paper we empirically evaluate how reinforcement learning agents can be encouraged to cooperate (without opening themselves up to exploitation) by selecting appropriate hyperparameters and environmental perceptions for the agent. Our results in the multi-agent scenario indicate that in hyperparameter-space there are isolated “oases” of mutual cooperation, and small changes in these hyperparameters can lead to sharp drops into non-cooperative behaviour.
Reinforcement Learning With Imperfect Safety Constraints

An obvious approach for ensuring safety in applying deep reinforcement learning is to use it with safety monitors. An ideal monitor captures all unsafe system states, but this is hardly possible in practical systems due to uncertainties in the environment, complicated system dynamics, and limited environment knowledge. Even with a perfect monitor, dynamic adjustments may still be required to account for sys-tem changes such as ageing and damage. Therefore, what to do if the monitors are imperfect? This paper proposes an approach for estimating the undetected states of imperfect monitors in conjunction with deep Q-learning. A new Q-value function is developed to capture the target states effectively, and a system is designed to manage control and near-critical safety separately. Our experiments show that the approach proposed in this paper better considers unsafe states than a classic learning-based approach and achieves a faster convergence to the maximum control reward.
Fast, Detailed, Accurate Simulation of a Thermal Car-Cabin Using Machine-Learning

Car-cabin thermal systems, including heated seats, air-conditioning, and radiant panels, use a large proportion of the energy budget of electric vehicles and thus reduce their effective range. Optimising these systems and their controllers might be possible with computationally efficient simulation. Unfortunately, state-of-the-art simulators are either too slow or provide little resolution of the cabin’s thermal environment. In this work, we propose a novel approach to developing a fast simulation by machine learning (ML) from measurements within the car cabin over a number of trials within a climatic wind tunnel. A range of ML approaches are tried and compared. The best-performing ML approach is compared to more traditional 1D simulation in terms of accuracy and speed. The resulting simulation, based on Multivariate Linear Regression, is fast (5 microseconds per simulation second), and yields good accuracy (NRMSE 1.8%), which exceeds the performance of the traditional 1D simulator. Furthermore, the simulation is able to differentially simulate the thermal environment of the footwell versus the head and the driver position versus the front passenger seat, but unlike a traditional 1D model cannot support changes to the physical structure. This fast method for obtaining computationally efficient simulators of car cabins will accelerate adoption of techniques such as Deep Reinforcement Learning for climate control.
The Extent and Pattern of Mariculture Impacts on Spatial and Seasonal Variations of Sediment Bacterial Communities Among Three Coastal Waters

Benthic sediment bacteria are important drivers for material circulation and energy flow in aquatic ecosystem, and they are sensitive to environmental changes. Large scale suspended mariculture in coastal waters induces high organic loading to the sediment through biodeposition, and causes changes in sediment biogeochemical features. However, the impacts of mariculture on sediment bacterial communities are not fully understood. In the present study, sediment samples were collected from three coastal semi-enclosed bays of China, i.e., Sanggou Bay, Daya Bay, and Maniao Bay, where large scale mariculture were carried out since the 1980s. High-throughput sequencing was used to examine the spatial and seasonal variations of bacterial communities. The results indicated that the dominant phyla of three bays were Proteobacteria (39.18–47.21%), Bacteroidetes (9.91–19.25%), and Planctomycetes (7.12–13.88%). Spatial variations played a greater role in shaping the bacterial communities than seasonal variation. The bacterial diversity indices (Chao1, Pielou’s evenness, and Shannon-Wiener index) of Sanggou Bay were significantly lower than those of Daya Bay and Maniao Bay. For seasonal variation, bacterial diversity indices in spring were significantly lower than that in autumn. Five keystone taxa belonging to Planctomycetes, Alphaproteobacteria, and Acidobacteria were identified in Sanggou Bay. Temperature, particulate organic carbon, pH, and salinity were the most important environmental factors shaping the spatial and seasonal variations of bacterial communities in the studied areas. The abundances of bacteria, particularly Bacteroidetes, Gammaproteobacteria, and Deltaproteobacteria, were significantly correlated with the mariculture-driven chemical properties of the sediment. These results indicated that intensive mariculture could induce profound and diverse impacts on the bacterial communities, and therefore modify their role in marine ecosystem. The interactions between mariculture and sediment bacterial communities should be considered in regard to mariculture management and carrying capacity.
Numerical Simulation of Unsteady Viscous Compressible Flows Applied to Blade Flutter Analysis

A numerical method for simulating quasi three-dimensional unsteady viscous compressible flow is developed and applied to blade flutter analysis. The Reynolds-averaged Navier-Stokes equations are solved in a time accurate manner on a continuously deforming computational mesh. Turbulence is accounted for by the inclusion of a two-layer algebraic turbulence model. This method is compared with measurements and a full velocity potential solver for two different subsonic compressor cascades and a range of flow conditions. Both methods are found to predict unsteady attached flows reasonably well. However, the Navier-Stokes solver picks up the pressure fluctuations associated with the unsteady leading edge separation bubble. These types of fluctuations have large amplitudes and tend to dominate the cascade’s aerodynamic damping behavior.Copyright © 1991 by ASME
Quality Management Tools on the Stage: Old but New Allies for Rigor and Standardization of Extracellular Vesicle Studies

As early as the second half of the 19th century, Charles Darwin hypothesized as a part of his pangenesis theory that every cell type in the body could generate minute size “gemmules” full of molecules to communicate with other cell types (Darwin, 1868). This seminal intuition fell unnoticed for more than 150 years until contemporary scientists may recognize extracellular vesicles (EVs) in Darwin’s gemmules (Liu andChen, 2018;Margolis and Sadovsky, 2019; Bergese et al., 2020). Nowadays it has been clearly shown that cells from different organisms, including eukaryotes, both animals (from yeast tomammals) and plants, but also prokaryotic cells, have been demonstrated to release vesicles into the extracellular environment either constitutively or following cell stimulation. EVs have also been isolated from diverse body fluids, including blood, urine, saliva, breast milk, amniotic fluid, cerebrospinal fluid, bile, and semen (Keller et al., 2011; Kalluri and LeBleu, 2020). All EVs are lipid-membrane encapsulated particles filled of cellular content, comprising proteins, metabolites, nucleic acids, lipids, and even entire organelles, some of them specifically sorted and enriched in EVpopulations with a pattern reflective of cell functions and conditions (Raposo and Stoorvogel, 2013; Yáñez-Mó et al., 2015;Meldolesi, 2018; VanNiel et al., 2018). Very far from representing a tool to eliminate waste material, as hypothesized at the beginning, EVs are able to target specific cells and deliver molecules that induce specific cell response (Van Niel et al., 2018; Kalluri and LeBleu, 2020; Mantile et al., 2020). Therefore, EV-based cell communication has become an extremely intriguing mechanism that attracted a lot of scientists for its great potential in basic as well as applied research.
“Existential Catastrophe Anxiety”: Phenomenology of Fearful Emotions in a Subset of Service Users With Severe Mental Health Conditions

A subset of people with severe mental health conditions feels they are on the verge of losing control, even in the absence of external threats or triggers. Some go to extreme ends to avoid affective arousal and associated expectations of a possible, impending catastrophe. We have learned about such phenomenological, emotional challenges in a group of individuals with severe, composite mental health problems and psychosocial disabilities. These individuals have had long treatment histories in the mental health care system. They have been encountered at a specialized inpatient ward offering exposure-based therapy that aims at restoring self-regulation and recovery. We describe the phenomenology of anxiety and fear presented by these service users, a fear we have coined existential catastrophe anxiety (ECa). We also suggest a set of underlying, interacting, psychological mechanisms that may give rise to ECa, before comparing ECa with three other constructs previously described in the literature—annihilation anxiety, ontological insecurity, and affect phobia. These comparisons show several similarities, but also unique qualities with ECa and its suggested underlying mechanisms. The conceptualization of ECa may aid clinicians in addressing extreme experiential turmoil and engage service users in empowering therapeutic projects.