Miracle-dz/UniFAR-FTUs huggingface dataset:
RIMI: Instruction-level Memory Isolation for Embedded Systems on RISC-V

With the advent of the Internet of Things, embedded systems have become widely used in various fields. Concurrently, the security of these systems has become a concern for many. However, security features that are already available for high-end systems have not been provided in low-end embedded systems due to its negative impact on cost and power consumption. Thus, to increase security with low overhead, many studies to implement the memory isolation approach to these systems have been conducted. However, existing techniques for this approach have suffered from problems in terms of scalability or performance. To mitigate such problems, we present RIMI, a new instruction extension to provide memory isolation in embedded systems. Thanks to instructions in RIMI, we can implement an instruction-level memory isolation where the access permission is bound to each memory and control transfer instructions. We implemented the RIMI prototype on a RISC-V architecture, which is a prominent open-source instruction set architecture (ISA). Our evaluation results show that existing security solutions, i.e., shadow stacks and in-process isolation, can be efficiently implemented with RIMI.
Somatic and anxiety symptoms of depression are associated with disability in late life depression

Abstract Objectives: To assess the relationships of somatic and anxiety symptoms of depression with functional disability in a sample of older adults with late life depression. Method: Data were analyzed from 78 older adults aged 65–88 with current major depression. Somatic and anxiety symptoms from the 24-item Hamilton Depression Rating Scale (HDRS) were summed to create variables measuring severity of these symptoms. Other symptoms of depression were also assessed using the remaining items of the HDRS. Current physical health burden was assessed using the Functional Comorbidity Index (FCI). Disability was measured with the Late Life Function and Disability Instrument (LLFDI) total limitation score. A linear regression analysis was performed to assess the association of somatic and anxiety symptoms with disability independent of other factors. Results: The model accounted for 26.6% of variance in disability, (F(6,51) = 3.1, p = . 01). Somatic (B = −1.9, p = .004) and anxiety (B = −3.7, p = .04) symptoms of depression were significantly associated with disability. Other depressive symptoms and physical illness burden were not associated with disability. Discussion: In older adults with major depression, somatic and anxiety symptoms of depression are associated with disability. Identification and treatment to remission of these symptoms may improve functional outcomes among older depressed adults.
Health-care needs of older women prisoners: Perspectives of the health-care workers who care for them

ABSTRACT The proportion of older incarcerated women is growing, yet little is known regarding their health-care needs. Using focus group methodology, this study sought to elucidate the unique health-care needs of older women prisoners through the perspectives of correctional health-care providers. Three organizing themes emerged regarding the health of older women prisoners: (a) the meaning of being “older” in the prison setting; (b) challenges impacting correctional health-care workers’ care delivery; and (c) unmet health-care-related needs. Correctional health-care workers’ insights can provide guidance regarding how to optimize the health of the increasing population of older women prisoners.
A Method for Large-Scale Parallel Simulation of Reactor Structural Mechanics

The structural mechanics simulation in reactor is an extremely complicated problem, which includes statics, fluid-induced vibration and wear. Because the entire structural mechanics simulation require dividing into 10 billion mesh elements, and then a large number of equations are generated to solve, the demand for high performance computing is urgent. Therefore, we conduct researches on E-class supercomputers, including reactor core geometric modeling, tens of billions of meshing, addition of physical parameters, regional decomposition, and solution of ultra-large sparse matrix equations. Furthermore, we propose a parallel workflow design, function design and architecture design scheme for large-scale parallel simulation of Generation IV reactors structural mechanics based on E-class supercomputer. By combining and optimizing of existing meshing tools and matrix equation solving methods, and finally verified its feasibility and correctness through calculation examples. In addition, the efficiency of the large-scale parallelism has also been significantly improved.
Comparison of Deep Learning Techniques for Semantic Classification of Ramps in Search and Rescue Arenas

Search and rescue activities should be carried out in indoor environments, after disasters such as earthquakes, floods, fire, and toxic substance spread. Since these activities are dangerous for humans and animals, the studies that address employing robots to the activities have been increased in recent years. However, robots must have capabilities such as autonomous navigation and situation awareness to operate in dangerous search and rescue environments. The National Institute of Standards and Technology presents reference test arenas to measure mobile robots’ autonomous navigation capability. The main motivation of this study is to compare PointNet, DGCNN, PointNet++, and PointCNN deep learning architectures for semantic classification of ramps that are placed in reference test arenas. Besides, the terrain and walls are considered since they carry essential information for navigation. To compare the architectures, the ESOGU RAMPS dataset was constructed and the architectures were trained and tested with the point cloud data that are situated in the dataset. The metric and visual test results were presented to analyze the positive and negative aspects of the architectures.
An Improved Link Prediction Algorithm of Complex Network

In recent years, link prediction has become a hot spot in complex network research area. Its goal is to calculate the possibility of future connection between the currently unconnected nodes through the known topology and other information in the network. At present, most prediction algorithms are based on local similarity and focus on the common neighbors and degree of nodes without considering the impact of compactness between nodes on the prediction. For this reason, in this paper an improved link prediction algorithm, named as CCRA(Resource Allocation with Clustering Coefficient) is proposed by combining the local similarity property with clustering coefficient. Furthermore, the accuracy of this algorithm's prediction is verified through experiments on real data sets. Consequently, conclusion on this algorithm is derived thereby.
Malware Family Classification using LSTM with Attention

As the damage caused by malware gets severer and involves a wider range of fields, the capability of detecting and classifying the malware becomes increasingly urgent and significant. Modern malware are usually equipped with metamorphic and polymorphic techniques, which means that malware from the same family might be modified. It is noteworthy that dynamic analysis can partially tackle those anti-static techniques. However, many malware authors have already realized that more dynamic methods are employed in malware analysis. Therefore, this field still suffers heavily from the anti-dynamic techniques. To address these problems, this paper first utilizes the static approach to disassembly the malware and obtains the assembly code. Harnessing the power of word embedding, our method then effectively learns the relationships of instructions within each block split by jump instructions and represents them as vectors. These vectors are fed into the LSTM to get each block's representative features. We then incorporate the attention mechanism to reduce these junk codes which belongs to one of the anti-static analysis techniques or atypical features, and thus our method can obtain significantly better feature representation capability of a malware file. Empirical experiments show that our method prevails its competitors and achieves the best performance with an accuracy of 94.25% and an f1 score of 0.95 on a dataset with 16,718 samples from 6 malware families.
Towards a Software Tool for Planning IHE-Compliant Information Systems

As hospital information systems are complex and the requirements for interoperability grow with the increasing networking in healthcare, careful planning becomes more and more necessary. The use of standards as described in IHE profiles, for example, are an important prerequisite for enabling interoperability. Enterprise Architecture Planning (EAP) methods should support this, but none of the currently available EAP methods offers the option of using IHE profiles. The 3LGM2IHE project wants to close this gap and implement the support of IHE profiles in the 3LGM2 tool. This paper describes how requirements for this tool were determined and presents the results.
Grooved Gold Grating-Assisted Integrated Planar Waveguide Based Localized Surface Plasmon Polariton Microbiosensor

The promising research and development of sensing technology initiates innovative sensors achieving cost effective to promote the simple, portable and experimental realization. Sensing performances of the proposed self-referenced localized surface plasmon resonance (LSPR) based grooved gold grating-assisted integrated planar waveguide refractive index microbiosensor have been investigated by using the finite element method in this work. The sensor achieves a maximum spectral and amplitude sensitivities of 4000 nm/RIU and 328 RIU$^{-1}$ in the analyte refractive-index from 1.33 to 1.34 for the chemical and biological applications.
Continuous Estimation of Left Ventricular Hemodynamic Parameters Based on Heart Sound and PPG Signals Using Deep Neural Network

Continuous estimation of left ventricular hemodynamic parameters is helpful to early diagnosis of cardiovascular diseases. Current non-invasive methods are somewhat inconvenient to monitor these parameters. Here, a deep neural network is built to noninvasively estimate left ventricular systolic pressure (LVSP), left ventricular diastolic pressure (LVDP), maximum rate of left ventricular pressure rise (+ dp/dt(max)) and minimum rate of left ventricular pressure drop (- dp/dt(min)) based on heart sound and PPG signals. The model consists of residual network and bidirectional recurrent neural network. Performance is evaluated on 2 beagle dogs’ experiment data with large ranges induced by epinephrine. Mean absolute errors and standard deviations between the estimated and the measured LVSP, LVDP, + dp/dt(max) and - dp/dt(min) are 7.23±8.33 mmHg, 2.12±3.0 mmHg, 298±406 mmHg/s, and 172±386 mmHg/s, respectively. The average correlation coefficients for LVSP, LVDP, + dp/dt(max) and - dp/dt(min) are 0.94, 0.86, 0.95 and 0.92. The results show that accurate intraventricular hemodynamic parameters can be achieved by non-invasive heart sound and PPG signals with deep neural networks. This technique suggests an easy way for real-time monitoring of intraventricular hemodynamics.
Enhancing Monkey to trigger malicious payloads in Android malware

Dynamic analysis is a prominent approach in analyzing the behavior of Android apps. To perform dynamic analysis, we need an event generator to execute the app. Monkey is the most popular event generator that is used in Android dynamic analysis. Monkey provides high code coverage, and yet high speed in generating events. However, in the case of malware analysis, Monkey suffers from several limitations. It only considers UI events but no system events. Moreover, it causes disconnecting the connectivity of the test environment during the analysis process. In this paper, we try to enhance Monkey to reduce its limitations while preserving its advantages. The proposed approach includes preparing Monkey with a facility for handling system events and keeping the connectivity of the test environment up during the analysis process. To evaluate the extended version of Monkey, we compare it with its original version regarding two important criteria in the case of malware analysis: the number of called sensitive APIs, and the code coverage. The evaluation process uses 100 randomly selected samples from AMD malware dataset. The results show that enhanced Monkey improves its ability to trigger sensitive APIs, and increases its code coverage.
Various Deep Learning Methods for Hyperspectral Images

Hyperspectral imagery is widely used in remote sensing applications that take into account thousands of spectral channel compositions over a single scene. Hyperspectral imagery requires accurate models of learning to extract the hyperspectral features in an image. Due to the presence of its spatial and spectral resolution, the image learning model presents a core challenge due to its complicated nature of image frames. In order to assist it during the learning process, several attempts have been made to address its complicated nature. However, these methods failed to provide the hyperspectral imagery with a deeper understanding. Because of the presence of mixed pixels, limited training samples and redundant data, the utilization of deep learning techniques addresses the problems. The deep learning process addresses the complex image data relationship. In this paper, various deep learning methods are studied which are used for the learning of hyperspectral imagery. Initially, we present an overview on various deep learning methods for various image processing techniques. A system review is then carried out on various hyperspectral image learning models based on deep learning.
Decentralised wireless networked model predictive control design for wind turbines *

An innovative Decentralised Wireless Networked Model Predictive Control (DWNMPC) is presented to regulate wind turbines speed and compensate the effect of communication constraints such as dropouts. A decentralised control system and an estimation algorithm have been developed as follows. The decentralised structure decomposes the wind farm into n turbines each with its local controller. A coordinated strategy where controllers share the turbine's status among other controllers is implemented to adjust the power generated by each turbine. A decentralised Kalman Filter (KF), based on the state-space model, is available for each subsystem to estimate the states locally. Then, the local control performance is optimised using the state estimation while considering input constraints. Experiments using the TrueTime network simulator and a 5 MW variable-speed pitch regulated wind turbine for below rated wind speed model are provided and the results demonstrate the effectiveness of the proposed DWNMPC approach in compensating for high percentages of dropouts while providing good performance and robustness.
Monocular-Based Pose Estimation of Non-Cooperative Space Targets Using EKF and EKPF

Relative pose estimation based on vision is widely used in various space navigation tasks. Considering the relative pose estimation problem of a spacecraft autonomous approaching to an unknown and non-cooperative target, a method for monocular-based pose estimation of non-cooperative space targets using Extended Kalman filter (EKF) and Extended Kalman Particle Filter (EKPF) is proposed. Compared with the existing methods, the proposed method does not depend on the prior information such as the size and shape of the target, and only uses the coordinates of the feature points of the target image as the filter input to realize the fast and accurate estimation of all pose parameters. Simulation results verify the effectiveness and feasibility of the proposed method.
A Comparative Study of Deep Learning Methods on Food Classification Problem

This paper gives a comparative study on the performances of several deep learning methods for the food images recognition challenge. The experiments were conducted on the UEC Food-100 dataset using ResNet-18, Inception-V3, Resnet-50, Densenet-121, Wide Resnet-50 and ResNext-50 with images of size 320x320 and 299x299. The limited size of the database required the transfer learning approach; that is, all models were trained with pretrained ImageNet weights. The best classification result was obtained using ResNext-50 with 87.7 % accuracy.
Design and Fabrication of Vertical Metal/TiO2/β-Ga2O3 Dielectric Heterojunction Diode With Reverse Blocking Voltage of 1010 V

This work provides insight into the design principles and the reasonable construction of Ga<sub>2</sub>O<sub>3</sub> heterojunction diode. We chose TiO<sub>2</sub> grown by atomic layer deposition (ALD) as a suitable insulator with a small conduction band offset of TiO<inline-formula> <tex-math notation="LaTeX">$_{2} \boldsymbol / \beta $ </tex-math></inline-formula>-Ga<sub>2</sub>O<sub>3</sub> heterojunction and a large dielectric constant. The type-I band alignment of the TiO<inline-formula> <tex-math notation="LaTeX">$_{2} \boldsymbol / \beta $ </tex-math></inline-formula>-Ga<sub>2</sub>O<sub>3</sub> heterojunction was obtained using X-ray photoelectron spectroscopy (XPS). We demonstrated a high-performance vertical metal/TiO<inline-formula> <tex-math notation="LaTeX">$_{2} \boldsymbol / \beta $ </tex-math></inline-formula>-Ga<sub>2</sub>O<sub>3</sub> dielectric heterojunction diode with reverse blocking voltage of 1010 V and differential ON-resistance of 3.3 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega \cdot $ </tex-math></inline-formula>cm<sup>2</sup>. The proposed design principles of dielectric heterojunction diode and the insulator selection can also provide a simple, effective, and feasible technology for other wideband and ultrawideband semiconductors in which bipolar doping is challenging to achieve high power device performance.
THz Radio Communication: Link Budget Analysis toward 6G

How well do upper millimeter-wave and terahertz frequency bands enable wireless communications? In this work, we approximate the current and estimate the future communication potential with emphasis on antenna and radio frequency hardware technologies, and radio propagation challenges. This is done by performing link budget evaluations with justified estimates of link budget calculus terms, such as the achievable or required noise figure, transmit power, and antenna gain. Estimates are based on current enabling technologies and needs to advance those. From the RF viewpoint, the bottlenecks are in generating sufficiently high transmit power and low noise with the support of very high antenna gains. As an example, we discuss opportunities around 300 GHz frequency. Challenges to support 100 Gb/s bit rate at 30 GHz bandwidth on 10 m link distance is analyzed for different kinds of devices.
Hybrid-Shield: Accurate and Efficient Cross-Layer Countermeasure for Run-Time Detection and Mitigation of Cache-Based Side-Channel Attacks

Cache-based Side-Channel Attacks (SCAs) exploit the emerging hardware vulnerabilities to steal secret information by observing cache access patterns of cryptographic applications. To address the challenges introduced by SCAs, existing solutions either rely on detecting them using the profiled hardware-related information of victim and attack programs, or mitigating the cache-based SCAs by focusing on cache partitioning techniques and/or randomization of cache mappings by modifying the underlying cache architecture of the processor. However, the former's detectors highly rely on the knowledge of attack programs that are not always available or could be obfuscated in real-world benign applications resulting in misidentifying attacks. On the other hand, the latter approach though effective, requires modification in the processor hardware which increases the complexity of architecture design and are not applicable to current as well as legacy architectures. To address the drawbacks, we propose Hybrid-Shield, an accurate and efficient cross-layer countermeasure for run-time detection and mitigation of cache-based side-channel attacks. For the detection stage, mi-croarchitectural information of victim under attack and under no attack conditions are collected for training machine learning classifiers. For the mitigation stage, Hybrid-Shield adapts hardware prefetchers and scales processor frequency to increase the noise level in observed cache access pattern attacks to induce secret information. The experimental results indicate that Hybrid-Shield can achieve 100% detection rate with 0% false alarm rate and detected attacks' error rate increases from less than 5% to above 35% with only 15% performance overhead.
Spatial Shift Point-Wise Quantization

Deep neural networks (DNN) have been applied to numerous artificial-intelligence applications because of their remarkable accuracy. However, computational requirements for deep neural networks are recently skyrocketing far beyond the Moore’s Law. In addition to the importance of accuracy, the industry’s demand for efficiency in model learning process is increasing. This has led to various attempts to make DNNs more lightweight. Hence, we propose a modeling technique that applies lightweight convolutional neural networks (CNN) to handle the model-learning processes for DNNs. The proposed spatial-shift pointwise quantization (SSPQ) model elegantly combines compact network-design techniques to revitalize DNN quantization efficiency with little accuracy loss. We set the depths of our SSPQ model to 20, 34, and 50 to test against CIFAR10, CIFAR100, and ImageNet datasets, respectively. By applying SSPQ20 to the CIFAR10 dataset, we reduced accuracy degradation by 2.95%, while reducing the number of parameters <inline-formula> <tex-math notation="LaTeX">$8.7\times $ </tex-math></inline-formula>. For the same dataset, our “wide” SSPQ20 variant reduced training parameters <inline-formula> <tex-math notation="LaTeX">$1.96\times $ </tex-math></inline-formula>, compared with the ResNet20 model architecture, which provided a 0.2% improvement. By applying SSPQ34 to the CIFAR100 dataset, we successfully reduced the number of learning parameters <inline-formula> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula>, compared with the ResNet34 model with an accuracy degradation of 4.57%. By applying SSPQ50 to the ImageNet dataset, we successfully reduced the number of parameters <inline-formula> <tex-math notation="LaTeX">$10.2\times $ </tex-math></inline-formula> over ResNet50 with an accuracy degradation of only 2.68%. Therefore, using only 9.78MB of learning parameter, the SSPQ50 model guarantees 73.6% accuracy. This is an improved performance by 1.46% in terms of accuracy and 3.62MB in terms of model size compared to the MobileNetV2 lightweight model. The source code is available at <uri>https://github.com/Eunhui-Kim/SSPQ</uri>.