Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of False Command and Response Injection Attacks for Cyber Physical Systems Security and Resilience

The operational cyber-physical system (CPS) state, safety and resource availability is impacted by the safety and security measures in place. This paper focused on i) command injection (CI) attack that alters the system behaviour through injection of false control and configuration commands into a control system and ii) response injection (RI) attacks that modifies the response from server to client, thereby providing false information about system state. In this project, we implemented deep learning (DL) multi-layered security model approach for securing industrial control system (ICS) against malicious CI and RI attacks. We validated this approach with two case studies: i) network transactions between a Remote Terminal Unit (RTU) and a Master Control Unit (MTU) in-house SCADA gas pipeline control system and ii) a case study of command and response injection attacks. Based on this project result, we show that the proposed approach achieved a significant attacks detection capability of 96.50%. Also, demonstrated that performance of attack detection techniques applied can be influences by the nature of network transactions with respect to the domain of application. Hence, robustness and resilience of operational CPS state and performance are influenced by the safety and security measures in place which is specific to the CPS device in question.
Analyzing the Performance of Plasmonic Resonators for Dielectric Sensing

The frequency shift of the plasmon resonances of metallic nanostructures upon changes in the surrounding bulk refractive index, coating with dielectric layers, and attachment of dielectric spheres is investigated. A general figure of merit (FOM) for the sensing capability of a given resonator is introduced and applied to those three situations. It is based on statistical analysis and an analytical expression for the resonance change. The latter is derived in a perturbation approach and yields a direct connection between the field localization and the resonator response. The scaling of this FOM with the size of the resonator and the analyte are discussed. We show that the optimal resonator strongly depends on the analyte under consideration. These concepts and their limitations are illustrated based on full numerical simulations of surface-supported crescent-shaped gold nanostructures.
Reliability of SSDs in Enterprise Storage Systems

This article presents the first large-scale field study of NAND-based SSDs in enterprise storage systems (in contrast to drives in distributed data center storage systems). The study is based on a very comprehensive set of field data, covering 1.6 million SSDs of a major storage vendor (NetApp). The drives comprise three different manufacturers, 18 different models, 12 different capacities, and all major flash technologies (SLC, cMLC, eMLC, 3D-TLC). The data allows us to study a large number of factors that were not studied in prior works, including the effect of firmware versions, the reliability of TLC NAND, and the correlations between drives within a RAID system. This article presents our analysis, along with a number of practical implications derived from it.
Age Inference on Twitter using SAGE and TF-IGM

Social media is increasingly influential in day-to-day life. People are more than ever sharing, posting, liking, and following different activities on disparate social media. Deriving specific attributes of users based on their online behavior is a growing research field. In this study, a novel methodology is proposed for determining the age of Twitter users. We classify three separate age groups, namely, 18--24, 25--54, 55 >. We compute numerous linguistic features from the tweets of users, obtain significant terms extracted by the SAGE algorithms, and retrieve relevant meta-data of users by extracting information on their followed interests on Twitter using TF-IGM. The final logistic regression model obtains a macro F1-score of 78%. This way, effectively combining NLP and IR techniques for attribute inference on social media.
A Non-Volatile Computing-In-Memory Framework With Margin Enhancement Based CSA and Offset Reduction Based ADC

Nowadays, deep neural network (DNN) has played an important role in machine learning. Non-volatile computing-in-memory (nvCIM) for DNN has become a new architecture to optimize hardware performance and energy efficiency. However, the existing nvCIM accelerators focus on system-level performance but ignore analog factors. In this paper, the sense margin and offset are considered in the proposed nvCIM framework. The margin enhancement based current-mode sense amplifier (MECSA) and the offset reduction based analog-to-digital converter (ORADC) are proposed to improve the accuracy of the ADC. Based on the above methods, the nvCIM framework is displayed and the experiment results show that the proposed framework has an improvement on area, power, and latency with the high accuracy of network models, and the energy efficiency is 2.3 - 20.4× compared to the existing RRAM based nvCIM accelerators.
Vision Control Unit in Fully Self Driving Vehicles using Xilinx MPSoC and Opensource Stack

Fully self-driving (FSD) vehicles are becoming increasing popular over the last few years and companies are investing significantly into its research and development. In the recent years, FSD technology innovators like Tesla, Google etc. have been working on proprietary autonomous driving stacks and have been able to successfully bring the vehicle to the roads. On the other end, organizations like Autoware Foundation and Baidu are fueling the growth of self-driving mobility using open source stacks. These organizations firmly believe in enabling autonomous driving technology for everyone and support developing software stacks through the open source community that is SoC vendor agnostic. In this proposed solution we describe a vision control unit for a fully self-driving vehicle developed on Xilinx MPSoC platform using open source software components.The vision control unit of an FSD vehicle is responsible for camera video capture, image processing and rendering, AI algorithm processing, data and meta-data transfer to next stage of the FSD pipeline. In this proposed solution we have used many open source stacks and frameworks for video and AI processing. The processing of the video pipeline and algorithms take full advantage of the pipelining and parallelism using all the heterogenous cores of the Xilinx MPSoC. In addition, we have developed an extensible, scalable, adaptable and configurable AI backend framework, XTA, for acceleration purposes that is derived from a popular, open source AI backend framework, TVM-VTA. XTA uses all the MPSoC cores for its computation in a parallel and pipelined fashion. XTA also adapts to the compute and memory parameters of the system and can scale to achieve optimal performance for any given AI problem. The FSD system design is based on a distributed system architecture and uses open source components like Autoware for autonomous driving algorithms, ROS and Distributed Data Services as a messaging middleware between the functional nodes and a real-time kernel to coordinate the actions. The details of image capture, rendering and AI processing of the vision perception pipeline will be presented along with the performance measurements of the vision pipeline.In this proposed solution we will demonstrate some of the key use cases of vision perception unit like surround vision and object detection. In addition, we will also show the capability of Xilinx MPSoC technology to handle multiple channels of real time camera and the integration with the Lidar/Radar point cloud data to feed into the decision-making unit of the overall system. The system is also designed with the capability to update the vision control unit through Over the Air Update (OTA). It is also envisioned that the core AI engine will require regular updates with the latest training values; hence a built-in platform level mechanism supporting such capability is essential for real world deployment.
On the Impact of Aging on Power Analysis Attacks Targeting Power-Equalized Cryptographic Circuits

Side-channel analysis attacks exploit the physical characteristics of cryptographic chip implementations to extract their embedded secret keys. In particular, Power Analysis (PA) attacks make use of the dependency of the power consumption on the data being processed by the cryptographic devices. To tackle the vulnerability of cryptographic circuits against PA attack, various countermeasures have been proposed in literature and adapted by industries, among which a branch of hiding schemes opt to equalize the power consumption of the chip regardless of the processed data. Although these countermeasures are supposed to reduce the information leak-age of cryptographic chips, they fail to consider the impact of aging occurs during the device lifetime. Due to aging, the specifications of transistors, and in particular their threshold-voltage, deviate from their fabrication-time specification, leading to a change of circuit’s delay and power consumption over time. In this paper, we show that the aging-induced impacts result in imbalances in the equalized power consumption achieved by hiding countermeasures. This makes such protected cryptographic chips vulnerable to PA attacks when aged. The experimental results extracted through the aging simulation of the PRESENT cipher protected by Sense Amplifier Based Logic (SABL), one of the well-known hiding countermeasures, show that the achieved protection may not last during the circuit lifetime.
The darkness and the light: diurnal rodent models for seasonal affective disorder

ABSTRACT The development of animal models is a critical step for exploring the underlying pathophysiological mechanisms of major affective disorders and for evaluating potential therapeutic approaches. Although most neuropsychiatric research is performed on nocturnal rodents, differences in how diurnal and nocturnal animals respond to changing photoperiods, combined with a possible link between circadian rhythm disruption and affective disorders, has led to a call for the development of diurnal animal models. The need for diurnal models is most clear for seasonal affective disorder (SAD), a widespread recurrent depressive disorder that is linked to exposure to short photoperiods. Here, we briefly review what is known regarding the etiology of SAD and then examine progress in developing appropriate diurnal rodent models. Although circadian disruption is often invoked as a key contributor to SAD, a mechanistic understanding of how misalignment between endogenous circadian physiology and daily environmental rhythms affects mood is lacking. Diurnal rodents show promise as models of SAD, as changes in affective-like behaviors are induced in response to short photoperiods or dim-light conditions, and symptoms can be ameliorated by brief exposure to intervals of bright light coincident with activity onset. One exciting avenue of research involves the orexinergic system, which regulates functions that are disturbed in SAD, including sleep cycles, the reward system, feeding behavior, monoaminergic neurotransmission and hippocampal neurogenesis. However, although diurnal models make intuitive sense for the study of SAD and are more likely to mimic circadian disruption, their utility is currently hampered by a lack of genomic resources needed for the molecular interrogation of potential mechanisms. Summary: We review the potential neural mechanisms by which light influences seasonal depression, focusing on research using diurnal rodent models.
Fast Tongue Image Extraction Fusing Multi-dimensional Information and Concurrent D-search Algorithm

Tongue image extraction is a primary and fundamental step in the objectification of the tongue diagnoses of traditional Chinese medicine. Aiming at the problems that some methods need to adjust parameters by manual and their accuracy and efficiency are not high, in this paper, a kind of novel concurrent d-search algorithm fusing multi-dimensional information is proposed for the application of tongue image extraction in order to realize automatic, fast and accurate tongue image extraction. In our method, original tongue image is considered as a pixel network. Starting from the seed pixel of central region, according to the intensity, hue and relative position information of the pixels of tongue image, a novel d-search algorithm is utilized to search tongue body region in original tongue image. Because tongue coating is surrounded by tongue substance in most cases, we propose a novel position operator to decide whether current pixel is surrounded by tongue substance in our d-search algorithm so as to extract tongue image with coating accurately. In the same time, to improve the efficiency of our algorithm, the original tongue image is divided into several regions (i.e. sub-images), concurrent techniques are utilized to search different tongue regions separately, so as to get the tongue image templates of different regions quickly, and then they are combined into one template. In addition, morphological operators including closing and opening are applied to the template after combination, so that small holes in the template image can be filled. In the end, an and operation is applied to the ultimate template and original tongue image, in order to get extracted tongue image. In our study, we conduct the comparison experiments of 4 methods including the method of manual extraction, our method, tongue image extraction based on greedy rules and tongue image extraction based on random walk. The comparison experiments are conducted from the aspects of accuracy and efficiency. The tongue images of the 5 typical kinds of colors including light red, light white, red, deep red and purple are utilized to test the 3 methods. Furthermore, a tongue image with thick white coating and a tongue image with thick yellow coating are also utilized to test the 3 methods. These tongue image samples cover all kinds of tongue colors and include tongue images with or without tongue coating. Therefore, the results of our tests are persuasive. In comparative experiments, our method achieves quite good effect in the aspect of accuracy and is superior to the current 2 methods in the aspect of efficiency, which can meet the accuracy and efficiency requirements of the objectification of tongue diagnoses. And we have implemented automatic tongue image extraction without adjusting any parameters by manual.
Semantic-aware Comment Analysis Approach for API Permission Mapping on Android

As Android platform is protected by permissions, which is one of the most powerful access control models of Android, it is possible to restrict the use of certain sensitive APIs. In contrast, many applications declare permissions more than they need, requiring API permission specification to detect these cases. However, prominent previous research has not been focused on java documents or comment from Android developers, despite a lot of information. To address this problem, we propose a novel method to analyze naturally written comments from Android developers for API permission map construction. We extract all comments and java documents from raw Android source code and extract permission information using natural language processing techniques. At the same time, we parse naturally written source code and extract API signature, to perform the mapping between permission. Moreover, we categorize all permissions and APIs according to their behavior explained in the comment to measure the potential risk level resulted from misuse. Our experiment on Android 10, which is the latest version, mapped 3,012 APIs with permission, and categorized semantically with seven different categories.
NVMM-Oriented Hierarchical Persistent Client Caching for Lustre

In high-performance computing (HPC), data and metadata are stored on special server nodes and client applications access the servers’ data and metadata through a network, which induces network latencies and resource contention. These server nodes are typically equipped with (slow) magnetic disks, while the client nodes store temporary data on fast SSDs or even on non-volatile main memory (NVMM). Therefore, the full potential of parallel file systems can only be reached if fast client side storage devices are included into the overall storage architecture. In this article, we propose an NVMM-based hierarchical persistent client cache for the Lustre file system (NVMM-LPCC for short). NVMM-LPCC implements two caching modes: a read and write mode (RW-NVMM-LPCC for short) and a read only mode (RO-NVMM-LPCC for short). NVMM-LPCC integrates with the Lustre Hierarchical Storage Management (HSM) solution and the Lustre layout lock mechanism to provide consistent persistent caching services for I/O applications running on client nodes, meanwhile maintaining a global unified namespace of the entire Lustre file system. The evaluation results presented in this article show that NVMM-LPCC can increase the average read throughput by up to 35.80 times and the average write throughput by up to 9.83 times compared with the native Lustre system, while providing excellent scalability.
A Novel DRAM-Based Process-in-Memory Architecture and its Implementation for CNNs

Processing-in-Memory (PIM) is an emerging approach to bridge the memory-computation gap. One of the key challenges of PIM architectures in the scope of neural network inference is the deployment of traditional area-intensive arithmetic multipliers in memory technology, especially for DRAM-based PIM architectures. Hence, existing DRAM PIM architectures are either confined to binary networks or exploit the analog property of the sub-array bitlines to perform bulk bit-wise logic operations. The former reduces the accuracy of predictions, i.e. Quality-of-results, while the latter increases overall latency and power consumption.In this paper, we present a novel DRAM-based PIM architecture and implementation for multi-bit-precision CNN inference. The proposed implementation relies on shifter based approximate multiplications specially designed to fit into commodity DRAM architectures and its technology. The main goal of this work is to propose an architecture that is fully compatible with commodity DRAM architecture and to maintain a similar thermal design power (i.e. < 1W ). Our evaluation shows that the proposed DRAM-based PIM has a small area overhead of 6.6% when compared with an 8 Gb commodity DRAM. Moreover, the architecture delivers a peak performance of 8.192 TOPS per memory channel while maintaining a very high energy efficiency. Finally, our evaluation also shows that the use of approximate multipliers results in a negligible drop in prediction-accuracy (i.e. < 2 %) in comparison with conventional CNN inference that relies on traditional arithmetic multipliers.
A CRISPR-Cas autocatalysis-driven feedback amplification network for supersensitive DNA diagnostics

A CRISPR-Cas–driven positive feedback circuit with exponential dynamic enables ultrasensitive detection of genomic DNA. Artificial nucleic acid circuits with precisely controllable dynamic and function have shown great promise in biosensing, but their utility in molecular diagnostics is still restrained by the inability to process genomic DNA directly and moderate sensitivity. To address this limitation, we present a CRISPR-Cas–powered catalytic nucleic acid circuit, namely, CRISPR-Cas–only amplification network (CONAN), for isothermally amplified detection of genomic DNA. By integrating the stringent target recognition, helicase activity, and trans-cleavage activity of Cas12a, a Cas12a autocatalysis-driven artificial reaction network is programmed to construct a positive feedback circuit with exponential dynamic in CONAN. Consequently, CONAN achieves one-enzyme, one-step, real-time detection of genomic DNA with attomolar sensitivity. Moreover, CONAN increases the intrinsic single-base specificity of Cas12a, and enables the effective detection of hepatitis B virus infection and human bladder cancer–associated single-nucleotide mutation in clinical samples, highlighting its potential as a powerful tool for disease diagnostics.
Melanoma classification using EfficientNets and Ensemble of models with different input resolution

Early and accurate detection of melanoma with data analytics can make treatment more effective. This paper proposes a method to classify melanoma cases using deep learning on dermoscopic images. The method demonstrates that heavy augmentation during training and testing produces promising results and warrants further research. The proposed method has been evaluated on the SIIM-ISIC Melanoma Classification 2020 dataset and the best ensemble model achieved 0.9411 area under the ROC curve on hold out test data.
Copy-on-Abundant-Write for Nimble File System Clones

Making logical copies, or clones, of files and directories is critical to many real-world applications and workflows, including backups, virtual machines, and containers. An ideal clone implementation meets the following performance goals: (1) creating the clone has low latency; (2) reads are fast in all versions (i.e., spatial locality is always maintained, even after modifications); (3) writes are fast in all versions; (4) the overall system is space efficient. Implementing a clone operation that realizes all four properties, which we call a nimble clone, is a long-standing open problem. This article describes nimble clones in B-ε-tree File System (BetrFS), an open-source, full-path-indexed, and write-optimized file system. The key observation behind our work is that standard copy-on-write heuristics can be too coarse to be space efficient, or too fine-grained to preserve locality. On the other hand, a write-optimized key-value store, such as a Bε-tree or an log-structured merge-tree (LSM)-tree, can decouple the logical application of updates from the granularity at which data is physically copied. In our write-optimized clone implementation, data sharing among clones is only broken when a clone has changed enough to warrant making a copy, a policy we call copy-on-abundant-write. We demonstrate that the algorithmic work needed to batch and amortize the cost of BetrFS clone operations does not erode the performance advantages of baseline BetrFS; BetrFS performance even improves in a few cases. BetrFS cloning is efficient; for example, when using the clone operation for container creation, BetrFS outperforms a simple recursive copy by up to two orders-of-magnitude and outperforms file systems that have specialized Linux Containers (LXC) backends by 3--4×.