Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on matrix transformation of AI media

Objective The development of artificial intelligence technology has led to the transformation of communication carrier, communication medium and communication mode. Methods on the basis of mathematician Arthur Cayley's mathematical matrix theory, through the existing AI media communication mode in-depth analysis and research. Conclusion This paper summarizes the transformation characteristics of matrix from the research on the transformation of AI media. Change one is based on the matrix transformation of AI media communication mode; Change 2: the transformation of large matrix communication based on the Central computing power; Change 3: Transformation of small matrix communication based on terminal; Change 4: Transformation of robot matrix based on AI media.
Convolutional neural network libraries benchmarking: a systematic mapping

Context: The growing use of Convolutional Neural Networks (CNNs) in scientific experiments and industry has resulted in the development of several Convolutional Neural Networks libraries. There is a need on the part of developers and scientists to identify scholarly works that evaluate the performance of these libraries on different computer architectures. Objective: Identify and systematize the CNN libraries benchmarkings. Method: A systematic literature mapping was conducted to analyze the scientific research in the field. Results: Systematic mapping found 12 papers that evaluate the performance of Convolutional Neural Networks on different computer architectures. Conclusion: The state of the art has been mapped, there are still few works that evaluate Convolutional Neural Networks libraries with statistical accuracy.
Low Voltage Cold Start-Up Ring Oscillator with Dynamic Body Biasing Technique for Battery-Assistance DC Energy Harvesting Systems

In this paper, a low voltage cold start-up ring oscillator with dynamic body biasing technique for battery-assistance DC energy harvesting systems is presented. The cold start-up ring oscillator consists of 21-stage low voltage delay elements. To overcome Meindl's fundamental limit on the minimal supply voltage for standard inverters, dynamic body biasing technique is introduced. Bodies of pull-down transistor and pull-up transistor are tied together. Meanwhile, a voltage level shifter (VLS) and a high voltage supply (VDDH) powered by extra battery are inserted to enlarge the voltage swing at bodies of pull-down transistor and pull-up transistor. In this paper, only one stage in the low voltage cold start-up ring oscillator is controlled by the VLS and others are controlled by stacked inverters (SI). The prototype is implemented in a standard 180nm CMOS process. The postlayout simulations show that the proposed cold start-up ring oscillator can maintain oscillation under a power supply 26mV and VDDH=100mV at a typical corner and room temperature.
Design Space Exploration of Heterogeneous-Accelerator SoCs with Hyperparameter Optimization

Modern SoC systems consist of general-purpose processor cores augmented with large numbers of specialized accelerators. Building such systems requires a design flow allowing the design space to be explored at the system level with an appropriate strategy. In this paper, we describe a methodology allowing to explore the design space of power-performance heterogeneous SoCs by combining an architecture simulator (gem5-Aladdin) and a hyperparameter optimization method (Hyperopt). This methodology allows different types of parallelism with loop unrolling strategies and memory coherency interfaces to be swept. The flow has been applied to a convolutional neural network algorithm. We show that the most energy efficient architecture achieves a 2x to 4x improvement in energy-delay-product compared to an architecture without parallelism. Furthermore, the obtained solution is more efficient than commonly implemented architectures (Systolic, 2D-mapping, and Tiling). We also applied the methodology to find the optimal architecture including its coherency interface for a complex SoC made up of six accelerated-workloads. We show that a hybrid interface appears to be the most efficient; it reaches 22% and 12% improvement in energy-delay-product compared to just only using non-coherent and only LLC-coherent models, respectively.
Mixed Precision Quantization for ReRAM-based DNN Inference Accelerators

ReRAM-based accelerators have shown great potential for accelerating DNN inference because ReRAM crossbars can perform analog matrix-vector multiplication operations with low latency and energy consumption. However, these crossbars require the use of ADCs which constitute a significant fraction of the cost of MVM operations. The overhead of ADCs can be mitigated via partial sum quantization. However, prior quantization flows for DNN inference accelerators do not consider partial sum quantization which is not highly relevant to traditional digital architectures. To address this issue, we propose a mixed precision quantization scheme for ReRAM-based DNN inference accelerators where weight quantization, input quantization, and partial sum quantization are jointly applied for each DNN layer. We also propose an automated quantization flow powered by deep reinforcement learning to search for the best quantization configuration in the large design space. Our evaluation shows that the proposed mixed precision quantization scheme and quantization flow reduce inference latency and energy consumption by up to 3.89× and 4.84×, respectively, while only losing 1.18% in DNN inference accuracy.
ACM SIGSOFT Empirical Standards Released

In October 2020, The ACM SIGSOFT Paper and Peer Review Quality Task Force released its first empirical standards. An empirical standard is "a brief public document that communicates expectations for a specific kind of study (e.g. a questionnaire survey)" [1]. (All quotations below are from the Empirical Standards report [1] unless otherwise noted.)
Machine Learning-based Structural Pre-route Insertability Prediction and Improvement with Guided Backpropagation*

With the development of semiconductor technology nodes, the sizes of standard cells become smaller and the number of standard cells is dramatically increased to bring into more functionality in integrated circuits (ICs). However, the shrinking of standard cell sizes causes many problems of ICs such as timing, power, and electromigration (EM). To tackle these problems, a new style structural pre-route (SPR) is proposed. Such type of pre-route is composed of redundant parallel metals and vias so that the low resistance and the redundant sub-structures can improve timing and yield. But the large area overhead becomes the major problem of inserting such pre-routes all over a design. In this paper, we propose a machine learning-based approach to predict the insertability of SPRs for placed designs. In addition, we apply a pattern visualization method by using a guided backpropagation technique to see in depth of our model and identify the problematic layout features causing SPR insertion failures. The experimental results not only show the excellent performance of our model, but also show that avoiding generating the identified critical features during legalization can improve SPR insertability compared to a commercial SPR-aware placement tool.
An Improved Phase Unwrapping Algorithm

InSAR technology plays an increasingly important role in detecting subsidence, while phase unwrapping is one of the most important steps in the InSAR process whose efficiency will directly affect digital elevation model. There has been no universal algorithm for phase unwrapping because the gradient between adjacent phases, such as noise, may be greater than &pgr;, which destroys the consistency. Goldstein algorithm is an excellent unwrapping algorithm, but there are some defects such as "isolated islands". In this paper, we first classify the branch cuts and connect the shorter cuts, then connect the longer branch cuts by using the improved branch-cut method and unwrap the isolated island region by using the quality map method. After verifying the simulation and the actual measured data, we can find that the algorithm reduces the probability of isolated islands, the length of the branch cuts and the unwrapping error, which shows the practicality and efficiency of the algorithm.
Exploiting Race Condition for Wi-Fi Denial of Service Attacks

Wi-Fi is a wireless communication technology that has been around since the late nineties. Nowadays, it is the most adopted wireless technology in various IoT (Internet of Things) applications. Although Wi-Fi security has significantly improved throughout the past years, it is still lagging behind. Many vulnerabilities exist allowing attackers to generate different types of attacks. These attacks can breach the authentication, confidentiality, and data integrity of Wi-Fi networks. In terms of attack impact, attacks on availability have a higher impact. In fact, breaching nowadays systems does not only result in data corruption but may also result in the loss of human lives. Therefore, more consideration should be brought to attacks on availability. In this paper, we present three attacks on Wi-Fi availability. These attacks cause a denial of service on Wi-Fi users by preventing them from connecting to a legitimate network. We adopt the evil twin scheme and exploit a race condition-based vulnerability to generate the attacks. Also, we propose countermeasures to fix the exploited vulnerability and mitigate the attacks.
A terrain classifier to improve the inclusion of objects in the collaborative mapping tool OpenStreetMap

In Geographic Information Systems (GIS) and, more specifically in the case of Volunteered Geographic Information (VGI), users actively participate in the processes of data inclusion, edition and deletion. Thus, the issue of data quality becomes a central topic, since it is essential to ascertain if some dimensions are being successfully achieved, such as accuracy, logical consistency, and completeness of the information within these types of system. In this context, the present paper develops the application QualiOSM, with the purpose of assisting users in the tasks of including and editing objects in the collaborative mapping tool OpenStreetMap (OSM). This paper focuses on the implementation of a terrain classifier, which was developed using the parallelepiped classifier technique and proved to be useful especially in identifying buildings of various shapes, contributing to a significant improvement in data consistency of the OSM platform.
Research on the Processing Technology of Improving the Sharpness of Li Brocade Pattern

In this paper, the color and pattern characteristics of Li brocade pattern in Hainan is firstly analyzed, and then, according to the different performance characteristics of Li brocade pattern and color, the gray scale and histogram equalization of Li brocade pattern are experimented and researched with different image processing methods. On this basis, Laplacian operator and improved Laplacian operator template are used to deal with the color, edge and contour of different Li brocade patterns. Different methods were used to process the different patterns of Li Brocade, and the processing results of different methods were obtained. By comparing and analyzing the experimental results, it can be seen that the Li brocade pattern with relatively large color contrast will become clearer and brighter after being processed by histogram equalization and gray scale. On the contrary, the effect is not very obvious. The improved Laplacian operator template method can make the Li brocade pattern clear, but at the same time, the local noise of the pattern increases.
Security of Neural Networks from Hardware Perspective: A Survey and Beyond

Recent advances in neural networks (NNs) and their applications in deep learning techniques have made the security aspects of NNs an important and timely topic for fundamental research. In this paper, we survey the security challenges and opportunities in the computing hardware used in implementing deep neural networks (DNN). First, we explore the hardware attack surfaces for DNN. Then, we report the current state-of-the-art hardware-based attacks on DNN with focus on hardware Trojan insertion, fault injection, and side-channel analysis. Next, we discuss the recent development on detecting these hardware-oriented attacks and the corresponding countermeasures. We also study the application of secure enclaves for the trusted execution of NN-based algorithms. Finally, we consider the emerging topic of intellectual property protection for deep learning systems. Based on our study, we find ample opportunities for hardware based research to secure the next generation of DNN-based artificial intelligence and machine learning platforms.
PARAMETER DEPENDENCE IN CONTINUUM ELECTROSTATIC CALCULATIONS : A STUDY USING PROTEIN SALT BRIDGES

Continuum electrostatic calculations were carried out for a data set of 21 salt bridges using three sets of parameters (charmm, parse, and a version of opls with slightly exaggerated atomic radii) and three representations of the dielectric boundary (approximate, exact, and smoothed versions of the analytic molecular surface). Comparisons of the overall results and a decomposition into terms due to desolvation, the direct salt bridge, and other interactions with the surrounding protein were used to analyze the effect of differences in parameter sets. It is concluded that (1) while numerical differences among the results can be significant, the underlying tension between desolvation penalty incurred and favorable interactions recovered upon folding is similar in all computations; (2) the largest variation occurs for the desolvation penalty, indicating the importance of fitting this property in parameter development; (3) the approximation of the molecular surface produces the largest desolvation penalties, .
Frequency-Domain Identification By Basis Pursuit

In this paper, we propose a new adaptive method for frequency-domain identification problem of discrete LTI systems. It is based on a dictionary that is consisting of normalized reproducing kernels. We prove that the singular values of the matrix generated by this dictionary converge to zero rapidly and this makes it quite efficient in representing the original systems with only a few elements. Three examples are presented to illustrate the idea.
When GDPR Meets CRAs (Credit Reference Agencies): Looking through the Lens of Twitter

Collecting information about consumers and businesses from various sources, Credit reference agencies (CRAs) help many organizations such as financial institutions to assess creditworthiness of applicants and customers of their services. CRAs’ business model depends on processing a high volume of personal data including highly sensitive ones, which must be processed within the relevant legal frameworks in different countries they operate their business, e.g., the European Union’s new GDPR (General Data Protection Regulation). This paper reports a data-driven analysis of CRA- and GDPR-related discussions on Twitter. Our analysis covers the three largest multi-national CRAs: Equifax, Experian and TransUnion and we also looked at the UK’s data protection authority, ICO, and two UK-based privacy-advocating NGOs, Privacy International and Open Rights Group (ORG). We have analyzed public tweets of their official Twitter accounts and other public tweets talking about them. Our analysis revealed a very surprising lack of awareness of CRA- and GDPR-related data privacy issues within the general public and an astonishing lack of active communications of CRAs to the general public on relevant GDPR-related privacy issues: out of 39,549 collected tweets we identified only 153 relevant tweets (0.387%). This small number of tweets are dominated by mentions of security issues (%73.2), especially data breaches affecting CRAs, not data subject rights or privacy issues directly. Other tweets are mainly about complaints regarding inaccurate data in credit files and questions about how to exercise right to rectification, just two of many data subject rights defined in the GDPR.
Drivers and influencing factors in mobile payment acceptance

The development and diffusion of smartphones has paved the way for several trends, among them an alternative payment solution in the form of mobile payment. Mobile payment is part of the overarching concept of mobile transactions, and experiences varied acceptance and adoption. This paper analyzes drivers and factors determining consumers' acceptance and adoption of mobile payment. For this reason, we examine the contribution of the two basic approaches, i.e. the "technology acceptance model" and the "unified theory of acceptance and use of technology" in the context of mobile payment. Based on empirical evidence, we identified several factors, which were found to have impact on primary adoption; a compelling user experience turned out to be the main driver of long-term usage.
Research on Meter Image Recognition Based on Improved LeNet-5

Because most mechanical instruments have no special intelligent interface, it is difficult to achieve automatic data acquisition, at the same time the domestic meter specifications have not been completely unified, so it is difficult to achieve high accuracy and high efficiency by manual data collection. In recent years, with the continuous development of information technology and computer hardware, people's demand for intelligence is also increasing. In this paper, through the optimization of the meter image preprocessing a convolution neural network model based on improved LeNet-5 is proposed to identify the meter image. Using ReLU function instead of traditional sigmoid function to solve the problem of gradient dispersion, and using max pooling to improve the sampling richness. Adding the dropout function in the full connection layer to prevent the neural network from over fitting. The experimental results show that the prediction results can be improved by optimizing the image preprocessing and improving the network model of LeNet-5. The accuracy rate can reach 99.8%, and the recognition speed is fast.
Categorizing Uses of Communications Metadata: Systematizing Knowledge and Presenting a Path for Privacy

Communications metadata can be used to determine a communication’s device, identify the user of the device, and profile the user’s personality and behavior. The current state of affairs is that the increase of attacks against user privacy based on using communications metadata vastly outpaces the ability of users to protect themselves. With few exceptions, protections are point solutions against a specific attack. In the current situation, the user loses. This paper is an initial step in a multi-step research effort to reset that balance. The main contribution of this paper is a categorization of the uses of communications metadata based on their privacy impact. Because of the technical complexity of the problem, including the wide variety of electronic communications, technology can only go so far in providing solutions to the privacy problems created by the use of communications metadata. Legal and policy intervention will also be needed. This categorization is intended to provide a start in developing legal and policy privacy protections for communications metadata. Along the way, I also provide an explanation for how it is that communications metadata has become so valuable, sometimes surpassing the value of content. This work provides both an intellectual framework for thinking about the privacy implications of the use of communications metadata and a roadmap, with first steps taken, for providing privacy protections for users of electronic communications.
Numerical Studies on the Impact of Equivalence Ratio Oscillations on Lean Premixed Flame Characteristics and Emissions

Laminar and turbulent one-dimensional lean premixed methane-air flames subject to equivalence ratio oscillations are investigated numerically. For the turbulent simulations, the Linear Eddy Model is employed. Harmonic perturbations at various frequencies and with various amplitudes are considered and their influence on CO and NO emissions, heat release rate fluctuations, and burning velocity is evaluated. The results indicate a strongly nonlinear behavior of the flame response for high forcing amplitudes attributed to nonlinear effects due to the interaction of burning velocity and equivalence ratio oscillations leading to nonsinusoidal oscillations at the flame front. Furthermore, the turbulent cases reveal decreasing mean burning velocities and heat release rates with increasing amplitudes due to damping of turbulent fluctuations induced by the oscillations.
Data-driven Key Features Selection for Fault Detection in a Complex System

Fault detection is a difficult but important problem for a complex system. This paper presents a fault detection method based on data-driven key feature selection for the complex system abbreviated as FD-DKFS. By regarding the observable parameters as original features, FD-DKFS first finds the missing correlations among original features and constructs potentially useful features for fault detection. Next, FD-DKFS provides a filter feature selection method to find the best feature subset for fault detection. Then, these selected features are used to detect the fault in a certain complex system with conventional classifiers. Compared with the other methods, the results of the experiment show that the proposed method is more accurate for fault detection in the complex system.
ML-augmented Methodology for Fast Thermal Side-channel Emission Analysis

Accurate side-channel attacks can non-invasively or semi-invasively extract secure information from hardware devices using "side- channel" measurements. The thermal profile of an IC is one class of side channel that can be used to exploit the security weaknesses in a design. Measurement of junction temperature from an on-chip thermal sensor or top metal layer temperature using an infrared thermal image of an IC with the package being removed can disclose secret keys of a cryptographic design through correlation power analysis. In order to identify the design vulnerabilities to thermal side channel attacks, design time simulation tools are highly important. However, simulation of thermal side-channel emission is highly complex and computationally intensive due to the scale of simulation vectors required and the multi-physics simulation models involved. Hence, in this paper, we have proposed a fast and comprehensive Machine Learning (ML) augmented thermal simulation methodology for thermal Side-Channel emission Analysis (SCeA). We have developed an innovative tile-based Delta-T Predictor using a data-driven DNN-based thermal solver. The developed tile based Delta-T Predictor temperature is used to perform the thermal side-channel analysis which models the scenario of thermal attacks with the measurement of junction temperature. This method can be 100-1000x faster depending on the size of the chip compared to traditional FEM-based thermal solvers with the same level of accuracy. Furthermore, this simulation allows for the determination of location- dependent wire temperature on the top metal layer to validate the scenario of thermal attack with top metal layer temperature. We have demonstrated the leakage of the encryption key in an 128-bit AES chip using both proposed tile-based temperature calculations and top metal wire temperature calculations, quantified by simulation MTD (Measurements-to-Disclosure).