Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on Machine Learning Against Hardware Trojan Attacks: Recent Advances and Challenges

The remarkable success of machine learning (ML) in a variety of research domains has inspired academic and industrial communities to explore its potential to address hardware Trojan (HT) attacks. While numerous works have been published over the past decade, few survey papers, to the best of our knowledge, have systematically reviewed the achievements and analyzed the remaining challenges in this area. To fill this gap, this article surveys ML-based approaches against HT attacks available in the literature. In particular, we first provide a classification of all possible HT attacks and then review recent developments from four perspectives, i.e., HT detection, design-for-security (DFS), bus security, and secure architecture. Based on the review, we further discuss the lessons learned in and challenges arising from previous studies. Despite current work focusing more on chip-layer HT problems, it is notable that novel HT threats are constantly emerging and have evolved beyond chips and to the component, device, and even behavior layers, therein compromising the security and trustworthiness of the overall hardware ecosystem. Therefore, we divide the HT threats into four layers and propose a hardware Trojan defense (HTD) reference model from the perspective of the overall hardware ecosystem, therein categorizing the security threats and requirements in each layer to provide a guideline for future research in this direction.
Some notes on the binary sequences of length 2n – 1 with the run property

In this paper, we calculate the number of binary sequences of length 2n – 1 that satisfy the run property, called run sequences. We know that only a small portion of those run sequences satisfy the span property. In this paper, in addition, we present some interesting properties of those run sequences with or without the span property.
Theta Synchronization and ERPs in Deception Detection

Although event related potentials (ERPs) have been used for the deception detection, deception-related changes in the electroencephalogram (EEG) remain to be established. The purpose of this research is to test the effect of theta oscillation in information concealing. EEG is recorded when healthy volunteers performed a deceptive task in a card-like version of the Concealed Information Test (CIT) paradigm. Data were analyzed by using event-related synchronization. A significant effect in theta power was recorded. The probe stimulus elicited larger theta synchronization than the irrelevant stimulus in the 200–500 ms post-stimulus data for deceptive participants. This effect was not observed in the control group. The theta synchronization effect overlapped with the classical ERP P300 effect but with marked difference in electrodes. The theta synchronization effect was strongest on the Fz electrode. The P300 effect was strongest on the Pz electrode. Our results indicated that theta synchronization is correlated with concealing information and might reflect some changes in related deception.
A Method to Locate and Recognize LCD Display Screen using FCN

This article proposed a more robust and universal method to locate the no-backlight LCD screen and recognize the number on the screen. It can work well in complex background and uneven illumination. The system uses a semantic segmentation neural network based on residual network to locate and segment the screen area roughly, then use the roughly segmentation and edge information of image to cut the screen area accurately. After these steps, the screen area will be rectification. Finally cut the characters and recognize them using CNN.
Mapping the Research Landscape of Deep Learning from 2001 to 2019

This paper analyzes recent literature to investigate the emerging topics and trends in deep learning research. In this context, a semantic content analysis based on probabilistic topic modeling has been performed on 22,279 journal articles on the subject of deep learning during the last 19 years between 2001 and 2019. As a consequence of this analysis, 18 topics mapping the research landscape of deep learning was revealed. The topic "Classification" has the highest ratio (10.57%). It is followed by the topics "Image feature representing" (8.90%), and "Data applications" (8.71%). In addition, with the purpose of identifying the time-dependent average and acceleration of these topics, the average number of articles for each topic, and the rate of increase in the number of articles for each topic were calculated. In this context, the topics were separated into three groups including "hot topics", "stable topics", and "cold topics". These discovered topics, which map the research landscape of deep learning, are expected to contribute to understanding the present and future of deep learning.
Ensembles of Deep Neural Networks for Action Recognition in Still Images

Despite the fact that notable improvements have been made recently in the field of feature extraction and classification, human action recognition is still challenging, especially in images, in which, unlike videos, there is no motion. Thus, the methods proposed for recognizing human actions in videos cannot be applied to still images. A big challenge in action recognition in still images is the lack of large enough datasets, which is problematic for training deep Convolutional Neural Networks (CNNs) due to the overfitting issue. In this paper, by taking advantage of pre-trained CNNs, we employ the transfer learning technique to tackle the lack of massive labeled action recognition datasets. Furthermore, since the last layer of the CNN has class-specific information, we apply an attention mechanism on the output feature maps of the CNN to extract more discriminative and powerful features for classification of human actions. Moreover, we use eight different pre-trained CNNs in our framework and investigate their performance on Stanford 40 dataset. Finally, we propose using the Ensemble Learning technique to enhance the overall accuracy of action classification by combining the predictions of multiple models. The best setting of our method is able to achieve 93.17% accuracy on the Stanford 40 dataset.
Why Do We Buy Virtual Goods?

This study identified the variables contribute to customer behavior in buying virtual goods. We hypothesized that Buying Intention, Perceived Value, and Flow as a significant predictor in buying virtual goods. Participants are online game players in Indonesia (N = 208). The results showed that there was a significant effect of Buying Intention, Perceived Value, and Flow on Virtual Goods Buying Behavior. The results also showed the proportion of variance in buying behavior explained by all independent variables = 0.612. Attitudes Against Behavior, Subjective Norms, and Perceived Behavioral Control, Visual Authority Value and Perceived Control significantly influence virtual goods buying behavior.
Representation of Words Over Vectors in Recurrent Convolutional Attention Architecture for Sentiment Analysis

Subjectivity in the text concerns to sentiment analysis, which entails Natural language processing (NLP) techniques to detain the casual way of communication. Many NLP undertakings used to capture the sentimental contextual information through distributed representations. Neural networks (NNs) like recurrent and convolutional neural networks have productively attained the remarkable outcomes in text classification. However, few of architectures learning through vector representation of words in the recurrent network for capturing long-term dependencies while in the convolutional for feature extraction along with pooling layer; a max-pooling task for dimensional reduction has been proposed, still, unable to seizure enough syntactic and semantic regularities for sentiment analysis. This paper empowers sentiment analysis by exploring unsupervised learning of vectors representation such as Word2Vec and GloVe, then acquired word vectors is input to the proposed architecture which is composed of RNN and CNN engaging with attention-mechanism referred to Recurrent Convolutional Attention Architecture “RCAA. ” Experimentations show that through unsupervised learning of representation of words into qualified vectors in interpreting the similarities related to syntactic and semantic context and the sentiments at adequate computational cost along with the combination neural architecture by succeeding accuracy on word2vec by 83.62%, GloVe by 85.72% as compared with Random initialization by 79.97% on rotten tomatoes test dataset respectively.
Brand usage detection via Audio Streams

With the constant creation of content on digital media platforms, it is becoming ever more difficult for companies to control the use of their brand. Such use can be found in a review of a competing product, feedback on a news article or merely part of a discussion in a public forum. The reference to the target brand can take the form of a direct textual mention, use of the corporate logo in an image form, or the mentioning of the brand in an audio format. Detection of textual and visual use of brand identity is well researched and considered in an advanced state, yet detection in audio formats is more challenging due to factors such as background noise, voice morphing and heavy accents to name a few. In this initial research, we seek to establish an ideal dataset for further research in this area and an initial review of current solutions for the detection of brand usage in audio streams. Our findings suggest that current solutions do not cater for heavy accents well, which will be the focus of the next stage of this research.
Semantic Segmentation-Based Real-Time Shallow Depth-of-Field Imaging Using a Single Smartphone Camera

Recently, consumer smartphones have various camera functions to satisfy a variety of consumer’s needs. Shallow depth-of-field imaging or shallow focusing technique can provide a photographic shallow focusing function. However, single camera-based economic implementation has limits in quality and accuracy. This paper presents a novel, efficient shallow focusing algorithm for consumer single-camera smartphones. The proposed method consists of three functional steps: i) classification of foreground object and background using semantic segmentation, ii) blurring the background, and iii) composing the segmented object and blurred image. The proposed method can perform shallow focusing without detecting human faces, and produces better performance than existing single-camera smartphones in the sense of subjective measure.
A Low Power and Low Latency Scan Algorithm for Bluetooth Low Energy Radios with Energy Detection Mechanisms

A novel Bluetooth Low Energy advertising scan algorithm is presented for hybrid radios that are additionally capable to measure energy on Bluetooth channels, e.g. as they would need to be compliant with IEEE 802.15. 4. Scanners applying this algorithm can achieve a low latency whilst consuming only a fraction of the power that existing mechanisms can achieve at a similar latency. Furthermore, the power consumption can scale with the incoming network traffic and in contrast to the existing mechanisms, scanners can operate without any frame loss given ideal network conditions. The algorithm does not require any changes to advertisers, hence, stays compatible with existing devices. Performance evaluated via simulation and experiments on real hardware shows a 37 percent lower power consumption compared to the best existing scan setting while even achieving a slightly lower latency which proves that this algorithm can be used to improve the quality of service of connection-less Bluetooth communication or reduce the connection establishment time of connection-oriented communication.
SPECIAL SECTION ON DATA MINING FOR INTERNET OF THINGS

In order to study the model and system design of music dance resource management, a human nervous system-like fog computing architecture is proposed. Based on the Internet of Things, the music dance resource management system is analyzed, and the contribution model of fog computing resources and the allocation model of fog computing resources are established. The traditional NSGA-II (Quick Nondominated Sorting Genetic Algorithms-II) algorithm is improved, and the improved NSGA-II algorithm is obtained to solve the resource allocation problem. Music dance resource management system is designed, implemented and tested. Through simulation and verification, the improved NSGA-II algorithm has better performance in average service delay time and average stability of task execution under different tasks and different fog nodes. In functional testing, the overall situation of music dance resource management system is good, and the design requirements are basically met. In performance testing, the response time is short and the throughput is high. Therefore, the system has high performance, and can meet the large amount of concurrent data access, which makes the user have better experience. INDEX TERMS Fog computing, human nervous system-like, Internet of Things, music dance resource management model, resource allocation model.
Performing Memristor-Aided Logic (MAGIC) using STT-MRAM

Modern computers suffer from a limited data transfer rate between the memory and the processing units. One of the attractive potential solutions to overcome this bottleneck is to combine processing and memory by performing computing in the same location where the data is stored. Processing-in-memory (PIM) has been demonstrated by memristor-aided logic (MAGIC) operations using resistive random access memory (RRAM) memristive devices within crossbar arrays. Nevertheless, RRAM devices are relatively slow and suffer from limited endurance. Spin-transfer torque magnetoresistive random access memory (STT-MRAM) is another memristive technology, which is faster and has practically unlimited endurance and is therefore considered to be an attractive technology for cache-level memories. In this paper, we demonstrate MAGIC operations within an STT-MRAM array by supplying voltages suitable to achieve the current required for MRAM device switching. The proposed circuit was evaluated in SPICE simulations with the GlobalFoundries 22nm CMOS-MRAM process, including Monte Carlo simulations to verify the proposed design in the presence of process variation and device mismatch. The circuit showed more than 90% chance of functioning for the {1 1} and {0 0} input cases while it was about 85% correct for the {0 1} case.
WINNER: a high speed high energy efficient Neural Network implementation for image classification

Nowadays, Neural Networks are often used in many applications to solve very complex tasks like speech recognition or image classification. In applications like image recognition, time is a critical constraint and a Neural Network must be able to correctly classify an image in a very short time: in these cases, hardware accelerators are necessary. In this work, a hardware implementation of a Neural Network is proposed. As a reference model, AlexNet has been chosen and implemented as an ASIC, by adopting an In-Memory computing design. The concept of In-Memory is based on the idea of placing small computational units near the memory element. This choice brings to relevant benefits, reducing the bottlenecks coming from a Von Neumann's system (such as fetching latency and energy inefficiency due to the communication between logic and memory). The architecture has been synthesized on a 45nm CMOS technology and highlights remarkable results, with very high frame-per-second (FPS) (comparable to binary neural network implementations) combined with low values of Energy/FPS and a maximum operating frequency of 281MHz. The system is also partially reconfigurable, enabling the mapping of different kind of neural networks.
A Novel Multi-Functional Plasmonic Logic Device by Angle Manipulation

Optical logic devices are indispensable for optical computing and data processing. In order to acquire high performance optical logic device, a novel plasmonic structure based on plasmon-induced transparency (PIT) effect is proposed. It can implement multiple logic functions simultaneously with high output contrast ratios between “0” and “1” states through a new manipulation method of angle rotation. The optical logic device consists of dual identical hexagonal resonators with rotatable ellipses embedded in resonators and two parallel metal-insulator-metal (MIM) waveguides. The specific rotation angles of ellipses are defined as the input logic states. In addition, according to the transmissions at output ports, the thresholds are set to distinguish the output logic states. Ultimately, four kinds of logic functions: AND, NAND, NOR and NOT can be implemented with fairly high output contrast ratios in this compact device. Most importantly, among these logic functions, AND, NAND, and NOR can be operated simultaneously with wavelength division multiplexing (WDM) technique. Therefore, this work supplies a potential application for optical logic integrated circuits.
Identification of Plasma Current Center by Neural Network Inference in EAST

For the efficient and safe operation of experimental advanced superconducting tokamak (EAST), it is necessary to accurately identify and control the plasma current and its central position. In this article, neural network is used to identify the position of the plasma current center. The model trained by the basic back-propagation neural network can well match the relationship between the electromagnetic diagnostic signals and plasma current center positions. Both noisy simulation data and experimental data are applied to train and test the neural network inference model. Adding 0.1% noise to the training data is proven to improve the noise immunity of the inference model. Basic neural networks trained with both noisy simulation data and actual experimental data show good results with sufficient inputs; however, in both cases, the performance degrades significantly when only the poloidal field coil currents are given as inputs. For this kind of time-series problem, the dynamic neural network containing delay and feedback architecture is introduced, and an improved model requiring much fewer inputs is trained and tested for current center inference. Some parameters of this model are compared and analyzed in this article. With suitable neural network architecture, the mapping between the controlled variables (poloidal field coil currents) and response variables (plasma current center) can be well-established.
Hybrid Linear Precoding Strategy for Multiuser Massive MIMO Millimeter Wave Systems

Multiuser-MIMO Millimeter wave based communication system has been considered as a current area of research for the future communication systems to serve multi-user simultaneously with a multiple data streams. Hybrid analog/digital architecture is the most satisfied architecture of the mmWave MIMO communication objectives and can be applied at both the base and mobile stations. Thus, exploiting the hybrid pre-coding/combining techniques offered by this architecture allow the realization of the multi-stream/multi-user transmission. We propose in this paper, a low computational complexity linear hybrid precoding schemes based on classical linear precoding and SVD decompostion of each channel user to avoid information exchange by the feedback where each user is powered by hybrid architecture. The simulation results demonstrate that our proposed two-stage hybrid linear precoding schemes can achieve a higher spectral efficiency compared with existing methods where each user is equipped with a single combiner and confirms its better management of the multi-user interference.
Automatic identification and analysis of epileptic seizure

Epilepsy is a common neurological disease that is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals are widely used to diagnose epilepsy. This paper presents an automated diagnostic method for epileptic detection using gaussian mixture models based statistical time domain features. The statistical time domain namely root mean square (RMS), Sekwens (Sk), kurtosis (Ku), crest factor (CF) and clearance indicator (CLI) parameters are extracted. These features are used to train gaussian mixture models (GMM) for classification of EEG signals into three classes normal, pre-ictal and ictal. The GMM classifier is able to discriminate the three types of EEG signals and achieved better results compared to other existing techniques with an average accuracy of 98.75%. The proposed work can be used for automatic diagnosis of seizure onsets which would help the patients to take appropriate precautions.
On Driver-Vehicle-Environment Integration for Multi-Actuated Ground Vehicles Safety Advancement: An Overview of the Interdisciplinary Training Network in Multi-Actuated Ground Vehicles

Transportation systems are invariably burdened with dynamically changing environmental conditions and ill-defined human factor. To raise ground vehicle safety on a new supreme level and to boost autonomous vehicles development driver-vehicle-environment cooperation is inevitable. In this paper, an overview of several existing driver-vehicle-environment integration methods with purpose of vehicle safety enhancement are stressed. Five unique and fundamentally different solutions are proposed, which have common similarity: the solutions are accomplished with machine learning algorithms. The methods aim at modelling drivers' or vehicles' behaviour with reasonable prediction accuracy under various complex scenarios. All five solutions are developed in individual projects in a framework of a continuous interdisciplinary European network ITEAM. The aim of the paper is to underline significant benefit of man-machine-environment integration in vehicle safety systems by exploiting fairly received tremendous attention machine learning methods.
Ambulatory blood pressure predicts end-organ damage only in subjects with reproducible recordings

Objective To determine whether the prediction of target-organ damage varies according to the reproducibility of 24 h blood pressure. Setting Seventeen hypertension clinics in northeast Italy. Main outcome measures Correlations of left ventricular mass index and albumin excretion rate with 24 h and office blood pressures in relation to tertiles of ambulatory blood pressure reproducibility. Patients and methods In 716 consecutive, stage I, hypertensives enrolled in the Hypertension and Ambulatory Recording Venetia Study (HARVEST), ambulatory blood pressure monitoring was performed twice, 3 months apart. In all subjects, the albumin excretion rate was measured by radioimmunoassay, and in 567, the left ventricular mass index was assessed by echocardiography. Results The subjects were divided into tertiles of ambulatory blood pressure consistency (between-monitoring differences, regardless of the sign). In the tertile of subjects with good reproducibility, correlation coefficients of systolic and diastolic ambulatory blood pressure with left ventricular mass and urinary albumin excretion were significant and higher than those of office blood pressure. In contrast, in the two tertiles with poorer reproducibility, the coefficients were barely or not significant for both pressures. The advantage of ambulatory blood pressure over office blood pressure in predicting target-organ damage was no longer present for systolic blood pressure differences greater than 3.8 mmHg and diastolic blood pressure differences greater than 3.1 mmHg. Conclusions These data indicate that ambulatory blood pressure is a better predictor of left ventricular mass and urinary albumin excretion than office blood pressure, but only in subjects with good pressure reproducibility. Therefore, the assessment of hypertensive patients should be based on duplicate blood pressure monitorings. Recordings with 24 h systolic and diastolic blood presssure differences greater than 4 and 3 mmHg, respectively, should be considered with caution.