Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual Range and High Data-Rate Intrabody Communication Transceiver based on Piezoelectric Micromachined Ultrasonic Transducers

In this paper, we demonstrate the implementation of a dual range (short $\mathrm{d}_{\mathrm{S}}=3.5\text{cm}$ and long $\mathrm{d}_{\mathrm{L}}=13.5\text{cm}$ distance application) and high bandwidth, high data-rate transceiver ($\text{BW}\approx 200\text{kHz}$ and $\text{Data}-\text{Rate}\approx 400\text{kbits}/\mathrm{s}$) for intrabody communication links based on Piezoelectric Micromachined Ultrasonic Transducers (pMUTs). The transceiver consists of a Quadrature Phase-Shift Keying (QPSK) modulation and demodulation scheme implemented in a Universal Software Defined Radio (USRP). The intrabody antennas consist of a $10\times 10$ uni-morph pMUT array based on Aluminum Nitride (AlN) with circular shape design and resonance frequency $\mathrm{f}\approx 700\text{kHz}$. The arrays are embedded in a tissue phantom to mimic human tissue properties and are coupled with ultrasound gel to avoid air gaps. The system was tested by serializing a $100\times 50\text{pixels}$ image in a bit stream and transmitting over the intrabody link. The sampling rate was set at twice the bandwidth of the pMUTs based on the Nyquist theorem. The detected Bit Error Rate (BER) was 1E-4 and 1E-1 for short and long range respectively, demonstrating the functionality of the pMUT intrabody transceiver. These levels of BER allow for perfect reconstruction of the original data by time-averaging successive frames.
Inconsistencies in the Diagnosis of Aggressive Posterior Retinopathy of Prematurity

Purpose: To determine the accuracy and reliability of diagnosing aggressive posterior retinopathy of prematurity (AP-ROP). Methods: A total of 1220 eye examinations from 230 infants were prospectively obtained at 8 major ROP centers. An ophthalmologist at each center provided a clinical diagnosis using indirect ophthalmoscopy. Wide-angle retinal images were then obtained, which were independently read by 2 ROP experts using a web-based system for an image-based diagnosis. Sensitivity and specificity of image-based AP-ROP diagnosis by the ROP experts were calculated using the clinical diagnosis as the reference standard. Agreement of AP-ROP diagnosis through image-based diagnosis and clinical diagnosis was calculated using the unweighted κ statistic. Results: One hundred four (9%) of the 1220 examinations had a clinical diagnosis of AP-ROP. Sensitivity and specificity for the presence of AP-ROP were 35% and 96% for expert 1 and 17% and 99% for expert 2. Using the κ statistic, expert image-based versus clinical diagnostic agreement for the diagnosis of AP-ROP was 0.34 (fair) for expert 1 and 0.24 (fair) for expert 2. Agreement for the diagnosis of AP-ROP between the image-based diagnoses of expert 1 and expert 2 was 0.49 (moderate). Conclusion: There are inconsistencies between the clinical diagnosis of AP-ROP (as determined by indirect ophthalmoscopy) and the image-based diagnosis of AP-ROP. This may have important implications for ROP management and the current international ROP classification system.
Breaking the Performance Tradeoffs in N-Path Mixer-First Receivers Using a Second-Order Baseband Noise-Canceling TIA

Input-match, noise figure (NF), in-band (IB) third-order input-referred intercept point (IIP3), out-of-band (OB) IIP3, and power consumption are five crucial performance metrics of a radio frequency (RF) receiver. In a typical passive mixer-first receiver, these five performance metrics are tightly coupled with each other. In this article, a second-order baseband noise-canceling (NC) scheme is proposed to break the performance tradeoffs in an N-path mixer-first receiver. Analytical expressions for input impedance, NF, IB-IIP3, and OB-IIP3 of the proposed NC receiver are derived and verified. A prototype mixer-first receiver is implemented in CMOS 180 nm technology and tested in the laboratory. In measurements, the receiver achieves an NF less than 4 dB and IB-IIP3 of +14.5 dBm over a frequency range of 0.2–1.2 GHz. The measured OB-IIP3 is +25 dBm at a frequency offset of 2.2 times the bandwidth while consuming a static power of 20 mW.
Design of Large-Scale Structures by an enhanced Metaheuristic utilizing Opposition-based Learning

Structural sizing problems are treated as rewarding optimization tasks characterized by non-differentiable constraints and objective functions. In this category, a number of new large-scale space trusses are offered here as structural benchmarks to declare effectiveness and efficiency of zero-order optimization methods. Furthermore, an optimization method is offered for such a task utilizing opposition-based learning that switches between certain vectors directed to the best individual. The proposed algorithm is developed with simple yet efficient operators as a specific-parameter-Iess method. Although it does not bring about high tuning effort for control parameters, numerical simulations exhibit competitive performance of the proposed method in structural sizing problems compared with recent meta-heuristic algorithms.
JCMathLib: Wrapper Cryptographic Library for Transparent and Certifiable JavaCard Applets

The JavaCard multi-application platform is now deployed to over twenty billion smartcards, used in various applications ranging from banking payments and authentication tokens to SIM cards and electronic documents. In most of those use cases, access to various cryptographic primitives is required. The standard JavaCard API provides a basic level of access to such functionality (e.g., RSA encryption) but does not expose low-level cryptographic primitives (e.g., elliptic curve operations) and essential data types (e.g., Integers). Developers can access such features only through proprietary, manufacturer-specific APIs. Unfortunately, such APIs significantly reduce the interoperability and certification transparency of the software produced as they require non-disclosure agreements (NDA) that prohibit public sharing of the applet’s source code.We introduce JCMathLib, an open library that provides an intermediate layer realizing essential data types and low-level cryptographic primitives from high-level operations. To achieve this, we introduce a series of optimization techniques for resource-constrained platforms that make optimal use of the underlying hardware, while having a small memory footprint. To the best of our knowledge, it is the first generic library for low-level cryptographic operations in JavaCards that does not rely on a proprietary API.Without any disclosure limitations, JCMathLib has the potential to increase transparency by enabling open code sharing, release of research prototypes, and public code audits. Moreover, JCMathLib can help resolve the conflict between strict open-source licenses such as GPL and proprietary APIs available only under an NDA. This is of particular importance due to the introduction of JavaCard API v3.1, which targets specifically IoT devices, where open-source development might be more common than in the relatively closed world of government-issued electronic documents.
FaultSee: Reproducible Fault Injection in Distributed Systems

Distributed systems are increasingly important in modern society, often operating on a global scale with stringent dependability requirements. Despite the vast amount of research and the development of techniques to build dependable systems, faults are inevitable as one can witness from regular failures of major providers of IT services. It is therefore fundamental to evaluate distributed systems under different fault patterns and adversarial conditions to assess their high-level behaviour and minimize the occurrence of failures. However, succinctly capturing the system configuration, environment, fault patterns and other variables affecting an experiment is very hard, leading to a reproducibility crisis. In this paper we propose the FaultSee toolkit. The two components of FaultSee are (1) the simple and descriptive FDSL language that captures the system, environment, workload and fault pattern characteristics; and (2) an easy-to-use platform to deploy and run the experiments described by the language. FaultSee allows to precisely describe and reproduce experiments and leads to a better assessment the impact of faults in distributed systems. We showcase the key features of FaultSee by studying the impact of faults with real deployments of Apache Cassandra and BFT-Smart.
Modeling of Buck Converter with 3D Air-Core Inductor

A high-efficiency and high-power density buck converter is presented in this work with in-package 3D air-core inductor (150 µm thick). To optimize the system efficiency at targeted power density, an analytical model for both the 3D inductor and power switches is developed. Compared to 3D EM simulation, the proposed inductor model has maximum modeling error of 12% and 25% for the inductance and resistance calculations respectively. With the proposed design methodology, an inductor Q factor up to 35 @ 300 MHz is achieved. 1/2-ratio buck converter can reach 90% efficiency @ 1–5W/mm2 based on three optimized inductor designs, with VOUT ranging from 0.7 V to 0.9 V and based on 28 nm CMOS.
EnsVAE: Ensemble Variational Autoencoders for Recommendations

Recommender systems are information software that retrieves relevant items for users from massive sources of data. The variational autoencoder (VAE) has proven to be a promising approach for recommendation systems, as it can explore high-level user-item relations and extract contingencies from the input effectively. However, the previous variants of VAE have so far seen limited application to domain-specific recommendations that require additional side information. Hence, The Ensemble Variational Autoencoder framework for recommendations (EnsVAE) is proposed. This architecture specifies a procedure to transform sub-recommenders’ predicted utility matrix into interest probabilities that allow the VAE to represent the variation in their aggregation. To evaluate the performance of EnsVAE, an instance — called the “Ensemblist GRU/GLOVE model” — is developed. It is based on two innovative recommender systems: 1-) a new “GloVe content-based filtering recommender” (GloVe-CBF) that exploits the strengths of embedding-based representations and stacking ensemble learning techniques to extract features from the item-based side information. 2-) a variant of neural collaborative filtering recommender, named “Gate Recurrent Unit-based Matrix Factorization recommender” (GRU-MF). It models a high level of non-linearities and exhibits interactions between users and items in latent embeddings, reducing user biases towards items that are rated frequently by users. The developed instance speeds up the reconstruction of the utility matrix with increased accuracy. Additionally, it can switch between one of its sub-recommenders according to the context of their use. Our findings reveal that EnsVAE instances retain as much information as possible during the reconstruction of the utility matrix. Furthermore, the trained VAE’s generative trait tackles the cold-start problem by accurately estimating the interest probabilities of newly-introduced users and resources. The empirical study on real-world datasets proves that EnsVAE significantly outperforms the state-of-the-art methods in terms of recommendation performances.
Discovering HTTPSified Phishing Websites Using the TLS Certificates Footprints

With the recent rise of HTTPS adoption on the Web, attackers have begun "HTTPSifying" phishing websites. HTTPSifying a phishing website has the advantage of making the website appear legitimate and evading conventional detection methods that leverage URLs or web contents in the network. Further, adopting HTTPS could also contribute to generating intrinsic footprints and provide defenders with a great opportunity to monitor and detect websites, including phishing sites, as they would need to obtain a public-key certificate issued for the preparation of the websites. The potential benefits of certificate-based detection include (1) the comprehensive monitoring of all HTTPSified websites by using certificates immediately after their issuance, even if the attacker utilizes dynamic DNS (DDNS) or hosting services; this could be overlooked with the conventional domain-registration-based approaches; and (2) to detect phishing websites before they are published on the Internet. Accordingly, we address the following research question: How can we make use of the footprints of TLS certificates to defend against phishing attacks? For this, we collected a large set of TLS certificates corresponding to phishing websites from Certificate Transparency (CT) logs and extensively analyzed these TLS certificates. We demonstrated that a template of common names, which are equivalent to the fully qualified domain names, obtained through the clustering analysis of the certificates can be used for the following promising applications: (1) The discovery of previously unknown phishing websites with low false positives and (2) understanding the infrastructure used to generate the phishing websites. We use our findings on the abuse of free certificate authorities (CAs) for operating HTTPSified phishing websites to discuss possible solutions against such abuse and provide a recommendation to the CAs.
Design of a Wideband Surface Mountable Suspended Integrated Strip-Line Technology

This paper presents a low-loss surface mountable suspended integrated strip-line (SISL) technology. A DC-20 GHz thru transmission line and an eleventh-order generalized Chebyshev lowpass filter (LPF), with a cutoff frequency of 18 GHz, are simulated, fabricated, and measured to demonstrate the first-ever surface mountable suspended strip-line designs. Measured results of the thru line show less than 1 dB of insertion loss and greater than 10 dB of return loss across the 20 GHz bandwidth. Moreover, measured results of the LPF show less than 1 dB of insertion loss and greater than 10 dB return loss across the 18 GHz passband, as well as greater than 30 dB of suppression above 19 GHz. The proposed SMT SISL technology also has advantages of compact size, light-weight, low cost, and elimination of the extended circuit to be a multi-layer board following the trend to reduce the size, weight, power, and cost (SWaP-C).
Variable Step-Size Widely Linear Complex-Valued Affine Projection Algorithm and Performance Analysis

In this paper, a variable step-size widely linear complex-valued affine projection algorithm (VSS-WLCAPA) is proposed for processing noncircular signals. The variable step-size (VSS) is derived by minimizing the power of the augmented noise-free a posteriori error vector, which speeds up the convergence and reduces the steady-state misalignment. By exploiting the evolution of the covariance matrix of the weight error vector, we provide insight into the theoretical behavior of the VSS-WLCAPA algorithm. In the analysis, we take into account the dependency between the weight error vector and the noise vector, which is useful for accuracy of the theoretical prediction. To evaluate the mean step-size, the probability density function of the magnitude of the error is derived by employing polar coordinate transformation. Moreover, a special case when the projection order reduces to one is analysed in detail. The presented theoretical analysis is different from existing methodologies for analyzing affine projection algorithms due to the use of the Kronecker product. Simulation results for system identification scenarios demonstrate the merits of the proposed algorithm and verify the accuracy of the theoretical analysis. Wind prediction experiments support the superiority of the proposed VSS-WLCAPA as well.
Maize hybrids and varieties for seed production in planting double rows

1Campo Experimental Valle de Mexico. Metepec-CEVAMEX-INIFAP. Carretera Toluca a Zitacuaro, km 4.5. Colonia San Jose Barbabosa. C. P. 51350, Zinacantepec, Estado de Mexico, Mexico. Tel. 01 722 278 43 31. (bzamudiog@yahoo.com.mx; aturrent37@yahoo.com.mx). 2Facultad de Estudios Superiores Cuautitlan. UNAM. Carretera Cuautitlan-Teoloyucan, km 2.5. Cuautitlan Izcalli, Estado de Mexico. C. P. 54714. A. P. 25. Tel. 01 55 56231971. §Autor para correspondencia: espinoale@ yahoo.com. mx.
Intelligent state evaluation method of marine engine room system

Based on system state classification and recognition, an intelligent state evaluation method was proposed for the intelligent maintenance and management in marine engine room. The method can be adopted to realize the condition-based maintenance and improve the efficiency and safety of ship operation. First, the radial basis function neural network was used to establish the state identification model of the marine engine room system to realize the state classification and identification of the system; Second, the recognized state was evaluated comprehensively by using the fuzzy analytical hierarchy process method; Finally, the validity of the results of the state identification and the fuzzy comprehensive evaluation was verified based on an actual ship system data. With the support of the system state data, the evaluation result shows a good match with the actual system state and has a high degree of credibility. On the premise of convenient data collection, the proposed method can further conduct a large-scale system state evaluation for multiple marine engine room systems. The process of the method proposed provides a new idea for the application of intelligent technology in the field of ships.
A Stochastic Framework for the Time Kinetics of Interface and Bulk Oxide Traps for BTI, SILC, and TDDB in MOSFETs

A stochastic reaction–diffusion drift model is used to simulate the time kinetics of interface and bulk oxide traps responsible for bias temperature instability (BTI), stress-induced leakage current (SILC), and time-dependent dielectric breakdown (TDDB) in MOSFETs. Trap generation and passivation are calculated using dissociation and repassivation of trap precursors and simultaneous diffusion and/or drift of atomic, molecular, and/or ionic species. The average of multiple stochastic simulations is used to qualitatively explain the measured BTI and SILC data. The difference in BTI and SILC time kinetics, variation in SILC time kinetics across reports, and oxide thickness dependence of TDDB Weibull slope variation are also qualitatively explained.
SOPNet Method for the Fine-Grained Measurement and Prediction of Precipitation Intensity Using Outdoor Surveillance Cameras

Surveillance cameras have been widely used in urban environments and are increasingly used in rural ones. Such cameras have mostly been used for security, but they can be applied to the problem of furnishing fine-grained measurements and predictions of precipitation intensity. In this study, we formulated a stacked order-preserving (OP) learning framework to train a network using time-series data. We constructed an OP module, which uses a three-dimensional (3D) convolution operation to extract features with spatial and temporal information and that are associated with ConvLSTM; this feature extraction is used to learn the short-term and OP time-series relationships between features. Furthermore, the OP modules are stacked to form a stacked OP network (SOPNet), which strengthens the relationship between features in long-term time-series image sequences. This SOPNet can be use to obtain fine-grained measurements and predictions of precipitation intensity from images captured by outdoor surveillance cameras. Our main contributions are threefold. First, the SOPNet strengthens the short-term and long-term time-series relationship between features. Second, the SOPNet simultaneously examines spatial and temporal information to measure and predict precipitation intensity. Third, we constructed a precipitation intensity database based on optical images captured by outdoor surveillance cameras. We experimentally evaluated our proposed architecture using our self-collected data set. We found that SOPNet yields better performance and greater accuracy relative to its well-known state-of-the-art counterparts with respect to various metrics.
Growth and Commoditization of Remote Access Trojans

In the last three decades there have been significant changes in the cybercrime world in terms of organization, type of attacks, and tools. Remote Access Trojans (RAT) are an intrinsic part of traditional cybercriminal activities but they have become a standard tool in advanced espionage and scams attacks. The overly specialized research in our community on Remote Access Trojans has resulted in a seemingly lack of general perspective and understanding on how RATs have evolved as a phenomenon. This work presents a new generalist perspective on Remote Access Trojans, an analysis of their growth in the last 30 years, and a discussion on how they have become a commodity in the last decade. We found that the amount of RATs increased drastically in the last ten years and that nowadays they have become standardized commodity products that are no very different from each other.
Shape Estimation Algorithm for Ultrasound Imaging by Flexible Array Transducer

A flexible ultrasonic array transducer able to be attached to the body has the potential to achieve long-term continuous unconstrained ultrasound (US) imaging. However, the quality of reconstructed US images is affected by the accuracy of the estimated array shape because the array shape is primitive for time-delay calculation in delay-and-sum beamforming. In this study, we propose an algorithm for estimating the array shape only from the backscattered US signal without using any external device. The proposed algorithm is based on the assumption that beam-summed images reconstructed using an array shape estimated at higher accuracy would have smaller entropy. The array shape is estimated by searching for the shape with minimal entropy, which was used as the index of the beam-summed image quality. Simulation experiments and phantom experiments were used to evaluate the proposed algorithm. In the simulation experiments, three different array shapes with 2.0 MHz, 20 elements, and 0.8-mm pitch transducers were estimated. In the phantom experiments, the array shapes of commercially available linear, convex, and concave transducers were estimated. The results showed that the proposed algorithm can estimate the correct array shape with an average element position error of less than one-eighth of the wavelength of the transmitted signals. These results indicate that the proposed algorithm can achieve sufficiently accurate shape estimation and has the potential to enable clear US imaging with flexible array transducers.
Attention Mechanism in Predictive Business Process Monitoring

Business process monitoring techniques have been investigated in depth over the last decade to enable organizations to deliver process insight. Recently, a new stream of work in predictive business process monitoring leveraged deep learning techniques to unlock the potential business value locked in process execution event logs. These works use Recurrent Neural Networks, such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), and suffer from misinformation and accuracy as they use the last hidden state (as the context vector) for the purpose of predicting the next event. On the other hand, in operational processes, traces may be very long, which makes the above methods inappropriate for analyzing them. In addition, in predicting the next events in a running case, some of the previous events should be given a higher priority. To address these shortcomings, in this paper, we present a novel approach inspired by the notion of attention mechanism, utilized in Natural Language Processing and, particularly, in Neural Machine Translation. Our proposed approach uses all hidden states to accurately predict future behavior and the outcome of individual activities. Experimental evaluation of real-world event logs revealed that the use of attention mechanisms in the proposed approach leads to a more accurate prediction.