Miracle-dz/UniFAR-FTUs huggingface dataset:
A Peak Detect & Hold circuit to measure and exploit RTN in a 65-nm CMOS PUF

A Physical Unclonable Function (PUF) that uses the Random Telegraph Noise (RTN) effect has been recently proposed. This PUF requires an analog sensing component whose design becomes critical to properly process the underlying entropy and thus provide a response to a given challenge. This analog sensing component needs to keep track of the RTN fluctuations through the detection and acquisition of the maximum and minimum excursions of a signal of interest that contains all the RTN information in a transistor. This paper describes the Peak Detect and Hold circuit that carries out that tracking, which has been designed with accuracy, reliability and fast dynamic response in mind. The proposed implementation has been designed in a 65-nm CMOS technology with a supply voltage of 1.2 V.
A Real-Time Text Analysis System

We live in an age of information overload. Manual information processing is increasingly overwhelmed with the enormous amount of information created by the explosive growth of news portals and online social networks. Such a situation calls for an automatic system that can support the process of handling, analyzing, and filtering information, especially information from online sources. In this work, we proposed a text analysis system that automatically collects, extracts, and analyses information from public-source-text documents such as news portals and social media networks. The proposed system can handle both long and short-text documents. It also has real-time features and is not restricted by any input data domain. The system can be used in different domains, such as scientific research, marketing, and security-related domains. Moreover, the system is engineered in modules and is flexible. Each module is an independent micro-service that can be used as a separate standalone application. The system is also extensible since new modules can be added easily. Index Terms—text analysis system, data mining, natural language processing
Machine Learning Aided Classification of Noise Distribution in Scanning Electron Microscopy Images

Noise estimation is a crucial part of any modern supervised denoiser. Various statistical approaches are studied to estimate the noise, but generally these depend on a manual analysis of the images. To remove the manual dependency, it is important to automate the noise estimation process. In this paper, the initial phase of noise estimation, which is to identify the types of noise distribution, is performed using machine learning (ML) techniques. To make the images workable with ML techniques, a feature extraction process was performed. Hu's moment invariants, Haralick's texture, and color histogram are extracted from the images and stacked horizontally by scaling with MinMax scale the three features into one. Label encoder is used for normalizing the labels. Multiple ML techniques are trained and validated, and then tested with unknown images. The result is that stacking multiple ML techniques can produce better results with an accuracy above 90%. Stacking with the test set produces the following scores for Precision, Recall, and F1-score: 0.98, 0.88, 0.93, and 0.89, 0.98, 0.93 for Gaussian and Poisson respectively, with an average precision of 88%. These promising results prove the capability of ML techniques for image noise classification tasks where noise is artificially added. However, in real case, i.e. when the images come from a Zeiss Auriga FE SEM, which is the initial target, the classification is not as efficient. Thus, it is not always possible to work in a real denoising scenario if the model is trained with synthetic data.
Detection of retinal microlesions through YOLOR-CSP architecture and image slicing with the SAHI algorithm

Diabetic retinopathy affects millions of working-age people worldwide. Of the countries in Latin America, Brazil has the highest incidence of cases. Diabetic retinopathy is detected through images of the fundus that contain lesions such as hard exudates, soft exudates, microaneurysms, and hemorrhages. Early identification of these lesions prevents the progression of the disease, which leads to a decrease in visual capacity. In addition, the early identification of these lesions allows the screening of patients who need priority care. The detection of these lesions occurs through the processing and analysis of fundus images using deep learning models. In this work, we present a new method that uses the You Only Learn One Representation with Cross Stage Partial Network (YOLOR-CSP) architecture combined with the Slicing Aided Hyper Inference (SAHI) framework to detect lesions. The proposed method was trained, adjusted, and evaluated using the Dataset for Diabetic Retinopathy (DDR) and the Indian Diabetic Retinopathy Image Dataset (IDRiD). The proposed method obtained in the data set DDR mAP equal to 38.08%, in the validation set, and 22.25% in the test set with SGD optimizer. The presented results were superior in the detection of eye fundus lesions in comparison with similar works found in the state-of-the-art literature.
Early Inner Race Fault Detection On A Ball Bearing Setup Using Histogram of Oriented Gradients and Wavelet Subselection

Predictive maintenance is an industrial practice to detect component failure ahead in time and before major damage is done to the system. Bearings are susceptible to such damage phenomena and should be replaced before critical failure, therefore early detection proves important. For wide applicability, these detection methods should work on easily available sensor data and have limited computational complexity. This study presents a method for classifying the health of a bearing based on the machines vibrational sensor data and with additional focus on computational complexity. The process involves converting the signals to the frequency spectrum using continuous wavelet transforms, and identifying specific frequency ranges associated with damage phenomena in bearings. Two approaches were used: analyzing the wavelet responses and creating a scalogram image to locate relevant areas. The results obtained on a bearing monitoring data set, created within the Flanders AI Research program, were consistent for both approaches and identified a specific set of scales that resulted in reduced computational load whilst attaining high failure detection rates. Consolidation is achieved by repeating the procedure on two public data sets.
Multi-level Interaction Network for Multi-Modal Rumor Detection

The rapid development of social platforms has intensified the creation and spread of rumors. Hence, automatic Rumor Detection (RD) is an important and urgent task to maintain public interests and social harmony. As one of the frontier subtasks in RD, Multi-Modal Rumor Detection (MMRD) has become a new research hotspot currently. Previous methods focused on inferring clues from media content, ignoring the rich knowledge contained in texts and images. Moreover, existing methods are limited to cascade operators to encode multi-modal relationships, which cannot reflect the interactions between multiple modalities. In this paper, we propose a novel Multi-level Interaction Network (MIN), which regards entities and their relevant external knowledge as priori knowledge to provide additional features. Meanwhile, in MIN, we design a Co-Attention Network (CAN) to implement three-level interactions (i.e., the interaction between entities and image, text and external knowledge, refined text and refined image) for multi-modal fusion. Experimental results on the three public datasets (i.e., Fakeddit, Pheme and Weibo) demonstrate that our MIN model outperforms the state-of-the-arts.
Effect of sintering temperature and Cu-rich secondary phase on dielectric properties of microwave processed CaCu3Ti4O12 ceramics

ABSTRACT Effect of sintering at different temperature on dielectric properties of microwave processed CaCu3Ti4O12 (CCTO) ceramics have been investigated over the frequency range from 100 Hz to 1 MHz at room temperature. Density (ρ) as well as dielectric constant (ϵr) increased with the sintering temperature up to 1050°C, beyond that “ρ” and ‘ϵr’ both decreased. At 1 KHz, maximum value of dielectric constant (ϵr) achieved ∼13,000 with a loss (D) ∼ 0.1. Very weak temperature dependence was found in dielectric constant bellow 150°C. Change in dielectric constant can be explained on the basis of IBLC model in presence of secondary CuO phase.
Towards Functional Analysis of IoT Malware Using Function Call Sequence Graphs and Clustering

The number of IoT malware variants infecting vulnerable IoT devices is growing. Many of these are created by modifying parts of publicly available source codes and adding, changing, or deleting functions. In a previous study, we defined a Function Call Sequence Graph (FCSG) as a directed graph representing the sequence of functions that a specimen calls. Then, we found the common functionality among specimens of malware variants. In this paper, we associate this method with IoT malware clustering outcomes, extracting representative subgraphs of FCSGs extracted from the elements of each cluster. We call each of the subgraphs as a representative FCSG (R-FCSG). We calculate the similarity between R-FCSGs to identify clusters exhibiting similar or identical functionality across CPU architectures. We also studied a method to resolve the family name of clusters by focusing on the similarity between R-FCSGs with no determined family name and R-FCSGs with family names.
In-Memory Versus On-Disk Databases: Best Practices, Use Cases and Architectural Designs

The performance of an information system is first and foremost determined by the database architecture's design. An unresponsive software application or an increased latency in transaction duration often indicates an incongruous compatibility between the implemented persistence mechanisms and the business features. This paper highlights the differences between in-memory and on-disk databases, starting with the memory types used by each, and their functionality differences. The research continues with the execution of performance tests on two different databases, conducted with the assistance of specific benchmarking tools for each type of tested technology. The investigation continues with a follow up analysis on the previous results, guided by performance indicators, that underlines the importance of choosing the most suitable solution imposed by the business requirements. A dedicated section is focused on the best practices that should be considered when designing a new database or refactoring a legacy architecture, in order to obtain a performant persistence mechanism.
Performance Evaluation of Optimum Number of Stages for ADPLL Ring Oscillator

An all-digital phase-locked loop (ADPLL) has been demanded among academics and industries due to its advantages in the complementary-metal-oxide semiconductor (CMOS) technology process. In the ADPLL, one of the crucial blocks is the digital-controlled oscillator (DCO), which is a combination of the digital-to-analog converter (DAC) and the voltage-controlled oscillator (VCO). In this work, the approach of the CMOS inverter ring oscillator is designed using Cadence OrCad Capture with CMOS 90 nm technology process. The ring oscillator is designed with different stages, which are 3-stage, 5-stage, and 7-stage configurations. At the output of every stage, a 0.001pF capacitor is connected as a capacitance load. The result shows that the oscillation period started for the 7-stage is 0.1 ns, which is faster than the 5-stage and the 3-stage of the ring oscillator, which are 0.6 ns and 0.15, ns respectively. For the power consumption performance, the 3-stage recorded $52.47 \mu \mathrm{W}$ which is the lowest power dissipated compared to others.
A Proposal for a Continuum-aware Programming Model: From Workflows to Services Autonomously Interacting in the Compute Continuum

This paper proposes a continuum-aware programming model enabling the execution of application workflows across the compute continuum: cloud, fog and edge resources. It simplifies the management of heterogeneous nodes while alleviating the burden of programmers and unleashing innovation. This model optimizes the continuum through advanced development experiences by transforming workflows into autonomous service collaborations. It reduces complexity in positioning/interconnecting services across the continuum. A meta-model introduces high-level workflow descriptions as service networks with defined contracts and quality of service, thus enabling the deployment/management of workflows as first-class entities. It also provides automation based on policies, monitoring and heuristics. Tailored mechanisms orchestrate/manage services across the continuum, optimizing performance, cost, data protection and sustainability while managing risks. This model facilitates incremental development with visibility of design impacts and seamless evolution of applications and infrastructures. In this work, we explore this new computing paradigm showing how it can trigger the development of a new generation of tools to support the compute continuum progress.
IoE simulation with Cisco Packet Tracer

This work aims to simulate several practical Internet of Everything (IoE) scenarios that one can experience in real world applications. The network components are based on IoE sensors. All devices are interconnected in the network architecture, and a backend intelligence executes logic and data analysis based on the gathered sensor data. This work uses a variety of Cisco components to provide a completely functional network (routers, switches, servers, etc). Also, there are illustrations of IoE smart devices that are already interconnected in the simulation. To provide setup examples for more complex applications, the programming of these sensors and the backend logic are both provided.
Leveraging Node Attributes for Link Prediction via Meta-path Based Proximity

Link prediction is an important task of graph data mining that facilitates a series of applications including recommendation system, network reconstruction, etc. Node attributes make it possible to predict potential links between isolate or unobserved nodes. That attracts more and more attention of academia and industry on leveraging node attributes for link prediction. Numerous link prediction methods are based on attributed network embedding, but they are not specially designed for link prediction and may suffer potential loss of accuracy due to reduction of dimension. In this paper, we propose a direct and efficient way to utilize path-based proximity for link prediction. In many works in the past, node attributes were regarded as a different property of networks compared to heterogeneity. This paper creatively treats attributes as a distinct type of nodes and extends attributed networks to heterogeneous networks. It proposes a framework based on transition probability of meta-path guided random walks to get attributes and structure incorporated proximity of nodes. Link prediction experiments on six real-world datasets were conducted by three types of baselines methods. The experimental results demonstrates that the proposed method has exceptional performance in terms of both robustness and accuracy on most datasets.
Optimizing Data Training Quantity for Bearing Condition Monitoring

Prognostics Health Management (PHM) in manu-facturing seeks to reduce the amount of unexpected downtime that inhibits manufacturing competitiveness. However, a common challenge for the manufacturing industry is the lack of known failure data to train a predictive classifier. This work optimizes the necessary quantity of required failure training data and healthy data for three different exemplar datasets by assessing classifier performance. Particle swarm optimization with penalty factors associated with the training data amount were used to identify the required training data amount for fault classification. Two separate analysis cases are considered: a binary classification and multi-class classification case termed the progressive case. In both analysis cases, the optimal training data depended on how separable the bearing data were between the different baseline and defect stages. In those instances where the differences in the data classes were apparent, the bearing data optimal training data amount was lower than in those instances where the data class differences were not present. Future work focuses on the investigation of these overlap cases to determine the best means for classifying progressive damage for remaining useful life calculations.
Deep Learning for Attack Detection in Industrial IoT Edge Devices

In recent times, system controllers are being fully deployed via the Industrial Internet of Things (IIoT), which significantly enhances the economy and manufacturing industry. Digital security concerns are also brought on by this evolution unfortunately. Due to the fact that a large portion of the value of IIoT systems are located at the edge level, attackers may find these as attractive targets. So, it is crucial to monitor edge system components and spot harmful activity using an effective diagnostic model in order to protect them. This study suggests a deep learning-based attack detection model that can be trained and tested using data gathered from a gas pipeline system. Improved Random Neural Network and Long Short Term Memory Networks are incorporated for the attack detection purposes. The proposed model achieves an accuracy of 97.8% and outperforms the other existing detection models.
Optimal Multisine Perturbations for Improved Dynamic System Identification using a Mechanical Platform: A Preliminary Simulation Study

This paper investigates the design of optimal inputs for dynamic system identification. Specifically, this paper concerns the perturbation design for system identification experiments where target human systems are perturbed by mechanical inputs produced by an active device. Although conventional perturbation design criteria are generally applicable, including the scenario described above, problems arise due to the dynamics of the active device. A low-bandwidth active device may distort the input signal and thereby void the optimality of the input. To address this issue, the paper formulates an optimization problem for optimal input design that explicitly incorporates the active device dynamics. The cost function is the determinant of a modified covariance lower bound that takes the active device dynamics into consideration. The proposed method is demonstrated with an identification of a linear dynamics model simulating human arm impedance. Simulation results show that, compared with a standard optimal input and an input with a flat spectrum, the proposed optimal input with active device compensation achieved a smaller parameter covariance. Furthermore, the proposed optimization problem suggests that the optimal covariance lower bound can be achieved by active devices with different dynamics properties. This allows the control design of the active device to satisfy a wide variety of requirements without sacrificing its ability to perform system identification.
Position Paper: The role of law in achieving privacy and security measures in smart buildings from the GDPR context

Background.Smart buildings are gaining more awareness and becoming adaptive to building occupant preferences, enabling by personal data processing given the implementation of sensors and pervasive computing. Although these technologies may provide great benefits, they can equally threaten the privacy of building occupants and raise data security concerns.Aim. The complexity of smart buildings poses difficulties when applying the General Data Protection Regulations (GDPR) in addressing the aforementioned concerns. This paper unpacks the specific ambiguities that make applying the GDPR in this context challenging and identify the key requirements for data controllers in managing data privacy. Methods. This paper takes on a doctrinal approach to research by drawing upon legal sources and existing scholarship in related fields, in particular, legal privacy scholars. Results. The pressing compliance problems stem from the debate on what is considered as 'personal data' in smart buildings in which dictates the compliance requirements, in particular, data protection impact assessments (DPIAs). We identify three facets that smart building controllers should focus on in complying with the GDPR: establishing personal data, identifying individual rights, and determining a high risk to the rights and freedom of natural persons. Conclusions. The law can help prevent harms and ensure that individual rights are protected in smart buildings. To achieve this, understanding the compliance pain points of smart environment in order to strike the balance between the privacy of the occupants and the benefits of smart buildings is crucial. This is a call for further empirical and interdisciplinary research to effectively address the issues surrounding smart environment compliance challenges.
Compressive Sensing-Based Automatic PPG Signal Quality Assessment Using CNN for Energy-Constrained Medical Devices

Smart wearable and portable healthcare devices are used for continuous patient health monitoring but have limited battery power and on-board memory. Therefore, there is a huge demand for an automated photoplethysmogram (PPG) signal quality assessment (SQA) for energy-constrained smart medical devices. In this article, we propose a novel convolutional neural network (CNN)-based compressive sensing PPG-SQA method for low-power wireless healthcare devices. The main focus of this paper is to find the best compressive sensing matrix to directly classify the compressed PPG segments into noise-free and noisy segments using the optimal one-dimensional CNN architecture. The proposed CS-PPG SQA method was evaluated using 4-layer and 32 filters with a rectified linear unit (ReLU) activation function. Evaluation results showed that the deterministic binary block diagonal (DBBD) sensing matrix with a compression factor of 2 outperforms the existing methods with original PPG signal. The proposed method had an accuracy (ACC) of 99.55% for noise-free PPG (NF-PPG) versus wrist-cup noisy PPG database (MA-DB01), 99.99% for NF versus random noise-added PPG (RN-PPG) segments and 72.71% for NF-PPG versus acceleration corrupted PPG database (MA-DB02). The proposed method can reduce false alarms by discarding noisy PPG segments and reduce model size, and computational time by 50% as compared to other existing SQA methods.
UvA-DARE (Digital Academic Repository) Extendable Text Analysis Service and its Usage in a Topic Monitoring Tool

xTAS is an extendable multi-user text analysis service for large scale multi-lingual document analysis developed at the University of Amsterdam. It can process large amounts of documents in a timely manner through a web interface that can be used by multiple users at once. In this demon-stration paper we present recent additions which include semanticization, on the ﬂy TF-IDF model generation and on the ﬂy co-occurrence metrics. Furthermore, we demonstrate ThemeStreams, a novel topic monitoring tool built on top of xTAS.