Reviewerly/exHarmony huggingface dataset:
Contact Tracing with the Exposure Notification Framework in the German Corona-Warn-App

Digital Contact Tracing (CT) protocols based on Bluetooth are best implemented at the system level to save resources and preserve security aspects. Combined with a government-monitored software platform, these CT-protocols can then be used to support controlling pandemics such as COVID-19. However, it is unclear how these protocols have to be parameterized to ensure the most accurate and reliable CT.This paper describes how we derived optimal parameters for a decentralized CT from extensive measurement campaigns that we carried out together with Deutsche Telekom (DT) and SAP under the supervision of the Robert Koch Institut (RKI). We examined the Google/Apple Exposure Notification Framework (ENF), which in combination with the front-end, i.e., the German Corona-Warn-App (CWA), enables digital CT in Germany. With centimeter accurate optical reference systems we show that optimal parameters are application-specific. However, they cause impractical high resource costs. In contrast, optimized general parameters offer an everyday compromise between energy costs, applicability, accuracy, and reliability of the ENF.
An Empirical Evaluation of the Effectiveness of Smart Contract Verification Tools

Blockchain has become popular due to its use in cryptocurrencies and potential to support different business-critical services (e.g., financial services, retail). The smart contract is at the center of blockchain systems and is a coded specification of an agreement between interacting partners in a transaction. Like other software artifacts, smart contracts are prone to carry residual faults. As many contracts are being used to handle financial transactions, huge losses may occur if a vulnerability is exploited. Also, a faulty contract cannot be corrected once it has been deployed on the blockchain, it can only be terminated and a new one must be deployed, which aggravates the cost of deploying contracts with faults and marks the reputation of the provider. Smart contract verification tools have been emerging, but limited knowledge is available regarding their real effectiveness. In this paper, we define a smart contract defect classification scheme based on the Orthogonal Defect Classification and apply it to a contract dataset, which has been extracted from multiple sources and holds different types of defects. We use the dataset to evaluate three state of the art verification tools regarding their fault detection performance. Results show the relatively low effectiveness of the tools and their complementarity.
Multiple Hypothesis Detection and Tracking Using Deep Learning for Video Traffic Surveillance

Moroccan Intelligent Transport System is the first Moroccan system that uses the latest advances in computer vision, machine learning and deep learning techniques to manage Moroccan traffic and road violations.In this paper, we propose a fully automatic approach to Multiple Hypothesis Detection and Tracking (MHDT) for video traffic surveillance.The proposed framework combines Kalman filter and data association-based tracking methods using YOLO detection approach, to robustly track vehicles in complex traffic surveillance scenes.Experimental results demonstrate that the proposed approach is robust to detect and track the trajectory of the vehicles in different situations such as scale variation, stopped vehicles, rotation, varying illumination and occlusion.The proposed approach shows a competitive results (detection: 94.10% accuracy, tracking: 92.50% accuracy ) compared to the state-of-the-art approaches.
Linear Regression Classification in the Quaternion and Reduced Biquaternion Domains

Linear regression classification (LRC) has proven to be a successful recognition tool in recent years. LRC depends on using the least square algorithm to get the solution of the linear regression equation. To improve the performance of the LRC algorithm, in this paper, we extend the LRC strategy to both quaternion and reduced biquaternion domains to consider image color information. We derive closed-form solutions from the properties of both domains<i>.</i> We also improve on the accuracy of the closed-form solutions using nonlinear optimization. Our experiments on three benchmark color face recognition databases demonstrate the effectiveness of the proposed methods for recognizing color faces.
Overview of Capsule Neural Networks

&lt;p&gt;As a vector transmission network structure, the capsule neural network has been one of the research hotspots in deep learning since it was proposed in 2017. In this paper, the latest research progress of capsule networks is analyzed and summarized. Firstly, we summarize the shortcomings of convolutional neural networks and introduce the basic concept of capsule network. Secondly, we analyze and summarize the improvements in the dynamic routing mechanism and network structure of the capsule network in recent years and the combination of the capsule network with other network structures. Finally, we compile the applications of capsule network in many fields, including computer vision, natural language, and speech processing. Our purpose in writing this article is to provide methods and means that can be used for reference in the research and practical applications of capsule networks.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Study on rare earth elements, heavy metals and organic contents in the soil of oil exploration site at Matruh Governorate, Egypt

Soil is an issue material for sustainable economic and development plans; besides it considers the most valuable natural resource in all fields of citizens' life. This study is carried out to assess the impacts of heavy metals and some organic constituents on the soil, and consequently on its pollution, in a sampling point left behind the exploration activities in a site subjected to oil and gas exploration nearby Matruh Governorate, Egypt. Moreover, it is trying to evaluate the contents of some added-value rare earth in the same area. Samples were collected from ten points in a bore at different depths starting at 13,000 feet with increasing down distance every 50 feet. The study area is located within latitude 30° 48′ to 36° 889 ̋ S and longitudes 26° 59′ to 16° 406 ̋ E. Geographically, Matruh is part of the Great Western Desert. Rare Earth Elements (REEs), Heavy Metals (HMs) and organic constituents in the soil samples were analyzed based on Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) and Fourier transform infrared spectroscopy (FT-IR). Geoaccumulation Index (Igeo), Enrichment factor (EF) and Pearson Correlation Coefficients (R) were calculated to consider the soil pollution in the studied point. Also, to evaluate the abundance of REEs and to follow the relations between those elements. The study illustrated that: the most organic soil pollutants in the oil field sewage were aliphatic and aromatic hydrocarbon, phenols, organic sulfide aliphatic acid and others. Based on the geoaccumulation Index (Igeo) and Enrichment Factor (EF) classifications, the soil quality of the studied site can be classified, in general, as class 0, (uncontaminated) to class 6, (extremely contaminated). Moreover, The potentiality of the rare earth should be subjected to intensive studies to decide their economical mining and element processing.
Highly Maneuverable Target Tracking Under Glint Noise via Uniform Robust Exact Filtering Differentiator With Intrapulse Median Filter

Highly maneuverable target tracking under glint noise and nonlinear uncertainties during course changes and terminal maneuvers has been solved suboptimally for many years via the interacting multiple model algorithm with the use of the Kalman filter (KF), unscented Kalman filter, and the extended Kalman filter (EKF). Also, nonlinear KFs such as the cubature Kalman filter have been proposed to improve nonlinear tracking without being able to filter out glint noise. To this end, the particle filter and some KF based on variational Bayesian approach have been proposed with very good results in filtering out glint noise. Nonetheless, it is difficult for state-of-the-art methods to achieve efficient filtering of glint noise and nonlinear tracking at the same time. On the other hand, robust exact differentiators, based on the super-twisting algorithm, have been used for many years in output-feedback control and state observation in order to obtain the derivatives of an input signal with theoretical finite-time exactness. However, their potential for target tracking applications has not been explored sufficiently. In this article, a uniform robust exact filtering differentiator with intrapulse median filtering is proposed to filter out glint noise at the sliding manifold, while offering nonlinear tracking robustness via high-degree super-twisting terms outside the sliding manifold. Numerical simulations comparing the proposed solution to other state-of-the-art methods were conducted, showing promising results.
Imbal-OL: Online Machine Learning from Imbalanced Data Streams in Real-world IoT

Typically a Neural Networks (NN) is trained on data centers using historic datasets, then a C source file (model as a char array) of the trained model is generated and flashed on IoT devices. This standard process impedes the flexibility of billions of deployed ML-powered devices as they cannot learn unseen/fresh data patterns (static intelligence) and are impossible to adapt to dynamic scenarios. Currently, to address this issue, Online Machine Learning (OL) algorithms are deployed on IoT devices that provide devices the ability to locally re-train themselves -continuously updating the last few NN layers using unseen data patterns encountered after deployment.In OL, catastrophic forgetting is common when NNs are trained using non-stationary data distribution. The majority of recent work in the OL domain embraces the implicit assumption that the distribution of local training data is balanced. But the fact is, the sensor data streams in real-world IoT are severely imbalanced and temporally correlated. This paper introduces Imbal-OL, a resource-friendly technique that can be used as an OL plugin to balance the size of classes in a range of data streams. When Imbal-OL processed stream is used for OL, the models can adapt faster to changes in the stream while parallelly preventing catastrophic forgetting. Experimental evaluation of Imbal-OL using CIFAR datasets over ResNet-18 demonstrates its ability to deal with imperfect data streams, as it manages to produce high-quality models even under challenging learning settings.
Amazon Alexa traffic traces

The number of devices that make up the Internet of Things (IoT) has been increasing every year, including smart speakers such as Amazon Echo devices. These devices have become very popular around the world where users with a smart speaker are estimated to be about 83 million in 2020. However, there has also been great concern about how they can affect the privacy and security of their users [1]. Responding to voice commands requires devices to continuously listen for the corresponding wake word, with the privacy implications that this entails. Additionally, the interactions that users may have with the virtual assistant can reveal private information about the user. In this document we publicly share two datasets that can help conduct privacy and security studies from the Amazon Echo Dot smart speaker. The included data contains 300.000 raw PCAP traces containing all the communications between the device and Amazon servers from 100 different voice commands on two different languages. The data can be used to train machine learning algorithms in order to find patterns that can characterize both, the voice commands and people using the device as well as Alexa as the device generating the traffic.
Improvement of Oracle Bone Inscription Recognition Accuracy: A Deep Learning Perspective

Deep learning techniques have been successfully applied in handwriting recognition. Oracle bone inscriptions (OBI) are the earliest hieroglyphs in China and valuable resources for studying the etymology of Chinese characters. OBI are of important historical and cultural value in China; thus, textual research surrounding the characters of OBI is a huge challenge for archaeologists. In this work, we built a dataset named OBI-100, which contains 100 classes of oracle bone inscriptions collected from two OBI dictionaries. The dataset includes more than 128,000 character samples related to the natural environment, humans, animals, plants, etc. In addition, we propose improved models based on three typical deep convolutional network structures to recognize the OBI-100 dataset. By modifying the parameters, adjusting the network structures, and adopting optimization strategies, we demonstrate experimentally that these models perform fairly well in OBI recognition. For the 100-category OBI classification task, the optimal model achieves an accuracy of 99.5%, which shows competitive performance compared with other state-of-the-art approaches. We hope that this work can provide a valuable tool for character recognition of OBI.
Shape-aware Multi-task Learning for Semi-supervised 3D Medical Image Segmentation

Semi-supervised learning has achieved many successes in medical image segmentation since it reduces the costs of manually annotating by leveraging abundant unlabeled data. However, these semi-supervised methods lack attention to ambiguous regions (e.g., some edges or corners around the targets), which may lead to meaningless and unreliable guidance. In this paper, we propose a novel semi-supervised segmentation method called Shape-aware Multi-task Learning (SMTL) to address the above issue. Our multi-task framework includes three tasks namely i) the main task for segmentation ii) one auxiliary task for signed distance regression iii) another auxiliary task for contour detection. The multi-task framework jointly predicts probabilistic segmentation maps, signed distance maps (SDMs) and edge maps to collect complementary information in the existing target label. Specifically, these two auxiliary tasks explicitly enforce shape-priors on the segmentation output to generate more accurate masks. Moreover, we design a region-attention-based adversarial learning strategy that enforces the consistency of two auxiliary tasks prediction distributions on the unlabeled and labeled data to make a meaningful and reliable guidance. We evaluate our SMTL on the datasets of the 2018 Atrial Segmentation Challenge and the 2017 Liver Tumor Segmentation Challenge. The results demonstrate that our SMTL achieves improvements and outperforms the state-of-the-art semi-supervised methods.
Quantum postselective measurements: Sufficient condition for overcoming the Holevo bound and the role of max-relative entropy

The operation of unambiguous state discrimination (USD) plays an important role in quantum information theory. Here, we study the generalization for this operation which can be applied where USD is not available, including the case of completely coinciding supports. We demonstrate the effect of overcoming the Holevo bound after postselective measurements, which works for a class of ensembles, including two arbitrary noncommuting quantum states. We also discuss the role of max-relative entropy in postselective measurements and give an alternative operational interpretation for this function.
Predicting User Susceptibility to Phishing Based on Multidimensional Features

While antiphishing techniques have evolved over the years, phishing remains one of the most threatening attacks on current network security. This is because phishing exploits one of the weakest links in a network system-people. The purpose of this research is to predict the possible phishing victims. In this study, we propose the multidimensional phishing susceptibility prediction model (MPSPM) to implement the prediction of user phishing susceptibility. We constructed two types of emails: legitimate emails and phishing emails. We gathered 1105 volunteers to join our experiment by recruiting volunteers. We sent these emails to volunteers and collected their demographic, personality, knowledge experience, security behavior, and cognitive processes by means of a questionnaire. We then applied 7 supervised learning methods to classify these volunteers into two categories using multidimensional features: susceptible and nonsusceptible. The experimental results indicated that some machine learning methods have high accuracy in predicting user phishing susceptibility, with a maximum accuracy rate of 89.04%. We conclude our study with a discussion of our findings and their future implications.
Hardware Constructions for Error Detection in Lightweight Authenticated Cipher ASCON Benchmarked on FPGA

Lightweight cryptography plays a vital role in securing resource-constrained embedded systems such as deeply-embedded systems, RFID tags, sensor networks, and the Internet of nano-Things. ASCON is one of the finalists for the National Institute of Standards and Technology (NIST) lightweight cryptography standardization competition advanced to the final round in April 2021. It is designed to provide authenticated encryption with associated data (AEAD) and hashing functionalities in hardware and software implementations. ASCON’s lightweight design utilizes a 320-bit permutation, bit-sliced into five 64-bit register words, providing 128-bit level security. This brief, for the first time, proposes error detection mechanisms for secure hardware implementations of ASCON. The proposed schemes, i.e., signature, interleaved signature, and cyclic redundancy check approaches are presented for both LUT and logic-based implementations of ASCON. The proposed error detection schemes are also benchmarked on two FPGA families (Spartan-7 and Kintex-7), achieving acceptable area, power, and delay overheads. The proposed mechanisms also provide a high error coverage (99.99%) shown via simulations performed for 640,000 injected faults. Hence, these approaches aim to make the respective ASCON architectures more reliable.
Security Analysis of LNMNT-LightWeight Crypto Hash Function for IoT

Conventional cryptographic techniques are inappropriate for resource-constrained applications in the Internet of Things (IoT) domain because of their high resources requirement.This paper introduces a new Lightweight (LWT) hash function termed Lightweight New Mersenne Number Transform (LNMNT) Hash function, suitable for many IoT applications.The proposed LWT hash function is evaluated in terms of randomness, confusion, diffusion, distribution of hash function, and different attacks.The randomness analysis is performed using the NIST test suit.The LNMNT LWT hash function has been benchmarked against other LWT hash functions in terms of execution time, cycles per byte, memory usage, and consumed energy.The analysis showed that the LNMNT LWT hash function has excellent randomness behavior and is highly sensitive to the slight change in the input message too.Moreover, it provides low execution time, memory usage, and power consumption against the other LWT hash functions.
Robust Deep Learning-Based End-to-End Receiver for OFDM System With Non-Linear Distortion

In this letter, we propose a deep learning-based receiver named one-dimensional transmit and recovery net (1D-TRNet), which is robust to non-linear clipping distortion for orthogonal frequency-division multiplexing (OFDM) systems. The proposed scheme uses a 1D U-shape structure with multiple gated recurrent units (GRUs). The 1D-convolution kernel can extract both the low-level (local) and high-level (global) features in the non-linearly distorted OFDM signals. Simulation results show that the proposed 1D-TRNet receiver achieves significant bit-error rate (BER) performance gain over the traditional OFDM receivers. Moreover, the proposed 1D-TRNet receiver outperforms the state-of-the-art generalized approximate message passing (GAMP) receiver under the same computational complexity constraint.
Approximate bang-bang control assisted rapid switching feedback stabilization for stochastic qubit systems

For stochastic qubit systems under measurement feedback, an approximate bang-bang control assisted rapid switching control scheme with fewer switching times between the two subsets of state space is proposed in this paper. An eigenstate for single-qubit systems and any GHZ entangled state for multi-qubit systems are prepared successfully. In our control scheme, the state space is partitioned into two parts: a subset S1 containing the target state and its complementary set S2. On each subset, a Lyapunov function is selected and the corresponding control law is designed, where the control laws in S1 are of approximate bang-bang form and therefore can speed up the control process and the control laws in S2 ensure the strictly monotonic descent of the corresponding Lyapunov function and therefore can reduce the switching times. In particular, for multi-qubit systems with degenerate measured observables, we use two control channels to distinguish the target state from other isospectral eigenstates. By properly constructing the control Hamiltonians and using stochastic Lyapunov stability theory, we prove that the switching control laws in this paper render the system trajectory to switch at most twice on the boundary of S1 and S2 and to converge to the target state contained in S1 rapidly. Numerical simulations verify the effectiveness and rapidity of the proposed rapid control scheme.
Confidence-and-Refinement Adaptation Model for Cross-Domain Semantic Segmentation

With the rapid development of convolutional neural networks (CNNs), significant progress has been achieved in semantic segmentation. Despite the great success, such deep learning approaches require large scale real-world datasets with pixel-level annotations. However, considering that pixel-level labeling of semantics is extremely laborious, many researchers turn to utilize synthetic data with free annotations. But due to the clear domain gap, the segmentation model trained with the synthetic images tends to perform poorly on the real-world datasets. Unsupervised domain adaptation (UDA) for semantic segmentation recently gains an increasing research attention, which aims at alleviating the domain discrepancy. Existing methods in this scope either simply align features or the outputs across the source and target domains or have to deal with the complex image processing and post-processing problems. In this work, we propose a novel multi-level UDA model named Confidence-and-Refinement Adaptation Model (CRAM), which contains a confidence-aware entropy alignment (CEA) module and a style feature alignment (SFA) module. Through CEA, the adaptation is done locally via adversarial learning in the output space, making the segmentation model pay attention to the high-confident predictions. Furthermore, to enhance the model transfer in the shallow feature space, the SFA module is applied to minimize the appearance gap across domains. Experiments on two challenging UDA benchmarks “GTA5-to-Cityscapes” and “SYNTHIA-to-Cityscapes” demonstrate the effectiveness of CRAM. We achieve comparable performance with the existing state-of-the-art works with advantages in simplicity and convergence speed.