Reviewerly/exHarmony huggingface dataset:
Improving key rates of the unbalanced phase-encoded BB84 protocol using the flag-state squashing model

All phase-encoded BB84 implementations have signal states with unbalanced amplitudes in practice. Thus the original security analyses a priori do not apply to them. Previous security proofs use signal tagging of multiphoton pulses to recover the behavior of regular BB84. This is overly conservative as for unbalanced signals the photon number splitting attack does not leak full information to Eve. In this work we exploit the flag-state squashing model to preserve some parts of the multiphoton-generated private information in our analysis. Using a numerical proof technique we obtain significantly higher key rates compared with previously published results in the low-loss regime. It turns out that the usual scenario of untrusted dark counts runs into conceptual difficulties in some parameter regimes. Thus we discuss the trusted dark-count scenario in this paper as well. We also report a gain in key rates when part of the total loss is known to be induced by a trusted device. We highlight that all these key rate improvements can be achieved without modification of the experimental setup.Received 30 July 2020Accepted 2 October 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.043172Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum communicationQuantum cryptographyQuantum Information
Clustering Depressed and Anti-Depressed keywords Based on a Twitter Event of Srilanka Bomb Blasts using text mining methods

Twitter users' post data on social websites that are casual, critical, emotional, and sharing in real-time. Many keywords related to an event will appear as tweet hashtags during an event and immediately after the event. Twitter allows a length of 140 characters as a hashtag keyword. Algorithms exist for event detection using several scientific methods and express the importance of the event and its features. Many of the earlier studies clustered the events based on the tweets. In this paper, we considered tweets with the bombing, depressed, and anti-depressed related keywords posted from Srilanka during the ‘Bomb’ blasts in April 2019. Similar tweets data also collected and analyzed from a normal period (during May 2019) to compare our results. Our results show that the keywords identified are related to the event. We could further cluster these two keywords sets into similar and dissimilar sets with a Twitter event. We applied Learning Quotient and Text mining methods, and our results support the clustering of keywords.
Extended Object Tracking Using Spatially Resolved Micro-Doppler Signatures

Recent progress in radar development enable high-resolution radar sensors to resolve multiple detections for an extended object. One of the main challenges in radar-based object detection and tracking is the handling of an uncertain measurement model problem dealing with wandering dominant scatter points on the extended object surface depending on the relative orientation and motion. This phenomenon needs to be considered for near extended object tracking in critical driving situations, e.g., for safety applications, which require fast and accurate target state estimation in high dynamic scenarios. This paper presents a method to detect rotating wheels of approaching targets. Using the position and velocity of detected wheels as input for a tracking procedure, instead of target detections, relaxes the uncertain measurement model problem. A generic radar signal processing method to detect the position and bulk velocities of rotating wheels for approaching target vehicles is presented. The method evaluates the micro-Doppler signals, generated by rotating wheels, of close targets. These wheel hypotheses serve as input for a subsequent sequential Monte Carlo tracking framework. The presented methods are evaluated on real experimental data where a target vehicle is used to reconstruct safety-critical dynamic evading maneuvers.
Utilizing Patch-Level Category Activation Patterns for Multiple Class Novelty Detection

For any recognition system, the ability to identify novel class samples during inference is an important aspect of the system’s robustness. This problem of detecting novel class samples during inference is commonly referred to as Multiple Class Novelty Detection. In this paper, we propose a novel method that makes deep convolutional neural networks robust to novel classes. Specifically, during training one branch performs traditional classification (referred to as global inference), and the other branch provides patch-level information to keep track of the class-specific activation patterns (referred to as local inference). Both global and local branch information are combined to train a novelty detection network, which is used during inference to identify novel classes. We evaluate the proposed method on four datasets (Caltech256, CUB-200, Stanford Dogs and FounderType-200) and show that the proposed method is able to identify novel class samples better compared to the other deep convolutional neural network-based methods.
A Secured Steganography Algorithm for Hiding an Image and Data in an Image Using LSB Technique

Recent advances in computer security have shown that the most effective way to secure information is to hide information rather than encrypt it. Hiding facts is that the art and technological know-how of hidden writing is a manner to hide records in a completely specific form of data. In addition, hidden textual content or pictures with the cover photograph and change some objects from the preliminary bit to the recipient. A record report or picture is hidden in the cowl photograph will steady the attacker's authentic facts or statistics. The least significant bit technique (LSB) may be an unremarkably used technique of activity data and is prone to attack owing to its simplicity. During this analysis, it shows that the way to use the operator's image to cover a picture and different knowledge. In this, a tendency to compare the signal-to-noise ratio (PSNR) and the error squared error (MSE) to determine the level at which the stego image is masked with the cover image.
Collaborative Clustering Through Optimal Transport

Significant results have been achieved recently by exchanging information between multiple learners for clustering tasks. However, this approaches still suffer from a few issues regarding the choice of the information to trade, the stopping criteria and the trade-of between the information extracted from the data and the information exchanged by the models. We aim in this paper to address this issues through a novel approach propelled by the optimal transport theory. More specifically, the objective function is based on the Wasserstein metric, with a bidirectional transport of the information. This formulation leads to a high stability and increase of the quality. It also allows the learning of a stopping criteria. Extensive experiments were conducted on multiple data sets to evaluate the proposed method, which confirm the advantages of this approach.
Classification of Breast Cancer using PNN Classifier based on GLCM Feature Extraction and GMM Segmentation

Breast cancer is the most cancer affecting women. Cases in this disease that continue to increase every year lead to the notion that this cancer cannot be cured. Doing early detection of breast cancer can be very helpful to minimize the effects of this disease on sufferers. This research proposes the classification of breast cancer based on mammographic images using Probabilistic Neural Network (PNN) as a classifier, to optimize PNN work preprocessing steps such as median filters are used to minimize noise, in addition to using Gaussian Mixture Model (GMM) segmentation, and extraction features of Gray Level Co-occurrence Matrix (GLCM). PNN classifier was chosen because it can work effectively on small datasets. Based on testing this method produces very good accuracy that is 100% accuracy.
Knowledge-guided semantic computing network

The excellent performance of deep neural networks mainly relies on the attributes of the dataset, such as the size, diversity, completeness. However, it is usually difficult to obtain a high-qualified training dataset for some scenarios. Inspired by the human visual cognition process with few sample learning, and strong robustness, we believe the experience knowledge is more powerful than large-scale data. To combine the power of knowledge and data, we propose a knowledge-guided semantic computing network (SCN) in this paper, which is constructed with a primary knowledge-guided semantic tree module and an auxiliary data-driven lightweight neural network module. The semantic tree module can calculate the classification results by a forward computing process rapidly. The lightweight neural network module can aid the semantic tree module for higher classification ability. We also propose a hinge cross-entropy loss function to train the SCN, which enables the SCN to focus on those misclassified training samples and further improve the classification accuracy. The experimental results on MNIST and GTSRB data sets prove that the SCN achieves excellent classification accuracy comparable to the state-of-the-art methods on the original training samples and higher classification accuracy than the state-of-the-art methods on few training samples. What is more, at BIM eps = 0.3 on MNIST and FGSM eps = 0.03 on GTSRB adversarial test samples, the proposed SCN(1/4) and SCN(1/8) obtain over 75% and 14% accuracy improvement than the original CapsNet.
Estimation of the distribution of the total net radiative flux from satellite and automatic weather station data in the Upper Blue Nile basin, Ethiopia

Abstract Net radiation is an important factor in studies of land–atmosphere processes, water resource management, and global climate change. This is particularly true for the Upper Blue Nile (UBN) basin, where significant parts of the basin are dry and evapotranspiration ( ET ) is a major mechanism for water loss. However, net radiation has not yet been appropriately parameterized in the basin. In this study, we estimated the instantaneous distribution of the net radiation flux in the basin using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra satellite and Automatic Weather Station (AWS) data. Downward shortwave radiation and air temperature usually vary with topography, so we applied residual kriging spatial interpolation techniques to convert AWS data for point locations into gridded surface data. Simulated net radiation outputs were validated through comparison with independent field measurements. Validation results show that our method successfully reproduced the downward shortwave, upward shortwave, and net radiation fluxes. Using AWS data and residual kriging spatial interpolation techniques makes our results robust and comparable to previous works that used satellite data at a finer spatial resolution than MODIS. The estimated net shortwave, longwave, and total radiation fluxes were in close agreement with ground truth measurements, with mean bias (MB) values of − 14.84, 5.7, and 20.53 W m −2 and root mean square error (RMSE) values 83.43, 32.54, and 78.07 W m −2 , respectively. The method presented here has potential applications in research focused on energy balance, ET estimation, and weather prediction for regions with similar physiographic features to those of the Nile basin.
Driver identification using in-vehicle digital data in the forensic context of a hit and run accident

One major focus in forensics is the identification of individuals based on different kinds of evidence found at a crime scene and in the digital domain. In the present study, we assessed the potential of using in-vehicle digital data to capture the natural driving behavior of individuals in order to identify them. Freely available data was used to classify drivers by their natural driving behavior. We formulated a forensic scenario of a hit and run car accident with three known suspects. Suggestions are provided for an understandable and useful reporting of model results in the light of the requirements in digital forensics. Specific aims of this study were 1) to develop a workflow for driver identification in digital forensics, 2) to apply a simple but sound method for model validation with time series data and 3) to transfer the model results to answers to the two forensic questions a) to which suspect does the evidence most likely belong to and b) how certain is the evidence claim. Based on freely available data (Kwak et al., 2017) the first question could be answered by unsupervised classification using a random forest model validated by random block splitting. To answer the second question we used model accuracy and false detection rate (FDR) which were 93% and 7%, respectively. Furthermore, we reported the random match probability (RMP) as well as the opportunity of a visual interpretation of the prediction on the time series for the evidence data in our hypothetical hit and run accident.
Machine learning applied to anthropogenic seismic events detection in Lai Chau reservoir area, Vietnam

Automatic detection of seismic events is a useful tool for routine data processing. Effective detection saves time and effort in phase picking and events’ location, especially in areas with moderate seismicity at regional and local scales. The Lai Chau area in northern Vietnam is a good example of such a region. An additional difficulty in detection is the anthropogenic origin of reservoir-triggered seismicity observed in this region, where seismicity is non-stationary and there was no prior seismic activity. Neural network event detection was prepared to aid event identifications and further processing of seismic data. An automatic detection system was utilized to reduce the effort of manual interpretation of seismic signals in the region of the Lai Chau dam in North Vietnam while maintaining the detection of weak events at the same level. For this reason, a Single Layer Recurrent Neural Network (SLRNN) was applied. Compared to deep learning algorithms, fewer examples were needed to train the SLRNN. This paper presents a modified version of SLRNN, which additionally uses polarization analysis and the multi-stage learning process. In the first stage, the training data consists of events detected manually and disturbances selected visually by the operator. In the next stages, the earlier trained detection is validated in the successive recording periods. False detections together with new seismic events are added to the training set and the detection is retrained. The multi-stage process significantly reduces false detections. The software allows SLRNN to be used for routine seismic data processing.
Real-time video monitoring of vehicular traffic and adaptive signal change using Raspberry Pi

Traffic congestion is a major predicament these days. Even though it pervades all over, urban cities are the ones most affected by it which leads to major wastage of time on traffic junctions. While insufficient road capacity and ever-increasing demand are somewhat interdependent, the delay of respective traffic lights is hardcoded and not reliant on traffic intensity. Therefore, the need for simulating and optimizing traffic control in real-time to better accommodate this increasing demand arises. This paper presents a method to use live video feed from the cameras at traffic junctions for real-time traffic density estimation. It also focuses on providing an effective traffic light switch timer according to the vehicle density, thereby aiming at reducing the traffic congestion on roads.
Practical Security Bounds against Trojan Horse Attacks in Continuous-Variable Quantum Key Distribution

As the existence of non-zero reflection coefficients in the real component of continuous-variable quantum key distribution (CV-QKD) systems, Eve can probe the system by sending the bright light pulses into Alice’s set-up. With the analysis of back-reflections, Eve only takes a few back-reflected photons to intercept information and obtain the raw key bit. In this paper, the attack problems are converted into the information leakage problems. First, we analyzed the Trojan horse attacks with different wavelengths and confirmed its side effects, such as crosstalk and anti-Stokes Raman scattering, by a numerical simulation. Then, based on the wavelength-dependent property of beam splitter, we presented a practical way to estimate the deviation of shot noise and therefore correct the excess noise by inserting different wavelength pulses under joint attacks. Finally, we specified the security bounds of the system through quantifying the excess noise bounds caused by the Trojan horse attacks and provided a theoretical reference for the secret key transmission of system. As a consequence, the transmission errors within the security bounds can be negligible and the legitimate users will not perceive the presence of Eve.
VR4VRT

Learning how to properly row is an intensive and injury prone process. Ergometers (i.e., rowing machines) are used as a preparatory step to train the basic steps of rowing, as well as to further improve technique and maintain stamina. In this paper we present three design iterations resulting in a new interactive system that utilizes the commercially available dynamic ergometer (RP3). Our proposed software-hardware combination uses the RP3 with the HTC Vive platform extended with three location trackers, from which the software is available on request. We will showcase how this facilitates a range of opportunities.
Particularities of Students’ Educational Trajectories and “Projectories”: A Psychosemantic Dimension

The paper deals with the analysis of psychological phenomenon of educational trajectories and their design for future electrical engineers which is based on the psychosemantic assessment of their meanings and experiences. It has been found out that in the consciousness of future electrical engineers educational trajectories are presented as a four-factor functional structure. The major part of it is played by the content regulatory component associated with such characteristics as relevance, cognition, responsibility, manageability. The emotional-dynamic component takes the second position by the degree of its significance for students. The third component testifies to the advantage of the collective nature of the educational trajectory of future electrical engineers over the individual one. The fourth component is related to the assessment of its progressiveness and innovation. The relevance of developing and implementing a system of psychological and educational design of students' individual educational trajectories has been substantiated.
Network Analysis for Fraud Detection in Portuguese Public Procurement

As technology evolves, frauds, in all their different forms, become increasingly more complex, as mega-processes such as Luanda Leaks or Panama Papers have recently shown. Fraud detection mechanisms must thus also resort to recent technological developments to ensure that even new forms of complex fraud are detected in time. In this paper we present a system to facilitate fraud detection in Public Procurement in Portugal. It relies on three main components. Data on public procurement and involved entities is acquired from public sources and integrated into a graph-oriented database. A rules-engine enriches these data with information additional information, using legal rules or custom rules defined by the users. Finally, a graph-oriented User Interface is used to support decision-making, allowing users to quickly and efficiently explore and filter information, in a natural and geo-referenced way. The main goal of this system is to increase transparency by facilitating access to relevant information, and in this way contribute to the fairness of the whole public procurement procedure.
Experiences and Challenges of Providing IoT-Based Care for Elderly in Real-Life Smart Home Environments

Elderly population across the world is on the rise and municipalities along with caregivers are struggling to provide care due to limited resources. Sweden's elderly population is set to grow significantly by 2050 where the number of people between 65–79 years and 80 years and over is expected to increase by 45% and 87% respectively [1]. The same trend continues within Europe where 25% of the population will be over 65 years of age by the year 2020, and the age group of 65–80 years is predicted to rise by 40% from the year 2010 to 2030 [2]. The rise in elderly population has increased the stress on municipalities and caregivers; and has created the need for new healthcare solutions that are feasible, affordable and easily accessible to all. Smart homes equipped with sensors have already made life easier for those living in them for many decades now by providing home automation solutions. We are also witnessing an increase in the use of Information Communication Technologies (ICT) to assist elderly population and decrease in operational costs. ICT systems in assisting elderly population have an immense potential for providing in-home care to the elderly [3]. The advent of the Internet of Things (IoT) with low-cost and prolific sensors has furthered this trend of home automation and monitoring solutions being used for elderly healthcare [4, 5]. Alongside, the field of ambient assisted homes has continuously paved the way for providing an improved quality of life for those in need such as patients with dementia or chronic conditions as well as elderly living alone at home [5–7].
Social media in language learning: a mixed-methods investigation of students’ perceptions

The literature on students’ perceptions towards using Social Media (SM) for language learning reports mixed findings: while some studies indicate learners’ positive perceptions of their use for academic purposes (e.g., Bani-Hani et al.), others suggest that learners’ perceptions might vary due to their proficiency in the language (e.g., Gamble & Wilkins). There is also evidence that students’ do not always wish to share their SM environments for educational purposes). This study investigates students’ attitudes towards the use of four popular SMs (WhatsApp, Snapchat, Instagram and Twitter) in learning English as a foreign language.Ninety-nine adult English learners at a university in Saudi Arabia, active users of SM, participated in this mixed-methods study, which consisted of individual surveys and interviews. A two-way analysis of variance revealed that there are differences between beginner and advanced students in their perceptions of the usefulness of SM applications for language learning, but not in their affective feelings towards SM use outside the classroom, nor their choice of SM application for learning. Frequency counts indicated that the groups’ choices of SM varied according to different language purposes and the skills to be learned (e.g., they preferred WhatsApp for communication with family and friends, Twitter for reading, and Snapchat for learning aural skills). Further qualitative analysis revealed that advanced learners were more reluctant to using SM for academic purposes.