Reviewerly/exHarmony huggingface dataset:
The Learning System by the Least Squares Support Vector Machine Method and its Application in Medicine

In the paper it has been presented the possibility of using the least squares support vector machine to the initial diagnosis of patients. In order to find some optimal parameters making the work of the algorithm more detailed, the following techniques have been used: K-fold Cross Validation, Grid-Search, Particle Swarm Optimization. The result of the classification has been checked by some labels assigned by an expert. The created system has been tested on the artificially made data and the data taken from the real database. The results of the computer simulations have been presented in two forms: numerical and graphic. All the algorithms have been implemented in the C# language.
Simulation Model of Biometric Authentication Using Multiagent Approach

In this article authors present the concept of application of multiagent approach in modeling biometric authentication systems. After short introduction, we presenta short primer to multiagent technology. Next, we depict current state of the art related to biometrics combined with multiagent approach. In the next part of the work we present four exemplary simulation models of biometric authentication environments as well as the results of their examination.
Acoustic emission wireless monitoring of structures

Acoustic emission (AE) monitoring is a powerful procedure used to detect and locate damage in mechanically loaded structures, materials, and components. This chapter presents the capability of a new data processing system based on wireless AE equipment that is very useful for long-term monitoring of concrete and masonry structures. Systems based on wireless transmission should be cost-efficient, easy to install, and adaptive to different types of structures and infrastructures. To this purpose, computer-based procedures are implemented. These procedures are performed by automatic AE data processing and used to evaluate the AE data in laboratory tests and in situ monitoring. The final output of the code returns a description, as complete as possible, of damage patterns and their evolution of the monitored structure. In the most critical cases, or in some cases requiring in situ, long observation periods, the AE monitoring method is fine-tuned for a telematics processing procedure of AE data clouds. Finally, to increase the safety of structures and infrastructural networks, the proposed AE monitoring system could also be used to determine the seismic risk of civil constructions and monuments subjected to earthquakes.
The V-Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data​

The Varieties of Democracy (V–Dem) project relies on country experts who code a host of ordinal variables, providing subjective ratings of latent - that is, not directly observable - regime characteristics over time. Sets of around five experts rate each case (country-year observation), and each of these raters works independently. Since raters may diverge in their coding because of either differences of opinion or mistakes, we require systematic tools with which to model these patterns of disagreement. These tools allow us to aggregate ratings into point estimates of latent concepts and quantify our uncertainty around these point estimates. In this paper we describe item response theory models that can that account and adjust for differential item functioning (i.e. differences in how experts apply ordinal scales to cases) and variation in rater reliability (i.e. random error). We also discuss key challenges specific to applying item response theory to expert-coded cross-national panel data, explain the approaches that we use to address these challenges, highlight potential problems with our current framework, and describe long-term plans for improving our models and estimates. Finally, we provide an overview of the different forms in which we present model output.
User-Ranking Video Summarization With Multi-Stage Spatio–Temporal Representation

Video summarization is a challenging task, mainly due to the difficulties in learning complicated semantic structural relations between videos and summaries. In this paper, we present a novel supervised video summarization scheme based on three-stage deep neural networks. The scheme takes a divide-and-conquer strategy to resolve the complicated task of 3D video summarization into a set of easy and flexible computational subtasks, and then to sequentially perform 2D CNNs, 1D CNNs, and long short-term memory to address the subtasks in an hierarchical fashion. The hierarchical modeling of spatio-temporal structure leads to high performance and efficiency. In addition, we propose a simple but effective user-ranking method to cope with the labeling subjectivity problem of user-created video summarization, leading to the labeling quality refinement for robust supervised learning. Experimental results show that our approach outperforms the state-of-the-art video summarization methods on two benchmark datasets.
Dynamical Learning of Dynamics

The ability of humans and animals to quickly adapt to novel tasks is difficult to reconcile with the standard paradigm of learning by slow synaptic weight modification. Here, we show that fixed-weight neural networks can learn to generate required dynamics by imitation. After appropriate weight pretraining, the networks quickly and dynamically adapt to learn new tasks and thereafter continue to achieve them without further teacher feedback. We explain this ability and illustrate it with a variety of target dynamics, ranging from oscillatory trajectories to driven and chaotic dynamical systems.
Achieving Data Truthfulness and Privacy Preservation in Data Markets

As a significant business paradigm, many online information platforms have emerged to satisfy society's needs for person-specific data, where a service provider collects raw data from data contributors, and then offers value-added data services to data consumers. However, in the data trading layer, the data consumers face a pressing problem, i.e., how to verify whether the service provider has truthfully collected and processed data? Furthermore, the data contributors are usually unwilling to reveal their sensitive personal data and real identities to the data consumers. In this paper, we propose TPDM, which efficiently integrates data Truthfulness and Privacy preservation in Data Markets. TPDM is structured internally in an Encrypt-then-Sign fashion, using partially homomorphic encryption and identity-based signature. It simultaneously facilitates batch verification, data processing, and outcome verification, while maintaining identity preservation and data confidentiality. We also instantiate TPDM with a profile matching service and a distribution fitting service, and extensively evaluate their performances on Yahoo! Music ratings dataset and 2009 RECS dataset, respectively. Our analysis and evaluation results reveal that TPDM achieves several desirable properties, while incurring low computation and communication overheads when supporting large-scale data markets.
Image Representations With Spatial Object-to-Object Relations for RGB-D Scene Recognition

Scene recognition is challenging due to the intra-class diversity and inter-class similarity. Previous works recognize scenes either with global representations or with the intermediate representations of objects. In contrast, we investigate more discriminative image representations of object-to-object relations for scene recognition, which are based on the triplets of <object, relation, object> obtained with detection techniques. Particularly, two types of representations, including co-occurring frequency of object-to-object relation (denoted as COOR) and sequential representation of object-to-object relation (denoted as SOOR), are proposed to describe objects and their relative relations in different forms. COOR is represented as the intermediate representation of co-occurring frequency of objects and their relations, with a three order tensor that can be fed to scene classifier without further embedding. SOOR is represented in a more explicit and freer form that sequentially describe image contents with local captions. And a sequence encoding model (e.g., recurrent neural network (RNN)) is implemented to encode SOOR to the features for feeding the classifiers. In order to better capture the spatial information, the proposed COOR and SOOR are adapted to RGB-D data, where a RGB-D proposal fusion method is proposed for RGB-D object detection. With the proposed approaches COOR and SOOR, we obtain the state-of-the-art results of RGB-D scene recognition on SUN RGB-D and NYUD2 datasets.
Degraded Image Semantic Segmentation With Dense-Gram Networks

Degraded image semantic segmentation is of great importance in autonomous driving, highway navigation systems, and many other safety-related applications and it was not systematically studied before. In general, image degradations increase the difficulty of semantic segmentation, usually leading to decreased semantic segmentation accuracy. Therefore, performance on the underlying clean images can be treated as an upper bound of degraded image semantic segmentation. While the use of supervised deep learning has substantially improved the state of the art of semantic image segmentation, the gap between the feature distribution learned using the clean images and the feature distribution learned using the degraded images poses a major obstacle in improving the degraded image semantic segmentation performance. The conventional strategies for reducing the gap include: 1) Adding image-restoration based pre-processing modules; 2) Using both clean and the degraded images for training; 3) Fine-tuning the network pre-trained on the clean image. In this paper, we propose a novel Dense-Gram Network to more effectively reduce the gap than the conventional strategies and segment degraded images. Extensive experiments demonstrate that the proposed Dense-Gram Network yields state-of-the-art semantic segmentation performance on degraded images synthesized using PASCAL VOC 2012, SUNRGBD, CamVid, and CityScapes datasets.
An Algebraic Attack on Rank Metric Code-Based Cryptosystems

The Rank metric decoding problem is the main problem considered in cryptography based on codes in the rank metric. Very efficient schemes based on this problem or quasi-cyclic versions of it have been proposed recently, such as those in the submissions ROLLO and RQC currently at the second round of the NIST Post-Quantum Cryptography Standardization Process. While combinatorial attacks on this problem have been extensively studied and seem now well understood, the situation is not as satisfactory for algebraic attacks, for which previous work essentially suggested that they were ineffective for cryptographic parameters. In this paper, starting from Ourivski and Johansson’s algebraic modelling of the problem into a system of polynomial equations, we show how to augment this system with easily computed equations so that the augmented system is solved much faster via Gröbner bases. This happens because the augmented system has solving degree r, $$r+1$$ or $$r+2$$ depending on the parameters, where r is the rank weight, which we show by extending results from Verbel et al. (PQCrypto 2019) on systems arising from the MinRank problem; with target rank r, Verbel et al. lower the solving degree to $$r+2$$, and even less for some favorable instances that they call “superdetermined”. We give complexity bounds for this approach as well as practical timings of an implementation using magma. This improves upon the previously known complexity estimates for both Gröbner basis and (non-quantum) combinatorial approaches, and for example leads to an attack in 200 bits on ROLLO-I-256 whose claimed security was 256 bits.
An accurate and real-time self-blast glass insulator location method based on faster R-CNN and U-net with aerial images

This paper proposes a new deep learning framework for the location of broken insulators (in particular the self-blast glass insulator) in aerial images. We address the broken insulators location problem in a low signal-noise-ratio (SNR) setting. We deal with two modules: 1) object detection based on Faster R-CNN, and 2) classification of pixels based on U-net. For the first time, our paper combines the above two modules. This combination is motivated as follows: Faster R-CNN is used to improve SNR, while the U-net is used for classification of pixels. A diverse aerial image set measured by a power grid in China is tested to validate the proposed approach. Furthermore, a comparison is made among different methods and the result shows that our approach is accurate in real time.
Integrating Cognitive Antecedents to UTAUT Model to Explain Adoption of Blockchain Technology Among Malaysian SMEs

Blockchain technology is gaining consideration more and more and will potentially revolutionize most of the industries. Bitcoin cryptocurrency which uses Blockchain platform, has even promoted this technology more. Blockchain is a decentralized source and encrypted database for storing transaction information. Instead of being dependent on a centralised mediator like bank, by using blockchain, parties can transfer fund promptly trough connected ledgers called blocks. Using this method transactions will significantly be more transparent for both parties. Consequently, transactions are performed based on the distributed trust among other blockchain users in the network. Blockchain will promote transparency in every industry, yet implementation of blockchain technology is still limited. This study focuses to study the possible factors affecting adoption of blockchain technology by focusing on literature review and using Unified Theory of Acceptance and Use of Technology as a theoretical basis.
Tight security bounds for decoy-state quantum key distribution

The BB84 quantum key distribution (QKD) combined with decoy-state method is currently the most practical protocol, which has been proved secure against general attacks in the finite-key regime. Thereinto, statistical fluctuation analysis methods are very important in dealing with finite-key effects, which directly affect secret key rate, secure transmission distance and even the most important security. There are two tasks of statistical fluctuation in decoy-state BB84 QKD. One is the deviation between expected value and observed value for a given expected value or observed value. The other is the deviation between phase error rate of computational basis and bit error rate of dual basis. Here, we provide the rigorous and optimal analytic formula to solve the above tasks, resulting higher secret key rate and longer secure transmission distance. Our results can be widely applied to deal with statistical fluctuation in quantum cryptography protocols.
LittleYOLO-SPP: A delicate real-time vehicle detection algorithm

Vehicle detection in real-time is a challenging and important task. The existing real-time vehicle detection lacks accuracy and speed. Real-time systems must detect and locate vehicles during criminal activities like theft of vehicle and road traffic violations with high accuracy. Detection of vehicles in complex scenes with occlusion is also extremely difficult. In this study, a lightweight model of deep neural network LittleYOLO-SPP based on the YOLOv3-tiny network is proposed to detect vehicles effectively in real-time. The YOLOv3-tiny object detection network is improved by modifying its feature extraction network to increase the speed and accuracy of vehicle detection. The proposed network incorporated Spatial pyramid pooling into the network, which consists of different scales of pooling layers for concatenation of features to enhance network learning capability. The Mean square error (MSE) and Generalized IoU (GIoU) loss function for bounding box regression is used to increase the performance of the network. The network training includes vehicle-based classes from PASCAL VOC 2007, 2012 and MS COCO 2014 datasets such as car, bus, and truck. LittleYOLO-SPP network detects the vehicle in real-time with high accuracy regardless of video frame and weather conditions. The improved network achieves a higher mAP of 77.44 % on PASCAL VOC and 52.95 % mAP on MS COCO datasets.
Quantum Communication with Time-Bin Encoded Microwave Photons

One of the most promising ways to transfer quantum information between superconducting qubits is with microwave photons. Enhancing direct quantum channels by time-bin encoding techniques, which map qubit states to single-photon states emitted at different times, allows the detection of photon-loss errors, and thus heralded quantum communication is possible. The authors realize and experimentally benchmark an error-detection scheme that allows them to select only experimental runs in which the photon state was transmitted successfully, yielding significantly improved fidelity of the transfer in this post-selected setting.
Discrete-Modulation Continuous-Variable Quantum Key Distribution Enhanced by Quantum Scissors

It is known that quantum scissors, as non-deterministic amplifiers, can enhance the performance of Gaussian-modulated continuous-variable quantum key distribution (CV-QKD) in noisy and long-distance regimes of operation. Here, we extend this result to a non-Gaussian CV-QKD protocol with discrete modulation. We show that, by using a proper setting, the use of quantum scissors in the receiver of such discrete-modulation CV-QKD protocols would allow us to achieve positive secret key rates at high loss and high excess noise regimes of operation, which would have been otherwise impossible. This also keeps the prospect of running discrete-modulation CV-QKD over CV quantum repeaters alive.
Non-Markovian super-superradiance in a linear chain of up to 100 qubits

We study non-Markovian enhancement effects in the spontaneous emission of a collective excitation in a linear chain of up to 100 qubits coupled to a 1D waveguide. We find that for a critical separation of qubits, the system exhibits super-superradiant (SSR) behavior leading to collective decay stronger than the usual Dicke superradiance. Here, time-delayed coherent quantum feedback effects are at play on top of the usual Dicke superradiance effects. We find a linear scaling for the SSR decay rate with increasing qubit number $N$ such that $\Gamma_{\rm SSR} \sim 2.277 N \gamma_0$, where $\gamma_0$ is the single emitter decay rate to a one-dimensional waveguide, as opposed to $\Gamma_{\rm Dicke}\sim N \gamma_0$ for Dicke superradiance. The SSR decay rate can be tuned with qubit separation distance and may therefore have application for quantum technologies.
Learning Deep Representations for Video-Based Intake Gesture Detection

Automatic detection of individual intake gestures during eating occasions has the potential to improve dietary monitoring and support dietary recommendations. Existing studies typically make use of on-body solutions such as inertial and audio sensors, while video is used as ground truth. Intake gesture detection directly based on video has rarely been attempted. In this study, we address this gap and show that deep learning architectures can successfully be applied to the problem of video-based detection of intake gestures. For this purpose, we collect and label video data of eating occasions using 360-degree video of 102 participants. Applying state-of-the-art approaches from video action recognition, our results show that (1) the best model achieves an F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score of 0.858, (2) appearance features contribute more than motion features, and (3) temporal context in form of multiple video frames is essential for top model performance.
Object Contour and Edge Detection with RefineContourNet

A ResNet-based multi-path refinement CNN is used for object contour detection. For this task, we prioritise the effective utilization of the high-level abstraction capability of a ResNet, which leads to state-of-the-art results for edge detection. Keeping our focus in mind, we fuse high, mid and low-level features in that specific order, which differs from many other approaches. The tensor with the highest-levelled features is set as the starting point to combine it layer-by-layer with features of a lower abstraction level until it reaches the lowest level. We train this network on a modified PASCAL VOC 2012 dataset for object contour detection and evaluate on a refined PASCAL-val dataset reaching an excellent performance and an Optimal Dataset Scale (ODS) of 0.752. Furthermore, by fine-training on the BSDS500 dataset, we reach state-of-the-art results for edge-detection with an ODS of 0.824.
All-optical long-distance quantum communication with Gottesman-Kitaev-Preskill qubits

Quantum repeaters are a promising platform for realizing long-distance quantum communication and thus could form the backbone of a secure quantum internet, a scalable quantum network, or a distributed quantum computer. Repeater protocols that encode information in single- or multiphoton states are limited by transmission losses and the cost of implementing entangling gates or Bell measurements. In this work, we consider implementing a quantum repeater protocol using Gottesman-Kitaev-Preskill (GKP) qubits. These qubits are natural elements for quantum repeater protocols, because they allow for deterministic Gaussian entangling operations and Bell measurements, which can be implemented at room temperature. The GKP encoding is also capable of correcting small displacement errors. At the cost of additional Gaussian noise, photon loss can be converted into a random displacement error channel by applying a phase-insensitive amplifier. Here we show that a similar conversion can be achieved in two-way repeater protocols by using phase-sensitive amplification applied in the postprocessing of the measurement data, resulting in less overall Gaussian noise per (sufficiently short) repeater segment. We also investigate concatenating the GKP code with higher-level qubit codes while leveraging analog syndrome data, postselection, and path-selection techniques to boost the rate of communication. We compute the secure key rates and find that GKP repeaters can achieve a comparative performance relative to methods based on photonic qubits while using orders-of-magnitude fewer qubits.
Tailoring Product Ownership in Large-Scale Agile Projects: Managing Scale, Distance, and Governance

In large-scale agile projects, product owners undertake a range of challenging and varied activities beyond those conventionally associated with that role. Using in-depth research interviews from 93 practitioners working in cross-border teams, from 21 organisations, our rich empirical data offers a unique international perspective into product owner activities. We found that the leaders of large-scale agile projects create product owner teams. Product owner team members undertake sponsor, intermediary and release plan master activities to manage scale. They undertake communicator and traveller activities to manage distance and technical architect, governor and risk assessor activities to manage governance. Based on our findings, we describe product owner behaviors that are valued by experienced product owners and their line managers.