Reviewerly/exHarmony huggingface dataset:
Continuous Variable Quantum Teleportation of a Thermal State in a Thermal Environment

The analytical formula for the fidelity of teleportation from Alice to Bob of a thermal state used as an input state is derived using the characteristic function approach. As a resource state it is used a continuous-variable two-mode bosonic state shared between Alice and Bob and the system is placed in a contact with a general environment. The quantum fidelity depends on the average thermal photon number of the input sate and on the parameters of the resource state and environment. In order to evaluate the parameters required for a successful quantum teleportation of a thermal state we estimate the evolution of the entanglement and fidelity of teleportation in the case when Alice and Bob share an initial squeezed thermal state in contact with a thermal bath. We work in the framework of the theory of open quantum systems based on completely positive dynamical semigroups and we use the Gorini-Kossakowski-Lindblad-Sudarshan master equation in order to describe the evolution of the system. The conditions for a successful quantum teleportation are the fidelity of teleportation larger than the threshold value of the classical fidelity of teleportation and entanglement present in the resource state. In order to assure the fulfillment of these conditions it is necessary to select the time of teleportation and to adjust the parameters characterizing the system (squeezing between the modes of the resource state, their average numbers of thermal photons and frequencies), and the temperature of the thermal reservoir. It is shown that in similar physical conditions, the fidelity of teleportation of the thermal states is larger than that one for a coherent state.
Using Artificial Intelligence in the Law Review Submissions Process

The use of artificial intelligence to help editors examine law review submissions may provide a way to improve an overburdened system. This Article is the first to explore the promise and pitfalls of using artificial intelligence in the law review submissions process. Technology-assisted review of submissions offers many possible benefits. It can simplify preemption checks, prevent plagiarism, detect failure to comply with formatting requirements, and identify missing citations. These efficiencies may allow editors to address serious flaws in the current selection process, including the use of heuristics that may result in discriminatory outcomes and dependence on lower-ranked journals to conduct the initial review of submissions. Although editors should not rely on a score assigned by an algorithm to decide whether to accept an article, technology-assisted review could increase the efficiency of initial screening and provide feedback to editors on their selection decisions. Uncovering potential human bias in the selection process may encourage editors to develop ways to minimize its harmful effects. Despite these benefits, using artificial intelligence to streamline the submissions process raises significant concerns. Technology-assisted review may enable efficient implementation of existing biases into the selection process, rather than correcting them. Artificial intelligence systems may rely on considerations that result in discriminatory effects and negatively impact groups that are not adequately represented during development. The tendency to defer to seemingly neutral and often opaque algorithms can increase the risk of adverse outcomes. With careful oversight, however, some of these concerns can be addressed. Even an imperfect system may be worth using in limited situations where the benefits substantially outweigh the potential harms. With appropriate supervision, circumscribed application, and ongoing refinement, artificial intelligence may provide a more efficient and fairer submissions experience for both editors and authors.
Predicting the Usefulness of Questions in Q&amp;A Communities: A Comparison of Classical Machine Learning and Deep Learning Approaches

Questioning and answering (Q&A) communities have become an important platform for online knowledge exchange. With a vast number of questions posted to elicit high-quality solutions as well as a large number of participants engaged in online knowledge sharing, a grand challenge for Q&A communities is thus to effectively and efficiently identify and rank useful questions. The current approach to solving this problem is either through user voting or by community moderators. However, such manual processes are limited in terms of efficiency and scalability, especially for large Q&A communities. Thus, automatically predicting the usefulness of questions has significant implications for the management of online Q&A communities. To provide guidelines for assessing the quality of online questions, this research investigates and compares various classical machine learning and deep learning methods for predicting question usefulness. A dataset collected from a large Q&A community was used to train and test those machine learning methods. The findings of this research provide important implications for both the research and practice of online Q&A communities.
Methodological Approach to Analyze Predictive Behavior of Alluvial Gold Mining Expansion in the Peruvian Amazon Using a Machine Learning Approach

Alluvial small-scale gold mining (ASGM) mining in the Amazon is expanding fiercely, generating severe environmental degradation including the disappearance of primary forests. Different factors motivate the expansion of mining activities. However, their interaction, influence and new directions when expanding to new gold mining areas are not well understood. Anticipating this growth is important to safeguard protected areas or to implement strategies to mitigate the related social and environmental impacts. This study uses machine learning (ML) techniques to explore gold mining expansion in the Peruvian Amazon, and to identify possible future hotspots. Unsupervised learning algorithm and a classification model (i.e., random forest) are used to achieve this objective. Results show that ML techniques allow anticipating new mining areas and subsequent deforestation. Two important areas of high mining probability were identified: Puerto Pariamanu and Puerto Maldonado. The model demonstrates to be useful to enhance decision making when implementing geo-spatial policies.
Performance of Computing Hash-Codes with Chaotically-Trained Artificial Neural Networks

The main goal of the research presented in this paper was to estimate the performance of applying neural networks trained with the usage of a chaotic model, that may serve as hashing functions. The Lorenz Attractor chaotic model was used for training data preparation, and Scaled Conjugate Gradient was used as a training algorithm. Networks consisted of two layers: a hidden layer with sigmoid neurons and an output layer with linear neurons. The method of bonding the input message with chaotic formula is presented. Created networks could return 256 or 512 bits of hash, however, this parameter can be easily adjusted before the training process. The performance analysis of networks is discussed (that is the time of hash computation) in comparison with popular standards SHA-256 and SHA-512 under the MATLAB environment. Further research may include analysis of networks’ training parameters (like mean squared error or gradient) or analysis of results of the statistical tests performed on networks output. The presented solution may be used as a security algorithm complementary to a certificated one (for example for additional data integrity checking).
An Auxiliary Heterogeneous Visual Localization Algorithm Based on Improved Hough Transform Line Matching

With the increasing demand for all-weather, uninterrupted aviation missions, heterogeneous visual navigation and positioning technology based on visible images and infrared images has been developed, and the key technology of heterogeneous image matching has become a research hotspot. Due to the different imaging mechanisms of heterogeneous images, it is difficult to directly apply the homogeneous image matching algorithm to heterogeneous images, and the feature extraction accuracy of algorithms based on line segment structure such as Hough transform needs to be improved. Therefore, an improved Hough transform line matching algorithm to assist heterogeneous visual positioning is proposed in this paper. Based on the heterogeneous image obtained by different optical sensors, the adaptive Canny operator of the improved maximum inter-class variance method is used for edge detection. The detected edge binary image is processed by Hough transform, and the line features in the heterogeneous images are extracted for matching. The experimental results show that the Hough transform line matching algorithm using the improved Canny operator has a line feature detection accuracy of 66%, which is 74.23% higher than the traditional Hough transform line matching algorithm. The number of correctly matched line features of the proposed algorithm is better than that of the traditional algorithm, which can provide ideas for the pose calculation based on heterogeneous images. The running time of the proposed algorithm is better than that of the traditional Hough transform line matching algorithm, and the total time is reduced by 46.62%.
Digital Educational Resources in Teaching

In the modern world, it is impossible to imagine a society without the various digital resources. The transition to a digital economy imposes new requirements for modern hardware and software. It is especially relevant in the field of training competitive personnel for the new economy. The positioning and opportunities of Russia in the global economic market depend on skilled professionals. Competencies, which were in demand a few years ago and seemed promising—such as, for example, computer literacy—have now become basic. In an ever-changing world, it is important to timely start training of future specialists for the digital economy, because retraining and training will require a lot of time. In this case, there is a need for teaching staff who would be able to train young professionals. The quality of training of future employees, and, consequently, the country’s transition rate to full digitalization depends on schoolteachers. In order to prepare future employees, teachers need to master the new competencies dictated by modern society and the labor market. Thus, there is a need for comprehensive retraining and professional development of schoolteachers #CSOC1120.
A Blended Learning Model: Results of Young Learners

The paper presents results of research that examined the learning of young learners within the blended learning model. The model included face-to-face lessons and homework done online distance. In total, 123 learners from the Slovak Republic (aged 10–12) participated in the research. In homework, the learners practised and fixed the learning content that was explained in face-to-face lessons. They worked either with printed materials or used an e-application. Two tools were exploited within the research: (1) didactic tests, that is, pretest that detected learners entrance knowledge, and posttest, that monitored their final knowledge; (2) a semi-structured interview in the focus group that proportionally included 16 learners. Five hypotheses were set to discover whether the learners achieved the expected learning objectives and outcomes. The results discovered that within the blended learning model learners achieved the required knowledge, irrespective they worked with printed materials or with e-application. Moreover, all test results were in favour of the e-app. Furthermore, in the semi-structured interview in the focus group, the learners reflected their experience with the blended learning model in four areas: hardware and software equipment, digital skills, autonomy in learning, and home environment. The results aspire to be applied in blended learning in the new normal era of post-pandemic education.
Effects of Kaplan-Shekhtman-Entin-Wohlman-Aharony Interactions on the Non-Markovian Dynamics of Quantum Entanglement and Communication

By using the non-Markovian Quantum State Diffusion method, the non-Markovian dynamics of entanglement, quantum dense coding and quantum teleportation in the spin chain system with Kaplan-Shekhtman-Entin-wohlman-Aharony interactions and Dzyaloshinskii-Moriya interactions are studied. In particular, the joint effects of Kaplan-Shekhtman-Entin-wohlmanAharony interactions and different external magnetic fields are discussed under zero and finite temperatures, respectively. The results show that the concurrence, channel capacity of quantum dense coding and average fidelity of quantum teleportation can be effectively increased via the increasing of Kaplan-Shekhtman-Entin-wohlman-Aharony interactions along the z-direction which can be attributed to the constructive role of Kaplan-Shekhtman-Entin-wohlman-Aharony interactions on the O(3) invariance in the spin chain. Non-uniform magnetic fields with the combination of Kaplan-Shekhtman-Entin-wohlman-Aharony interactions are found to be more effective in the increasing of the parameters unfer discussion.
Sort1 Algorithm: A New Sorting Algorithm

This paper explains about a new data arranging algorithm, “Sort1 Algorithm” which is compared with bubble sort, selection sort, and insertion sort algorithms. Here input is divided into two parts. On the second half numbers, arrange the second half numbers by last number to first number in simple comparison. On first half numbers, arrange the first half numbers by left to right numbers in simple comparison.
Forecasting Crop Yields Based on Fuzzy Analysis of the Dynamics of Remote Sensing Multispectral Data

Modern technologies for satellite monitoring of the Earth’s surface provide agricultural producers with useful information about the health status of crops. The remote sensor’s ability to detect subtle differences in vegetation makes it a useful tool for quantifying variability within a given field, estimating crop growth, and managing land based on current conditions. Remote sensing data, collected on a regular basis, allows producers and agronomists to draw up a current vegetation map that reflects the condition and strength of crops, analyze the dynamics of changes in plant condition, and predict yields in a particular area under crops. To interpret these data, the most effective means are various vegetation indices calculated empirically, that is, by operations with different spectral ranges of satellite monitoring multispectral data. Based on the time series of one of these vegetation indices, the paper considers the annual dynamics of the development of a plant culture in a particular field. The possibility of predicting the yield of the given crop is considered based on fuzzy modeling of time series for the corresponding spectral ranges of vegetation reflection obtained from satellite monitoring images. The proposed fuzzy models of time series are investigated for adequacy and suitability in terms of analyzing the features of the intra-annual of average long-term dynamics of the vegetation index, typical for the given area under crop.
BEUD: Bifold-Encoder Uni-Decoder Based Network for Anomaly Detection

Anomaly detection is a very critical and significant data analysis mission given the raft of cyber-attacks these days. Used to identify thought-provoking and emerging patterns, predispositions, and irregularities in the data, it is an important tool to perceive abnormalities in many different domains, including security, finance, power automation, health, computer network intrusion detection, etc. Deep learning-based AutoEncoders have shown great potential in identifying anomalies. However, state-of-the-art anomaly scores are still based on reconstruction errors, which do not take advantage of the available anomalous data samples during the training phase. Towards this direction, we demonstrate a novel extension to the AutoEncoder that not only maintains the AutoEncoder’s ability to discover non-linear features of non-anomalies but also uses the existing anomalous samples to assist in learning the features of the data better. Since the model architecture is designed to have two encoders and one decoder network, we name our model as Bifold-Encoder Uni-Decoder (BEUD) network. In this paper, we discuss two different ways of using the BEUD model to predict the anomalies in the data. BEUD is conceptually analogous to AutoEncoder but empirically more powerful. The experimental results of this architecture demonstrated a fairly good performance compared to the standard AutoEncoder architecture evaluated for the anomaly detection task.
Dynamic Multi-swarm Particle Swarm Optimization with Center Learning Strategy

In this paper, we propose a novel variant of particle swarm optimization, called dynamic multi-swarm particle swarm optimization with center learning strategy (DMPSOC). In DMPSOC, all particles are divided into several sub-swarms. Then, a center-learning strategy is designed, in which each particle within the sub-swarms will learn from the historical optimal position of a particle or the center position in a sub-swarm. Also, an alternative learning factor is given to determine the particle learning strategy, which can be classified as center-learning or optimum-learning. Four benchmark functions are used in order to compare the performance of DMPSOC algorithm with the standard particle swarm optimization (SPSO). Experiments conducted illustrate that the proposed algorithm outperform SPSO in terms of convergence rate and solution accuracy.
On the Evaluation of Video-Based Crowd Counting Models

Crowd counting is a challenging and relevant computer vision task. Most of the existing methods are image-based, i.e., they only exploit the spatial information of a single image to estimate the corresponding people count. Recently, video-based methods have been proposed to improve counting accuracy by also exploiting temporal information coming from the correlation between adjacent frames. In this work, we point out the need to properly evaluate the temporal information's specific contribution over the spatial one. This issue has not been discussed by existing work, and in some cases such evaluation has been carried out in a way that may lead to overestimating the contribution of the temporal information. To address this issue we propose a categorisation of existing video-based models, discuss how the contribution of the temporal information has been evaluated by existing work, and propose an evaluation approach aimed at providing a more complete evaluation for two different categories of video-based methods. We finally illustrate our approach, for a specific category, through experiments on several benchmark video data sets.
Blockchain Based Freelancing System

The introduction of Blockchain technology, originally used for the bitcoin cryptocurrency has opened many opportunities to create services that can leverage its features. A few of these features include immutable record creation, decentralization, and the implementation of smart contracts. These features allow developers to implement applications that store critical data in a more secure and hassle-free manner. Blockchain’s features have made it a prime choice for a variety of applications such as electronic voting and data backup. This project looks into a freelancing platform as a possible feasible application of Blockchain. Freelancing involves requesting another person or party to complete a task (usually one that involves programming) in exchange for a fee. A Blockchain could help solve several key issues with the current system of freelancing platforms, including security, vulnerability of participants, and complexity. This project hence looks into how a freelancer marketplace can be implemented through the power of the Ethereum Blockchain with the use of smart contracts. It provides some insight into ways in which the Ethereum Blockchain can aid online marketplaces in general through its decentralized, immutable nature and the use of smart contracts.
Auditing of AI in Railway Technology – A European Legal Approach

Artificial Intelligence (AI) promises major gains in productivity, safety and convenience through automation. Despite the associated euphoria, care must be taken to ensure that no immature, unsafe products enter the market, especially in high-risk areas. Artificial Intelligence systems are therefore to be integrated into the European Union's existing product safety system through the planned AI Regulation. This is done by horizontally linking the draft AI Regulation (AI-Act) with the existing harmonizing legal acts that ensure the safety of products and provide for a complex regime of approval and testing concepts for this purpose. The railway sector is no exception, which is why potential AI systems designed for monitoring tracks or simple and accurate train detection are also subject to this approval regime. The following article highlights the challenges that exist in the railway sector on the way to verifiable AI systems in this regulatory context.
Automated Diagnosis of Breast Cancer from Mammogram Using Wavelet, Curvelet Features, and Convolutional Neural Network

Breast cancer is the utmost generally occurring cancer in women and the second most communal cancer. The ground truth standard used in real-time clinical application for the diagnosis is a mammogram. A novel approach is projected in this paper for the automated diagnosis of breast cancer from mammogram images composed from the MIAS data set using curvelet/wavelet transform-based features and a convolutional neural network. The following sequences of operations are involved, namely pre-processing, application of curvelet/wavelet transform, statistical and gray level co-occurrence matrix-based features extracted from curvelet/wavelet coefficients followed by a selection of highly discriminative features by statistical p-test. Initially, pre-trained models VGG16 and VGG19 are used for classification, and Deep convolutional neural network architecture is constructed for which feature matrix is given as input. Pretrained models are used for classification using the concept of transfer learning. The constructed architecture hyperparameters are adjusted, and the highest classification precision of 93% is achieved. The obtained results outperform the state of art methods available in the state of art.
Cross-lingual Inflection as a Data Augmentation Method for Parsing

We propose a morphology-based method for low-resource (LR) dependency parsing.We train a morphological inflector for target LR languages, and apply it to related rich-resource (RR) treebanks to create cross-lingual (xinflected) treebanks that resemble the target LR language.We use such inflected treebanks to train parsers in zero-(training on x-inflected treebanks) and few-shot (training on x-inflected and target language treebanks) setups.The results show that the method sometimes improves the baselines, but not consistently.
Cable-Driven Parallel Robot Controlled by Servo-Vision System

This work proposes the implementation of a control system based servo-vision in a cable-driven parallel robot with four degrees of freedom. In the first instance, a servo-vision based position control system is designed for a correct positioning of the mobile platform (end-effector) within the working space of the fixed platform (frame). Also, this part integrates the application of techniques for object detection in images. The second part verifies the system by tracking trajectories and positioning of the end-effector in the workspace. Moreover, seedling monitoring will be performed in a seedbed located inside the robot, which consists of taking real-time video of the robot’s end-effector, for the location and monitoring of the seedlings.