Reviewerly/exHarmony huggingface dataset:
Comprehensive evaluation model of driving behavior under intelligent network supervision based on entropy weight method

With the increase of transportation demand and the progress of technology and management, it has become a trend for enterprises to apply intelligent network platform for driving safety management. Its monitoring information plays a positive role in improving driving safety and improving the operation efficiency of transportation enterprises. According to the previous research, the enterprise lacks an evaluation index system integrating the comprehensive supervision data provided by the intelligent network platform in the process of enterprise and driver safety management. The dynamic information of the supervision platform is not included in the management in the traditional evaluation system, so the driver cannot be more finely managed and intervened. Therefore, by analyzing the 4G monitoring and driving early warning records of the intelligent network platform, this paper constructs a comprehensive evaluation system for driver driving behavior, and calculates the index weight of the index monitoring value obtained by the intelligent network platform through the entropy weight method, to establish a comprehensive evaluation model for drivers, to provide a more refined management system for enterprises. Finally, taking 16 transportation enterprises in Jiaozuo City as an example, this paper uses the constructed driving comprehensive evaluation system for modeling and analysis, and puts forward management suggestions for transportation enterprises according to the weight of evaluation indexes. The research results are of great significance to effectively reduce traffic accidents and improve the road traffic safety.
The Perception of Use of Social Media and Online Pharmacies by Lebanese Pharmacists: A Quantitative Study

The International Conferences on Economics and Social Sciences (ICESS) organized by Bucharest University of Economic Studies provide an opportunity for all those interested in Economics and Social Sciences to discuss and exchange research ideas. The conference takes place in Bucharest University of Economic Studies, Piata Romana 6, Bucharest, Romania.Contact: conference.icess[@]ase.ro The papers presented at the Conference are available online in the Conference Proceedings series (ISSN 2704-6524): Volume 2019 Collaborative Research for Excellence in Economics and Social Sciences, ISBN 9788366675322 Volume 2020 Innovative Models to Revive the Global Economy, ISBN 9788395815072 Volume 2021 Resilience and Economic Intelligence Through Digitalization and Big Data Analytics, ISBN 9788366675704 The past years’ uncertainties and pressures resulted from the pandemic, alongside new developments in social technology, 5G, cloud computing, augmented and virtual reality, generated the perfect setting for the metaverse to gain traction. Several companies found there’s a critical need to redefine the work environment and started exploring the metaverse concept beyond the entertainment sector. Thus, new tools for collaboration and data visualization within a digital shared space are being created, aiming for a fully immersive interaction between the virtual and the physical worlds within the next few
Phishing campaign targets Japanese MICARD and Amex users

Network SecurityVol. 2022, No. 9 Threat IntelligencePhishing campaign targets Japanese MICARD and Amex usersTom McVeyTom McVeyMenlo SecuritySearch for more papers by this authorTom McVeyPublished Online:11 Oct 2022https://doi.org/10.12968/S1353-4858(22)70054-2AboutSectionsView articleView Full TextView EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article References 1. ‘Data Breach Investigations Report 2020’. Verizon. Accessed Sep 2022. https://itb.dk/wp-content/uploads/2020/07/verizon-data-breach-investigations-report-2020.pdf. Google Scholar2. ‘GDP per Capita by Country 2022’. World Population Review. Accessed Sep 2022. https://worldpopulationreview.com/country-rankings/gdp-per-capita-by-country. Google Scholar3. Micard phishing advisory (in Japanese). Accessed Sep 2022. https://www2.micard.co.jp/notice/201030notice.html. Google Scholar4. ‘Phishing Activity Trends Report: 4th Quarter 2021’. Anti-Phishing Working Group (APGW), 23 Feb 2022. Accessed Sep 2022. https://docs.apwg.org/reports/apwg_trends_report_q4_2021.pdf. Google Scholar FiguresReferencesRelatedDetails 1 September 2022Volume 2022Issue 9ISSN (print): 1353-4858ISSN (online): 1872-9371 Metrics History Published online 11 October 2022 Published in print 1 September 2022 Information© MA Business Ltd.
Argumentative Language Resources at Språkbanken Text

Sprakbanken Text at the University of Gothenburg is a CLARIN B-centre providing language resources in Swedish, as well as tools to use them, for a wide range of disciplines. In 2017, we began exploring the field of argument mining - the process of automatically identifying and classifying arguments in text - partly aimed at establishing language resources and tools for argument analysis and mining in Swedish.
Animalogy: Learning through Analogy and Technology

The idea for our project came about when we decided to join the Product Innovation Project (PiP) program at Belgium’s top research university, KU Leuven. PiP is a course unit at the university where an interdisciplinary team of students empathize over a societal challenge presented by an external sponsor. Through the course of the academic year, we tried to devise and work out a solution using the design-thinking method. We were presented with a challenge by an Indian NGO, Destiny, which works on rescuing and rehabilitating women and young girls that have been trafficked into sex work. This challenge was even unique for PiP since it was the first time that an organization outside Europe participated. After the project we decided to move forward with the collaboration with Destiny, and so far, we have enjoyed the journey getting to where we are now: close to starting a social enterprise where we will be using modern technology to educate children from all social backgrounds.
Weakly Supervised Learning of the Motion Resistance of a Locomotive Powered by Liquefied Natural Gas

A model of the running resistance of a locomotive powered by liquefied natural gas is proposed. The model uses operating data and does not require specific instrumentation. The input data consists of a succession of instantaneous speed and electrical power measurements of a diesel-electric locomotive. The slope at each point along the route is unknown and the speed is measured with a digital sensor that quantifies the signal, so acceleration estimates are also unreliable. From these data, a weakly supervised learning problem is defined that makes use of a fuzzy rule-based system to indirectly predict the effective slope, and is able to estimate the power demand of the locomotive with a margin of error close to 5%.
Active Labeling: Streaming Stochastic Gradients

The workhorse of machine learning is stochastic gradient descent. To access stochastic gradients, it is common to consider iteratively input/output pairs of a training dataset. Interestingly, it appears that one does not need full supervision to access stochastic gradients, which is the main motivation of this paper. After formalizing the "active labeling" problem, which focuses on active learning with partial supervision, we provide a streaming technique that provably minimizes the ratio of generalization error over the number of samples. We illustrate our technique in depth for robust regression.
Adaptive supervised learning on data streams in reproducing kernel Hilbert spaces with data sparsity constraint

Data are generated at an unprecedented rate and scale these days across many disciplines. The field of streaming data analysis has emerged as a result of new data collection and storage technologies in various areas, such as air pollution monitoring, detection of traffic congestion, disease surveillance, and recommendation systems. In this paper, we consider the problem of model estimation for data streams in reproducing kernel Hilbert spaces. We propose an adaptive supervised learning method with a data sparsity constraint that uses limited storage spaces and can handle nonstationary models. We demonstrate the competitive performance of the proposed method using simulations and analysis of the bike sharing dataset.
Control of Quadrotor in 2-D for a Commanded Trajectory

The usage of drones has been increased and they are now being used for safety operations as well as in the security forces. They are being used in military, police forces, in geodesy companies. These applications demand accurate control of the drone, and the drone should follow the desired trajectory accurately so that less errors made in decision making of such complicated application. These applications require that the drones can be controlled using proper controller for controlling the trajectory. They follow the commanded trajectory very accurately while minimizing the error. These controllers get the difference between the actual trajectory and the desired trajectory, that is the error which they try to minimize. In this paper we will discuss about the control of a 2-D quadrotor using a Proportional-Derivative controller and simulate it in MATLAB. The PD controller tries to minimize the error and the derivative of the error. The model takes into account the mass of the drone, the moment of inertia about x-axis, the actual coordinates and the actual roll angle. The errors in this case is the difference between the actual position and the desired position.
A study on the R&amp;D professionals post-evaluation of Tucson NX4 design process based on grounded theory method

This research employing grounded theory as a research methodology is based on the in-depth interviews of designers and engineers who played a pivotal role in developing the Tucson 4th Generation Model NX4, Hyundai Motor's flagship C-Segment SUV model, which was released in 2020. Hyundai Motor's Tucson NX4 recorded 18,842 units (2020.9) on the first day of pre-contracting in Korea, and is currently receiving positive reviews in overseas markets for its design and stable performance. In particular, in the early stages of Hyundai's design innovation process, DDD (Digital Driven Design) process, the global simultaneous launch of NX4 was successful, which requires different dimensions and complex design and legal conditions for each local markets, including domestic demand, North America, Europe, and China. The development process behind the development of the internally and externally acclaimed C-Segment SUV Tucson NX4 is post-evaluated qualitatively, objectively and structurally based on grounded theory for practical R&amp;D vehicle developers, rather than for conventional quantitative assessments. The result laid four decisive factors that could have succeeded the project, and based on the results of this study, it will serve as supplementing and improving the DDD process in the future.
Optimal single-error-type entanglement generation via coherent-state transmission (Conference Presentation)

Entanglement is a fundamental resource not only for quantum communication but also for distributed quantum computation. Especially, entanglement including only one type of error is favorable, compared with one including multiple types of noise. In this talk, we consider protocol that presents single-error-type entanglement for distant qubits via coherent-state transmission over lossy channels. This protocol is considered to be a subroutine to serve entanglement for larger protocol to yield a final output, such as ebits or pbits. A protocol based on remote non-destructive parity measurement (RNPM) [K. Azuma, H. Takeda, M. Koashi, and N. Imoto, Phys. Rev. A 85, 062309 (2012)] is identified as a subroutine which achieves the global optimal for typical yield functions monotonically non-decreasing with respect to the singlet fraction, such as an arbitrary convex function of a singlet fraction and two-way distillable entanglement/key.
Quantum state engineering by zero-photon subtraction (Conference Presentation)

Single-photon subtraction (SPS) is useful for engineering optical quantum states and can be accomplished experimentally by heralding on the detection of one photon in the output port of a beamsplitter. Alternatively, conditioning on zero reflected photons modifies states by “zero-photon subtraction” (ZPS). Here we experimentally demonstrate that ZPS reduces the mean photon number of superpositions/mixtures of Fock states. The observed trends in attenuation show a dependence on the Mandel Q parameter for various input states, resulting in complementary behavior between SPS and ZPS. Theoretical results also show higher-order effects on the photon number distribution, beyond reduction in mean photon number.
IoT Droplocks: Wireless Fingerprint Theft Using Hacked Smart Locks

Electronic locks can provide security- and convenience-enhancing features, with fingerprint readers an increasingly common feature in these products. When equipped with a wireless radio, they become a smart lock and join the billions of IoT devices proliferating our world. However, such capabilities can also be used to transform smart locks into fingerprint harvesters that compromise an individual’s security without their knowledge. We have named this the droplock attack. This paper demonstrates how the harvesting technique works, shows that off-the-shelf smart locks can be invisibly modified to perform such attacks, discusses the implications for smart device design and usage, and calls for better manufacturer and public treatment of this issue.
Persistent spin-helix laser based on liquid crystal optical microcavity (Conference Presentation)

We realize a tunable laser based on a liquid crystal optical microcavity doped with the pyrromethene 580 organic dye. The tunable range reaches 40 nm. By transforming the system into the Rashba-Dresselhaus coupling regime, the laser action takes place from the bottoms of two oppositely polarized valleys shifted apart in reciprocal space. Measurements of emissions in real space show the persistent spin-helix lasing, which is a consequence of the spin coherence of the system. The platform that we propose can be used in quantum communication, in which information is encoded through light polarization.
Faktor-Faktor yang Berhubungan dengan Self Efficacy dalam Kegiatan Pembelajaran Siswa SMP Kartini 2 Kota Batam

Background: Self-efficacy is a belief in an individual's ability to successfully deal with challenges and have the confidence to achieve their desired outcomes. The purpose of this study was to determine the Factors Associated with Self Efficacy in the Learning Activities of Students in SMP Kartini 2 Batam.&#x0D; Methods: The study uses a descriptive analytic survey research design with cross sectional study approach. The sampling technique was simple random sampling with a sample size of 48 students. Collecting data using a questionnaire sheet and measuring intellectual intelligence with the assistance of a psychologist. Analyzing the data using Spearman's Rho statistics.&#x0D; Results: Spearman's Rho test showed that the experience of success, the experience of others, and psychological conditions had a relationship with self-efficacy, while intellectual intelligence did not. The p-value for experience of success is 0.004, experience of others is 0.039, verbal persuasion is 0.000, psychological condition is 0.007, and intellectual intelligence is 0.823. The correlation coefficient, the experience of success is 0.404, the experience of others is 0.300, verbal persuasion is 0.515, psychological conditions are 0.383, and intellectual intelligence is 0.033.&#x0D; Conclusion: Based on the results of this study, there is a significant relationship between the experience of success, the experience of others, verbal persuasion, and psychological conditions with self-efficacy and there is no significant relationship between intellectual intelligence and self-efficacy. The most influential factor on student learning is verbal persuasion.
3.119 Improvement in Mood, Reading Skills, and Growth Mindset in Children with Reading Difficulties and Comorbid ADHD, Anxiety, and/or Depression After Completing the “Read to Heal” Program, a Pilot Study

Unaddressed reading disabilities (RDs) can potentially impact the psychosocial health of children, resulting in mood and behavioral disturbances. “Read to Heal” is an intervention to address the needs of children with possible RDs presenting to the inpatient and outpatient services of an urban university hospital with comorbid mental health problems. The program targets students from minority groups who otherwise could not access similar services. It consists of Reading Training, CBT, and Growth Mindset.
Use of Information Technology in Academic Library Practice

At the organizational level, libraries focus on the development of physical collections of books and other materials, on the development of library materials by persons who are profiled in the selection, enrichment, organization, classification, cataloging and circulation of these materials, for stakeholders, who are primarily university students. The information technology programs used by libraries go beyond the physical collection of books, documents, and scientific journals, so we have a transformation of the library into the virtual world of the Internet. The advancement of libraries in the technological aspect has influenced us to have a change in the communication process between the library and the student. So, the purpose of this paper is to present the changes that have been made to the library in its structure, the programs they offer, the impact they have on the communication process between stakeholders, with a specific focus on changes that have been implemented at the AAB College Library in Prishtina, Kosovo.
A New Model of Deliberative Democracy: 'Friendly Conversation Meetings' for Prosperous Local Education in Suncheon City, Jeonnam Province.

Since October 2018 to the present (2022), deliberative democracy conferences, named 'Friendly Conversation Meetings' have been held every month in Suncheon City, Jeonnam Province, with citizens and stakeholders interested in local education. This meeting can be considered successful as it has been held regularly and for a long time (longer than 3 years), attracting citizens‘ voluntary participation and influencing local government policy. A survey was conducted to find out which elements of deliberative democracy was the best implemented and which element was the most important for the success, and a focus group interview was conducted to find out what was the background of such success. The survey respondents of 109 citizens assessed that 'diverse participants' was the most important, and 'boosting local education' was the best implemented, among 8 elements of deliberative democracy. At the focus group interview, the people who had the central role in designing and implementing this deliberation program answered that four backgrounds were important: human capital, social capital, the way the meeting was managed, and the independence of the management organization. This ’Friendly Conversation Meeting' seems to be an efficient model of deliberative democracy as it needs small amount of budget and it can achieve the goal of deliberative democracy.
Offline Writer Identification using Convolutional Recurrent Neural Network

Writer identification can be used to find fakes and help with forensic science. Handwritten text is the only way to identify the author offline. Recently, convolutional neural networks (CNNs) have become the best way to classify images on a large scale. The recurrent neural network (RNN) models the spatial relationship between the fragment sequences in order to improve the local fragment feature's capacity for discrimination. To get the best of both models, combine CNN and RNN to create a Convolution Recurrent Neural Network (CRNN). Therefore, the CRNN model for offline writer identification is suggested in this paper. The proposed method achieved 96% accuracy in classifying 690 writers using CRNN. The suggested method can make efficient and strong writer identification based on different sizes, different orientations, or both. The efficiency of the proposed system has been demonstrated. Finally, an accuracy comparison of CRNN with CNN and RNN has been conducted.
How to describe collective decay of uncoupled modes in the input–output formalism

We extend the input–output formalism to study the behavior of uncoupled discrete modes (bosonic cavity modes and fermionic qubits) when they decay to the same Markovian continuum. When the continuum interacts with only a single mode, this decay is irreversible. However, when multiple modes decay to the same Markovian continuum they develop correlations and decay collectively. In the input–output formalism these correlations manifest in additional terms in the quantum Langevin equation. For two modes, this collective decay can dramatically extend the lifetimes of both modes (Dicke subradiance) and, within the single-mode subsystem, induces non-Markovian memory effects including energy backflow.
Cluster Weighted Model Based on TSNE algorithm for High-Dimensional Data

Abstract Similar to many Machine Learning models, both accuracy and speed of the Cluster weighted models (CWMs) can be hampered by high-dimensional data, leading to previous works on a parsimonious technique to reduce the effect of ”Curse of dimensionality” on mixture models. In this work, we review the background study of the cluster weighted models (CWMs). We further show that parsimonious technique is not sufficient for mixture models to thrive in the presence of huge high-dimensional data. We discuss a heuristic for detecting the hidden components by choosing the initial values of location parameters using the default values in the ”FlexCWM” R package. We introduce a dimensionality reduction technique called T-distributed stochastic neighbor embedding (TSNE) to enhance the parsimonious CWMs in high-dimensional space. Originally, CWMs are suited for regression but for classification purposes, all multi-class variables are transformed logarithmically with some noise. The parameters of the model are obtained via expectation maximization algorithm. The effectiveness of the discussed technique is demonstrated using real data sets from different fields.
QKD in the NISQ era: enhancing secure key rates via quantum error correction

Error mitigation is one of the key challenges in realising the full potential of quantum cryptographic protocols. Consequently, there is a lot of interest in adapting techniques from quantum error correction (QEC) to improve the robustness of quantum cryptographic protocols. In this work, we benchmark the performance of different QKD protocols on noisy quantum devices, with and without error correction. We obtain the secure key rates of BB84, B92 and BBM92 QKD protocols over a quantum channel that is subject to amplitude-damping noise. We demonstrate, theoretically and via implementations on the IBM quantum processors, that B92 is the optimal protocol under amplitude-damping and generalized amplitude-damping noise. We then show that the security of the noisy BBM92 protocol crucially depends on the type and the mode of distribution of an entangled pair. Finally, we implement an error-corrected BB84 protocol using dual-rail encoding on a noisy quantum processor, and show that the dual-rail BB84 implementation outperforms the conventional BB84 in the presence of noise. Our secure key rate calculation also takes into account the effects of CNOT imperfections on the error rates of the protocols.