Reviewerly/exHarmony huggingface dataset:
The Effects of Nursing Students' Learning Motivation and Learning Commitment on Adaptation to College Life

Purpose: This study attempted to find out the relationship between nursing students'' learning motivation, learning flow and adaptation to college life. Methods: The data of this study were collected using a structured questionnaire for 147 nursing students at two four-year university located in D city. For the data analysis method, descriptive statistics, t-test, ANOVA, Scheffe test, Pearson''s correlation analysis, and multiple regression analysis were used. Results: As a result of the study, the average of leaning motivation for study was 3.49±0.42 points, learning flow was 3.27±0.36, and adaptation to college life was 2.05±0.49. The relationship between learning motivation, learning flow, and adaptation to college life showed a significant positive correlation. In the results of multiple regression analysis, the factors influencing nursing students'' adaptation to college life were found to be learning motivation and learning flow. Conclusion: Therefore, in order to increase the adaptation to college life for nursing students, it is necessary to develop specific strategies to improve learning motivation and various programs to immerse students in learning.
Towards Educational Systems-of-Information Systems

Virtual Learning Environments (VLE), such as Moodle and Canvas, are often used in online learning to support both classroom and semi-classroom courses. Considering the abundance of VLE and the different functionalities offered by them, teachers/professors (and students, as a consequence) are often required to use multiple tools at the same time to fulfill all the objectives of their courses. Hence, it is necessary to excessive time and effort for VLE management (on the teachers’ side), leading to difficulties and misunderstanding for the students. Currently, VLEs are independent, highly-coupled, built with different technologies, and supported by various organizations. Moreover, VLEs offer specific functionalities that, frequently, must be complemented with other technologies to provide more complete solutions. In this perspective, VLEs present characteristics of System-of-Systems (SoS), i.e., large software systems whose operations depend on the collaboration and interactions between individual systems that are managerial and operational independent. This work presents preliminary results of constructing Educa-SoIS, an Educational Systems-of-Information-Systems that combines the individual capabilities of several VLEs in a single system offering a more complete solution to teachers and students. We describe the Educa-SoIS missions (i.e., high-level objectives that this educational SoIS needs to achieve), the types of independent systems (existing VLEs) that can compose the Educa-SoIS , the Educa-SoIS architecture, as well as an implementation of this architecture. Preliminary results reveal (i) a demand for this type of system, (ii) the feasibility of its existence, and (iii) the need for further investigation.
Can Diverse Futuring Strategies Inform an Ecology-Centred Speculative Design Practice?

{BIO}DIVERSE FUTURES is a doctoral research project that aims to explore the potential of non-normative and ecological futuring strategies as a way to create speculative design tools and techniques that are diverse, ecology-centred (rather than human-centred), and impactful. The hope is that such strategies can form part of a more sustainable design practice, and contribute to positive social change, and biodiversity and community activism.
Generalized Conditioned Dialogue Generation Based on Pre-trained Language Model.

We investigate the general problem of conditioned dialogue, in which a condition label is used as input to designate the type of the target response such as a persona. A major challenge for conditioned dialogue generation is the lack of substantial dialogue data labeled with conditions. Thus, we propose to complement the labeled dialogue data with labeled non-dialogue text data, and fine-tune BERT based on them. Our fine-tuning approach utilizes BERT for both encoder and decoder via different input representations and self-attention masks in order to distinguish the source and target side. On the target (generation) side, we use a new attention routing mechanism to choose between generating a generic word or condition-related word at each position. Our model is instantiated to persona- and topic-related dialogue. Experimental results in both cases show that our approach can produce significantly better responses than the state-of-the-art baselines.
Location-Aware Privacy Preserving Scheme in SDN-Enabled Fog Computing

Fog computing, as a novel computing paradigm, aims at alleviating data loads of cloud computing and brings computing resources closer to end users. This is achieved through fog nodes such as access points, sensors, and fog servers. According to the fog computing location awareness capabilities, a large quantity of devices exists in the physical environment with a short cover range. This leads to location privacy exposure by the connection triggered. Adversaries can pry into more private data through the commodiously accessible location information. Although the existing privacy-preserving schemes can address some issues such as differential privacy, it cannot meet various privacy expectations in practice for fog computing variants. Motivated by this, we propose a location-aware dynamic dual $$\epsilon $$ -differential privacy preservation scheme to provide the ultimate protection. We start by establishing the first scheme by clustering fog nodes with SDN-enabled fog computing. In addition, we customize $$\epsilon $$ -differential privacy preservation scheme to tailor-made for the variant fog computing services. Furthermore, we employ a modified Laplacian mechanism to generate noise, with which we find the optimal trade-off. Extensive experimental results confirm the significance of the proposed model in terms of privacy protection level and data utility.
Correction: Similarities and differences in spatial and non-spatial cognitive maps

[This corrects the article DOI: 10.1371/journal.pcbi.1008149.].
Quantum Ground States from Reinforcement Learning

Finding the ground state of a quantum mechanical system can be formulated as an optimal control problem. In this formulation, the drift of the optimally controlled process is chosen to match the distribution of paths in the Feynman--Kac (FK) representation of the solution of the imaginary time Schrodinger equation. This provides a variational principle that can be used for reinforcement learning of a neural representation of the drift. Our approach is a drop-in replacement for path integral Monte Carlo, learning an optimal importance sampler for the FK trajectories. We demonstrate the applicability of our approach to several problems of one-, two-, and many-particle physics.
Understanding Multilingual Correlation of Geo-Tagged Tweets for POI Recommendation

This paper presents a multilingual analysis of Twitter for recommending POIs based on psychographic preferences. People who belong to different countries have different behavioral activities and speak different languages. According to psychographic analysis, for example, people who visit other countries are interested in eating the food of their home country. For this, we aim to clarify psychographic preferences for user behaviors by analyzing geo-tagged tweets based on times, locations, and languages. In this work, we focused on the differences between locations and languages in geo-tagged tweets from European countries. A key feature of the proposed system is the ability to suggest POIs (for tourists) in regions where very few geo-tagged tweets are available in a specific language by using the weighted similarity by others’ preferences. Specifically, we first extract languages of tweets, and we identify tweeting countries based on the latitude and longitude of tweet locations. Then, we extract feature words from tweets of a specific language in a specific region by using a tf-idf based approach. In this paper, we discuss the POI preferences of different language users based on the linguistic correlation between feature words of tweets in the region.
Solutions to Improve the Quality of Higher Education in Vietnam in the Context of Industrial Revolution 4.0

In the digital age, higher education varies widely from the educational environment, the role of teachers and learners to teaching methods. Currently, Vietnam as well as other countries around the world is facing the great challenges of the shortage of a highly skilled workforce. The quality of workforce educated in university has not met the demands of socio-economic development and international integration. Besides, there is a lack of international experienced researchers in higher education institutions. The connection between universities and businesses is not focused, which makes learners not be able to meet job requirements after graduating. Therefore, it is critical to improve the quality of Vietnam’s higher education in the context of Industry Revolution 4.0.
Sharp Bias-variance Tradeoffs of Hard Parameter Sharing in High-dimensional Linear Regression.

Hard parameter sharing for multi-task learning is widely used in empirical research despite the fact that its generalization properties have not been well established in many cases. This paper studies its generalization properties in a fundamental setting: How does hard parameter sharing work given multiple linear regression tasks? We develop new techniques and establish a number of new results in the high-dimensional setting, where the sample size and feature dimension increase at a fixed ratio. First, we show a sharp bias-variance decomposition of hard parameter sharing, given multiple tasks with the same features. Second, we characterize the asymptotic bias-variance limit for two tasks, even when they have arbitrarily different sample size ratios and covariate shifts. We also demonstrate that these limiting estimates for the empirical loss are incredibly accurate in moderate dimensions. Finally, we explain an intriguing phenomenon where increasing one task's sample size helps another task initially by reducing variance but hurts eventually due to increasing bias. This suggests progressively adding data for optimizing hard parameter sharing, and we validate its efficiency in text classification tasks.
Implementation of an IT security measurement method for the evaluation of IT security in micro-enterprises

This paper presents a proposal for an application-oriented implementation of an existing multidimensional IT security measurement method. The result is a software tool (IT-Tool) which is used to measure the internal and external perspective on the IT security of an enterprise. The measured values are being classified with the help of a defined metric into different IT security levels. The aim of the self-measurement IT-Tool is to increase the IT security awareness of the enterprise by comparing the internal and external perspective as well as to derive concrete measures to improve the IT security of the enterprise. The entire IT security measurement method is based on the multiple German industry IT security framework conditions and is initially designed for craftwork micro-enterprises (1 - 9 employees). In addition, a suggestion for the evaluation of the tool presented here is described.
Experimental analysis of moisture content with involuntary irrigation structure in soil

Agriculture is the backbone of this world. It is an important segment growing worldwide due to population growth. The main problem in the agricultural segment is the development of farm yields with good agricultural supremacy. It could be achieved with continuous automated monitoring to gather the fast-growing food requirement. Climate changes are the biggest issue in the agricultural sector due to the increase in the world population. In this research work, a sophisticated farming technique has been adapted based on Internet of Things (IoT) to deal with the unfavorable conditions. This proposed farming method can be provided with highest possible accuracy of crop control and collection of various functional statistics with computerized control. Temperature and soil humidity are monitored with suitable observation system by an intellectual agriculture pasture. Without human participation, the sensing data shall be analyzed and the necessary steps can be taken with the above data. In this research work, Measurement parameters temperature and soil moisture are taken as measurement parameters. With the sufficient statistical analysis, they could be processed with the help of ThingSpeak cloud application.
Tip/tilt Alignment for a Fizeau Interferometer via Defocus-Based Sub-Spot Separation

It is a crucial premise for Fizeau interferometric imaging that all the optical axes of sub-apertures are well aligned. What is challenging in tip/tilt alignment of such interferometers is the superposition of all the sub-aperture spots on the focal plane for generation of fringes. In this paper, we propose and demonstrate a simple tip/tilt alignment scheme via defocus-based sub-spot separation (TADS). The focal interference function is divided into sparse defocused sub-spots at a long defocusing distance. Then the servo-control technology with a centroid extracting algorithm is used to align all the sub-aperture optical axes, each corresponding to a defocused sub-spot. The pointing correction performance using TADS has been verified through closed-loop experiments performed on a Fizeau telescope array testbed newly built in our laboratory. Compared with the current means, the proposed TADS eliminates the requirement of complex hardware design and the limitation of sub-aperture amounts, which is compatible with most existing segmented mirrors and telescope arrays.
ThunderX3 Next-Generation Arm-Based Server

This article consists only of a collection of slides from the author's conference presentation.
Distributionally Robust Learning for Unsupervised Domain Adaptation

We propose a distributionally robust learning (DRL) method for unsupervised domain adaptation (UDA) that scales to modern computer-vision benchmarks. DRL can be naturally formulated as a competitive two-player game between a predictor and an adversary that is allowed to corrupt the labels, subject to certain constraints, and reduces to incorporating a density ratio between the source and target domains (under the standard log loss). This formulation motivates the use of two neural networks that are jointly trained --- a discriminative network between the source and target domains for density-ratio estimation, in addition to the standard classification network. The use of a density ratio in DRL prevents the model from being overconfident on target inputs far away from the source domain. Thus, DRL provides conservative confidence estimation in the target domain, even when the target labels are not available. This conservatism motivates the use of DRL in self-training for sample selection, and we term the approach distributionally robust self-training (DRST). In our experiments, DRST generates more calibrated probabilities and achieves state-of-the-art self-training accuracy on benchmark datasets. We demonstrate that DRST captures shape features more effectively, and reduces the extent of distributional shift during self-training.
Verifying Safety Properties of Robotic Plans Operating in Real-World Environments via Logic-Based Environment Modeling

These days, robotic agents are finding their way into the personal environment of many people. With robotic vacuum cleaners commercially available already, comprehensive cognition-enabled agents assisting around the house autonomously are a highly relevant research topic. To execute these kinds of tasks in constantly changing environments, complex goal-driven control programs, so-called plans, are required. They incorporate perception, manipulation, and navigation capabilities among others. As with all technological innovation, consequently, safety and correctness concerns arise. In this paper, we present a methodology for the verification of safety properties of robotic plans in household environments by a combination of environment reasoning using Discrete Event Calculus (DEC) and Symbolic Execution for effectively handling symbolic input variables (e. g. object positions). We demonstrate the applicability of our approach in an experimental evaluation by verifying safety properties of robotic plans controlling a two-armed, human-sized household robot packing and unpacking a shelf. Our experiments demonstrate our approach’s capability to verify several robotic plans in a realistic, logically formalized environment.
Comp-O: An OWL-S Extension for Composite Service Description

Component-based software engineering is a paradigm that fosters software flexibility and emphasizes composability and reuse of software components. These are runtime units that provide services and, in turn, may require other services to operate. Assembling components consists in binding components’ required services to provided ones to deliver composite services with added value. Building a composite service is a challenging task as it requires identifying components and services that are compatible, binding them to implement the service, and describe it for discovery. For that, the vocabulary used to describe component-based services (i.e., services offered by components or assemblies) must support the description of required services, and descriptions must be combinable in order to automatically generate composite service descriptions. However, existing solutions are limited to the description and composition of provided (and not required) services. In this paper, we consider ontologies to describe component-based services implemented by component assemblies. After comparing existing service ontologies, we present an extension of OWL-S called Comp-O. Through a proof-of-concept, we demonstrate how the added semantics can be handled to automatically build composite service descriptions.
High-recall causal discovery for autocorrelated time series with latent confounders

We present a new method for linear and nonlinear, lagged and contemporaneous constraint-based causal discovery from observational time series in the presence of latent confounders. We show that existing causal discovery methods such as FCI and variants suffer from low recall in the autocorrelated time series case and identify low effect size of conditional independence tests as the main reason. Information-theoretical arguments show that effect size can often be increased if causal parents are included in the conditioning sets. To identify parents early on, we suggest an iterative procedure that utilizes novel orientation rules to determine ancestral relationships already during the edge removal phase. We prove that the method is order-independent, and sound and complete in the oracle case. Extensive simulation studies for different numbers of variables, time lags, sample sizes, and further cases demonstrate that our method indeed achieves much higher recall than existing methods for the case of autocorrelated continuous variables while keeping false positives at the desired level. This performance gain grows with stronger autocorrelation. At this https URL we provide Python code for all methods involved in the simulation studies.
WITHDRAWN: A proposal of bidirectional grid long short term memory based model for user intention identification in on-line search query using text

Abstract Whenever any search query is entered in to a search engine by the user, the set of web pages which are deemed to be relevant for the query being made are identified with help of an index and some algorithms to rank the web pages order at the time of display. Any result set of search query would be effective if it matches exactly with the user requirements and for this to happen understanding user intention behind the search query entered plays vital role. Research in Machine learning algorithms has shown tremendous promising results in identifying user intention. In this paper authors have done a brief survey of work related to working and application of machine learning methods in identifying user intention behind query entered and based on observations, the authors propose a Bi Directional Grid Long-Short Term Memory RNN frame work for enhancing the efficiency of user intention identification.
Production of Superposition of Coherent States Using Kerr Non Linearity

The coherent states are often called as Glauber coherent states and were named after the American Scientist Glauber who was first to realize the extraordinary usefulness of these coherent states for explanation and analysis of many optical phenomena. These states were first introduced by Sudarshan also and are now been extensively studied and applied to quantum-optical problems. The most explicit form of these states are expressed as, $$ \left| {\,\alpha \,} \right\rangle = \,\sum\nolimits_{n = 0}^{\infty } {e^{{ - \frac{1}{2}\,\left| {\,\alpha \,} \right|^{2} }} \frac{{\alpha^{n} }}{\sqrt n !}\,\left| {\,n\,} \right\rangle } $$ where, the Fock states $$ \left| {\,n\,} \right\rangle $$ is the eigen state of the number operator $$ N = \,a^{{{\dag }}} a $$ , i.e., $$ N\,\left| {\,n\,} \right\rangle = \,n\,\left| {\,n\,} \right\rangle $$ and $$ \alpha \, = \,\alpha_{r} + \,i\alpha_{i} $$ is a complex number. These Glauber coherent states are the eigen states of annihilation operator and are well known. They play a very important role in many applications of quantum information processing including quantum teleportation. But it has been a long dream for physicists to generate these superposed coherent states in the most general desired form $$ \left| {\,\psi \,} \right\rangle \, = \,N\,\left( {\cos \,\frac{\theta }{2}\,\left| {\,\alpha \,} \right\rangle \, \pm \sin \,\frac{\theta }{2}\,e^{i\,\varphi } \,\left| { - \alpha \,} \right\rangle \,} \right)\, $$ where, N is the normalization factor. In this paper, we propose a scheme to generate any such general superposition of coherent states $$ \left| {\,\alpha \,} \right\rangle $$ and $$ \left| { - \alpha \,} \right\rangle $$ using Kerr effect, two beams in coherent states, a single photon beam and optical devices like polarization beam splitter and mirrors. In the output, if a single photon is detected in a polarization state defined by angle $$ \theta $$ and $$ \varphi $$ , the desired superposition of coherent states $$ \left| {\,\alpha \,} \right\rangle $$ and $$ \left| { - \alpha \,} \right\rangle $$ results. If the photon is detected in an orthogonal polarization state (the state in which the electric field strength at a given point in space is normal to the direction of propagation), a superposition state different from the desired one results.
Synthesizing Control for a System with Black Box Environment, Based on Deep Learning

We study the synthesis of control for a system that interacts with a black-box environment, based on deep learning. The goal is to minimize the number of interaction failures. The current state of the environment is unavailable to the controller, hence its operation depends on a limited view of the history. We suggest a reinforcement learning framework of training a Recurrent Neural Network (RNN) to control such a system. We experiment with various parameters: loss function, exploration/exploitation ratio, and size of lookahead. We designed examples that capture various potential control difficulties. We present experiments performed with the toolkit DyNet.