Reviewerly/exHarmony huggingface dataset:
Investigating the Effective Climate on Transfer of Lessons Learned and Providing Solutions to Improve the Effectiveness of HSE Training Courses

Background: Today's businesses spend a lot of money on educating their personnel. What matters is that people use their knowledge to their jobs. The goal of this study was to look into the environment that affects learning transfer and come up with a solution to increase the effectiveness of health, safety and environment (HSE) courses.&#x0D; Methods: In 2020, a cross-sectional study was done at the Tehran Oil Refining Company. The number of samples was 200, according to Cochran's formula. The major data gathering technique was a 20-item questionnaire created by the researcher. The multivariate regression model was used to analyze the study data, which was done with IBM SPSS software.&#x0D; Results: The questionnaire's content validity and reliability were estimated to be 0.83 and 0.929, respectively. 3.68±0.22 was the atmospheric indicator that proved effective in transferring learning and providing a way to increase the effectiveness of HSE training. The climate index affecting the transfer of learning had a significant link with the parameters of work experience (p = 0.02), education.(p = 0.03), and kind of employment (P = 0.01), according to the results of linear multivariate regression analysis.&#x0D; Conclusion: The atmospheric index influencing learning transfer and proposing a solution to increase the efficacy of HSE courses in the Tehran Oil Refining Company was deemed favorable. The outcomes of this study revealed that supervisors on job units in this business provide a supportive environment that is perfectly aligned with encouraging learners to enroll in training courses.
Using the SMART System for Organizing Mobile Routes in the Open Education System

The publication presents a prototype of Smart-system for student mobility in open education system and methodological recommendations for its use. For the first time developed a working prototype of Smart-system in the form of an integrated IT-environment with a unified interface for making optimal decisions when students independently choose the parameters and resources of the planned trip. The practical significance is the enrichment of online education platforms with a new functionality for the organization of educational travel (mobile education).
Implementing Performance Portable Graph Algorithms Using Task-Based Execution

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Designing flexible graph kernels that can run well on various platforms is a crucial research problem due to the frequent usage of graphs for modeling data and recent architectural advances and variety. In this talk, I will present our recent graph processing model and framework, PGAbB, for modern shared-memory heterogeneous platforms. PGAbB implements a block-based programming model. This allows a user to express a graph algorithm using functors that operate on an ordered list of blocks (subgraphs). Our framework deploys these computations to all available resources in a heterogeneous architecture. We will demonstrate that one can implement a diverse set of graph algorithms in our framework, and task-based execution enables graph computations even on large graphs that do not fit in GPU device memory. Our experimental results show that PGAbB achieves competitive or superior performance compared to hand-optimized implementations or existing state-of-the-art graph computing frameworks.
Dual-stream spatio-temporal decoupling network for video deblurring

It is very important to obtain spatio-temporal information in video deblurring based on deep learning. The existing methods usually jointly learn the spatio-temporal information of blurred videos through single-stream networks, which inevitably limit spatio-temporal information learning and video deblurring performance of networks. Therefore, we propose a dual-stream spatio-temporal decoupling network (STDN), which can learn the spatio-temporal information of blurred videos more flexibly and efficiently with the decoupled temporal stream and spatial stream, for solving this problem. Firstly, in the temporal stream of STDN, we propose a video deblurring pipeline, that is motion compensation plus 3D CNNs, for solving the drawback of 3D CNNs that its receptive field cannot effectively cover the same but misplaced contents of different frames. Thus, the temporal stream can aggregate temporal information of frame sequences and handle inter-frame misalignments more effectively. Specifically, we design a novel deformable convolution compensation module (DCCM) to achieve motion compensation of this pipeline more accurately. Then, we develop a 3DConv module optimized by the designed temporal, spatial, and channel decoupling attention block, named the CTS, to achieve 3D CNNs of this pipeline. Secondly, we design a spatial stream in which two types of wide-activation residual modules are stacked, for learning more spatial features of the central frame to supplement the temporal stream. Finally, extensive experiments on the baseline datasets demonstrate that the proposed STDN has better performance than the latest methods. Remarkably, using the proposed temporal stream alone already can achieve competitive video deblurring performance than the existing methods.
Similarity Learning between Patients with Large Age-Gap: Model Development and Validation Study (Preprint)

<sec> <title>BACKGROUND</title> Similarity-based machine-learning methodologies are suitable for personalized prediction and recommendation research, which is actively applied in healthcare field along with the generalization of EHR data. In particular, the similarity learning model which carefully reflects age can be efficiently used in predicting chronic diseases, closely related to ageing. </sec> <sec> <title>OBJECTIVE</title> We aimed to design a similarity model for patients in different age-groups in order to predict the two major chronic diseases: Diabetes and Hypertension. </sec> <sec> <title>METHODS</title> We developed an idea about learning the overlapping periods of two individuals by moving the viewpoint of them to future and past respectively. From this idea, we build separated similarity learning models through three sequential age-group intervals; 30-40, 40-50, 50-60 age-groups intervals. Each model has same structure based on deep neural network. For similarity learning, we set several demographic/bi-annual check-up information and diagnosis records as input features and disease based yes-or-no similarity labels as output features. </sec> <sec> <title>RESULTS</title> As a result of applying hypertension patients’ pair, diabetes patients’ pair, and non-diabetes/diabetes patient pair to our methodology, the similarity value was very high, close to 1 in the former two cases, and the similarity value was low, close to zero, in the last case. This proves that similarity learning appropriately reflects the disease status between individuals. In addition, we tried to find out how the conventional single-timepoint methodology and our methodology differ in the measurement of similarity for several special cases in which the patient's disease condition changes. As a result, it was found that the similarity results between the existing methodology and our methodology differ from at least 0.2 to at most 0.9 in four special cases where the patient's condition changes. This suggests that our methodology responds more sensitively to the patient's condition changing over time and can be applied more efficiently to disease prediction in those cases. </sec> <sec> <title>CONCLUSIONS</title> We developed an age-sensitive similarity learning model for personalized prediction of chronic diseases targeting Koreans. As a result, for the cases that patient's disease pattern changes, by designing and learning a deep similarity learning model using divided age groups which has not been previously attempted, we have shown that similarity learning results are better than conventional single-timepoint methodology. Moreover, we proposed the possibility of overcoming data shortage limitations that occur frequently in medical datasets through a similarity learning model considering patients’ age differences. </sec>
Checking Agent Intentions in Games

Rational agents’ decisions are driven by their intentions, in the sense that agents execute actions that most probably lead to situations where their intentions are achieved. Using that insight, this paper proposes a method for ‘intention checking’: let a description of a game, a state and the action executed by the agent at that state be given, the method checks whether the agent acted with the intention to reach a situation where some proposition ‘p’ is true. We use a logic with epistemic and temporal operators to reason about games and extend it with an intention operator ‘IX’. Formulas of the form ‘IX(p)’ are defined to be true in the situations where the intention check method verifies that the agent acts with the intention to achieve ‘p’ in the next state of the game. We show that this operator satisfies the principles of Bratman’s Asymmetry Thesis, and we also compare it to other theories of intention.
Flexible Power-Normal Models with Applications

The main object of this paper is to propose a new asymmetric model more flexible than the generalized Gaussian model. The probability density function of the new model can assume bimodal or unimodal shapes, and one of the parameters controls the skewness of the model. Three simulation studies are reported and two real data applications illustrate the flexibility of the model compared with traditional proposals in the literature.
Claiming on the Net

The availability of EDI (Electronic Data Interchange) to process routine serial claims is attractive to librarians, agents and publishers. This workshop presented one such project using the electronic claiming module developed by Innovative Interfaces Inc. The preparation required by the vendor and library was demonstrated, and advantages, problems and future enhancements were discussed. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: getinfo@haworth.com ]
Bagging Based Cross-Media Retrieval Algorithm

Abstract It is very challenging to propose a strong learning algorithm with high prediction accuracy of cross-media retrieval, while finding a weak learning algorithm which is slightly higher than that of random prediction is very easy. Inspired by this idea, we propose an imaginative Bagging based cross-media retrieval algorithm (called BCMR) in this paper. First, we utilize bootstrap sampling to carry out random sampling of the original training set. The amount of the sample abstracted by bootstrap is set to be same as the original dataset. Second, 50 bootstrap replicates are used for training 50 weak classifiers independently. We take advantage of homogenous individual classifiers and integrate eight different baseline methods in our experiments. Finally, we generate the final strong classifier from the 50 weak classifiers by the integration strategy of sample voting. We use collective wisdom to eliminate bad decisions so that the generalization ability of the integrated model could be greatly enhanced. Extensive experiments performed on three datasets show that BCMR can effectively improve the accuracy of cross-media retrieval.
Wordless Storytelling in a Surreal World

Creaks, released in summer 2020, was the first videogame I worked on. But it’s not the first game by Amanita Design. The studio was founded in 2003 alongside the release of Amanita’s first game Samorost. Today there are a couple of independent teams working at the same time on different games in this studio and a new team was formed to work on Creaks. I worked on the game as a designer and artist.
Strategia didattica per sviluppare la comprensione della lettura negli studenti di Italiano I presso un’università privata di Lima

The purpose of this research study is to design a didactic strategy that develops the reading comprehension of students of Italian I course at a private university in Lima. The study is framed within the interpretative paradigm, with a qualitative approach and within the research type of applied education. The sample consisted on two Italian teachers, the academic coordinator of other languages department and thirty-five Beca 18 students. The students were registered in eight Italian I courses of the 2018-01 semester at this private university. The participants were selected using a non-probabilistic convenient sampling. The research study is based on the different theories, approaches, models and strategies related to the reading comprehension of Italian as a foreign language. The research tools used for the data collection process were participant-observation, depth interview, student survey and an Italian reading comprehension test (CILS). The diagnosis performed in this study is based on current developments on the state of reading comprehension of Italian I course, in order to understand the level of competence achieved by these students and their perceptions of in classroom reading comprehension activities. In addition, this study also observed the teachers’ theoretical and practical knowledges inside the teaching and learning processes. The didactic strategy proposed in this study aims to respond coherently to the problem identified and to provide an understanding of this complex mental process through which the student interprets, assimilates, organizes and values texts in Italian.
Adapted discriminative coupled mappings for low-resolution face recognition with one-shot

Low-resolution face recognition with one-shot is a prevalent problem encountered in law enforcement, where it generally requires to recognize the low-resolution face images captured by surveillance cameras with the only one high-resolution profile face image in the database. The problem is very tough because the available samples is quite few and the quality of unknown images is quite low. To effectively address this issue, this paper proposes Adapted Discriminative Coupled Mappings (AdaDCM) approach, which integrates domain adaptation and discriminative learning. To achieve good domain adaptation performance for small size dataset, a new domain adaptation technique called Bidirectional Locality Matching-based Domain Adaptation (BLM-DA) is first developed. Then the proposed AdaDCM is formulated by unifying BLM-DA and discriminative coupled mappings into a single framework. AdaDCM is extensively evaluated on FERET, LFW, and SCface databases, which includes LR face images obtained in constrained, unconstrained, and real-world environment. The promising results on these datasets demonstrate the effectiveness of AdaDCM in LR face recognition with one-shot.
PROPOSAL OF A DATA MODEL FOR MANAGING BLOKCHAIN-BASED TRUST BETWEEN ACTORS IN AN ELECTORAL PROCESS

In this paper we propose a tool (reference data model) for the improvement of trust, based on blokchain technologies, between the different actors of an electoral process. Our contribution focuses in a first step on the implementation of a data model having all these public attributes and thus public classes whose methods are coupled to cryptographic techniques. In a second step, we propose an underlying formalism of this model using a matrix representation of the different actors, the different transactions and the working criteria allowing to validate these transactions and blockchains. This formalism allows to find a transaction performed by one of the actors of the electoral chain and also the data block in which this transaction is validated. Also, an algorithm allowing to reinforce the trust is proposed.
Variability class dependent evaluation of the CAMS Solar Radiation Service

&amp;lt;p&amp;gt;The Copernicus Atmospheric Monitoring Service (CAMS) offers Solar radiation services (CRS) providing information on surface solar irradiance (SSI). The service is currently derived from Meteosat Second Generation (MSG) and the service evolution includes its extension to other parts of the globe. CRS provides clear and all sky time series combining satellite data products with numerical model output from CAMS on aerosols, water vapour and ozone. These products are available from 2004 until yesterday. A regular quality control of input parameters, quarterly benchmarking against ground measurements and automatic consistency checks ensure the service quality.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;Variability of solar surface irradiances in the 1-minute range is of interest especially for solar energy applications. The variability classes can be defined based on ground as well as satellite-based measurements. This study will present the evaluation of the CAMS CRS based on the eight variability classes derived from ground observations of direct normal irradiation (DNI) (Schroedter-Homscheidt et al., 2018). Such an analysis will help assess the impact of recent improvements in the derivation of all sky irradiance under different cloudy conditions.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;References:&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;Schroedter-Homscheidt, M., S. Jung, M. Kosmale, 2018: Classifying ground-measured 1 minute temporal variability within hourly intervals for direct normal irradiances. &amp;amp;#8211; Meteorol. Z. 27, 2, 160&amp;amp;#8211;179. DOI:10.1127/metz/2018/0875.&amp;lt;/p&amp;gt;
Overcoming the ordinal imbalanced data problem by combining data processing and stacked generalizations

Ordinal imbalanced datasets are pervasive in real world applications but remain challenging to analyse as they require specific methods to account for the ordering information and imbalanced classes. Failure to account for both those characteristics can substantially impact the model predictive performance. However, existing methods tend to focus either on ordinality or imbalance, rather than addressing both simultaneously. The few approaches that do account for both characteristics are not always easy to implement for non-advanced analysts and simpler approaches are needed to facilitate appropriate data processing. Here, we developed a general approach using some of the most popular machine learning algorithms to ensure appropriate processing of ordinal imbalanced datasets and to optimize the predictions of all classes. After transforming the multi-class ordinal problem into a well-known binary problem, we implemented several different resampling methods in a decision-tree classifier. We then used a stacked generalization algorithm to combine the classifiers to improve model predictive performance. To test our approach, we used two ordinal imbalanced datasets on student performance and wine quality. Individual resampling techniques tended to improve the accuracy of minority classes, while simultaneously increasing the number of false positives in those classes. This resulted in a decrease, sometimes substantial, in accuracy of other classes. The stacking model offered a good compromise between improvement in accuracy of minority classes and mitigation of reduced accuracy in other classes. Our approach provided useful insights into modelling strategies that should be favoured for implementation in production that involve these common datasets, depending on the end-user interests.
Musical instruments

All musical instruments have a few fundamental elements in common: an energy source, an excitation device, an oscillator, and a radiator. If the excitation is periodic, the oscillator resonates with the excitation, as in pipes or bowed strings, then there is resonance and feedback. If the excitation is a single event, as in drumheads, plucked or hammered strings, there is no resonance, and no feedback. Feedback from the oscillator locks the frequency of the excitation at the natural frequency of the oscillator, that is, makes it so that the excitation and the oscillator resonate. A few details about the construction of various musical instruments are discussed, with particular attention to the generation of edge tones in organ pipes of recorders, bowing of violin strings, the piano key-hammer-string mechanism, but also fingerboard frets, finger holes, tone holes, keys, and valves that change the length of strings or wind instruments.
Quantizer Optimization Based on Uniform Iterative Expansion of Quantization Intervals

We propose a quantizer optimization algorithm using uniform iterative expansion of quantization intervals. The algorithm allows you to optimize quantizers for an arbitrary quality measure. When optimized, the quantizers remain valid in terms of the given constraints. We carry out computational experiments to study the proposed algorithm when optimizing the quantizer in terms of the entropy quality measure with a constraint on the RMSE within the framework of image compression. We use a compression method based on the representation of the image in the form of a quadtree. We experimentally prove that the proposed optimization algorithm allows one to construct quantizers whose efficiency is insignificantly different from the optimal quantizer.
The future of NLP research

This chapter concludes the series by offering a summary of both the evidence for NLP applications in a clinical context and the gaps, concerns or controversies that remain. The authors provide a debate about the place for practitioner led research alongside rigorous clinical trials. Recommendations are made for future research directions.
Privilege Escalation Focused Offensive Security Training Platform

While the offensive cybersecurity training market is growing on a regular basis, the industry still lacks the focus on skills required for internal privilege escalation operations. The purpose of this project is to design and build a training system that focuses mainly on improving the escalation of privileges skills among offensive security personnel. The project aims to ultimately fill the existent skill gap when it comes to privilege escalation techniques and methodologies. This gap can negatively impact the way organizations perceives the security status of their internal digital systems. With the “assume compromise” approach increasingly adopted by large enterprises, this project emphasizes the importance of possessing the knowledge and skills that cyber adversaries would use in a post-compromise situation in order to develop and implement the necessary countermeasures. This project proposes a system that utilizes some of the latest containerization and virtualization technologies to design and build portable and highly scalable training exercises inspired by real-world privilege escalation scenarios. The development process of the system proposed in this project will follow the Iterative and Incremental Development (IID) software development methodology. The IID methodology will divide the system into multiple increments that are developed and integrated over multiple iterations. In conclusion, this project is believed to be of a noticeable contribution to the offensive cybersecurity training industry which might open the doors to more sophisticated contributions and improvements.