Reviewerly/exHarmony huggingface dataset:
The effect of motivation on achieving job satisfaction through organizational justice - A field study on employees of the General Directorates of Education in the Sultanate of Oman –: أثر التحفيز على تحقيق الرضا الوظيفي من خلال العدالة التنظيمية - دراسة ميدانية على موظفي المديريات العامة للتربية والتعليم بسلطنة عُمان –

The study aims at examining the validity of the influence of financial and moral motivation on job satisfaction through organizational justice as a mediator (an influential) variable of personnel at the general directorates of general education in the Sultanate of Oman, as well as investigating the nature of relations within the virtual model of the study, The researcher used a questionnaire as an instrument that was designed based on previous studies of each variable and was distributed to a random sample of (381) individuals from a population of (4137) individuals working in each of the education directorates in sultanate of Oman. The results of the study showed that there is a positive influence of financial and moral motivation on achieving job satisfaction, as the values of the standard evaluations showed that moral incentives will contribute (by 40%) in achieving job satisfaction and that financial incentives will contribute (by 60%) in achieving job satisfaction. The study also concluded that there are significant differences in the moral significance of the influential variable on the relationship between financial and moral motivation and job satisfaction in favor of the samples that support the organizational justice in the concerned public directorates. The study recommends raising the level of job satisfaction of personnel by setting a system for incentives based on justice and objectivity and providing an environment that helps the staff feel positive towards the directorates.
A Uniform Quantum Computing Model based on Virtual Quantum Processors

What else than Hybrid Quantum Computing will dominate data centers, just within a decade? But until now, it is unclear how precisely such high-performance computers will be constructed, even the fundamental functionality is in question. Thus, it is very hard to prepare today, from a programmer perspective, in-depth for the anticipated near-term programming paradigm shift due to quantum computers. Furthermore, the industry is reluctant to invest heavily into quantum software development right now, since under this uncertainty regarding the future development of hardware and Cloud services, no one would like to see all these efforts to be ruined by a novel technology, an unforeseen new machine, which would eradicate the software in place, which the industry had developed so costly. This calls for a more generic view on Hybrid Quantum Computing, on one hand hardware agnostic, but still anticipating the fundamental laws of nature which rule any future quantum computing system, regardless of its engineered excellence. This generalization can be done with the introduction of a Virtual Quantum Processor, a piece of imaginary hardware, which is described detailed enough, to emulate a generic hybrid quantum machine based on a set of instructions within a Turing machine. If this could be accomplished, we would retrieve a Uniform Computing Model for Hybrid Quantum Software, which can be applied later to any physical representation of quantum computing hardware, but would run already today on our current machines.
Specular Reflections Detection and Removal Based on Deep Neural Network for Endoscope Images

Specular reflections have always been undesirable when processing endoscope vision for clinical purpose. Scene afflicted with strong specular reflection could result in visual confusion for the operation of surgical robot. In this paper, we propose a novel model based on deep learning framework, known as Surgical Fix Deep Neural Network (SFDNN). This model can effectively detect and fix the reflection points in different surgical videos hence opening up a whole new approach in handling undesirable specular reflections.
DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation

In this paper, we provide a bilingual parallel human-to-human recommendation dialog dataset (DuRecDial 2.0) to enable researchers to explore a challenging task of multilingual and cross-lingual conversational recommendation. The difference between DuRecDial 2.0 and existing conversational recommendation datasets is that the data item (Profile, Goal, Knowledge, Context, Response) in DuRecDial 2.0 is annotated in two languages, both English and Chinese, while other datasets are built with the setting of a single language. We collect 8.2k dialogs aligned across English and Chinese languages (16.5k dialogs and 255k utterances in total) that are annotated by crowdsourced workers with strict quality control procedure. We then build monolingual, multilingual, and cross-lingual conversational recommendation baselines on DuRecDial 2.0. Experiment results show that the use of additional English data can bring performance improvement for Chinese conversational recommendation, indicating the benefits of DuRecDial 2.0. Finally, this dataset provides a challenging testbed for future studies of monolingual, multilingual, and cross-lingual conversational recommendation.
Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline

Aligning sentences in a reference summary with their counterparts in source documents was shown as a useful auxiliary summarization task, notably for generating training data for salience detection. Despite its assessed utility, the alignment step was mostly approached with heuristic unsupervised methods, typically ROUGE-based, and was never independently optimized or evaluated. In this paper, we propose establishing summary-source alignment as an explicit task, while introducing two major novelties: (1) applying it at the more accurate proposition span level, and (2) approaching it as a supervised classification task. To that end, we created a novel training dataset for proposition-level alignment, derived automatically from available summarization evaluation data. In addition, we crowdsourced dev and test datasets, enabling model development and proper evaluation. Utilizing these data, we present a supervised proposition alignment baseline model, showing improved alignment-quality over the unsupervised approach.
A Generic Approach for Relative Regular-Frequent Pattern Mining in Retail Supermarket

Regular-Frequent item-sets mining is a challenging data mining concept to discover interesting and useful patterns in retail transactional market databases. It offers the most extreme advantages to a retailer for product placements, segmentation, layout designs, promoting strategies and different vending decisions to enhance customer satisfaction as well as increase sales. Most of the existing studies consider only the frequent patterns that strictly satisfy the user defined maximum regularity threshold level for all of its periods in the transactional database. Many frequent patterns satisfy the threshold level in almost all of its periods but these patterns are not considered regular-frequent pattern due to the strict periodicity measure. The retail market is highly seasonal. Many seasonal item-sets regularly appears at particular time intervals in a transactional database, those aren't regular frequent at all. In this paper, we propose a generic and flexible approach for mining relative seasonal regular-frequent patterns in retail transactional databases. The dataset has been generated from transaction slips in a large Supermarket of Bangladesh. Our results represent the superiority over the traditional systems in terms of discovering many hidden useful patterns in the transactional database for making easy merchandising decisions.
Recent Advances in Automated Question Answering In Biomedical Domain.

The objective of automated Question Answering (QA) systems is to provide answers to user queries in a time efficient manner. The answers are usually found in either databases (or knowledge bases) or a collection of documents commonly referred to as the corpus. In the past few decades there has been a proliferation of acquisition of knowledge and consequently there has been an exponential growth in new scientific articles in the field of biomedicine. Therefore, it has become difficult to keep track of all the information in the domain, even for domain experts. With the improvements in commercial search engines, users can type in their queries and get a small set of documents most relevant for answering their query, as well as relevant snippets from the documents in some cases. However, it may be still tedious and time consuming to manually look for the required information or answers. This has necessitated the development of efficient QA systems which aim to find exact and precise answers to user provided natural language questions in the domain of biomedicine. In this paper, we introduce the basic methodologies used for developing general domain QA systems, followed by a thorough investigation of different aspects of biomedical QA systems, including benchmark datasets and several proposed approaches, both using structured databases and collection of texts. We also explore the limitations of current systems and explore potential avenues for further advancement.
Predicting Text Readability from Scrolling Interactions

Judging the readability of text has many important applications, for instance when performing text simplification or when sourcing reading material for language learners. In this paper, we present a 518 participant study which investigates how scrolling behaviour relates to the readability of English texts. We make our dataset publicly available and show that (1) there are statistically significant differences in the way readers interact with text depending on the text level, (2) such measures can be used to predict the readability of text, and (3) the background of a reader impacts their reading interactions and the factors contributing to text difficulty.
Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation

Human explanation (e.g., in terms of feature importance) has been recently used to extend the communication channel between human and agent in interactive machine learning. Under this setting, human trainers provide not only the ground truth but also some form of explanation. However, this kind of human guidance was only investigated in supervised learning tasks, and it remains unclear how to best incorporate this type of human knowledge into deep reinforcement learning. In this paper, we present the first study of using human visual explanations in human-in-the-loop reinforcement learning (HRL). We focus on the task of learning from feedback, in which the human trainer not only gives binary evaluative good or bad feedback for queried state-action pairs, but also provides a visual explanation by annotating relevant features in images. We propose EXPAND (EXPlanation AugmeNted feeDback) to encourage the model to encode task-relevant features through a context-aware data augmentation that only perturbs irrelevant features in human salient information. We choose five tasks, namely Pixel-Taxi and four Atari games, to evaluate the performance and sample efficiency of this approach. We show that our method significantly outperforms methods leveraging human explanation that are adapted from supervised learning, and Human-in-the-loop RL baselines that only utilize evaluative feedback.
Convergence and Alignment of Gradient Descent with Random Backpropagation Weights

Stochastic gradient descent with backpropagation is the workhorse of artificial neural networks. It has long been recognized that backpropagation fails to be a biologically plausible algorithm. Fundamentally, it is a non-local procedure -- updating one neuron's synaptic weights requires knowledge of synaptic weights or receptive fields of downstream neurons. This limits the use of artificial neural networks as a tool for understanding the biological principles of information processing in the brain. Lillicrap et al. (2016) propose a more biologically plausible "feedback alignment" algorithm that uses random and fixed backpropagation weights, and show promising simulations. In this paper we study the mathematical properties of the feedback alignment procedure by analyzing convergence and alignment for two-layer networks under squared error loss. In the overparameterized setting, we prove that the error converges to zero exponentially fast, and also that regularization is necessary in order for the parameters to become aligned with the random backpropagation weights. Simulations are given that are consistent with this analysis and suggest further generalizations. These results contribute to our understanding of how biologically plausible algorithms might carry out weight learning in a manner different from Hebbian learning, with performance that is comparable with the full non-local backpropagation algorithm.
A practical perspective on connective generation

In data-driven natural language generation, we typically know what relation should be expressed and need to select a connective to lexicalize it. In the current contribution, we analyse whether a sophisticated connective generation module is necessary to select a connective, or whether this can be solved with simple methods (such as random choice between connectives that are known to express a given relation, or usage of a generic language model). Comparing these methods to the distributions of connective choices from a human connective insertion task, we find mixed results: for some relations, it is acceptable to lexicalize them using any of the connectives that mark this relation. However, for other relations (temporals, concessives) either a more detailed relation distinction needs to be introduced, or a more sophisticated connective choice module would be necessary.
SISTEM PAKAR DIAGNOSA PENYAKIT KUCING DENGAN METODE FORWARD CHAINING BERBASIS WEB

The purpose of this research is to design and build the Expert System for Diagnosis Disease of Cats Using the Method of Forward Chaining Web-Based. The development method used is the method of ESDLC and inference engine Forward Chaining. The algorithm used by researchers in this expert system uses an Algorithm Depth-First Search (DFS). The results of this research are expert systems that can diagnose the disease and Treatment how to handle it.Keywords: Cat Disease, DFS, ESDLC, Expert System, Forward Chaining.
Open Access beyond Publications: Data, Software, Samples

Open Access not only promotes the open availability of peerreviewed research articles, but of all research outputs including data, software, and samples.Open access to data, software, and samples is essential to build trust in science and to accelerate discovery and creation of new knowledge.In order to fulfill these promises, data, samples, and code need to be managed in a way that they are Findable, Accessible, Interoperable, and Reusable (FAIR).This presentation will discuss the status of Open and FAIR data and samples in Geochemistry, identify current gaps and challenges, and propose actions for overcoming these.The increasing implementation of open data policies at funding agencies and publishers has improved data sharing in Geochemistry over recent years.Many journals in the Earth Sciences have adopted the FAIR principles and no longer accept data as supplementary files, but rather request that data supporting publications are submitted to trusted repositories and linked to the related papers via persistent identifiers.Similar demands are implemented by funders.Geochemical data repositories such as the EarthChem Library have consequently seen a substantial increase in data submissions.Globally, new data repositories have emerged that host geochemical data.The primary challenge remaining today is the lack of common protocols and vocabularies for formatting and documenting geochemical data so they can be trusted, reused with confidence, and easily integrated with equivalent datasets for advanced data analysis.Editors of journals need community-endorsed guidelines for data reporting in publications that authors should comply with.Similarly, data repositories need communityendorsed guidelines to ensure that the data they curate are not only findable and accessible online, but that they are reusable and interoperable with those of other repositories.Standards are also needed to enable an ecosystem of interoperable tools that help researchers and labs manage geochemical data through their life time from collection to archiving.International initiatives such as OneGeochemistry aim to take on the development and promotion of data standards for geochemistry.A workshop is planned for 2021 to start the conversation between members of geochemical societies, repositories that house geochemical data, editors and publishers of geochemical journals, and funders.
Scalable Rule-Based Representation Learning for Interpretable Classification

Rule-based models, e.g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity. However, rule-based models are hard to optimize, especially on large data sets, due to their discrete parameters and structures. Ensemble methods and fuzzy/soft rules are commonly used to improve performance, but they sacrifice the model interpretability. To obtain both good scalability and interpretability, we propose a new classifier, named Rule-based Representation Learner (RRL), that automatically learns interpretable non-fuzzy rules for data representation and classification. To train the non-differentiable RRL effectively, we project it to a continuous space and propose a novel training method, called Gradient Grafting, that can directly optimize the discrete model using gradient descent. An improved design of logical activation functions is also devised to increase the scalability of RRL and enable it to discretize the continuous features end-to-end. Exhaustive experiments on nine small and four large data sets show that RRL outperforms the competitive interpretable approaches and can be easily adjusted to obtain a trade-off between classification accuracy and model complexity for different scenarios. Our code is available at: this https URL.
Structured Denoising Diffusion Models in Discrete State-Spaces

Denoising diffusion probabilistic models (DDPMs) (Ho et al. 2020) have shown impressive results on image and waveform generation in continuous state spaces. Here, we introduce Discrete Denoising Diffusion Probabilistic Models (D3PMs), diffusion-like generative models for discrete data that generalize the multinomial diffusion model of Hoogeboom et al. 2021, by going beyond corruption processes with uniform transition probabilities. This includes corruption with transition matrices that mimic Gaussian kernels in continuous space, matrices based on nearest neighbors in embedding space, and matrices that introduce absorbing states. The third allows us to draw a connection between diffusion models and autoregressive and mask-based generative models. We show that the choice of transition matrix is an important design decision that leads to improved results in image and text domains. We also introduce a new loss function that combines the variational lower bound with an auxiliary cross entropy loss. For text, this model class achieves strong results on character-level text generation while scaling to large vocabularies on LM1B. On the image dataset CIFAR-10, our models approach the sample quality and exceed the log-likelihood of the continuous-space DDPM model.
Exploring Decomposition for Table-based Fact Verification

Fact verification based on structured data is challenging as it requires models to understand both natural language and symbolic operations performed over tables. Although pre-trained language models have demonstrated a strong capability in verifying simple statements, they struggle with complex statements that involve multiple operations. In this paper, we improve fact verification by decomposing complex statements into simpler subproblems. Leveraging the programs synthesized by a weakly supervised semantic parser, we propose a program-guided approach to constructing a pseudo dataset for decomposition model training. The subproblems, together with their predicted answers, serve as the intermediate evidence to enhance our fact verification model. Experiments show that our proposed approach achieves the new state-of-the-art performance, an 82.7\% accuracy, on the TabFact benchmark.
Neural Correlates of Adaptive Behavior: Structure, Dynamics, and Information Processing

Dissertation (Ph.D.) - Indiana University, School of Informatics, Computing, and Engineering, 2019
The gross domestic product and its effect on spending on human resource development and community health in the Kingdom of Saudi Arabia - period. 2000 – 2017: الناتج المحلي الإجمالي وأثره في الإنفاق على تنمية الموارد البشرية وصحة المجتمع في المملكة العربية السعودية – للفترة 2000-2017 م

The objective of this study is to analyze the gross domestic product (GDP) and its impact on the expenses on the development of human resources and the community health in the kingdom of Saudi Arabia period 2000/2017. In fact, the GDP has been used as a separate variable while the expenditure on both the human resource development and community health in Saudi Arabia is used as a follow-up variable. an analytical prescriptive approach has been followed concluding that there is a strong moral effect of spending on human resource development as a percentage of GDP amounted to 1.03. In fact, through a descriptive analytical approach, this study concluded that there is a relationship with a strong moral impact of spending on both human resource development, as a proportion of the gross domestic product, and health, as a percentage of GDP amounted to 1.09.. The study also recommended the increase of the general public spending on human resources, as a percentage of local production and the increase of the proportion of public spending allocated to community health and localization of treatment within the Kingdom of Saudi Arabia.
Aggressive Q-Learning with Ensembles: Achieving Both High Sample Efficiency and High Asymptotic Performance

Recent advances in model-free deep reinforcement learning (DRL) show that simple model-free methods can be highly effective in challenging high-dimensional continuous control tasks. In particular, Truncated Quantile Critics (TQC) achieves state-of-the-art asymptotic training performance on the MuJoCo benchmark with a distributional representation of critics; and Randomized Ensemble Double Q-Learning (REDQ) achieves high sample efficiency that is competitive with state-of-the-art model-based methods using a high update-to-data ratio and target randomization. In this paper, we propose a novel model-free algorithm, Aggressive Q-Learning with Ensembles (AQE), which improves the sample-efficiency performance of REDQ and the asymptotic performance of TQC, thereby providing overall state-of-the-art performance during all stages of training. Moreover, AQE is very simple, requiring neither distributional representation of critics nor target randomization. The effectiveness of AQE is further supported by our extensive experiments, ablations, and theoretical results.