Reviewerly/exHarmony huggingface dataset:
INFORMAL RELATIONSHIPS ON EMPLOYEE JOB PERFORMANCE: A COMPARATIVE STUDY

This research is aimed at determining and comparing the relationship between informal relationships and employee job performance in the selected technical secondary schools (TSSs) of Muhanga District, Southern Province, in Rwanda. The study was conducted in Ecole Secondaire Bulinga and College Saint Peter of Shyogwe. During this research, both quantitative and qualitative approaches were used in collecting and analyzing data. The population of the study was 43 employees where only 21 out of 26 employees of Ecole Secondaire Bulinga and 17 of College Saint Peter of Shyogwe contributed to the study. The primary data was collected using questionnaires, interviews, and observation; numerical data was analyzed by SPSS software version 22 using descriptive method and analyzed using Pearson correlation coefficient to come up with its interpretation. This study was guided by a null hypothesis which presented no significant influence of informal relationships on employee job performance in the selected TSSs. The statistical evidence depicts that there is a moderate significant relationship between those variables as Pearson Correlation Coefficient is .659 showing a Positive Correlation for ESB while that of College Saint Peter of Shyogwe was .564 which is also positive and significant. Therefore, the null hypothesis was rejected while alternative hypothesis accepted and the researcher confirmed that there exists an influence of informal relationships on employee job performance in the selected TSSs. In the view of the findings derived from the study, some recommendations were made to the selected TSSs like setting informal relationships policies and strategies in order to prevent any negative outcome which can result from relationships that are detrimental to employee job performance and the overall organizational performance.
Categorizing user stories in the software engineering classroom

User story documentation is a significant aspect of Agile development in which developers document possible actions that users may take within a software system. It is essential to educate students...
Numerical modeling of sub-surface drainage systems in Citraland, West Surabaya

Abstract Flooding occurs when the existing drainage system is unable to accommodate the water flow, that is due to a decreased drainage system capacity, increased flow of water, or a combination of both. On January 31, 2019, two hours of heavy rain caused Citraland housing to be flooded up to one meter high. This study aims to overcome the existing flooding problems through the assessment of sub-surface drainage systems for hilly areas in the Citraland residential area, Surabaya. Annual rainfall data from the rain stations around the residential area will be processed according to the proportion of the area to get a rain analysis plan that will be used in modeling. Plan rainfall data, area data and types of land cover, and catchment area data are analyzed hydrologically to get the design discharge. The hydraulic data of the dimensions of the existing drainage system will be analyzed for the capacity in flowing of Flow Plan. The Drainage System is then modeled using the Storm Water Management Model (SWMM) application. Through using topographic data and land cover, four alternative solutions have been modeled which include the addition of water reservoirs in various housing locations to overcome existing flooding. The results of this study are the second scenario through the addition of one water reservoirs which can handle flooding most effectively because it reduces 34.61% of the inundation volume that occurs. In addition, the fourth scenario through the addition of three water reservoirs can reduce 57.82% of the inundation volume that occurs.
A Deep Learning Based Method to Measure the Similarity of Long Text

For complex text data, especially for long text data, in order to measure the text similarity, the traditional methods are not accurate enough. We found that it is mainly because the feature representation ability is not strong enough. To improve the accuracy of long text similarity, an algorithm based on pre-training deep learning model is proposed to extract features of long text. On the benchmark data set of THUCNews corpus, the accuracy of our method is 5.4% higher than that of the traditional algorithm. Besides, we perform ablation experiments to test the improvement of fine-tuning technology.
Corrigendum: BIIGLE 2.0 - Browsing and Annotating Large Marine Image Collections

In the original article, there is an error. Readers were directed to the link "https://dias.cebitec.unibielefeld.de/" for the web application that was presented in the original article. This link is now no longer valid and should be changed to "https://biigle.de".A correction has been made to Section 1 (Introduction), last paragraph:In this paper we present a new version of our early proposed system BIIGLE (Ontrup et al., 2009), which was introduced 8 years ago with a primary focus on collecting annotations to train machine learning algorithms. However, BIIGLE's mission transformed more and more to be used as a tool for collaborative web-based manual marine image annotation. The next generation in BIIGLE's history was the (BIIGLE-) DIAS tool, which was a rapid prototype to be used offshore during two cruises as part of the JPIO Ecomining project. The new version presented in this paper is referred to as BIIGLE 2.0 and in the next section the software architecture is motivated and described. Afterwards we will show, how the three aspects I-III are addressed with the functions of BIIGLE 2.0 using different data sets provided by the collaborators and users. The paper concludes with a discussion and outlook. The web application can be found at https:// biigle .de/ using biiglepaper@example.com as username and frontiersinm as password.
Design of Control Systems Using Active Uncertainty Reduction-Based Reinforcement Learning

Abstract Model-free reinforcement learning based methods such as Proximal Policy Optimization, or Q-learning typically require thousands of interactions with the environment to approximate the optimal controller which may not always be feasible in robotics due to safety and time consumption. Model-based methods such as PILCO or BlackDrops, while data-efficient, provide solutions with limited robustness and complexity. To address this tradeoff, we introduce active uncertainty reduction-based virtual environments, which are formed through limited trials conducted in the original environment. We provide an efficient method for uncertainty management, which is used as a metric for self-improvement by identification of the points with maximum expected improvement through adaptive sampling. Capturing the uncertainty also allows for better mimicking of the reward responses of the original system. Our approach enables the use of complex policy structures and reward functions through a unique combination of model-based and model-free methods, while still retaining the data efficiency. We demonstrate the validity of our method on several classic reinforcement learning problems in OpenAI gym. We prove that our approach offers a better modeling capacity for complex system dynamics as compared to established methods.
Review of the monograph by V. S. Kruglyk "The system of training of future software engineers for the professional activity at higher educational institutions"

In the publication the scientific-theoretical and methodological monographic study of V. S. Kruglyk "The system of training of future software engineers for the professional activity at higher educational institutions" has been analyzed, the study reflects the author conceptual approach to the scientific understanding of the problem of training software engineers for professional activities. The structure and content of the monograph have been analyzed; the scientific novelty of the research which consists in the development and substantiation of the author pedagogical system of training future software engineers for their professional activity and the structure of professional competence of a software engineer has been highlighted.
Free-Form Image Inpainting via Contrastive Attention Network

Most deep learning based image inpainting approaches adopt autoencoder or its variants to fill missing regions in images. Encoders are usually utilized to learn powerful representational spaces, which are important for dealing with sophisticated learning tasks. Specifically, in image inpainting tasks, masks with any shapes can appear anywhere in images (i.e., free-form masks) which form complex patterns. It is difficult for encoders to capture such powerful representations under this complex situation. To tackle this problem, we propose a self-supervised Siamese inference network to improve the robustness and generalization. It can encode contextual semantics from full resolution images and obtain more discriminative representations. we further propose a multi-scale decoder with a novel dual attention fusion module (DAF), which can combine both the restored and known regions in a smooth way. This multi-scale architecture is beneficial for decoding discriminative representations learned by encoders into images layer by layer. In this way, unknown regions will be filled naturally from outside to inside. Qualitative and quantitative experiments on multiple datasets, including facial and natural datasets (i.e., Celeb-HQ, Pairs Street View, Places2 and ImageNet), demonstrate that our proposed method outperforms state-of-the-art methods in generating high-quality inpainting results.
FTBNN: Rethinking Non-linearity for 1-bit CNNs and Going Beyond

Binary neural networks (BNNs), where both weights and activations are binarized into 1 bit, have been widely studied in recent years due to its great benefit of highly accelerated computation and substantially reduced memory footprint that appeal to the development of resource constrained devices. In contrast to previous methods tending to reduce the quantization error for training BNN structures, we argue that the binarized convolution process owns an increasing linearity towards the target of minimizing such error, which in turn hampers BNN's discriminative ability. In this paper, we re-investigate and tune proper non-linear modules to fix that contradiction, leading to a strong baseline which achieves state-of-the-art performance on the large-scale ImageNet dataset in terms of accuracy and training efficiency. To go further, we find that the proposed BNN model still has much potential to be compressed by making a better use of the efficient binary operations, without losing accuracy. In addition, the limited capacity of the BNN model can also be increased with the help of group execution. Based on these insights, we are able to improve the baseline with an additional 4~5% top-1 accuracy gain even with less computational cost. Our code will be made public at https://github.com/zhuogege1943/ftbnn.
Traffic Data Prediction Based on Complex-Valued S-System Model

To predict traffic data accurately could make an important role in network management. In order to improve forecasting accuracy, this paper proposes complex-valued S-system model (CVSS) forecast small-scale traffic data. According to the form of CVSS model, complex-valued restricted gene expression programming (CVRGEP) is utilized to search the optimal the representation of CVSS. Complex-valued differential evolution (CVDE) is proposed to evolve the parameters of model. The small-scale traffic data is utilized to test our method. Our method has better prediction performances than neural network (NN), radial basis function neural network (RBF), flexible neural tree (FNT), ordinary differential equation (ODE) and S-system.
Research and Practice of Computer-assisted Japanese Intensive Reading Education

Abstract With economic globalization, China’s foreign exchanges have been increasing. As a neighbouring country of China, Japan has close trade relations with China and has a large amount of cultural output. People’s learning of Japanese is also becoming more common. Using computer tools to make Japanese intensive reading education a higher level is the goal of educators’ current research and practice. This article will explain from three aspects. First, the author will clarify the teaching content and teaching goals of Japanese intensive reading. Second, the author will analyze the current use of basic computer-aided tools and analyze the advantages and disadvantages, including the related auxiliary word software MOJi as an example. Software suitable for training reading is based on the Asahi Shimbun, and computer-assisted translation tools are based on TRADOS. Finally, the author will summarize and prospect the current computer tools.
A short note on the decision tree based neural turing machine

Turing machine and decision tree have developed independently for a long time. With the recent development of differentiable models, there is an intersection between them. Neural turing machine(NTM) opens door for the memory network. It use differentiable attention mechanism to read/write external memory bank. Differentiable forest brings differentiable properties to classical decision tree. In this short note, we show the deep connection between these two models. That is: differentiable forest is a special case of NTM. Differentiable forest is actually decision tree based neural turing machine. Based on this deep connection, we propose a response augmented differential forest (RaDF). The controller of RaDF is differentiable forest, the external memory of RaDF are response vectors which would be read/write by leaf nodes.
DeepRite: Deep Recurrent Inverse TreatmEnt Weighting for Adjusting Time-varying Confounding in Modern Longitudinal Observational Data

Counterfactual prediction is about predicting outcome of the unobserved situation from the data. For example, given patient is on drug A, what would be the outcome if she switch to drug B. Most of existing works focus on modeling counterfactual outcome based on static data. However, many applications have time-varying confounding effects such as multiple treatments over time. How to model such time-varying effects from longitudinal observational data? How to model complex high-dimensional dependency in the data? To address these challenges, we propose Deep Recurrent Inverse TreatmEnt weighting (DeepRite) by incorporating recurrent neural networks into two-phase adjustments for the existence of time-varying confounding in modern longitudinal data. In phase I cohort reweighting we fit one network for emitting time dependent inverse probabilities of treatment, use them to generate a pseudo balanced cohort. In phase II outcome progression, we input the adjusted data to the subsequent predictive network for making counterfactual predictions. We evaluate DeepRite on both synthetic data and a real data collected from sepsis patients in the intensive care units. DeepRite is shown to recover the ground truth from synthetic data, and estimate unbiased treatment effects from real data that can be better aligned with the standard guidelines for management of sepsis thanks to its applicability to create balanced cohorts.
High-accuracy optical extensometer realized by two parallel cameras and two-dimensional digital image correlation

A conventional optical extensometer realized by a single common camera and two-dimensional digital image correlation (2D-DIC) often provides unsatisfactory strain results owing to the out-of-plane motion of the specimen. In this work, we propose an improved optical extensometer based on two parallel cameras and 2D-DIC. In the proposed extensometer, the gauge points are selected at the image centers of two cameras, which are negligibly affected by the out-of-plane translation and rotation, leading to higher accuracy of strain measurement as compared with the conventional optical extensometer. A rigid out-of-plane translation experiment and four repeated uniaxial tensile tests were conducted to verify the feasibility, reliability, and accuracy of the proposed method. Experimental results indicate that the proposed method has a strong ability to resist the effect of out-of-plane motion and experimental vibrations. Moreover, the strain measurement results obtained with the proposed method were found to be in excellent agreement with those obtained with a strain gauge, and the strain errors between them were only a few microstrains. Given that no compensation method is required, the proposed method is easy to implement with 2D-DIC and can be used for specimens of different sizes by adjusting the distance between the two cameras.
Generalized Sorting with Predictions

Generalized sorting problem, also known as sorting with forbidden comparisons, was first introduced by Huang et al. together with a randomized algorithm which requires $\tilde O(n^{3/2})$ probes. We study this problem with additional predictions for all pairs of allowed comparisons as input. We propose a randomized algorithm which uses $O(n \log n+w)$ probes with high probability and a deterministic algorithm which uses $O(nw)$ probes, where $w$ is the number of mistakes made by prediction.
ANALISIS PERAN PARTISIPASI MASYARAKAT DAN PEMERINTAH DESA DALAM UPAYA PENGELOLAAN DAN PELESTARIAN CAGAR BUDAYA DI DESA KOTA KAPUR KECAMATAN MENDO BARAT KABUPATEN BANGKA

Indonesia is a country that is rich in historical heritage, one of which is a cultural heritage site which is a legacy that must be maintained and managed properly. As a national asset, cultural heritage sites have historical values that can be used as lessons for the nation's future generations. A famous historical relic in the Province of Bangka Belitung Islands is the site of the city of Kapur which is located in Kota Kapur Village, Mendo Barat District, Bangka Regency. From various information obtained, the site of the city of Kapur is closely related to the Srivijaya kingdom. In the area of the site, the city of Kapur Inscription was found and other findings that have been studied by archaeologists. Seeing the importance of the historical value of the Kapur city site, researchers are interested in conducting research on the role of the community and local village government in relation to the management and preservation of the Lime City site. This type of research is a descriptive study by presenting all field findings in an explanatory and empirical way
GEOCHEMICAL AND SEDIMENTOLOGICAL EVIDENCE OF THE PALAEOPROTEROZOIC OCEANIC ARC-BACKARC DEPOSITIONAL ENVIRONMENT OF THE GRANITES-TANAMI OROGEN, AUSTRALIA

The Palaeoproterozoic Granites-Tanami Orogen in central Australia is an important orogenic gold province. Gold mineralisation is hosted by structurally-controlled epigenetic quartz veins with a suggested mineralisation age of ~1805 and ~1795 Ma associated with granitic intrusions. The quartz veins are emplaced in the Tanami Group, which consists of mostly lower-greenschist metamorphic grade volcaniclastic-sedimentary rocks and basalts. The Tanami Group depositional age is between ~1900 and 1850 Ma, and was deposited in an oceanic back-arc basin setting. Due to lack of exposure and the complex structural setting, the stratigraphic evolution of this basin is poorly understood, and regional stratigraphic correlations are difficult. In this presentation, we present sedimentological interpretations together with newly developed geochemical tools for regional-scale correlation. High field strength elements (HFSE) were used to classify basalts in different study areas and are interpreted to be geochemically affiliated with either backarc basin (Supplejack area) or volcanic arc (Capstan and Bluebush areas). Massive pyroclastic flow deposits and interflow sedimentary rocks have a mafic geochemical signature close to a basaltic composition of intermittent basalt flows. In contrast, thinly bedded to laminated volcaniclastic-sedimentary rocks have an increasingly more andesitic to dacitic composition and are overlying the massive mafic-derived successions. It is interpreted that the change in composition is related to changing igneous composition in a maturing volcanic arc. Therefore, we can use the geochemical composition for the volcaniclastic-sedimentary rocks of this succession as relative age and depositional environment indicator in a deformed, partly preserved oceanic basin succession that is poorly exposed and lacks drill core data.
Effect of Audio Visual Learning Media on Student Learning Motivation of Islamic Education

The main problem in this study is the influence of the use of PAI learning media to increase the motivation of learning students in SMP N 2 Srandakan. The purpose in this study is to find out how the use of Audio-Visual learning media affects the motivation of learning students in PAI maple in SMP N 2 Srandakan. In this study, researchers obtained data using interviews, written tests, and documentation, intended to collect data procedurally, then processed and analyzed according to the collected data. The result of this study is the influence of the use of audio visual learning media on the motivation of learning students in pai maple, this is evidenced by the test results of students after learning by using audio visual media in the form of videos about congregational prayer. Students feel more interested in delving into the material being taught in the form of a learning video than reading matrial text.
Microsaccades around the visual field

[Goal] Visual performance fields (PFs) are characterized by two asymmetries: Performance is better along the horizontal than vertical meridian (Horizontal-Vertical Anisotropy) and along the lower than upper vertical meridian (Vertical Meridian (VM) Asymmetry). Here, we investigated how the directionality of microsaccades (MS, fixational eye movements <1°) vary during a visual discrimination task with stimuli placed along the cardinal meridia, for which discriminability varies (Experiment 1) or is equated across all locations (Experiment 2). [Methods] Observers performed a 2AFC orientation-discrimination task, which depends on contrast sensitivity, while maintaining fixation. On every trial, 4 Gabors briefly appeared simultaneously at 4 cardinal, isoeccentric (6.4°) locations. Shortly thereafter, a response cue indicated the target. In Experiment 1, contrast was fixed across locations. In Experiment 2, we equated task performance across all locations using different contrast thresholds per location. Eye fixation was monitored with an eye-tracker. Microsaccades were detected with a standard velocity-based detection algorithm (Engbert & Kliegl, 2003). [Results] We found typical PFs in the discrimination task for Experiment 1, and same discrimination across locations (by design) in Experiment 2. However, MS patterns were similar in both experiments. From trial- to response- cue onset, MS were more frequent toward the horizontal and upper-vertical locations than the lower-vertical location. But immediately after response cue onset, observers made more MS toward the target meridia; i.e. horizontal (vertical) bias when targets were on the horizontal (vertical) meridian, and more MS toward the upper- than lower- VM. [Conclusions] MS-direction follow the same pattern regardless of whether discriminability differs or is similar across isoeccentric locations. Interestingly, when stimuli are placed along the cardinal meridia, there is no overall MS horizontal bias. Once observers know the target location, MS are biased along the corresponding meridia, and in particular to the location where discriminability is typically lowest (upper-VM).