Reviewerly/exHarmony huggingface dataset:
PRACTICES, PITFALLS AND GUIDELINES IN VISUALISING LAGRANGIAN OCEAN ANALYSES

Abstract. The Lagrangian analysis of particulate matter, biota and drifters, which are dispersed by turbulent fluid currents, is a cornerstone of oceanographic studies, covering diverse study objectives. The results of Lagrangian simulations and observations is predominantly visualised by means of easy-access plotting interfaces and simple presentation techniques. We analysed over 50 publications from the years 2010–2020 with respect to their visual design to deduce common visualisation practices in the domain. Individual figures are analysed towards adherence to visualisation best-practices, algebraic visualisation guidelines and the IPCC visual style guide. In this article, we present the resulting best-practices and common pitfalls in the design of Lagrangian ocean visualisations. Based on this visual study, we highlight that raising awareness of established visual guidelines may have a higher impact on improving the visual quality of publications in oceanography than the vigorous development of more general-purpose visualisation tools.
Predictive modeling of parafoveal information processing during reading

Abstract Skilled reading requires information processing of the fixated and the not-yet-fixated words to generate precise control of gaze. Over the last 30 years, experimental research provided evidence that word processing is distributed across the perceptual span, which permits recognition of the fixated (foveal) word as well as preview of parafoveal words to the right of fixation. However, theoretical models have been unable to differentiate the specific influences of foveal and parafoveal information on saccade control. Here we show how parafoveal word difficulty modulates spatial and temporal control of gaze in a computational model to reproduce experimental results. In a fully Bayesian framework, we estimated model parameters for different models of parafoveal processing and carried out large-scale predictive simulations and model comparisons for a gaze-contingent reading experiment. We conclude that mathematical modeling of data from gaze-contingent experiments permits the precise identification of pathways from parafoveal information processing to gaze control, uncovering potential mechanisms underlying the parafoveal contribution to eye-movement control.
Finding Optimal Paths Using Networks Without Learning—Unifying Classical Approaches

Trajectory or path planning is a fundamental issue in a wide variety of applications. In this article, we show that it is possible to solve path planning on a maze for multiple start point and endpoint highly efficiently with a novel configuration of multilayer networks that use only weighted pooling operations, for which no network training is needed. These networks create solutions, which are identical to those from classical algorithms such as breadth-first search (BFS), Dijkstra's algorithm, or TD(0). Different from competing approaches, very large mazes containing almost one billion nodes with dense obstacle configuration and several thousand importance-weighted path endpoints can this way be solved quickly in a single pass on parallel hardware.
Research on Colour Modelling and Detecting System Based on Computer Big Data

Abstract Through the statistics and analysis of the three colour components of dynamic background pixels in RGB space, the paper found that the difference between the three components fluctuates in a narrow area. Based on this fact, it proposes RGB colour based on computer big data Background modelling method of component statistics. This method fully considers the correlation of the three colour components of RGB, and the foreground detection process is faster. Experimental results show that compared with other colour invariant methods, this algorithm can more accurately reduce the computational complexity, time consumption and memory consumption of foreground detection.
The voice of the voiceless? Decoloniality and online radical discourses in South Africa

South Africa combines one of the highest internet penetration rates on the African continent with a vibrant civil society borne of its struggle against apartheid. Contrary to digital media’s promise to give a “voice to the voiceless”, recent literature suggests that the online space may reproduce offline inequalities. At the same time, opinions and viewpoints which would normally be chastised by traditional media (e.g. racism) can and do find expression online. In this chapter, the nature of radical discourses within the digital public sphere is explored in relation to contentious issues rooted in historical processes, such as gender-based violence, capital punishment, white genocide and land expropriation. The research takes the form of a thematic analysis of content in online publications and forums as well as various social networks. The approach is a decolonial one, concerned with bringing to light and interrogating the relations of power and cultural domination at play in constructing radical externality. The study contributes a critical analysis of popular culture forms such as social media in relation to epistemic disobedience, delinking and reconstruction within a developing democracy as well as their relationship with other forms of political action and communication.
High involvement HR practices as a precursor to innovation performance: a study of Indian IT firms

The present paper is an attempt to understand as to how high involvement HR practices help strengthen innovation performance in IT employees. The study has also tried to interrogate work engagement as a connecting medium between the two and the role of resource commitment in influencing the interactions between work engagement and innovation performance. The methodology used for data collection was through survey questionnaires from the superior as well as subordinates of the managerial cadre of the IT firms in the Pune region, India. For a good model fit, confirmatory factor analysis was done on the available dataset eliminating the outliers and SPSS macro, and PROCESS was used for further analysis. Results showed that HIHRP has an influence on innovation performance and work engagement mediates the relationship between HIHRP and innovation performance, additionally resource commitment positively mitigates the interaction between work engagement and innovation performance. The present study is the first of its kind which has empirically examined the mediation/moderation interactions between HIHRP, work engagement, resource commitment and innovation performance.
A Discontinuity of the Energy of Quantum Walk in Impurities

We consider the discrete-time quantum walk whose local dynamics is denoted by a common unitary matrix C at the perturbed region {0,1,⋯,M−1} and free at the other positions. We obtain the stationary state with a bounded initial state. The initial state is set so that the perturbed region receives the inflow ωn at time n(|ω|=1). From this expression, we compute the scattering on the surface of −1 and M and also compute the quantity how quantum walker accumulates in the perturbed region; namely, the energy of the quantum walk, in the long time limit. The frequency of the initial state of the influence to the energy is symmetric on the unit circle in the complex plain. We find a discontinuity of the energy with respect to the frequency of the inflow.
Activity-Aware Vital Sign Monitoring Based on a Multi-Agent Architecture

Vital sign monitoring outside the clinical environment based on wearable sensors ensures better support in assessing a patient’s health condition, and in case of health deterioration, automatic alerts can be sent to the care providers. In everyday life, the users can perform different physical activities, and considering that vital sign measurements depend on the intensity of the activity, we proposed an architecture based on the multi-agent paradigm to handle this issue dynamically. Different types of agents were proposed that processed different sensor signals and recognized simple activities of daily living. The system was validated using a real-life dataset where subjects wore accelerometer sensors on the chest, wrist, and ankle. The system relied on ontology-based models to address the data heterogeneity and combined different wearable sensor sources in order to achieve better performance. The results showed an accuracy of 95.25% on intersubject activity classification. Moreover, the proposed method, which automatically extracted vital sign threshold ranges for each physical activity recognized by the system, showed promising results for remote health status evaluation.
Masked SABL: A Long Lasting Side-Channel Protection Design Methodology

As an outstanding cell-level countermeasure to defeat power analysis attacks, dual-rail pre-charge logics rely on balanced complementary paths. During the circuit lifetime, the gates undergo unavoidable changes due to the so-called device aging, hence imbalancing the dual rails. Here, we focus on Sense Amplifier Based Logic (SABL), and highlight the vulnerability of corresponding circuits when the device is aged. By integrating gate-level masking, we introduce a modified variant of SABL, maintaining its resistance in presence of device aging. The corresponding results, covering both dynamic and static power profiles, show the prominent impact of our construction on extending the protection of circuits for their entire lifetime.
Is rice price a major source of inflation in the Philippines? A panel data analysis

We estimate a panel vector auto-regression model using monthly prices for 17 regions in the Philippines from 2007 to 2019. Our results suggest that the effect of rice price on inflation is larger than the effect of fuel price and remittances. The results obtained are robust to various model specifications, different sub-periods, and inflation proxies. The impulse response functions for sub-samples of regions show two remarkable results. First, the response of inflation to a rice price shock varies according to poverty incidence by region. Second, the magnitude and persistence of this effect are larger for regions with high poverty rates. These results suggest that the Central Bank should keep alert in monitoring and assessing key developments in rice prices and supply.
Structural Knowledge Distillation: Tractably Distilling Information for Structured Predictor

Knowledge distillation is a critical technique to transfer knowledge between models, typically from a large model (the teacher) to a more fine-grained one (the student).The objective function of knowledge distillation is typically the cross-entropy between the teacher and the student's output distributions.However, for structured prediction problems, the output space is exponential in size; therefore, the cross-entropy objective becomes intractable to compute and optimize directly.In this paper, we derive a factorized form of the knowledge distillation objective for structured prediction, which is tractable for many typical choices of the teacher and student models.In particular, we show the tractability and empirical effectiveness of structural knowledge distillation between sequence labeling and dependency parsing models under four different scenarios: 1) the teacher and student share the same factorization form of the output structure scoring function; 2) the student factorization produces more fine-grained substructures than the teacher factorization; 3) the teacher factorization produces more fine-grained substructures than the student factorization; 4) the factorization forms from the teacher and the student are incompatible.
Densely Connected Feature Pyramid Network for Image Segmentation

Image segmentation is a specific image processing technique used to segment a picture into two or more semantic regions. This paper proposes a densely connected feature pyramid segmentation network and applies it to the segmentation of images in real driving scenarios. The feature pyramid network is a kind of feature extractor, which is originally used for object detection. This paper applies it to image segmentation tasks. The densely connected network is used as a part of feature pyramid network to extract features from bottom to top. Through the lateral connection, the features of the bottom-up part and the top-down part are merged. In the final merge stage, the extracted features are transformed to the same size through upsampling and then concatenated together, and finally the segmentation map is output. The experiment is conducted on the CamVid dataset, which is a dataset in actual driving scenarios. In the experiment, the generalization ability of the segmentation model is improved through data enhancement. Based on the densely connected feature pyramid segmentation network, the F1-score on the test set is 0.8895, and the IoU-score is 0.8209.
Babies, Variables, and Connectionist Networks

This chapter focuses on G. F. Marcus et al ’s experiments, showing how an associationist device, a particular neural network architecture, can learn the patterns in the experiments, generalizing to novel sequences, and how the account, rather than being simply an uninteresting implementation of a symbolic model, makes novel predictions about the learning of sequences. A neural network needs two features to generalize over “grammatical” patterns like those in Marcus et al ’s experiments. Playpen is a neural network architecture which is designed to represent and learn relational knowledge and to deal with simple sequential patterns. Micw-relation units (MRU) in the network represented possible binary relations between the syllables in a sequence, and connections between the MRUs represented correlations between the simple binary relations. In the network the similarity of the test sequences to the training sequences had an effect on generalization.
A Unified Framework for Planning with Learned Neural Network Transition Models

Automated planning with neural network transition models is a two stage approach to solving planning problems with unknown transition models. The first stage of the approach learns the unknown transition model from data as a neural network model, and the second stage of the approach compiles the learned model to either a Mixed-Integer Linear Programming (MILP) model or a Recurrent Neural Network (RNN) model, and optimize it using an off-the-shelf solver. The previous studies have shown that both models have their advantages and disadvantages. Namely, the MILP model can be solved optimally using a branch-and-bound algorithm but has been experimentally shown not to scale well for neural networks with multiple hidden layers. In contrast, the RNN model can be solved effectively using a gradient descent algorithm but can only work under very restrictive assumptions. In this paper, we focus on improving the effectiveness of solving the second stage of the approach by introducing (i) a novel Lagrangian RNN architecture that can model the previously ignored components of the planning problem as Lagrangian functions, and (ii) a novel framework that unifies the MILP and the Lagrangian RNN models such that the weakness of one model is complemented by the strength of the other. Experimentally, we show that our unifying framework significantly outperforms the standalone MILP model by solving 80% more problem instances, and showcase the ability of our unifying framework to find high quality solutions to challenging automated planning problems with unknown transition models.
Predicting cross-linguistic adjective order with information gain

Languages vary in their placement of multiple adjectives before, after, or surrounding the noun, but they typically exhibit strong intralanguage tendencies on the relative order of those adjectives (e.g., the preference for 'big blue box' in English, 'grande boîte bleue' in French, and 'als .undūq al'azraq alkabīr' in Arabic).We advance a new quantitative account of adjective order across typologically-distinct languages based on maximizing information gain.Our model addresses the left-right asymmetry of French-type ANA sequences with the same approach as AAN and NAA orderings, without appeal to other mechanisms.We find that, across 32 languages, the preferred order of adjectives mirrors an efficient algorithm of maximizing information gain.
Turnover Intention and Productivity of Local Bureaucrats: Focusing on Influences of Local Politicians Intervention, Central Government Control, and Citizen Compliance

Among the external environment factors faced by local officials are intervention by local council politicians, central government control and citizen policy non-compliance. These factors, together with organizational management factors and individual factors, affect the turnover intentions and productivity of local government bureaucrats in various ways. An empirical analysis of the Local Bureaucrats Perception Survey shows that intervention by politicians has a negative effect on individual and organizational turnover intention and productivity. Central government has no significant effect on individual turnover intention but increases organizational turnover intention and productivity. Citizen policy compliance has a negative effect on individual turnover intention and no significant effect on productivity. The most harmful effect on local bureaucrats is the negative intervention of local congressman because they increase turnover intention but decrease productivity. Turnover intention and Productivity are also important in that these factors improve organizational competitiveness.
Basics of Python Programming

Python is a general-purpose dynamic programming language that was created by Dutch programmer Guido van Rossum in 1989. It is the most commonly used programming language in the field of data science. Since it is easier to learn and write code in Python than other languages, it is an optimal choice for beginners. The widespread use of Python is also attributed to the fact that it is free and open source. The number of scientific libraries and packages developed by the Python community allows for data scientists to work with data-intensive real-time applications. Some of the leading organizations such as Google, Dropbox, and Netflix are using Python at various levels to enhance their software. In this chapter, we will discuss Python installation on the Windows operating system, different Python IDEs, the fundamental data types available with Python, control flow statements, Python functions, and different Python libraries for data science.
Cloud Computing Issues And Challenges

Cloud Computing may be a sort of net based mostly computing which provides services via the web and accesses the resources within the user enterprise either in an exceedingly private-own-cloud or on a third-party server On Demand. The model is characterized by three attributes: quantifiability, pay-per-use, self-services. Many industries like banking, healthcare, Retail, Education, Manufacturing and business are adopting this cloud technique due to potency of services provided by pay-per-use pattern which helps in accessing the networks, storage, servers, services and applications, while not physically getting them. The and Traffic Hijacking
Users Want Diverse, Multiple, and Personalized Behavior Change Support: Need-Finding Survey

Behavior change research usually takes a theory-driven or application-specific approach. We took a user-centered view of real-world user needs and conducted a survey with 53 participants to investigate the overall behavior change goals and support preferences of everyday users. Our survey revealed three key themes. First, individual users have multiple behavior change goals, desired context types for behavior change reminders, and desired activities for self-tracking. Second, users have diverse and personalized desired actions, implementations, contexts, and reminders for their behavior change goals, as well as diverse preferences for behavior change support features and sensors. Third, users want to set custom personalized goals, reminder contexts, reminder messages, and even train custom machine learning models. Thus, users want multiple, diverse, and personalized behavior change support in the real world. We suggest a ‘convergence with connection and customization’ approach to meet the diverse, multiple, and personalized behavior change needs of everyday users.
Loop Detection in the PANDA Planning System

The International Planning Competition (IPC) in 2020 was the first one for a long time to host tracks on Hierarchical Task Network (HTN) planning. HyperTensioN, the winner of the tack on totally-ordered problems, comes with an interesting technique: it stores parts of the decomposition path in the state to mark expanded tasks and forces its depth first search to leave recursive structures in the hierarchy. This can be seen as a form of loop detection (LD) – a technique that is not very common in HTN planning. This might be due to the spirit of encoding enough advice in the model to find plans (so that loop detection is simply not necessary), or because it becomes a computationally hard task in the general (i.e. partially-ordered) setting. We integrated several approximate and exact techniques for LD into the progression search of the HTN planner PANDA. We test our techniques on the benchmark set of the IPC 2020. Both in the partial ordered and total ordered track, PANDA with LD performs better than the respective winner of the competition.
Self-aware Operation of Heterogeneous Compute Nodes using the Learning Classifier System XCS

No abstract available.
Adaptive Light Weight Encryption Algorithm for Securing Multi-Cloud Storage

In several organisations the use of cloud computing has rapidly inflated. In terms of low cost and accessibility to information, cloud computing offers several advantages. Next generation design of the IT Company was described as Cloud Computing. It moves software and expertise to centralised huge data centres where management of data and services cannot be entirely reliable. Its cost-effectiveness is the main advantage of this technology. The disadvantage is significant, however, and the security problems associated with information stored in clouds are also disadvantageous. We first identified the potential security problems and the difficulties in fully expanding cloud information and restore availability. Previous efforts have ensured the integrity of distant knowledge, but lack public verification and access to knowledge. This ensures the integrity of knowledge and ensures public verification and accessibility. We have created an elegant verification with access rights to ensure the seamless integration of these two key options in our protocol style. This paper attempts to achieve public verification by changing the traditional Merkle Hash Tree into newly materialised multi-cloud or inter-cloud. It has been shown that we can ensure the availability of knowledge with the DepSky System model for multi-clouds.