Reviewerly/exHarmony huggingface dataset:
Replicability and reproducibility in evolutionary optimization

tutorial Open AccessReplicability and reproducibility in evolutionary optimization Authors: Luís Paquete University of Coimbra, Portugal University of Coimbra, PortugalView Profile , Manuel López-Ibáñez University of Málaga, Spain University of Málaga, SpainView Profile Authors Info & Claims GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2021 Pages 454–462https://doi.org/10.1145/3449726.3461405Published:08 July 2021Publication History 1citation156DownloadsMetricsTotal Citations1Total Downloads156Last 12 Months49Last 6 weeks12 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
State, global and local parameter estimation using local ensemble Kalman filters: applications to online machine learning of chaotic dynamics

&amp;lt;div&amp;gt;In a recent methodological paper, we have shown how a (local) ensemble Kalman filter can be used to learn both the state and the dynamics of a system in an online framework. The surrogate model is fully parametrised (for example, this could be a neural network) and the update is a two-step process: (i) a state update, possibly localised, and (ii) a parameter update consistent with the state update. In this framework, the parameters of the surrogate model are assumed to be global. &amp;lt;br&amp;gt;&amp;lt;br&amp;gt;In this presentation, we show how to extend the method to the case where the surrogate model, still fully parametrised, admits both global and local parameters (typically forcing parameters). In this case, localisation can be applied not only to the state update, but also to the local parameters update. This results in a collection of new algorithms, depending on the localisation method (covariance localisation or domain localisation) and on whether localisation is applied to the state update, or to both the state and local parameter update. The algorithms are implemented and tested with success on the 40-variable Lorenz model. Finally, we show a two-dimensional illustration of the method using a multi-layer Lorenz model with radiance-like non-local observations.&amp;lt;/div&amp;gt;
Automatic Sliding Gate System using Tap Water Pressure

Current automatic gates control utilizes electrically powered motor, which is relatively expensive in term of installation, operation, and maintenance that increases the cost of automatic gates.For this, a new idea with automatic gate control system that uses a source directly from tap water pressure to operate the movement of gate that is controlled with fluid power valves and cylinders is developed.Tap water is directly connected to the system and does not use any additional pump to increase the pressure.A double acting cylinder, solenoid valves and a remote-control switch are the main components in this product.The type of gate that are consider for this paper are swing gate and sliding gate.For sliding gate type, an additional device that is able to extend the maximum reach of the piston cylinder is required.It is because the length of the piston cylinder is not long enough to open the gate.After choosing the best design from some design, the selected design was further analysed and simulated using Computer Aided Design (CAD) software.Then the CAD software could determine the motion by simulation what the distance is through which the gate can move or travel.The comparison between simulation data and actual data had a percentage of error of 24.68% which refers to the movement or distance of gate travelled.Finally, both type of gate refers to swing gate and sliding gate able to work using directly tap water.In conclusion the newly developed automatic gate control for each type is cheaper in term of initial and operating cost.
A Chatbot that Uses a Multi-agent Organization to Support Collaborative Learning

This work investigates and apply the use of a multi-agent system to assist in the coordination of group tasks, specifically in educational environments, in which the interaction occurs indirectly, that is, asynchronously. The system has a web interface integrated with a chatbot for more natural interaction. The chatbot communicates with the multi-agent system that is responsible for the organization of the group, that is, it contains information about the tasks and members of the groups, in addition to restrictions that can be imposed according to the organization of the group, and it is also able to return the requested information in natural language through the chatbot. This approach was validated in a practical undergraduate course of software engineering. The students assessed the functionalities and usability of the system while working in groups in order to develop software collaboratively. Our system was used to assist students in a real project. With this assessment, it was found that the system was able to support the development of the group tasks, ensuring quick and consistent responses to the student’s request.
QR code as speckle pattern for reinforced concrete beams using digital image correlation

Digital Image Correlation technique (DIC) is a non-contact optical method for rapid structural health monitoring of critical infrastructure. An innovative approach to DIC is presented using QR (Quick Response) code based random speckle pattern. Reinforced Cement Concrete (RCC) beams of size 1800 mm X 150 mm X 200 mm are tested in flexure. DIC is used to extract Moment (M) – Curvature (k) relationships using random speckle patterns and QR code based random speckle patterns. The QR code based random speckle pattern is evaluated for 2D DIC measurements and the QR code speckle pattern performs satisfactorily in comparison with random speckle pattern when considered in the context of serving a dual purpose. Characteristics of QR code based random speckle pattern are quantified and its applicability to DIC is explored. The ultimate moment-curvature values computed from the QR code based random speckled pattern are found to be in good agreement with conventional measurements. QR code encrypts the structural information which enables integration with building information modelling (BIM).
Evaluation of semi-frozen semi-fixed neural network for efficient computer vision inference

Deep neural networks have been utilized in an increasing number of computer vision tasks, demonstrating superior performance. Much research has been focused on making deep networks more suitable for efficient hardware implementation, for low-power and low-latency real-time applications. In [1], Isikdogan et al. introduced a deep neural network design that provides an effective trade-off between flexibility and hardware efficiency. The proposed solution consists of fixed-topology hardware blocks, with partially frozen/partially trainable weights, that can be configured into a full network. Initial results in a few computer vision tasks were presented in [1]. In this paper, we further evaluate this network design by applying it to several additional computer vision use cases and comparing it to other hardware-friendly networks. The experimental results presented here show that the proposed semi-fixed semi-frozen design achieves competitive performanc on a variety of benchmarks, while maintaining very high hardware efficiency.
Subjective Well-Being: Perspectives and Programs on Happiness and Well-Being as Basis for Company-Based Policies

Subjective well-being is an individual’s own evaluation of life that includes both affective and cognitive aspects like happiness and satisfaction that are contributory to success. As a trend in the UAE, government and non-government organization now strive to introduce policies related to employee engagement, well-being, and empowerment that will lead to programs that will cater to the organization’s happiness and wellness. It is in this context that the United Arab Emirates (UAE) established initiatives such as the National Program for Happiness and Wellbeing, resulting to being in the 20th rank in the UN World’s Happiness Report 2018. This study utilized data through exploratory, qualitative-descriptive, case study methods. The impact of happiness on employee well-being was ascertained and the programs expounded, with the aim to be amongst world’s top five happiest countries in 2021. Recommendations related to happiness and well-being are also offered in this paper for private organizations to have a more structured policies that will support well-bring programs.
Supervised Authorship Segmentation of Open Source Code Projects

Abstract Source code authorship attribution can be used for many types of intelligence on binaries and executables, including forensics, but introduces a threat to the privacy of anonymous programmers. Previous work has shown how to attribute individually authored code files and code segments. In this work, we examine authorship segmentation, in which we determine authorship of arbitrary parts of a program. While previous work has performed segmentation at the textual level, we attempt to attribute subtrees of the abstract syntax tree (AST). We focus on two primary problems: identifying the primary author of an arbitrary AST subtree and identifying on which edges of the AST primary authorship changes. We demonstrate that the former is a difficult problem but the later is much easier. We also demonstrate methods by which we can leverage the easier problem to improve accuracy for the harder problem. We show that while identifying the author of subtrees is difficult overall, this is primarily due to the abundance of small subtrees: in the validation set we can attribute subtrees of at least 25 nodes with accuracy over 80% and at least 33 nodes with accuracy over 90%, while in the test set we can attribute subtrees of at least 33 nodes with accuracy of 70%. While our baseline accuracy for single AST nodes is 20.21% for the validation set and 35.66% for the test set, we present techniques by which we can increase this accuracy to 42.01% and 49.21% respectively. We further present observations about collaborative code found on GitHub that may drive further research.
Building Tapis v3 Streams API Support for Real-Time Streaming Data Event-Driven Workflows

The Tapis framework, an NSF-funded project, is an open-source, scalable API platform that enables researchers to perform distributed computational experiments securely and achieve faster scientific results with increased reproducibility. Tapis Streams API focuses on supporting scientific use cases that require working with real-time sensor data. The Streams Service, built on the top of the CHORDS time-series data service, allows storing, processing, annotating, querying, and archiving time-series data. This paper focuses on the new Tapis Streams API functionality that enables researchers to design and execute real-time data-driven event workflow for their research. We describe the architecture and design choices towards achieving this new capability with Streams API. Specifically, we demonstrate the integration of Streams API with Kapacitor, a native data processing engine for time-series database InfluxDB, and Abaco, an NSF Funded project, web service, and distributed computing platform providing function-as-a-Service (FaaS). The Streams API, which includes a wrapper interface for the Kapacitor alerting system, can define and enable alerts. Finally, simulation results from the water-quality use case depict that Streams API’s new capabilities can support real-time streaming data event-driven workflows.
Balancing Policy Improvement and Evaluation in Risk-Sensitive Satisficing Algorithm

Reducing the search space is one of the challenges in reinforcement learning. One of the satisficing reinforcement learning algorithms, commonly known as RS+GRC, reduces large search space by setting an aspiration level. However, a lag between policy evaluation and improvement, due to policy feedback, prevents proper exploration. Therefore, we propose an eligibility trace-based RS( $$\lambda $$ ) method, which eliminated the lag. We demonstrated that RS( $$\lambda $$ ) exhibited efficient learning toward behavior policy-based satisfaction.
Interactive Qualitative Data Visualization for Educational Assessment

Data visualization accelerates the communication of quantitative measures across many fields, including education, but few visualization methods exist for qualitative data in educational fields that capture both the context-specific information and summarize trends for instructors. In this paper, we design an interface to visualize students’ weekly journal entries collected as formative educational assessments from an undergraduate data visualization course and a statistics course. Using these qualitative data, we present an interactive WordStream and word cloud to show the temporal and topic-based organization of students’ development during instruction and explore the patterns, trends, and diversity of student ideas in a context-specific way. Informed by the Technology Acceptance Model, we used an informal user study to evaluate the perceived ease of use and usefulness of the tool for instructors using journal entries. Our evaluation found the tool to be intuitive, clear, and easy-to-use to explore student entries, especially words of interest, but might be limited by focusing on word frequencies rather than underlying relationships among the student’s ideas or other measures in assessment. Implications and challenges for bridging qualitative data for educational assessment with data visualization methods are discussed.
Unexpected guests: When disconfirmed predictions linger

Author(s): Rich, Stephanie; Harris, Jesse | Abstract: Previous literature suggests that the language processor generates expectations about upcoming material. Several studies have found evidence for a prediction error cost in cases where the comprehender encountered not the predicted word but a plausible unexpected continuation instead. This cost is argued to be a result of an inhibitory process that suppresses activation of the originally predicted word. Others have found no such evidence for a prediction cost. In a probe recognition memory task, we find evidence for interference from an incorrectly predicted word, and in a self-paced reading study, we find evidence for facilitation when the originally predicted word is encountered later on. Taken together, our results provide evidence against a strong version of the suppression account, in which incorrectly predicted words are fully inhibited. Instead we argue in favor of a passive lingering activation account, in which activation for the disconfirmed prediction gradually decays over time.
No-reference Image Quality Assessment of Underwater Images Using Multi-Scale Salient Local Binary Patterns

Images acquired in underwater scenarios may contain severe distortions due to light absorption and scattering, color distortion, poor visibility, and contrast reduction. Because of these degradations, researchers have proposed several algorithms to restore or enhance underwater images. One way to assess these algorithms’ performance is to measure the quality of the restored/enhanced underwater images. Unfortunately, since reference (pristine) images are often not available, designing no-reference (blind) image quality metrics for this type of scenario is still a challenge. In fact, although the area of image quality has evolved a lot in the last decades, estimating the quality of enhanced and restored images is still an open problem. In this work, we present a no-reference image quality evaluation metric for enhanced underwater images (NR-UWIQA) that uses an adapted version of the multi-scale salient local binary pattern operator to extract image features and a machine learning approach to predict quality. The proposed metric was tested on the UID-LEIA database and presented good accuracy performance when compared to other state-of-the-art methods. In summary, the proposed NR-UWQIA method can be used to evaluate the results of restoration techniques quickly and efficiently, opening a new perspective in the area of underwater image restoration and quality assessment.
ZLeaks: Passive Inference Attacks on Zigbee Based Smart Homes

Zigbee is an energy-efficient wireless IoT protocol that is increasingly being deployed in smart home settings. In this work, we analyze the privacy guarantees of Zigbee protocol. Specifically, we present ZLeaks, a tool that passively identifies in-home devices or events from the encrypted Zigbee traffic by 1) inferring a single application layer (APL) command in the event’s traffic, and 2) exploiting the device’s periodic reporting pattern and interval. This enables an attacker to infer user’s habits or determine if the smart home is vulnerable to unauthorized entry. We evaluated ZLeaks’ efficacy on 19 unique Zigbee devices across several categories and 5 popular smart hubs in three different scenarios; controlled RF shield, living smart-home IoT lab, and third-party Zigbee captures. We were able to i) identify unknown events and devices (without a-priori device signatures) using command inference approach with 83.6% accuracy, ii) automatically extract device’s reporting signatures, iii) determine known devices using the reporting signatures with 99.8% accuracy, and iv) identify APL commands in a public capture with 91.2% accuracy. In short, we highlight the trade-off between designing a low-power, low-cost wireless network and achieving privacy guarantees. We have also released ZLeaks tool for the benefit of the research community.
Penerapan Permainan Ular Tangga dalam Peningkatan Hasil Belajar Geometri Bangun Ruang di Kelas v SD 006 Rambah Samo

Abstract This study aims to determine the increase in student learning outcomes through learning media for the game of snakes and ladders. The subjects of this study were 15 students of grade V SD 006 Rambah Samo, which amounted to 15 people in the 2020/2021 school year. This study consisted of 2 cycles consisting of the stages of planning, action, observation, and reflection. The instruments used to measure the improvement of student learning outcomes are test instruments and non-test instruments in the form of observation sheets, field notes, and documentation. The data obtained from the research results were analyzed using quantitative descriptive research, namely data in the form of numbers and can be expressed in units of count. The results of the data analysis showed that there was an increase in student learning outcomes in class V SD 006 Rambah Samo, after applying the playing method using snake and ladder medis by 26,67%. This can be seen from the increase in the average percentage of student learning outcomes in cycle 1 of 6,66% and in cycle 2 amounted to 33,33%. The small percentage of student learning outcomes who reach the KKM is due to constraints in online learning that were less effective during the Covid-19 pandemic.
The effect of Non-Monetary Incentives &amp; work environment on employee’s job satisfaction

This study aimed to investigate the effect of Non-Monetary Incentives &amp; work environment on employee’s job satisfaction. A Stratified random sample, which was chosen, consisted of 234 academic staff within Erbil Polytechnic University, of a population of 601 people. To achieve the objectives of the study, the analytical descriptive approach and SPSS were used. The results indicated that there is a positive statistically significant relationship between the study's two independent variables (non-monitory incentives, and the work environment), reveal that non-monetary incentives are positively correlated with job satisfaction. Also, the results show that employee non-monetary incentives have significant positive effects on employees' job satisfaction so non-monetary incentives have significant positive effects on employees' job satisfaction.
Are Convolutional Neural Networks or Transformers more like human vision

Author(s): Tuli, Shikhar; Dasgupta, Ishita; Grant, Erin; Griffiths, Tom | Abstract: Modern machine learning models for computer vision exceed humans in accuracy on specific visual recognition tasks, notably on datasets like ImageNet. However, high accuracy can be achieved in many ways. The particular decision function found by a machine learning system is determined not only by the data to which the system is exposed, but also the inductive biases of the model, which are typically harder to characterize. In this work, we follow a recent trend of in-depth behavioral analyses of neural network models that go beyond accuracy as an evaluation metric by looking at patterns of errors. Our focus is on comparing a suite of standard Convolutional Neural Networks (CNNs) and a recently-proposed attention-based network, the Vision Transformer (ViT), which relaxes the translation-invariance constraint of CNNs and therefore represents a model with a weaker set of inductive biases. Attention-based networks have previously been shown to achieve higher accuracy than CNNs on vision tasks, and we demonstrate, using new metrics for examining error consistency with more granularity, that their errors are also more consistent with those of humans. These results have implications both for building more human-like vision models, as well as for understanding visual object recognition in humans.
Experiment Selection in Meta-Analytic Piecemeal Causal Discovery

Scientists try to design experiments that will yield maximal information. For instance, given the available evidence and a limitation on the number of variables that can be observed simultaneously, it may be more informative to intervene on variable X and observe the response of variable Y than to intervene on X and observe Z; in other situations, the opposite may be true. Scientists must often make these decisions without primary data. To address this problem, in previous work, we created software for annotating aggregate statistics in the literature and deriving consistent causal explanations, expressed as causal graphs. This meta-analytic pipeline is useful not only for synthesizing evidence but also for planning experiments: one can use it strategically to select experiments that could further eliminate causal graphs from consideration. In this paper, we introduce interpretable policies for selecting experiments in the context of piecemeal causal discovery, a common setting in biological sciences in which each experiment can measure not an entire system but rather a strict subset of its variables. The limits of this piecemeal approach are only beginning to be fully characterized, with crucial theoretical work published recently. With simulations, we show that our experiment-selection policies identify causal structures more efficiently than random experiment selection. Unlike methods that require primary data, our meta-analytic approach offers a flexible alternative for those seeking to incorporate qualitative domain knowledge into their search for causal mechanisms. We also present a method that categorizes hypotheses with respect to their utility for identifying a system's causal structure. Although this categorization is usually infeasible to perform manually, it is critical for conducting research efficiently.