Reviewerly/exHarmony huggingface dataset:
Ensemble Reservoir Computing for Dynamical Systems: Prediction of Phase-Space Stable Region for Hadron Storage Rings

We investigate the ability of an ensemble reservoir computing approach to predict the long-term behaviour of the phase-space region in which the motion of charged particles in hadron storage rings is bounded, the so-called dynamic aperture. Currently, the calculation of the phase-space stability region of hadron storage rings is performed through direct computer simulations, which are resource- and time-intensive processes. Echo State Networks (ESN) are a class of recurrent neural networks that are computationally effective, since they avoid backpropagation and require only cross-validation. Furthermore, they have been proven to be universal approximants of dynamical systems. In this paper, we present the performance reached by ESN based on an ensemble approach for the prediction of the phase-space stability region and compare it with analytical scaling laws based on the stability-time estimate of the Nekhoroshev theorem for Hamiltonian systems. We observe that the proposed ESN approach is capable of effectively predicting the time evolution of the extent of the dynamic aperture, improving the predictions by analytical scaling laws, thus providing an efficient surrogate model.
FedCliP: Federated Learning with Client Pruning

The prevalent communication efficient federated learning (FL) frameworks usually take advantages of model gradient compression or model distillation. However, the unbalanced local data distributions (either in quantity or quality) of participating clients, contributing non-equivalently to the global model training, still pose a big challenge to these works. In this paper, we propose FedCliP, a novel communication efficient FL framework that allows faster model training, by adaptively learning which clients should remain active for further model training and pruning those who should be inactive with less potential contributions. We also introduce an alternative optimization method with a newly defined contribution score measure to facilitate active and inactive client determination. We empirically evaluate the communication efficiency of FL frameworks with extensive experiments on three benchmark datasets under both IID and non-IID settings. Numerical results demonstrate the outperformance of the porposed FedCliP framework over state-of-the-art FL frameworks, i.e., FedCliP can save 70% of communication overhead with only 0.2% accuracy loss on MNIST datasets, and save 50% and 15% of communication overheads with less than 1% accuracy loss on FMNIST and CIFAR-10 datasets, respectively.
Causal Models with Constraints

Causal models have proven extremely useful in offering formal representations of causal relationships between a set of variables. Yet in many situations, there are non-causal relationships among variables. For example, we may want variables $LDL$, $HDL$, and $TOT$ that represent the level of low-density lipoprotein cholesterol, the level of lipoprotein high-density lipoprotein cholesterol, and total cholesterol level, with the relation $LDL+HDL=TOT$. This cannot be done in standard causal models, because we can intervene simultaneously on all three variables. The goal of this paper is to extend standard causal models to allow for constraints on settings of variables. Although the extension is relatively straightforward, to make it useful we have to define a new intervention operation that $disconnects$ a variable from a causal equation. We give examples showing the usefulness of this extension, and provide a sound and complete axiomatization for causal models with constraints.
Knowledge Acquisition and Completion for Long-Term Human-Robot Interactions using Knowledge Graph Embedding

In Human-Robot Interaction (HRI) systems, a challenging task is sharing the representation of the operational environment, fusing symbolic knowledge and perceptions, between users and robots. With the existing HRI pipelines, users can teach the robots some concepts to increase their knowledge base. Unfortunately, the data coming from the users are usually not enough dense for building a consistent representation. Furthermore, the existing approaches are not able to incrementally build up their knowledge base, which is very important when robots have to deal with dynamic contexts. To this end, we propose an architecture to gather data from users and environments in long-runs of continual learning. We adopt Knowledge Graph Embedding techniques to generalize the acquired information with the goal of incrementally extending the robot's inner representation of the environment. We evaluate the performance of the overall continual learning architecture by measuring the capabilities of the robot of learning entities and relations coming from unknown contexts through a series of incremental learning sessions.
HanoiT: Enhancing Context-aware Translation via Selective Context

Context-aware neural machine translation aims to use the document-level context to improve translation quality. However, not all words in the context are helpful. The irrelevant or trivial words may bring some noise and distract the model from learning the relationship between the current sentence and the auxiliary context. To mitigate this problem, we propose a novel end-to-end encoder-decoder model with a layer-wise selection mechanism to sift and refine the long document context. To verify the effectiveness of our method, extensive experiments and extra quantitative analysis are conducted on four document-level machine translation benchmarks. The experimental results demonstrate that our model significantly outperforms previous models on all datasets via the soft selection mechanism.
Rapid quantum approaches for combinatorial optimisation inspired by optimal state-transfer

We propose a new design heuristic to tackle combinatorial optimisation problems, inspired by Hamiltonians for optimal state-transfer. The result is a rapid approximate optimisation algorithm. We provide numerical evidence of the success of this new design heuristic. We find this approach results in a better approximation ratio than the Quantum Approximate Optimisation Algorithm at lowest depth for the majority of problem instances considered, while utilising comparable resources. This opens the door to investigating new approaches for tackling combinatorial optimisation problems, distinct from adiabatic-influenced approaches.
The use of Octree in point cloud analysis with application to cultural heritage

In this article we present the effects of our work on the subject of the technical approach to the 3D point cloud data analysis through the use of the Octree method to compress, analyse and compute the initial data.
Solving a new application of asymmetric TSP by modified migrating birds optimization algorithm

In this study, we first introduce a new application of the asymmetric traveling salesman problem (ATSP) which is about a small restaurant with one cook and a single stove. Once a meal has started cooking on the stove, the cook prepares the next meal on the table where the preparation time is dependent on the previous meal prepared. For the solution of this problem, besides several simple construction algorithms and a new version of the simulated annealing algorithm, we focus on enhanced versions of the recently introduced migrating birds optimization (MBO) algorithm. The original MBO algorithm might suffer from early convergence. Here we introduce several different ways of handling this problem. The extensive numerical experimentation conducted shows the superiority of the enhanced MBO over the original MBO (about 2.62 per cent) and over the simulated annealing algorithm (about 1.05 per cent).
Long Range Pooling for 3D Large-Scale Scene Understanding

Inspired by the success of recent vision transformers and large kernel design in convolutional neural networks (CNNs), in this paper, we analyze and explore essential reasons for their success. We claim two factors that are critical for 3D large-scale scene understanding: a larger receptive field and operations with greater non-linearity. The former is responsible for providing long range contexts and the latter can enhance the capacity of the network. To achieve the above properties, we propose a simple yet effective long range pooling (LRP) module using dilation max pooling, which provides a network with a large adaptive receptive field. LRP has few parameters, and can be readily added to current CNNs. Also, based on LRP, we present an entire network architecture, LRPNet, for 3D understanding. Ablation studies are presented to support our claims, and show that the LRP module achieves better results than large kernel convolution yet with reduced computation, due to its nonlinearity. We also demonstrate the superiority of LRPNet on various benchmarks: LRPNet performs the best on ScanNet and surpasses other CNN-based methods on S3DIS and Matterport3D. Code will be made publicly available.
The SwaNNFlight System: On-the-Fly Sim-to-Real Adaptation via Anchored Learning

Reinforcement Learning (RL) agents trained in simulated environments and then deployed in the real world are often sensitive to the differences in dynamics presented, commonly termed the sim-to-real gap. With the goal of minimizing this gap on resource-constrained embedded systems, we train and live-adapt agents on quadrotors built from off-the-shelf hardware. In achieving this we developed three novel contributions. (i) SwaNNFlight, an open-source firmware enabling wireless data capture and transfer of agents' observations. Fine-tuning agents with new data, and receiving and swapping onboard NN controllers -- all while in flight. We also design SwaNNFlight System (SwaNNFS) allowing new research in training and live-adapting learning agents on similar systems. (ii) Multiplicative value composition, a technique for preserving the importance of each policy optimization criterion, improving training performance and variability in learnt behavior. And (iii) anchor critics to help stabilize the fine-tuning of agents during sim-to-real transfer, online learning from real data while retaining behavior optimized in simulation. We train consistently flight-worthy control policies in simulation and deploy them on real quadrotors. We then achieve live controller adaptation via over-the-air updates of the onboard control policy from a ground station. Our results indicate that live adaptation unlocks a near-50\% reduction in power consumption, attributed to the sim-to-real gap. Finally, we tackle the issues of catastrophic forgetting and controller instability, showing the effectiveness of our novel methods. Project Website: https://github.com/BU-Cyber-Physical-Systems-Lab/SwaNNFS
No reference image quality assessment metric based on regional mutual information among images

With the inclusion of camera in daily life, an automatic no reference image quality evaluation index is required for automatic classification of images. The present manuscripts proposes a new No Reference Regional Mutual Information based technique for evaluating the quality of an image. We use regional mutual information on subsets of the complete image. Proposed technique is tested on four benchmark natural image databases, and one benchmark synthetic database. A comparative analysis with classical and state-of-art methods indicate superiority of the present technique for high quality images and comparable for other images of the respective databases.
Learning Financial Literacy Through Savings Activities in Islamic Elementary Schools: Empirical Evidence at Islamic Elementary School

Understanding and saving habits from an early age is the most important practice in the financial literacy of the Indonesian people, especially for children who are currently attending elementary school, including students of MI Al-Ustmani. The researcher used descriptive qualitative research. The research subjects used were students of MI Al-Ustmani. Data collection techniques in this study are interviews, observation, and documentation. Data analysis used in this research is data organization, data reading, describing data into code, classifying coding into themes, interpreting data, and presenting data. Researchers found empirical evidence that financial literacy learning at MI Al-Ustmani has been carried out through saving activities that are practiced by students every day. MI Al-Ustmani has included financial literacy both intra-curriculum and extra-extra-curriculum as well as direct practicum in the classroom. Learning financial literacy education through saving activities for MI Al-Ustmani students requires cooperation and synergy from various parties so that financial education is truly internalized in the mindset and can be manifested in the daily behavior of MI Al-Ustmani students.
Enhancing Green Waqf For Carbonization Technology: Opportunities for Sustainable Development Goals (SDGs) in Indonesia

Introduction/Main Objectives: Green economy, a new concept of combating global warming and climate change through economic activities, becomes a prominent theme for politicians, scientists, activists, and businessmen. The main purpose of this research is to propose green economy by enhancing green waqf for Carbonization Technology. Background Problems: Waste countinuos to be one of the major problems facing the stable development of the economy and society. Managing waste is needed to reduce environmental damage and provide benefits for all. Novelty: Proposing green waqf for carbonization technology is a new concept. Research Methods: This research used qualitative approach by studying related literatures. Finding/Results: The results show that green waqf is strongly recommended to support green economy. Waqf as a charitable institution in Islam is expected to play its significant role to carry out environmental protection. Environmental protection could be achieved by waste management. Waste management by Carbonization Technology could supply renewable power and plays a crucial role in preserving the environment, improving living standards, and accelerating economic power. Conclusion: Enhanching green waqf for Carbonization Technology should be a prioritized program to support green economy. Hopefully, this is in line with SDG 7 (sustainable energy), SDG 11 (sustainable cities and communities), and SDG 13 (climate action).
Can Social Media-Style Fanfiction Stories Enrich Students’ Narrative Writing Mastery?

With the rise of pop culture, social media-style Fanfiction is a new style of fanfiction that are mostly read by students. However, students still find it hard to write narrative stories. This study is aimed at enhancing students’ narrative writing skills by employing social media-style Fanfiction stories as learning media. The design of the study is a pre-experimental design with one-group pre-test and post-test design. The samples of the study were 30 students from the eleventh grade of SMA Negeri 5 Depok. The data were collected using pre-test, treatment, and post-test and the data were calculated using a paired sample t-test to see the significant difference. Based on the data analysis, the findings showed that the students’ narrative writing skills were improved after using social media-style fanfiction as a learning media. In learning processes, students are involved in various writing activities such as practicing writing topic sentences, supporting sentences, and narrative features of narrative texts. It was proven with students’ writing achievement in the post-test. The score mean was increased from 66,58 to 81,04, and from the t-score it is -10,308 from t-table -2,045 which means that the hypothesis H1 is accepted. It can be concluded that the use of social media-style fanfiction story was able to enrich students’ narrative writing mastery at eleventh grade students.
Taxation of Palm Oil Production Results Using on Regression Model (PT. Menthobi Makmur Sustainable)

PT Menthobi Makmur Lestari is a growing industrial company in the field of palm oil production. The company is targeting increased palm oil production to forecast capacity plans and manufacturing facilities. One of the prediction methods used is multiple linear regression. The free variables used for prediction are the age of the tree, land area (ha), number of trees, number of vines, and the bound variable is the yield of oil palm production. The results of the correlation test using multiple regression showed a significant correlation of 0.05 or less. Hypothesis testing includes multiple linear regression and correlation using the t test and f test with a significance level of α = 0.05. The value of multiple correlation analysis (R) is 0.947 and the coefficient of determination is 90%. The performance of the multiple regression equation is the accuracy of predictions with an average absolute error percentage (MAPE) of 21%, which is formed from the validation of training and testing data.
Semantic Enhancement Loss Function Based on Attention Mechanism

Panoramic segmentation is an important research direction in computer vision. Considering that different applications have different requirements for semantic segmentation accuracy, a semantic enhancement loss function based on attention mechanism is proposed. By adding attention mechanism, it can enhance the sensitivity to the semantic information of task attention and improve the classification accuracy of specific objects and backgrounds. The experimental results show that the semantic enhancement loss function can effectively improve the classification accuracy of semantic categories required by tasks.
How to Design Data Access for Researchers: A Legal and Software Development Perspective

Public scrutiny of platforms has been limited by a lack of transparency. In response, EU law increasingly requires platforms to provide data to researchers. The Digital Services Act and the proposed Regulation on the Transparency and Targeting of Political Advertising in particular require platforms to provide access to data through ad libraries and in response to data access requests. However, these obligations leave platforms considerable discretion to determine how access to data is provided. As the history of platforms’ self-regulated data access projects shows, the technical choices involved in designing data access significantly affect how researchers can use the provided data to scrutinise platforms. Ignoring the way data access is designed therefore creates a danger that platforms’ ability to limit research into their services simply shifts from controlling what data is available to researchers, to how data access is provided. This article explores how the Digital Services Act and proposed Political Advertising Regulation should be used to control the operationalisation of data access obligations that enable researchers to scrutinise platforms. It argues that it is necessary to approach the operationalisation of data access regimes as not only a legal, but also a software design problem. This allows us to better understand how the development process for data access regimes should be organised to ensure data access obligations are operationalised in a way that meets the needs of the researchers that will use them. The article closes by exploring the legal mechanisms available in the Digital Services Act and proposed Political Advertising Regulation to exercise control over the design of data access regimes, and makes five recommendations for ways in which these mechanisms should be used to enable research into platforms.
The Hacker Strikes Back: Examining the Lawfulness of “Offensive Cyber” Under the Laws of Australia

Over the past ten years, criminal offending utilising or involving computers and information systems has risen to become one of the most prevalent global security threats. With a low cost to entry and high potential benefits, cybercrime is a significant challenge for traditional forms of law enforcement investigation. In response, many Western democracies have passed laws permitting officers of policing and intelligence agencies to “hack back” – that is, to use computers to attack, infiltrate, damage or disrupt the information systems of criminal offenders. Yet the contours and boundaries of those laws are underexamined in the literature. How do these agencies pursue criminals operating extraterritorially? On what legal basis can police, intelligence services or even the military attack the computers of a criminal group? This paper seeks to chart the legal parameters of the use of cyber capabilities by Australian national security agencies on a domestic basis.
Optimized Teammate Selection with a Focus on Member Compatibility to Domain Roles

The goal of this project is to develop a Machine Learning tool for predicting the performance of a team of individuals based solely on the strengths and weaknesses of its members in past experiences prior to the team’s formation. There exist models in the field for predicting an existing team’s performance based on that team’s past performance but having a model that helps optimize team formation can assist and improve existing processes further. We tested our theory by developing a tool to assist with the drafting process in FIRST Robotics competitions, which involves the top eight competitors in each tournament selecting their teammates from the other competitors based on their performance in a series of qualification matches. We developed a model to evaluate individual and team strengths and weaknesses, optimize team formation based on the model, and perform game winner prediction with 80% validation accuracy.
Forecasting Service Sales Turnover Using Double Exponential Smoothing Method (case Study: Kinclong Sub)

As the lifestyle of modern society develops, service companies are businesses in the form of services that sell specialskills in order to make it easier for customers, one of which is shoe washing services. However, the problem thatusually occurs is that the number of service requests is not certain which results in raw materials that sometimesaccumulate or run out every month, errors in predicting the sales turnover of shoe washing services can result inowners experiencing losses if the target raw materials are not appropriate.This study aims to create a forecasting system for the amount of turnover from each service sale by Kinclong Sub usingthe Double Exponential Smoothing method, so that the raw material needs needed every month can be optimal. Thebest turnover forecasting results for each service on Kinclong Sub for the following month, May 2022, were Rp.10.006,248 with a MAPE value of 17.86% for DeepClean services. And Unyellowing got Rp. 1493,374 with a MAPEvalue of 17.18 %. So it can be concluded that the service that has the most turnover for the next month is DeepClean,the forecasting results can make it easier for Kinchlong Sub shop owners to provide raw materials from services thatare most in demand by consumers.