Reviewerly/exHarmony huggingface dataset:
Learner interaction with, and response to, AI-programmed automated writing evaluation feedback in EFL writing: An exploratory study

Abstract Recently, artificial intelligence (AI)-programmed automated writing evaluation (AWE) has attracted increasing attention in language research. Using a small data set arising from an analysis of five Chinese university-level English as a foreign language (EFL) students’ submissions, this paper examined in detail how EFL students interacted with the feedback of Pigai, the largest AI-programmed AWE in China. The analysis started with the intention of capturing the machine feedback on the five students’ submissions and the exchanges between the participants and Pigai over repeated submissions, ranging from 3 to 12 submissions. The analysis showed that the learners’ interactions with Pigai focused on error corrective feedback in the initial two submissions. In the case of one student who had 12 submissions, the non-error corrective feedback increased gradually over time, providing rich linguistic resources but without examples and contextual information. The students’ take-up rates of feedback with linguistic resources were much lower than that of error corrective and general feedback. A terrain model to map the stages and nature of student responses showed a more complete dynamic process, in which students’ responses changed from the initial mechanical responses at the discrete language level to more considered approaches in response to machine feedback. The findings of this study have implications for both language pedagogy and the future design and development of AWE for second or foreign language learning.
DE-net: Dynamic Text-Guided Image Editing Adversarial Networks

Text-guided image editing models have shown remarkable results. However, there remain two problems. First, they employ fixed manipulation modules for various editing requirements (e.g., color changing, texture changing, content adding and removing), which results in over-editing or insufficient editing. Second, they do not clearly distinguish between text-required and text-irrelevant parts, which leads to inaccurate editing. To solve these limitations, we propose: (i) a Dynamic Editing Block (DEBlock) that composes different editing modules dynamically for various editing requirements. (ii) a Composition Predictor (Comp-Pred), which predicts the composition weights for DEBlock according to the inference on target texts and source images. (iii) a Dynamic text-adaptive Convolution Block (DCBlock) that queries source image features to distinguish text-required parts and text-irrelevant parts. Extensive experiments demonstrate that our DE-Net achieves excellent performance and manipulates source images more correctly and accurately.
Meta-Reinforcement Learning Based on Self-Supervised Task Representation Learning

Meta-reinforcement learning enables artificial agents to learn from related training tasks and adapt to new tasks efficiently with minimal interaction data. However, most existing research is still limited to narrow task distributions that are parametric and stationary, and does not consider out-of-distribution tasks during the evaluation, thus, restricting its application. In this paper, we propose MoSS, a context-based Meta-reinforcement learning algorithm based on Self-Supervised task representation learning to address this challenge. We extend meta-RL to broad non-parametric task distributions which have never been explored before, and also achieve state-of-the-art results in non-stationary and out-of-distribution tasks. Specifically, MoSS consists of a task inference module and a policy module. We utilize the Gaussian mixture model for task representation to imitate the parametric and non-parametric task variations. Additionally, our online adaptation strategy enables the agent to react at the first sight of a task change, thus being applicable in non-stationary tasks. MoSS also exhibits strong generalization robustness in out-of-distributions tasks which benefits from the reliable and robust task representation. The policy is built on top of an off-policy RL algorithm and the entire network is trained completely off-policy to ensure high sample efficiency. On MuJoCo and Meta-World benchmarks, MoSS outperforms prior works in terms of asymptotic performance, sample efficiency (3-50x faster), adaptation efficiency, and generalization robustness on broad and diverse task distributions.
Revolutionizing Solar Power Production with Artificial Intelligence: A Sustainable Predictive Model

Photovoltaic (PV) power production systems throughout the world struggle with inconsistency in the distribution of PV generation. Accurate PV power forecasting is essential for grid-connected PV systems in case the surrounding environmental conditions experience unfavourable shifts. PV power production forecasting requires the consideration of critical elements, such as grid energy management, grid operation and scheduling. In the present investigation, multilayer perceptron and adaptive network-based fuzzy inference system models were used to forecast PV power production. The developed forecasting model was educated using historical data from October 2011 to February 2022. The outputs of the proposed model were checked for accuracy and compared by considering the dataset from a PV power-producing station. Three different error measurements were used—mean square error, root-mean-square error, and Pearson’s correlation coefficient—to determine the robustness of the suggested method. The suggested method was found to provide better results than the most recent and cutting-edge models. The MLP and ANFIS models achieved the highest performance (R = 100%), with less prediction errors (MSE = 1.1116 × 10−8) and (MSE = 1.3521 × 10−8) with respect to MLP and ANFIS models. The study also predicts future PV power generation values using previously collected PV power production data. The ultimate goal of this work is to produce a model predictive control technique to achieve a balance between the supply and demand of energy.
A Genetic Algorithm-Based Virtual Machine Allocation Policy for Load Balancing Using Actual Asymmetric Workload Traces

Load balancing is a very important concept in cloud computing. In this work, studies are conducted on workload traces at Los Alamos National Lab (LANL). The jobs in this trace are asymmetric in nature as most of them have small time limit. Two hybrid algorithms, a Genetic Algorithm combined with First Come First Serve (GA_FCFS) and Genetic Algorithm combined with Round Robin (GA_RR), are proposed here. The results obtained are compared with the existing First Come First Serve (FCFS), Round Robin (RR) and Genetic Algorithm (GA). Makespan and Resource Utilization are used for the comparison of results. In terms of Makespan, it is observed that GA_RR outperforms the other methods for all the batch sizes. Although the performance of GA_FCFS is much better than that of the other three well-established algorithms FCFS, RR and GA, it is still worse than that of the GA_RR algorithm for all the cases. GA_RR performs best in terms of Resource Utilization also and GA_FCFS is a close competitor. Overall, GA_RR outperforms all the other algorithms.
Hydro-climate information services for smallholder farmers: FarmerSupport app principles, implementation, and evaluation

Rainfed agriculture constitutes the backbone of the economy in many regions of the Global South. Historically, smallholder farmers used their local knowledge to forecast the weather. However, with the increase in climatic variability, they can no longer solely rely on their experience to accurately forecast the weather. FarmerSupport app (FSapp) is a hydro-climate information service developed through a co-production approach to address the weather and climate information needs of farmers. The app gathers weather forecast from both local farmers and scientific sources, and presents this information to users to enable them to make informed decisions regarding agriculture. To test its proof-of-concept, the FSapp was implemented in two rice communities in northern Ghana during the 2020 crop season. The app was introduced to farmers, who received training on it use, as well as built their capacity on weather and climate-related phenomena and the use of Information and Communication Technologies (ICT). Following the end of the cropping season, farmers evaluated the app and the results revealed that co-production of information played a crucial role to its adoption in relation to other similar platforms. Farmers consider the app as a relatively accurate and reliable source of information for planning agricultural activities. They additionally demonstrated a significant level of knowledge about weather phenomena as a result to their engagement and capacity building. Further implementation of the app in other farming communities is needed to address smallholder farmers weather and climate information needs and unlock their agriculture potential.
A Hierarchical Federated Learning Algorithm Based on Time Aggregation in Edge Computing Environment

Federated learning is currently a popular distributed machine learning solution that often experiences cumbersome communication processes and challenging model convergence in practical edge deployments due to the training nature of its model information interactions. The paper proposes a hierarchical federated learning algorithm called FedDyn to address these challenges. FedDyn uses dynamic weighting to limit the negative effects of local model parameters with high dispersion and speed-up convergence. Additionally, an efficient aggregation-based hierarchical federated learning algorithm is proposed to improve training efficiency. The waiting time is set at the edge layer, enabling edge aggregation within a specified time, while the central server waits for the arrival of all edge aggregation models before integrating them. Dynamic grouping weighted aggregation is implemented during aggregation based on the average obsolescence of local models in various batches. The proposed algorithm is tested on the MNIST and CIFAR-10 datasets and compared with the FedAVG algorithm. The results show that FedDyn can reduce the negative effects of non-independent and identically distributed (IID) data on the model and shorten the total training time by 30% under the same accuracy rate compared to FedAVG.
Chat Dialogue System with Context Understanding

Many difficulties arise in developing a dialogue system that can perform conversation in the manner of humans, even for casual conversations.Recent research on chat conversation has led to the development of dialogue systems that can respond to users in a wide range of topics, which was the first major challenge of chat conversation.However, it is still difficult to construct dialogue systems that can properly respond to user utterances according to the dialogue context, and this has often made users feel that the system did not understand what they said.In this article, we introduce our work on a chat dialogue system that has the ability to understand the dialogue context.
Blockchain for IoT-enabled Healthcare

Emerging technologies including such Internet of Things (IoT) and blockchain contribute significantly to the improvement of health services. The purpose of this chapter is to achieve and democratize services through the provision of medical care as a service. The result was the development of medical gadgets integrating healthcare sensors. It links medical equipment like the temperature controller to the cloud environment of medical doctors and staff. This study introduced the combination of IoT and Blockchain as a secure platform to reduce the scarcity of nurses. Blockchain was employed for storing and validating patient information in the proposed operating framework. A significant reduction in nursing gaps for large-scale patients has been shown. All technological specifications have been given to allow the prototyping execution of these suggested medical services simply adaptable. This article deals with Blockchain technology inclusion in Remote Medical Monitoring Devices Internet of Things (IoT) security. The document provides the advantages of Blockchain based safety methods and practical barriers in remote health monitoring via IoT devices. The study also examines several cryptographic methods appropriate for IoT implementation.
Unauthorized access detection system to the equipments in a room based on the persons identification by face recognition

Face recognition is a very active research topic due to the number of potential applications that use this technique and the number of challenges that still require efforts to solve them. However, new research problems arise when dealing with devices operating in real-time, and using a non-controlled image source such as surveillance cameras, due to the needs and context of end-use. A robust and efficient face recognition system is proposed in this paper to monitor unauthorized access to equipments in a room using a surveillance camera. The system is based on a person detection algorithm adapted to the context of use, a person tracking algorithm robust to occlusion, a face detection algorithm robust to orientation and scale, and a face recognition algorithm robust to illumination change. To enhance the efficiency of person identification, a novel strategy for estimating the confidence of face recognition is proposed in this paper. This is achieved by leveraging the general conditions of the image used for this purpose. This technique allows to increase the identification rate while minimizing the false alarm rate. An effort is also made to optimize the implementation of the developed algorithms to guarantee a real-time operation while keeping a very high efficiency. Also, the evaluations of the proposed algorithms on public and private data show an improvement of the accuracy on the whole context of use of the proposed system. Furthermore the evaluation of the system in general shows a high efficiency, 100% detection of unauthorized accesses and 0 false alarms.
Fall Detection Systems Supported by TinyML and Accelerometer Sensors: An Approach for Ensuring the Safety and Quality of Life of the Elderly

Many elderly individuals live alone in their homes, which can lead to significant health and safety concerns due to the risk of falls. Falls not only cause physical injuries but also have social, psychological, and economic impacts that negatively affect the quality of life for older adults. In this context, early detection of falls and implementation of preventive measures are of great importance. Edge computing-based fall detection systems have been developed to effectively address the safety of older adults in such situations. In the present study, a fall detection system is proposed that utilizes edge computing and TinyML technologies, operating on an embedded platform. This system is designed for the interpretation of accelerometer sensor data and processes the data collected through sensors to obtain valuable information. The Edge Impulse platform is used for training an extensive dataset consisting of various fall examples for older adults, allowing the proposed system to achieve a 98.5% recognition accuracy. This cost-effective and user-friendly novel approach combines a portable accelerometer sensor and artificial intelligence software to target early detection and prevention of falls in older adults. This study contributes significantly to the field of edge computing and provides effective solutions to enhance the quality of life for elderly individuals.
Chaotic attractors of discrete dynamical systems used in the core of evolutionary algorithms: state of art and perspectives

This paper delves into the intersection between discrete dynamical systems and bio-inspired metaheuristic algorithms. Chaos, an inherent phenomenon manifested by both continuous and discrete dynamic nonlinear systems has become an essential component of engineering design. This study examines the relationship between chaos and bio-inspired metaheuristic algorithms from two distinct angles: the presence of chaos within the realm of algorithms, and the application of bio-inspired algorithms for the identification, control, or synthesis of complex systems. Furthermore, this paper offers an in-depth exploration of the interplay between metaheuristics and complex nonlinear dynamics, including potential avenues for future research. The presented simulation studies, along with the design of objective functions for optimization and the implementation of metaheuristics, serve as a valuable foundation for subsequent experiments involving discrete nonlinear systems and complex systems with time delays. Such systems may benefit from multiparametric or multicriteria optimization approaches, paving the way for novel advancements in the field.
A variational level set model based on additive decomposition for segmenting noisy images with intensity inhomogeneity

The variational level set model is widely used in image segmentation. However, it suffers from the limited performance when the images are contaminated by noise and intensity inhomogeneity. To solve this problem, a variational level set model based on additive decomposition is proposed in this paper. First, we decompose the image into three components: structure, intensity inhomogeneity and noise, which are regularized by different metrics. Specially, the structure component is regularized as a piecewise constant function in bounded variation (BV) space; the intensity inhomogeneity is modeled as a smooth function that is regularized by H1 space, and the residual containing noise is measured by L2 norm. Furthermore, a new indirect regularization term for level set function is designed to enhance the accuracy of the segmentation outcomes. And then, alternate direction iteration algorithm combining with gradient descent and ADMM to solve the proposed model. Experiments on both synthetic and real images validate the proposed model. Compared with five state-of-the-art models, the experimental results demonstrate that the proposed model exhibits the significant improvement in both accuracy and efficiency, outperforming all other methods. Quantitative evaluations show the average segmentation coefficients for Jaccard, Dice and Accuracy against ground truth are 0.93, 0.95 and 0.96, respectively, and the average running time is 0.80 seconds, further confirming its superior performance.
Neural Belief Propagation Decoding of Quantum LDPC Codes Using Overcomplete Check Matrices

The recent success in constructing asymptotically good quantum low-density parity-check (QLDPC) codes makes this family of codes a promising candidate for error-correcting schemes in quantum computing. However, conventional belief propagation (BP) decoding of QLDPC codes does not yield satisfying performance due to the presence of unavoidable short cycles in their Tanner graph and the special degeneracy phenomenon. In this work, we propose to decode QLDPC codes based on a check matrix with redundant rows, generated from linear combinations of the rows in the original check matrix. This approach yields a significant improvement in decoding performance with the additional advantage of very low decoding latency. Furthermore, we propose a novel neural belief propagation decoder based on the quaternary BP decoder of QLDPC codes which leads to further decoding performance improvements.
Multi-User Privacy Mechanism Design with Non-zero Leakage

A privacy mechanism design problem is studied through the lens of information theory. In this work, an agent observes useful data Y = (Y <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> ,…,Y <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</inf> ) that is correlated with private data X = (X <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> ,…,X <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</inf> ) which is assumed to be also accessible by the agent. Here, we consider K users where user i demands a sub-vector of Y, denoted by C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> . The agent wishes to disclose C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> to user i. A privacy mechanism is designed to generate disclosed data U which maximizes a linear combinations of the users utilities while satisfying a bounded privacy constraint in terms of mutual information. In a similar work it has been assumed that X <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> is a deterministic function of Y <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> , however in this work we let X <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> and Y <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> be arbitrarily correlated.First, an upper bound on the privacy-utility trade-off is obtained by using a specific transformation, Functional Representation Lemma and Strong Functional Representation Lemma, then we show that the upper bound can be decomposed into N parallel problems. Next, lower bounds on privacy-utility tradeoff are derived using Functional Representation Lemma and Strong Functional Representation Lemma. The upper bound is tight within a constant and the lower bounds assert that the disclosed data is independent of all $\left\{ {{X_j}} \right\}_{i = 1}^N$ except one which we allocate the maximum allowed leakage to it. Finally, the obtained bounds are studied in special cases.
Patient Graph Deep Learning to Predict Breast Cancer Molecular Subtype

Breast cancer is a heterogeneous disease consisting of a diverse set of genomic mutations and clinical characteristics. The molecular subtypes of breast cancer are closely tied to prognosis and therapeutic treatment options. We investigate using deep graph learning on a collection of patient factors from multiple diagnostic disciplines to better represent breast cancer patient information and predict molecular subtype. Our method models breast cancer patient data into a multi-relational directed graph with extracted feature embeddings to directly represent patient information and diagnostic test results. We develop a radiographic image feature extraction pipeline to produce vector representation of breast cancer tumors in DCE-MRI and an autoencoder-based genomic variant embedding method to map variant assay results to a low-dimensional latent space. We leverage related-domain transfer learning to train and evaluate a Relational Graph Convolutional Network to predict the probabilities of molecular subtypes for individual breast cancer patient graphs. Our work found that utilizing information from multiple multimodal diagnostic disciplines improved the model's prediction results and produced more distinct learned feature representations for breast cancer patients. This research demonstrates the capabilities of graph neural networks and deep learning feature representation to perform multimodal data fusion and representation in the breast cancer domain.