Reviewerly/exHarmony huggingface dataset:
FedSIGN: A sign-based federated learning framework with privacy and robustness guarantees

Federated learning enables clients to train a global model jointly without sharing their private local datasets. Despite its benefits, due to the untrustworthiness of clients and the server, traditional federated learning faces the risk of privacy leakage and poisoning attacks. Privacy-preserving methods change the original model parameters whereas robust aggregation algorithms required accurate parameters. To solve such a dilemma, we propose a new framework named FedSIGN. On the one hand, it utilizes the sign of local model update to update the global model to protect privacy and improve efficiency. On the other hand, focus on the Sybil-based poisoning attack: malicious client controlled by a single adversary who directs those clients to launch a poisoning attack, we design a Poisoning Attack Detector to identify malicious clients based on the similarity between sign vectors. Experimental results show that FedSIGN resists privacy and poisoning attacks while maintaining better global model performance. Especially, FedSIGN is not affected by the number of malicious clients and is effective in both the IID and non-IID scenarios.
Are neural architecture search benchmarks well designed? A deeper look into operation importance

Neural Architecture Search (NAS) benchmarks significantly improved the capability of developing and comparing NAS methods while at the same time drastically reduced the computational overhead by providing meta-information of trained neural networks. However, tabular benchmarks have several drawbacks that can hinder fair comparisons and provide unreliable results. These usually focus on a small pool of operations in heavily constrained search spaces – usually cell-based neural networks with pre-defined outer-skeletons. In this work, we conducted an empirical analysis of the widely used NAS-Bench-101, NAS-Bench-201 and TransNAS-Bench-101 benchmarks in terms of their generability and how different operations influence the performance of the generated architectures. We found that only a subset of the operation pool is required to generate architectures close to the upper-bound of the performance range. More, the performance distribution is negatively skewed, with many architectures clustered near the upper accuracy bound. Further experiments revealed that convolution layers have the highest impact on the architecture's performance and that specific combinations of operations favor top-scoring architectures. Overall, our results demonstrate the need for benchmarks with greater operation diversity and less constrained search spaces. We provide suggestions for improving future benchmark design and evaluation of NAS methods when using existing benchmarks. The code used to conduct the evaluations is available at https://github.com/VascoLopes/NAS-Benchmark-Evaluation.
UAV Obstacle Avoidance by Human-in-the-Loop Reinforcement in Arbitrary 3D Environment

This paper focuses on the continuous control of the unmanned aerial vehicle (UAV) based on a deep reinforcement learning method for a large-scale 3D complex environment. The purpose is to make the UAV reach any target point from a certain starting point, and the flying height and speed are variable during navigation. In this work, we propose a deep reinforcement learning (DRL)-based method combined with human-in-the-loop, which allows the UAV to avoid obstacles automatically during flying. We design multiple reward functions based on the relevant domain knowledge to guide UAV navigation. The role of human-in-the-loop is to dynamically change the reward function of the UAV in different situations to suit the obstacle avoidance of the UAV better. We verify the success rate and average step size on urban, rural, and forest scenarios, and the experimental results show that the proposed method can reduce the training convergence time and improve the efficiency and accuracy of navigation tasks. The code is available on the website https://github.com/Monnalo/UAV_navigation.
Optimization Algorithm to Reduce Training Time for Deep Learning Computer Vision Algorithms Using Large Image Datasets With Tiny Objects

The optimization of convolutional neural networks (CNN) generally refers to the improvement of the inference process, making this as fast and precise as possible. While inference time is an essential factor in using these networks in real time, the training of CNNs using very large datasets can be very costly in terms of time and computing power. This paper proposes a technique to reduce training time by an average of 75% without altering the results of CNN training with an algorithm which partitions the dataset and discards superfluous objects (targets). This algorithm is a tool that pre-processes the original dataset, generating a smaller and more condensed dataset to be used for network training. The effectiveness of this tool depends on the type of dataset used for training the CNN and is particularly effective with sequential images (video), large images and images with tiny targets generally from drones or traffic surveillance cameras (but applicable to any other type of image which meets the requirements). The tool can be parameterized to meet the characteristics of the initial dataset.
The Potential Impact of Chatbots on Student Engagement and Learning Outcomes

Emerging technologies such as Chatbots are redefining communication, engagement, and outcomes in the digital space in contemporary times. Academic institutions are now left behind as the majority of students who are Gen Z feel comfortable in this space, thus leaving the institutions of higher learning with no choice but to embrace these technologies to improve students' engagement and lead to a better learning outcome. However, despite its myriads of potentials, there are also consequences. It is based on this premise that this chapter will examine the usage of chatbots on students' engagement and learning outcomes by taking into perspective its potentials and consequences. Vygostsky's sociocultural theory and the Ranganathan law of library science will underpin this chapter. The chapter is expected to contribute to practice, society, policy, and theory based on the conceptual model that would be developed.
Training variational quantum algorithms with random gate activation

A new training strategy with progressive random gate activation for variational quantum algorithms greatly mitigates barren plateaus and avoids the local minima while being quantum resource efficient and noise resilient.
Analysis of Program Representations Based on Abstract Syntax Trees and Higher-Order Markov Chains for Source Code Classification Task

In this paper we consider the research and development of classifiers that are trained to predict the task solved by source code. Possible applications of such task detection algorithms include method name prediction, hardware–software partitioning, programming standard violation detection, and semantic code duplication search. We provide the comparative analysis of modern approaches to source code transformation into vector-based representations that extend the variety of classification and clustering algorithms that can be used for intelligent source code analysis. These approaches include word2vec, code2vec, first-order and second-order Markov chains constructed from abstract syntax trees (AST), histograms of assembly language instruction opcodes, and histograms of AST node types. The vectors obtained with the forementioned approaches are then used to train such classification algorithms as k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP). The obtained results show that the use of program vectors based on first-order AST-based Markov chains with an RF-based classifier leads to the highest accuracy, precision, recall, and F1 score. Increasing the order of Markov chains considerably increases the dimensionality of a vector, without any improvements in classifier quality, so we assume that first-order Markov chains are best suitable for real world applications. Additionally, the experimental study shows that first-order AST-based Markov chains are least sensitive to the used classification algorithm.
PIPTO: Precise Inertial-Based Pipeline for Threshold-Based Fall Detection Using Three-Axis Accelerometers

After traffic-related incidents, falls are the second cause of human death, presenting the highest percentage among the elderly. Aiming to address this problem, the research community has developed methods built upon different sensors, such as wearable, ambiance, or hybrid, and various techniques, such as those that are machine learning- and heuristic based. Concerning the models used in the former case, they classify the input data between fall and no fall, and specific data dimensions are required. Yet, when algorithms that adopt heuristic techniques, mainly using thresholds, are combined with the previous models, they reduce the computational cost. To this end, this article presents a pipeline for detecting falls through a threshold-based technique over the data provided by a three-axis accelerometer. This way, we propose a low-complexity system that can be adopted from any acceleration sensor that receives information at different frequencies. Moreover, the input lengths can differ, while we achieve to detect multiple falls in a time series of sum vector magnitudes, providing the specific time range of the fall. As evaluated on several datasets, our pipeline reaches high performance results at 90.40% and 91.56% sensitivity on MMsys and KFall, respectively, while the generated specificity is 93.96% and 85.90%. Lastly, aiming to facilitate the research community, our framework, entitled PIPTO (drawing inspiration from the Greek verb "πι´πτω", signifying "to fall"), is open sourced in Python and C.
Moving into the metaverse: libraries in virtual worlds

Purpose As traditional physical spaces grapple with evolving reading habits and information consumption patterns, the notion of libraries within the metaverse poses intriguing questions about accessibility, knowledge dissemination and the evolving role of librarians. The purpose of this paper is to explore the role of metaverse in virtual libraries. Design/methodology/approach This paper analyzes various literature relating to libraries and the metaverse in the virtual worlds through a review of the literature. Findings In the ever-evolving landscape of information and technology, the emergence of libraries in virtual worlds presents a transformative paradigm that brings forth both tremendous advantages and intricate challenges. These advantages are underscored by enhanced accessibility for a global audience, interactive engagements that cater to diverse learning styles and the unprecedented scalability that digital environments provide. Virtual libraries hold the potential to reshape the way people access, share and engage with knowledge, while also fostering a sense of community that transcends geographical boundaries. Originality/value To the best of the authors’ knowledge, this paper is the original idea that highlights the importance of metaverse in virtual libraries.
A Blockchain-Based Framework to Make the Rice Crop Supply Chain Transparent and Reliable in Agriculture

Rice is one of the major food crops across the globe, and its quality and safety highly influence human health. It is the basis of many different products, including rice flour, rice bread, noodles, rice vinegar, and others. Therefore, the rice supply chain has garnered increasing attention due to the high demand for food safety. Furthermore, malpractices in the rice supply chain can impact farmers by generating low revenues despite their great efforts in rice cultivation. In addition, they would cause governments to suffer significant economic losses due to the high cost of importing rice crops from other countries during the off-season. These issues derive from the lack of reliability, trust, transparency, traceability, and security in the rice supply chain. In this research, we propose a secure, trusted, reliable, and transparent framework based on a Blockchain for rice crop supply chain’s traceability from farm to fork. A new crypto token, the Rice Coin (RC), is introduced to keep a record of all transactions between the stakeholders of the rice supply chain. Moreover, the proposed framework includes an economic model and a crypto wallet and introduces an Initial Coin Offering (ICO) for the RC. Based on smart contracts, a transaction processing system was developed for the transparency and traceability of rice crops, including the conversion of the RC to fiat currency. Furthermore, the InterPlanetary File System (IPFS) is proposed in this research to store encrypted data of companies, retailers, and farmers, so to increase data security, transparency, and availability. In the end, the experimental results showed a better performance of the proposed framework compared to already available supply chain solutions in terms of transaction verification time, transaction average gas cost, and new block latency.
An Optimized Intelligent Computational Security Model for Interconnected Blockchain-IoT System &amp; Cities

Blockchain technology may provide a potential solution to the Internet of Things (IoT) security challenges by providing a decentralized and secure method for storing, managing, and sharing data. The Secure Hash Algorithm (SHA-256) hashed value of preliminary data (block) is retained in one block along with transaction data in tree form and timestamp in a chain of blocks. However, there are observations about blockchain limitations such as higher energy consumption, secure data, self-maintenance reliance, and higher cost. These constraints can be overcome by incorporating encryption algorithms into accepting blocks of data. In this paper, we propose a secure intelligent computational model for a large-scale interconnected IoT environment; an analytical modeling technique is considered for the proposed system. The proposed system takes advantage of the potential security feature of blockchain, which is considered the most appropriate secure communication system in an IoT. A computational model is built using the proposed blockchain technology to incorporate a secure and intelligent communication system. The proposed system uses the enhanced McEliece encryption approach's potential to link the blockchain due to the faster mode of encryption and decryption process with a highly reduced number of steps.
Unmanned Aerial Vehicle-Assisted Federated Learning Method Based on a Trusted Execution Environment

In the face of increasing concerns around privacy and security in the use of unmanned aerial vehicles (UAVs) for mobile edge computing (MEC), this study proposes a novel approach to secure UAV-assisted federated learning. This research integrates a trusted execution environment (TEE) into UAV-assisted federated learning and proposes a robust aggregation algorithm based on cosine distance, denoted as CosAvg. This study further designs and evaluates a TEE-based federated learning model, comparing its resource overhead with other secure aggregation frameworks, like homomorphic encryption (HE) and differential privacy (DP). Experimental results indicate a significant reduction in resource overhead for TEE against DP and HE. Moreover, the proposed CosAvg algorithm demonstrated superior robustness against adversarial scenarios, maintaining high accuracy in the presence of malicious clients. The integration of TEE and the CosAvg algorithm provides a secure and robust solution for UAV-assisted federated learning, effectively defending both gradient inversion attacks and byzantine attacks.
A solar panel dataset of very high resolution satellite imagery to support the Sustainable Development Goals

Effectively supporting the United Nations' Sustainable Development Goals requires reliable, substantial, and timely data. For solar panel installation monitoring, where accurate reporting is crucial in tracking green energy production and sustainable energy access, official and regulated documentation remains inconsistent. Reports of solar panel installations have been supplemented with object detection models developed and used on openly available aerial imagery, a type of imagery collected by aircraft or drones and limited by cost, extent, and geographic location. We address these limitations by providing a solar panel dataset derived from 31 cm resolution satellite imagery to support rapid and accurate detection at regional and international scales. We also include complementary satellite imagery at 15.5 cm resolution with the aim of further improving solar panel detection accuracy. The dataset of 2,542 annotated solar panels may be used independently to develop detection models uniquely applicable to satellite imagery or in conjunction with existing solar panel aerial imagery datasets to support generalized detection models.
Comparison of ground-based and floating solar photovoltaic systems performance based on monofacial and bifacial modules in Ghana

This research aims to compare the energy output potential of land-based and floating bifacial photovoltaic (PV) systems of 50 MW and 400 kW with an existing land-based and floating monofacial PV system of the same dimensions and design in Bui, Ghana. The study uses ground weather files and PV system configurations to compare the energy yield from simulations of the different PV systems based on capacity factor, performance ratio, and monthly, final/annual energy yield. The results show that the bifacial gain of land-based and floating bifacial PV systems is 2.51% and 4.57%, respectively, and the capacity factor and performance ratio of the bifacial PV system are higher than the monofacial PV system. However, the additional energy generated by the bifacial PV system is not significant enough to justify the installation of a new system. Therefore, the study recommends optimizing system parameters such as albedo, tilt angle, and ground cover ratio to 0.5, 15°, and 0.3 respectively to enhance the performance and increase the bifacial gain of the PV system. Overall, this study provides valuable insights into the potential of bifacial PV systems in Bui, Ghana, and highlights the importance of optimizing system parameters for maximum energy output.
Comparison of Five Spectral Indices and Six Imagery Classification Techniques for Assessment of Crop Residue Cover Using Four Years of Landsat Imagery

Determining residue cover on agricultural land is an important task. Residue cover helps reduce soil erosion and helps sequester carbon. Many studies have used either spectral indices or classification techniques to map residue cover using satellite imagery. Unfortunately, most of these studies use only a few spectral indices or classification techniques and generally only study an area for a single year with a certain level of success. This manuscript presents an investigation of five spectral indices and six classification techniques over four years to determine if a single spectral index or classification technique performs consistently better than the others. A second objective is to determine whether using the coefficient of determination (R2) from the relationship between residue cover and a spectral index is a reasonable substitute for calculating accuracy. Field visits were conducted for each of the years studied and used to create the correlations with the spectral indices and as ground truth for the classification techniques. It was found that no spectral index/classification technique is consistently better than all the others. Classification techniques tended to be more accurate in 2011 and 2013, while spectral indices tended to be more accurate in 2015 and 2018. The combination of spectral indices/classification techniques outperformed the individual approach. For the second objective, it was found that R2 is not a great indicator of accuracy. Root mean square error (RMSE) is a better indicator of accuracy than R2. However, simply calculating the accuracy would be the best of all.
HSFL: Efficient and Privacy-Preserving Offloading for Split and Federated Learning in IoT Services

Distributed machine learning methods like Federated Learning (FL) and Split Learning (SL) meet the growing demands of processing large-scale datasets under privacy restrictions. Recently, FL and SL are combined in hybrid SLFL (SFL) frameworks to exploit both methods’ advantages to facilitate ubiquitous intelligence in the Internet of Things (IoT), for example, smart finance. Despite its significant impact on the performance and costs of SFL, model decomposition that splits an ML model into the client-server pair has not been sufficiently studied, especially for SFL in a large-scale dynamic IoT environment. In this paper, we propose a new SFL framework HSFL with a lightweight model decomposition method to offload a part of model training to the edge server. Specifically, we develop a method for estimating the training latency of HSFL and designed a metric for measuring privacy leakage in HSFL, based on which we formulate model decomposition in HSFL as an optimization problem with privacy protection as a constraint. Then, we transform the formulated problem into a contextual bandit problem and design an efficient algorithm to solve it. We have conducted thorough evaluations of the proposed HSFL framework through extensive experiments on a prototype testbed and a simulation platform. The experimental results validate the superiority of HSFL over the state-of-the-art benchmarks in terms of training latency, efficiency, scalability, and privacy protection.
Sentiment Analysis for Curriculum of Independent Learning Based on Naïve Bayes with Laplace Estimator

Curriculum changes have a significant impact on the field of education in Indonesia, especially in universities. This is because the curriculum is used as a means to achieve the goals of educational success as well as guidelines in the implementation of teaching at every level of education. This study aims to determine public sentiment regarding the implementation curriculum of independent learning (MBKM) through Twitter based on Naïve Bayes with Laplace Estimator. Tweet search results use the keywords “kurikulum mbkm” and “mbkm”. The data used in this study amounted to 2500 tweet data which will be divided into 2000 data as training data with details of 1000 positive tweets and 1000 negative tweets and 500 data as testing data. The results showed the performance of Naïve Bayes using Laplace Estimator which resulted in an accuracy value of 80.20%, precision value of 82.00%, and recall value of 79.00%. Meanwhile, the performance of Naïve Bayes without Laplace Estimator resulted in an accuracy value of 77.00%, precision value of 80.00%, and recall value of 74.00%. Naive Bayes with Laplace Estimator reached the highest value in all metrics.
A Personalized Reinforcement Learning Summarization Service for Learning Structure from Unstructured Data

The exponential growth of textual data has created a crucial need for tools that assist users in extracting meaningful insights. Traditional document summarization approaches often fail to meet individual user requirements and lack structure for efficient information processing. To address these limitations, we propose Summation, a hierarchical personalized concept-based summarization approach. It synthesizes documents into a concise hierarchical concept map and actively engages users by learning and adapting to their preferences. Using a Reinforcement Learning algorithm, Summation generates personalized summaries for unseen documents on specific topics. This framework enhances comprehension, enables effective navigation, and empowers users to extract meaningful insights from large document collections aligned with their unique requirements.