Reviewerly/exHarmony huggingface dataset:
Challenges in GIS practice in Geography teacher training during COVID 19 pandemic: Students' perspective from private Islamic university in Indonesia

During the COVID 19 pandemic, a vast number of universities need to limit their face-to-face teaching activities, including laboratory access. For Geography education students, this situation forced them to use Geographic Information System (GIS) as a major skill set in a virtual learning model, even only with minimum infrastructures such as laptops, mobile phones, and unstable internet connections. In the current study, the perception of undergraduate students from the Geography teaching program at Universitas Muhammadiyah Purwokerto, Central Java, Indonesia, about the effectiveness and readiness of online learning platforms as the impact of the global pandemic were examined. The results showed that students were relatively satisfied and motivated in GIS online learning modules, although classical learning was still preferable. Nevertheless, some students were facing electricity and unstable internet connectivity issues. Finally, in the future, distance learning for GIS teaching programs should be integrated at the national level with sufficient infrastructure support, including supporting training on developing modules and evaluation to increase the student learning experience.
Bringing Stories to Life in 1001 Nights: A Co-creative Text Adventure Game Using a Story Generation Model

How can the stories we tell be turned from abstractions in our own minds into concrete elements in a digital environment that we can interact with? To immerse everyday storytelling into digital interactions, we created a game that turns entities in a story into digital assets that have functional roles. Taking the classic folklore as inspiration, we created 1001 Nights, a co-creative, mixed-initiative storytelling game using an existing AI creative writing system. In this game, Shahrzad (driven by the player) tells stories through a dialogue interface, while the King (driven by the AI model) continues the player's story in turn. Text from the story is used in the game mechanics, so that if the player enters keywords such as 'sword' and 'shield', they are turned into equipment that can be used in battles. Players who are more engaged with the game, measured by the length of their inputs, are rewarded with better achievements. The game aims to facilitate player engagement and creativity through natural language interactions in an empowering setting. This paper presents the game design, a breakdown of the development process and an analysis of user data, including instrumented gameplay data from 2055 players and comments from 422 players. The player feedback indicates that they enjoyed the creative interactions, the game mechanics and the narratives they constructed.
A comparative analysis of pose estimation models as enablers for a smart-mirror physical rehabilitation system

Smart mirrors are gaining attention as a smart device that could integrate a set of functionalities intended to assist older adults in their day-to-day life. These devices are seamlessly integrated in the environment, providing a user-friendly interface and naturally fitting into the daily-care routines. People face a mirror several times a day, thus ensuring that any application running on a smart mirror will have several guaranteed interactions per day. It is therefore essential to detect when the user is in front of the mirror and also to interpret what he or she is doing. Very powerful and accurate libraries are currently available, but the limited computational resources and the need to work in real time limit the valid options for smart mirror devices. This paper therefore analyses and evaluates several body pose estimation models in order to determine which one can be deployed in a smart mirror-like device dedicated to supporting older adults in their physical rehabilitation routines.
Optimized and Adaptive Federated Learning for Straggler-Resilient Device Selection

Federated Learning (FL) has evolved as a promising distributed learning paradigm in which data samples are disseminated over massively connected devices in an IID (Identical and Independent Distribution) or non-IID manner. FL follows a collaborative training approach where each device uses local training data to train local models, and the server generates a global model by combining the local model's parameters. However, FL is vulnerable to system heterogeneity when local devices have varying computational, storage, and communication capabilities over time. The presence of stragglers or low-performing devices in the learning process severely impacts the scalability of FL algorithms and significantly delays convergence. To mitigate this problem, we propose Fed-MOODS, a Multi-Objective Optimization-based Device Selection approach to reduce the effect of stragglers in the FL process. The primary criteria for optimization are to maximize: (i) the availability of the processing capacity of each device, (ii) the availability of the memory in devices, and (iii) the bandwidth capacity of the participating devices. The multi-objective optimization prioritizes devices from fast to slow. The approach involves faster devices in early global rounds and gradually incorporating slower devices from the Pareto fronts to improve the model's accuracy. The overall training time of Fed-MOODS is 1.8× and 1.48× faster than the baseline model (FedAvg) with random device selection for MNIST and FMNIST non-IID data, respectively. Fed-MOODS is extensively evaluated under multiple experimental settings, and the results show that Fed-MOODS has significantly improved model's convergence and performance. Fed-MOODS maintains fairness in the prioritized participation of devices and the model for both IID and non-IID settings.
Development of A Dodecacopter using Pixhawk 2.4.8 Autopilot Flight Controller

This research focused on the development of unmanned aerial vehicle (UAV) of a dodecacopter system.The dodecacopter is controlled through a Pixhawk 2.4.8 firmwarebased flight controller.The communication between the dodecacopter and flight controller is connected through wireless communication system.The dodecacopter is well equipped with latest technology from Pixhawk flight controller for efficient and smooth controlling system.The dodecacopter balancing system is using the technology of barometer MS5611 and magnetometer IST8310 sensors.The developed dodecacopter is equipped with 12 Tarot 4006 brushless motors to increase the payload capability.Based on the results, the experiment shows that the dodecacopter can hover and maintain its position with optimum stability.Observation shows that the dodecacopter performances can be increased by using high-powered motors, lighter batteries.
VCI-LSTM: Vector Choquet Integral-Based Long Short-Term Memory

Choquet integral is a widely used aggregation operator on 1-D and interval-valued information, since it is able to take into account the possible interaction among data. However, there are many cases where the information taken into account is vectorial, such as long short-term memories (LSTM). LSTM units are a kind of recurrent neural networks that have become one of the most powerful tools to deal with sequential information since they have the power of controlling the information flow. In this article, we first generalize the standard Choquet integral to admit an input composed by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -dimensional vectors, which produces an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -dimensional vector output. We study several properties and construction methods of vector Choquet integrals (VCIs). Then, we use this integral in the place of the summation operator, introducing in this way the new VCI-LSTM architecture. Finally, we use the proposed VCI-LSTM to deal with two problems: 1) sequential image classification; 2) text classification.
Content-Aware Robust JPEG Steganography for Lossy Channels Using LPCNet

Most robust steganographic methods pursue insignificantly zero-bit error rate of hidden messages for ensuring the reliability of communication. Cover JPEG images are required to recompress many times for enhancing the robustness, that reduces the security. In this letter, we propose a content-aware steganographic scheme for hiding speech signals into JPEG images by utilizing the redundancy of speech signals. Firstly, we design a steganographic communication model that combines speech coding with embedding process by using embedded redundancy of speech messages. It is suitable for all lossy channels. Secondly, for improving the robustness of speech signals under a given embedding rate, we propose a content-aware protection method by exploiting different effects of speech coding parameters on speech quality after transcoding. Finally, an optimized linear prediction net (LPCNet) model is implemented to improve the end-to-end quality of speech signals. Compared with existing algorithms, experimental results show that the end-to-end quality of embedding speech is improved by 95% and the transmission efficiency is raised by 2.16 times. Meanwhile, our scheme can resist the steganalysis attack based on the JPEG recompression feature.
Verification of Solar Energy Measurements by (ERA-5) and Its Impact on Electricity Costs in North Africa

The target of this research is to estimate the distribution of global solar radiation (GSR) and reanalysis datasets (ERA-5) for development of PV cost reduction and predict of level cost energy over five countries in North Africa during the period time from 2011 to 2020.The effectiveness of reanalysis datasets (ERA-5) for North African countries was evaluated against high-quality surfaces measured using statistical analysis.The average values of mean bias error (MBE), root mean square error (RMSE) and mean absolute error (MAE) of the reanalysis data of solar radiation vary from 0.079 to 0.222, 0.055 to 0.178, and 0.0145 to 0.198 respectively during the period time in the present research.The correlation coefficient (R 2 ) varies from 0.93 to 99% in the present research.North African countries are among the most vulnerable regions to the potential impacts of climate change.The increasing impact of climate change shows the need to build up a reliable energy mix and improve the resilience of existing and new energy systems.The development of PV cost reduction and the predicted of level cost of energy (LCOE) are discussed and used one PV Module to calculate the total cost for five countries in North Africa.This research's objective is to provide a reliable representation of the world's solar radiation to aid in the use of solar energy in all sectors.
Blockchain and Trust in Supply Chain Management: A Conceptual Framework

Yet we cannot guarantee that the information that is shared among supply chain entities is correct. Blockchain has been proposed to solve the lack of trust in financial transactions. This paper examines whether blockchain enables entities in the supply chain to trust the shared information regarding the physical flow. A conceptual framework is proposed to explain how and why supply chain entities can trust the shared information when using blockchain. This paper deepens our knowledge of the use of blockchain in the supply chain. It also provides valuable insights to those working in information and supply chain management related-fields.
Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error Analysis

Model poisoning attacks on federated learning intrude in the entire system via compromising an edge model, resulting in malfunctioning of machine learning models. Such compromised models are tampered with to perform adversary-desired behaviors. In particular, we considered a semi-targeted situation where the source class is predetermined however the target class is not. The goal is to cause the global classifier to misclassify data of the source class. Though approaches such as label flipping have been adopted to inject poisoned parameters into federated learning, it has been shown that their performances are usually class-sensitive varying with different target classes applied. Typically, an attack can become less effective when shifting to a different target class. To overcome this challenge, we propose the Attacking Distance-aware Attack (ADA) to enhance a poisoning attack by finding the optimized target class in the feature space. Moreover, we studied a more challenging situation where an adversary had limited prior knowledge about a client's data. To tackle this problem, ADA deduces pair-wise distances between different classes in the latent feature space from shared model parameters based on the backward error analysis. We performed extensive empirical evaluations on ADA by varying the factor of attacking frequency in three different image classification tasks. As a result, ADA succeeded in increasing the attack performance by 1.8 times in the most challenging case with an attacking frequency of 0.01.
Multi-UAV Cooperative Short-Range Combat via Attention-Based Reinforcement Learning using Individual Reward Shaping

In this paper, we propose a novel distributed method based on attention-based deep reinforcement learning using individual reward shaping, for multiple unmanned aerial vehicles (UAVs) cooperative short-range combat mission. Specifically, a two-level attention distributed policy, composed of observation-level and communication-level attention networks, is designed to enable each UAV to selectively focus on important environmental features and messages, for enhancing the effectiveness of the cooperative policy. Moreover, due to the high complexity and stochasticity of the UAV combat mission, the learning of UAVs is tricky and low efficient. To embed knowledge to accelerate the policy learning, a potential-based individual reward function is constructed by implicitly translating the individual reward into the specific form of dynamic action potentials. In addition, an actor-critic training algorithm based on the centralized training and decentralized execution framework is adopted to train the policy network of UAV maneuver decision. We build a three-dimensional UAV simulation and training platform based on Unity for multi-UAV short-range combat missions. Simulation results demonstrate the effectiveness of the proposed method and the superiority of the attention policy and individual reward shaping.
Approximate Co-Location-Resistant VM Placement Strategy With Low Energy Consumption

In cloud computing, muti-tenancy enables virtual machines to co-host the same server to reduce the power expenses. Although, there may exist co-hosts that attempt to conduct co-location attacks, which cause data leakage. To address this challenge, the most recent works present Virtual machines placement strategies minimizing co-location attacks and energy consumption. Few works establish a security before-hand threshold to constraint attack probability with respect to the provider/users priorities. Thus, we adopt this approach and introduce a novel virtual machines allocation strategy that considerably reduces energy consumption. Finally, we deploy our strategy and prove its high performance on the simulation platform CloudSim.
Adversarial Security Verification of Data-Driven FDC Systems

Data-driven fault detection and classification (FDC) systems play an important role in ensuring the stability and security of modern industry. However, the security issue of the data-driven FDC itself poses new challenges, where the model prediction can be seriously damaged by maliciously manipulated imperceptible perturbations, known as the adversarial attack. Since the adversarial attacks may threaten the FDC models and even the whole safety-critical industrial systems, there is an urgent need for the guarantee of data-driven model security. In this article, a scheme is presented for formally and completely verifying the security properties by treating the target problem as a convex mathematical programming. The major contribution on methodology is the verification under multiple norms via multiobjective optimization with a novel Pareto front approximation algorithm. Moreover, this work studies security verification for both supervised (fault classification) and unsupervised (fault detection) models under all mainstream norms simultaneously. For four basic data-driven models on two industrial datasets, our exclusive verification scheme provides deep and novel security insight into FDC systems. Moreover, we compare with related works to validate the algorithm performances of verification and Pareto front approximation.
A modified artificial bee colony algorithm for classification optimisation

The promising capabilities, easily implementable and customisable structures of the meta-heuristic algorithms have increased the researchers' attentions to the well-known problems and their new approximations that are suitable to be solved with the meta-heuristics directly. In this study, an attempt was made to solve with an artificial bee colony (ABC)-based technique called classifierABC algorithm, a new approximation that defines the classification problem by using a set of linear equations. The performance of the classifierABC was investigated in detail by using various datasets and assigning different values to the algorithm specific control parameters. The results obtained by the classifierABC algorithm were also compared with the results of the other meta-heuristics including particle swarm optimisation (PSO), differential evaluation (DE), fireworks algorithm (FWA) and different variants of the FWA. Comparative studies showed that the classifierABC solves the new problem approximation more robustly and its solutions determine the classes of instances in sets with high accuracies.
A Multi-level Cyber-Security Reference Model in Support of Vulnerability Analysis

This paper reports on the second engineering cycle of a reference model for end-to-end cyber-security by design in the electricity sector. In our previous work, we proposed a reference model that relies on the integrated consideration of two fragmented, but complementary, reference models: NISTIR 7628 and powerLang. To align these reference models, we rely on multi-level modeling, specifically on the Flexible Meta Modeling and Execution Language (FMMLx), and integrated modeling and programming. Within this paper, we strengthen the bottom-up design of the reference model's application by integrating a semi-automated threat analysis. This enables the identification of possible points of improvement in the actual architecture design, as well as a future analysis of business-level impact of different threats. To demonstrate our approach, we rely on the well-studied Ukraine scenario from 2016.
MART: Mask-Aware Reasoning Transformer for Vehicle Re-Identification

As a significant topic in Intelligent Transportation Systems (ITS), vehicle Re-Identification (Re-ID) has attracted increasing research attention. However, the variation of shooting scenes and the similar appearance among the vehicles with the same type and color lead to large intra-class variances and small inter-class variances, respectively. To address the problems, we propose a novel Mask-Aware Reasoning Transformer (MART) to extract the background-unrelated global features and perspective-invariant local features. The MART contains three effective modules including Foreground Global Features Extraction (FGFE), Mask-guided Local Features Extraction (MLFE) and Cross-images Local Features Reasoning (CLFR). Firstly, due to the complexity of background information in images, identity representation inevitably contains background elements, which may impact identity matching. To address this issue, we propose the FGFE to extract background-independent global features by introducing the mask semantic information to the inputs of Vision Transformer (ViT). Secondly, to fill the gap that the previous local features extraction methods cannot be directly applied to ViT, the MLFE is presented to extract distinctive local features by recombining token features according to vehicle mask. Thirdly, when local components are invisible in the image due to the occlusion problem, the corresponding local information is absent from the image, leading to unreliable local features. To solve this problem, the CLFR is proposed to reason the occluded local features by exploiting the correlation between cross-image local features. We carry out comprehensive experiments to illustrate the effectiveness of the MART on two challenging datasets.
Discovering Novel Categories in Sar Images in Open Set Conditions

In this paper, we deal with the issue of discovering data of novel categories for Synthetic Aperture Radar (SAR) images under open-set conditions. The traditional SAR image classification methods are trained under the closed-set setting where all categories in testing data are seen in training data. It does not always meet the requirements of the real SAR imagery interpretation applications. With a labelled SAR image dataset, we propose a multi-stage approach to effectively pick out images belonging to new classes in another unlabelled dataset and then cluster them into correct number of novel categories. To do so, our pipeline is composed of three major steps: (1) train a powerful feature extractor leveraging both the labelled and unlabelled dataset by semi-supervised inference; (2) identify the unknown data by openset detection; (3) cluster these unknown data based on the features generated by the extractor to discover novel categories. The proposed method is validated on a Sentinel-1 SAR image dataset OpenSARUrban [1].
A Study of the Emerging Trends in SIM Swapping Crime and Effective Countermeasures

SIM swapping is a novel criminal modus operandi which uses copied SIM cards for the purpose of taking over a mobile service subscription and thereby accessing sensitive personal and financial data. Authorities around the world have reported a surge in SIM Swapping crimes, mainly driven by criminals taking advantage of the increasingly virtual lifestyle precipitated by the COVID-19 pandemic. This study examines related cases around the world, and categorizes them according to their specific modi operandi, as well as the initial responses of national authorities to this emerging crime. By analyzing the dynamics of this new crime trend, this study aims to propose preemptive and preventative measures to address this new threat. Research has identified three main steps in SIM Swapping crime: 1) personal data theft, 2) fraudulent copying of SIM card, and 3) exploitation of falsely-obtained mobile service for perpetration. Research has also found that the subscriber authentication procedure involved in replacing a SIM card is vulnerable to identity theft, especially in jurisdictions which have implemented eSIM. Therefore, it will be upon governments to enforce a stronger user authentication and information security regime for mobile carriers, introduce an online payment system devised with a data-sharing mechanism connecting mobile carriers and financial services, and raise public awareness on SIM Swapping and information security in general.