Reviewerly/exHarmony huggingface dataset:
A Multiscale Fusion Lightweight Image-Splicing Tamper-Detection Model

The easy availability and usability of photo-editing tools have increased the number of forgery attacks, primarily splicing attacks, thereby increasing cybercrimes. Because of an existing image-splicing tamper-detection algorithm based on deep learning with high model complexity and weak robustness, a multiscale fusion lightweight model for image-splicing tamper detection is proposed. For the above problems and to improve MobileNetV2, the structural block of the classification part of the original network structure was removed, the stride of the sixth largest structural block of the network was changed to 1, the dilated convolution was used instead of downsampling, and the features extracted from the second and third large structural blocks in the network were downsampled with maximal pooling; then, the constraint on the backbone network was increased by jumping connections. Combined with the pyramid pooling module, the acquired feature layers were divided into regions of different sizes for average pooling; then, all feature layers were fused. The experimental results show that it had a low number of parameters and required a small amount of computation, achieving 91.0% and 96.4% precision on CASIA and COLUMB, respectively, and 83.2% and 88.1% F-measure on CASIA and COLUMB, respectively.
The four Rs and crystal structure analysis: reliability, reproducibility, replicability and reusability

Within science, of which crystallography is a key part, there are questions posed to all fields that challenge the trust in results. The US National Academies of Sciences, Engineering and Medicine published a thorough report in 2019 on the Reproducibility and Replicability of Science: replicability being where a totally new study attempts to confirm if a phenomenon can be seen independently of another study. Data reuse is a key term in the FAIR data accord [Wilkinson et al. (2016). Sci. Data, 3, 160018], where the acronym FAIR means findable, accessible, interoperable and reusable. In the social sciences, the acronym FACT (namely fairness, accuracy, confidentiality and transparency) has emerged, the idea being that data should be FACTual to ensure trust [van der Aalst et al. (2017). Bus. Inf. Syst. Eng.59, 311-313]. A distinction also must be made between accuracy and precision; indeed, the authors' lectures at the European Crystallography School ECS6 independently emphasized the need for use of other methods as well as crystal structure analysis to establish accuracy in biological and chemical/material functional contexts. The efforts by disparate science communities to introduce new terms to ensure trust have merit for discussion in crystallographic teaching commissions and possible adoption by crystallographers too.
RCOA Scheduler: Rider Cuckoo Optimization Algorithm for Task Scheduling in Cloud Computing

Cloud computing, a term which has become very popular on the internet platform which has evolved over time and gained more attention due to various features that are being introduced like multiplex numerous users on the identical physical structure, computing of resource on request and many more.Cloud computing is considered to be an enormous pool having essential resources which the users can make use of to complete their task through the internet.Therefore, in order to complete the tasks requested by the users, a scheduling algorithm is required making scheduling a vital part of cloud computing.Though numerous solutions with respect to scheduling techniques have been proposed in in recent literature, researcher's continuously carryout performance upgradation by considering a mixture of QoS parameters thereby enhancing the cloud performance.In this regard, we had introduced an optimal and ideal task scheduling algorithm and challenge the other prevailing algorithms in terms of QoS parameters like makespan and energy.The proposed algorithm increases the performance of the scheduling in cloud by making use of an enhanced meta-heuristic hybrid algorithm called Rider Cuckoo Optimization Algorithm (RCOA), which is an advanced version of the already existing Rider Optimization Algorithm.The proposed RCOA technique had produced an improvement of 5.6%, 4.36% and 2.27% for makespan and had reduced the energy to the tune of 16.8%, 19.18%, and 16.15% when compared with Particle Swarm Optimization (PSO), Cuckoo Search (CS), Rider Optimization Algorithm (ROA) algorithms respectively when used with 25 VMs.Also the proposed approach attains the objective of optimal scheduling swiftly and achieves convergence in a small duration of time.
Binary image classification using collective optical modes of an array of nanolasers

Recent advancements in nanolaser design and manufacturing open up unprecedented perspectives in terms of high integration densities and ultra-low power consumption, making these devices ideal for high-performance optical computing systems. In this work, we exploit the symmetry properties of the collective modes of a nanolaser array for a simple binary classification task of small digit images. The implementation is based on a 8 × 8 nanolaser array and relies on the activation of a collective optical mode of the array—the so-called “zero-mode”—under spatially modulated pump patterns.
An adaptation algorithm for personalised virtual reality exposure therapy

Anxiety disorders are highly prevalent in mental health problems. The lives of people suffering from an anxiety disorder can be severely impaired. Virtual Reality Exposure Therapy (VRET) is an effective treatment, which immerses patients in a controlled Virtual Environment (VE). This creates the opportunity to confront feared stimuli and learn how to deal with them, which may result in the reduction of anxiety. The configuration of these VEs requires extensive effort to maximise the potential of Virtual Reality (VR) and the effectiveness of the therapy. Manual configuration becomes infeasible when the number of possible virtual stimuli combinations is infinite. Due to the growing complexity, acquiring the skills to truly master a VR system is difficult and it increases the threshold for psychotherapists to use such useful systems. We therefore developed a prototype of a supportive algorithm to facilitate the use of VRET in a clinical setting. This automatised system assists psychotherapists to use the wide range of functionalities without burdening them with technical challenges. Thus, psychotherapists can focus their attention on the patient. In this paper both the prototype of the algorithm and a first proof of concept are described. The algorithm suggests environment configurations for VRET, tailored to the individual therapeutic needs of each patient. The system aims to maximise learning during exposure therapy for different combinations of stimuli by using the Rescorla–Wagner model as a predictor for learning. In a first proof of concept, the VE configurations suggested by the algorithm for three anonymised clinical vignettes were compared with prior manual configurations by two psychotherapists. The prototype of the algorithm and a first proof of concept are described. The first proof of concept demonstrated the relevance and potential of the proposed system, as it managed to propose similar configurations for the clinical vignettes compared to those made by therapists. Nonetheless, because of the exploratory nature of the study, no claims can yet be made about its efficacy. With the increasing ubiquity of immersive technologies, this technology for assisted configuration of VEs could make VRET a valuable tool for psychotherapists.
Offline Signature Verification with Transformers

Signature verification is a frequently-used forensics technology. Although the previous convolution neural network (CNN) based methods have made a great progress, the limitation of local neighborhood operation of CNN impedes reasoning about the relation of global signature strokes. To overcome this weakness, in this paper, we propose a novel holistic-part unified model named TransOSV based on the transformer framework. Signature images are encoded into patch sequences by the proposed holistic encoder to learn global representation. Considering the subtle local difference between the genuine signature and forged signature, we design a contrast based part decoder that is utilized to learn discriminative part features. To reduce the influence of sample imbalance, we formulate a new focal contrast loss function. Extensive experimental results and ablation studies prove the potential of the proposed model.
Controlling Fleets of Autonomous Mobile Robots with Reinforcement Learning: A Brief Survey

Controlling a fleet of autonomous mobile robots (AMR) is a complex problem of optimization. Many approached have been conducted for solving this problem. They range from heuristics, which usually do not find an optimum, to mathematical models, which are limited due to their high computational effort. Machine Learning (ML) methods offer another potential trajectory for solving such complex problems. The focus of this brief survey is on Reinforcement Learning (RL) as a particular type of ML. Due to the reward-based optimization, RL offers a good basis for the control of fleets of AMR. In the context of this survey, different control approaches are investigated and the aspects of fleet control of AMR with respect to RL are evaluated. As a result, six fundamental key problems should be put on the current research agenda to enable a broader application in industry: (1) overcoming the “sim-to-real gap”, (2) increasing the robustness of algorithms, (3) improving data efficiency, (4) integrating different fields of application, (5) enabling heterogeneous fleets with different types of AMR and (6) handling of deadlocks.
3LegRace: Privacy-Preserving DNN Training over TEEs and GPUs

Leveraging parallel hardware (e.g. GPUs) for deep neural network (DNN) training brings high computing performance. However, it raises data privacy concerns as GPUs lack a trusted environment to protect the data. Trusted execution environments (TEEs) have emerged as a promising solution to achieve privacypreserving learning. Unfortunately, TEEs’ limited computing power renders them not comparable to GPUs in performance. To improve the trade-off among privacy, computing performance, and model accuracy, we propose an asymmetric model decomposition framework, AsymML, to (1) accelerate training using parallel hardware; and (2) achieve a strong privacy guarantee using TEEs and differential privacy (DP) with much less accuracy compromised compared to DP-only methods. By exploiting the low-rank characteristics in training data and intermediate features, AsymML asymmetrically decomposes inputs and intermediate activations into low-rank and residual parts. With the decomposed data, the target DNN model is accordingly split into a trusted and an untrusted part. The trusted part performs computations on low-rank data, with low compute and memory costs. The untrusted part is fed with residuals perturbed by very small noise. Privacy, computing performance, and model accuracy are well managed by respectively delegating the trusted and the untrusted part to TEEs and GPUs. We provide a formal DP guarantee that demonstrates that, for the same privacy guarantee, combining asymmetric data decomposition and DP requires much smaller noise compared to solely using DP without decomposition. This improves the privacy-utility trade-off significantly compared to using only DP methods without decomposition. Furthermore, we present a rank bound analysis showing that the low-rank structure is preserved after each layer across the entire model. Our extensive evaluations on DNN models show that AsymML delivers 7.6× speedup in training compared to the TEE-only executions while ensuring privacy. We also demonstrate that AsymML is effective in protecting data under common attacks such as model inversion and gradient attacks.
An Effective Negotiation Strategy for Quantitative and Qualitative Issues in Multi-Agent Systems

Automated negotiation is an efficient approach for interaction in multi-agent systems in which agents exchange offers and counteroffers to conclude an agreement. This paper addresses the problem of offer formulation during the interaction between buyer and seller software agents for the purpose of reaching an agreement over quantitative and qualitative issues at once. In order to improve the outcome of the negotiation process, a hybrid negotiation method is presented and verified. Offer formulation is based on fuzzy similarity and preference-based methods. The preference-based mechanism is used for quantitative issues, while the fuzzy similarity technique is used for qualitative issues. The preference-based mechanism takes into account the preferences of the opponent when generating offers; the agent makes greater concessions on the issues which the opponent prefers more. The fuzzy-similarity method formulates an offer that considers offering a deal that is more similar to the one received by the opponent during the last round of negotiation. The experiments consists of two parts. The first part compares the hybrid strategy with the basic one. The findings reveal that the hybrid strategy is better in all performance measures, namely, utility rate, agreement rate, and Nash product rate. The second part of the experimental work compares four mechanisms of offer generating mechanisms: basic, preference-based, fuzzy similarity, and hybrid. The results show that the hybrid negotiation strategy performs equal or better that other negotiation strategies. More details can be found in the paper.
Learning Generative Design Methods: Higher Education Students Developing Toolkits

Abstract In order to achieve successful products and services, design research nowadays is searching for new methods that unify collaboration between professional designers and future users. Developing and using these new design methods is also of interest in design education. In this study, design toolkits are developed to practise participatory design with a user‐centric orientation. The purpose of this study is to analyse a case in higher education where students of art and design aimed at becoming craft teachers developed toolkits in design situations appointed beforehand. The literature review of the toolkit shows that a diverse and multidisciplinary field of applications has been developed. The data of the study consist of 24 virtual or physical toolkits made by the students as well as their reflections on their work. The results showed that executing a design process where students in a design situation had to enter into the future user's world creates empathy for designers. Also, various elements connected with the unifying of senses and physical activities were built into the toolkits as well as certain game‐like features. The results of this study can be used as basic data concerning making educational insights in design education in higher education.
E2EGI: End-to-End Gradient Inversion in Federated Learning

A plethora of healthcare data is produced every day due to the proliferation of prominent technologies such as Internet of Medical Things (IoMT). Digital-driven smart devices like wearable watches, wristbands and bracelets are utilized extensively in modern healthcare applications. Mining valuable information from the data distributed at the owners' level is useful, but it is challenging to preserve data privacy. Federated learning (FL) has swiftly surged in popularity due to its efficacy in dealing privacy vulnerabilities. Recent studies have demonstrated that Gradient Inversion Attack (GIA) can reconstruct the input data by leaked gradients, previous work demonstrated the achievement of GIA in very limited scenarios, such as the label repetition rate of the target sample being low and batch sizes being smaller than 48. In this paper, a novel method of End-to-End Gradient Inversion (E2EGI) is proposed. Compared to the state-of-the-art method, E2EGI's Minimum Loss Combinatorial Optimization (MLCO) has the ability to realize reconstructed samples with higher similarity, and the Distributed Gradient Inversion algorithm can implement GIA with batch sizes of 8 to 256 on deep network models (such as ResNet-50) and ImageNet datasets. A new Label Reconstruction algorithm is developed that relies only on the gradient information of the target model, which can achieve a label reconstruction accuracy of 81% in one batch sample with a label repetition rate of 96%, a 27% improvement over the state-of-the-art method. This proposed work can underpin data security assessments for healthcare federated learning.
Projection pursuit Gaussian process regression

A primary goal of computer experiments is to reconstruct the function given by the computer code via scattered evaluations. Traditional isotropic Gaussian process models suffer from the curse of dimensionality, when the input dimension is relatively high given limited data points. Gaussian process models with additive correlation functions are scalable to dimensionality, but they are more restrictive as they only work for additive functions. In this work, we consider a projection pursuit model, in which the nonparametric part is driven by an additive Gaussian process regression. We choose the dimension of the additive function higher than the original input dimension, and call this strategy “dimension expansion”. We show that dimension expansion can help approximate more complex functions. A gradient descent algorithm is proposed for model training based on the maximum likelihood estimation. Simulation studies show that the proposed method outperforms the traditional Gaussian process models.
Static-gesture word recognition in Bangla sign language using convolutional neural network

Sign language is the communication process of people with hearing impairments. For hearing-impaired communication in Bangladesh and parts of India, Bangla sign language (BSL) is the standard. While Bangla is one of the most widely spoken languages in the world, there is a scarcity of research in the field of BSL recognition. The few research works done so far focused on detecting BSL alphabets. To the best of our knowledge, no work on detecting BSL words has been conducted till now for the unavailability of BSL word dataset. In this research, a small static-gesture word dataset has been developed, and a deep learning-based method has been introduced that can detect BSL static-gesture words from images. The dataset, "BSLword" contains 30 static-gesture BSL words with 1200 images for training. The training is done using a multi-layered convolutional neural network with the Adam optimizer. OpenCV is used for image processing and TensorFlow is used to build the deep learning models. This system can recognize BSL static-gesture words with 92.50% accuracy on the word dataset.
Psychoeducation on Stress and Anxiety Using Virtual Reality: A Mixed-Methods Study

Virtual reality can help individuals to manage stress and anxiety. In addition to its efficacy in reducing stress and anxiety through relaxation, virtual reality can be helpful for psychoeducation. However, to date, few studies have explored this topic. Therefore, this mixed-methods sequential explanatory study, aimed to investigate the usability, sense of presence, emotional response, and effect on learning of MIND-VR, a psychoeducational virtual experience created to offer information on stress and anxiety. Twenty healthcare workers (60% female; mean age 43 ± 10) tried MIND-VR. Psychometric outcomes served as quantitative variables, while participant interviews provided qualitative data. Results showed that the virtual reality psychoeducational experience: (a) was highly usable and satisfying; (b) increased positive emotions (i.e., happiness and surprise) and decreased negative emotions (i.e., fear, sadness) and state anxiety; (c) elicited a strong sense of presence, especially spatial presence; and (d) was practical, simple, motivating, and engaging for learning information about stress and anxiety. These findings offer promising insights into that virtual reality may be an effective tool to provide psychoeducation.
RDFtex: Knowledge Exchange Between LaTeX-Based Research Publications and Scientific Knowledge Graphs

Scientific Knowledge Graphs (SciKGs) aim to integrate scientific knowledge in a machine-readable manner. For populating SciKGs, research publications pose a central source of knowledge. The goal is to represent both contextual information, i.e., metadata, and contentual information, i.e., original contributions like definitions and experimental results, of research publications in SciKGs. However, typical forms of research publications like traditional papers do not provide means of integrating contributions into SciKGs. Furthermore, they do not support making direct use of the rich information SciKGs provide. To tackle this, the present paper proposes RDFtex, a framework enabling (1) the import of contributions represented in SciKGs to facilitate the preparation of -based research publications and (2) the export of original contributions to facilitate their integration into SciKGs. As a proof of concept, an RDFtex implementation is provided. We demonstrate the framework’s functionality using the example of the present paper itself since it was prepared using this implementation.
Prospects of federated machine learning in fluid dynamics

Physics-based models have been mainstream in fluid dynamics for developing predictive models. In recent years, machine learning has offered a renaissance to the fluid community due to the rapid developments in data science, processing units, neural network based technologies, and sensor adaptations. So far in many applications in fluid dynamics, machine learning approaches have been mostly focused on a standard process that requires centralizing the training data on a designated machine or in a data center. In this article, we present a federated machine learning approach that enables localized clients to collaboratively learn an aggregated and shared predictive model while keeping all the training data on each edge device. We demonstrate the feasibility and prospects of such a decentralized learning approach with an effort to forge a deep learning surrogate model for reconstructing spatiotemporal fields. Our results indicate that federated machine learning might be a viable tool for designing highly accurate predictive decentralized digital twins relevant to fluid dynamics.
Attack Agnostic Dataset: Towards Generalization and Stabilization of Audio DeepFake Detection

Audio DeepFakes allow the creation of high-quality, convincing utterances and therefore pose a threat due to its potential applications such as impersonation or fake news. Methods for detecting these manipulations should be characterized by good generalization and stability leading to robustness against attacks conducted with techniques that are not explicitly included in the training. In this work, we introduce Attack Agnostic Dataset - a combination of two audio DeepFakes and one anti-spoofing datasets that, thanks to the disjoint use of attacks, can lead to better generalization of detection methods. We present a thorough analysis of current DeepFake detection methods and consider different audio features (front-ends). In addition, we propose a model based on LCNN with LFCC and mel-spectrogram front-end, which not only is characterized by a good generalization and stability results but also shows improvement over LFCC-based mode - we decrease standard deviation on all folds and EER in two folds by up to 5%.