Reviewerly/exHarmony huggingface dataset:
Hyperspectral Imagery Denoising Using Minimum Noise Fraction and Video Non-Local Bayes Algorithms

Hyperspectral imagery (HSI) denoising is a popular research topic in remote sensing. In this paper, we propose a novel method for HSI denoising by performing Minimum Noise Fraction (MNF) to the original HSI data cube, thresholding the noisy output bands with the Video Non-Local Bayes (VNLB) algorithm, and then conducting the inverse MNF transform to obtain the denoised data cube. Our experiments demonstrate that the proposed method usually achieves the best denoising results among several existing denoising methods for two HSI data cubes. In addition, it is much faster for HSI denoising than the VNLB algorithm which was originally developed for video denoising.
Addressing Values in Co-Design Projects: Lessons Learned From Two Case Studies in Sensitive Contexts

Abstract Human–computer interaction (HCI) scholars and others have advocated treating design as inevitably implicating political and ethical sensitivities. A subset of those considerations has been the attempt to deal with the often conflicting interests of stakeholders through ‘value sensitivity’. Drawing on value-sensitive design (VSD) as an inspiration, we emphasize the necessary way in which the evolving contextuality of the values in question shaped our research collaborations. This paper presents a retrospective analysis of two case studies from long-term user-centered design projects in fields with explicit ambitions for value-driven HCI research and concerned with emancipation and empowerment. The first, a 3-year project, entailed an explicit commitment to feminist policy initiatives with female participants that aimed at fostering values of gender equality. The second, a 4-year project, dealt with HCI research with and for older adults, where a multimodal mobility platform for ridesharing and public transportation was developed. We show how we translated general commitment into pragmatic, co-design research goals and infrastructures. The long-term ambition of our endeavors and integration of a broad stakeholder base were vital to support this. We additionally provide insights into how our approach offered safe spaces for trustful collaboration and flexibility when adapting methods to specific contexts.
Video Segmentation and Characterisation to Support Learning

The predominance of using videos for learning has become a phenomenon for generations to come. This leads to a prevalence of videos generating and using open learning platforms. However, learners may not be able to detect the main points in the video and relate them to the domain for their study. This can hinder the effectiveness of using videos for learning. To address these challenges, our research aims to develop automatic ways to segment videos, characterise them and finalise the segmentation work by aggregating adjacent segments within a video with the same focus of domain topic(s) or topic-concept(s). We present a framework for automated video segmenting and characterising to support learning (VISC-L). We assume that the domain we are processing videos from has been computationally presented (via ontology). We are using the Deep learning BERT-BASE-Uncased model with a binary classifier to identify the focus topic of each segment. Then we use a semantic tagging algorithm to identify the focus concept within the topic. The adjacent segments within a video with the same focus topic/concept are aggregated to generate the final characterised video segments. We have evaluated the usefulness of watching the identified segments and characterisations compared with video segmentation provided by Google.
Evaluating Tangle Distributed Ledger for Access Control Policy Distribution in Multi-region Cloud Environments

Nowadays, an increasing number of applications, services, and devices are shifting towards a less centralized cloud computing model. Cloud providers allow customers to deploy applications across multiple regions of their infrastructures to achieve high availability, be compliant with data privacy laws and regulations, and provide more robustness. Although an application can be deployed in different areas, it still has to behave as a single service that undergoes the same access control policies. However, verifying access control is a challenging task since the desired requirements are often in conflict (i.e., high availability and strict consistency) according to the use-case scenario taken into account. Different solutions have been proposed to distribute access control policies that are used to grant or deny user requests to access data or services. In this direction, Distributed Ledger Technologies (DLTs) seem to be a promising solution to distribute policies across different regions while still enabling their integrity, authenticity, and confidentiality. In this work, we propose an access control framework that uses IOTA, a novel DLT implemented through a directed acyclic graph named Tangle, to distribute policies. We implemented a prototype of our solution and compared it with its corresponding where the Tangle was replaced by a globally distributed NoSQL database.
Automated Threat Modeling Approaches: Comparison of Open Source Tools

The software systems of modern architectures are characterized by high heterogeneity and by the use of a model that delegates the control of individual components to third parties, making these systems more vulnerable to cyber-attacks. As a consequence, best practices, such as the Security-by-Design development methodologies, suggest taking into account security all over the systems life cycle, starting from the very early stages (e.g. from initial requirement analysis). Thus, one of the most relevant practices is Threat Modeling (TM), i.e. the activity devoted to identifying the possible threats that may affect the system. According to most security-related best practices, TM should be done as early as possible, in order to help in the requirement elicitation. Threat Modeling is a complex activity, that requires security experts with consolidated skills, able to predict and anticipate the possible issues: as a consequence, it is a costly activity, both in terms of time and money. Due to the continuous need of enforcing security, the effect of new regulation and the wide diffusion of ICT systems, there is a recent growth of tools and techniques that support and aims at automatizing Threat modelling activities. This work illustrates the approach adopted by our research team and compares the results of our technique with two other existing tools, in order to offer a brief overview of the state of the art of threat modelling automation techniques and of state of art limits and open research topics. It is worth noting that our comparison does not aims at being complete and focuses only on open tools (or on their free/community version), but offers a basis for understanding the progress of security automation processes in terms of threat modelling.
Promoting Universal Design for Learning Through Digital Assistive Tools in GamesHUB

The topic of inclusive education for underrepresented groups in the mainstream education system has long been an issue, mainly because of questions about the modalities and resources used. Today, this matter is much more affordable, thanks to digital technology. Indeed, in an education system that is becoming blended, and in which the computer plays an essential role in the presentation of information, the means are now more varied and the issue of heterogeneity of student profiles can be better addressed. These perspectives relate closely to Universal Design for Learning (UDL). This article presents a full Web inclusive platform for gamified learning, named GamesHUB. The platform offers special assistance to students with special needs (dyslexic students, L2 speakers, students with language disorders…) through Digital Assistive Tools (DAT) such as text-to-speech, spaced text for dyslexics, high-contrasted display, real-time translation in their native language… The choice of these DAT and their functions will be explained through didactic and psycholinguistic arguments. GamesHUB platform is developed by the “anonymized laboratory” (Switzerland). It is mainly aimed at primary learning in ordinary classes in the French-speaking part of Switzerland.
Characteristic sets profile features: Estimation and application to SPARQL query planning

RDF dataset profiling is the task of extracting a formal representation of a dataset’s features. Such features may cover various aspects of the RDF dataset ranging from information on licensing and provenance to statistical descriptors of the data distribution and its semantics. In this work, we focus on the characteristics sets profile features that capture both structural and semantic information of an RDF dataset, making them a valuable resource for different downstream applications. While previous research demonstrated the benefits of characteristic sets in centralized and federated query processing, access to these fine-grained statistics is taken for granted. However, especially in federated query processing, computing this profile feature is challenging as it can be difficult and/or costly to access and process the entire data from all federation members. We address this shortcoming by introducing the concept of a profile feature estimation and propose a sampling-based approach to generate estimations for the characteristic sets profile feature. In addition, we showcase the applicability of these feature estimations in federated querying by proposing a query planning approach that is specifically designed to leverage these feature estimations. In our first experimental study, we intrinsically evaluate our approach on the representativeness of the feature estimation. The results show that even small samples of just 0.5 % of the original graph’s entities allow for estimating both structural and statistical properties of the characteristic sets profile features. Our second experimental study extrinsically evaluates the estimations by investigating their applicability in our query planner using the well-known FedBench benchmark. The results of the experiments show that the estimated profile features allow for obtaining efficient query plans.
A multiple hierarchical clustering ensemble algorithm to recognize clusters arbitrarily shaped

As a research hotspot in ensemble learning, clustering ensemble obtains robust and highly accurate algorithms by integrating multiple basic clustering algorithms. Most of the existing clustering ensemble algorithms take the linear clustering algorithms as the base clusterings. As a typical unsupervised learning technique, clustering algorithms have difficulties properly defining the accuracy of the findings, making it difficult to significantly enhance the performance of the final algorithm. AGglomerative NESting method is used to build base clusters in this article, and an integration strategy for integrating multiple AGglomerative NESting clusterings is proposed. The algorithm has three main steps: evaluating the credibility of labels, producing multiple base clusters, and constructing the relation among clusters. The proposed algorithm builds on the original advantages of AGglomerative NESting and further compensates for the inability to identify arbitrarily shaped clusters. It can establish the proposed algorithm’s superiority in terms of clustering performance by comparing the proposed algorithm’s clustering performance to that of existing clustering algorithms on different datasets.
Efficient convolution-based pairwise elastic image registration on three multimodal similarity metrics

This paper proposes a complete convolutional formulation for 2D multimodal pairwise image registration problems based on free-form deformations. We have reformulated in terms of discrete 1D convolutions the evaluation of spatial transformations, the regularization term, and their gradients for three different multimodal registration metrics, namely, normalized cross correlation, mutual information, and normalized mutual information. A sufficient condition on the metric gradient is provided for further extension to other metrics. The proposed approach has been tested, as a proof of concept, on contrast-enhanced first-pass perfusion cardiac magnetic resonance images. Execution times have been compared with the corresponding execution times of the classical tensor product formulation, both on CPU and GPU. The speed-up achieved by using convolutions instead of tensor products depends on the image size and the number of control points considered, the larger those magnitudes, the greater the execution time reduction. Furthermore, the speed-up will be more significant when gradient operations constitute the major bottleneck in the optimization process.
Dependable learning-enabled multiagent systems

We are concerned with the construction, formal verification, and safety assurance of dependable multiagent systems. For the case where the system (agents and their environment) can be explicitly modelled, we develop formal verification methods over several logic languages, such as temporal epistemic logic and strategy logic, to reason about the knowledge and strategy of the agents. For the case where the system cannot be explicitly modelled, we study multiagent deep reinforcement learning, aiming to develop efficient and scalable learning methods for cooperative multiagent tasks. In addition to these, we develop (both formal and simulation-based) verification methods for the neural network based perception agent that is trained with supervised learning, considering its safety and robustness against attacks from an adversarial agent, and other approaches (such as explainable AI, reliability assessment, and safety argument) for the analysis and assurance of the learning components. Our ultimate objective is to combine formal methods, machine learning, and reliability engineering to not only develop dependable learning-enabled multiagent systems but also provide rigorous methods for the verification and assurance of such systems.
Resilience, reliability, and coordination in autonomous multi-agent systems

Multi-agent systems is an evolving discipline that encompasses many different branches of research. The long-standing Agents at Aberdeen ( A 3 ) group undertakes research across several areas of multi-agent systems, focusing in particular on aspects related to resilience, reliability, and coordination. In this article we introduce the group and highlight past research successes in those themes, building a picture of the strengths within the group. We close the paper outlining the future direction of the group and identify key open challenges and our vision towards solving them.
Developing, evaluating and scaling learning agents in multi-agent environments

The Game Theory &amp; Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simulating social dilemmas in rich spatial environments and training 3-d humanoids in difficult team coordination tasks. A signature aim of our group is to use the resources and expertise made available to us at DeepMind in deep reinforcement learning to explore multi-agent systems in complex environments and use these benchmarks to advance our understanding. Here, we summarise the recent work of our team and present a taxonomy that we feel highlights many important open challenges in multi-agent research.
IMLBoost for intelligent diagnosis with imbalanced medical records

Class imbalance of medical records is a critical challenge for disease classification in intelligent diagnosis. Existing machine learning algorithms usually assign equal weights to all classes, which may reduce classification accuracy of imbalanced records. In this paper, a new Imbalance Lessened Boosting (IMLBoost) algorithm is proposed to better classify imbalanced medical records, highlighting the contribution of samples in minor classes as well as hard and boundary samples. A tailored Cost-Fitting Loss (CFL) function is proposed to assign befitting costs to these critical samples. The first and second derivations of the CFL are then derived and embedded into the classical XGBoost framework. In addition, some feature analysis skills are utilized to further improve performance of the IMLBoost, which also can speed up the model training. Experimental results on five UCI imbalanced medical datasets have demonstrated the effectiveness of the proposed algorithm. Compared with other existing classification methods, IMLBoost has improved the classification performance in terms of F1-score, G-mean and AUC.
Entanglement duality in spin-spin interactions

We examine entanglement of thermal states for spin-12 dimers in external magnetic fields. Entanglement transition in the temperature-magnetic field plane demonstrates a duality in spin-spin interactions. This identifies dual categories of symmetric and anti-symmetric dimers with each category classified into toric entanglement classes. The entanglement transition line is preserved from each toric entanglement class to its dual toric class. The toric classification is an indication of topological nature of the entanglement.
Chinese Named Entity Recognition Using the Improved Transformer Encoder and the Lexicon Adapter

As a basic work of natural language processing (NLP), named entity recognition (NER) has attracted wide attention. Many methods of fusing the potential word representations in a Chinese sentence into the corresponding Chinese character representations have been applied to the Long-Short Term Memory (LSTM) model with good results in the Chinese NER task. However, the structure of LSTM cannot take full advantage of the parallelism of GPUs. Hence, we design a character-word attention adapter in the embedding layer to accelerate information fusion. Recently, the Transformer encoder has been popular in NLP for its parallel computing performance and the advantage of modeling the long-distance context. Nevertheless, the native Transformer encoder performs poorly on the NER task. We have deeply analyzed some of the shortcomings of the Transformer encoder. On these bases, we have further refined the position embedding and the self-attention calculation method in the Transformer encoder. Finally, we propose a new architecture of Chinese NER using the improved Transformer encoder and the lexicon adapter. On the four datasets of the Chinese NER task, our model achieves better performance than other models.
DT2I: Dense Text-to-Image Generation from Region Descriptions

Despite astonishing progress, generating realistic images of complex scenes remains a challenging problem. Recently, layout-to-image synthesis approaches have attracted much interest by conditioning the generator on a list of bounding boxes and corresponding class labels. However, previous approaches are very restrictive because the set of labels is fixed a priori. Meanwhile, text-to-image synthesis methods have substantially improved and provide a flexible way for conditional image generation. In this work, we introduce dense text-to-image (DT2I) synthesis as a new task to pave the way toward more intuitive image generation. Furthermore, we propose DTC-GAN, a novel method to generate images from semantically rich region descriptions, and a multi-modal region feature matching loss to encourage semantic image-text matching. Our results demonstrate the capability of our approach to generate plausible images of complex scenes using region captions.
Pancreatic Image Augmentation Based on Local Region Texture Synthesis for Tumor Segmentation

High-accuracy segmentation of lesions in pancreatic images is essential for computer-aided precision diagnosis and treatment. The segmentation accuracy of deep learning-based segmentation models depends on the number of annotated pancreatic tumor images. Due to the high cost of labeling, the size of the training set for segmentation models is usually small. This paper proposes an image augmentation model based on local region texture generation. For pancreas images (background) and tumor images (foreground) with ablated regions, the model can generate image textures for the remaining blank areas after combining the two images to obtain new samples. To improve the texture continuity between the tumor region and surrounding tissues in the generated image, this paper constructs a three-level loss function to constrain the training of the augmented model. Simulation experiments on the pancreatic tumor image set provided by the partner hospital show that the Dice coefficient of the segmentation model trained on the dataset augmented by the proposed model improves by 2.4% compared with the current optimal method when the number of real images is sparse, which proves its effectiveness and feasibility.
Sector steganographic algorithm with column modification on Raspberry hardware

The article analyzes the proposed sector steganographic algorithm for embedding messages using a single-board computer Raspberry Pi. The proposed steganographic algorithm is based on the LSB method, using a column modification to embed the hidden message. Experimental results from the program implementation of the proposed algorithm have been performed. Data analysis proves a high degree of invisibility and good performance of the steganographic algorithm in the single-board computer.
Connecting the methods of psychology and philosophy: Applying Cognitive-Affective Maps (CAMs) to identify ethical principles underlying the evaluation of bioinspired technologies

One major challenge of the 21st century is the increasingly rapid development of new technologies and their evaluation. In this article we argue for an interdisciplinary approach to meet this demand for evaluating new and specifically bioinspired technologies. We combine the consideration of normative principles in the field of ethics with psychological-empirical research on attitudes. In doing so, the paper has a twofold concern: first, we discuss how such an interdisciplinary collaboration can be implemented by using the method of Cognitive-Affective Mapping. Cognitive-Affective Maps (CAMs) enable a graphical representation of attitudes, including cognitive and affective aspects. Second, we argue that CAMs can be helpful to remedy the deficits of traditional ethical approaches. We applied CAMs in the context of an ethics seminar in which students were instructed to create CAMs based on bioinspired technologies twice – prior to the seminar to assess their evaluation on bioinspired technologies per se (pre-assessment) and after the seminar to assess how their evaluation might have changed and especially which normative ethical principles might have been additionally considered (post-assessment). As could be shown, CAMs can visualize the students’ attitudes, including the valence of ethical principles. Further, comparing pre- and post-CAMs indicated students’ attitude change.
Capturing constrained constructor patterns in matching logic

Reachability logic for rewrite theories consists of a specification of system states that are given by constrained constructor patterns, a transition relation that is given by a rewrite theory, and reachability properties expressed as pairs of state specifications. Matching logic has been recently proposed as a unifying foundation for programming languages, specification and verification. It is known that reachability properties can be naturally expressed in matching logic. In this paper, we show that constrained constructor patterns can be faithfully specified as a matching logic theory. As a result, we obtain a full encoding of reachability logic for rewrite theories as matching logic theories, by combining the two encodings. We also show that the main properties of constrained constructor patterns can be specified and proved within matching logic, using the existing proof system.