Reviewerly/exHarmony huggingface dataset:
Patient positioning by visualising surgical robot rotational workspace in augmented reality

Surgical robots have been the focal point of minimally invasive approaches in the last decades, gradually changing laparoscopic procedures. Robotic platforms offer many advantages that assist the surgeon, including a magnified view of the operative field, motion stability, and 3D visualisation.Understanding the characteristics and limitations of the robot's workspace is crucial for a successful intervention. Proper positioning of the patient with respect to the robot ensures optimal intraoperative reachability of the desired target regions by the robot and eliminates the need to reposition.In this work, we present a workspace visualisation system using augmented reality. We aim to assist the user wearing a head-mounted display with placing the patient in an optimal position in preparation for the robotic surgery. We evaluated three possible modes of presenting rotational workspace information to the user and quantitatively assessed the placement accuracy. We demonstrated the effectiveness of the implemented solution to increase the robot's effective range of motion at the surgical port location.
Verifiable Authentication and Issuance of Academic Certificates Using Permissioned Blockchain Network

Fake certificates pose a severe problem in today's world; they vouch for an individual's false skillset and put an organization's reputation at risk. Moreover, the existing verification process is performed in a centralized manner, often too cumbersome and time-consuming to the end-user, lacking transparency in the educational institutions' Issuance of certificates. Of-late, blockchain is a promising technology that provides transparent, secure, and reliable features, which offers solutions to the education sector. This paper provides the solution to the educational certification problem by employing the blockchain network. We proposed a permissioned blockchain network that identifies, authenticates the Issuer, adequate verification, securely shares academic records to the recipients, and stores the certificate credentials in the blockchain in a distributed manner.
PYREF: Refactoring Detection in Python Projects

Refactoring, the process of improving the internal code structure of a software system without altering its external behavior, is widely applied during software development. Understanding how developers refactor source code can help gain better understanding of the software development process and the relationship between various versions of a system. Refactoring detection tools have been developed for many popular programming languages, such as Java (e.g., REFACTORINGMINER and REF-FINDER) but, quite surprisingly, this is not the case for Python, a widely used programming language.Inspired by REFACTORING MINER, we present PYREF, a tool that automatically detects method-level refactoring operations in Python projects. We evaluated PYREF against a manually built oracle and compared it with a PYTHON-ADAPTED REFACTOR-INGMINER, which converts Python program to Java and detects refactoring operations with REFACTORING MINER. Our results indicate that PYREF can achieve satisfactory precision and detect more refactorings than the current state-of-the-art.
Federated Learning: Issues in Medical Application

Since the federated learning, which makes AI learning possible without moving local data around, was introduced by google in 2017 it has been actively studied particularly in the field of medicine. In fact, the idea of machine learning in AI without collecting data from local clients is very attractive because data remain in local sites. However, federated learning techniques still have various open issues due to its own characteristics such as non identical distribution, client participation management, and vulnerable environments. In this presentation, the current issues to make federated learning flawlessly useful in the real world will be briefly overviewed. They are related to data/system heterogeneity, client management, traceability, and security. Also, we introduce the modularized federated learning framework, we currently develop, to experiment various techniques and protocols to find solutions for aforementioned issues. The framework will be open to public after development completes.
An adaptive differential evolution algorithm using fitness distance correlation and neighbourhood-based mutation strategy

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MPHELL: A fast and robust library with unified and versatile arithmetics for elliptic curves cryptography

We propose a new versatile elliptic curves cryptography library based on unified arithmetics and various low-level arithmetics with a focus on protection against simple power analysis and an abstract layer for easy customisations. The implementations are oriented toward industrial applications and embedded devices. The number arithmetic used in the library is partly inherited from GMP with several improvements using adapted Montgomery representation and windowing techniques. We also present an improved AMNS (Adapted Modular Number System) arithmetic with competitive running time. The abstraction layer allows for the integration of external arithmetics (e.g., other libraries or hardware co-processor), general number systems and randomization of arithmetics. The library has the advantage of proposing standard elliptic curves but gives also the possibility to use curves in different settings such as Weierstrass form in co-Z coordinates, Jacobi quartic or Edwards forms (as well as their associated conversions functions). It has been extensively tested on x86-64, ARM 32/64 bits, STM32 architectures and also in real-world applications. We present some comparative elliptic curves signatures timings for different curves without taking into account the specificity of the curves in our library (as opposed to OpenSSL for instance).
Reflective Learning Classifier Systems for Self-Adaptive and Self-Organising Agents

Learning is a key capability in self-adaptive and self-organising systems to deal with dynamically changing conditions, unanticipated operational incidences, and open system constellations. Learning Classifier Systems, especially variants of the X CS Classifier System, have been demonstrated to be successfully applicable to the self-adaptation task concerning condition-aware re-configuration of parameters of the productive system. Compared to recent alternatives such as control theoretic approaches or (deep) reinforcement learning, XCS has advantages in the interpretability and continual evolution of knowledge, rendering it particularly applicable to real-world control problems. In this paper, we argue that the algorithmic concept of X CS and its integration in cooperative system constellations needs to be augmented with concepts for self-reflection, flexibility and transferability of knowledge to cover still unsolved challenges of real-world control problems. We present a brief introduction to the field and derive core challenges leading to a research agenda to achieve extended, reflective XCS-based self-adaptive and self-organising agents.
Neural Point Light Fields

We introduce Neural Point Light Fields that represent scenes implicitly with a light field living on a sparse point cloud. Combining differentiable volume rendering with learned implicit density representations has made it possible to synthesize photo-realistic images for novel views of small scenes. As neural volumetric rendering methods require dense sampling of the underlying functional scene representation, at hundreds of samples along a ray cast through the volume, they are fundamentally limited to small scenes with the same objects projected to hundreds of training views. Promoting sparse point clouds to neural implicit light fields allows us to represent large scenes effectively with only a single radiance evaluation per ray. These point light fields are as a function of the ray direction, and local point feature neighborhood, allowing us to interpolate the light field conditioned training images without dense object coverage and parallax. We assess the proposed method for novel view synthesis on large driving scenarios, where we synthesize realistic unseen views that existing implicit approaches fail to represent. We validate that Neural Point Light Fields make it possible to predict videos along unseen trajectories previously only feasible to generate by explicitly modeling the scene.
Towards a Quantum Modeling Approach to Reactive Agents

Quantum computing offers a new approach to the problem modeling and solving. This paper deals with the quantum modeling of reactive agents. It also proposes a quantum algorithm to implement the subsumption architecture, widely used by reactive agents, particularly in robotics. This work shows the contribution of the formalism proposed by quantum mechanics to the modeling and the proof of certain properties of the agent behavior. After, the definition of the reactive agent state modeling, the paper suggests a behavior modeling approach based on two steps for subsumption architecture. The first one models the preset behavior that links each action to the perception states. The second one determines, among several actuated actions, the one that the robot must achieve. The subsumption architecture raises the challenge of modeling hierarchical priority of actions. To this end, a multipartite entanglement is used in the second step. More precisely, the paper proposes and generalizes a W-state circuit in order to be used for modeling hierarchical priority actions and controlling the robot accordingly. The result of both steps provides a formal model that links the robot’s perception (input) to the actions (output), with respect to the subsumption architecture. The proposed model of agent is simulated using IBM quantum computer. The simulation shows that the model can either be served as a control unit of the robot (CU) to obtain the suitable action or to simulate the robot behavior.
Minimum spanning tree‐based cluster analysis: A new algorithm for determining inconsistent edges

Abstract In recent years, graph‐based data clustering algorithms have become popular as they perform connectivity‐based rather than centroid‐based partitioning. Methods related to minimum spanning tree (MST)‐based data clustering are types of graph‐based algorithms that can recognize arbitrary shapes of clusters by eliminating inconsistent edges from MST graphs. In all MST‐based data clustering algorithms, definition of inconsistent edges is the main problem that needs to be addressed. The longest edges in MST graphs are considered as inconsistent edges under ideal conditions. Nevertheless, outliers often exist in real‐world tasks, which makes the longest edges inaccurate cluster separation indicators. In this paper, we propose a new data clustering algorithm using MST and a critical distance method. The proposed algorithm solves the main issue of MST‐based data clustering, namely identifying and removing inconsistent edges to obtain clusters even in the event that the dataset contains some outliers. It begins by constructing the MST over a given weighted graph based on Euclidean distance and then splits up the graph into clusters by eliminating inconsistent edges using critical distance as a threshold. Integration of the advantages of both MST and critical distance methodology to obtain optimal clusters is the main contribution of this work. The conducted experimental analysis and results using different datasets prove that our proposed clustering algorithm yields better overall performance compared with the most common data clustering algorithms. Taking the Liver and Tumor datasets as an example, the proposed algorithm outperforms all other clustering algorithms with clustering accuracy equal to 0.579 and 0.660, respectively.
Human object interaction detection using two-direction spatial enhancement and exclusive object prior

Human-Object Interaction (HOI) detection aims to detect visual relations between humans and objects in images. One significant problem of HOI detection is that non-interactive human-object pair can be easily mis-grouped and misclassified as an action, especially when the humans are close and performing similar actions in the scene. To address the mis-grouping problem, we propose a spatial enhancement approach to enforce fine-level spatial constraints in two directions between human body parts and object parts. At inference, we propose a human-object regrouping approach for object-exclusive actions by considering the object-exclusive property of the interactive object, where the target object should not be shared by more than one human. By suppressing non-interactive pairs, our approach can decrease the false positives. Experiments on V-COCO and HICO-DET datasets demonstrate our approach is more robust compared to the existing methods under the presence of multiple humans and objects in the scene.
Revisiting Fault Adversary Models – Hardware Faults in Theory and Practice

Fault injection attacks are considered as powerful techniques to successfully attack embedded cryptographic implementations since various fault injection mechanisms from simple clock glitches to more advanced techniques like laser fault injection can lead to devastating attacks. Given these critical attack vectors, researchers came up with a long list of dedicated countermeasures to thwart such attacks. However, the security validation of proposed countermeasures is mostly performed on custom adversary models that are often not tightly coupled with the actual physical behavior of available fault injection mechanisms and, hence, fail to model the reality accurately. Furthermore, using custom models complicates comparison between different designs and evaluation results. As a consequence, we aim to close this gap by proposing a simple, generic, and consolidated fault injection adversary model that can be perfectly tailored to existing fault injection mechanisms and their physical behavior in hardware. To demonstrate the advantages, we apply it to a cryptographic primitive and evaluate it based on different attack vectors. We further show that our proposed adversary model can be integrated into the state-of-the-art fault verification tool VerFI. Finally, we provide a discussion on the benefits and differences of our approach compared to already existing evaluation methods.
Generalized Zero-Shot Learning using Identifiable Variational Autoencoders

Deep learning tasks rely heavily on a large amount of training data, but collecting and annotating data daily is not practical. Therefore, Zero-shot learning (ZSL) has become important for the applications, where no labeled data is available during training. ZSL aims at recognizing unseen classes by semantic transfer of information from seen to unseen classes. In this paper, we have proposed an identifiable VAE (iVAE) based generative model to address conventional and generalized ZSL. The key to our approach is learning disentangled representations, where each dimension is statistically independent and responsible for generating data. Thus, VAE is a commonly used model for learning disentangled independent factors of variation from the data. Our goal is to learn a latent space representing significant information, that approximates the actual data distribution. Extensive experiments on five benchmark datasets, i.e. CUB, AWA1, AWA2, SUN and aPY, are performed for further evaluation in both settings.
Extreme learning machine based sub-surface crack detection and quantification method for ACFM

In this paper, a quantification method for sub-surface crack evaluation based on extreme learning machine (ELM) neural network is proposed to the application of alternating current field measurement (ACFM). According to simulation results of finite element model, the dimensions of cracks can be described by four features of the Bz and Bx curves. An optimized ELM feasible for ACFM detection is trained and validated based on the selected features. Taking the advantage of our proposed sub-surface crack sizing method, the experimental results indicate that the detection and quantification for sub-surface crack is conducted readily. The maximum errors of length and depth predicted by ELM can be limited to 5.65 % and 6.27 %, respectively.
Fast &amp; Accurate Gaussian Kernel Density Estimation

Kernel density estimation (KDE) models a discrete sample of data as a continuous distribution, supporting the construction of visualizations such as violin plots, heatmaps, and contour plots. This paper draws on the statistics and image processing literature to survey efficient and scalable density estimation techniques for the common case of Gaussian kernel functions. We evaluate the accuracy and running time of these methods across multiple visualization contexts and find that the combination of linear binning and a recursive filter approximation by Deriche efficiently produces pixel-perfect estimates across a compelling range of kernel bandwidths.
SEXTAMT: A systematic map to navigate the wide seas of factors affecting expert judgment software estimates

Software projects involve technical and managerial activities, including software estimation. Inaccurate estimates are harmful and improving estimation methods is not enough: we need to understand more of the factors that impact estimates. Our study aims to identify the existing evidence about the factors that affect estimates in software projects when using expert judgment. We executed a Systematic Literature Mapping (SLM) based on database and snowballing searches, selecting papers by first reading their titles and abstracts and later reading the full text. Researchers investigated a wide range of different factors employing mostly laboratory research strategies and relying primarily on differences of estimates and participants’ perceptions to measure the factors’ effects. Resulting from our analysis, we present the SEXTAMT (Software Estimates of eXperts: A Map of influencing facTors), a map of factors affecting estimates built on three dimensions: project/iteration phase, stakeholders, and type of effect. Over the years, researchers have investigated a varied set of factors. Many of them were explored in different studies, employing diverse research strategies. Such studies provide compelling evidence on the elements that influence expert judgment estimates, which can be used to assess and improve everyday estimation in the software industry.
Adaptation of Semantic Rule-Based Sentiment Analysis Approach for Russian Language

The paper describes application of the semantic rule-based sentiment analysis approach, which was earlier developed and tested on English texts, to the Russian language. In order to take into account specificity of Russian it was adapted, particularly representation of the rules as patterns over a list of words was replaced with algorithms over the syntax tree of a sentence. The experiments on a quarter of a corpus of sentences extracted from hotel reviews allowed to perform the error analysis and refinement of the approach. The final results on the whole corpus allowed to achieve the results close to the state-of-the-art methods based on neural networks. The advantages of the approach, including simple interpretability of its results and absence of the need of learning, make it perspective for further research in sentiment analysis.
Register variation in school EFL textbooks

Abstract This study applies additive Multi-Dimensional Analysis (MDA) ( Biber 1988 ) to explore the linguistic characteristics of ‘school English’ or ‘textbook English’. It seeks to find out how text registers commonly featured in English as a Foreign Language (EFL) textbooks differ from comparable registers found outside the EFL classroom. To this end, a Textbook English Corpus (TEC) of 43 coursebooks used in European schools is mobilised. The texts from six textbook register subcorpora and three target language corpora are mapped onto Biber’s (1998) ‘Involved vs. Informational’ dimension of General English. Register accounts for 63% of the variance in these dimension scores in the TEC. Additional factors such as textbook level, series and country of publication/use only play a marginal role in mediating textbook register variation. Textbook dialogues score considerably lower than the Spoken BNC2014, whereas Textbook Fiction scores closest to its corresponding reference Youth Fiction Corpus. Pedagogical and methodological implications are discussed.
LIBRA: An Economical Hybrid Approach for Cloud Applications with Strict SLAs

Function-as-a-Service (FaaS) has recently emerged to reduce the deployment cost of running cloud applications compared to Infrastructure-as-a-Service (IaaS). FaaS follows a serverless “pay-as-you-go” computing model; it comes at a higher cost per unit of execution time but typically application functions experience lower provisioning time (startup delay). IaaS requires the provisioning of Virtual Machines, which typically suffer from longer cold-start delays that cause higher queuing delays and higher request drop rates. We present LIBRA, a balanced (hybrid) approach that leverages both VM-based and serverless resources to efficiently manage cloud resources for the applications. LIBRA closely monitors the application demand and provisions appropriate VM and serverless resources such that the running cost is minimized and Service-Level Agreements are met. Unlike state of the art, LIBRA not only hides VM cold-start delays, and hence reduces response time, by leveraging serverless, but also directs a low-rate bursty portion of the demand to serverless where it would be less costly than spinning up new VMs. We evaluate LIBRA on real traces in a simulated environment as well as on the AWS commercial cloud. Our results show that LIBRA outperforms other resource-provisioning policies, including a recent hybrid approach - LIBRA achieves more than 85% reduction in SLA violations and up to 53% cost savings.
Confidence Propagation Cluster: Unleash Full Potential of Object Detectors

It's been a long history that most object detection methods obtain objects by using the non-maximum suppression (NMS) and its improved versions like Soft-NMS to remove redundant bounding boxes. We challenge those NMS-based methods from three aspects: 1) The bounding box with highest confidence value may not be the true positive having the biggest overlap with the ground-truth box. 2) Not only suppression is required for redundant boxes, but also confidence enhancement is needed for those true positives. 3) Sorting candidate boxes by confidence values is not necessary so that full parallelism is achievable. In this paper, inspired by belief propagation (BP), we propose the Confidence Propagation Cluster (CP-Cluster) to replace NMS-based methods, which is fully parallelizable as well as better in accuracy. In CP-Cluster, we borrow the message passing mechanism from BP to penalize redundant boxes and enhance true positives simultaneously in an iterative way until convergence. We verified the effectiveness of CP-Cluster by applying it to various mainstream detectors such as FasterRCNN, SSD, FCOS, YOLOv3, YOLOv5, Centernet etc. Experiments on MS COCO show that our plug and play method, without retraining detectors, is able to steadily improve average mAP of all those state-of-the-art models with a clear margin from 0.3 to 1.9 respectively when compared with NMS-based methods.