Reviewerly/exHarmony huggingface dataset:
Simple phase unwrapping method with continuous convex minimization

Phase unwrapping is a problem to reconstruct true phase values from modulo 2π phase values measured using various phase imaging techniques. This procedure is essentially formulated as a discrete optimization problem. However, most energy minimization methods using continuous optimization techniques have ignored the discrete nature and solved it as a continuous minimization problem directly, leading to losing exactness of the algorithms. We propose a new minimum norm method that can yield the optimal solution of the discrete problem by minimizing a continuous energy function. In contrast to the graph-cuts method, which is state of the art in this field, the proposed method requires much less memory space and a very simple implementation. Therefore, it can be simply extended to 3D or 4D phase unwrapping problems.
A multi-components approach to monitoring process structure and customer behaviour concept drift

A method for detecting sudden and gradual drifts in process event data.• The method can detect structural and behavioural process drifts.• A sliding window framework associated with the proposed drift detector.• The method can detect, localize and rationalize process drifts.
MergedNET: A simple approach for one-shot learning in siamese networks based on similarity layers

Classifiers trained on disjointed classes with few labelled data points are used in one-shot learning to identify visual concepts from other classes. Recently, Siamese networks and similarity layers have been used to solve the one-shot learning problem, achieving state-of-the-art performance on visual-character recognition datasets. Various techniques have been developed over the years to improve the performance of these networks on fine-grained image classification datasets. They focused primarily on improving the loss and activation functions, augmenting visual features, employing multiscale metric learning, and pre-training and fine-tuning the backbone network. We investigate similarity layers for one-shot learning tasks and propose two frameworks for combining these layers into a MergedNet network. On all four datasets used in our experiment, MergedNet outperformed the baselines based on classification accuracy, and it generalises to other datasets when trained on miniImageNet.
Tumor grading model employing geometric analysis of histopathological images with characteristic nuclei dictionary

Histopathological study has been shown to improve diagnosis of various disease classifications effectively as any disease condition is correlated to characteristic set of changes in the tissue structure. This study aims at developing an automated neural network system for grading brain tumors (Glioblastoma Multiforme) from histopathological images within the Whole Slide Images (WSI) of hematoxylin and eosin (H&E) stains with significant accuracy. Hematoxylin channels are extracted from the histopathological image patches using color de-convolution. Cell nuclei are precisely segmented using three level Otsu thresholding. From each segmented image, nuclei boundaries are extracted to extract nucleus level features based on their shape and size. Geometric features including ellipse eccentricities, nucleus perimeter, area, and polygon edge counts are extracted using geometric algorithms to define the nuclei boundaries of the segmented image. These features are collected for a large number of nuclei and the nuclei are clustered using the K-Means algorithm in order to create a dictionary. One of the major contributions involves the creation of dictionary of a fixed number of representative cell nuclei to speed up patch level classification. This optimal dictionary is used for clustering extracted cell nuclei and a fixed length histogram of counts on different types of nuclei is obtained. The proposed system has been tested with a total of 239600 TCGA patches of GBM and 206000 patches of LGG collected from GDC data portal and it showed good diagnosis performance with auto-classification accuracy of 97.2% compared to other state-of-art methods. Our results on segmentation and classification are encouraging, with better attainment with regard to precision and accuracy in contrast with previous models. The auto grading proposed system will act as a potential guide for pathologists to make more accurate decisions.
An Artificial Intelligence Assisted Defensive Framework for Securing Cyberspace

Since the computer industry has started, the Internet has reached every organization and individual. Everyone is sharing or using some data in this digital world through various online activities. Now, this industry is increasing, and so is the need for advancement in cybersecurity to keep the data safe, maintain privacy, and prevent attacks. Securing cyberspace is becoming more difficult nowadays as vulnerabilities are increasing and being exploited in a more sophisticated way. Advancements in technology, especially artificial intelligence (AI), can help us in cybersecurity by finding the security flaws and learning their patterns to handle any such similar situation in the future. In this paper, an intelligent defensive framework based on cognitive working with reliable and massive data sets has been proposed. The proposed framework consists of four different cyber analytics methods based on data sources, learning methods, data mining algorithms, and end objectives. Each of the cyber analytics methods is unique that can work independently or in combination with other methods based on the event, response time, attack type, the scale of the attack, and risk associated.
Class Biased Sarcasm Detection Using Variational LSTM Autoencoder

Bhattacharjee, Saranya Maity, Soumyajit Sen, Rahul Chatterjee, SankhadeepWith the emergence of social media as a predominant source of propagating information, the detection of sarcastic posts has become imperative. Studies have found that efficient detection of sarcastic posts suffers from the class-biased distribution of sarcastic posts and non-sarcastic posts. Traditional approaches have used data level resampling algorithms on various text-level features like bag-of-words, tf-idf, etc. to mitigate the effect of imbalanced classes. However, such representations are unable to capture the semantic information of social media posts. Hence, in the current study, a Variational LSTM autoencoder has been used to learn the most efficient latent vector representation of the posts extracted from Twitter. Latent space vectors are then resampled to balance the dataset. Next, the balanced set of tweets have been used to train different classifiers and their performances have been measured in terms of precision, recall, Area Under Curve (AUC), F1-score, and Geometric mean. Performance analysis has revealed that SVM-SMOTE hybrid resampling technique has improved the performance of the classifiers to a greater extent.
Diverse Contemporary Algorithms to Resolve Load Balancing Issues in Cloud Computing—A Comparative Study

In the past couple of years, cloud technology has experienced a massive growth as it offers a wide range of benefits like very less capital expenditure, increased flexibility, scalability, etc. This massive growth has made effective resource utilization in cloud environment, an important area of concern to ensure user satisfaction. Various load balancing approaches have been proposed in the past in order to overcome this concern. The present work presents a review of different load balancing techniques in cloud environment. It also presents a clear comparative study of six contemporary algorithms, viz. Round Robin, throttled, ant colony, honey bee, ESCEL and PSO based on salient cloud load balancing metrics and also highlights the pros and cons of each algorithm.
Research on the Use of Open Teaching Mode Based on IoT Applications in College Piano Courses

The existing piano curriculum education system in colleges has been very backward. With the rapid economic development, it is also necessary to add new teaching technical means to meet the teaching needs of the new era and study the application of open teaching mode in the piano curriculum in colleges. This paper examines the current state of piano instruction in colleges and puts forward some problems, such as the serious lag of teaching mode, the backwardness of curriculum design education, and the failure of evaluation mechanism to keep pace with the times. The design is based on the open teaching mode, designs the piano course in colleges, establishes the open teaching mode, designs the open and inclusive teaching content, improves the piano teaching evaluation mechanism, and obtains a new teaching method of piano course in colleges. In the case analysis, the test results of this teaching mode and traditional teaching mode are compared. The results of the pretest and posttest show that the open teaching mode can greatly improve the teaching performance of piano courses and can be applied to practical teaching.
Visual analysis of action using machine learning and distributed expectation-maximization algorithm

We propose the novel visual analysis system Video of Things (VoT) using machine learning and a distributed Expectation-Maximization (EM) method. The VoT-enabled approach is proposed to perform a visual analysis of action in the context of wireless sensor systems. For the technical impact analysis of VoT, we used the EM algorithm. Furthermore, we investigate the notion of merging the clustered grid with the uniform filter in the target tracking of the distributed EM of particle filtering. We grid the network monitoring region with the technical impact analysis of VoT, then clusters the whole sensor nodes, build the system structure using a real scenario of visual actions in the VoT, and thoroughly analyze the system implementation process. Finally, we present investigations to validate the performance of the proposed research approach with a technical impact analysis of VoT. According to the study findings, the proposed VoT-based system can play an essential role in the recognition and technical impact analysis of table tennis activity.
The AssureMOSS security certification scheme

In the AssureMOSS project we aim to improve the security of MOSS (Multi-party Open Software and Services), which faces challenges of increasing complexity, high-frequency update cycles and high costs of evaluation. In this quest we will create a methodology to evaluate and certify MOSS projects regularly to improve and maintain a higher level of security in those projects. We aim to make the methodology and tools related to this effort open source for the benefit of the open projects. As a first step, this document presents the state of the art in security certification through showing the most popular and relevant – from the point of view in AssureMOSS – certification schemes. Moreover, as some shortcomings of these schemes are presented, we present the motivation behind the creation of the AssureMOSS scheme was necessary. In AssureMOSS we concentrate on the domain of MOSS, where constant recertification caused by the rapid release cycles of a product would cause extreme overhead in the budget and for developers as well if they need to maintain a documentation suitable for security evaluation. The AssureMOSS scheme would fill a void in the cloud- and microservices domain by employing the concept on delta evaluation in a lightweight certification scheme. Building on the capabilities of the AssureMOSS tools and implementing the methodology of the hereby presented AssureMOSS scheme we will build on the DeltAICert tool, which will be able to help the work of security evaluators by automating the security evaluation and certification process via delta evaluation, which concentrates the evaluation effort on the changes between the certified and new version of the target of evaluation (ToE).
ConSenseIoT: A Consensus Algorithm for Secure and Scalable Blockchain in the IoT context

Data protection and privacy is a major concern in the Internet of Things (IoT) ecosystem, and the excessive use of IoT devices may risk the security of the network. Blockchain solutions are used to enhance the trustworthiness and eliminate the need for trusted third parties by providing mechanisms to reach consensus in a network of trustless participants. The consensus algorithms employed by blockchain architectures ensure the integrity of the data stored in the blockchain, the resiliency of the network and manage the security of devices. However, current solutions are compute intensive affecting the performance of the network and consuming much energy. In this work, we introduce a consensus algorithm for offering secure distributed consensus among IoT devices without affecting the performance of the network. The algorithm is inspired by existing solutions, employs decentralised identities, verifiable credentials and a decentralised trust management mechanism to guarantee security, privacy and trustworthiness of transactions. Finally, our algorithm combines technologies for operating in a distributed manner which favors the scalability and allow the effective integration in large scale networks.
The Owner, the Provider and the Subcontractors: How to Handle Accountability and Liability Management for 5G End to End Service

The adoption of 5G services depends on the capacity to provide high-value services. In addition to enhanced performance, the capacity to deliver Security Service Level Agreements (SSLAs) and demonstrate their fulfillment would be a great incentive for the adoption of 5G services for critical 5G Verticals (e.g., service suppliers like Energy or Intelligent Transportation Systems) subject to specific industrial safety, security or service level rules and regulations (e.g., NIS or SEVESO Directives). Yet, responsibilities may be difficult to track and demonstrate because 5G infrastructures are interconnected and complex, which is a challenge anticipated to be exacerbated in future 6G networks. This paper describes a demonstrator and a use case that shows how 5G Service Providers can deliver SSLAs to their customers (Service Owners) by leveraging a set of network enablers developed in the INSPIRE-5Gplus project to manage their accountability, liability and trust placed in subcomponents of a service (subcontractors). The elaborated enablers are in particular a novel sTakeholder Responsibility, AccountabIity and Liability deScriptor (TRAILS), a Liability-Aware Service Management Referencing Service (LASM-RS), an anomaly detection tool (IoT-MMT), a Root Cause Analysis tool (IoT-RCA), two Remote Attestation mechanisms (Systemic and Deep Attestation), and two Security-by-Orchestration enablers (one for the 5G Core and one for the MEC).
All that is Solid Melts into Air: Towards Decentralized Cryptographic Access Control

Access control languages are traditionally based on centralized trust models when achieving their security goals. One important reason for a lack of decentralized trust models for access control has been difficulties in referring to and accessing cryptographic key material in a decentralized fashion. This lack of a working and secure decentralized public key infrastructure could be fatal for projects like Berners-Lee’s Solid. To solve this problem, we propose repurposing the access control scheme SDSI (Simple Distributed Security Infrastructure). SDSI is a long-standing and well-studied alternative to the hierarchical PKI infrastructure, but it failed to be adopted due to problems with key discovery and revocation, both of which can be solved with blockchain technology. In this concept note, we outline the next steps for how SDSI could be used as a decentralized trust model using blockchain technology.
ZEKRO: Zero-Knowledge Proof of Integrity Conformance

In the race toward next-generation systems of systems, the adoption of edge and cloud computing is escalating to deliver the underpinning end-to-end services. To safeguard the increasing attack landscape, remote attestation lets a verifier reason about the state of an untrusted remote prover. However, for most schemes, verifiability is only established under the omniscient and trusted verifier assumption, where a verifier knows the prover’s trusted states, and the prover must reveal evidence about its current state. This assumption severely challenges upscaling, inherently limits eligible verifiers, and naturally prohibits adoption in public-facing security-critical networks. To meet current zero trust paradigms, we propose a general ZEro-Knowledge pRoof of cOnformance (ZEKRO) scheme, which considers mutually distrusting participants and enables a prover to convince an untrusted verifier about its state’s correctness in zero-knowledge, i.e., without revealing anything about its state.
Explainability-based Debugging of Machine Learning for Vulnerability Discovery

Machine learning has been successfully used for increasingly complex and critical tasks, achieving high performance and efficiency that would not be possible for human operators. Unfortunately, recent studies have shown that, despite its power, this technology tends to learn spurious correlations from data, making it weak and susceptible to manipulation. Explainability techniques are often used to identify the most relevant features contributing to the decision. However, this is often done by taking examples one by one and trying to show the problem locally. To mitigate this issue, we propose in this paper a systematic method to leverage explainability techniques and build on their results to highlight problems in the model design and training. With an empirical analysis on the Devign dataset, we validate the proposed methodology with a CodeBERT model trained for vulnerability discovery, showing that, despite its impressive performances, spurious correlations consistently steer its decision.
Opportunities and risks for engaging research participants with self-logged menstrual health data

Many people use health tracking apps to keep track of their menstrual cycles, often in the hopes of better understanding their own health, and being able to identify when something might be wrong. However, it can be very difficult to interpret this data alone. Meanwhile, it is becoming increasingly common for researchers to use data from these apps to learn more about menstrual health. In this work we ask, how could more participatory approaches to conducting menstrual health research benefit both participants and researchers? We identify key challenges and risks of this kind of engagement, and propose four design guidelines for human-in-the-loop data analysis tools that engage participants with large-scale, quantitative menstrual health research: surface and elicit feedback on the data cleaning and analysis procedure; convey information relative to other users and clinical guidance; structure engagement to ensure valid analyses; and support social engagement and learning. For each of these, we highlight key open research questions relevant to the HILDA and visualization research communities. We plan to for evaluate and iterate on these guidelines through design workshops with users, researchers, and healthcare providers.
Towards Image Hashing Robust Against Cropping and Rotation

Image recognition is an important mechanism used in various scenarios. In the context of multimedia forensics, its most significant task is to automatically detect already known child and adolescent pornography in a large set of images. For this purpose, numerous methods based on robust hashing and feature extraction are already known, and recently also supported by machine learning. However, in general, these methods are either only partially robust to changes such as rotation and pruning, or they require a large amount of data and computation. We present a method based on a simple block hash that is efficient to compute and memory efficient. To be robust against cropping and rotation, we combine the method with image segmentation and a method to normalize the rotation of the objects. Our evaluation shows that the method produces results comparable to much more complex approaches, but requires fewer resources.
Extremal Dependence Modelling of Global Horizontal Irradiance with Temperature and Humidity: An Application Using South African Data

The interaction between global horizontal irradiance (GHI) and temperature helps determine the maximum amount of solar power generated. As temperature increases, GHI increases up to the point that it increases at a decreasing rate and then decreases. Therefore, system operators need to know the maximum possible solar power which can be generated. Using the multivariate adaptive regression splines, extreme value theory and copula models, the present paper seeks to determine the maximum temperature that will result in the generation of the maximum GHI ceteris paribus. The paper also discusses extremal dependence modelling of GHI with temperature and relative humidity (RH) at one radiometric station using South African data from 16 November 2015 to 16 November 2021. Empirical results show that the marginal increases of GHI converge to 0.12 W/m2 when temperature converges to 44.26 °C and the marginal increases of GHI converge to −0.1 W/m2 when RH converges to 103.26%. Conditioning on GHI, the study found that temperature and RH variables have a negative extremal dependence on large values of GHI. Due to the nonlinearity and different structure of the dependence on GHI against temperature and RH, unlike previous literature, we use three Archimedean copula functions: Clayton, Frank and Gumbel, to model the dependence structure. The modelling approach discussed in this paper could be useful to system operators in power utilities who must optimally integrate highly intermittent renewable energies on the grid.
Proposal for the Analysis of the State of Learning in University Students with the Inclusion of ICT in the Classroom

The inclusion of information and communication technologies in education has become a priority for all universities. To meet this need, there are several research works that have dealt with the subject for several decades. However, for its inclusion, the analysis of each institution is necessary since the needs of the university population and the resources for its application change according to each situation. This work seeks to create a method that allows establishing the needs and doubts of students about the use of educational technologies in the classroom without affecting their performance. For this, a process has been designed that identifies learning needs, through the validation of data obtained from surveys and the monitoring of the academic efficiency and learning of a cohort of students. The follow-up includes a period of four years from 2019 to 2022. This follow-up allowed establishing three different realities, in 2019 the academic data was analyzed in a face-to-face education model, from 2020 to 2021 the follow-up was included in a remote model with the use of technologies as a communication channel and in 2022 these were included as a learning component, which marked an in-depth analysis of student performance and how technology affected their learning.