Reviewerly/exHarmony huggingface dataset:
Effect of Noise on Segmentation Evaluation Parameters

Lung cancer is the killing disease that maximum vertexes due to drugs, smoking chewing of tobacco. The affliction of this disease is 14% than any other neoplasm, curtailing the functioning and existence of the diseased by 14 years. The overall relative survival rate is less than 18%. Early diagnosis of lung abnormality is a key challenge to improve the survival rates. Identification of malignant nodules from the medical image is a critical task as the image may contain noise during the processing that can be unseen and also having similar intensities of unwanted tissue thickening. This may debase the image standard and lead to wrong predictions. To process and reconstruct a medical image noise is to be eliminated. To exactly diagnose the disease, image is to be properly segmented from the other regions as to identify the lesions. Accuracy of ROI identification depends on the selection of segmentation operators. In this paper the performance of reconstruction is evaluated by using morphological operations and segmentation filters in noisy environment. The analysis is done between the original extracted ROI and noise image based on the evaluation parameters, Global Consistency Error (GCE) and Variation of Information (VOI). The best suitable operator can be used to obtain the ROI which can help for early diagnosis of the disease so as to control the cancer incidence and mortality rates.
Hacking the AI - the Next Generation of Hijacked Systems

Within the next decade, the need for automation, intelligent data handling and pre-processing is expected to increase in order to cope with the vast amount of information generated by a heavily connected and digitalised world. Over the past decades, modern computer networks, infrastructures and digital devices have grown in both complexity and interconnectivity. Cyber security personnel protecting these assets have been confronted with increasing attack surfaces and advancing attack patterns. In order to manage this, cyber defence methods began to rely on automation and (artificial) intelligence supporting the work of humans. However, machine learning (ML) and artificial intelligence (AI) supported methods have not only been integrated in network monitoring and endpoint security products but are almost omnipresent in any application involving constant monitoring, complex or large volumes of data. Intelligent IDS, automated cyber defence, network monitoring and surveillance as well as secure software development and orchestration are all examples of assets that are reliant on ML and automation. These applications are of considerable interest to malicious actors due to their importance to society. Furthermore, ML and AI methods are also used in audio-visual systems utilised by digital assistants, autonomous vehicles, face-recognition applications and many others. Successful attack vectors targeting the AI of audio-visual systems have already been reported. These attacks range from requiring little technical knowledge to complex attacks hijacking the underlying AI.With the increasing dependence of society on ML and AI, we must prepare for the next generation of cyber attacks being directed against these areas. Attacking a system through its learning and automation methods allows attackers to severely damage the system, while at the same time allowing them to operate covertly. The combination of being inherently hidden through the manipulation made, its devastating impact and the wide unawareness of AI and ML vulnerabilities make attack vectors against AI and ML highly favourable for malicious operators. Furthermore, AI systems tend to be difficult to analyse post-incident as well as to monitor during operations. Discriminating a compromised from an uncompromised AI in real-time is still considered difficult.In this paper, we report on the state of the art of attack patterns directed against AI and ML methods. We derive and discuss the attack surface of prominent learning mechanisms utilised in AI systems. We conclude with an analysis of the implications of AI and ML attacks for the next decade of cyber conflicts as well as mitigations strategies and their limitations.
Human Psychophysiological Activity Estimation Based on Smartphone Camera and Wearable Electronics

This paper presents a study related to human psychophysiological activity estimation based on a smartphone camera and sensors. In recent years, awareness of the human body, as well as human mental states, has become more and more popular. Yoga and meditation practices have moved from the east to Europe, the USA, Russia, and other countries, and there are a lot of people who are interested in them. However, recently, people have tried the practice but would prefer an objective assessment. We propose to apply the modern methods of computer vision, pattern recognition, competence management, and dynamic motivation to estimate the quality of the meditation process and provide the users with objective information about their practice. We propose an approach that covers the possibility of recognizing pictures of humans from a smartphone and utilizes wearable electronics to measure the user’s heart rate and motions. We propose a model that allows building meditation estimation scores based on these parameters. Moreover, we propose a meditation expert network through which users can find the coach that is most appropriate for him/her. Finally, we propose the dynamic motivation model, which encourages people to perform the practice every day.
Using LSTM for Context Based Approach of Sarcasm Detection in Twitter

In this research, we propose a sarcasm detection by taking into consideration its many varying contexts, related to the word or phrase in a tweet. To get the related context, we extract the information with paragraph2vec to simplify the process of finding the contextual meaning. The result paragraph2vec will provide the features to help classification in Long Short Term Memory (LSTM). Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. We applied a sarcasm detection method to identify sarcasm in two different languages: English and Indonesian and classification with balanced and imbalanced data. It aims to measure the reliability of the proposed approach and how effective the method is in detecting sarcasm. The result of the experiment shows that in Indonesian, balanced data has a good accuracy of 88.33 % and imbalanced data of 76.66 %, whereas in English the balanced data has an accuracy of 79% and imbalanced data of 54.5%.
A Hardware Accelerator for the Inference of a Convolutional Neural network

Convolutional Neural Networks (CNNs) are becoming increasingly popular in deep learning applications, e.g. image classification, speech recognition, medicine, to name a few. However, the CNN inference is computationally intensive and demanding a large among of memory resources. In this work is proposed a CNN inference hardware accelerator, which was implemented in a co-processing scheme. The aim is to reduce the hardware resources and achieve the better possible throughput. The design was implemented in the Digilent Arty Z7-20 development board, which is based on System on Chip (SoC) Zynq-7000 of Xilinx. Our implementation achieved a of accuracy for the MNIST database using only 12-bits fixed-point format. The results show that the co-processing scheme operating at a conservative speed of 100 MHz can identify around 441 images per second, which is about 17% times faster than a 650 MHz - software implementation. It is difficult to compare our results against other implementations based on Field-Programmable Gate Array (FPGA), because the others implementations are not exactly like ours. However, some comparisons, regarding the logical resources used and accuracy, suggest that our work could be better than previous works.
A distributed averaging-based evidential Expectation-Maximization algorithm for density estimation in unreliable sensor networks

In this paper, a novel distributed averaging-based evidential Expectation-Maximization algorithm is presented for data clustering and probability density function estimation in unreliable sensor networks in the presence of uncertain observations. All observations of the sensor network are statistically modelled by a Gaussian mixture model. A generalized likelihood function is maximized in a distributed manner for density estimation. For this purpose, the parameters of the mixture model such as weighting coefficients, mean vectors, and covariance matrices are updated by using a distributed averaging method. The proposed algorithm can be applied to any network with a mesh topology. The convergence of algorithm is also investigated and guaranteed by using a mathematical analysis. In the simulation result section, the proposed algorithm has been applied to a simulated uncertain sensor network with a mesh topology. The simulations confirm the favourable performance of the distributed averaging-based evidential EM algorithm.
<i>AVIKOM</i>

This paper introduces the novel Augmented Reality (AR) assistance system AVIKOM, a joint endeavour of three research groups together with four small and medium-sized enterprises (SME) as well as network partners and a diaconal institution. In particular, we investigate how AR-enabled assistance systems can be tailored to individual requirements of workers with diverse cognitive and physical capabilities for today's real-world industrial applications in the areas of (manual) assembly, logistics and operation of industrial machinery. We combine best practices from the domains of artificial intelligence, machine learning, user experience engineering, ethics research, and cognitive science with state-of-the-art insights for multi-modal system development to create a cognitive action assistance system that recognizes and adapts to individual users in various situational contexts, such as picking and training.
MoViz

Motion capture data is useful for machine learning applications in a variety of domains (e.g. movement improvisation, physical therapy, character animation in games), but many of these domains require large, diverse datasets with data that is difficult to label. This has precipitated the use of unsupervised learning algorithms for analyzing motion capture datasets. However, there is a distinct lack of tools that aid in the qualitative evaluation of these unsupervised algorithms. In this paper, we present the design of MoViz, a novel visualization tool that enables comparative qualitative evaluation of otherwise "black-box" algorithms for pre-processing and clustering large and diverse motion capture datasets. We applied MoViz to the evaluation of three different gesture clustering pipelines used in the LuminAI improvisational dance system. This evaluation revealed features of the pipelines that may not otherwise have been apparent, suggesting directions for iterative design improvements. This use case demonstrates the potential for this tool to be used by researchers and designers in the field of movement and computing seeking to better understand and evaluate the algorithms they are using to make sense of otherwise intractably large and complex datasets.
Diversity preservation in minimal criterion coevolution through resource limitation

Minimal Criterion Coevolution (MCC) is a recently-introduced algorithm that demonstrates how interactions between two populations, each subject to a simple reproductive constraint, can produce an open-ended search process. Unlike conventional quality diversity (QD) algorithms, which also promote divergence, MCC does not require an explicit characterization of behavior or a comparison of performance, thereby addressing bottlenecks introduced by an intrinsically-finite behavior descriptor and by an assessment of comparative quality. Genetic speciation, a common method of diversity preservation, maintains population diversity in MCC; however, it requires an unnatural explicit comparison of genetic similarity. In nature, organisms are implicitly segregated into niches that each have a carrying capacity dictated by the amount of available resources. To show that MCC can be simpler and more natural while still working effectively, this paper introduces a method of diversity preservation through resource limitation, thereby alleviating the need to formalize and compare genetic distance. Experimental results in a maze navigation domain demonstrate that resource limitation not only maintains higher population diversity in both the maze and agent populations, but also accelerates evolution by forcing individuals to explore new niches, thereby suggesting that resource limitation is an effective, simpler, and more natural alternative for diversity preservation in MCC.
Towards Understanding and Arguing with Classifiers: Recent Progress

Abstract Machine learning and argumentation can potentially greatly benefit from each other. Combining deep classifiers with knowledge expressed in the form of rules and constraints allows one to leverage different forms of abstractions within argumentation mining. Argumentation for machine learning can yield argumentation-based learning methods where the machine and the user argue about the learned model with the common goal of providing results of maximum utility to the user. Unfortunately, both directions are currently rather challenging. For instance, combining deep neural models with logic typically only yields deterministic results, while combining probabilistic models with logic often results in intractable inference. Therefore, we review a novel deep but tractable model for conditional probability distributions that can harness the expressive power of universal function approximators such as neural networks while still maintaining a wide range of tractable inference routines. While this new model has shown appealing performance in classification tasks, humans cannot easily understand the reasons for its decision. Therefore, we also review our recent efforts on how to “argue” with deep models. On synthetic and real data we illustrate how “arguing” with a deep model about its explanations can actually help to revise the model, if it is right for the wrong reasons.
Model-Based Hypothesis Engineering for Supporting Adaptation to Uncertain Customer Needs

To build successful products, the developers have to adapt their product features and business models to uncertain customer needs. This adaptation is part of the research discipline of Hypotheses Engineering (HE) where customer needs can be seen as hypotheses that need to be tested iteratively by conducting experiments together with the customer. So far, modeling support and associated traceability of this iterative process are missing. Both, in turn, are important to document the adaptation to the customer needs and identify experiments that provide most evidence to the customer needs. To target this issue, we introduce a model-based HE approach with a twofold contribution: First, we develop a modeling language that models hypotheses and experiments as interrelated hierarchies together with a mapping between them. While the hypotheses are labeled with a score level of their current evidence, the experiments are labeled with a score level of maximum evidence that can be achieved during conduction. Second, we provide an iterative process to determine experiments that offer the most evidence improvement to the modeled hypotheses. We illustrate the usefulness of the approach with an example of testing the business model of a mobile application.
A Prediction Model of Microsatellite Status from Histology Images

Machine learning approaches have received sufficient attention in tumor detection in histopathology, and the very recent researches show their potential in extraction of molecular information for biomarker prediction. However, as we can only obtain label information of the whole slide, the patch-wise prediction classification results are simply summarized to reach the final diagnosis in previous work. In this paper, we develop a novel framework to precisely predict biomarker from hematoxylin and eosin (H&E) stained histology slides, where microsatellite instability (MSI) status in colorectal cancer is used as a case study. We develop a patch-wise binary classifier to detect tumor tissue as biomarker is tightly associated with tumor tissues. To obtain a precise predication of MSI status, a noise-robust convolutional neural network is trained by relabeling patch-wise output iteratively and feed back as input information. We employ a distillation framework for the dataset relabeling task. We also design a mathematical algorithm to sort out representative patches towards the final MSI status prediction. The model is evaluated by a large patient cohort from The Cancer Genome Atlas (TCGA), and tested on the state-of-the-art deep learning device NVIDIA GPU TeslaTM V100. The experimental results demonstrate the improved reliability in MSI prediction from histology images.
PizzaBlock

This pictorial describes in detail the design, and multiple iterations, of PizzaBlock - a role-playing game and design workshop to introduce non-technical participants to decentralised identity management systems. We have so far played this game with six different audiences, with over one hundred participants - iterating the design of the artefacts and gameplay each time. In this pictorial, we reflect on this RtD project to unpack: a) How we designed artefacts and roleplay to explore decentralised technologies and networks; b) How we communicated the key challenges and parameters of a complex system, through the production of a playable, interactive, analogue representation of that technology; c) How we struck a balance between playful tangible gameplay and high-fidelity technical analogy; and d) How approaches like PizzaBlock invite engagement with complex infrastructures and can support more participatory approaches to their design.
mu-term: Verify Termination Properties Automatically (System Description)

We report on the new version of mu-term, a tool for proving termination properties of variants of rewrite systems, including conditional, context-sensitive, equational, and order-sorted rewrite systems. We follow a unified, logic-based approach to describe rewriting computations. The automatic generation of logical models for suitable first-order theories and formulas provides a common basis to implement the proofs.
Discovering and incorporating latent target-domains for domain adaptation

In this paper, we aim to address the unsupervised domain adaptation problem where the data in the target domain are much more diverse compared with the data in the source domain. In particular, this problem is formulated as discovering and incorporating latent domains underlying target data of interest for unsupervised domain adaptation. More specifically, the discovery of the latent target domains is based on three criteria, including the maximization of compactness and distinctiveness of the data in the individual latent target-domain, as well as the minimization of total divergence from the latent target-domains to the source domain. For each pair formed by a latent target domain and the source domain, we learn a feature space where the discrepancy between the source domain and the specific latent target domain is shrunk. Finally, we consider the projected source domain data on the learned latent feature spaces as different views of the source domain, and propose an extended multiple kernel learning algorithm to train a more robust and precise classifier for predicting the unlabeled target data. The effectiveness of our proposed method is demonstrated on various benchmark datasets for object recognition and human activity recognition. Moreover, we also show that our proposed method can be treated as an effective complement to the deep learning based unsupervised domain adaptation.
Transformation of the UML Deployment Model into a Distributed Ledger Network Configuration

A distributed ledger is a decentralized database spread across many participants. Various models describe software architecture and represent different architectural views. The paper concentrates on the deployment view. Model-Driven Development (MDD) is a software engineering approach that leverages models and transformations. The paper describes the UML2Deployment transformation of the distributed ledger’s deployment model into its deployment script. The deployment model, expressed in Unified Modeling Language (UML), is augmented with stereotypes and tagged values from UML Profile for Distributed Ledger Deployment. The target of the transformation is Gradle Groovy Domain Specific Language (DSL) deployment script for DLT network configuration. The transformation has been designed for R3 Corda framework. The authors propose the complete solution. The transformation has been incorporated into Visual Paradigm modeling tool.
An improved method for high hiding capacity based on LSB and PVD

In this chapter, we propose an improved image steganography method using the concept of least significant bit (LSB) substitution and pixel value differencing (PVD). The major contributions of the proposed method are (i) increase of the hiding capacity (HC), (ii) avoiding fall off boundary problem (FOBP), and (iii) resistance to regular and singular (RS) and pixel difference histogram (PDH) attack. Initially, the cover image is partitioned into blocks of three consecutive pixels. A reference pixel is chosen from each block, and then the pixels are readjusted. Further, the difference values are found using the smallest pixel of the block. Finally, the stego-pixels are obtained using the difference value and the smallest pixel. Additionally, to avoid FOBP, the pixel shifting process has been carried out for the pixels that fall out of the boundary. The observed result outperforms other existing state-of-the-art methods in terms of the steganographic parameters like peak signal-to-noise ratio (PSNR), HC, the number of fall off boundary pixels, and structural similarity index metric (SSIM).
Ultraviolet Laser Pulses with Multigigahertz Repetition Rate and Multiwatt Average Power for Fast Trapped-Ion Entanglement Operations

The conventional approach to perform two-qubit gate operations in trapped ions relies on exciting the ions on motional sidebands with laser light, which is an inherently slow process. One way to implement a fast entangling-gate protocol requires a suitable pulsed laser to increase the gate speed by orders of magnitude. However, the realization of such a fast entangling-gate operation presents a big technical challenge, as such the required laser source is not available off the shelf. For this, we engineer an ultrafast entangling-gate source based on a frequency comb. The source generates bursts of several hundred mode-locked pulses with pulse energy approximately 800 pJ at 5-GHz repetition rate at 393.3 nm and complies with all requirements for implementing a fast two-qubit gate operation. Using a single, chirped ultraviolet pulse, we demonstrate a rapid adiabatic passage in a ${\mathrm{Ca}}^{+}$ ion. To verify the applicability and projected performance of the laser system for inducing entangling gates we run simulations based on our source parameters. The gate time can be faster than a trap period with an error approaching ${10}^{\ensuremath{-}4}$.