Reviewerly/exHarmony huggingface dataset:
The function of narrative in interactive documentary

Interactive documentary theory has oversimplified concepts of narrative in order to accentuate the contrast between traditional documentary and interactive documentary. In doing so, there is a risk of imagining the interactive documentary as an impermeable and incomprehensible artefact, resistant to hermeneutic examination. This paper will show that by approaching the question of narrative coherence as a phenomenological proposition, a theory of narrative can be developed which is able to account for interactive documentary traits which are not shared with traditional documentaries. In order to reimagine the role of narrative in interactive documentary, the philosophical approach of Paul Ricoeur will be adapted and extended. Ricoeur’s combination of phenomenology and hermeneutics encourages a nuanced and philosophically rigorous approach to narrative, which helps us expand on the role of narrative in interactive documentary.
Pathadin – The essential set of tools to start with whole slide analysis

Pathadin (https://gitlab.com/Digipathology/Pathadin) was designed as a WSI oriented open-source set of tools for beginners to experience the advantages of computer-assisted image analysis and cover essential features, frequently strenuous to access with the alternative programs. It is mainly oriented to work with histology slides but also includes a significant part of modern image formats. Introducing Pathadin, the manuscript aims to improve understanding of contemporary image analysis components, resolve technophobia and misbeliefs in the computational field, simplifying pathology research work, and shifting it into a quantitative paradigm. Despite being easy to use, Pathadin includes both basic and advanced analytical algorithms, as the application of machine learning. The functionality of Pathadin is demonstrated by AI-enhanced quantification of epithelial and stromal changes in prostate carcinoma, and their dependence on ISUP grade. The material included 5 radical prostatectomy samples for training and 83 (including 11 autopsies) samples for analysis. The analytical material was stained with Masson’s trichrome and Ki67, as widely available and potentially prognostic markers. An integrated solution by HistomicsTK for Ki67 evaluation was used. A U-net model for separating glands and stroma was trained, simplifying the independent analysis of these components. During the process, the model successfully highlighted glands and stroma. Masson’s trichrome stain demonstrated a gradual increase in collagen expression, being statistically significant between controls vs. G1, and G3 vs. G4. Although there was considerable overlap between adjacent groups, there was only a minor overlap in collagen amount between high- and low-grade carcinomas, affirming that with further research, stroma could be an additional diagnostic marker in prostate adenocarcinoma. Ki67 showed a statistically significant gradual increase in all groups except G1 vs. G2 and G4 vs. G5. Pathadin demonstrates that there is no need for significant resources to experience the advantages of modern computer-assisted analysis.
Color Image Generation from Range and Reflection Data of LiDAR

Recently, it has been reported that a camera-captured-like color image can be generated from the reflection data of 3D light detection and ranging (LiDAR). In this paper, we present that the color image can also be generated from the range data of LiDAR. We propose deep learning networks that generate color images by fusing reflection and range data from LiDAR point clouds. In the proposed networks, the two datasets are fused in three ways-early, mid, and last fusion techniques. The baseline network is the encoder-decoder structured fully convolution network (ED-FCN). The image generation performances were evaluated according to source types, including reflection data-only, range data-only, and fusion of the two datasets. The well-known KITTI evaluation data were used for training and verification. The simulation results showed that the proposed last fusion method yields improvements of 0.53 dB, 0.49 dB, and 0.02 in gray-scale peak signal-to-noise ratio (PSNR), color-scale PSNR, and structural similarity index measure (SSIM), respectively, over the conventional reflection-based ED-FCN. Besides, the last fusion method can be applied to real-time applications with an average processing time of 13.56 ms per frame. The methodology presented in this paper would be a powerful tool for generating data from two or more heterogeneous sources.
Two constructions of low-hit-zone frequency-hopping sequence sets

&lt;p style='text-indent:20px;'&gt;In this paper, we present two constructions of low-hit-zone frequen-cy-hopping sequence (LHZ FHS) sets. The constructions in this paper generalize the previous constructions based on &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ m $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-sequences and &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ d $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-form functions with difference-balanced property, and generate several classes of optimal LHZ FHS sets and LHZ FHS sets with optimal periodic partial Hamming correlation (PPHC).&lt;/p&gt;
Netfox detective: A novel open-source network forensics analysis tool

Network forensics is a major sub-discipline of digital forensics which becomes more and more important in an age where everything is connected. In order to cope with the amounts of data and other challenges within networks, practitioners require powerful tools that support them. In this paper, we highlight a novel open-source network forensic tool named – Netfox Detective – that outperforms existing tools such as Wireshark or NetworkMiner in certain areas. For instance, it provides a heuristically based engine for traffic processing that can be easily extended. Using robust parsers (we are not solely relying on the RFC description but use heuristics), our application tolerates malformed or missing conversation segments. Besides outlining the tool's architecture and basic processing concepts, we also explain how it can be extended. Lastly, a comparison with other similar tools is presented as well as a real-world scenario is discussed.
Conjecture: 100% of Elliptic Surfaces Over $$\mathbb {Q}$$ have Rank Zero

Based on an equation for the rank of an elliptic surface over \(\mathbb {Q}\) which appears in the work of Nagao, Rosen, and Silverman, we conjecture that 100% of elliptic surfaces have rank 0 when ordered by the size of the coefficients of their Weierstrass equations, and present a probabilistic heuristic to justify this conjecture. We then discuss how it would follow from either understanding of certain L-functions, or from understanding of the local behaviour of the surfaces. Finally, we make a conjecture about ranks of elliptic surfaces over finite fields, and highlight some experimental evidence supporting it.
Differential Evolution based Hyperparameters Tuned Deep Learning Models for Disease Diagnosis and Classification

With recent advancements in medical filed, the quantity of healthcare care data is increasing at a faster rate.Medical data classification is considered as a major research topic and numerous research works have been already existed in the literature.Presently, deep learning (DL) models offers an efficient method for developing a dedicated model to determine the class labels of the respective medical data.But the performance of the DL is mainly based on the hyperparameters such as, learning rate, batch size, momentum, and weight decay, which need expertise and wide-ranging trial and error.Therefore, the process of identifying the optimal configuration of the hyper parameters of a DL is still remains a major issue.To resolve this issue, this paper presents a new hyperparameters tuned DL models for intelligent medical diagnosis and classification.The proposed model is mainly based on four major processes namely pre-processing, feature extraction, classification and parameter tuning.The proposed method makes use of simulated annealing (SA) based feature selection.Then, a set of DL models namely recurrent neural network (RNN), gated recurrent units (GRU) and long short term memory (LSTM) are used for classification.To further increase the classification performance, differential evolution (DE) algorithm is applied to tune the hyperparameters of the DL models.A detailed simulation analysis takes place using three benchmark medical dataset namely Diabetes, EEG Eye State and Sleep stage dataset.The simulation outcome indicated that the DE-LSTM model have shown better performance with the maximum accuracy of 97.59%, 88.52% and 93.18% on the applied diabetes, EEG Eye State and Sleep Stage dataset.
A Dynamic Resource Allocation Framework for Apache Spark Applications

In this paper we design and implement a middleware service for dynamically allocating computing resources for Apache Spark applications on cloud platforms, and consider two different approaches to allocate resources. In the first approach, based on limited execution data of an application, we estimate the amount of resource adjustment (i.e., Delta) for each application separately a priori which is static during the execution of that particular application (i.e., Approach - I). In the second approach, we adjust the value of Delta dynamically during runtime based on execution pattern in real-time (i.e., Approach - II). Our evaluation using six different Apache Spark applications on both physical and virtual clusters demonstrates that our approaches can improve application performance while reducing resource requirements significantly in most cases compared to static resource allocation strategies.
The Oxymoron of the Internet Voting in Illiberal and Hybrid Political Contexts

This paper explores the phenomenon of e-voting, in particular, new i-voting technologies, within the context of hybrid and authoritarian political regimes. While e-voting and i-voting are not particularly widespread, more and more illiberal countries are implementing these innovations, which has been overlooked in the academia so far. The paper attempts to fill in this gap. Firstly, we provide a general overview of the problem and identify the key features of non-democracies adopting e-voting and i-voting. Secondly, we explore the case of Russia, a hybrid regime, which may become a role model for other countries in the near future. The research exposes the potential of e-voting, and in particular, i-voting as a tool for the regime stability and provides some avenues of the future research.
The Use of Grey Literature and Google Scholar in Software Engineering Systematic Literature Reviews

Objective of the study is to calculate: a) grey literature evidence in the selected Systematic literature reviews (SLRs); b) Google Scholar indexing for the extracted primary studies from the selected SLRs. We have randomly selected 20+ SLRs from Science Direct, IEEE Xplore, Springer Link and ACM. Result: a) Random selection of 20+ SLRs and grey literature calculation verifies that the grey literature percentage ranges around 5.7% to 9.1%; b) The second phase showed that Google Scholar was successful in retrieving around ~91% of the primary studies.
EI-MTD: Moving Target Defense for Edge Intelligence against Adversarial Attacks

Edge intelligence has played an important role in constructing smart cities, but the vulnerability of edge nodes to adversarial attacks becomes an urgent problem. A so-called adversarial example can fool a deep learning model on an edge node for misclassification. Due to the transferability property of adversarial examples, an adversary can easily fool a black-box model by a local substitute model. Edge nodes in general have limited resources, which cannot afford a complicated defense mechanism like that on a cloud data center. To address the challenge, we propose a dynamic defense mechanism, namely EI-MTD. The mechanism first obtains robust member models of small size through differential knowledge distillation from a complicated teacher model on a cloud data center. Then, a dynamic scheduling policy, which builds on a Bayesian Stackelberg game, is applied to the choice of a target model for service. This dynamic defense mechanism can prohibit the adversary from selecting an optimal substitute model for black-box attacks. We also conduct extensive experiments to evaluate the proposed mechanism, and results show that EI-MTD could protect edge intelligence effectively against adversarial attacks in black-box settings.
Discriminative Model for Identifying Motion Primitives Based on Virtual Reality-Based IADL

Although the number of patients with dementia has been increasing with the aging of society, no efficient treatment for advanced dementia forms has been proposed yet. Therefore, it is important to detect mild cognitive impairment (MCI) to prevent dementia from further progressing. According to a recent study, people with MCI tend to perform more inefficiently compared with healthy older adults during performance-based tests on instrumental activities of daily living (IADL). In this research, we aim to develop a discriminative model to identify motion primitives based on virtual reality-based IADL. In the experiment, finger movement was measured through the Lunchbox Task simulating meal preparation using a touch panel and a three-dimensional motion sensor. The time series velocity was estimated from the obtained position data, and segmentation was conducted based on the property of the reaching motion between two points. Considering the data of each segment interval, feature extraction and coding were performed according to the predefined motion primitives, and modeling based on machine learning was implemented. As a result, the identification accuracy of motion primitives was 97.1%. Sensitivity by category was 99% for stationary actions, 93% for pointing, 97% for drag, and 93% for click or release. These results indicate that hand movements during VR-IADL can be classified into four categories based on their behavioral characteristics. On this basis, the automatic identification of the motion primitive in VR-IADL is deemed realizable.
Bio-Inspired Agent-Based Architecture for Fraud Detection

The information accessible on the Internet has been drastically increased over the last years. This information is not always produced by trusted sources. This fact leads to the emergence of fraudulent websites containing unappropriated information or having malicious intentions. The analysis of this large amount of information to detect possible fraud situations tends to be a very demanding task for human experts. Thus, it becomes a key issue to automatise these operations. This paper presents a Multi-Agent System (MAS) model and its implementation focused on automatically detecting fraudulent websites. The INGENIAS methodology and the MESA framework have been selected for this purpose. The system consists of a bio-inspired agent-based architecture based on insect colonies. Several basic agents carry out simple and distributed operations, while there is just one agent which aggregates the individual outcomes to obtain the final result. Some websites have been selected to illustrate the viability of the proposal.
A measurement method for three-dimensional inner and outer surface profiles and spatial shell uniformity of laser fusion capsule

We propose a novel laser differential confocal measurement method with high spatial resolution for the simultaneous measurement of 3D inner and outer surface profiles and spatial shell uniformity of capsules. The zero-crossing point of characteristic curve of a laser differential confocal sensor (LDCS) exactly corresponds to the focal point; therefore, the proposed method performs high-precision and fast fixed-focus measurements on the vertexes of the surfaces of the capsule along its radial direction using the sensor. Then through the precision rotation measurement of the circumference trace and multi-directional meridian contour measurement of the capsule around the horizontal axis, the simultaneous high-precision measurement of 3D inner and outer surface profiles and spatial shell uniformity of the capsule can be achieved. To the best of our knowledge, the proposed method is the first to achieve high-precision and non-destructive measurement of 3D inner and outer surface profiles and spatial shell uniformity of the capsule simultaneously, and it provides a novel technical approach for achieving high-precision and non-destructive measurement of geometric parameters of ICF capsules.
Automated Assignment of Helpdesk Email Tickets: An Artificial Intelligence Life‐Cycle Case Study

In this article, we present an end‐to‐end automated helpdesk email ticket assignment system driven by high accuracy, coverage, business continuity, scalability, and optimal usage of computational resources. The primary objective of the system is to determine the problem mentioned in an incoming email ticket and then automatically dispatch it to an appropriate resolver group with high accuracy. While meeting this objective, it should also meet the objective of being able to operate at desired accuracy levels in the face of changing business needs by automatically adapting to the changes. The proposed system uses a system of classifiers with separate strategies for handling frequent and sparse resolver groups augmented with a semiautomatic rule engine and retraining strategies to ensure that it is accurate, robust, and adaptive to changing business needs. Our system has been deployed in the production of six major service providers in diverse service domains and currently assigns 100,000 emails per month, on an average, with an accuracy close to ninety percent and covering at least ninety percent of email tickets. This translates to achieving human‐level accuracy and results in a net savings of more than 50,000 man‐hours of effort per annum. To date, our deployed system has already served more than two million tickets in production.
Breathing, voice, and synchronized movement

When humans vocalize along with rhythmic arm movements, the recorded voice shows the same rhythmic pattern as these movements (1). When others listen to this voice, their rhythmic movements synchronize with those of the unseen vocalizer (1). Pouw et al.’s (1) outstanding multimodal research connects voice and gesture, and—we suggest—even more so breathing and movement. Pouw et al. (1) report rhythmic modulation of fundamental frequency, which is the rate at which vocal folds vibrate. They attribute this rhythmicity to voice modulation. However, many mechanisms can give the voice a rhythmic character; the most common one is breathing. Try exhaling while rhythmically moving your arm; the sound of your breath will become rhythmic. Could breathing alone explain Pouw et al.’s results (1, 2) (Fig. 1)? Their data may show that muscular contractions for movement propagate … [↵][1]1To whom correspondence may be addressed. Email: andrea.ravignani{at}gmail.com or sonja.kotz{at}maastrichtuniversity.nl. [1]: #xref-corresp-1-1
Secure, Global Quantum Communications Networks

The research in quantum communications networks (QCNs), where multiple users desire to generate a common quantum-secured key, is still in its beginning stage. To solve for the problems of both discrete variable (DV)-and continuous variable (CV)-QKD schemes in a simultaneous manner and enable the next generation of quantum communication networking, in this invited paper we describe a scenario where disconnected terrestrial QCNs are coupled through LEO satellite quantum network forming heterogeneous satellite-terrestrial QCN. This concept will provide an important step in enabling ultimate security for future network infrastructure in the world. The proposed QCNs will be robust against channel impairments, over heterogeneous links. Moreover, the proposed QCNs will provide an unprecedented security level for beyond 5G wireless networks, Internet-of-Things (IoT), optical networks, and autonomous vehicles, to mention few.
Privacy-Preserving Nonlinear SVM Classifier Training Based on Blockchain

The SVM classifier has been a significant and prevailing technique in machine learning applications. Training a high-quality SVM classifier usually requires a huge amount of data, which makes collaborative training by multiple parties become an inevitable trend. However, it causes privacy risks when sharing sensitive data with others. There are some existing methods to solve this problem. These methods mainly contain computation-intensive cryptographic techniques which are inefficient and not suitable for practical use. Therefore, it is important to realize efficient SVM classifier training while protecting privacy. In this paper, we propose a novel privacy-preserving SVM classifier training scheme based on blockchain. We establish a blockchain-based SVM classifier training mechanism which realizes collaboratively training while protecting privacy. We adopt the additive secret sharing technique to design several computation protocols, which are much more efficient than the schemes which contain complex cryptographic primitives. We conduct a thorough analysis of the security properties of our scheme. Experiments over a real dataset show that our scheme achieves high accuracy and practical efficiency.
A Formal Analysis of Moving Target Defense

Static system configuration provides a significant advantage for the adversaries to discover the assets and launch attacks. Configuration-based moving target defense (MTD) reverses the cyber warfare asymmetry by mutating certain configuration parameters to disrupt the attack planning or increase the attack cost significantly. In this research, we present a methodology for the formal verification of MTD techniques. We formally modeled MTD techniques and verified them against constraints. We use Random Host Mutation (RHM) as a case study for MTD formal verification. The RHM transparently mutates the IP addresses of end-hosts and turns into untraceable moving targets. We apply the formal methodology to verify the correctness, safety, mutation, mutation quality, and deadlock-freeness of RHM using the model checking tool. An adversary is also modeled to validate the effectiveness of the MTD technique. Our experimentation validates the scalability and feasibility of the formal verification methodology.
Calibration method of shear amount based on the optical layout of point source microscope for lateral shearing interferometric wavefront sensor

A calibration method of shear amount based on the optical layout of point source microscope (PSM) for lateral shearing interferometric (LSI) wavefront sensor is proposed. A simulation model of quadriwave LSI is introduced to analyze the influence of the window size of the spatial filter and the shear amount on the accuracy of the shearing wavefront feature extraction (SWFE) method. Simulation results show that the accuracy of the SWFE method deteriorates at small shear amounts. The proposed calibration method makes use of the optical layout of PSM to generate spot images of the point source at the detector plane of the sensor itself. The shear amount is calibrated by the relationship between the lateral distance of the spot images and the distribution of the diffraction orders of the grating. In the experiments, an SID4 wavefront sensor and a circular-aperture modified Hartmann mask LSI wavefront sensor are calibrated by the proposed method. A phase plate etched with patterns has been manufactured as the test specimen. The etching depth is characterized by the two wavefront sensors, and the testing results are compared with that by a commercial ZYGO® interferometer. The feasibility and accuracy of the proposed calibration method are validated. This calibration method provides an easily conducted approach for calibrating the shear amount of LSI wavefront sensor with high accuracy.