Reviewerly/exHarmony huggingface dataset:
A novel generalized fuzzy intelligence-based ant lion optimization for internet of things based disease prediction and diagnosis

In the modern healthcare system, the function of the Internet of Things (IoT) and the data mining methods with cloud computing plays an essential role in controlling a large number of big data for predicting and diagnosing various categories of diseases. However, when the patients suffer from more than one disease, the physician may not identify it properly. Therefore, in this research, the predictive method using the cloud with IoT-based database is proposed for forecasting the diseases that utilized the biosensors to estimate the constraints of patients. In addition, a novel Generalized Fuzzy Intelligence-based Ant Lion Optimization (GFIbALO) classifier along with a regression rule is proposed for predicting the diseases accurately. Initially, the dataset is filtered and feature extracted using the regression rule that data is processed on the proposed GFIbALO approach for classifying diseases. Moreover, suppose the patient has been affected by any diseases, in that case, the warning signal will be alerted to the patients via text or any other way, and the patients can get advice from doctors or any other medical support. The implementation of the proposed GFIbALO classifier is done with the use of the MATLAB tool. Subsequently, the results from the presented model are compared with state of the art techniques, and it shows that the presented method is more beneficial in diagnosis and disease forecast.
JEM++: Improved Techniques for Training JEM

Joint Energy-based Model (JEM) [12] is a recently proposed hybrid model that retains strong discriminative power of modern CNN classifiers, while generating samples rivaling the quality of GAN-based approaches. In this paper, we propose a variety of new training procedures and architecture features to improve JEM’s accuracy, training stability, and speed altogether. 1) We propose a proximal SGLD to generate samples in the proximity of samples from previous step, which improves the stability. 2) We further treat the approximate maximum likelihood learning of EBM as a multi-step differential game, and extend the YOPO framework [47] to cut out redundant calculations during backpropagation, which accelerates the training substantially. 3) Rather than initializing SGLD chain from random noise, we introduce a new informative initialization that samples from a distribution estimated from training data. 4) This informative initialization allows us to enable batch normalization in JEM, which further releases the power of modern CNN architectures for hybrid modeling. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Phase-reference-intensity attack on continuous-variable quantum key distribution with a local local oscillator

In practical continuous-variable quantum key distribution system using local local oscillator (LLO CV-QKD), the phase noise related to coherent detection and phase-reference pulse intensity can be locally calibrated at the receiver side. Therefore, it can be regarded as trusted noise in order to obtain a higher secret key rate and transmission distance. However, security loopholes will be opened for the eavesdropper to launch attacks without appropriate countermeasures. To this end, we propose two practical phase-reference pulse intensity attack strategies, where Eve can reduce the trusted phase noise by manipulating the intensity of the phase-reference pulse, thereby hiding her attack on the signal pulse. The performance of the LLO CV-QKD system under these attacks has been analyzed. We show that precisely monitoring the intensity of the phase-reference pulse in real time is an essential countermeasure to prevent the proposed attacks. Moreover, an intensity-monitoring scheme for the phase-reference pulse is proposed to strengthen the security of the practical LLO CV-QKD system and make the trusted phase noise model more robust.
Tracking of Maneuvering Extended Target Using Modified Variable Structure Multiple-Model Based on Adaptive Grid Best Model Augmentation

Maneuvering extended target tracking, an important but challenging research field, has attracted increasing attention in the field of radar signal processing. Variable structure multiple-model (VSMM) estimation is the current mainstream tracking algorithm, it possesses high tracking performance but depends largely on the model set used. However, the existing model set design methods still cannot offer higher tracking performance due to the complex maneuverability of the target. The current best model augmentation (BMA) algorithm is an efficient and universal model set design method, but it cannot adapt well to complex maneuvering situations because of its fixed basic and candidate model set. Hence, this paper proposes a modified BMA algorithm for VSMM based on adaptive grid, it can make full use of the previous information to realize an adaptation and time variation of the model set. Overall, two different scenarios were considered, wherein the digital simulation shows that the proposed method can provide a more reliable estimation of the kinematic state and shape of the extended target.
vexptoolbox: A software toolbox for human behavior studies using the Vizard virtual reality platform

Virtual reality (VR) is a powerful tool for researchers due to its potential to study dynamic human behavior in highly naturalistic environments while retaining full control over the presented stimuli. Due to advancements in consumer hardware, VR devices are now very affordable and have also started to include technologies such as eye tracking, further extending potential research applications. Rendering engines such as Unity, Unreal, or Vizard now enable researchers to easily create complex VR environments. However, implementing the experimental design can still pose a challenge, and these packages do not provide out-of-the-box support for trial-based behavioral experiments. Here, we present a Python toolbox, designed to facilitate common tasks when developing experiments using the Vizard VR platform. It includes functionality for common tasks like creating, randomizing, and presenting trial-based experimental designs or saving results to standardized file formats. Moreover, the toolbox greatly simplifies continuous recording of eye and body movements using any hardware supported in Vizard. We further implement and describe a simple goal-directed reaching task in VR and show sample data recorded from five volunteers. The toolbox, example code, and data are all available on GitHub under an open-source license. We hope that our toolbox can simplify VR experiment development, reduce code duplication, and aid reproducibility and open-science efforts.
STRAIDE: A Research Platform for Shape-Changing Spatial Displays based on Actuated Strings

We present STRAIDE, a string-actuated interactive display environment that allows to explore the promising potential of shape-changing interfaces for casual visualizations. At the core, we envision a platform that spatially levitates elements to create dynamic visual shapes in space. We conceptualize this type of tangible mid-air display and discuss its multifaceted design dimensions. Through a design exploration, we realize a physical research platform with adjustable parameters and modular components. For conveniently designing and implementing novel applications, we provide developer tools ranging from graphical emulators to in-situ augmented reality representations. To demonstrate STRAIDE’s reconfigurability, we further introduce three representative physical setups as a basis for situated applications including ambient notifications, personal smart home controls, and entertainment. They serve as a technical validation, lay the foundations for a discussion with developers that provided valuable insights, and encourage ideas for future usage of this type of appealing interactive installation.
Short Text Topic Learning Using Heterogeneous Information Network

With the explosive growth of short texts on users' preferences, learning discriminative and coherent latent topics from short texts is a critical work, since many practical applications require semantic understandings that short texts convey explicitly and implicitly. However, existing short text topic learning methods face the challenge of fully capturing semantically related co-occurrence phrases. Therefore, this paper proposes a novel Heterogeneous Information Network-based Short Text Topic learning approach (HIN-ShoTT) in terms of parts of speech, without depending on any auxiliary information. Specifically, HIN-ShoTT can be decomposed into three phases: i) seeking semantic relations among words, where HIN-ShoTT models multiple semantic relations among words based on a Heterogeneous Information Network (HIN) in terms of parts of speech; ii) extracting co-occurrence phrases and filtering noises, where HIN-ShoTT defines parts-of-speech meta structures to guide co-occurrence phrase extraction and a self-adapting threshold filtering module is proposed for discarding noises; and iii) inferring topics, where HIN-ShoTT models the generative process of co-occurrence phrases to make topic learning effective with the abundant corpus-level information. Our experimental results on three real-world datasets not only show that HIN-ShoTT performs well, but also demonstrate that it is feasible to incorporate HIN into short text topic learning for accuracy improvement.
Authoring Tool for Automatic Generation of Augmented Reality Instruction Sequence for Manual Operations

Despite the large-scale digitalization and automation of production lines, human operators still play a vital role in the shopfloor. Operators provide flexibility and agile responsiveness with their actions, as well as capability to react autonomously based on their technical knowledge and experience. Bridging the gap between modern digital systems of information creation and management and humans in the shopfloor is a current challenge for both the academia and the technology integrators. One emerging way for delivering instructions to the operators is Augmented Reality (AR). AR allows the visualization of the instructions in the operator’s field of view, using digital designs, animations and text instructions. As this technology gains increasingly more ground in the shopfloor, there is a need for agile generation of content, automatizing most of the instructions’ generation process. Towards that end, this paper presents an authoring tool for generating digital instructions for manual operations. The authoring tool allows the identification of the (dis-)assembly sequence of a product and also to include intermediate manual operations, such as surface treatment, and visualizations. In order to determine which assembly tasks and instructions are the most commonly needed, contextual inquiries were conducted in collaboration with the industry. These findings, together with the existing literature on manual processes on the shopfloor, served as a starting point for a taxonomy of assembly task types that are also presented in this paper. The taxonomy serves as a guide to define the processes on which digital instructions and visualizations can be provided. The result can be delivered as digital on-screen instructions or as an augmented reality application that may target mobile devices and headsets. The proposed approach is validated in an industrial use case of a compressor assembly.
An End-to-End Video Steganography Network Based on a Coding Unit Mask

Steganography hides secret messages inside the covers while ensuring imperceptibility. Different from traditional steganography, deep learning-based steganography has an adaptable and generalized framework without needing expertise regarding the embedding process. However, most steganography algorithms utilize images as covers instead of videos, which are more expressive and more widely spread. To this end, an end-to-end deep learning network for video steganography is proposed in this paper. A multiscale down-sampling feature extraction structure is designed, which consists of three parts including an encoder, a decoder, and a discriminator network. Furthermore, in order to facilitate the learning ability of network, a CU (coding unit) mask built from a VVC (versatile video coding) video is first introduced. In addition, an attention mechanism is used to further promote the visual quality. The experimental results show that the proposed steganography network can achieve a better performance in terms of the perceptual quality of stego videos, decoding the accuracy of hidden messages, and the relatively high embedding capacity compared with the state-of-the-art steganography networks.
A Light‐Hole Germanium Quantum Well on Silicon

The quiet quantum environment of holes in solid-state devices is at the core of increasingly reliable architectures for quantum processors and memories. However, due to the lack of scalable materials to properly tailor the valence band character and its energy offsets, the precise engineering of light-hole (LH) states remains a serious obstacle toward coherent optical photon-spin interfaces needed for a direct mapping of the quantum information encoded in photon flying qubits to stationary spin processors. Herein, to alleviate this long-standing limitation, an all-group-IV low-dimensional system is demonstrated, consisting of a highly tensile strained germanium quantum well grown on silicon allowing new degrees of freedom to control and manipulate the hole states. Wafer-level, high bi-isotropic in-plane tensile strain (<1%) is achieved using strain-engineered, metastable germanium-tin alloyed buffer layers yielding quantum wells with LH ground state, high g-factor anisotropy, and a tunable splitting of the hole sub-bands. The epitaxial heterostructures display sharp interfaces with sub-nanometer broadening and show room-temperature excitonic transitions that are modulated and extended to the mid-wave infrared by controlling strain and thickness. This ability to engineer quantum structures with LH selective confinement and controllable optical response enables manufacturable silicon-compatible platforms relevant to integrated quantum communication and sensing technologies.
Consensus protocols as a model of trust in blockchains

Blockchain is a decentralised, replicated, transparent and immutable data store. Blockchains are best described not as 'trustless', but on the basis of distributed trust: trusting everyone in aggregate. Consensus protocols are the heart and soul of the blockchains as they help in achieving this distributed trust. Blockchains are updated via the consensus protocols that guarantee their consistency and integrity over geographically distributed network nodes. Various algorithms can be applied to achieve a consensus based on the requirements like performance, security, scalability, consistency, and failure redundancy. Creating a global fair decentralised consensus protocol is of prime importance, in order to address above-mentioned requirements sufficiently. This paper focuses on analysing the already proposed consensus protocols adopted by popular blockchain platforms to determine their feasibility and efficiency. Parameters that are critical in evaluating blockchain consensus protocol are also discussed. This paper also analyses the hardness of achieving the fair decentralised trust with proof.
Multi-feature-based approach for white blood cells segmentation and classification in peripheral blood and bone marrow images

In this paper, we propose a complete automated framework for white blood cells differential count in peripheral blood and bone marrow images, in order to reduce the analysis time and increase the accuracy of several blood disorders diagnosis. A new colour transformation is first proposed to highlight the white blood cells regions. Then, a marker controlled watershed algorithm is used to segment the region of interest. The nucleus and cytoplasm are subsequently separated. In the identification step, a set of colour, texture and morphological features are extracted from both nucleus and cytoplasm regions. Next, the performances of a random forest classifier on a set of microscopic images are compared and evaluated. The obtained results reveal high recognition accuracies for both segmentation and classification stage.
Teaching accessibility awareness with games

Serious games have become more common in higher education as tools for delivering experiential learning. They are particularly useful during times of crisis like the COVID-19 pandemic, with almost all learning switching to virtual as populations isolate and people learn from home. Creating such games for typical able learners that are also accessible to learners with disabilities can be challenging, with few existing examples to learn from. The Accessibility Maze was created to teach web accessibility basics through experiential learning, to model accessible web-based game design, and as a game playable independently with a screen reader.
Evaluating Zotero, SHERPA/RoMEO, and Unpaywall in an institutional repository workflow

East Tennessee State University developed a workflow to add journal publications to their institutional repository and faculty profiles using three tools: Zotero for entering metadata, SHERPA/RoMEO for checking copyright permissions, and Unpaywall for locating full-text documents. This study evaluates the availability and accuracy of the information and documents provided by Zotero, SHERPA/RoMEO, and Unpaywall for journal publications in four disciplines. The tools were less successful with works authored by arts and humanities and education faculty in comparison to works authored by medicine and health sciences and social and behavioral sciences faculty. The findings suggest that publisher practices contributed to the disciplinary differences.
SymPy

In the previous chapters, we used NumPy to program our arrays, along with mathematical capabilities. In this chapter, we introduce SymPy. This Python library provides special mathematical functions and polynomials as well as powerful symbolic mathematics. You can include SymPy in your programs by means of the following directive:
Policy to Avoid a Privacy Disaster

This paper argues that in the current data-rich environment, organizations need formal policies for privacy as a way to avoid “privacy disasters”. Privacy disasters can occur when a company uses consumer data in a way that is legal, but violates public norms for acceptable use. The paper uses a case study to illustrate the elements that often characterize privacy disasters, and describes the principles and processes that can serve as the basis of a privacy policy capable of helping organizations avoid these negative events. The paper also highlights the implications of big data for privacy policy.
IBM/ObserveIT: Cost of Insider Threats 2020

When Michael Corleone says “keep your friends close but your enemies closer” in The Godfather Part II (a quote often misattributed to Sun Tzu, Machiavelli or Petrarch) he clearly didn't have computers in mind. But the truth is that, for most organisations, their biggest threat is already as close as it can be – their own staff.
Handwritten Character Recognition using Deep Learning

In day to day human life, handwritten documents are a general purpose for communication and restoring their information. In the field of computer science, character recognition using Deep Learning has more attention. DL has a massive set of pattern recognition tools that can apply to speech recognition, image processing, natural language processing and has a remarkable capability to find out a solution for complex machine learning problems. DL can focus on the specific feature of an image to character recognition for enhancing efficiency and accuracy.
Encoding–decoding strategy based resilient state estimation for bias-corrupted stochastic nonlinear systems

This paper is concerned with the resilient state estimation problem for a type of stochastic nonlinear systems, in which the possible dynamical bias is considered that is depicted by a dynamical equation. In pursuit of enhancing the robustness of the propagated data, a binary encoding strategy (BES) is exploited in the binary symmetric channel (BSC). While the random bit errors caused by the channel noise may take place during the propagation of the binary bit string via the memoryless BSC. To characterize the occurrence of the bit errors, a series of Bernoulli distributed random variables is adopted. More specifically, in order to deal with the possible gain fluctuation of the estimator in the execution process, a resilient state estimator is employed. This paper intends to put forward a novel resilient estimation scheme under the BES, which can assure that the estimation error dynamics is exponentially ultimately bounded in mean square. A sufficient criterion is first acquired for the existence of the expected resilient estimator and the estimator parameter is achieved by solving a convex optimization problem. Finally, an illustrative simulation example is provided to verify the validity of the theoretical results.
A prediction–correction method for fast and accurate initial displacement field estimation in digital image correlation

Abstract Initial displacement estimation is one of the most critical issues in digital image correlation. A better initial value can greatly improve the convergence rate and accuracy of the algorithms with subpixel accuracy. This paper developed an efficient estimation method to yield high-quality initial displacement fields. This method finds the initial displacement of each subset in a prediction–correction way, in which the displacement of the seed point is found by exhaustive search, but the other subsets are first predicted by an extrapolation scheme and then corrected by a monotonous search strategy. This method was tested by extensive experiments and validated by comparing with the well-known exhaustive search and adaptive rood pattern search methods, and then it was combined with the inverse compositional Gauss–Newton algorithm to perform subpixel-optimization experiments. The results demonstrated excellent features of accuracy, effectiveness, and convergence. Finally, we presented a three-dimensional surface reconstruction experiment using the proposed method, obtaining a geometric accuracy with a relative error of 0.016%.
A systematic review of digital technology and innovation and its potential to address anti-corruption, transparency, and accountability in the pharmaceutical supply chain

Introduction The urgent need to acquire medical supplies amidst the COVID-19 pandemic has led to bypassing of controls that govern the global pharmaceutical supply chain, increasing the risk of corruption. Hence, promoting anti-corruption, transparency, and accountability (ACTA) in supply chain and procurement has never been more important. The adoption of digital tools, if designed and implemented appropriately, can reduce the risks of corruption.Areas Covered Following PRISMA guidelines, we conducted an interdisciplinary systematic review of health/medicine, humanities/social sciences, engineering, and computer science literature, with the aims of identifying technologies used for pharmaceutical supply chain and procurement optimization and reviewing whether they address ACTA mechanisms to strengthen pharmaceutical governance. Our review identified four distinct categories of digital solutions: e-procurement and open contracting; track-and-trace technology; anti-counterfeiting technology; and blockchain technology.Expert Opinion Findings demonstrate an increase in research of technologies to improve pharmaceutical supply chain and procurement functions; however, most technologies are not being leveraged to directly address ACTA or global health outcomes. Some blockchain and RFID technologies incorporated ACTA mechanisms and mentioned specific policy/governance frameworks, but more purposeful linkage is needed. Findings point to the need for targeted policy development and governance to activate these innovative technologies to improve global health .