Reviewerly/exHarmony huggingface dataset:
An automatic level set method for hippocampus segmentation in MR images

Hippocampus segmentation in MR images is beneficial for the diagnosis of many diseases and pathologies such as Alzheimer’s disease. Manual segmentation of the hippocampus is highly time-consuming and has low reproducibility; however, automated methods have introduced substantial gains in this regard. In this study, we used a novel level-set method for hippocampus segmentation in combination with the SBGFRLS (Selective Binary and Gaussian Filtering Regularised Level Set) and LAC (Localising Region-Based Active Contours) algorithms. The proposed method avoided the algorithms which required a large database and instead used a more complex level set approach to obtain comparable accuracy. This method was applied to a set of 36 MRI scans provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI), using the Harmonised Hippocampal Protocol (HarP) as the gold standard. In addition, the results were compared with the outputs of the Freesurfer software package. In regards to the similarity indices, the results of our algorithm (mean Dice = 0.847) were more comparable with the gold standard compared to those of Freesurfer. Classification results for AD vs control and MCI vs control showed a high degree of accuracy (91% and 75%, respectively). Therefore, this method can be an option for accurate and robust segmentation of the hippocampus.
Real-World and Virtual-World Practices for Virtual Reality Games: Effects on Spatial Perception and Game Performance

Researchers have been investigating ways to improve users’ spatial perception in virtual environments. Very limited studies have focused on the context of virtual reality (VR) games. Tutorials with practices, a common element in games, are good opportunities to implement measures that improve players’ spatial perception. Using an experiment, this paper investigates how two types of practices (real-world and virtual-world practices) influence players’ spatial perception, game performance, and immersion in VR games. Given that spatial perception is viewed as an essential aspect of VR applications, the moderating role of spatial perception on the effect of practices in game performance is also explored. The results demonstrate that virtual-world practice is effective in improving players’ spatial perception of the virtual environment of VR games. Real-world practice is suggested to be effective in enhancing spatial perception when it is averaged over multiple sessions. The results also suggest that spatial perception moderates the effects of practices on game performance. The results imply that practices in game tutorial can be a transitional environment for new players to enter a VR game.
The Hitchhiker’s guide to nonlinear filtering

Nonlinear filtering is used in online estimation of a dynamic hidden variable from incoming data and has vast applications in different fields, ranging from engineering, machine learning, economic science and natural sciences. We start our review of the theory on nonlinear filtering from the simplest ‘filtering’ task we can think of, namely static Bayesian inference. From there we continue our journey through discrete-time models, which are usually encountered in machine learning, and generalize to continuous-time filtering theory. The idea of changing the probability measure connects and elucidates several aspects of the theory, such as the parallels between the discrete- and continuous-time problems and between different observation models. Furthermore, it provides insight into the construction of particle filtering algorithms. This tutorial is targeted at scientists and engineers and should serve as an introduction to the main ideas of nonlinear filtering, and as a segway to more advanced and specialized literature.
On Forks and Fork Characteristics in a Bitcoin-Like Distribution Network

Chain growth in a permissionless blockchain-based ledger is mostly defined by the characteristics of the distribution network and the chosen consensus protocol. In this paper, we investigate the performance of block propagation in a Bitcoin-like peer-to-peer distribution network, and highlight the impact of the Nakamoto consensus protocol on the dynamics of blockchain growth. We use a simulated network with nodes located in different geographic regions, each with its own propagation characteristics; the values of network parameters are chosen to match available data for the Bitcoin peer-to-peer network. We show that the latency of block propagation is mainly affected by the mean round-trip time; that forks occur more often when mean round-trip time is longer, and that the ratio of the number of nodes opting for one or the other of competing blocks as the main chain tip can occur in almost any ratio; finally, that the mean time to resolve a fork is approximately equal to block inter-arrival time.
Spectral bounding: Strictly satisfying the 1-Lipschitz property for generative adversarial networks

Imposing the 1-Lipschitz constraint is a problem of key importance in the training of Generative Adversarial Networks (GANs), which has been proved to productively improve stability of GAN training. Although some interesting alternative methods have been proposed to enforce the 1-Lipschitz property, these existing approaches (e.g., weight clipping, gradient penalty (GP), and spectral normalization (SN)) are only partially successful. In this paper, we propose a novel method, which we refer to as spectral bounding (SB) to strictly enforce the 1-Lipschitz constraint. Our method adopts very cost-effective terms of both 1-norm and ∞-norm, and yet allows us to efficiently approximate the upper bound of spectral norms. In this way, our method provide important insights to the relationship between an alternative of strictly satisfying the Lipschitz property and explainable training stability improvements of GAN. Our proposed method thus significantly enhances the stability of GAN training and the quality of generated images. Extensive experiments are conducted, showing that the proposed method outperforms GP and SN on both CIFAR-10 and ILSVRC2015 (ImagetNet) dataset in terms of the standard inception score.
Retouching and restoration of missing image fragments by means of the iterative calculation of their spectra

This paper discusses the use of the interpolation method for the sequential calculation of the Fourier spectrum (IMSCS) for retouching and restoration of missing (shaded) image fragments. The proposed approach can be used with any form of a missing image fragment. Such image processing can give good results even at a significantly high percentage of missing image fragments. The method of digital virtual image reconstruction proposed here is strictly based on a scientific approach; as the source data, it uses all the data available (the image itself is the object to be recovered). Therefore, it is free from the human factor, because of which subjective changes can be introduced in the image under processing. The results presented indicate a significant increase in the quality of digital images (increasing the information content), which can offer helpful auxiliary tools for professionals using these images for their practical purposes.
Robust Head Pose Estimation Using Extreme Gradient Boosting Machine on Stacked Autoencoders Neural Network

Head pose estimation is an important sign in helping robots and other intelligence machines understand human. It plays a vital role in designing human computer interaction systems because many applications rely on precise results of head pose angles such as human behavior analysis, gaze estimation, 3D head reconstruction etc. This study presents a robust approach for estimating the head pose angles in a single image. More specifically, the proposed system first encodes the global features extracted from Histogram of Oriented Gradients in a multi stacked autoencoders neural network. Based on the hidden nodes in deep layers, Autoencoder has been proposed for feature reduction while maintaining the key information of data. A scalable gradient boosting machine is then employed to train and classify the embedded features. Experiences have evaluated on the Pointing 04 dataset and show that the proposed approach outperforms the state-of-the-art methods with the low head pose angle errors in pitch and yaw as 6.16° and 7.17°, respectively.
Discrete Atomic Compression of Digital Images: A Way to Reduce Memory Expenses

Digital image compression technology based on the application of atomic functions is presented. The fundamentals of this algorithm and the results of its comparison to the algorithm JPEG are given. It is shown that Discrete Atomic Compression saves more memory than JPEG with the same quality of results. It follows that application of Discrete Atomic Compression makes it possible in the future to reduce the cost of storing, processing and transferring a huge number of digital images via communication networks.
A Visual Object Tracking Algorithm Based on Improved TLD

Visual object tracking is an important research topic in the field of computer vision. Tracking–learning–detection (TLD) decomposes the tracking problem into three modules—tracking, learning, and detection—which provides effective ideas for solving the tracking problem. In order to improve the tracking performance of the TLD tracker, three improvements are proposed in this paper. The built-in tracking module is replaced with a kernelized correlation filter (KCF) algorithm based on the histogram of oriented gradient (HOG) descriptor in the tracking module. Failure detection is added for the response of KCF to identify whether KCF loses the target. A more specific detection area of the detection module is obtained through the estimated location provided by the tracking module. With the above operations, the scanning area of object detection is reduced, and a full frame search is required in the detection module if objects fails to be tracked in the tracking module. Comparative experiments were conducted on the object tracking benchmark (OTB) and the results showed that the tracking speed and accuracy was improved. Further, the TLD tracker performed better in different challenging scenarios with the proposed method, such as motion blur, occlusion, and environmental changes. Moreover, the improved TLD achieved outstanding tracking performance compared with common tracking algorithms.
Automatic identification of knowledge‐transforming content in argument essays developed from multiple sources

Abstract Developing knowledge‐transforming skills in writing may help students increase learning by actively building knowledge, regardless of the domain. However, many undergraduate students struggle to transform knowledge when drafting essays based on multiple sources. Writing analytics can be used to scaffold knowledge transforming as writers bring evidence to bear in supporting claims. We investigated how to automatically identify sentences representing knowledge transformation in argumentative essays. A synthesis of cognitive theories of writing and Bloom's typology identified 22 linguistic features to model processes of knowledge transforming in a corpus of 38 undergraduates' essays. Findings indicate undergraduates mostly paraphrase or copy information from multiple sources rather than engage deeply with sources' content. Eight linguistic features were important for discriminating evidential sentences as telling versus transforming source knowledge. We trained a machine learning algorithm that accurately classified nearly three of four evidential sentences as knowledge‐telling or knowledge‐transforming, offering potential for use in future research.
Post-Non-Classical Artificial Intelligence and its Pioneer Practical Applications

Artificial intelligence (AI) approaches and methods have been developed since the beginning of the last century. This paper identifies three stages in the history of AI development: classical, non-classical, and post-non-classical, where each stage includes the capabilities of the previous one. Classical and non-classical approaches include logical constructions, deep learning, genetic and ant algorithms, denotative (formalised) semantics and ontologies. Post-non-classical approaches aim to embrace latent parts of consciousness by indirect methods, including convergent approaches, quantum semantics, topologies and category theories, inverse problem solving, cognitive modelling and the like. The convergent approach takes into account the collective unconscious and can help to enrich conceptual models with cognitive (non-formalised) semantics. The continual power of cognitive semantics is tens of orders of magnitude greater than that of denotative semantics, but there are as yet no methods for using this potential. Examples of the real practical implementation of the components of post-non-classical AI approaches are given, and a design for a digital platform is suggested.
Fine-grained image classification with factorized deep user click feature

The advantages of user click data greatly inspire its wide application in fine-grained image classification tasks. In previous click data based image classification approaches, each image is represented as a click frequency vector on a pre-defined query/word dictionary. However, this approach not only introduces high-dimensional issues, but also ignores the part of speech (POS) of a specific word as well as the word correlations. To address these issues, we devise the factorized deep click features to represent images. We first represent images as the factorized TF-IDF click feature vectors to discover word correlation, wherein several word dictionaries of different POS are constructed. Afterwards, we learn an end-to-end deep neural network on click feature tensors built on these factorized TF-IDF vectors. We evaluate our approach on the public Clickture-Dog dataset. It shows that: 1) the deep click feature learned on click tensor performs much better than traditional click frequency vectors; and 2) compared with many state-of-the-art textual representations, the proposed deep click feature is more discriminative and with higher classification accuracies.
FAST: Fast and Accurate Scale Estimation for Tracking

In visual object tracking, robust and accurate scale estimation of a target is a challenging task. Despite the associated computational expense, existing tracking methods cannot accommodate large scale variations. Here, we propose a scale searching scheme that obtains robust and accurate scale estimation by incorporating a novel and robust criterion, the average peak-to-correlation energy, into a multi-resolution translation filter framework. To address the problem of computational expense, we introduce an expeditious search strategy. The resulting system is named FAST: Fast and Accurate Scale estimation for Tracking. Comprehensive evaluation using the publicly available tracking benchmark datasets demonstrates that the proposed scale searching framework can accommodate large scale variation while also yielding computational efficiency.
Reconstruction of functions and digital images using sign representations

The paper deals with the reconstruction of implicitly defined functions or digital images. Functions are defined using observations, each of which is the result of a pairwise comparison of the function values for two random arguments. The analysis of the current state of research for particular statements of this problem is presented: the method of pairwise comparisons used in decision-making for a finite set of alternatives; reconstruction of preference/utility function in multicriteria tasks; sign representations of images used for the description and analysis of digital images. A unified approach to reconstructing functions and images according to their sign representations is proposed, based on mapping in a high-dimensional space and constructing a linear (when reconstructing a function and images) or non-linear (including non-parametric) classifier (when reconstructing preferences). For a number of classification algorithms, experimental studies have been conducted to evaluate the effectiveness of the proposed approach using the example of the reconstruction of the utility function in problems of decision theory and reconstruction of the brightness function of real images.
Robust weighted linear loss twin multi-class support vector regression for large-scale classification

Least square twin multi-class support vector machine (LST-KSVC) is an efficient algorithm under “one-versus-one-versus-rest” approach. However, it has two drawbacks. One is the use of quadratic loss punishing the rest class samples causes both decision hyperplanes passing through the rest class, resulting in some rest class samples misclassified. The other is LST-KSVC gives the same weight to normal samples and outliers, which may cause sensitivity to outliers. To overcome these drawbacks, this paper presents a novel classifier called robust weighted linear loss based twin multi-class support vector regression (WLT-KSVC). First, WLT-KSVC punishes the rest class samples by a weighted linear loss with two advantages: to put the rest class samples in a reasonable region to improve classification accuracy, and to reduce the impact of outliers in the rest class. Second, WLT-KSVC reduces the impact of outliers in positive and negative classes by giving different samples different weights obtained by kernel-based possibilistic c-means algorithm. Finally, WLT-KSVC adds a regularization term in the objection function to improve model generalization ability. Notably, low computational complexity makes WLT-KSVC suitable for solving large-scale problems. Experimental results on UCI datasets and NDC datasets demonstrate that our WLT-KSVC achieves better performance than previous methods.
Accounting for Design Activism: On the Positionality and Politics of Designerly Intervention

We use two cases of design activism to examine designers' forms of positionality—or, the relations that enter into the formation of design interventions and the ways that a designer's situation affects the matter of those designs. We argue, by recognizing the stakes of their interventions and by mapping their contingencies, designers call into question the promise of their reforms—opening opportunities for responsive revision.
Minimum-Risk Path Planning for Long-Range and Low-Altitude Flights of Autonomous Unmanned Aircraft

We present the architecture, implementation and benchmark results of a minimum-risk path planning framework targeted towards long-range unmanned flights at low altitudes. Application scenarios, such as transportation, remote sensing or surveillance missions, often require long-range flights beyond the visual line-of-sight of a pilot on the ground. Autonomy is a key enabler for such operations as it decreases the dependency on command-and-control links and human interaction. An essential technology for autonomous unmanned aircraft is onboard and online path planning. It requires highly efficient planning algorithms to be integrated in onboard systems with limited computational resources. In this work, we demonstrate how semantic geospatial datasets and sampling-based planning algorithms can be used to calculate long-range and low-altitude flight paths within seconds. We present benchmark results from a set of mission scenarios of realistic complexity and extent. The results indicate that in-flight and onboard path planning for autonomous aircraft can be realized through effective pre-processing of input data and careful design, integration and tuning of planning algorithms.
Evaluation of Pre-Flight and On Orbit Training Methods Utilizing Virtual Reality

Operational training for Space missions is often limited to textbook information and hands-on training within physical mock-ups on Earth. Virtual Reality technology provides an alternative training method that is becoming increasingly more available for preliminary training tasks. Unlike many physical mock-ups, virtual mock-ups allow for an accurately simulated external environment similar to a spacecraft in orbit or aircraft during flight. This adds a layer of fidelity not possible in the physical world, and can save time and cost during development due to the lack of physical hardware needs. Key challenges for operational training include how well a task can be interpreted, remembered, and executed. This study aims to assess differences in human performance between a guided instructional task in a Virtual Reality environment and in a physical environment. Specifically, each test subject is given a set of instructions and a physical set of parts or a virtual set of 3D models that they are tasked with assembling into a certain configuration. A series of pre-task and post-task survey questions are used to evaluate cognitive workload, retention of concepts, and success of execution. This paper evaluates the differences in performance characteristics of an assembly task in VR compared to assembling the physical model.
Self-Learning Hot Data Prediction: Where Echo State Network Meets NAND Flash Memories

Well understanding the access behavior of hot data is significant for NAND flash memory due to its crucial impact on the efficiency of garbage collection (GC) and wear leveling (WL), which respectively dominate the performance and life span of SSD.Generally, both GC and WL rely greatly on the recognition accuracy of hot data identification (HDI).However, in this paper, the first time we propose a novel concept of hot data prediction (HDP), where the conventional HDI becomes unnecessary.First, we develop a hybrid optimized echo state network (HOESN), where sufficiently unbiased and continuously shrunk output weights are learnt by a sparse regression based on L 2 and L 1/2 regularization.Second, quantum-behaved particle swarm optimization (QPSO) is employed to compute reservoir parameters (i.e., global scaling factor, reservoir size, scaling coefficient and sparsity degree) for further improving prediction accuracy and reliability.Third, in the test on a chaotic benchmark (Rossler), the HOESN performs better than those of six recent state-of-the-art methods.Finally, simulation results about six typical metrics tested on five real disk workloads and on-chip experiment outcomes verified from an actual SSD prototype indicate that our HOESN-based HDP can reliably promote the access performance and endurance of NAND flash memories.
Augmented Reality for Human-Robot Cooperation in Aircraft Assembly

Augmented Reality (AR) is often discussed as one of the enabling technologies in Industrie 4.0. In this paper, we describe a practical application, where Augmented Reality glasses are used not only for assembly assistance, but also as a means of communication to enable the orchestration of a hybrid team consisting of a human worker and two mobile robotic systems. The task of the hybrid team is to rivet so-called stringers onto an aircraft hull. While the two robots do the physically demanding, unergonomic and possibly hazardous tasks (squeezing and sealing rivets), the human takes over those responsibilities that need experience, multi-sensory sensitiveness and specialist knowledge. We describe the working scenario, the overall architecture and give design and implementation details on the AR application.