Reviewerly/exHarmony huggingface dataset:
Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.
A Fast Distributed Variational Bayesian Filtering for Multisensor LTV System With Non-Gaussian Noise

For multisensor linear time-varying system with non-Gaussian measurement noise, how to design distributed robust estimator to increase the accuracy and robustness to outliers at a relatively low computation and communication cost is a fundamental task. This paper proposes a fast distributed variational Bayesian (VB) filtering algorithm to recursively estimate the state and noise distribution over three conventional sensor networks: 1) incremental-based; 2) diffusion-based; and 3) consensus-based. To be specific, the non-Gaussian measurement noise of each sensor is modeled as Student-t distribution, and the system state and the parameters of the distribution are estimated via VB approach in each iteration step. An interaction scheme is then added to obtain the global optimal parameter by fusing the local optimal parameters over incremental, diffusion, and consensus communication topology. An efficient sensor selection criterion under these topologies based on the Cramér-Rao lower bound is proposed to reduce the communication and computation burden. Compared with the existing centralized VB filtering algorithms, the proposed algorithm in this paper can extensively increase the robustness to node or link failure at a lower computation cost with acceptable estimation performance and communication load. The theoretic results and simulation results are given to show the efficiency of our proposed algorithm.
Technology-Enabled Assessment of Functional Health

The maturation of pervasive computing technologies has dramatically altered the face of healthcare. With the introduction of mobile devices, body area networks, and embedded computing systems, care providers can use continuous, ecologically valid information to overcome geographic and temporal barriers and thus provide more effective and timely health assessments. In this paper, we review recent technological developments that can be harnessed to replicate, enhance, or create methods for assessment of functional performance. Enabling technologies in wearable sensors, ambient sensors, mobile technologies, and virtual reality make it possible to quantify real-time functional performance and changes in cognitive health. These technologies, their uses for functional health assessment, and their challenges for adoption are presented in this paper.
Antibunched Photons Emitted by a dc-Biased Josephson Junction

We show experimentally that a dc biased Josephson junction in series with a high-enough-impedance microwave resonator emits antibunched photons. Our resonator is made of a simple microfabricated spiral coil that resonates at 4.4 GHz and reaches a $1.97\text{ }\text{ }\mathrm{k}\mathrm{\ensuremath{\Omega}}$ characteristic impedance. The second order correlation function of the power leaking out of the resonator drops down to 0.3 at zero delay, which demonstrates the antibunching of the photons emitted by the circuit at a rate of $6\ifmmode\times\else\texttimes\fi{}{10}^{7}$ photons per second. Results are found in quantitative agreement with our theoretical predictions. This simple scheme could offer an efficient and bright single-photon source in the microwave domain.
A novel reconstructed training-set SVM with roulette cooperative coevolution for financial time series classification

In real applications, noises are often present in the obtained data, which would considerably affect the performance of machine learning models. Although support vector machine (SVM) is a classic and efficient learning model, however, it is sensitive to noises in the training data. In this paper, a novel support vector machine named as reconstructed training-set SVM (RTS-SVM) is proposed to implement classification for high-noise data, where the roulette cooperative coevolution algorithm (R-CC) is used to optimize the parameters of RTS-SVM. The proposed SVM model is applicable to make the classification of high-noise data by tackling with the sensitive effect of the ``soft margin'' of SVM on the original training set. By means of the hierarchical relations existing in feature sets, hierarchical grouping (HG) algorithm is applied to construct feature subsets, based on which R-CC coordinates the parameters of RTS-SVM to achieve the optimization of the whole model. The application of the proposed scheme in the classification of financial time series is mainly discussed. Besides, the proposed model is also verified by using synthetic data with high noises and daily life data sets. Examples are provided to illustrate the effectiveness and practicability of the proposed algorithm.
Rubber hand illusion modulates the influences of somatosensory and parietal inputs to the motor cortex

The rubber hand illusion (RHI) paradigm experimentally produces an illusion of rubber hand ownership and arm shift by simultaneously stroking a rubber hand in view and a participant's visually occluded hand. It involves visual, tactile, and proprioceptive multisensory integration and activates multisensory areas in the brain, including the posterior parietal cortex (PPC). Multisensory inputs are transformed into outputs for motor control in association areas such as PPC. A behavioral study reported decreased motor performance after RHI. However, it remains unclear whether RHI modifies the interactions between sensory and motor systems and between PPC and the primary motor cortex (M1). We used transcranial magnetic stimulation (TMS) and examined the functional connections from the primary somatosensory and association cortices to M1 and from PPC to M1 during RHI. In experiment 1, short-latency afferent inhibition (SAI) and long-latency afferent inhibition (LAI) were measured before and immediately after a synchronous (RHI) or an asynchronous (control) condition. In experiment 2, PPC-M1 interaction was measured using two coils. We found that SAI and LAI were reduced in the synchronous condition compared with baseline, suggesting that RHI decreased somatosensory processing in the primary sensory and the association cortices projecting to M1. We also found that greater inhibitory PPC-M1 interaction was associated with stronger RHI assessed by questionnaire. Our findings suggest that RHI modulates both the early and late stages of processing of tactile afferent, which leads to altered M1 excitability by reducing the gain of somatosensory afferents to resolve conflicts among multisensory inputs. NEW & NOTEWORTHY Perception of one's own body parts involves integrating different sensory information and is important for motor control. We found decreased effects of cutaneous stimulation on motor cortical excitability during rubber hand illusion (RHI), which may reflect decreased gain of tactile input to resolve multisensory conflicts. RHI strength correlated with the degree of inhibitory posterior parietal cortex-motor cortex interaction, indicating that parietal-motor connection is involved in resolving sensory conflicts and body ownership during RHI.
Image Encryption Based on Dynamic Filtering and Bit Cuboid Operations

As one of the most widely used media types, images play an important role in the era of the Internet. And hence how to enhance the security of images has become a hot topic in the field of information security. However, due to some intrinsic characteristics of images, image security is still a challenging task. For the purpose of coping with this issue, in this paper, we propose a novel algorithm that combines a hyperchaotic system, dynamic filtering, and bit cuboid operations, namely, DFBC, for image encryption. Specifically, the proposed DFBC consists of four steps: firstly, a 7D Lorenz hyperchaotic system is utilized to generate a pseudorandom sequence; secondly, variable 1D filters are derived from the pseudorandom sequence, and dynamic filtering is conducted on each pixel of an image; thirdly, a diffusion scheme is performed and then the image is transformed to a bit cuboid; and, finally, various types of permutation (rearranging, symmetry, rotation, zigzag, and global bit permutation) are performed on the bit cuboid. The experiments on several testing images demonstrate that the DFBC achieves state-of-the-art results in terms of several evaluation criteria, showing that the DFBC is promising for image encryption.
CheriABI

The CHERI architecture allows pointers to be implemented as capabilities (rather than integer virtual addresses) in a manner that is compatible with, and strengthens, the semantics of the C language. In addition to the spatial protections offered by conventional fat pointers, CHERI capabilities offer strong integrity, enforced provenance validity, and access monotonicity. The stronger guarantees of these architectural capabilities must be reconciled with the real-world behavior of operating systems, run-time environments, and applications. When the process model, user-kernel interactions, dynamic linking, and memory management are all considered, we observe that simple derivation of architectural capabilities is insufficient to describe appropriate access to memory. We bridge this conceptual gap with a notional abstract capability that describes the accesses that should be allowed at a given point in execution, whether in the kernel or userspace. To investigate this notion at scale, we describe the first adaptation of a full C-language operating system (FreeBSD) with an enterprise database (PostgreSQL) for complete spatial and referential memory safety. We show that awareness of abstract capabilities, coupled with CHERI architectural capabilities, can provide more complete protection, strong compatibility, and acceptable performance overhead compared with the pre-CHERI baseline and software-only approaches. Our observations also have potentially significant implications for other mitigation techniques.
Competitive Mechanism For The Distribution Of Labor Resources In The Transport Objective

This article proposes a close labor force allocation model based on the use of a competitive mechanism. Many practical economic activity tasks and some economic theory issues are associated with the tasks of determining the optimal variant for solving the problem of the distribution of labor resources. One of these solutions is a compromise set. This article is devoted to the search for this set in a non-antagonistic non-coalition game related to the transport problem of integer programming.
Uniform and Variational Deep Learning for RGB-D Object Recognition and Person Re-Identification

In this paper, we propose a uniform and variational deep learning (UVDL) method for RGB-D object recognition and person re-identification. Unlike most existing object recognition and person re-identification methods, which usually use only the visual appearance information from RGB images, our method recognizes visual objects and persons with RGB-D images to exploit more reliable information such as geometric and anthropometric information that are robust to different viewpoints. Specifically, we extract the depth feature and the appearance feature from the depth and RGB images with two deep convolutional neural networks, respectively. In order to combine the depth feature and the appearance feature to exploit their relationship, we design a uniform and variational multi-modal auto-encoder at the top layer of our deep network to seek a uniform latent variable by projecting them into a common space, which contains the whole information of RGB-D images and has small intra-class variation and large inter-class variation, simultaneously. Finally, we optimize the auto-encoder layer and two deep convolutional neural networks jointly to minimize the discriminative loss and the reconstruction error. The experimental results on both RGB-D object recognition and RGB-D person re-identification are presented to show the efficiency of our proposed approach.
Named Entity Recognition From Biomedical Texts Using a Fusion Attention-Based BiLSTM-CRF

Biomedical named entity recognition (BNER) is the basis of biomedical text mining and one of the core sub-tasks of information extraction.Previous BNER models based on conventional machine learning rely on time-consuming feature engineering.Though most neural network methods improve the problems with automatic learning, they cannot pay attention to the significant areas when capturing features.In this paper, we propose an attention-based BiLSTM-CRF model.First, this model adopts a bidirectional long short-term memory network (BiLSTM) to obtain more complete context information.At the same time, the attention mechanism is proposed to improve the vector representations in BiLSTM.We design different attention weight redistribution methods and fuse them.It effectively prevents the significant information loss when extracting features.Finally, combining BiLSTM with conditional random field (CRF) layer effectively solves the problems of the inability to handle the strong dependence of tags in the sequence.With the simple architecture, our model achieves a reasonable performance on the JNLPBA corpus.It obtains an F1-score of 73.50.Our model can enhance the ability of the neural network to extract significant information and does not rely on any feature engineering, with only general pre-training word vectors.It makes our model have high portability and extendibility.
Personalized Saliency and Its Prediction

Nearly all existing visual saliency models by far have focused on predicting a universal saliency map across all observers. Yet psychology studies suggest that visual attention of different observers can vary significantly under specific circumstances, especially a scene is composed of multiple salient objects. To study such heterogenous visual attention pattern across observers, we first construct a personalized saliency dataset and explore correlations between visual attention, personal preferences, and image contents. Specifically, we propose to decompose a personalized saliency map (referred to as PSM) into a universal saliency map (referred to as USM) predictable by existing saliency detection models and a new discrepancy map across users that characterizes personalized saliency. We then present two solutions towards predicting such discrepancy maps, i.e., a multi-task convolutional neural network (CNN) framework and an extended CNN with Person-specific Information Encoded Filters (CNN-PIEF). Extensive experimental results demonstrate the effectiveness of our models for PSM prediction as well their generalization capabilityfor unseen observers.
A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning

In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of glioblastomas. The method combines a contrast-adaptive generative model for whole-brain segmentation with a new spatial regularization model of tumor shape using convolutional restricted Boltzmann machines. We demonstrate experimentally that the method is able to adapt to image acquisitions that differ substantially from any available training data, ensuring its applicability across treatment sites; that its tumor segmentation accuracy is comparable to that of the current state of the art; and that it captures most organs-at-risk sufficiently well for radiation therapy planning purposes. The proposed method may be a valuable step towards automating the delineation of brain tumors and organs-at-risk in glioblastoma patients undergoing radiation therapy.
Guiding CTC Posterior Spike Timings for Improved Posterior Fusion and Knowledge Distillation

Conventional automatic speech recognition (ASR) systems trained from frame-level alignments can easily leverage posterior fusion to improve ASR accuracy and build a better single model with knowledge distillation. End-to-end ASR systems trained using the Connectionist Temporal Classification (CTC) loss do not require frame-level alignment and hence simplify model training. However, sparse and arbitrary posterior spike timings from CTC models pose a new set of challenges in posterior fusion from multiple models and knowledge distillation between CTC models. We propose a method to train a CTC model so that its spike timings are guided to align with those of a pre-trained guiding CTC model. As a result, all models that share the same guiding model have aligned spike timings. We show the advantage of our method in various scenarios including posterior fusion of CTC models and knowledge distillation between CTC models with different architectures. With the 300-hour Switchboard training data, the single word CTC model distilled from multiple models improved the word error rates to 13.7%/23.1% from 14.9%/24.1% on the Hub5 2000 Switchboard/CallHome test sets without using any data augmentation, language model, or complex decoder.
A Framework Strategy to Overcome Trust Issues on Cloud Computing Adoption in Higher Education

Cloud computing has the potential of adding strategic value to the higher education domain owing to exemplary growth in ubiquitous data and communication services ranging from student access to educational materials to developing teaching and research practices. Despite the wide adoption of CC in HEIs, there is a paucity of research that specifically addresses the issue of trust in cloud adoption in the UK HEI context, as well as identifying smarter and more efficient strategies to overcome the existing CC trust issue in this domain. The authors propose a five-stage strategic roadmap to address the trust issues impacting the uptake of cloud services in UK universities. They conclude that IT and management participation and support are the keys to the success of the strategic framework.
Qubit allocation as a combination of subgraph isomorphism and token swapping

In 2016, the first quantum processors have been made available to the general public. The possibility of programming an actual quantum device has elicited much enthusiasm. Yet, such possibility also brought challenges. One challenge is the so called Qubit Allocation problem: the mapping of a virtual quantum circuit into an actual quantum architecture. There exist solutions to this problem; however, in our opinion, they fail to capitalize on decades of improvements on graph theory. In contrast, this paper shows how to model qubit allocation as the combination of Subgraph Isomorphism and Token Swapping. This idea has been made possible by the publication of an approximative solution to the latter problem in 2016. We have compared our algorithm against five other qubit allocators, all independently designed in the last two years, including the winner of the IBM Challenge. When evaluated in "Tokyo", a quantum architecture with 20 qubits, our technique outperforms these state-of-the-art approaches in terms of the quality of the solutions that it finds and the amount of memory that it uses, while showing practical runtime.
Limitations of the empirical Fisher approximation for natural gradient descent

Natural gradient descent, which preconditions a gradient descent update with the Fisher information matrix of the underlying statistical model, is a way to capture partial second-order information. Several highly visible works have advocated an approximation known as the empirical Fisher, drawing connections between approximate second-order methods and heuristics like Adam. We dispute this argument by showing that the empirical Fisher---unlike the Fisher---does not generally capture second-order information. We further argue that the conditions under which the empirical Fisher approaches the Fisher (and the Hessian) are unlikely to be met in practice, and that, even on simple optimization problems, the pathologies of the empirical Fisher can have undesirable effects.
On properties of a new decomposable entropy of Dempster-Shafer belief functions

We define entropy of belief functions in the Dempster-Shafer (D-S) theory that satisfies a compound distributions property that is analogous to the property that characterizes Shannon's definitions of entropy and conditional entropy for probability mass functions. None of the existing definitions of entropy for belief functions in the D-S theory satisfy this property. We describe some important properties of our definition, and discuss its semantics as a measure of dissonance and not uncertainty. Finally, we compare our definition of entropy with some other definitions that are similar to ours in the sense that these definitions measure dissonance and not uncertainty.
Faster Algorithms for Edge Connectivity via Random 2-Out Contractions

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms (SODA)Faster Algorithms for Edge Connectivity via Random 2-Out ContractionsMohsen Ghaffari, Krzysztof Nowicki, and Mikkel ThorupMohsen Ghaffari, Krzysztof Nowicki, and Mikkel Thoruppp.1260 - 1279Chapter DOI:https://doi.org/10.1137/1.9781611975994.77PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract We provide a simple new randomized contraction approach to the global minimum cut problem for simple undirected graphs. The contractions exploit 2-out edge sampling from each vertex rather than the standard uniform edge sampling. We demonstrate the power of our new approach by obtaining better algorithms for sequential, distributed, and parallel models of computation. Our end results include the following randomized algorithms for computing edge connectivity, with high probability1: Two sequential algorithms with complexities O(m log n) and O(m + n log3 n). These improve on a long line of developments including a celebrated O(m log3 n) algorithm of Karger [STOC'96] and the state of the art O(m log2 n(log log n)2) algorithm of Henzinger et al. [SODA'17]. Moreover, our O(m + n log3 n) algorithm is optimal when m = Ω (n log3 n). An round distributed algorithm, where D denotes the graph diameter. This improves substantially on a recent breakthrough of Daga et al.[STOC'19], which achieved a round complexity of , hence providing the first sublinear distributed algorithm for exactly computing the edge connectivity. The first O(1) round algorithm for the massively parallel computation setting with linear memory per machine. Previous chapter Next chapter RelatedDetails Published:2020eISBN:978-1-61197-599-4 https://doi.org/10.1137/1.9781611975994Book Series Name:ProceedingsBook Code:PRDA20Book Pages:xxii + 3011