Reviewerly/exHarmony huggingface dataset:
A Greedy Jellyfish Search Optimization Algorithm

Jellyfish Search Optimizer (JSO) is one of the latest additions in the list of nature-inspired metaheuristic algorithms. This paper proposes a modified variant of JSO called Greedy Jellyfish Search Optimizer (GJSO). GJSO modifies the ocean current movement of JSO and adds greedy behaviour in the search process. The proposed variant has been tested on 20 diverse functions. The results are compared with two other peer algorithms. The results show superiority of the proposal over unimodal functions and comparative performance over multimodal functions.
Framework to select refining parameters in Total fringe order photoelasticity (TFP)

What makes Total fringe order photoelasticity (TFP) the ideal digital photoelastic technique for industrial applications is the sufficiency of a single colour isochromatic image for fringe order evaluation and the convenience of capturing this image using easily accessible, relatively cheap hardware. In TFP, the colour information in the application image is matched with an experimental colour calibration table. A simple least squares colour matching is insufficient for this due to errors caused by the repetition of colour in the isochromatics. This is rectified through a process called fringe order refinement. Among the existing fringe order refining methods, the combination of – flexible seeding, window search method, and FRSTFP scanning – has proved to be the best choice for solving a variety of problems for the complete model domain. The successful application of this combination requires the right choice of parameter values for its constituent methods. A systematic parametric study of these refining parameters is a lacuna in the literature, which is addressed in this work, by selecting a variety of problems. The study has revealed the interconnection between the refining parameters, which has helped to formulate a set of recommendations for selecting the same. It is found that some of the existing understandings on refining parameters require a relook. The difficulty in refining fringe fields having multiple fringe gradient reversals with a single set of parameters is illustrated, and how this can be overcome by local refinement is brought out. Additionally, a methodology to track the source of refining error is proposed.
Enhancing Nurse–Robot Engagement: Two-Wave Survey Study

Robots are introduced into health care contexts to assist health care professionals. However, we do not know how the benefits and maintenance of robots influence nurse-robot engagement.This study aimed to examine how the benefits and maintenance of robots and nurses' personal innovativeness impact nurses' attitudes to robots and nurse-robot engagement.Our study adopted a 2-wave follow-up design. We surveyed 358 registered nurses in operating rooms in a large-scale medical center in Taiwan. The first-wave data were collected from October to November 2019. The second-wave data were collected from December 2019 to February 2020. In total, 344 nurses participated in the first wave. We used telephone to follow up with them and successfully followed-up with 331 nurses in the second wave.Robot benefits are positively related to nurse-robot engagement (β=.13, P<.05), while robot maintenance requirements are negatively related to nurse-robot engagement (β=-.15, P<.05). Our structural model fit the data acceptably (comparative fit index=0.96, incremental fit index=0.96, nonnormed fit index=0.95, root mean square error of approximation=0.075).Our study is the first to examine how the benefits and maintenance requirements of assistive robots influence nurses' engagement with them. We found that the impact of robot benefits on nurse-robot engagement outweighs that of robot maintenance requirements. Hence, robot makers should consider emphasizing design and communication of robot benefits in the health care context.
Energy efficient resource optimization algorithm for cloud infrastructure

The proliferation of cloud computing infrastructure has increased the energy demand remarkably. Energy-efficient resource management is essential for running a cost effective and environment friendly data center. Virtual Machine (VM) consolidation is a well-accepted method for reducing the energy consumption of the cloud data center. Quality of service is an equally important aspect of cloud services. VM migrations caused by consolidation often cause degradation in QoS. These two parameters have been dealt with individually in most research and very few addressed both energy efficiency and QoS simultaneously. We have proposed a new Energy and QoS Efficient (EQSE) VM selection and placement method for improving the energy efficiency along with quality of service (QoS). VM selection and placement are two critical steps of VM consolidation. EQSE uses Resource Gap Minimization (RGM) algorithm for VM selection and Utilization-Aware Best-Fit Decreasing (UABFD) algorithm for placement of these VMs. EQSE along with dynamic thresholds reduces energy consumption and improves the quality of service by reducing the number of VM migrations. CloudSim simulation performed on PlanetLab data establishes the superiority of the proposed method compared to the existing state of the art methods of VM consolidation.
Image Entropy Equalization for Autoencoder-Based One-Class Classification

Autoencoder (AE) is a common technique for one-class classification (OCC). Reconstruction error (RE) is used to classify one seen class or other unseen classes. However, AE-based OCC (OCAE) does not provide a high AUC score. This study considers the hypothesis that RE is related to image entropy, and the OCAE is biased due to the image entropy differences. Based on such a hypothesis, this paper proposes image entropy equalization as the preprocessing technique. In which, image pixels are replaced by a defined set of pixels. Entropy equalized images are experimented with OCAE using MNIST, Fashion MNIST, and CIFAR10 datasets. Image Entropy Equalization improves AUC scores with several seen classes, where the improved classes have relatively high entropy on original images.
Experience of Online Education for Students in Hungary

The March 2020 epidemic in Europe required a shift to online education at all levels of education. Many teachers were caught unprepared, compounded by a lack of the necessary hardware tools and a lack of mastery in selecting and managing software that supports teaching properly. For the second wave in autumn 2020, educational institutions were better prepared in Hungary and universities had already experienced hybrid education to some extent. Now, after the third wave, a lot of experience has been gathered from both the teaching and student sides. Based on the students' experiences, we analyse how they would like to listen to lectures, practicals and labs at university. The results of the analysis show that students are open to online learning for lectures, but still prefer face-to-face classes for tutorials and labs. The students' views will help to inform the way in which higher education institutions should deliver courses at the end of the pandemic period, which will also help to rationalise the way universities operate in the future in Hungary.
Dual-position features fusion for head pose estimation for complex scene

Head pose estimation (HPE) is widely used in attention detection, behavior analysis, and expression recognition. Nevertheless, in some complex scenes (such as facial occlusion, large head deflection angle, and multi-person in one scene), HPE still has the problem of low estimation accuracy. To solve this problem, we propose a dual position feature fusion method for estimating head pose. First, the RGB input is replaced with a standard luminance, which reduces the effect of extraneous light factors. Subsequently, the center offset loss is used to detect the head and body position, and dynamic adjustment strategy is used to deflate the border, aiming to not only obtain the best confidence level but also improve the capability of multi-person HPE. Finally, the estimate results under head position and body position are fused to further reduce the estimate loss. We tested our approach on the popular public AFLW2000, BIWI, and UPNA datasets, the results show the superiority of our approach in solving the occlusion, deflection, and multi-person scene problems.
Enhancement of formant regions in magnitude spectra to develop children’s KWS system in zero resource scenario

The mismatches due to pitch, speaking rate, formant dispersion and ambient noise deteriorate the performance of an automatic keyword spotting (KWS) system. The work presented in this paper aims at reducing the aforementioned mismatches through front-end signal processing. In the proposed approach, the short-term magnitude spectra (ST-MS) are firstly computed with a smaller frameshift and then averaged over the adjacent frames to enhance the formant regions. The formant regions in the ST-MS have a higher magnitude than the nearing frequency regions. Consequently, temporal averaging of ST-MS over adjacent frames suppresses the high-frequency variation due to pitch, and formant dispersion. Furthermore, the formant peaks can be more accurately detected from the temporal averaged ST-MS when compared to detection from the original ST-MS. The Mel frequency cepstral coefficients (MFCC) computed from the temporally averaged magnitude spectra (TAS-MFCC) are pitch robust compared to the MFCC, and MFCC extracted from the reported spectral smoothing approaches employing variational mode decomposition (VMD-MFCC), pitch adaptive cepstral truncation (PACT-MFCC) and single-pole filter (SPS-MFCC). Performance of TA-MFCC feature in mismatched test condition is further improved by appending five logarithmically compressed resonant peaks at least separated by 400 Hz, here this feature is termed as TAS-MFCC-ARP. The spectral peaks mostly represent the formants in ST-MS. The performances of the deep neural network-hidden Markov model-based children's KWS system reported in this work show that the TAS-MFCC-ARP provides a relative performance improvement of 103.83% compared to MFCC. The performance of the KWS system is further improved by data-augmented training through duration modification.
Transformed ROIs for capturing visual transformations in videos

Modeling the visual changes that an action brings to a scene is critical for video understanding. Currently, CNNs process one local neighborhood at a time, thus contextual relationships over longer ranges, while still learnable, are indirect. We present TROI, a plug-and-play module for CNNs to reason between mid-level feature representations that are otherwise separated in space and time. The module relates localized visual entities such as hands and interacting objects and transforms their corresponding regions of interest directly in the feature maps of convolutional layers. With TROI, we achieve state-of-the-art action recognition results on the large-scale datasets Something–Something-V2 and EPIC-Kitchens-100.
Unveil the potential of siamese framework for visual tracking

Most of the existing Siamese tracking methods follow the overall framework of SiamRPN, adopting its general network architecture and the local and linear cross-correlation operation to integrate search and template features, which restricts the introduction of more sophisticated structures for expressive appearance representation as well as the further improvements on tracking performance. Motivated by the recent progresses in vision Transformer and MLP, we first explore to accomplish a global, nonlinear and scale-invariant similarity measuring manner called Dynamic Cross-Attention (DCA). Specifically, template features are first decomposed along the spatial and channel dimension and then the Transformer Encoders are applied to adaptively excavate the long-range feature interdependency, producing reinforced kernels. As the kernels are successively multiplied to the search feature map, similarity scores between all the pixels on feature maps are estimated at once while the spatial scale of search features remains constant. Furthermore, we redesign each part of our Siamese network to further remedy the framework limitation with the assistant of DCA. Comprehensive experimental results on large-scale benchmarks indicate that our Siamese method realizes the efficient feature extraction, aggregation, refinement and interaction, outperforming state-of-the-art trackers.
A sorting algorithm based on ordered block insertions

Among the comparison-based algorithms, insertionsort is recognized as one of the fastest methods to sort relatively small data sets, or when the elements are relatively ordered. However, due to not offering good asymptotic complexity in its runtime, it performs very poorly both in the worst case and in the average case for most large data collections. In this article we offer a new sorting algorithm based on orderer block insertions with worst case optimal time. At the cost of an additional memory space of nk words, for any constant k, our algorithm is able to easily transform it into an in-place algorithm with a time of o(n2) in the worst case. Empirically, our method outperforms insertionsort in all the different cases tested, even for small input collections. Furthermore, our experiments show that, for small or large datasets from either of the two main probability distributions—Uniform and Normal; our algorithms also outperform any traditional method like quicksort, mergesort or heapsort, and even better than the efficient hybrid algorithm introsort —std::sort() method provided by the GNU C++ Standard Library.
Method for generating randomly perturbed density operators subject to different sets of constraints

This paper presents a general method for producing randomly perturbed density operators subject to different sets of constraints. The perturbed density operators are a specified “distance" away from the state described by the original density operator. This approach is applied to a bipartite system of qubits and used to examine the sensitivity of various entanglement measures on the perturbation magnitude. The constraint sets used include constant energy, constant entropy, and both constant energy and entropy. The method is then applied to produce perturbed random quantum states that correspond with those obtained experimentally for Bell states on the IBM quantum device ibmq_manila. The results show that the methodology can be used to simulate the outcome of real quantum devices where noise, which is important both in theory and simulation, is present.
Taming stateful computations in Rust with typestates

As our lives become increasingly more reliant on software, the impact of its failures grows as well; these failures have diverse causes and their impact ranges from negligible to life-threatening; thus, it is our duty as developers to minimize their occurrence, just as other fields do. To that end, we build abstractions, move complexity from component to component, and much more, just to stop the end-user from shooting themselves in the foot. However, building said abstractions still requires the original author to know where the pitfalls lie and how to avoid them, an implicit contract that does not constitute a guarantee that they will not shoot themselves and their users in the feet. Rust aims to minimize the amount of handguns users have at their disposal, locking them behind special unsafe blocks and restricting the set of possible programs through static analysis; this analysis is performed by the compiler which ensures that the program does not contain memory related errors such as use-after-free bugs. While Rust is able to succeed in the previous domain, other error classes persist, such as errors related to API misusage. Our work aims to tackle that domain, providing a tool which enables developers to write safer APIs using typestates. We propose a macro which embeds a typestate description DSL in Rust which allows developers to specify typestates for their APIs; the typestate is checked at compile-time for common mistakes and to ensure the correct usage of the typestate, we leverage Rust's type system. Our work only requires a Rust compiler, avoiding workflow bloat and keeping the development experience simple; it is open-source and available at https://github.com/rustype/typestate-rs.
Homomorphic encryption for stochastic computing

Abstract Homomorphic encryption (HE) method can be used to realize arithmetic operations on encrypted data. This method, however, is limited owing to its low efficiency in performing certain functions, especially those involving several multiplications. As a solution, this paper proposes a new HE-based secure computation scheme, termed as the HE for stochastic computing (HESC); this scheme can homomorphically evaluate both the stochastic addition and multiplication operations, without any bootstrapping. This HESC scheme is constructed based on additive/multiplicative HE, which only supports homomorphic addition/multiplication, and realizes the homomorphic evaluation of stochastic multiplication. The HESC employs the features of stochastic computing (SC) for homomorphic stochastic operations, where stochastic additions and multiplications are performed using random multiplexing and bit-parallel logic operations, respectively. This paper first presents a basic HESC scheme based on additive/multiplicative HE. It then presents an efficient HESC scheme that utilizes the parallelism of lattice-based cryptography (i.e., plaintext packing and vectorized homomorphic evaluation). A new stochastic addition operation is also introduced in this study, which can be used for the HESC instantiated by lattice-based cryptography. This new stochastic addition operation significantly improves the accuracy of the HESC, albeit with the trade-off of increased ciphertext size. Accordingly, this paper also proposes a technique that can reduce the size of ciphertexts, while maintaining the accuracy of the scheme. The basic performance of the HESC implemented with various HEs is demonstrated, along with its applications in polynomial functions and an oblivious inference with a neural network. Lastly, the results thus obtained indicate that the proposed scheme is more advantageous than the conventional schemes. This paper is concluded with some implications/research directions for HESC from perspectives of cryptography and HE implementations.
A Typological Examination of Modern Chinese OV Word Order Based on Deep Learning and the Design of Stance Expression Methods

Object-verb (OV) inflections are an important grammatical device in Chinese with denotative utility. The decorativeness of OV inflections shows different levels in Chinese: OV nouns denote things in reality and are the most denotative; OV independent structures can denote both denotation and trait; OV structures that are definite are not self-sufficient and denote a certain trait. The verb category of V is worn out in denotative OV structures, and OV phrases must repair the wear and tear of V’s category and enhance V’s declarativity when forming small sentences. In this paper, we propose an attention-based approach to Chinese stance representation based on modern Chinese OV order types; firstly, we use bidirectional (bidirectional) long and short-term memory neural networks (LSTM) and convolutional neural networks (CNNs) to obtain text representation vectors and local convolutional features, respectively, and then, we use attention mechanisms to add influence weight information to the local convolutional features and finally fuse the two features for classification. Experiments on the relevant corpus show that the method achieves better stance representation results, and the addition of attention mechanisms can effectively improve the accuracy of stance representation.
Measuring and achieving test coverage of attack simulations extended version

Designing secure and reliable systems is a difficult task. Threat modeling is a process that supports the secure design of systems by easing the understanding of the system’s complexity, as well as identifying and modeling potential threats. These threat models can serve as input for attack simulations, which are used to analyze the behavior of attackers within the system. To ensure the correct functionality of these attack simulations, automated tests are designed that check if an attacker can reach a certain point in the threat model. Currently, there is no way for developers to estimate the degree to which their tests cover the attack simulations and, thus, they cannot determine the quality of their tests. To resolve this shortcoming, we analyze structural testing methods from the software engineering domain and transfer them to the threat modeling domain by following an action design research approach. Further, we develop a first prototype, which is able to assess the test coverage in an automated way and provide a first approach to achieve full coverage. This will enable threat modeler to determine the quality of their tests and, simultaneously, increase the quality of the threat models.
Several classes of bent functions over finite fields

Inspired by the works of Mesnager (IEEE Trans Inf Theory 60(7):4397–4407, 2014) and Tang et al. (IEEE Trans Inf Theory 63(10):6149–6157, 2017), we study a class of bent functions of the form $$f(x)=g(x)+F(f_1(x),f_2(x),\ldots ,$$ $$f_{\tau }(x))$$ , where g(x) is a function from $$\mathbb {F}_{p^{n}}$$ to $${\mathbb {F}}_{p}$$ , $$f_i(x)=\prod _{j=1}^{\kappa _i}{\text {Tr}}(u_{ij}x)$$ for $$1\le i\le \tau $$ , $$\kappa _i\ge 1$$ and $$F(x_1,\ldots ,x_{\tau })$$ is a reduced polynomial in $${\mathbb {F}}_{p}[x_1,\ldots ,x_{\tau }]$$ . As a consequence, we obtain a generic result on the Walsh transform of f(x) in terms of g(x) and characterize the bentness of f(x) for the cases $$F(x_1,\ldots ,x_{\tau })$$ without and with restrictions respectively, which enables us to generalize some earlier works and derive new bent functions from known ones. In addition, we study the construction of bent functions f(x) when g(x) is not bent for the first time and present a class of bent functions from non-bent Gold functions.
Image-Based 3D Shape Estimation of Wind Turbine from Multiple Views

This paper addresses the problem of reconstructing depth and silhouette images of wind turbine from its photos of multiple views using deep learning approaches, which aims for wind turbine blade fault diagnosis. Some previous multi-view based methods have extracted each photo’s silhouette and combined them into separate channels as the input of convolution; others use LSTM to combine a series of views for reconstruction. These approaches inevitably need a fixed number of views and the output result is divergent if the order of the input views is changed. So, we refer to a network, SiDeNet (Wiles and Zisserman, Learning to predict 3d surfaces of sculptures from single and multiple views. Int J Comp Vision, 2018), which has a flexible number of input views and will not be affected by the input order. It integrates both viewpoint and image information from each view to learn a latent 3D shape representation and use it to predict the depth of wind turbine at input views. Also, this representation could generalize to the silhouette of unseen views. We make the following contributions to SiDeNet: improving the resolution of predicted images by deepening network structure, adopting 6D camera pose to increase the degrees of freedom of viewpoint to capture a wider range of views, optimizing the loss function of silhouette by applying weights on edge points, and implementing silhouette refinement with point-wise optimizing. Additionally, we conduct a set of prediction experiments and prove the network’s generalization ability to unseen views. Evaluating predicted results on a realistic wind turbine dataset confirms the high performance of the network on both given views and unseen views.