Reviewerly/exHarmony huggingface dataset:
Manta ray foraging and Gaussian mutation-based elephant herding optimization for global optimization

Abstract The elephant herding optimization (EHO) algorithm is a novel metaheuristic optimizer inspired by the clan renewal and separation behaviors of elephant populations. Although it has few parameters and is easy to implement, it suffers from a lack of exploitation, leading to slow convergence. This paper proposes an improved EHO algorithm called manta ray foraging and Gaussian mutation-based EHO for global optimization (MGEHO). The clan updating operator in the original EHO algorithm is replaced by the somersault foraging strategy of manta rays, which aims to optimally adjust patriarch positions. Additionally, a dynamic convergence factor is set to balance exploration and exploitation. The gaussian mutation is adopted to enhance the population diversity, enabling MGEHO to maintain a strong local search capability. To evaluate the performances of different algorithms, 33 classical benchmark functions are chosen to verify the superiority of MGEHO. Also, the enhanced paradigm is compared with other advanced metaheuristic algorithms on 32 benchmark functions from IEEE CEC2014 and CEC2017. Furthermore, a scalability test, convergence analysis, statistical analysis, diversity analysis, and running time analysis demonstrate the effectiveness of MGEHO from various aspects. The results illustrate that MGEHO is superior to other algorithms in terms of solution accuracy and stability. Finally, MGEHO is applied to solve three real engineering problems. The comparison results show that this method is a powerful auxiliary tool for handling complex problems.
A hybrid deep kernel incremental extreme learning machine based on improved coyote and beetle swarm optimization methods

Abstract The iteration times and learning efficiency of kernel incremental extreme learning machines are always affected by the redundant nodes. A hybrid deep kernel incremental extreme learning machine (DKIELM) based on the improved coyote and beetle swarm optimization methods was proposed in this paper. A hybrid intelligent optimization algorithm based on the improved coyote optimization algorithm (ICOA) and improved beetle swarm optimization algorithm (IBSOA) was proposed to optimize the parameters and determine the number of effectively hidden layer neurons for the proposed DKIELM. A Gaussian global best-growing operator was adopted to replace the original growing operator in the intelligent optimization algorithm to improve COA searching efficiency and convergence. In the meantime, IBSOA was designed based on tent mapping inverse learning and dynamic mutation strategies to avoid falling into a local optimum. The experimental results demonstrated the feasibility and effectiveness of the proposed DKIELM with encouraging performances compared with other ELMs.
Genetic Programming for Manifold Learning: Preserving Local Topology

Manifold learning methods are an invaluable tool in today's world of increasingly huge datasets.Manifold learning algorithms can discover a much lower-dimensional representation (embedding) of a high-dimensional dataset through non-linear transformations that preserve the most important structure of the original data.State-of-the-art manifold learning methods directly optimise an embedding without mapping between the original space and the discovered embedded space.This makes interpretability -a key requirement in exploratory data analysis -nearly impossible.Recently, genetic programming has emerged as a very promising approach to manifold learning by evolving functional mappings from the original space to an embedding.However, genetic programming-based manifold learning has struggled to match the performance of other approaches.In this work, we propose a new approach to using genetic programming for manifold learning, which preserves local topology.This is expected to significantly improve performance on tasks where local neighbourhood structure (topology) is paramount.We compare our proposed approach with various baseline manifold learning methods and find that it often outperforms other methods, including a clear improvement over previous genetic programming approaches.These results are particularly promising, given the potential interpretability and reusability of the evolved mappings.
Identifying and describing functional discourse units in the BNC Spoken 2014

Abstract On the surface, it appears that conversational language is produced in a stream of spoken utterances. In reality conversation is composed of contiguous units that are characterized by coherent communicative purposes. A large number of important research questions about the nature of conversational discourse could be addressed if researchers could investigate linguistic variation across functional discourse units. To date, however, no corpus of conversational language has been annotated according to functional units, and there are no existing methods for carrying out this type of annotation. We introduce a new method for segmenting transcribed conversation files into discourse units and characterizing those units based on their communicative purposes. In this paper, the development and piloting of this method is described in detail and the final framework is presented. We conclude with a discussion of an ongoing project where we are applying this coding framework to the British National Corpus Spoken 2014.
Advances in Digital Pathology: From Artificial Intelligence to Label-Free Imaging

Digital pathology, in its primary meaning, describes the utilization of computer screens to view scanned histology slides. Digitized tissue sections can be easily shared for a second opinion. In addition, it allows tissue image analysis using specialized software to identify and measure events previously observed by a human observer. These tissue-based readouts were highly reproducible and precise. Digital pathology has developed over the years through new technologies. Currently, the most discussed development is the application of artificial intelligence to automatically analyze tissue images. However, even new label-free imaging technologies are being developed to allow imaging of tissues by means of their molecular composition.This review provides a summary of the current state-of-the-art and future digital pathologies. Developments in the last few years have been presented and discussed. In particular, the review provides an outlook on interesting new technologies (e.g., infrared imaging), which would allow for deeper understanding and analysis of tissue thin sections beyond conventional histopathology.In digital pathology, mathematical methods are used to analyze images and draw conclusions about diseases and their progression. New innovative methods and techniques (e.g., label-free infrared imaging) will bring significant changes in the field in the coming years.
Exploring finite temperature properties of materials with quantum computers

Thermal properties of nanomaterials are crucial to not only improving our fundamental understanding of condensed matter systems, but also to developing novel materials for applications spanning research and industry. Since quantum effects arise at the nano-scale, these systems are difficult to simulate on classical computers. Quantum computers can efficiently simulate quantum many-body systems, yet current quantum algorithms for calculating thermal properties of these systems incur significant computational costs in that they either prepare the full thermal state on the quantum computer, or they must sample a number of pure states from a distribution that grows with system size. Canonical thermal pure quantum (TPQ) states provide a promising path to estimating thermal properties of quantum materials as they neither require preparation of the full thermal state nor require a growing number of samples with system size. Here, we present an algorithm for preparing canonical TPQ states on quantum computers. We compare three different circuit implementations for the algorithm and demonstrate their capabilities in estimating thermal properties of quantum materials. Due to its increasing accuracy with system size and flexibility in implementation, we anticipate that this method will enable finite temperature explorations of relevant quantum materials on near-term quantum computers.
Cybercrime Victimisation and Polyvictimisation in Finland—Prevalence and Risk Factors

Abstract This study examines the prevalence of different types of cybercrime victimisation and their shared risk factors among the population of Finland. We examine how respondents’ socio-economic background variables, past offline victimisation experiences, online activity, user skills, and protective measures impact the risk of the most common forms of online victimisation and online polyvictimisation. Our nationally representative survey data were collected from 5455 Finns aged 15 to 74 years (response rate 39%) as part of the Finnish National Crime Survey in 2018. According to our findings, the five most common forms of victimisation were malware, harassment, sexual harassment, hacking, and fraud. Online routines and exposure to potential offenders, along with past offline victimisation experiences, served as notable risk factors for a range of different victimisation experiences online. Our findings show slightly different SES risk factors for victimisation of different online offences, thereby indicating the diverse nature of different types of online victimisation. Our findings also show that young age, better financial situation, high internet use, and user skills, along with past offline victimisation of property crime and violence, associate with increased risk of online polyvictimisation. High user protection decreased the risk of online polyvictimisation.
EMOVIE: A Mandarin Emotion Speech Dataset with a Simple Emotional Text-to-Speech Model

Recently, there has been an increasing interest in neural speech synthesis. While the deep neural network achieves the state-of-the-art result in text-to-speech (TTS) tasks, how to generate a more emotional and more expressive speech is becoming a new challenge to researchers due to the scarcity of high-quality emotion speech dataset and the lack of advanced emotional TTS model. In this paper, we first briefly introduce and publicly release a Mandarin emotion speech dataset including 9,724 samples with audio files and its emotion human-labeled annotation. After that, we propose a simple but efficient architecture for emotional speech synthesis called EMSpeech. Unlike those models which need additional reference audio as input, our model could predict emotion labels just from the input text and generate more expressive speech conditioned on the emotion embedding. In the experiment phase, we first validate the effectiveness of our dataset by an emotion classification task. Then we train our model on the proposed dataset and conduct a series of subjective evaluations. Finally, by showing a comparable performance in the emotional speech synthesis task, we successfully demonstrate the ability of the proposed model.
Robust Optimal Classification Trees against Adversarial Examples

Decision trees are a popular choice of explainable model, but just like neural networks, they suffer from adversarial examples. Existing algorithms for fitting decision trees robust against adversarial examples are greedy heuristics and lack approximation guarantees. In this paper we propose ROCT, a collection of methods to train decision trees that are optimally robust against user-specified attack models. We show that the min-max optimization problem that arises in adversarial learning can be solved using a single minimization formulation for decision trees with 0-1 loss. We propose such formulations in Mixed-Integer Linear Programming and Maximum Satisfiability, which widely available solvers can optimize. We also present a method that determines the upper bound on adversarial accuracy for any model using bipartite matching. Our experimental results demonstrate that the existing heuristics achieve close to optimal scores while ROCT achieves state-of-the-art scores.
Query complexity of generalized Simon's problem

Simon's problem plays an important role in the history of quantum algorithms, as it inspired Shor to discover the celebrated quantum algorithm solving integer factorization in polynomial time. Besides, the quantum algorithm for Simon's problem has been recently applied to break symmetric cryptosystems. Generalized Simon's problem, denoted by GSP(p,n,k), is a natural extension of Simon's problem: Given a function f:Zpn→X where X is a finite set and the promise that for any x,y∈Zpn,f(x)=f(y) iff x−y∈S for a subgroup S≤Zpn of rank k<n, the goal is to find S. In this paper we consider the query complexity of the problem, that is, the minimum number of queries to f required to find S. First, it is not difficult to design a quantum algorithm solving the above problem with query complexity of O(n−k). However, so far it is not clear what is the classical query complexity of the problem, and revealing this complexity is necessary for clarifying the computational power gap between quantum and classical computing on the problem. To tackle this problem, we prove that any classical (deterministic or randomized) algorithm for GSP(p,n,k) has to query at least Ω(max⁡{k,pn−k}) values and any classical nonadaptive deterministic algorithm for GSP(p,n,k) has to query at least Ω(max⁡{k,k⋅pn−k}) values. Hence, we clearly show the classical computing model is less powerful than the quantum counterpart, in terms of query complexity for the generalized Simon's problem. Moreover, we obtain an upper bound O(max⁡{k,k⋅pn−k}) on the classical deterministic query complexity of GSP(p,n,k), by devising a subtle classical algorithm based on group theory and the divide-and-conquer approach. Therefore, we have an almost full characterization of the classical deterministic query complexity of the generalized Simon's problem.
Collaborative Training of Acoustic Encoders for Speech Recognition

On-device speech recognition requires training models of different sizes for deploying on devices with various computational budgets. When building such different models, we can benefit from training them jointly to take advantage of the knowledge shared between them. Joint training is also efficient since it reduces the redundancy in the training procedure's data handling operations. We propose a method for collaboratively training acoustic encoders of different sizes for speech recognition. We use a sequence transducer setup where different acoustic encoders share a common predictor and joiner modules. The acoustic encoders are also trained using co-distillation through an auxiliary task for frame level chenone prediction, along with the transducer loss. We perform experiments using the LibriSpeech corpus and demonstrate that the collaboratively trained acoustic encoders can provide up to a 11% relative improvement in the word error rate on both the test partitions.
Improving AdaBoost Classifier to Predict Enterprise Performance after COVID-19

Because COVID-19 occurred in 2019, the behavioxr of humans has been changed and it will influence the business model of enterprise. Enterprise cannot predict its development according to past knowledge and experiment; so, it needs a new machine learning framework to predict enterprise performance. The goal of this research is to modify AdaBoost to reasonably predict the enterprise performance. In order to justify the usefulness of the proposed model, enterprise data will be collected and the proposed model can be used to predict the enterprise performance after COVID-19. The test data correct rate of the proposed model will be compared with some of the traditional machine learning models. Compared with the traditional AdaBoost, back propagation neural network (BPNN), regression classifier, support vector machine (SVM) and support vector regression (SVR), the proposed method possesses the better classification ability (average correct rate of the proposed method is 88.04%) in handling two classification problems. Compared with traditional AdaBoost, one-against-all SVM, one-against-one SVM, one-against-all SVR and one-against-one SVR, the classification ability of the proposed method is also relatively better for coping with the multi-class classification problem. Finally, some conclusions and future research will be discussed at the end.
The UW Virtual Brain Project: An immersive approach to teaching functional neuroanatomy.

Learning functional neuroanatomy requires forming mental representations of 3D structure, but forming such representations from 2D textbook diagrams can be challenging. We address this challenge in the UW Virtual Brain Project by developing 3D narrated diagrams, which are interactive, guided tours through 3D models of perceptual systems. Lessons can be experienced in virtual realty (VR) or on a personal computer monitor (PC). We predicted participants would learn from lessons presented on both VR and PC devices (comparing pre-test/post-test scores), but that VR would be more effective for achieving both content-based learning outcomes (i.e test performance) and experience-based learning outcomes (i.e., reported enjoyment and ease of use). All participants received lessons about the visual system and auditory system, one in VR and one on a PC(order counterbalanced). We assessed content learning using a drawing/labeling task on paper (2D drawing) in Experiment 1 and a Looking Glass autostereoscopic display (3D drawing) in Experiment 2. In both experiments, we found that the UW Virtual Brain Project lessons were effective for teaching functional neuroanatomy, with no difference between devices. However, participants reported VR was more enjoyable and easier to use. We also evaluated the VR lessons in our Classroom Implementation during an undergraduate course on perception. Students reported that the VR lessons helped them make progress on course learning outcomes, especially for learning system pathways. They suggested lessons could be improved byadding more examples and providing more time to explore in VR.
Does Mode of Access Make a Difference? Mobile Learning and online student engagement

Even though student use of mobile devices for educational purposes has increased in recent years, the research on the possible impact on student success or engagement has been minimal. This study investigated the impact of mobile device use on student engagement and student success in online courses. The theory of Self-Regulated Learning (SRL) was a foundation for understanding and measuring engagement behaviors of online students. The Online Self-Regulated Learning Questionnaire (OSLQ) was the basis of the survey instrument. Participants were categorized into three groups of mobile device use (low, moderate, high). These three groups were used for comparison in each of the research questions. The research questions ask what impacts mobile learning has on student engagement, as measured with SRL; what impact mobile learning has on the SRL constructs of environment structuring, task management, and time management; and what associations mobile learning might have with student success and persistence. The ANOVA showed that mobile learning had a moderate impact on engagement for students in the high group. Students in each group engaged in environment structuring behaviors more than task strategies or time management behaviors. Students in the moderate and high groups engaged in task strategies more than the low group. Students in the high group engaged in time management behaviors more than the other groups. The Crosstab analysis did not show an association between levels of mobile learning and course grade or persistence. These findings have positive implications for online pedagogy and course design. Keywords: Mobile Learning; online learning; student engagement; Self-Regulated Learning; student success
An Algebraic Axiomatisation of ZX-calculus

ZX-calculus is a graphical language for quantum computing which is complete in the sense that calculation in matrices can be done in a purely diagrammatic way.However, all previous universally complete axiomatisations of ZX-calculus have included at least one rule involving trigonometric functions such as sin and cos which makes it difficult for application purpose.In this paper we give an algebraic complete axiomatisation of ZX-calculus instead such that there are only ring operations involved for phases.With this algebraic axiomatisation of ZX-calculus, we are able to establish for the first time a simple translation of diagrams from another graphical language called ZH-calculus and to derive all the ZX-translated rules of ZH-calculus.As a consequence, we have a great benefit that all techniques obtained in ZH-calculus can be transplanted to ZX-calculus, which can't be obtained by just using the completeness of ZX-calculus.
Segmentation of pectoral muscle from digital mammograms with depth-first search algorithm towards breast density classification

Digital mammography acts as a unique screening technology to protect the lives of females against breast cancer for the past few decades. Mammographic breast density is a well-known biomarker and plays a substantial role in breast cancer prediction and treatments. Breast density is calculated based on the opacity of fibro-glandular tissue reflected on digital mammograms concerning the whole area of the breast. The opacity of pectoral muscle and fibro-glandular tissue is similar to each other; hence, the small presence of the pectoral muscle in the breast area can hamper the accuracy of breast density classification. Successful removal of pectoral muscle is challenging due to changes in shape, size, and texture of pectoral muscle in every MLO and LMO views of mammogram. In this article, the depth-first search (DFS) algorithm is proposed to remove artifacts and pectoral muscle from digital mammograms. In the proposed algorithm, image enhancement is performed to improve the pixel quality of the input image. The whole breast as a single connected component is identified from the background region to remove the artifacts and tags. The depth-first search method with and without the heuristic approach is used to delineate the pectoral muscle, and then final suppression is performed on it. This algorithm is tested on 2675 images of the DDSM dataset, which is further divided into four density classes as per BIRADs classification. Segmentation results are calculated individually on each BIRADs density class of the DDSM dataset. Results are validated subjectively by the expert's Radiologist's ground truth with segmentation accuracy and objectively by the Jaccard coefficient and a dice similarity coefficient. This algorithm is found robust on each density class and provides overall segmentation accuracy of 86.18%, a mean value of Jaccard index, and a Dice similarity coefficient of 0.9315 and 0.9548, respectively. The experimental results show that the proposed algorithms applied for pectoral muscle removal follow the ground truth marked by an expert radiologist. The proposed algorithm can be part of the pre-processing unit of breast density measurement and breast cancer detection system used during clinical practice.