Reviewerly/exHarmony huggingface dataset:
Classification at the accuracy limit: facing the problem of data ambiguity

Data classification, the process of analyzing data and organizing it into categories or clusters, is a fundamental computing task of natural and artificial information processing systems. Both supervised classification and unsupervised clustering work best when the input vectors are distributed over the data space in a highly non-uniform way. These tasks become however challenging in weakly structured data sets, where a significant fraction of data points is located in between the regions of high point density. We derive the theoretical limit for classification accuracy that arises from this overlap of data categories. By using a surrogate data generation model with adjustable statistical properties, we show that sufficiently powerful classifiers based on completely different principles, such as perceptrons and Bayesian models, all perform at this universal accuracy limit under ideal training conditions. Remarkably, the accuracy limit is not affected by certain non-linear transformations of the data, even if these transformations are non-reversible and drastically reduce the information content of the input data. We further compare the data embeddings that emerge by supervised and unsupervised training, using the MNIST data set and human EEG recordings during sleep. We find for MNIST that categories are significantly separated not only after supervised training with back-propagation, but also after unsupervised dimensionality reduction. A qualitatively similar cluster enhancement by unsupervised compression is observed for the EEG sleep data, but with a very small overall degree of cluster separation. We conclude that the handwritten letters in MNIST can be considered as 'natural kinds', whereas EEG sleep recordings are a relatively weakly structured data set, so that unsupervised clustering will not necessarily re-cover the human-defined sleep stages.
A benchmark for automatic medical consultation system: frameworks, tasks and datasets

In recent years, interest has arisen in using machine learning to improve the efficiency of automatic medical consultation and enhance patient experience. In this article, we propose two frameworks to support automatic medical consultation, namely doctor-patient dialogue understanding and task-oriented interaction. We create a new large medical dialogue dataset with multi-level fine-grained annotations and establish five independent tasks, including named entity recognition, dialogue act classification, symptom label inference, medical report generation and diagnosis-oriented dialogue policy.We report a set of benchmark results for each task, which shows the usability of the dataset and sets a baseline for future studies.Both code and data are available from https://github.com/lemuria-wchen/imcs21.Supplementary data are available at Bioinformatics online.
Distributed fuzzy clustering algorithm for mixed-mode data in Apache SPARK

Abstract Fuzzy clustering is an invaluable data mining technique that allows each data point to belong to more than one cluster with some degree of membership. It is widely employed in exploratory data mining to discover overlapping communities in social networks, find structure in spectral data, and capture user interests in recommendation systems. Nowadays, the variety and volume of data are increasing at a tremendous rate. Data is power; the massive data, along with an effective technique, can unravel valuable information. The existing fuzzy clustering algorithms do not perform well on massive heterogeneous datasets. Processing an enormous amount of data is beyond the capacity of a single processor. The need of the hour is to develop fuzzy clustering techniques that can work on a distributed framework for Big Data processing and can handle heterogeneous data. In this research, we evaluate the performance of the recently proposed algorithm for the Fuzzy clustering of mixed-mode data FCMD-MD (D’Urso and Massari in Inf Sci 505:513–534, 2019) with different real-world datasets. We develop a distributed FCMD-MD, a fuzzy clustering algorithm for mixed-mode data in Apache SPARK. The experimental results show that the algorithm is scalable, performs well in a distributed environment, and clusters enormous heterogeneous data with high accuracy. We also compared the performance of distributed FCMD-MD and the distributed k-medoid algorithm.
Cyber threat: its origins and consequence and the use of qualitative and quantitative methods in cyber risk assessment

Purpose Consumers increasingly rely on organisations for online services and data storage while these same institutions seek to digitise the information assets they hold to create economic value. Cybersecurity failures arising from malicious or accidental actions can lead to significant reputational and financial loss which organisations must guard against. Despite having some critical weaknesses, qualitative cybersecurity risk analysis is widely used in developing cybersecurity plans. This research explores these weaknesses, considers how quantitative methods might address the constraints and seeks the insights and recommendations of leading cybersecurity practitioners on the use of qualitative and quantitative cyber risk assessment methods. Design/methodology/approach The study is based upon a literature review and thematic analysis of in-depth qualitative interviews with 16 senior cybersecurity practitioners representing financial services and advisory companies from across the world. Findings While most organisations continue to rely on qualitative methods for cybersecurity risk assessment, some are also actively using quantitative approaches to enhance their cybersecurity planning efforts. The primary recommendation of this paper is that organisations should adopt both a qualitative and quantitative cyber risk assessment approach. Originality/value This work provides the first insight into how senior practitioners are using and combining qualitative and quantitative cybersecurity risk assessment, and highlights the need for in-depth comparisons of these two different approaches.
Fast, Robust and Accurate Detection of Cache-Based Spectre Attack Phases

Modern processors achieve high performance and efficiency by employing techniques such as speculative execution and sharing resources such as caches. However, recent attacks like Spectre and Meltdown exploit the speculative execution of modern processors to leak sensitive information from the system. Many mitigation strategies have been proposed to restrict the speculative execution of processors and protect potential side-channels. Currently, these techniques have shown a significant performance overhead. A solution that can detect memory leaks before the attacker has a chance to exploit them would allow the processor to reduce the performance overhead by enabling protections only when the system is at risk.
Automatic Assessment of Functional Movement Screening Exercises with Deep Learning Architectures

(1) Background: The success of physiotherapy depends on the regular and correct unsupervised performance of movement exercises. A system that automatically evaluates these exercises could increase effectiveness and reduce risk of injury in home based therapy. Previous approaches in this area rarely rely on deep learning methods and do not yet fully use their potential. (2) Methods: Using a measurement system consisting of 17 inertial measurement units, a dataset of four Functional Movement Screening exercises is recorded. Exercise execution is evaluated by physiotherapists using the Functional Movement Screening criteria. This dataset is used to train a neural network that assigns the correct Functional Movement Screening score to an exercise repetition. We use an architecture consisting of convolutional, long-short-term memory and dense layers. Based on this framework, we apply various methods to optimize the performance of the network. For the optimization, we perform an extensive hyperparameter optimization. In addition, we are comparing different convolutional neural network structures that have been specifically adapted for use with inertial measurement data. To test the developed approach, it is trained on the data from different Functional Movement Screening exercises and the performance is compared on unknown data from known and unknown subjects. (3) Results: The evaluation shows that the presented approach is able to classify unknown repetitions correctly. However, the trained network is yet unable to achieve consistent performance on the data of previously unknown subjects. Additionally, it can be seen that the performance of the network differs depending on the exercise it is trained for. (4) Conclusions: The present work shows that the presented deep learning approach is capable of performing complex motion analytic tasks based on inertial measurement unit data. The observed performance degradation on the data of unknown subjects is comparable to publications of other research groups that relied on classical machine learning methods. However, the presented approach can rely on transfer learning methods, which allow to retrain the classifier by means of a few repetitions of an unknown subject. Transfer learning methods could also be used to compensate for performance differences between exercises.
Capital in illegal online drug markets: How digital capital changes the cultural environment of drug dealing

Digital societies demand technological competence, including for actors in illegal activity. Inspired by Bourdieu’s theory of cultural capital and related criminological concepts such as street capital, this study analyses digital capital as a wider concept relating to digital drug markets that capture both technological and cultural competences. We pursue this empirically via interview data ( N = 107) on social media and darknet drug markets. The overall need for digital competence erodes the earlier divide in drug markets based on either subculture or networks. The need to be familiar with mainstream technological tools and behaviours connects digital drug markets to more general cultural competencies. Consequently, illegal activities become connected with mainstream cultural capital because both fields value the same competencies.
The trajectory CPHD filter for spawning targets

This paper proposes a new trajectory cardinality probability hypothesis density (TCPHD) filter to track multiple spawning targets, referred to as the S-TCPHD filter. Firstly, we build the spawning events of single trajectory as a zero-inflated Poisson process and present corresponding trajectory set dynamic model. Then, the prediction equations of the S-TCPHD filter is derived based on the dynamic model, while the update step is the same as the TCPHD. In addition, we also develop the linear Gaussian mixture (LGM) implementations of the prediction step. The resulting S-TCPHD filter can estimate the trajectory information of targets, thereby being versatile and suitable for spawning target applications. The trajectory probability hypothesis density (TPHD) filter for spawning targets, namely S-TPHD, can be directly obtained from the S-TCPHD filter. Finally, the simulation results verify the tracking performance of the proposed S-TCPHD/S-TPHD filters in a multiple spawning targets tracking scenario.
Advancing cardiovascular medicine with machine learning: Progress, potential, and perspective

Recent advances in machine learning (ML) have made it possible to analyze high-dimensional and complex data-such as free text, images, waveforms, videos, and sound-in an automated manner by successfully learning complex associations within these data. Cardiovascular medicine is particularly well poised to take advantage of these ML advances, due to the widespread digitization of medical data and the large number of diagnostic tests used to evaluate cardiovascular disease. Various ML approaches have successfully been applied to cardiovascular tests and diseases to automate interpretation, accurately perform measurements, and, in some cases, predict novel diagnoses from less invasive tests, effectively expanding the utility of more widely accessible diagnostic tests. Here, we present examples of some impactful advances in cardiovascular medicine using ML across a variety of modalities, with a focus on deep learning applications.
3D face recognition algorithm based on deep Laplacian pyramid under the normalization of epidemic control

Under the normalization of epidemic control in COVID-19, it is essential to realize fast and high-precision face recognition without feeling for epidemic prevention and control. This paper proposes an innovative Laplacian pyramid algorithm for deep 3D face recognition, which can be used in public. Through multi-mode fusion, dense 3D alignment and multi-scale residual fusion are ensured. Firstly, the 2D to 3D structure representation method is used to fully correlate the information of crucial points, and dense alignment modeling is carried out. Then, based on the 3D critical point model, a five-layer Laplacian depth network is constructed. High-precision recognition can be achieved by multi-scale and multi-modal mapping and reconstruction of 3D face depth images. Finally, in the training process, the multi-scale residual weight is embedded into the loss function to improve the network's performance. In addition, to achieve high real-time performance, our network is designed in an end-to-end cascade. While ensuring the accuracy of identification, it guarantees personnel screening under the normalization of epidemic control. This ensures fast and high-precision face recognition and establishes a 3D face database. This method is adaptable and robust in harsh, low light, and noise environments. Moreover, it can complete face reconstruction and recognize various skin colors and postures.
SolarGAN: Synthetic annual solar irradiance time series on urban building facades via Deep Generative Networks

Building Integrated Photovoltaics (BIPV) is a promising technology to decarbonize urban energy systems via harnessing solar energy available on building envelopes. While methods to assess solar irradiation, especially on rooftops, are well established, the assessment on building facades usually involves a higher effort due to more complex urban features and obstructions. The drawback of existing physics-based simulation programs are that they require significant manual modeling effort and computing time for generating time resolved deterministic results. Yet, solar irradiation is highly intermittent and representing its inherent uncertainty may be required for designing robust BIPV energy systems. Targeting on these drawbacks, this paper proposes a data-driven model based on Deep Generative Networks (DGN) to efficiently generate stochastic ensembles of annual hourly solar irradiance time series on building facades with uncompromised spatiotemporal resolution at the urban scale. The only input required are easily obtainable fisheye images as categorical shading masks captured from 3D models. In principle, even actual photographs of urban contexts can be utilized, given they are semantically segmented. The potential of our approach is that it may be applied as a surrogate for time-consuming simulations, when facing lacking information (e.g., no 3D model exists), and to use the generated stochastic time-series ensembles in robust energy systems planning. Our validations exemplify a good fidelity of the generated time series when compared to the physics-based simulator. Due to the nature of the used DGNs, it remains an open challenge to precisely reconstruct the ground truth one-to-one for each hour of the year. However, we consider the benefits of the approach to outweigh the shortcomings. To demonstrate the model’s relevance for urban energy planning, we showcase its potential for generative design by parametrically altering characteristic features of the urban environment and producing corresponding time series on building facades under different climatic contexts in real-time.
Multi-scale random walk driven adaptive graph neural network with dual-head neighboring node attention for CT segmentation

Segmenting objects with indistinct boundaries and large variations from CT volumes is a challenging issue due to overlapping intensity distributions from neighboring tissues or long-distance semantic relations. We propose a multi-scale random walk (RW) driven graph neural network (GNN) to address this issue. A graph is first initialized to represent image regions and deep semantic features from the segmentation encoder by graph nodes and attributes. We then propose a multi-scale graph reasoning model where for each scale, graph node attribute embedding is obtained by an adaptive GNN with dual-head neighboring node attention, while graph topology is evolved by RW. The neighboring-node attention mechanism is designed to learn and incorporate the importance and influence of neighboring nodes on their connected nodes. Random walking to multi-order neighbors enhance the contextual information formulation and diffusion along graph edges. Finally, multi-scale knowledge learnt from graphs is adaptively fused by a new graph-wise attention fusion module before reshaping and feeding to the segmentation decoder. We evaluate the contributions of major innovations by ablation studies, comparison with other state-of-the-art models on public kidney and tumor segmentation dataset. The generalization ability of our model is validated by different segmentation backbones. Experimental results show that the novel multi-scale adaptive graph reasoning architecture and RW-enhanced GNN model improved the segmentation of objects from adjacent tissues.
Evaluating WebAssembly for Orchestrated Deployment of Serverless Functions

Serverless computing has made a significant impact in the cloud computing landscape, and has even been extended beyond the cloud, up to the edge of the network. Existing serverless platforms which use containers and micro virtual machines as function runtimes incur a significant startup latency, hindering the performance and scalability of the executed functions. One potential solution to this problem is the use of WebAssembly. In this paper we discuss recent developments which allow WebAssembly to be used for serverside applications, as well as serverless functions, and evaluate potential orchestration options with the end goal of integrating WebAssembly with existing cloud and edge infrastructure. We conclude that while WebAssembly is a solution to the cold start problem, further work is needed in this area. To realize the endgoal of seamless and user-friendly serverless platforms that can be deployed across the edge-cloud continuum and can dynamically adapt to compute and latency requirements, WebAssembly should not be seen as the exclusive technology, and instead multiple runtime environments should be supported in addition to WebAssembly.
Expression site agnostic histopathology image segmentation framework by self supervised domain adaption

With the sites of antigen expression different, the segmentation of immunohistochemical (IHC) histopathology images is challenging, due to the visual variances. With H&E images highlighting the tissue structure and cell distribution more broadly, transferring more salient features from H&E images can achieve considerable performance on expression site agnostic IHC images segmentation.To the best of our knowledge, this is the first work that focuses on domain adaptive segmentation for different expression sites. We propose an expression site agnostic domain adaptive histopathology image semantic segmentation framework (ESASeg). In ESASeg, multi-level feature alignment encodes expression site invariance by learning generic representations of global and multi-scale local features. Moreover, self-supervision enhances domain adaptation to perceive high-level semantics by predicting pseudo-labels.We construct a dataset with three IHCs (Her2 with membrane stained, Ki67 with nucleus stained, GPC3 with cytoplasm stained) with different expression sites from two diseases (breast and liver cancer). Intensive experiments on tumor region segmentation illustrate that ESASeg performs best across all metrics, and the implementation of each module proves to achieve impressive improvements.The performance of ESASeg on the tumor region segmentation demonstrates the efficiency of the proposed framework, which provides a novel solution on expression site agnostic IHC related tasks. Moreover, the proposed domain adaption and self-supervision module can improve feature domain adaption and extraction without labels. In addition, ESASeg lays the foundation to perform joint analysis and information interaction for IHCs with different expression sites.
Fitness Action Counting Based on MediaPipe

AI fitness has become a new and practical way of fitness, but most of the mainstream fitness apps focus on guiding and planning fitness activities, ignoring the detection and evaluation of users' fitness movements. Aiming at this phenomenon, this paper proposed a method to classify and count basic fitness movements based on Google Mediapipe framework. The method consists of three steps: First, a single fitness action is divided into two detection states: up and down, and the corresponding picture samples are collected and trained. Secondly, based on the generated training set (csv file), KNN algorithm was used to identify and classify different fitness actions. Finally, the classification results are processed and the fitness actions are counted. The best recognition angle and threshold are obtained through the test accuracy. Compared with the mainstream human pose recognition frameworks such as Openpose and Alphapose, Mediapipe's Blazepose algorithm has lower performance requirements, faster recognition speed and a high level of accuracy, which is more suitable for personalized needs for smart fitness on mobile devices today.
A Comprehensive Survey on Quantum Machine Learning and Possible Applications

Machine learning is a branch of artificial intelligence that is being used at a large scale to solve science, engineering, and medical tasks. Quantum computing is an emerging technology that has a very high computational ability to solve complex problems. Classical machine learning with traditional systems has some limitations for problem-solving due to a large amount of data availability. Quantum machine learning has high performance and computational ability that can effectively be used to solve computation tasks. This study reviews the latest articles in quantum computing and quantum machine learning. Building blocks of quantum computing and different flavors of quantum algorithms are also discussed. The latest work in quantum neural networks is also presented. In the end, different possible applications of quantum computing are also discussed.
Learning Sparrow Algorithm With Non-Uniform Search for Global Optimization

Sparrow Algorithm as a New Swarm Intelligence Search Algorithm, the sparrow algorithm has good optimization ability, but in complex environments, it still has certain limitations, such as weak learning ability. Therefore, this paper proposes a learning sparrow search algorithm for non-uniform search(Sparrow search algorithm with non-uniform search, NSSSA). A learning behavior selection strategy is proposed, and saltation learning and a random walk learning are introduced respectively.To a certain extent, the algorithm avoided alling into the local optimum, and a non-uniform variable spiral search is proposed to balance the development and search capabilities of the algorithm. In the experimental simulation, the effectiveness of the NSSSA algorithm is verified by using the benchmark function, and it is tested on the CEC 2013 test set. Compared with the algorithms with better performance in recent years, the results show that the NSSSA algorithm has better universality . Finally, the NSSSA algorithm is applied to the WSN coverage optimization problem. The results show that NSSSA achieves more than 90% and 96% coverage on the two models of 50×50 and 100×100, respectively, which verifies the practicability of the algorithm.