Reviewerly/exHarmony huggingface dataset:
Probabilistic Grammatical Evolution

Grammatical Evolution (GE) is one of the most popular Genetic Programming (GP) variants, and it has been used with success in several problem domains. Since the original proposal, many enhancements have been proposed to GE in order to address some of its main issues and improve its performance. In this paper we propose Probabilistic Grammatical Evolution (PGE), which introduces a new genotypic representation and new mapping mechanism for GE. Specifically, we resort to a Probabilistic Context-Free Grammar (PCFG) where its probabilities are adapted during the evolutionary process, taking into account the productions chosen to construct the fittest individual. The genotype is a list of real values, where each value represents the likelihood of selecting a derivation rule. We evaluate the performance of PGE in two regression problems and compare it with GE and Structured Grammatical Evolution (SGE). The results show that PGE has a better performance than GE, with statistically significant differences, and achieved similar performance when comparing with SGE.
An Ontology-Based Approach for Curriculum Mapping in Higher Education

Programs offered by academic institutions in higher education need to meet specific standards that are established by the appropriate accreditation bodies. Curriculum mapping is an important part of the curriculum management process that is used to document the expected learning outcomes, ensure quality, and align programs and courses with industry standards. Semantic web languages can be used to express and share common agreement about the vocabularies used in the domain under study. In this paper, we present an approach based on ontology for curriculum mapping in higher education. Our proposed approach is focused on the creation of a core curriculum ontology that can support effective knowledge representation and knowledge discovery. The research work presents the case of ontology reuse through the extension of the curriculum ontology to support the creation of micro-credentials. We also present a conceptual framework for knowledge discovery to support various business use case scenarios based on ontology inferencing and querying operations.
Translating Videos into Synthetic Training Data for Wearable Sensor-Based Activity Recognition Systems Using Residual Deep Convolutional Networks

Human activity recognition (HAR) using wearable sensors has benefited much less from recent advances in Deep Learning than fields such as computer vision and natural language processing. This is, to a large extent, due to the lack of large scale (as compared to computer vision) repositories of labeled training data for sensor-based HAR tasks. Thus, for example, ImageNet has images for around 100,000 categories (based on WordNet) with on average 1000 images per category (therefore up to 100,000,000 samples). The Kinetics-700 video activity data set has 650,000 video clips covering 700 different human activities (in total over 1800 h). By contrast, the total length of all sensor-based HAR data sets in the popular UCI machine learning repository is less than 63 h, with around 38 of those consisting of simple mode of locomotion activities like walking, standing or cycling. In our research we aim to facilitate the use of online videos, which exist in ample quantities for most activities and are much easier to label than sensor data, to simulate labeled wearable motion sensor data. In previous work we already demonstrated some preliminary results in this direction, focusing on very simple, activity specific simulation models and a single sensor modality (acceleration norm). In this paper, we show how we can train a regression model on generic motions for both accelerometer and gyro signals and then apply it to videos of the target activities to generate synthetic Inertial Measurement Units (IMU) data (acceleration and gyro norms) that can be used to train and/or improve HAR models. We demonstrate that systems trained on simulated data generated by our regression model can come to within around 10% of the mean F1 score of a system trained on real sensor data. Furthermore, we show that by either including a small amount of real sensor data for model calibration or simply leveraging the fact that (in general) we can easily generate much more simulated data from video than we can collect its real version, the advantage of the latter can eventually be equalized.
Overview of CCKS 2020 Task 3: Named Entity Recognition and Event Extraction in Chinese Electronic Medical Records

The China Conference on Knowledge Graph and Semantic Computing (CCKS) 2020 Evaluation Task 3 presented clinical named entity recognition and event extraction for the Chinese electronic medical records. Two annotated data sets and some other additional resources for these two subtasks were provided for participators. This evaluation competition attracted 354 teams and 46 of them successfully submitted the valid results. The pre-trained language models are widely applied in this evaluation task. Data argumentation and external resources are also helpful.
Presence, flow, and narrative absorption: an interdisciplinary theoretical exploration with a new spatiotemporal integrated model based on predictive processing

Presence, flow, narrative absorption, immersion, transportation, and similar subjective phenomena are studied in many different disciplines, mostly in relation to mediated experiences (books, film, VR, games). Moreover, since real, virtual, or fictional agents are often involved, concepts like identification and state empathy are often linked to engaging media use. Based on a scoping review that identified similarities in the wording of various questionnaire items conceived to measure different phenomena, we categorize items into the most relevant psychological aspects and use this categorization to propose an interdisciplinary systematization. Then, based on a framework of embodied predictive processing, we present a new cognitive model of presence-related phenomena for mediated and non-mediated experiences, integrating spatial and temporal aspects and also considering the role of fiction and media design. Key processes described within the model are: selective attention, enactment of intentions, and interoception. We claim that presence is the state of perceived successful agency of an embodied mind able to correctly enact its predictions. The difference between real-life and simulated experiences ("book problem," "paradox of fiction") lays in the different precision weighting of exteroceptive and interoceptive signals.
On the value of filter feature selection techniques in homogeneous ensembles effort estimation

Abstract Software development effort estimation (SDEE) remains as the principal activity in software project management planning. Over the past four decades, several methods have been proposed to estimate the effort required to develop a software system, including more recently machine learning (ML) techniques. Because ML performance accuracy depends on the features that feed the ML technique, selecting the appropriate features in the preprocessing data step is important. This paper investigates three filter feature selection techniques to check the predictive capability of four single ML techniques: K ‐nearest neighbor, support vector regression, multilayer perceptron, and decision trees and their homogeneous ensembles over six well‐known datasets. Furthermore, the single and ensembles techniques were optimized using the grid search optimization method. The results suggest that the three filter feature selection techniques investigated improve the reasonability and the accuracy performance of the four single techniques. Moreover, the homogeneous ensembles are statistically more accurate than the single techniques. Finally, adopting a random process (i.e., random subspace method) to select the inputs feature for ML technique is not always effective to generate an accurate homogeneous ensemble.
The Conceptual Flaws of Constant Product Automated Market Making

Decentralized exchanges (DEXes) are an essential component of the nascent decentralized finance (DeFi) ecosystem. The most common DEXes are so-called automated market makers (AMMs), smart contracts that pool liquidity and process trades as atomic swaps of tokens. AMMs price transactions with a deterministic liquidity invariance rule that only uses the AMM's token deposits as inputs and that has no precedent in traditional finance. Yet in the context of transparent and open blockchain operations, any liquidity invariance pricing function allows so-called sandwich attacks (akin to front-running) that increase the cost of trading and threaten the long-term viability of the DeFi eco-system. Linear price rules that commonly emerge in economic models have similar problems, except for uniform pricing for which sandwich attack profits are limited and smaller, but which invites excessive order splitting.
A Global–Local Attentive Relation Detection Model for Knowledge-Based Question Answering

Knowledge-based question answering (KBQA) is an essential but challenging task for artificial intelligence and natural language processing. A key challenge pertains to the design of effective algorithms for relation detection. Conventional methods model questions and candidate relations separately through the knowledge bases (KBs) without considering the rich word-level interactions between them. This approach may result in local optimal results. This article presents a global-local attentive relation detection model (GLAR) that utilizes the local module to learn the features of word-level interactions and employs the global module to acquire nonlinear relationships between questions and their candidate relations located in KBs. This article also reports on the application of an end-to-end retrieval-based KBQA system incorporating the proposed relation detection model. Experimental results obtained on two datasets demonstrated GLAR's remarkable performance in the relation detection task. Furthermore, the functioning of end-to-end KBQA systems was significantly improved through the relation detection model, whose results on both datasets outperformed even state-of-the-art methods. Impact Statement-Knowledge-based question answering (KBQA) aims at answering user questions posed over the knowledge bases (KBs). KBQA helps users access knowledge in the KBs more easily, and it works on two subtasks: entity mention detection and relation detection. While existing relation detection algorithms perform well on the global representation of questions and relations sequences, they ignore some local semantic information on interaction cases between them. The technology proposed in this article takes both global and local interactions into account. With superior improvement on two relation detection tasks and two KBQA end tasks, the technology provides more precise answers. It could be used in more applications, including intelligent customer service, intelligent finance, and others.
A benchmark bone marrow aspirate smear dataset and a multi-scale cell detection model for the diagnosis of hematological disorders

Research on pathological diagnosis of hematopoietic disorders based on bone marrow aspirate smear images has attracted more and more attention with the development of deep learning methods. However, high quality bone marrow aspirate smear image datasets are not readily available because of the time, the efforts, and the medical knowledge required in the acquisition and manual annotation images. In order to facilitate the research of automated diagnosis of hematological disorders, we constructed a high quality Bone Marrow Aspirate Smear Image Dataset (BMASID), which contains 230 bone marrow aspirate smear images, all with the corresponding labeled images. We used additional clinical images as testing data, which are more challenging because of image noise, cell overlap, cell adhesion, blurred borders of cells and ambiguous types of cells. We also proposed a Cell Recognition Network (CRNet) that was trained on this benchmark dataset. CRNet is comprised of a cell detector to locate and recognize cells in the bone marrow aspirate images, and a cell classifier to classify the types of cells. New anchors and novel evaluation metrics are proposed and applied in CRNet. Benchmark evaluations of the proposed CRNet demonstrated the satisfactory performance of our state-of-the-art methods. Experimental results show that the detection precision by detector is more than 83%, and it is better when compared with other detection methods. After the cell type confirmation by the cell classifier, the precision is more than 95%. Compared with the most popular evaluation metrics Intersection over Union (IoU) and the newly proposed Generalized Intersection over Union (GIoU) used in the object detection benchmarks, our evaluation metrics are more suitable for the cell detection task with ambiguous cell boundaries. The proposed bone marrow aspirate smear image dataset and the proposed evaluation metrics can be used in the training and the evaluation of cell detection models, which contributes to future research in the pathological analysis and auxiliary diagnostic methods of hematological disorders. The codes are available at: https://github.com/SuJie-Med/hematolgical-disorders.
Attenuation of sensory processing in the primary somatosensory cortex during rubber hand illusion

Abstract The neural representation of the body is easily altered by the integration of multiple sensory signals in the brain. The “rubber hand illusion” (RHI) is one of the most popular experimental paradigms to investigate this phenomenon. During this illusion, a feeling of ownership of the rubber hand is created. Some studies have shown that somatosensory processing in the brain is attenuated when RHI occurs. However, it is unknown where attenuation of somatosensory processing occurs. Here, we show that somatosensory processing is attenuated in the primary somatosensory cortex. We found that the earliest response of somatosensory evoked potentials, which is thought to originate from the primary somatosensory cortex, was attenuated during RHI. Furthermore, this attenuation was observed before the occurrence of the illusion. Our results suggest that attenuation of sensory processing in the primary somatosensory cortex is one of the factors influencing the occurrence of the RHI.
Towards quantitative and intuitive percutaneous tumor puncture via augmented virtual reality

In recent years, the radiofrequency ablation (RFA) therapy has become a widely accepted minimal invasive treatment for liver tumor patients. However, it is challenging for doctors to precisely and efficiently perform the percutaneous tumor punctures under free-breathing conditions. This is because the traditional RFA is based on the 2D CT Image information, the missing spatial and dynamic information is dependent on surgeons' experience. This paper presents a novel quantitative and intuitive surgical navigation modality for percutaneous respiratory tumor puncture via augmented virtual reality, which is to achieve the augmented visualization of the pre-operative virtual planning information precisely being overlaid on intra-operative surgical scenario. In the pre-operation stage, we first combine the signed distance field of feasible structures (like liver and tumor) where the puncture path can go through and unfeasible structures (like large vessels and ribs) where the needle is not allowed to go through to quantitatively generate the 3D feasible region for percutaneous puncture. Then we design three constraints according to the RFA specialists consensus to automatically determine the optimal puncture trajectory. In the intra-operative stage, we first propose a virtual-real alignment method to precisely superimpose the virtual information on surgical scenario. Then, a user-friendly collaborative holographic interface is designed for real-time 3D respiratory tumor puncture navigation, which can effectively assist surgeons fast and accurately locating the target step-by step. The validation of our system is performed on static abdominal phantom and in vivo beagle dogs with artificial lesion. Experimental results demonstrate that the accuracy of the proposed planning strategy is better than the manual planning sketched by experienced doctors. Besides, the proposed holographic navigation modality can effectively reduce the needle adjustment for precise puncture as well. Our system shows its clinical feasibility to provide the quantitative planning of optimal needle path and intuitive in situ holographic navigation for percutaneous tumor ablation without surgeons' experience-dependence and reduce the times of needle adjustment. The proposed augmented virtual reality navigation system can effectively improve the precision and reliability in percutaneous tumor ablation and has the potential to be used for other surgical navigation tasks.
Hash Bit Selection via Collaborative Neurodynamic Optimization With Discrete Hopfield Networks

Hash bit selection (HBS) aims to find the most discriminative and informative hash bits from a hash pool generated by using different hashing algorithms. It is usually formulated as a binary quadratic programming problem with an information-theoretic objective function and a string-length constraint. In this article, it is equivalently reformulated in the form of a quadratic unconstrained binary optimization problem by augmenting the objective function with a penalty function. The reformulated problem is solved via collaborative neurodynamic optimization (CNO) with a population of classic discrete Hopfield networks. The two most important hyperparameters of the CNO approach are determined based on Monte Carlo test results. Experimental results on three benchmark data sets are elaborated to substantiate the superiority of the collaborative neurodynamic approach to several existing methods for HBS.
The extended coset leader weight enumerator of a twisted cubic code

Abstract The extended coset leader weight enumerator of the generalized Reed–Solomon $$[q+1,q-3,5]_q$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mo>[</mml:mo> <mml:mi>q</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mi>q</mml:mi> <mml:mo>-</mml:mo> <mml:mn>3</mml:mn> <mml:mo>,</mml:mo> <mml:mn>5</mml:mn> <mml:mo>]</mml:mo> </mml:mrow> <mml:mi>q</mml:mi> </mml:msub> </mml:math> code is computed. In this computation methods in finite geometry, combinatorics and algebraic geometry are used. For this we need the classification of the points, lines and planes in the projective three space under projectivities that leave the twisted cubic invariant. A line in three space determines a rational function of degree at most three and vice versa. Furthermore, the double point scheme of a rational function is studied. The pencil of a true passant of the twisted cubic, not in an osculation plane gives a curve of genus one as double point scheme. With the Hasse–Weil bound on $${\mathbb F}_q$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:mi>q</mml:mi> </mml:msub> </mml:math> -rational points we show that there is a 3-plane containing the passant.
Power ramp rates and variability of individual and aggregate photovoltaic systems using measured production data at the municipal scale

Intermittency from photovoltaic systems can negatively impact electricity grid stability due to increased power ramp rates and temporal misalignment between generation and load. Distributed systems, especially rooftop residential systems, present a range of orientations and spatial separation that when aggregated introduce temporal diversity. If these features reduce negative impacts, they should be weighed against increased costs per delivered energy compared to larger commercial/industrial systems. We compare power production data from 60 distributed residential PV systems spread across a large municipality with medium and large commercial rooftop systems located centrally in the municipality. Power ramp rates and power output variability were calculated. The aggregated distributed residential system ramp rates never exceed 13% per 5 min, while the medium/large commercial systems experienced ramp rates up to 61% and 68% per 5 min. In aggregate the residential output variability is 3 times lower than the commercial sites. Consistent with previous literature the output variability decreases as the number of systems grows. There are however diminishing returns due to the limited geographic area, with a relative output variability for the municipality approaching 20% of the variability of a single system. Residential data was scaled up to represent 10% of electrical load and resulted in 10% more electricity grid ramp rates exceeding ±5 MW per 5 min compared to baseline. Finally, a comparison between pyranometer and photovoltaic system ramps rates shows the latter are less severe, likely because pyranometers are single points sources.
3E-LDA

Linear discriminant analysis (LDA) is one of the important techniques for dimensionality reduction, machine learning, and pattern recognition. However, in many applications, applying the classical LDA often faces the following problems: (1) sensitivity to outliers, (2) absence of local geometric information, and (3) small sample size or matrix singularity that can result in weak robustness and efficiency. Although several researchers have attempted to address one or more of the problems, little work has been done to address all of them together to produce a more effective and efficient LDA algorithm. This article proposes 3E-LDA, an enhanced LDA algorithm, that deals with all three problems as an attempt to further improve LDA. It proposes to learn a weighted median rather than the mean of the samples to deal with (1), to embed both between-class and within-class local geometric information to deal with (2), and to calculate the projection vectors in the null space of the matrix to deal with (3). Experiments on six benchmark datasets show that these three enhancements enable 3E-LDA to markedly outperform state-of-the-art LDA baselines in both accuracy and efficiency.
Adversarial self-supervised clustering with cluster-specificity distribution

As we embark on the big data era, numerous unlabeled data has generated increasingly, and thus deep clustering analysis has become prevalent in artificial intelligence. Yet, most existing deep clustering methods still have the following demerits: 1) They fail to take cluster-specificity distribution into account, resulting in suboptimal latent representation. 2) They suffer from the scale issue of the distributions between the given sample and cluster centers, resulting in unstable clustering performance. 3) They fail to utilize the obtained clustering labels, resulting in suboptimal clustering performances. To fill these gaps, we propose a new deep clustering solution, namely Adversarial Self-supervised Clustering With Cluster-specificity Distribution (ASC2D). Specifically, by imposing the cluster-specificity constraint, which is measured by the ℓ1,2-norm, the learned latent representation well encodes the cluster structure. Meanwhile, by introducing the thought of adversarial learning, ASC2D well eliminates the gaps between distributions. Moreover, ASC2D utilize the clustering label to supervise the learning of representation, where the latter is used in turn to conduct the subsequent clustering. By this way, clustering and representation learning are seamlessly connected, with the aim to achieve better clustering performance. Extensive experimental results show that ASC2D is superior to 14 state-of-the-art baselines on six image datasets in terms of three evaluation metrics, especially on Fashion-MNIST datasets, ASC2D brings about 4.1% and 7.1% improvement over the best baseline in terms of ACC and NMI metrics.