Miracle-dz/UniFAR-FTUs huggingface dataset:
Leveraging Human Prior Knowledge to Learn Sense Representations

Conventional distributed word representation learning, which learns a single vector for each word, is unable to represent different meanings of polysemous words. To address this issue, a number of approaches were proposed to model individual word senses in recent years. However, most of these sense representations are hard to be integrated into downstream tasks. In this paper, we pro-pose a knowledge-based method to learn word sense representations that can offer effective support in downstream tasks. More speciﬁ-cally, we propose to capture the semantic information of prior human knowledge from sememes, the minimum semantic units of meaning, to build global sense context vectors and perform a reliable soft word sense disambiguation for polysemous words. We extend the framework of Skip-gram model with a contextual attention mechanism to learn an individual embedding for each sense. The intrinsic experimental results show that our proposed method can capture the distinct and exact meanings of senses and outperform previous work on the classic word similarity task. The extrinsic experiment and further analysis show that our sense embeddings can be utilized to effectively improve performance and mitigate the impact of polysemy in multiple real-word downstream tasks.
Link Prediction by Analyzing Common Neighbors Based Subgraphs Using Convolutional Neural Network

Link prediction (LP) in social networks is to infer if a link is likely to be formed in the future. Social networks (SN) are ubiquitous and have different types such as human interaction and protein-protein networks. LP uses heuristic methods including common neighbors and resource allocation to ﬁnd the formation of future links. These heuristics are sensitive to different types of social networks. Certain types of heuristics work better for some SN types, but not for others. Selecting the appropriate heuristic method for the SN type is often a trial and error process. Recent ground-breaking methods, WLMN and SEAL, demonstrated that this selection process can be automated for the different types of SN. While these methods are promising, in some types of SN they still suffer from low accuracy. The objective of this paper is to address this weakness by introducing a novel framework called PLACN that incorporates the analysis of common neighbors of nodes on target link and a combination of heuristic features through a deep learning method. PLACN is driven by a new method to efﬁciently extract the subgraphs for a target link based on the common neighbors. Another novelty is the method for labeling subgraphs based on the average hop and average weight. Furthermore, we introduce a method to evaluate the approximate number of nodes in the subgraph. Our model converts link prediction to an image classiﬁcation problem and uses a convolutional neural network. We tested our model on seven real-world networks and compared against traditional LP methods as well as two recent state-of-the-art methods based on subgraphs. Our results out-performed those LP methods reaching above 96% of AUC in benchmark SNs.
Argumentative Reflections of Approximation Fixpoint Theory

In this paper we show that non-monotonic formalisms that are represented by approximation ﬁxpoint theory can also be represented by formal argumentation frameworks. By this, we are able not only to recapture and generalize many forms of non-monotonic reasoning in the context of argumentation theory, but also introduce new argumentative representations that have not been considered so far.
Dispersion of long-nosed fur seals (Arctocephalus forsteri) determined by tagging

Abstract. Long-nosed fur seals (Arctocephalus forsteri) were tagged as pups in colonies on Kangaroo Island, South Australia in eight consecutive pupping seasons from 1988–89 to 1995–96. Thirty-nine tagged animals were sighted on the southern Australian coast, being 0.89% of those tagged. They were aged from 9 months to 14 years 6 months, with half in their second and third years. Most records (88%) were of animals that moved eastwards. The most distant records were from Sydney in the east (1700 km), south of Tasmania in the south (1240 km) and Head of Bight in the west (700 km). One animal was seen twice, both times on the north coast of Kangaroo Island, once underwater and two years later ashore. Satellite telemetry studies of juvenile A. forsteri from Kangaroo Island showed that they typically forage in pelagic waters ∼1000 km further south in association with the subtropical front. The study reported here shows that some animals tagged as pups disperse widely as juveniles around the southern Australian coast. The possibility of genetic interchange between breeding colonies is suggested by sightings of three tagged females aged 4 years and older at non-natal colonies.
Changes in risk perception and self-reported protective behaviour during the first week of the COVID-19 pandemic in the United States

Efforts to change behaviour are critical in minimizing the spread of highly transmissible pandemics such as COVID-19. However, it is unclear whether individuals are aware of disease risk and alter their behaviour early in the pandemic. We investigated risk perception and self-reported engagement in protective behaviours in 1591 United States-based individuals cross-sectionally and longitudinally over the first week of the pandemic. Subjects demonstrated growing awareness of risk and reported engaging in protective behaviours with increasing frequency but underestimated their risk of infection relative to the average person in the country. Social distancing and hand washing were most strongly predicted by the perceived probability of personally being infected. However, a subgroup of individuals perceived low risk and did not engage in these behaviours. Our results highlight the importance of risk perception in early interventions during large-scale pandemics.
Does conspicuousness scale linearly with colour distance? A test using reef fish

To be effective, animal colour signals must attract attention—and therefore need to be conspicuous. To understand the signal function, it is useful to evaluate their conspicuousness to relevant viewers under various environmental conditions, including when visual scenes are cluttered by objects of varying colour. A widely used metric of colour difference (ΔS) is based on the receptor noise limited (RNL) model, which was originally proposed to determine when two similar colours appear different from one another, termed the discrimination threshold (or just noticeable difference). Estimates of the perceptual distances between colours that exceed this threshold—termed ‘suprathreshold’ colour differences—often assume that a colour's conspicuousness scales linearly with colour distance, and that this scale is independent of the direction in colour space. Currently, there is little behavioural evidence to support these assumptions. This study evaluated the relationship between ΔS and conspicuousness in suprathreshold colours using an Ishihara-style test with a coral reef fish, Rhinecanthus aculeatus. As our measure of conspicuousness, we tested whether fish, when presented with two colourful targets, preferred to peck at the one with a greater ΔS from the average distractor colour. We found the relationship between ΔS and conspicuousness followed a sigmoidal function, with high ΔS colours perceived as equally conspicuous. We found that the relationship between ΔS and conspicuousness varied across colour space (i.e. for different hues). The sigmoidal detectability curve was little affected by colour variation in the background or when colour distance was calculated using a model that does not incorporate receptor noise. These results suggest that the RNL model may provide accurate estimates for perceptual distance for small suprathreshold distance colours, even in complex viewing environments, but must be used with caution with perceptual distances exceeding 10 ΔS.
Cross-Border Medical Research Using Multi-Layered and Distributed Knowledge

As medical research becomes ever ﬁner-grained, experiments require healthcare data in quantities that single countries cannot provide. Cross-jurisdictional data collection remains, however, extremely challenging due to the diverging legal, professional, linguistic, normative, and technological contexts of the participating countries. Medical data heterogeneity, in particular, is still a largely unsolved problem on the international level, due to the complexity of data combined with strict precision and data protection constraints. We propose a scalable solution based on a novel knowledge architecture and the corresponding knowledge graph integration methodology. Medical knowledge that drives the scalable integration process is divided into multiple functional layers and is maintained in a distributed manner across participating countries. We successfully applied the approach in the context of a research experiment across Scotland and Italy, and are currently adapting it within other initiatives of Europe-wide health data interoperability.
The Rr Form of the Kedem–Katchalsky–Peusner Model Equations for Description of the Membrane Transport in Concentration Polarization Conditions

The paper presents the Rr matrix form of Kedem–Katchalsky–Peusner equations for membrane transport of the non-homogeneous ternary non-electrolyte solutions. Peusner’s coefficients Rijr and det [Rr] (i, j ∈ {1, 2, 3}, r = A, B) occurring in these equations, were calculated for Nephrophan biomembrane, glucose in aqueous ethanol solutions and two different settings of the solutions relative to the horizontally oriented membrane for concentration polarization conditions or homogeneity of solutions. Kedem–Katchalsky coefficients, measured for homogeneous and non-homogeneous solutions, were used for the calculations. The calculated Peusner’s coefficients for homogeneous solutions depend linearly, and for non-homogeneous solutions non-linearly on the concentrations of solutes. The concentration dependences of the coefficients Rijr and det [Rr] indicate a characteristic glucose concentration of 9.24 mol/m3 (at a fixed ethanol concentration) in which the obtained curves for Configurations A and B intersect. At this point, the density of solutions in the upper and lower membrane chamber are the same. Peusner’s coefficients were used to assess the effect of concentration polarization and free convection on membrane transport (the ξij coefficient), determine the degree of coupling (the rijr coefficient) and coupling parameter (the QRr coefficient) and energy conversion efficiency (the (eijr)r coefficient).
Model-Free Non-Stationarity Detection and Adaptation in Reinforcement Learning

In most Reinforcement Learning (RL) studies, the considered task is assumed to be stationary, i.e., it does not change its behavior or its characteristics over time, as this allows to generate all the convergence properties of RL techniques. Unfortunately, this assumption does not hold in real-world scenarios where systems and environments typically evolve over time. For instance, in robotic applications, sensor or actuator faults would induce a sudden change in the RL settings, while in ﬁnancial applications the evolution of the market can cause a more gradual variation over time. In this paper, we present an adaptive RL algorithm able to detect changes in the environment or in the reward function and react to these changes by adapting to the new conditions of the task. At ﬁrst, we develop a ﬁgure of merit onto which a hypothesis test can be applied to detect changes between two different learning iterations. Then, we extended this test to sequentially operate over time by means of the CU-mulative SUM (CUSUM) approach. Finally, the proposed change-detection mechanism is combined (following an adaptive-active approach) with a well known RL algorithm to make it able to deal with non-stationary tasks. We tested the proposed algorithm on two well-known continuous-control tasks to check its effectiveness in terms of non-stationarity detection and adaptation over a vanilla RL algo-rithm.
Relaxed Per-Stage Requirements for Training Cascades of Classifiers

Historically ﬁrst training algorithms for cascades of clas-siﬁers were guided by constant per-stage requirements (constraints) imposed on false alarm and detection rates. Those constant values were calculated according to the geometric mean rule, implied by a pair of ﬁnal requirements predeﬁned for the whole cascade. We provide and prove a theoretical result demonstrating that the presence of slack between the constant requirements and actual rates observed while learning, allows to introduce new relaxed requirements for each successive stage and still complete the training procedure successfully (with ﬁnal requirements satisﬁed). The relaxed requirements can be met more easily, using fewer features. This creates a potential possibility to reduce the expected number of features used by an operating cascade — the crucial quantity we focus on in the paper. Taking advantage of the relaxation, we propose new stage-wise training algorithms that apply two approaches: uniform or greedy. They differ in the way the slack cumulated so far becomes “consumed” later on. Reported experimental results pertain to cascades trained to work as face or letter detectors, with Haar-like features or Zernike moments being the input information, respectively. The results conﬁrm shorter operating times of cascades obtained by the proposed technique, owing to the reduction in the number of extracted features.
Data-Dependent Conditional Priors for Unsupervised Learning of Multimodal Data †

One of the major shortcomings of variational autoencoders is the inability to produce generations from the individual modalities of data originating from mixture distributions. This is primarily due to the use of a simple isotropic Gaussian as the prior for the latent code in the ancestral sampling procedure for data generations. In this paper, we propose a novel formulation of variational autoencoders, conditional prior VAE (CP-VAE), with a two-level generative process for the observed data where continuous z and a discrete c variables are introduced in addition to the observed variables x. By learning data-dependent conditional priors, the new variational objective naturally encourages a better match between the posterior and prior conditionals, and the learning of the latent categories encoding the major source of variation of the original data in an unsupervised manner. Through sampling continuous latent code from the data-dependent conditional priors, we are able to generate new samples from the individual mixture components corresponding, to the multimodal structure over the original data. Moreover, we unify and analyse our objective under different independence assumptions for the joint distribution of the continuous and discrete latent variables. We provide an empirical evaluation on one synthetic dataset and three image datasets, FashionMNIST, MNIST, and Omniglot, illustrating the generative performance of our new model comparing to multiple baselines.
Option Portfolio Selection with Generalized Entropic Portfolio Optimization

In this third and final paper of our series on the topic of portfolio optimization, we introduce a further generalized portfolio selection method called generalized entropic portfolio optimization (GEPO). GEPO extends discrete entropic portfolio optimization (DEPO) to include intervals of continuous returns, with direct application to a wide range of option strategies. This lays the groundwork for an adaptable optimization framework that can accommodate a wealth of option portfolios, including popular strategies such as covered calls, married puts, credit spreads, straddles, strangles, butterfly spreads, and even iron condors. These option strategies exhibit mixed returns: a combination of discrete and continuous returns with performance best measured by portfolio growth rate, making entropic portfolio optimization an ideal method for option portfolio selection. GEPO provides the mathematical tools to select efficient option portfolios based on their growth rate and relative entropy. We provide an example of GEPO applied to real market option portfolio selection and demonstrate how GEPO outperforms traditional Kelly criterion strategies.
Cognitive Vision and Perception

Semantic interpretation of dynamic visuospatial imagery calls for a general and systematic integration of methods in knowledge representation and computer vision. Towards this, we highlight research articulating & developing deep semantics , characterised by the existence of declarative models –e.g., pertaining space and motion – and corresponding formalisation and reasoning methods supporting capabilities such as semantic question-answering, relational visuospatial learning, and (non-monotonic) visuospatial explanation. We position a working model for deep semantics by highlighting select recent / closely related works from IJCAI [8, 4], AAAI [10], ILP [7], and ACS [9]. We posit that human-centred, explainable visual sensemaking necessitates both high-level semantics and low-level visual computing, with the highlighted works providing a model for systematic, modular integration of diverse multifaceted techniques developed in AI, ML, and Computer Vision.
Evaluation of Feature Extraction and Classification for Lower Limb Motion Based on sEMG Signal

The real-time and accuracy of motion classification plays an essential role for the elderly or frail people in daily activities. This study aims to determine the optimal feature extraction and classification method for the activities of daily living (ADL). In the experiment, we collected surface electromyography (sEMG) signals from thigh semitendinosus, lateral thigh muscle, and calf gastrocnemius of the lower limbs to classify horizontal walking, crossing obstacles, standing up, going down the stairs, and going up the stairs. Firstly, we analyzed 11 feature extraction methods, including time domain, frequency domain, time-frequency domain, and entropy. Additionally, a feature evaluation method was proposed, and the separability of 11 feature extraction algorithms was calculated. Then, combined with 11 feature algorithms, the classification accuracy and time of 55 classification methods were calculated. The results showed that the Gaussian Kernel Linear Discriminant Analysis (GK-LDA) with WAMP had the highest classification accuracy rate (96%), and the calculation time was below 80 ms. In this paper, the quantitative comparative analysis of feature extraction and classification methods was a benefit to the application for the wearable sEMG sensor system in ADL.
Forecasting Bitcoin Trends Using Algorithmic Learning Systems

This research has examined the ability of two forecasting methods to forecast Bitcoin’s price trends. The research is based on Bitcoin—USA dollar prices from the beginning of 2012 until the end of March 2020. Such a long period of time that includes volatile periods with strong up and downtrends introduces challenges to any forecasting system. We use particle swarm optimization to find the best forecasting combinations of setups. Results show that Bitcoin’s price changes do not follow the “Random Walk” efficient market hypothesis and that both Darvas Box and Linear Regression techniques can help traders to predict the bitcoin’s price trends. We also find that both methodologies work better predicting an uptrend than a downtrend. The best setup for the Darvas Box strategy is six days of formation. A Darvas box uptrend signal was found efficient predicting four sequential daily returns while a downtrend signal faded after two days on average. The best setup for the Linear Regression model is 42 days with 1 standard deviation.
Joint Modeling of Local and Global Behavior Dynamics for Session-Based Recommendation

Session-based recommendation is critical in modern recommender systems, which aims to predict the next interested item given anonymous behavior sequences of users. While prior works have made efforts to addressing the session-based recommendation problem, two signiﬁcant limitations exist: i) They ignore the fact that items may be correlated with other across different session units; ii) existing solutions are also limited in their assumption of rigidly ordered pattern over intra-session item transition, which may not be true in practice. To address these above limitations, we propose a Local-Global Session-based Recommendation framework–LGSR which generalizes the modeling of behavior dynamics from two perspectives: we ﬁrst design a cross-session item dependency encoder to learn the inter-session item relation structures from a global perspective. Additionally, a dual-stage attentive aggregation module is developed to capture local item transition dynamics, without the restriction of rigid sequential process for jointly modeling user’s current interest and intra-session purpose. With the exploration of both complex intra-and inter-session interest transitional regularities, our LGSR model enables the representation learning of user behavior dynamics via jointly mapping local and global signals into the same latent space. The experimental results on two real-world datasets demonstrate the superiority of the proposed LGSR framework over state-of-the-art methods.
Document and Word Representations Generated by Graph Convolutional Network and BERT for Short Text Classification

In many studies, the graph convolution neural networks were used to solve different natural language processing (NLP) problems. However, few researches employ graph convolutional network for text classiﬁcation, especially for short text classiﬁcation. In this work, a special text graph of the short-text corpus is created, and then a short-text graph convolutional network (STGCN) is developed. Speciﬁcal-ly, different topic models for short text are employed, and a short text short-text graph based on the word co-occurrence, document word relations, and text topic information, is developed. The word and sentence representations generated by the STGCN are considered as the classiﬁcation feature. In addition, a pre-trained word vector obtained by the BERTs hidden layer is employed, which greatly improves the classiﬁcation effect of our model. The experimental results show that our model outperforms the state-of-the-art models on multiple short text datasets.
Knowledge Extraction with Interval Temporal Logic Decision Trees

Multivariate temporal, or time, series classification is, in a way, the temporal generalization of (numeric) classification, as every instance is described by multiple time series instead of multiple values. Symbolic classification is the machine learning strategy to extract explicit knowledge from a data set, and the problem of symbolic classification of multivariate temporal series requires the design, implementation, and test of ad-hoc machine learning algorithms, such as, for example, algorithms for the extraction of temporal versions of decision trees. One of the most well-known algorithms for decision tree extraction from categorical data is Quinlan's ID3, which was later extended to deal with numerical attributes, resulting in an algorithm known as C4.5, and implemented in many open-sources data mining libraries, including the so-called Weka, which features an implementation of C4.5 called J48. ID3 was recently generalized to deal with temporal data in form of timelines, which can be seen as discrete (categorical) versions of multivariate time series, and such a generalization, based on the interval temporal logic HS, is known as Temporal ID3. In this paper we introduce Temporal C4.5, that allows the extraction of temporal decision trees from undiscretized multivariate time series, describe its implementation, called Temporal J48, and discuss the outcome of a set of experiments with the latter on a collection of public data sets, comparing the results with those obtained by other, classical, multivariate time series classification methods.