Reviewerly/exHarmony huggingface dataset:
Multi-perspective enhanced representation for effective session-based recommendation

Session-based recommendation uses the click history of anonymous users to recommend the next possible click item for a given point in time. GNN-based models have returned promising results. However, these models still have some limitations. First, when GNN encodes the session data into session graphs, the order of the items is ignored; second, the GNN layer limit makes it impossible to capture some long-term dependencies in the session; third, noise items in the final generated session embedding will interfere with the model’s predictions. We propose a new multiperspective enhanced session representation recommendation model (MPER) to address the above problems. First, we introduce learnable position embedding to record the sequential relationships of the items, and propose the SLD-encoder to capture richer global information. Second, we propose an adaptive sparse target attention mechanism to alleviate the effect of the noise items in the traditional target embeddings. Third, a linear integration layer is added to the model to ensure that the session and item embeddings are in the same representation space. We conduct experiments on three real datasets, and the experimental results show that our model outperforms the SOTA baseline studies.
A Class of Power Mappings with Low Boomerang Uniformity

Let $$f(x)=x^{\frac{q-3}{2}}$$ be a power mapping over $${\mathbb {F}}_{q}$$ , where q is an odd prime power. The differential uniformity of f was determined by Helleseth and Sandberg [14] in 1997. In this paper, we study the boomerang uniformity of f via its differential properties. It is shown that f has low boomerang uniformity when $$q \equiv 3 \pmod 4$$ .
Optimally Designing Cybersecurity Insurance Contracts to Encourage the Sharing of Medical Data

Though the sharing of medical data has the potential to lead to breakthroughs in health care, the sharing process itself exposes patients and health care providers to various risks. Patients face risks due to the possible loss in privacy or livelihood that can occur when medical data is stolen or used in non-permitted ways, whereas health care providers face risks due to the associated liability. For medical data, these risks persist even after anonymizing/deidentifying, according to the standards defined in existing legislation, the data sets prior to sharing, because shared medical data can often be deanonymized/reidentified using advanced artificial intelligence and machine learning methodologies. As a result, health care providers are hesitant to share medical data. One possible solution to encourage health care providers to responsibly share data is through the use of cybersecurity insurance contracts. This paper studies the problem of designing optimal cybersecurity insurance contracts, with the goal of encouraging the sharing of the medical data. We use a principal-agent model with moral hazard to model various scenarios, derive the optimal contract, discuss its implications, and perform numerical case studies. In particular, we consider two scenarios: the first scenario is where a health care provider is selling medical data to a technology firm who is developing an artificial intelligence algorithm using the shared data. The second scenario is where a group of health care providers share health data amongst themselves for the purpose of furthering medical research using the aggregated medical data.
Distributed Finite-Time k-means Clustering with Quantized Communucation and Transmission Stopping

In this paper, we present a distributed version of the k-means algorithm for multi-agent systems with directed communication links. The goal of k-means is to partition the network’s agents in mutually exclusive sets (groups) such that agents in the same set have (and possibly share) similar information and are able to calculate a representative value for their group. Our distributed algorithm allows each node to transmit quantized values in an event-driven fashion, and exhibits distributed stopping capabilities. Transmitting quantized values leads to more efficient usage of the available bandwidth and reduces the communication bottleneck, whereas distributed stopping preserves available resources. We characterize the properties of the proposed distributed algorithm and show that its execution (on any static and strongly connected digraph) will partition all agents in mutually exclusive clusters in finite time. We conclude with examples that illustrate the operation, performance, and potential advantages of the proposed algorithm.
Logic for Timed Agent Network Topologies

We define µTGL, a spatio-temporal logic with fixed points and first-order agent quantification whose expressive power allows the definition of topological properties of networks of communicating agents. The existence of temporal operators and fixed points requires particular care when defining its semantics. We demonstrate the logic’s usefulness on an example, where we monitor a complex property that ensures resilient consensus.
A J-Spectral Factorization Condition for the Physical Realiazability of a Transfer Function Matrix with only Direct Feedthrough Quantum Noise

This paper gives a J-spectral factorization condition for the implementation of a strictly proper transfer function matrix as a physically realizable quantum system using only direct feedthrough quantum noise. A necessary frequency response condition is also presented. Examples are included to illustrate the main results.
Online education through the eyes of Turkish EFL instructors: A study on metaphorical perceptions

Online education has been one of the most discussed educational phenomena in literature.However, it has not been a common experience for all shareholders of educational settings until the global pandemic.Compulsory online transmission in educational settings has turned into a common experience for all shareholders of educational practices around the globe rather than an option for traditional classroom-based instruction.The present study aims to explore the perceptions of EFL instructors and faculty regarding their online teaching practices through elicited metaphor analysis.To this end, 75 participants' metaphoric utterances were collected and coded through content analysis according to their gender, general teaching experience, and online teaching experience.Two categories and 17 themes have been identified.The results indicate a nuance between the positive and negative perceptions about online teaching according to gender and online teaching experience and a slight difference between general teaching experience and perceptions.Overall, participants have adopted a negative perception of their online teaching experience. ÖZÇevrimiçi eğitim, literatürde en çok tartışılan eğitim olgularından biri olmuştur.Ancak küresel salgına kadar eğitim ortamlarının tüm paydaşları için ortak bir deneyim olmamıştır.Eğitim ortamlarında zorunlu çevrimiçi aktarım, geleneksel sınıf temelli öğretim için bir seçenek olmaktan çok, dünyadaki tüm eğitim uygulamalarının tüm paydaşları için ortak bir deneyime dönüşmüştür.Bu çalışma, metafor analizi tekniği ile İngilizce öğretim görevlilerinin ve öğre
Reinforcement Learning with Unbiased Policy Evaluation and Linear Function Approximation

We provide performance guarantees for a variant of simulation-based policy iteration for controlling Markov decision processes that involves the use of stochastic approximation algorithms along with state-of-the-art techniques that are useful for very large MDPs, including lookahead, function approximation, and gradient descent. Specifically, we analyze two algorithms; the first algorithm involves a least squares approach where a new set of weights associated with feature vectors is obtained via least squares minimization at each iteration and the second algorithm is a two-time-scale algorithm taking several steps of gradient descent towards the least squares solution before obtaining the next iterate using a stochastic approximation algorithm.
On the Global Convergence of Stochastic Fictitious Play in Stochastic Games with Turn-based Controllers

This paper presents a learning dynamic with almost sure convergence guarantee for any stochastic game with turn-based controllers (on state transitions) as long as stage-payoffs have stochastic fictitious-play-property. For example, two-player zero-sum and n-player potential strategic-form games have this property. Note also that stage-payoffs for different states can have different structures such as they can sum to zero in some states and be identical in others. The dynamics presented combines the classical stochastic fictitious play with value iteration for stochastic games. There are two key properties: (i) players play finite horizon stochastic games with increasing lengths within the underlying infinite-horizon stochastic game, and (ii) the turn-based controllers ensure that the auxiliary stage-games (induced from the continuation payoff estimated) have the stochastic fictitious-play-property.
A Homotopic Approach to Policy Gradients for Linear Quadratic Regulators with Nonlinear Controls

We study the convergence of deterministic policy gradient algorithms in continuous state and action space for the prototypical Linear Quadratic Regulator (LQR) problem when the search space is not limited to the family of linear policies. We first provide a counterexample showing that extending the policy class to piecewise linear functions results in local minima of the policy gradient algorithm. To solve this problem, we develop a new approach that involves sequentially increasing a discount factor between iterations of the original policy gradient algorithm. We finally prove that this homotopic variant of policy gradient methods converges to the global optimum of the undiscounted Linear Quadratic Regulator problem for a large class of Lipschitz, non-linear policies.
Accurate Guaranteed State Estimation for Uncertain LPVs using Constrained Convex Generators

Guaranteed state estimation for autonomous vehicles in GPS-denied areas that resort to landmarks detection and onboard sensors requires set-membership techniques that are capable of representing heterogeneous bounds using hyperplanes and ellipsoids. Recently, in the literature, the concept of Convex Constrained Generators (CCGs) has been introduced for the case where the dynamical system can be represented by a Linear Parameter-Varying (LPV) model. However, in practical applications, dynamics have uncertain parameters caused by noise-corrupted measurements of quantities of interest such as mass or orientation angles. In this paper, we first explore a closed-form solution for the convex hull of polytopes to showcase the main challenges of guaranteed state estimation for uncertain LPVs. We then propose the use of CCGs to have low conservatism when in the presence of distance measurements and avoid the exponential growth of the generators used in the state representation by performing an approximation using ray-shooting. Simulations illustrate the ability of CCGs to accurately model distance measurements with the corresponding decrease in volume without adding additional constraints.
Uncertainty-Aware Feature Mixing and Cross-Decoupled Pseudo Supervision for Semi-Supervised Semantic Segmentation

Semi-supervised semantic segmentation aims to maximize the training performance for a limited annotation cost. Existing methods such as cross pseudo supervision have shown excellent performance, yet ignore potential information interactions between labeled and unlabeled data, and suffer from misleading incorrect pseudo labels. This paper takes two ways to improve each of these shortcomings. Firstly, we perform feature-level mixing and cross-decoupling using labeled and unlabeled data to establish potential interactions between the two types of data. Secondly, an uncertainty-aware loss re-weighting method based on information entropy is used to mitigate the negative effects of incorrect pseudo labels. Experimentally, our method further improves the previous cross pseudo supervision method with competitive performance on PASCAL VOC 2012 dataset under various data partition protocols.
Safe Hierarchical Navigation in Crowded Dynamic Uncertain Environments

This paper describes a hierarchical solution consisting of a multi-phase planner and a low-level safe controller to jointly solve the safe navigation problem in crowded, dynamic, and uncertain environments. The planner employs dynamic gap analysis and trajectory optimization to achieve collision avoidance with respect to the predicted trajectories of dynamic agents within the sensing and planning horizon and with robustness to agent uncertainty. To address uncertainty over the planning horizon and real-time safety, a fast reactive safe set algorithm (SSA) is adopted, which monitors and modifies the unsafe control during trajectory tracking. Compared to other existing methods, our approach offers theoretical guarantees of safety and achieves collision-free navigation with higher probability in uncertain environments, as demonstrated in scenarios with 20 and 50 dynamic agents.
A Sequential Greedy Approach for Training Implicit Deep Models

Recent works in deep learning have demonstrated impressive performance using "implicit deep models," wherein conventional architectures composed of forward-propagating, differentiable parametric layers are replaced by more expressive models composed of an implicitly defined fixed-point equation together with a prediction equation. Methods for training implicit deep models are currently restricted to end-to-end optimization, which relies on solving a matrix-variable fixed-point equation to compute the gradient and an expensive projection step at every iteration. In this work, we extend the idea of greedy layer-wise training, an approach found to yield state-of-the-art performance in conventional deep learning, to a sequential greedy training algorithm for implicit deep models with a strictly upper block triangular structure. We show that such implicit models can be regarded as generalized dense block modules of Dense Convolutional Networks (DenseNets), and thus inherit the underlying parameter efficiency property. For models trained with the Euclidean loss, we develop an alternating minimization subroutine for our sequential optimization algorithm, which consists of alternating between efficiently-solvable least squares problems and single hidden-layer training problems. Furthermore, we theoretically prove that training a non-strictly upper triangular ReLU implicit model is equivalent to training a strictly upper block triangular one, allowing for the application of our algorithm to even more general models. Experiments on smooth and nonsmooth function interpolation, and on MNIST and Fashion-MNIST classification tasks, show that our algorithm consistently converges to models that outperform state-of-the-art end-to-end implicit learning.
Outliers Detection and Repairing Technique for Measurement Data in the Distribution System

In any power network, data perform a very critical role in the operation, management, and regulations of power systems. However, most of the data contain anomalies, which may have an impact on the outcomes of data-driven applications. Therefore, to avoid problems during operations it is very important to detect these outliers and remove them from the data. This research investigates the anomalies cleaning approach for measuring data in the distribution system in order to enhance data quality. This approach includes a set of association rule (AR) that are built automatically using past measuring data. This study demonstrates a data-mining approach based on a mix of density-based spatial clustering of applications with noise (DBSCAN) clustering and auto-generated association rules using historical data. Following that, a novel cost function based on Mahalanobis distance is developed and used for data restoration; this function describes the similarity between different data points. Finally, simulation results show that the suggested model outperforms existing detection and repair strategies. The evaluation section of this research demonstrates that as the number of historical data increases, so does the resilience of the suggested method.
Feature engineering techniques for skeleton-based two-person interaction classification in video

An LSTM-based approach to two-person interaction classification in image sequences, based on human skeleton detection in single frames, is proposed. The main contribution is the design of different preliminary feature extraction techniques from many skeleton data sets. The approach consists of four main stages. First, we employ the OpenPose engine in order to estimate silhouettes of persons in single video frames. Next, the skeleton data sets are tracked over time and particular joints are corrected whenever needed. Then, three different versions of features are defined, based on the stream of refined skeleton joints. In the final stage, a classifier network, based on LSTM, is developed and experimentally studied. The models are trained and evaluated on the interaction subset of the NTU RGB+D data set. The most efficient model achieved a classification accuracy of 94.5% (in the CV mode). This is an excellent performance, comparable with the best reported Graph CNN-based classifiers for this subset.
Convergence Rates of Asynchronous Policy Iteration for Zero-Sum Markov Games under Stochastic and Optimistic Settings

We consider an asynchronous policy iteration method for solving the problem of sequential zero-sum Markov games. This method can be viewed as a recursive iteration for finding the fixed point of a contractive Bellman operator. Our focus in this paper is to derive the convergence rates of this method under two scenarios, namely, stochastic and optimistic settings. In the first scenario, we assume that we only have access to an unbiased estimate of the underlying Bellman operator, resulting in a stochastic variant of the policy iteration method. In the second, we consider the popular optimistic setting of the policy iteration method, where the policy improvement steps are approximately estimated. In both scenarios, we show that the asynchronous policy iteration method converges with a sublinear rate $\mathcal{O}(1/k)$, where k is the number of iterations. We also provide numerical simulations to illustrate our theoretical results.
Reachability Analysis of Neural Feedback Loops using Sparse Polynomial Optimisation

Neural networks have seen a recent increased use in control feedback systems. However, providing robustness guarantees on these feedback systems has proven challenging and to combat these issues, there is a significant amount of current research. One of the biggest shortcoming of neural networks is how sensitive they are to adversarial inputs. Given that feedback systems are usually subject to external perturbations, this issue surrounding neural networks must be overcome before they can be used in safety-critical applications. One method to tackle this problem is to compute outer-approximations of the reachable sets, through bounding the activation functions in the neural network controller. Our approach is to use these bounds in a sparse polynomial optimisation framework in conjunction with the Positivstellensatz. The sparsity property is able to exploit the natural cascading structure of the neural network to allow for tractable solve times. The Positivstellensatz is able to provide more accurate bounds over similar methods by asserting the emptiness of a semi-algebraic set. We show through examples that our method can provide tighter bounds over similar methods, with reasonable computational time. Our approach is also able to deal with non-linear polynomial dynamics due to the polynomial optimisation framework we use, while other methods need to use alternative work-around solutions to incorporate the non-linearities.
Multi-Agent Actor-Critic Multitask Reinforcement Learning based on GTD(1) with Consensus

In this paper, a new distributed multi-agent off-policy Actor-Critic algorithm for collaborative multitask reinforcement learning is proposed. The Critic stage is based on the distributed gradient temporal difference algorithm GTD(1), while the Actor stage is derived from a predefined global criterion function and consists of a complementary consensus-based exact policy gradient algorithm. A proof that the Feller-Markov properties hold for the derived algorithm at the Actor stage is derived. The weak convergence of the algorithm to the set of stationary points of an attached ODE is proved under mild conditions using the two-time-scale stochastic approximation arguments. An experimental verification of the algorithm properties is given, demonstrating its high efficiency and practical applicability.
A new k-NN based Open-Set Recognition method

Traditional pattern classification methods can handle the objects whose categories are contained in the given (known) categories of training data. In open-set scenarios, objects to classify may belong to the ignorant (unknown) classes that is not included in training data set. Open-Set Recognition (OSR) tries to detect these unknown class objects and classify known class objects. In this paper, we propose a simple OSR method based on k-Nearest Neighbors (k-NNs). The test data (objects to classify) is put together with the labeled training data, so that the labeled training instances and the unlabeled objects to classify can appear in the k-NNs of other objects. Then, the probability of object lying in the given classes can be determined according to the k-NNs of this object. If the labeled training data is the majority of k-NNs, this object most likely belongs to one of the given classes, and the distances between the object and its neighbors are taken into account here. Then the objects with high probability are marked with the estimated probability. However, if most of k-NNs are the unlabeled test objects, the class of object cannot be classified in this step because the k-NNs of the object is uncertain. In this paper, the probability of the other uncertain objects belonging to known classes is re-calculated based on the labeled training instances and the objects marked with the estimated probability. Such iteration will not stop until all the probabilities of objects belonging to known classes are not changed. Then, the Otsu's method is employed to obtain the optimal threshold for the final recognition. If the probability of object belonging to known classes is smaller than this threshold, it will be assigned to the unknown class. The other objects will be considered as known classes and then committed to a specific class by a pre-trained classifier. The effectiveness of the proposed method has been validated using some experiments.
Global Ontology Entities Embeddings

Ontologies are among the most widely used types of knowledge representation formalisms. The application of deep learning techniques in the field of ontology engineering has reinforced the need to learn and generate representations of ontological data. This allows ontologies to be exploited by such models, and thus automate various ontology engineering tasks, where most of the existing tools and machine learning approaches require a numerical feature vectors associated with each concept. This paper outlines a novel approach for learning global ontology entities embeddings by exploiting the structure and the various taxonomic and semantic relationships present in ontologies, taking into account all the information present in the ontological graph and carried by the OWL/RDF triples. Thus, producing global ontology entities embeddings capturing the global ontological graph semantics and similarities enclosed in the source ontology. Three different neural network models have been proposed based on two architectures: multi-input and multi-output, trained using the contrastive estimation technique. The evaluation on OWL/RDF ontologies and word semantic similarity tasks using various graph and WordNet based similarity measures, show that our approach yields competitive results outperforming the state-of-the-art ontology and word embedding models.