Reviewerly/exHarmony huggingface dataset:
Learning to Adapt Dynamic Clinical Event Sequences with Residual Mixture of Experts

Clinical event sequences in Electronic Health Records (EHRs) record detailed information about the patient condition and patient care as they occur in time. Recent years have witnessed increased interest of machine learning community in developing machine learning models solving different types of problems defined upon information in EHRs. More recently, neural sequential models, such as RNN and LSTM, became popular and widely applied models for representing patient sequence data and for predicting future events or outcomes based on such data. However, a single neural sequential model may not properly represent complex dynamics of all patients and the differences in their behaviors. In this work, we aim to alleviate this limitation by refining a one-fits-all model using a Mixture-of-Experts (MoE) architecture. The architecture consists of multiple (expert) RNN models covering patient sub-populations and refining the predictions of the base model. That is, instead of training expert RNN models from scratch we define them on the residual signal that attempts to model the differences from the population-wide model. The heterogeneity of various patient sequences is modeled through multiple experts that consist of RNN. Particularly, instead of directly training MoE from scratch, we augment MoE based on the prediction signal from pretrained base GRU model. With this way, the mixture of experts can provide flexible adaptation to the (limited) predictive power of the single base RNN model. We experiment with the newly proposed model on real-world EHRs data and the multivariate clinical event prediction task. We implement RNN using Gated Recurrent Units (GRU). We show 4.1% gain on AUPRC statistics compared to a single RNN prediction.
Variational Autoencoder-Based Hybrid Recommendation With Poisson Factorization for Modeling Implicit Feedback

Hybrid recommendation, which is based on collaborative filtering and supplemented with auxiliary content information, is being actively researched due to its ability to overcome the coldstart problem.Many proposed hybrid methods make recommendations using Gaussian distribution-based collaborative filtering even though they handle variables that tend to be non-Gaussian, such as the number of interactions.We present a method that uses a hybrid recommendation framework based on collaborative filtering that models the number of interactions as a Poisson-distributed and variational autoencoder-based content information generation process that shares latent variables with collaborative filtering.As a prior for the shared latent variables, we use a gamma distribution, which is a conjugate prior of a Poisson distribution.An implicit-derivative-based reparameterization trick enables the use of a gamma distribution in a variational autoencoder.The latent variables in the generative model are inferred using the stochastic gradient variational Bayes approach, taking the number of interactions corresponding to users and items and content information as input.In accordance with the inference, unobserved interactions between users and items are predicted for recommendation.The use of a neural-network-based generative model for content information enables the framework to handle various types of content information.Experimental results show that the proposed method utilizes content information effectively for predicting the number of interactions and that it should aid in overcoming the cold-start problem.
Augmented Reality (AR) and Virtual Reality (VR) as Tools to Empower Vulnerable Communities: Opportunities and Challenges for Designers

In the Anthropocene complexity, information and technology play a transcendental role in how we relate to our identity, context and otherness. The already widespread AR (Augmented Reality) and VR (Virtual Reality) technologies allow to play, teach, train, or educate, presenting also the potentiality to make people empathize with other realities and to envision healthier social scenarios. With the aim to define the benefits of applying AR/VR technologies to design in vulnerable communities, this research analyses several cases studies, underlying their effects on social interactions. Since contemporary projects of applications to vulnerable communities are limited, this work studied cases applied to (1) vulnerable groups (The Machine to Be Another, Carne y Arena, and Autism Empathy Tools) and (2) local communities (Within, Mondly Lenguages VR, Spatial, Alspace VR). An analytical comparison between these two kinds of applications shows how the application of these technologies can bring to benefits in terms of empathy, identity and alterity applied to several fields of life: works, leisure, education and sociality. Nevertheless, the research's results are very clear in underlining how is extremely urgent to define a new narrative to efficiently represent the features of a vulnerable community.
How Distributed are Distributed Representations? An Observation on the Locality of Syntactic Information in Verb Agreement Tasks

This work addresses the question of the localization of syntactic information encoded in the transformers representations.We tackle this question from two perspectives, considering the object-past participle agreement in French, by identifying, first, in which part of the sentence and, second, in which part of the representation syntactic information is encoded.The results of our experiments using probing, causal analysis and feature selection method, show that syntactic information is encoded locally in a way consistent with the French grammar.
An End-to-End Personalized Preference Drift Aware Sequential Recommender System With Optimal Item Utilization

The user preference is dynamic and requires drift detection to capture changes for delivering relevant recommendations.A sequential recommender system with drift detection was proposed, where drift points are indicated by comparing characteristics of consecutive items.The model leverages drift points to retrieve only interactions with preferences relevant to the current user preference.Nonetheless, the number of utilized items is pre-defined and may not be optimal.It is also not a unified architecture that requires optimizing each part individually.Recently, a Content-Based Transformer has been proposed to consider only items with similar characteristics by leveraging a similarity function.Content-Based Transformer is trained in an end-to-end approach and can be applied for the sequential recommendation task, where the drift of the user preference is indicated as the point where the item's group changes.However, Content-Based Transformer provides the item's group as the hard label, ignoring the item characteristics in a realworld scenario where items can exist in many groups.For instance, most movies have multiple genres.This work proposes a unified sequential recommender system that detects the personalized drift pattern of user preference.It groups similar items with soft labels and utilizes the optimal amount of items.The model is trained in an end-to-end approach to jointly optimize for group items with similar characteristics and deliver relevant recommendations.We conducted the experiments to verify the effectiveness of the proposed method by comparing it with Content-Based Transformers and related methods.The evaluation results show that the proposed method consistently outperforms the baselines.
Towards Second-Wave Architectural Ecopedagogies

Buildings are responsible for 38% of all global CO2 emissions: the responsibility of architectural educators to prepare future architects for this reality demands radical action. Considering the tendency of architectural practice and education to pursue an abstractive relationship to the non-human environment, this chapter explores a lineage of three first-wave architectural ecopedagogies: Arcosanti, the Women’s School of Planning and Architecture, and the Centre for Alternative Technology in Wales. It uses these three very different endeavours to speculate about the need for radical responses to the climate emergency and about the challenges that will be faced by emergent second-wave architectural ecopedagogies.
Learning to Predict User-Defined Types

TypeScript is a widely adopted gradual typed language where developers can optionally type variables, functions, parameters and more. Probabilistic type inference approaches with ML (machine learning) work well especially for commonly occurring types such as <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">boolean</monospace> , <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">number</monospace> , and <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">string</monospace> . TypeScript permits a wide range of types including developer defined class names and type interfaces. These developer defined types, termed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">user-defined types</i> , can be written within the realm of language naming conventions. The set of user-defined types is boundless and existing bounded type guessing approaches are an imperfect solution. Existing works either under perform in user-defined types or ignore user-defined types altogether. This work leverages a BERT-style pre-trained model, with multi-task learning objectives, to learn how to type user-defined classes and interfaces. Thus we present <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DiverseTyper</small> , a solution that explores the diverse set of user-defined types by uniquely aligning classes and interfaces declarations to the places in which they are used. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DiverseTyper</small> surpasses all existing works including those that model user-defined types.
Freshness Authentication for Outsourced Multi-Version Key-Value Stores

Data outsourcing is a promising technical paradigm to facilitate cost-effective real-time data storage, processing, and dissemination. In data outsourcing, a data owner proactively pushes a stream of data records to a third-party cloud server for storage, which in turn processes various types of queries from end users on the data owner’s behalf. However, the popular outsourced multi-version key-value stores pose a critical security challenge that a third-party cloud server cannot be fully trusted to return both authentic and fresh data in response to end users’ queries. Although several recent attempts have been made on authenticating data freshness in outsourced key-value stores, they either incur excessively high communication cost or can only offer very limited real-time guarantee. To fill this gap, this article introduces KV-Fresh, a novel freshness authentication scheme for outsourced key-value stores that offers strong real-time guarantee for both point query and range query. KV-Fresh is designed based on a novel data structure, Linked Key Span Merkle Hash Tree, which enables highly efficient freshness proof by embedding chaining relationship among records generated at different time. Extensive simulation studies using a synthetic dataset generated from real data confirm the efficacy and efficiency of KV-Fresh.
Perceptual Image Hashing with Bidirectional Generative Adversarial Networks for Copy Detection

The traditional perceptual hashing algorithm is generally achieved by artificially extracting features from an image and quantizing them into a hash code. However, it is often hard to capture the inherent features of an image in practice. In order to improve the performance of image perceptual hashing, an unsupervised data-driven generative adversarial framework to generate the image perceptual hash code is proposed in this paper. Firstly, the original image is normalized and the encoder network is employed to generate the perceptual hash code; then, the generator network is used to formulate a data distribution as similar as possible to the original image from random noise in the same dimension as the perceptual hash code. Thirdly, the discriminator network is adopted to distinguish the hash code from the noise modified by the generated network and the source of the generated image and the original image. Finally, the encoder can generate image hash codes with good perceptual robustness and recognition accuracy by jointly training of the three networks. Various experiments have been executed on an extensive test database in this paper. The results show that the proposed perceptual image hashing algorithm has stronger robustness than other state-of-the-art schemes.
Information Fusion Based on Score/Weight Classifier Fusion

By applying different classifiers to the classification, multiple classification scores will be generated. Generally, different classifiers enjoy their own advantages and disadvantages, and only a simple classifier usually fails to robustly achieve the classification. To jointly exploit the advantages of different classifiers, it is significant to use the score fusion. This chapter proposes two score fusion methods and applies them to classification. After reading this chapter, people can have preliminary knowledge on score fusion algorithms.
An Underwater Image Color Correction Algorithm Based on Underwater Scene Prior and Residual Network

Color distortion in underwater scenes affect the accuracy of pattern recognition, visual understanding and key feature extraction work of underwater robots. In this paper, we propose a method to correct color distortion in underwater images by exploiting prior knowledge of the underwater scene and combining it with a residual network. A dataset with the color deviation only is created by the proposed method according to the underwater image imaging model. The residual network is adopted to train the dataset with only color shift so that they have the ability to improve color deviation. Training the network only in the presence of color-biased dataset provides the network to be more specialized, extract single features even better and the number of training samples is also reduced. The experimental results demonstrate that the method in this paper can fully preserve the local details of the image with the color correction, leading to good qualitative and quantitative results in comparison with other image restoration and enhancement methods.
Adaptive Viewpoint Feature Enhancement-Based Binocular Stereoscopic Image Saliency Detection

Modeling 3D visual saliency has received great attention due to the development of emerging 3D display technologies. Traditional methods relying on low-level features may not be efficient in interpreting 3D visual content from high-level semantic perspective. Despite numerous efforts dedicated to this area, existing 3D visual saliency detection methods do not necessarily excel in exploring the stereoscopic image saliency driven by the intra-view and inter-view dependencies among left and right views. In this paper, we propose a visual saliency detection method for stereoscopic images grounded on adaptive viewpoint feature enhancement via binocular vision. More specifically, the correlation among left and right views is investigated through a delicately designed binocular stereoscopic saliency feature aggregation module, enabling the generation of more representative saliency features towards binocular vision. Subsequently, to further aggregate the saliency features in multiple scales, we design a progressive attention-based saliency feature pyramid extraction module to effectively integrate the features from top-level to down-level based on the network hierarchy mechanism. The saliency maps are ultimately produced for stereoscopic images by evaluating the obtained saliency features. In addition, we create a stereoscopic image saliency dataset (SIS-3D) that includes 1086 stereoscopic image pairs with various content and their corresponding human eye fixation annotations, aiming to further facilitate the research on visual saliency detection for stereoscopic images. Extensive experiments demonstrate that our proposed method improves CC by an average of 4.02% compared to representative counterparts on the newly built saliency dataset and another publicly available dataset.
The Assessment Pattern Catalog for a Distant Education: The Study of the Classroom Applicability

A crucial part of education is knowledge assessment. In the distant setting it seems even more important. A knowledge assessment provides a lot of important feedbacks, among others, students’ acquired knowledge. The COVID-19 pandemic forced teachers and students online for a few semesters. Quite a few knowledge assessments were implemented online during that period. They provided an extensive collection of recurring practices. Within the research we collected and categorized them into an assessment pattern catalog for distant education. We also validated and rated the catalog using empirical research with a help of practitioners. In this paper, we present the catalog and its definition process, emphasizing the top-rated patterns in each category. However, practice and experience show that some patterns could also be used in a classroom setting. This is why we also investigated the classroom pattern appropriateness. In this paper we present the top-rated patterns in each category that are, according to our survey participants, suitable for classroom use. Since our lives are getting back to normal and the learning process is moving back to the classroom, this paper demonstrates that most patterns within the catalog, although formed for distant education, could also be applied in a classroom knowledge assessment.
Discussion of Novel Filters and Models for Color Space Conversion

In the era of artificial intelligence perceptual algorithms used in state-of-the-art Advanced Driver Assistance Systems (ADAS), algorithm validation is not an easy task. To ensure the highest possible safety level of the solution, the performance of the algorithm must be evaluated under a variety of challenging conditions. To test the algorithms, simulators are used to emulate the virtual environment around the car taking into account road traffic, infrastructure and vehicles dynamics. Sensor models are necessary for virtual testing to provide required data to ADAS algorithms. This article introduces the issue of modeling the color filter spaces that are used in the automotive industry. The images generated by the simulator usually have RGB color. In contrast, the automotive industry uses filters such as RCCC and RYYCy. In this paper, the methods for transforming color space from RGB to RYYCy are discussed. Three novel approaches are introduced to solve this problem: analytical, polynomial, and based on a neural network. Moreover, comparative discussion of the presented solutions is shown and with the set of experiments the conversion accuracy and execution time of each algorithm are compared. In addition, introduced solution were compared with modified models that are presented in the literature.
Rule-Based Collaborative Learning with Heterogeneous Local Learning Models

Collaborative learning such as federated learning enables to train a global prediction model in a distributed way without the need to share the training data. However, most existing schemes adopt deep learning models and require all local models to have the same architecture as the global model, making them unsuitable for applications using resource- and bandwidth-hungry devices. In this paper, we present CloREF, a novel rule-based collaborative learning framework, that allows participating devices to use different local learning models. A rule extraction method is firstly proposed to bridge the heterogeneity of local learning models by approximating their decision boundaries. Then a novel rule fusion and selection mechanism is designed based on evolutionary optimization to integrate the knowledge learned by all local models. Experimental results on a number of synthesized and real-world datasets demonstrate that the rules generated by our rule extraction method can mimic the behaviors of various learning models with high fidelity (>0.95 in most tests), and CloREF gives comparable and sometimes even better AUC compared with the best-performing model trained centrally.
A Civil Aircraft Cockpit Control Device Design Using Mixed Reality Device

Mixed Reality are considered enabling technologies for the Industry 4.0 paradigm. In this paper, the cockpit control device design and evaluation system based on mixed reality technology is established, which is suitable for rapid iteration in the initial stage of civil aircraft design. It can provide aircraft designers with a more efficient and realistic design environment. It also provides a user evaluation environment, allowing pilots to participate in the cockpit design at an early stage of the design. According to the operation task and operation pattern classifying of common control equipment in the cockpit, also determines the control unit from the industrial model to virtual reality model of the conversion process. The conversion process of the control device from industrial model to virtual reality model is determined. The paper also puts forward the design mode of double coordinate system based on design eye reference point and controller panel, which is suitable for the design process of civil aircraft cockpit. For the system, we also conducted a preliminary user research and analysis. The experimental and analysis results are used for the subsequent system design and upgrading. It provides useful guidance for the design of human-computer interaction interface in mixed reality environment.
Evolutionary Algorithm Based Adaptive Load Balancing (EA-ALB) in Cloud Computing Framework

In the present decade, the development of cloud computing framework is witnessed for providing computational resources by dynamic service providing methods.There are many problems in load balancing in cloud, when there is a huge demand for resources.The objective of load balancing is to equilibrate the cloud server computations for avoiding overloading problems.On addressing the issue, this paper develops a new model called Evolutionary Algorithm based Adaptive Load Balancing (EA-ALB) for enhancing the efficacy and user satisfaction of cloud services.Efficient Scheduling Scheme for the virtual machines using machine learning algorithm is proposed in this work.Initially, process of K-means clustering is used for computing optimal min-max rates and then, local search capability for solving the load balancing problems in cloud model is determined with the incorporation of Evolutionary Algorithm.The results show that the proposed model achieves better results in terms of load balancing factors, Virtual Machine (VM) migration, energy consumption and so on, when compared to the existing model.