Miracle-dz/UniFAR-FTUs huggingface dataset:
User-Oriented Objective Prioritization for Meta-Featured Multi-Objective Recommender Systems

Multi-Objective Recommender Systems (MO-RS) consider several objectives to produce useful recommendations. Besides accuracy, other important quality metrics include novelty and diversity of recommended lists of items. Previous research up to this point focused on naive combinations of objectives. In this paper, we present a new and adaptable strategy for prioritizing objectives focused on users' preferences. Our proposed strategy is based on meta-features, i.e., characteristics of the input data that are influential in the final recommendation. We conducted a series of experiments on three real-world datasets, from which we show that: (i) the use of meta-features leads to the improvement of the Pareto solution set in the search process; (ii) the strategy is effective at making choices according to the specificities of the users' preferences; and (iii) our approach outperforms state-of-the-art methods in MO-RS.
A Hierarchical Bayesian Linear Regression Model with Local Features for Stochastic Dynamics Approximation

One of the challenges in model-based control of stochastic dynamical systems is that the state transition dynamics are involved, and it is not easy or efficient to make good-quality predictions of the states. Moreover, there are not many representational models for the majority of autonomous systems, as it is not easy to build a compact model that captures the entire dynamical subtleties and uncertainties. In this work, we present a hierarchical Bayesian linear regression model with local features to learn the dynamics of a micro-robotic system as well as two simpler examples, consisting of a stochastic mass-spring damper and a stochastic double inverted pendulum on a cart. The model is hierarchical since we assume non-stationary priors for the model parameters. These non-stationary priors make the model more flexible by imposing priors on the priors of the model. To solve the maximum likelihood (ML) problem for this hierarchical model, we use the variational expectation maximization (EM) algorithm, and enhance the procedure by introducing hidden target variables. The algorithm yields parsimonious model structures, and consistently provides fast and accurate predictions for all our examples involving large training and test sets. This demonstrates the effectiveness of the method in learning stochastic dynamics, which makes it suitable for future use in a paradigm, such as model-based reinforcement learning, to compute optimal control policies in real time.
CL-SciSumm Shared Task - Team Magma

Finding the cited text spans of a scientific article based on the citation text is a challenging task. In this paper, we present our novel system to identify cited sentence(s) and their residential sections in a reference paper, given a citing text. We define this task as a binary classification problem. We use domain-specific features obtained from ACL terminology. The predictions of the system are generated by a logistic regression classifier, with additional predictions from an Adaboost-decision tree added if the logistic regression predictions do not show sufficient diversity according to a threshold.
Essential Tensor Learning for Multi-View Spectral Clustering

Recently, multi-view clustering attracts much attention, which aims to take advantage of multi-view information to improve the performance of clustering. However, most recent work mainly focuses on the self-representation-based subspace clustering, which is of high computation complexity. In this paper, we focus on the Markov chain-based spectral clustering method and propose a novel essential tensor learning method to explore the high-order correlations for multi-view representation. We first construct a tensor based on multi-view transition probability matrices of the Markov chain. By incorporating the idea from the robust principle component analysis, tensor singular value decomposition (t-SVD)-based tensor nuclear norm is imposed to preserve the low-rank property of the essential tensor, which can well capture the principle information from multiple views. We also employ the tensor rotation operator for this task to better investigate the relationship among views as well as reduce the computation complexity. The proposed method can be efficiently optimized by the alternating direction method of multipliers (ADMM). Extensive experiments on seven real-world datasets corresponding to five different applications show that our method achieves superior performance over other state-of-the-art methods.
Exploring the Impact of Elaborateness and Indirectness on User Satisfaction in a Spoken Dialogue System

We present a study addressing the questions of how varying communication styles of a spoken user interface are perceived by users and whether there exist global preferences in the communication styles elaborateness and indirectness. A total of 60 participants had two conversations each with Amazon's Alexa where Alexa used varying wordings for its output. In a post-survey, the participants had to rate statements to subjectively assess each dialogue as well as indicate which dialogue they preferred. The results show that the system's communication style has a direct influence on the user's satisfaction level as well as the user's perception of the dialogue and imply that the preference in the system's communication style is individual for every person. This emphasises the need for adaptive user interfaces.
Autoregulation of Acetylcholine Release and Micro-Pharmacodynamic Mechanisms at Neuromuscular Junction: Selective Acetylcholinesterase Inhibitors for Therapy of Myasthenic Syndromes

Neuromuscular junctions (NMJs) are directly involved into such indispensable to life processes as respiration and locomotion. However, motor nerve forms only one synaptic contact at each muscle fiber. This unique configuration requires specific properties and constrains to be effective. The very high density of acetylcholine receptors (AChRs) of muscle type in synaptic cleft and an excess of acetylcholine (ACh) released under physiological conditions make this synapse extremely reliable. Nevertheless, under pathological conditions such as myasthenia gravis and congenital myasthenic syndromes, the safety factor can be markedly reduced. Drugs used for short-term symptomatic therapy of these pathological states, cause partial inhibition of cholinesterases (ChEs). These enzymes catalyze the hydrolysis of ACh, thus terminate its action on AChRs. Extension of the lifetime of ACh molecules compensates muscular AChRs abnormalities and, consequently, rescues muscle contractions. In this mini review, we will first outline the functional organization of the NMJ, and then, consider the concept of the safety factor and how it may be changed. This will be followed by a look at autoregulation of ACh release that influences the safety factor of NMJs. Finally, we will consider the morphological features of NMJs as a putative reserve to increase effectiveness of pathological muscle weakness therapy by ChEs inhibitors due to opportunity to use micro-pharmacodynamic mechanisms.
Robust Beamforming Design in a NOMA Cognitive Radio Network Relying on SWIPT

This paper studies a multiple-input single-output non-orthogonal multiple access cognitive radio network relying on simultaneous wireless information and power transfer. A realistic non-linear energy harvesting model is applied and a power splitting architecture is adopted at each secondary user (SU). Since it is difficult to obtain perfect channel state information (CSI) in practice, instead either a bounded or Gaussian CSI error model is considered. Our robust beamforming and power splitting ratio are jointly designed for two problems with different objectives, namely, that of minimizing the transmission power of the cognitive base station and that of maximizing the total harvested energy of the SUs, respectively. The optimization problems are challenging to solve, mainly because of the non-linear structure of the energy harvesting and CSI errors models. We converted them into convex forms by using semi-definite relaxation. For the minimum transmission power problem, we obtain the rank-2 solution under the bounded CSI error model, while for the maximum energy harvesting problem, a two-loop procedure using a 1-D search is proposed. Our simulation results show that the proposed scheme significantly outperforms its traditional orthogonal multiple access counterpart. Furthermore, the performance using the Gaussian CSI error model is generally better than that using the bounded CSI error model.
Risk factors for prolonged hospitalization in patients undergoing laparoscopic adrenalectomy

Introduction Even though laparoscopic adrenalectomy is currently a standard, there are important variations between different centres in short-term treatment results such as length of hospital stay (LOS) or morbidity. Aim To determine the factors affecting LOS in patients after laparoscopic transperitoneal lateral adrenalectomy (LTA). Material and methods The study enrolled 453 patients (173 men and 280 women, mean age 57 years) who underwent LTA between 2009 and 2017. Discharge from hospital after more than median hospital stay was considered as prolonged LOS. We evaluated factors that potentially may influence LOS (primary length of stay after surgery, excluding readmissions). Logistic regression models were used in univariate and corrected multivariate analyses, in order to identify the factors related to prolonged LOS. Results The median LOS after LTA in the studied group was 2 days. One hundred seventy-five (38.5%) patients required prolonged hospitalization. Univariate logistic regression showed that the following factors were related to prolonged LOS: presence of any comorbidity, cardiovascular disease, intraoperative complications, postoperative complications, day of the week of operation (surgery on Thursday or Friday), intraoperative blood loss, need for transfusion, hormonal activity, postoperative drainage, ASA (III–IV) and histological type – pheochromocytoma. Multivariate logistic regression showed that only complications (OR = 3.86; 95% CI: 1.84–8.04), day of the week of operation (Thursday or Friday) (OR = 4.85; 95% CI: 3.04–7.73), need for drainage (OR = 3.63; 95% CI: 1.55–8.52), and histological type – pheochromocytoma (OR = 2.48; 95% CI: 1.35–4.54) prolonged LOS. Conclusions Prolonged length of hospital stay following laparoscopic transperitoneal lateral adrenalectomy is strongly associated with the presence of postoperative complications, day of the week of operation (Thursday or Friday), need for drainage, and histological type – pheochromocytoma.
A Parallel Algorithm for Tracking Dynamic Communities based on Apache Flink

Real world social networks are highly dynamic environments consisting of numerous users and communities, rendering the tracking of their evolution a challenging problem. In this work, we propose a parallel algorithm for tracking dynamic communities between consecutive timeframes of the social network, where communities are represented as undirected graphs. Our method compares the communities based on the widely adopted Jaccard similarity measure and is implemented on top of Apache Flink, a novel framework for parallel and distributed data processing. We evaluate the benefits, in terms of execution time, that parallel processing brings to community tracking on datasets carrying different quantitative characteristics, derived from two popular social media platforms; Twitter and Mathematics Stack Exchange Q&A. Experiments show that our parallel method has the ability to calculate the similarity of communities within seconds, even for large social networks, consisting of more than 600 communities per timeframe.
Exploiting Contextual and External Data for Hotel Recommendation

The recommendation problem in the hotel industry introduces several interesting and unique challenges leading to the insufficiency of classical approaches. Traveling is not a frequent activity and users tend to have multifaceted behaviors affected by their specific context. While context-aware recommender systems are a promising way to address this problem, the context's dimensions do not contribute equally to the decision-making process and users are not equally sensible to all of the dimensions. In this paper, we propose novel context-aware methods for addressing the hotel recommendation problem, taking into account geography, temporality, textual reviews extracted from social media, and the trip's intent. We present the architecture of the system developed in industry, combining the proposed approaches and addressing each user segment differently. Our experiments show the impact of considering contextual data, external data, and user segmentation on improving the quality of recommendation.
Accelerating differential evolution using multiple exponential cauchy mutation

Differential Evolution (DE) is a robust optimization algorithm, but it suffers from the stagnation problem in which individuals may not escape from a local optimum. In this article, we proposed a new Cauchy mutation using multiple exponential recombination, self-adaptive parameter control, and linear failure threshold reduction. The proposed method is a simple yet efficient to mitigate the stagnation problem, and it can improve the convergence speed of DE. Our experimental results show that the proposed method can find more accurate solutions to complicated problems.
Control a robot via internet using a block programming platform for educational purposes

This paper deals with a system consisting of a robot named El Greco that can be programmed via internet and livestreaming from any place, anywhere, to move on a play room as well as perform other functions such as to produce voice in many languages, recognize voice commands, recognize faces, perceive its environment, perform combined movements and provide information by searching the Internet. All these capabilities of the robot can be programmed using a friendly block programming platform that has been developed to be used by students for educational purposes. The system was used by a number of students and the results of two questionnaires before and after the use are reported.
The Current State and Future of CRISPR-Cas9 gRNA Design Tools

Recent years have seen the development of computational tools to assist researchers in performing CRISPR-Cas9 experiment optimally. More specifically, these tools aim to maximize on-target activity (guide efficiency) while also minimizing potential off-target effects (guide specificity) by analyzing the features of the target site. Nonetheless, currently available tools cannot robustly predict experimental success as prediction accuracy depends on the approximations of the underlying model and how closely the experimental setup matches the data the model was trained on. Here, we present an overview of the available computational tools, their current limitations and future considerations. We discuss new trends around personalized health by taking genomic variants into account when predicting target sites as well as discussing other governing factors that can improve prediction accuracy.
Using declarative programming for network data management in smart grids

In the control and management of smart grids, from steady state to real-time, the objective is to handle and to treat any change in the system as fast as possible, with as less resources as possible. In this context, this paper proposes a new language, called Smartlog, designed as declarative programming. Smartlog is developed for distributed computing in real-time and distributed database management. Compared to imperative programming, based on anticipation rather than reaction, the interest is to not ignore the meaning of some data, or to collect and analyze data without interest, and thus loose bandwidth and computational time. Smartlog is designed for operating smart grids, which are defined as abstract structures of large and scalable distributed databases. After its definition, the main features of the Smartlog language are its compactness, its simplicity and its scalability are shown. The language is tested on the application of a frequency and voltage secondary control of an islanded micro-grid in an experimental test-case, using a realtime simulator connected to Raspberry Pis. The characteristics of Smartlog are illustrated thanks to a comparison with an imperative programming implementation of the same regulation.
The Importance of Song Context and Song Order in Automated Music Playlist Generation

The automated generation of music playlists can be naturally regarded as a sequential task, where a recommender system suggests a stream of songs that constitute a listening session. In order to predict the next song in a playlist, some of the playlist models proposed so far consider the current and previous songs in the playlist (i.e., the song context) and possibly the order of the songs in the playlist. We investigate the impact of the song context and the song order on next-song recommendations by conducting dedicated off-line experiments on two datasets of hand-curated music playlists. Firstly, we compare three playlist models, each able to consider a different song context length: a popularity-based model, a song-based Collaborative Filtering (CF) model and a Recurrent-Neural-Network-based model (RNN). We also consider a model that predicts next songs at random as a reference. Secondly, we challenge the RNN model (the only model from the first experiment able to consider the song order) by manipulating the order of songs within playlists. Our results indicate that the song context has a positive impact on the quality of next-song recommendations, even though this effect can be masked by the bias towards very popular songs. Furthermore, in our experiments the song order does not appear as a crucial variable to predict better next-song recommendations.
Performance assessment of multi-objective evolutionary algorithms with the R package ecr

Assessing the performance of stochastic optimization algorithms in the field of multi-objective optimization is of utmost importance. Besides the visual comparison of the obtained approximation sets, more sophisticated methods have been proposed in the last decade, e. g. , a variety of quantitative performance indicators or statistical tests. In this paper, we present tools implemented in the R package ecr, which assist in performing comprehensive and sound comparison and evaluation of multi-objective evolutionary algorithms following recommendations from the literature.
RECURRENT NEURAL NETWORKS WITH FLEXIBLE GATES USING KERNEL ACTIVATION FUNCTIONS

Gated recurrent neural networks have achieved remarkable results in the analysis of sequential data. Inside these networks, gates are used to control the flow of information, allowing to model even very long-term dependencies in the data. In this paper, we investigate whether the original gate equation (a linear projection followed by an element-wise sigmoid) can be improved. In particular, we design a more flexible architecture, with a small number of adaptable parameters, which is able to model a wider range of gating functions than the classical one. To this end, we replace the sigmoid function in the standard gate with a non-parametric formulation extending the recently proposed kernel activation function (KAF), with the addition of a residual skip-connection. A set of experiments on sequential variants of the MNIST dataset shows that the adoption of this novel gate allows to improve accuracy with a negligible cost in terms of computational power and with a large speed-up in the number of training iterations.
Levels of DNA cytosine methylation in the Drosophila genome

Insects provide an accessible system to study the contribution of DNA methylation to complex epigenetic phenotypes created to regulate gene expression, chromatin states, imprinting and dosage compensation. The members of genus Drosophila have been used as a model system to study aspects of biology like development, behaviour and genetics. Despite the popularity of Drosophila melanogaster as a genetic and epigenetic model organism, DNA methylation studies are limited due to low levels of genomic 5-methylcytosine. Our study employs a sensitive liquid chromatography-mass spectrometry (LCMS) based method to quantify the levels of 5-methylcytosine from the genomic DNA in different members of the genus Drosophila. Our results reveal that, despite being phylogenetically related, there is a marked variation in the levels of 5-methylcytosine between the genomes of the members of genus Drosophila. Also, there is a change in the genomic levels of 5-methylcytosine through each life cycle stage of holometabolous development in D. melanogaster.
Photos Don't Have Me, But How Do You Know Me?: Analyzing and Predicting Users on Instagram

This paper presents a comprehensive analysis on social media use and engagement by age and gender on Instagram. We define five user age groups (from 10s to 50s) and two user gender groups (males and females), and compare them based on three aspects: activity, image object, and tag. We especially excluded the information that indicates human (e.g., selfies, faces, body) for each aspect in order to examine whether users are still identifiable without that information. Our study results indicate that each user group exhibits unique characteristics and the features from each aspect can be used to develop user classification models (82% for gender and 43% for age classification) without relying on the information that specifically indicates age and gender.
Towards a Fuzzy Rule-based Systems Approach for Adaptive Interventions in Menopause Self-care

We present a novel approach in the design of self-care applications aimed at women in pre-menopause and menopause, which is based on the use fuzzy rule-based systems (FRBS) for the description of women's health status, behavior, and personality traits, as well as the provision of adaptive interventions. Our main goal is to develop a personalized solution that informs users and promotes healthy behavioral changes in them, as many women are not conscious of the health consequences caused by menopause. To this end, we follow just-in-time adaptive interventions (JITAI), a design framework for the rule-based provision of personalized and on-demand interventions through mobile applications, according to user's health status and behavior. However, these health-related concepts are usually vague and difficult to formalize in a well-constrained manner. Therefore, to overcome these issues, our approach seeks to leverage the knowledge of health professional experts and the capacity of fuzzy logic modelling and inference to formally define such vague concepts and rules. Although we discuss this approach in the context of menopause self-care, in which prior user data may be insufficient for alternative techniques, it could also be applied in other self-care applications facing similar challenges.