Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Content-based Recommender Systems Exploiting Recurrent Neural Networks and Linked Open Data

In this paper we present a deep content-based recommender system (DeepCBRS) that exploits Bidirectional Recurrent Neural Networks (BRNNs) to learn an effective representation of the items to be recommended based on their textual description. Next, such a representation is extended by introducing structured features extracted from the Linked Open Data (LOD) cloud, as the genre of a book, the director of a movie, in order to enrich the available content with new and very descriptive information. In the experimental session we evaluate the effectiveness of our approach in a top-N recommendation scenario: first, we compare the representation based on BRNNs to that obtained through different deep learning techniques. Next, we demonstrate that the exploitation of features gathered from the LOD cloud improves the overall accuracy of our DeepCBRS.
Impact of aging on diaphragm muscle function in male and female Fischer 344 rats

The diaphragm muscle (DIAm) is the primary inspiratory muscle in mammals and is active during ventilatory behaviors, but it is also involved in higher‐force behaviors such as those necessary for clearing the airway. Our laboratory has previously reported DIAm sarcopenia in rats and mice characterized by DIAm atrophy and a reduction in maximum specific force at 24 months of age. In Fischer 344 rats, these studies were limited to male animals, although in other studies, we noted a more rapid increase in body mass from 6 to 24 months of age in females (~140%) compared to males (~110%). This difference in body weight gain suggests a possible sex difference in the manifestation of sarcopenia. In mice, we previously measured transdiaphragmatic pressure (Pdi) to evaluate in vivo DIAm force generation across a range of motor behaviors, but found no evidence of sex‐related differences. The purpose of this study in Fischer 344 rats was to evaluate if there are sex‐related differences in DIAm sarcopenia, and if such differences translate to a functional impact on Pdi generation across motor behaviors and maximal Pdi (Pdimax) elicited by bilateral phrenic nerve stimulation. In both males and females, DIAm sarcopenia was apparent in 24‐month‐old rats with a ~30% reduction in both maximum specific force and the cross‐sectional area of type IIx and/or IIb fibers. Importantly, in both males and females, Pdi generated during ventilatory behaviors was unimpaired by sarcopenia, even during more forceful ventilatory efforts induced via airway occlusion. Although ventilatory behaviors were preserved with aging, there was a ~20% reduction in Pdimax, which likely impairs the ability of the DIAm to generate higher‐force expulsive airway clearance behaviors necessary to maintain airway patency.
Infections in Myelodysplastic Syndrome in Relation to Stage and Therapy

Infections remain a significant problem in myelodysplastic syndromes (MDS) in treated as well in non-treated patients and assume a particular complexity. The susceptibility to infections is due, in the absence of intensive chemotherapies, mainly to functional defects in the myeloid lineage with or without neutropenia. Furthermore, MDS includes a heterogeneous group of patients with very different prognosis, therapy and risk factors regarding survival and infections. You should distinguish risk factors related to the disease, like as neutrophils function impairment, neutropenia, unfavorable cytogenetics and bone marrow insufficiency; factors related to the patient, like as age and comorbidities, and factors related to the therapy. When the patients with MDS are submitted to intensive chemotherapy with and without hematopoietic stem cell transplantation (HSCT), they have a risk factor for infection very similar to that of patients with acute myeloid leukemia (AML), and mostly related to neutropenia. Patients with MDS treated with supportive therapy only or with demethylating agent or lenalidomide or immunosuppressive drugs should have a tailored approach. Most of the infections in MDS originate from bacteria, and the main risk factors are represented by neutropenia, thrombocytopenia, and unfavorable cytogenetics. Thus, it is reasonable to give antibacterial prophylaxis to patients who start the therapy with demethylating agents with a number of neutrophils <500 × 109/L, or with thrombocytopenia and unfavorable cytogenetics. The antifungal prophylaxis is not considered cost/benefit adequate and should be taken into consideration only when there is an antecedent fungal infection or presence of filamentous fungi in the surveillance cultures. Subjects submitted to immunosuppression with ATG+CSA have a high rate of infections, and when severely neutropenic should ideally be nursed in isolation, should be given prophylactic antibiotics and antifungals, regular mouth care including an antiseptic mouthwash.
Framework for integration of domain knowledge into logistic regression

Traditionally, machine learning extracts knowledge solely based on data. However, huge volume of knowledge is available in other sources which can be included into machine learning models. Still, domain knowledge is rarely used in machine learning. We propose a framework that integrates domain knowledge in form of hierarchies into machine learning models, namely logistic regression. Integration of the hierarchies is done by using stacking (stacked generalization). We show that the proposed framework yields better results compared to standard logistic regression model. The framework is tested on the binary classification problem for predicting 30-days hospital readmission. Results suggest that the proposed framework improves AUC (area under the curve) compared to logistic regression models unaware of domain knowledge by 9% on average.
Reusing Search Data in Ranking and Selection

It is tempting to reuse replications taken during a simulation optimization search as input to a ranking-and-selection procedure. However, even when the random inputs used to generate replications are identically distributed and independent within and across systems, we show that for searches that use the observed performance of explored systems to identify new systems, the replications are conditionally dependent given the sequence of returned systems. Through simulation experiments, we demonstrate that reusing the replications taken during search in selection and subset-selection procedures can result in probabilities of correct and good selection well below the guaranteed levels. Based on these negative findings, we call into question the guarantees of established ranking-and-selection procedures that reuse search data. We also rigorously define guarantees for ranking-and-selection procedures after search and discuss how procedures that only provide guarantees in the preference zone are ill-suited to this setting.
Non-invasive evaluation of cardiac index by impedance cardiography in patients undergoing percutaneous nephrolithotomy

Introduction Percutaneous nephrolithotomy (PNL) is an endoscopic treatment of renal lithiasis. It is usually two-staged: it begins in the lithotomy position for ureteral catheter placement and retrograde pyelography, and subsequently an optimal renal access is obtained in the prone position. Some patients under epidural anesthesia do not tolerate the prone position and the PNL cannot be continued. This may be related to changes occurring within the circulatory system. The aim of this prospective randomized double-blind study was to evaluate the changes of the cardiac index (CI) during PNL. Material and methods In a group of 50 patients, with ASA physical status grade 1–2, epidural anesthesia with either 0.2% ropivacaine or 0.25% bupivacaine was performed and then the CI was evaluated by impedance cardiography. Results Forty-five patients were included in the analysis; all tolerated the PNL well. After turning prone, a decrease in the CI was always recorded, a maximum after 10–15 min – 22.58 ±11.47%. There was significant variability of recorded values. The average CI dropped from 2.96 ±0.42 l/min/m2 to 2.28 ±0.39 l/min/m2. In 7 patients the decrease in the CI was greater than 35%. No correlation was observed with the arterial blood pressure or the heart rate. The decrease in the CI occurred irrespective of the type of local anesthetic used (p = 0.91). Conclusions A decrease in the CI was observed in every case, and it should be taken into consideration during qualification for PNL in the prone position.
Classifier generalization for comprehensive classifiers subsumption in XCS

We proposed XCS-VRc3 that can extract useful rules (classifiers) from data and verify its effectiveness. The difficulty of mining real world data is that not only the type of the input state but also the number of instances varies. Although conventional method XCS-VRc is able to extract classifiers, the generalization of classifiers was insufficient and lack of human readability. The proposed XCS-VRc3 incorporating "generalization mechanism by comprehensive classifier subsumption" to solves this problem. Specifically, (1) All classifiers of the matching set subsume other classifiers, (2) Abolition of the inappropriate classifier deletion introduced by XCS-VRc (3) Preferentially select classifier with small variance of output in genetic algorithm. To verify the effectiveness of XCS-VRc3, we applied on care plan planning problem in a nursing home (in this case, identifying daytime behavior contributing to increase the ratio of deep sleep time). Comparing the association rules obtained by Apriori, and classifiers obtained by XCS-VRc3, the followings was found. First, abolishing the inappropriate classifier deletion and comprehensively subsuming promotes various degrees of generalization. Second, parent selection mechanism can obtain classifiers with small output variance. Finally, XCS-VRc3 is able to extract a small number classifiers equivalent to large number of rules found in Apriori.
Reflection Companion

Mobile, wearable and other connected devices allow people to collect and explore large amounts of data about their own activities, behavior, and well-being. Yet, learning from-, and acting upon such data remain a challenge. The process of reflection has been identified as a key component of such learning. However, most tools do not explicitly design for reflection, carrying an implicit assumption that providing access to self-tracking data is sufficient. In this paper, we present Reflection Companion, a mobile conversational system that supports engaging reflection on personal sensed data, specifically physical activity data collected with fitness trackers. Reflection Companion delivers daily adaptive mini-dialogues and graphs to users' mobile phones to promote reflection. To generate our system's mini dialogues, we conducted a set of workshops with fitness trackers users, producing a diverse corpus of 275 reflection questions synthesized into a set of 25 reflection mini dialogues. In a 2-week field deployment with 33 active Fitbit users, we examined our system's ability to engage users in reflection through dialog. Results suggest that the mini-dialogues were successful in triggering reflection and that this reflection led to increased motivation, empowerment, and adoption of new behaviors. As a strong indicator of our system's value, 16 of the 33 participants elected to continue using the system for two additional weeks without compensation. We present our findings and describe implications for the design of technology-supported dialog systems for reflection on data.
Bridging the Gap Between User-centric and Offline Evaluation of Personalized Recommendation Systems

In this paper, we propose to evaluate recommender systems by conducting both offline and user-centric evaluations, while considering multiple quality aspects in realistic settings. This comprehensive evaluation would provide insight on how to improve the algorithms, and how to design better evaluation metrics, particularly for offline settings where it is cheaper to conduct evaluations. We present the preliminary offline evaluation results of several algorithms, using accuracy, novelty, and coverage metrics while considering the impact of dataset density. We propose to complement this offline evaluation with a user-centric evaluation that measures the users' perceived quality of the same algorithms.
Identification of a tyrosine switch in copper-haem nitrite reductases

Structural characterization of the copper-haem nitrite reductase (haem CuNiR) from Ralstonia pickettii (RpNiR) revealed a tyrosine residue blocking the substrate-entry channel and binding site. The treatment of crystals with NO triggers a movement of tyrosine that allows NO- and NO2 −-bound species to be captured to provide the first information on ligand-bound species in this class of nitrite reductases. The use of tyrosine as a switch in activating the redox enzyme RpNiR may have wider significance as this tyrosine is found to be totally conserved in all known haem CuNiRs.
PathGraph: querying and exploring big data graphs

With the widespread diffusion of social networks and the dawn of data-intensive scientific applications, graphs became one of the foundations for modern data management applications. A key role in graph querying and analysis is played by Regular Path Queries, their extensions, and, in particular, GXPath. In this demo we will present PathGraph, a distributed GXPath query processor, and its web-based graphical interface.
Detection of Negation in the Serbian Language

The number of documents written in the Serbian language is constantly increasing, hence the need for the computer processing of the same is increasingly relevant. It is impossible to process natural language without processing the individual phenomena which occur in the syntax of a language. One such phenomenon is negation, and its processing contributes to the improvement of the quality of document processing. In this paper, we present an algorithm for the detection of negation and the extent of negation that includes specific syntax rules. It is shown that at least 7.59% of the classical approach to negation processing and detection of its scope has been improved. We applied the proposed method for negation processing on the sentiment analysis of tweets and an improvement in the accuracy of sentiment determination of 9.66% was obtained.
Are You Reaching Your Audience?: Exploring Item Exposure over Consumer Segments in Recommender Systems

Many state-of-the-art recommender systems are known to suffer from popularity bias, which means that they have a tendency to recommend items that are already popular, making those items even more popular. This results in the item catalogue being not fully utilised, which is far from ideal from the business' perspective. Issues of item exposure are actually more complex than simply overexposure of popular items. In this paper we look at the exposure of individual items to different groups of consumers, the item's audience, and address the question of whether recommender systems reach each item's potential audience. Thus, we go beyond state-of-the-art analyses that have simply addressed the extent to which items are recommended, regardless of whether they are reaching their target audience. We conduct an empirical study on the MovieLens 20M dataset showing that recommender systems do not fully utilise items' audiences, and existing sales diversity optimisers do not improve their exposure.
Understanding Privacy Dichotomy in Twitter

Balancing personalization and privacy is one of the challenges marketers commonly face. The privacy dilemmas associated with personalized services are particularly concerning in the context of social networking websites, wherein the privacy dichotomy problem is widely observed. To prevent potential privacy violations, businesses need to employ multiple safeguards beyond the current privacy settings of users. As a possible solution, companies can utilize user social footprints to detect user privacy preferences. To take a step towards this goal, we first ran a series of experiments to examine if the privacy preference attribute is homophilous in social media. As a result, we found a set of clues that users' privacy preferences are similar to the privacy behaviour of their social contacts, signaling that privacy homophily exists in social networks. We further studied users located in different neighbourhoods with varying degrees of privacy and found a set of characteristics that are specific to public users located in private neighbourhoods. These identified features can be used in a predictive model to identify public user accounts that are intended to be private, supporting companies to make an informed decision whether or not to exploit one's publicly available data for personalization purposes.
Using a one-class compound classifier to detect in-vehicle network attacks

The Controller Area Network (CAN) in vehicles provides serial communication between electronic control units that manage engine, transmission, steering and braking. Researchers have recently demonstrated the vulnerability of the network to cyber-attacks which can manipulate the operation of the vehicle and compromise its safety. Some proposals for CAN intrusion detection systems, that identify attacks by detecting packet anomalies, have drawn on one-class classification, whereby the system builds a decision surface based on a large number of normal instances. The one-class approach is discussed in this paper, together with initial results and observations from implementing a classifier new to this field. The Compound Classier has been used in image processing and medical analysis, and holds advantages that could be relevant to CAN intrusion detection.
When deep learning meets security

Deep learning is an emerging research field that has proven its effectiveness towards deploying more efficient intelligent systems. Security, on the other hand, is one of the most essential issues in modern communication systems. Recently many papers have shown that using deep learning models can achieve promising results when applied to the security domain. In this work, we provide an overview for the recent studies that apply deep learning techniques to the field of security.
Endoscopic drainage combined with percutaneous drainage in treatment of walled-off pancreatic necrosis – a single-center experience

Introduction In last three decades we have been observing development of minimally invasive walled-off pancreatic necrosis (WOPN) treatment techniques. The choice of access to the necrosis and technique of treatment depends not only on the position and spread of necrosis, but in the first place on the experience of the medical center. Aim To assess the effectiveness and safety of combined endoscopic and percutaneous drainage of WOPN. Material and methods We performed a retrospective analysis of 64 consecutive patients with symptomatic WOPN, who underwent endoscopic treatment in our department between 2011 and 2013. Results Additional percutaneous drainage was executed during endoscopic treatment in 20/64 (31.25%) patients. Complications of treatment occurred in 4/20 (20%) patients. Complications of treatment occurred in 4/20 (20%) patients. All these complications were related to endoscopic treatment. No complications related to percutaneous drainage were noted. There were no deaths. Therapeutic success was achieved in all 20 patients. No patients required surgery. The average time of endoscopic drainage was 41.4 (11–173) days. The mean number of endoscopic procedures was 4.2 (2–12). The average time of percutaneous drainage was 11.3 (5–20) days. The medium time of follow-up was 54 (48–64) months. During the observation the recurrence of WOPN was noted in 2/20 (10%) patients. Long-term success of combined drainage was achieved in 18/20 (90%) patients. Conclusions In selected patients with symptomatic WOPN combined endoscopic and percutaneous drainage enables a high success rate with a low procedure-related complication rate.
The Presynaptic Protein Mover Is Differentially Expressed Across Brain Areas and Synapse Types

The assembly and function of presynaptic nerve terminals relies on evolutionarily conserved proteins. A small number of presynaptic proteins occurs only in vertebrates. These proteins may add specialized functions to certain synapses, thus increasing synaptic heterogeneity. Here, we show that the vertebrate-specific synaptic vesicle (SV) protein mover is differentially distributed in the forebrain and cerebellum of the adult mouse. Using a quantitative immunofluorescence approach, we compare the expression of mover to the expression of the general SV marker synaptophysin in 16 brain areas. We find that mover is particularly abundant in the septal nuclei (SNu), ventral pallidum (VPa), amygdala and hippocampus. Within the hippocampus, mover is predominantly associated with excitatory synapses. Its levels are low in layers that receive afferent input from the entorhinal cortex, and high in layers harboring intra-hippocampal circuits. In contrast, mover levels are high in all nuclei of the amygdala, and mover is associated with inhibitory synapses in the medioposterior amygdala. Our data reveal a striking heterogeneity in the abundance of mover on three levels, i.e., between brain areas, within individual brain areas and between synapse types. This distribution suggests a role for mover in providing specialization to subsets of synapses, thereby contributing to the functional diversity of brain areas.
Draft Genome Sequence of Pectobacterium atrosepticum PB72 and Complete Genome Sequence of the Specific Bacteriophage PP90

We present the draft genome sequence of Pectobacterium atrosepticum strain PB72 infecting potatoes in Russia. PB72 is similar to the previously reported strain 21A. ABSTRACT We present the draft genome sequence of Pectobacterium atrosepticum strain PB72 infecting potatoes in Russia. PB72 is similar to the previously reported strain 21A. Considering potential biocontrol of this pathogen, an infectious bacteriophage was isolated and characterized. Phage vB_PatP_PP90 is a lytic podovirus of narrow host range belonging to the KP34virus genus.
SceneEDNet: A Deep Learning Approach for Scene Flow Estimation

Estimating scene flow in RGB-D videos is attracting much interest of the computer vision researchers, due to its potential applications in robotics. The state-of-the-art techniques for scene flow estimation typically rely on the knowledge of scene structure of the frame and the correspondence between frames. However, with the availability of large RGB-D data captured from depth sensors, learning representations for estimation of scene flow has become possible. This paper introduces a first effort to apply a deep learning method for direct estimation of scene flow by presenting a fully convolutional neural network with an encoder-decoder (ED) architecture. The proposed network SceneEDNet involves estimation of three dimensional motion vectors of all the scene points from sequence of stereo images. The training for direct estimation of scene flow is done using consecutive pairs of stereo images and corresponding scene flow ground truth.