Miracle-dz/UniFAR-FTUs huggingface dataset:
Time and Space Efficient Large-Scale Link Discovery using String Similarities

This paper proposes and evaluates time and space efficient methods for discovering links between matching entities in large data sets, using state of the art methods for measuring edit distance as a string similarity metric. The paper proposes and compares three filtering methods that build on a basic blocking technique to organize the target dataset, facilitating efficient pruning of dissimilar pairs. The proposed filtering methods are compared in terms of runtime and memory usage: The first method exploits the blocking structure using the triangle inequality in conjunction to the substring-matching criterion. The second method uses only the substring-matching criterion, while the third method uses the substring-matching criterion in conjunction to the frequency-matching criterion. Evaluation results show the pruning power of the different criteria used, also in comparison to the string matching functionality provided in LIMES and SILK, which are state-of-the-art tools for large-scale link discovery.
Parallax Bundle Adjustment on Manifold with Convexified Initialization

Bundle adjustment (BA) with parallax angle based feature parameterization has been shown to have superior performance over BA using inverse depth or XYZ feature forms. In this paper, we propose an improved version of the parallax BA algorithm (PMBA) by extending it to the manifold domain along with observation-ray based objective function. With this modification, the problem formulation faithfully mimics the projective nature in a camera's image formation, BA is able to achieve better convergence, accuracy and robustness. This is particularly useful in handling diverse outdoor environments and collinear motion modes. Capitalizing on these properties, we further propose a pose-graph simplification to PMBA, with significant dimensionality reduction. This pose-graph model is convex in nature, easy to solve and its solution can serve as a good initial guess to the original BA problem which is intrinsically non-convex. We provide theoretical proof that our global initialization strategy can guarantee a near-optimal solution. Using a series of experiments involving diverse environmental conditions and motions, we demonstrate PMBA's superior convergence performance in comparison to other BA methods. We also show that, without incremental initialization or via third-party information, our global initialization process helps to bootstrap the full BA successfully in various scenarios, sequential or out-of-order, including some datasets from the "Bundle Adjustment in the Large" database.
Comparative cytogenetics and derived phylogenic relationship among Sitophilus grain weevils (Coleoptera, Curculionidae, Dryophthorinae)

Abstract Cytogenetic characteristics and genome size are powerful tools for species characterization and identification of cryptic species, providing critical insights into phylogenetic and evolutionary relationships. Sitophilus Linnaeus, 1758 grain weevils can benefit from such tools as key pest species of stored products and also as sources of archeological information on human history and past urban environments. Moreover, the phylogenetic relationship among these weevil species remains controversial and is largely based on single DNA fragment analyses. Therefore, cytogenetic analyses and genome size determinations were performed for four Sitophilus grain weevil species, namely the granary weevil Sitophilus granarius (Linnaeus, 1758), the tamarind weevil S. linearis (Herbst, 1797), the rice weevil S. oryzae (Linnaeus, 1763), and the maize weevil S. zeamais Motschulsky, 1855. Both maize and rice weevils exhibited the same chromosome number (2n=22; 10 A + Xyp). In contrast, the granary and tamarind weevils exhibited higher chromosome number (2n=24; 11 A + Xyp and 11 A + neo-XY, respectively). The nuclear DNA content of these species was not proportionally related to either chromosome number or heterochromatin amount. Maize and rice weevils exhibited similar and larger genome sizes (0.730±0.003 pg and 0.786±0.003 pg, respectively), followed by the granary weevil (0.553±0.003 pg), and the tamarind weevil (0.440±0.001 pg). Parsimony phylogenetic analysis of the insect karyotypes indicate that S. zeamais and S. oryzae were phylogenetically closer than S. granarius and S. linearis, which were more closely related and share a more recent ancestral relationship.
Deep Reference Mining From Scholarly Literature in the Arts and Humanities

We consider the task of reference mining: the detection, extraction and classification of references within the full text of scholarly publications. Reference mining brings forward specific challenges, such as the need to capture the morphology of highly abbreviated words and the dependence among the elements of a reference, both following codified reference styles. This task is particularly difficult, and little explored, with respect to the literature in the arts and humanities, where references are mostly given in footnotes. We apply a deep learning architecture for reference mining from the full text of scholarly publications. We explore and discuss three architectural components: word and character-level word embeddings, different prediction layers (Softmax and Conditional Random Fields) and multi-task over single-task learning. Our best model uses both pre-trained word embeddings and characters embeddings, and a BiLSTM-CRF architecture. We test our solution on a dataset of annotated references from the historiography on Venice and, using a linear-chain CRF classifier as a baseline, we show that this deep learning architecture improves by a considerable margin. Furthermore, multi-task learning performs almost on par with a single-task approach. We thus confirm that there are important gains to be had by adopting deep learning for the task of reference mining.
Analyzing effects of various trust in product recalls using a social simulation with a co-evolution model

To improve product recall systems, we studied social simulation using a multi-agent system with a co-evolution model. This research is important because empirical approaches are no longer adequate for complex and diverse modern societies. Results of a simulation experiment have revealed the possibility that improving consumer trust in product recall actions is useful for producers and makes it possible to sell expensive products. We believe this work can contribute to support of government staff for improving product recall systems and to support of executive officers of product companies deliberating about recall decision strategies.
Monitoring Immune Checkpoint Regulators as Predictive Biomarkers in Hepatocellular Carcinoma

The global burden of hepatocellular carcinoma (HCC), one of the frequent causes of cancer-related deaths worldwide, is rapidly increasing partly due to the limited treatment options available for this disease and recurrence due to therapy resistance. Immune checkpoint inhibitors that are proved to be beneficial in the treatment of advanced melanoma and other cancer types are currently in clinical trials in HCC. These ongoing trials are testing the efficacy and safety of a few select checkpoints in HCC. Similar to observations in other cancers, these immune checkpoint blockade treatments as monotherapy may benefit only a fraction of HCC patients. Studies that assess the prevalence and distribution of other immune checkpoints/modulatory molecules in HCC have been limited. Moreover, robust predictors to identify which HCC patients will respond to immunotherapy are currently lacking. The objective of this study is to perform a comprehensive evaluation on different immune modulators as predictive biomarkers to monitor HCC patients at high risk for poor prognosis. We screened publically available HCC patient databases for the expression of previously well described immune checkpoint regulators and evaluated the usefulness of these immune modulators to predict high risk, patient overall survival and recurrence. We also identified the immune modulators that synergized with known immune evasion molecules programmed death receptor ligand-1 (PD-L1), programmed cell death protein-1 (PD-1), and cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) and correlated with worse patient outcomes. We evaluated the association between the expression of epithelial-to-mesenchymal transition (EMT) markers and PD-L1 in HCC patient tumors. We also examined the relationship of tumor mutational burden with HCC patient survival. Notably, expression of immune modulators B7-H4, PD-L2, TIM-3, and VISTA were independently associated with worse prognosis, while B7-H4, CD73, and VISTA predicted low recurrence-free survival. Moreover, the prognosis of patients expressing high PD-L1 with high B7-H4, TIM-3, VISTA, CD73, and PD-L2 expression was significantly worse. Interestingly, PD-L1 expression in HCC patients in the high-risk group was closely associated with EMT marker expression and prognosticates poor survival. In HCC patients, high tumor mutational burden (TMB) predicted worse patient outcomes than those with low TMB.
Modeling and Predicting News Consumption on Twitter

While much is known about how people tweet and interact on Twitter, surprisingly little is known about how the news items tweeted by journalists -- news tweets -- act as a distribution channel for the news that is spread by social media reading and sharing. This paper aims to fill this gap by analyzing the dynamics of news on Twitter, by revealing what drives users to consume news, and by developing a news consumption prediction model. We present the Twitter News Model (TNM), a computational data-driven approach to elucidate the dynamics of news consumption on Twitter. We apply the TNM to a dataset of interactions between users and journalists/newspapers to reveal what drives users' consumption of news on Twitter, and predictively relate users' news beliefs, motivations, and attitudes to their consumption of news. Our findings reveal that news motivations, followed by news attitudes and news beliefs, impact users' behavior of news consumption on Twitter.
Controlling Spotify Recommendations: Effects of Personal Characteristics on Music Recommender User Interfaces

The "black box'' nature of today's recommender systems raises a number of challenges for users, including a lack of trust and limited user control. Providing more user control is interesting to enable end-users to help steer the recommendation process with additional input and feedback. However, different users may have different preferences with regard to such control. To the best of our knowledge, no research has investigated the effect of personal characteristics on visual control techniques in the music recommendation domain. In this paper, we present results of a user study on the web using two different visualisation techniques (a radar chart and sliders) that allows users to control Spotify recommendations. A within-subject design withLatin Square counterbalancing measures was used for the study. Results indicate that the radar chart helped the participants discover a significantly higher number of new songs compared to the sliders. We also found that users' experience with Spotify had an influence on their interaction with different musical attributes. The participants who used Spotify frequently and users with a high individual musical sophistication interacted with the attributes significantly more with the radar chart compared to the sliders. Individual musical sophistication also had a significant impact on their interaction with the interaction techniques. The participants with high musical sophistication interacted significantly more with the radar chart in comparison to the sliders. Based on the feedback from our participants, we provide design suggestions to further improve user control in music recommendation.
Using genetic algorithms based on neighbor list mechanism to reduce handover latency for IEEE 802.11 WLAN

In IEEE 802.11 Wireless Local Area Network (WLAN), the mobile stations (STAs) have to perform handover to keep network connections when they move out of the range of an access point (AP). However, the STAs collect the information of surrounding APs by channel scanning, which will cause high handover latency and degrade the quality of mobility. Therefore, minimizing the scanning delay is key to enabling seamless communications over WLAN. In this paper, we propose a genetic algorithm (GA) algorithm combined with neighbor list mechanism to reduce handover latency. Using this proposed approach, the handover latency is minimized by reducing both the number of scanned channels and the waiting time for each channel. Simulation results demonstrate that our approach improves throughput up to 9.5% and reduces handover delay up to 74% compared to standard IEEE 802.11.
A strategic framework for identifying the critical factors of 4G technology diffusion in I.R. Iran - A Fuzzy DEMATEL approach

As the most prominent representative of 4G, Long term evolution (LTE) technology has become a focal point for mobile network operators all over the world. However, although Iranian main operators like MCI and Irancell have hugely invested on deployment of this technology, its diffusion has been very slow with a penetration rate of 0.06 at the end of spring 2017. Nevertheless, if this rate doesn't increase, it will yield some negative unintended consequences for telecom operators such as (I) Failure to provide a large number of high quality services (II) Inability to compete with OTT technologies (III) Loss of many revenue opportunities (IV) Prolongation of payback period and (V) The lack of technological integrability with fifth generation networks (5G) and loss of many IOT opportunities. Through discussing the literature of technology adoption and diffusion both generally and specifically, identifying the major limitations of these studies and establishing a comprehensive factor set based on four major groups of (I) mobile handset and operators-related factors (II) subscribers-related biological factors, (III) subscribers-related perceptual factors and (IV) subscribers-related contextual factors, a novel fuzzy DEMATEL model has been developed by which all ICT policy makers can not only get a clear knowledge of factors influencing technology adoption but also know the critical success factors (CSFs) influencing Iranians' mindsets towards LTE adoption. Therefore, they can make effective and actionable policies to scale up LTE diffusion or other ICT-related technologies throughout the society.
Prefrontal BDNF Levels After Anodal Epidural Direct Current Stimulation in Rats

This study measured levels of brain-derived neurotrophic factor (BDNF) in the prefrontal cortex (PFC) after single (S) and repetitive (R) anodal epidural DC stimulation (eDCS) over the left medial prefrontal cortex (mPFC). Male Wistar rats (n = 4 per group) received single application of sham (S-sham) or anodal eDCS (S-eDCS) (400 μA for 11 min) and had their PFC removed 15, 30, or 60 min later. For repetitive brain stimulation, rats received sham (R-sham) or anodal eDCS (R-eDCS) once a day, five consecutive days, and their PFC were removed 24 h after the last application. BDNF isoforms levels were measured by Western blot assays. It was observed that animals receiving S-eDCS showed smaller (p < 0.01) levels of BDNF 15 min after stimulation when compared to S-sham, especially in its mature form (mBDNF p < 0.001). Levels of BDNF, including mBDNF, were almost like the S-sham at 30 and 60 min intervals after stimulation, but not proBDNF, which was significantly smaller (p < 0.05) than S-sham at these intervals. After five sessions, BDNF levels were higher in the PFC of R-eDCS animals, notably the proBDNF (p < 0.01) when compared to R-sham. This study showed that levels of BDNF in the PFC, especially the proBDNF, were lower after a single and higher after repetitive anodal eDCS applied over the left mPFC when compared to sham. Therefore, changes of prefrontal BDNF levels may disclose molecular changes underlying the plasticity induced by cortical anodal DC stimulation, which may be opposite if applied in single or multiple sessions.
MuscleIO

Receiving and reacting to notifications on mobile devices can be cumbersome. We propose MuscleIO, the use of electrical muscle stimulation (EMS) for notification output and electromyography (EMG) for reacting to notifications. Our approach provides a one-handed, eyes-free, and low-effort way of dealing with notifications. We built a prototype that interleaves muscle input and muscle output signals using the same electrodes. EMS and EMG alternate such that the EMG input signal is measured in the gaps of the EMS output signal, so voluntary muscle contraction is measured during muscle stimulation. Notifications are represented as EMS signals and are accepted or refused either by a directional or a time-based EMG response. A lab user study with 12 participants shows that the directional EMG response is superior to the time-based response in terms of reaction time, error rate, and user preference. Furthermore, the directional approach is the fastest and the most intuitive for users compared to a button-based smartwatch interface as a baseline.
Dynamic Allocation of Serverless Functions in IoT Environments

The IoT area has grown significantly in the last few years and is expected to reach a gigantic amount of 50 billion devices by 2020. The appearance of serverless architectures, specifically highlighting FaaS, raises the question of the suitability of using them in IoT environments. Combining IoT with a serverless architectural design can effective when trying to make use of local processing power that exists in a local network of IoT devices and creating a fog layer that leverages computational capabilities that are closer to the end-user. In this approach, which is placed between the device and the serverless function, when a device requests for the execution of a serverless function will decide based on previous metrics of execution if the serverless function should be executed locally, in the fog layer of a local network of IoT devices, or if it should be executed remotely, in one of the available cloud servers. Therefore, this approach allows dynamically allocating functions to the most suitable layer.
Prediction of the Effect of Sleep Deprivation on Response Inhibition via Machine Learning on Structural Magnetic Resonance Imaging Data

Sleep deprivation (SD) impairs the ability of response inhibition. However, few studies have explored the quantitative prediction of performance impairment using Magnetic Resonance Imaging (MRI) data. In this study, structural MRI data were used to predict the change in response inhibition performance (ΔSSRT) measured by a stop-signal task (SST) after 24 h of SD in 52 normal young subjects. For each subject, T1-weighted MRI data were acquired and the gray matter (GM) volumes were calculated using voxel-based morphometry (VBM) analysis. First, the regions in which GM volumes correlated with ΔSSRT were explored. Then, features were extracted from these regions and the prediction process was performed using a linear regression model with four-fold cross-validation. We found that the GM volumes of the left middle frontal gyrus (L_MFG), pars opercularis of right inferior frontal gyrus (R_IFG), pars triangularis of left inferior frontal gyrus, pars opercularis of right rolandic area, left supplementary motor area (L_SMA), left hippocampus, right lingual gyrus, right postcentral gyrus and left middle temporal gyrus (L_MTG) could predict the ΔSSRT with a low mean square error of 0.0039 ± 0.0011 and a high Pearson’s correlation coefficient between the predicted and actual values of 0.948 ± 0.0503. In conclusion, our results demonstrated that a linear combination of structural MRI data could accurately predict the change in response inhibition performance after SD. Further studies with larger sample sizes and more comprehensive sample may be necessary to validate these findings.
To Distribute or Not to Distribute? Why Licensing Bugs Matter

Software licenses dictate how source code or binaries can be modified, reused, and redistributed. In the case of open source projects, software licenses generally fit into two main categories, permissive and restrictive, depending on the degree to which they allow redistribution or modification under licenses different from the original one(s). Developers and organizations can also modify existing licenses, creating custom licenses with specific permissive/restrictive terms. Having such a variety of software licenses can create confusion among software developers, and can easily result in the introduction of licensing bugs, not necessarily limited to well-known license incompatibilities. In this work, we report a study aimed at characterizing licensing bugs by (i) building a catalog categorizing the types of licensing bugs developers and other stakeholders face, and (ii) understanding the implications licensing bugs have on the software projects they affect. The presented study is the result of the manual analysis of 1,200 discussions related to licensing bugs carried out in issue trackers and in five legal mailing lists of open source communities. Our findings uncover new types of licensing bugs not addressed in prior literature, and a detailed assessment of their implications.
Irisin Maternal Plasma and Cord Blood Levels in Mothers with Spontaneous Preterm and Term Delivery

Irisin, an adipomyokine identified in 2012, has been investigated in association with common pregnancy complications, including gestational diabetes mellitus, preeclampsia, and intrauterine growth restriction. The objective of this study is to examine the potential role of irisin in preterm birth (PTB) by comparing its level between mothers with term and preterm labor. Maternal peripheral blood and cord blood samples were collected from 30 mothers who delivered prematurely and from 35 mothers who delivered at term. Irisin concentrations were measured in all samples using ELISA, and four common single nucleotide polymorphisms in the irisin gene were determined (rs16835198, rs726344, rs3480, and rs1746661). Univariable and multivariable regression modeling was applied to evaluate maternal and cord blood irisin concentrations in relation to preterm/term labor. Irisin concentration in umbilical cord blood was found to be associated with PTB in the univariable model (p = 0.046). On the other hand, no differences in maternal blood irisin levels between mothers with preterm and term deliveries were established. To the best of our knowledge, this is the first study determining irisin levels in term and preterm deliveries in maternal peripheral blood and umbilical cord blood. Our study shows a possible association between cord blood irisin concentration and PTB occurrence.
Using Pre-trained Full-Precision Models to Speed Up Training Binary Networks For Mobile Devices

Binary Neural Networks (BNNs) are well-suited for deploying Deep Neural Networks (DNNs) to small embedded devices but state-of-the-art BNNs need to be trained from scratch for a long time. We show how weights from a pre-trained full-precision model can be used to speed-up training of binary networks. We show that for CIFAR-10, accuracies within 1% of the full-precision model can be achieved in just 5 epochs.