Miracle-dz/UniFAR-FTUs huggingface dataset:
Active learning: a step towards automating medical concept extraction

OBJECTIVE This paper presents an automatic, active learning-based system for the extraction of medical concepts from clinical free-text reports. Specifically, (1) the contribution of active learning in reducing the annotation effort and (2) the robustness of incremental active learning framework across different selection criteria and data sets are determined. MATERIALS AND METHODS The comparative performance of an active learning framework and a fully supervised approach were investigated to study how active learning reduces the annotation effort while achieving the same effectiveness as a supervised approach. Conditional random fields as the supervised method, and least confidence and information density as 2 selection criteria for active learning framework were used. The effect of incremental learning vs standard learning on the robustness of the models within the active learning framework with different selection criteria was also investigated. The following 2 clinical data sets were used for evaluation: the Informatics for Integrating Biology and the Bedside/Veteran Affairs (i2b2/VA) 2010 natural language processing challenge and the Shared Annotated Resources/Conference and Labs of the Evaluation Forum (ShARe/CLEF) 2013 eHealth Evaluation Lab. RESULTS The annotation effort saved by active learning to achieve the same effectiveness as supervised learning is up to 77%, 57%, and 46% of the total number of sequences, tokens, and concepts, respectively. Compared with the random sampling baseline, the saving is at least doubled. CONCLUSION Incremental active learning is a promising approach for building effective and robust medical concept extraction models while significantly reducing the burden of manual annotation.
Real-time compressive tracking with motion estimation

Visual tracking is challenging due to appearance changes caused by motion, illumination, occlusion and pose, among others. For these local changes, appearance model based tracking algorithms, such as MILtracker [8], have adopted local features and most recently extended to compressive domain, namely Compressive Tracking [13], for the real-time performance. However, the motion information is missed out from these trackers and assumptions on target motion have been made by predefined search radii. In this paper, the motion information has been integrated into appearance model based tracking by introducing motion estimator, i.e., particle filters. The experiments show that motion estimator could improve the performance of appearance based trackers especially when the target is with motion variety.
A resource scheduling method based on QoS for device-to-device communication systems

In this paper, we proposed a novel resource scheduling method based on QoS for D2D networks. We considered not only the geographical position and power control as traditional methods, but also the D2D user's specific QoS requirement. When the base station receives different QoS message from different D2D users, an independent message including reused cellular user's frequency and optimal power information will be sent back to corresponding D2D users. As we pay more attention to QoS level to determine the reused user and resource allocation, this new method can both improve the system throughput and satisfy different QoS requirement of all users. Simulation results verify the effectiveness of this new method.
Workload and Resource Aware Proactive Auto-scaler for PaaS Cloud

Elasticity is a key feature in Cloud Computing where virtualized resources are provisioned and de-provisioned via auto-scaling. However, auto-scaling in most Platform-as-a-Service (PaaS) systems is based on reactive, threshold-driven approaches. Such systems are incapable of catering to rapidly varying workloads, unless the associated thresholds are sufficiently low. Alternatively, maintaining low thresholds leads to resource over-provisioning under relatively stable workloads. Moreover, thresholds are not a good indication of QoS compliance, which is a key performance indicator of a cloud application. Hence, it is nontrivial to determine an optimum threshold while minimizing costs and meeting QoS demands. We propose inteliScaler, a proactive and cost-aware auto-scaling solution to address these issues by combining a predictive model, cost model, and a smart killing feature. An ensemble workload prediction mechanism is introduced based on time series and machine learning techniques for making accurate predictions on drastically different workload patterns. Utility of the solution is demonstrated using both simulations and empirical evaluations using Apache Stratos PaaS (deployed on the AWS EC2), as well as RUBiS and real-world workload traces. Results show significant QoS improvements and cost reductions by inteliScaler compared to a typical reactive and threshold-based PaaS auto-scaling solution.
NP-Hard Problems of Learning from Examples

As designing practical algorithms of learning from examples, one has to deal with some optimization problems. The major optimization problems are: the smallest feature subset selection, the smallest decision tree induction, and the smallest k-DNF induction. In this paper, we show that all these optimization problems listed as above are NP-hard, and we present new greedy algorithms for solving these problems.
Augmented reality target finding based on tactile cues

This study is based on a user scenario where augmented reality targets could be found by scanning the environment with a mobile device and getting a tactile feedback exactly in the direction of the target. In order to understand how accurately and quickly the targets can be found, we prepared an experiment setup where a sensor-actuator device consisting of orientation tracking hardware and a tactile actuator were used. The targets with widths 5°, 10°, 15°, 20°, and 25° and various distances between each other were rendered in a 90° -wide space successively, and the task of the test participants was to find them as quickly as possible. The experiment consisted of two conditions: the first one provided tactile feedback only when pointing was on the target and the second one included also another cue indicating the proximity of the target. The average target finding time was 1.8 seconds. The closest targets appeared to be not the easiest to find, which was attributed to the adapted scanning velocity causing the missing the closest targets. We also found that our data did not correlate well with Fitts' model, which may have been caused by the non-normal data distribution. After filtering out 30% of the least representative data items, the correlation reached up to 0.71. Overall, the performance between conditions did not differ from each other significantly. The only significant improvement in the performance offered by the close-to-target cue occurred in the tasks where the targets where the furthest from each other.
Using Kernel Methods and Model Selection for Prediction of Preterm Birth

We describe an application of machine learning to the problem of predicting preterm birth. We conduct a secondary analysis on a clinical trial dataset collected by the National In- stitute of Child Health and Human Development (NICHD) while focusing our attention on predicting different classes of preterm birth. We compare three approaches for deriving predictive models: a support vector machine (SVM) approach with linear and non-linear kernels, logistic regression with different model selection along with a model based on decision rules prescribed by physician experts for prediction of preterm birth. Our approach highlights the pre-processing methods applied to handle the inherent dynamics, noise and gaps in the data and describe techniques used to handle skewed class distributions. Empirical experiments demonstrate significant improvement in predicting preterm birth compared to past work.
Unsupervised Relation Extraction for E-Learning Applications

In this modern era many educational institutes and business organisations are adopting the e-Learning approach as it provides an effective method for educating and testing their students and staff. The continuous development in the area of information technology and increasing use of the internet has resulted in a huge global market and rapid growth for e-Learning. Multiple Choice Tests (MCTs) are a popular form of assessment and are quite frequently used by many e-Learning applications as they are well adapted to assessing factual, conceptual and procedural information. In this thesis, we present an alternative to the lengthy and time-consuming activity of developing MCTs by proposing a Natural Language Processing (NLP) based approach that relies on semantic relations extracted using Information Extraction to automatically generate MCTs. Information Extraction (IE) is an NLP field used to recognise the most important entities present in a text, and the relations between those concepts, regardless of their surface realisations. In IE, text is processed at a semantic level that allows the partial representation of the meaning of a sentence to be produced. IE has two major subtasks: Named Entity Recognition (NER) and Relation Extraction (RE). In this work, we present two unsupervised RE approaches (surface-based and dependencybased). The aim of both approaches is to identify the most important semantic relations in a document without assigning explicit labels to them in order to ensure broad coverage, unrestricted to predefined types of relations. In the surface-based approach, we examined different surface pattern types, each implementing different assumptions about the linguistic expression of semantic relations between named entities while in the dependency-based approach we explored how dependency relations based on dependency trees can be helpful in extracting relations between named entities. Our findings indicate that the presented approaches are capable of achieving high precision rates.
Performance Evolution of Mobile Web-Based Services

The mobile Web's widespread diffusion opens many interesting design and management issues about server infrastructures that must satisfy present and future client demand. Future mobile Web-based services will have growing computational costs. Even requests for the same Web resource will require services to dynamically generate content that takes into account specific devices, user profiles, and contexts. The authors consider the evolution of the mobile Web workload and trends in server and client devices with the goal of anticipating future bottlenecks and developing management strategies.
A cellular cofactor facilitates efficient 3CD cleavage of the poliovirus P1 precursor

The production of poliovirus capsid proteins from a capsid protein precursor (P1) is mediated by virus-encoded proteinase 3CD and involves a complicated set of proteinase-substrate interactions. In addition to substrate and enzymatic determinants required for this interaction, we describe a cellular cofactor, which facilitates 3CD recognition of the P1 precursor. Cellular cofactor activity is 3CD dependent and salt dependent. Our analysis shows that proteolytic cleavage of the P1 precursor at the VP0/VP3 cleavage site exhibits a greater dependency on the cellular cofactor than cleavage at the VP3/VP1 site. Such a greater dependency on cellular cofactor activity can be relieved (in part) by the substitution of an Ala residue for the Pro residue at the -4 position of the VP0/VP3 cleavage site. However, mutant viruses containing Pro-to-Ala substitutions at the -4 position of the VP0/VP3 site exhibit defects in viral growth.
THE INTERACTION OF COGNITIVE APTITUDES WITH SEQUENCES OF FIGURAL AND SYMBOLIC TREATMENTS OF MATHEMATICAL INEQUALITIES

The goal of this research was to replicate and extend an earlier Aptitude Treatment Interaction (ATI) study (Young, 1973) in which ordinal and disordinal interactions between aptitude measures of general ability (Hidden Figures) and previous achievement (Pretest) and treatment sequences were detected. Replication was sought in the sense of using again those aptitude measures that seemed most promising in the earlier study. Several extensions were made. One was to incorporate other aptitude measures that might prove to be appropriate in revealing interaction. Because of the lack of success with some aptitude measures in the earlier study, it was hoped that others might be more appropriate for revealing expected but elusive interactions (see Cronbach & Snow, 1969). Another extension involved the testing for interactions for each treatment independently prior to their sequencing. Another incorporated greater emphasis in the design on prior learning as an aptitude measure. Finally, it was hoped that with a larger population and improved treatments the earlier interactions could be convincingly replicated and that others might be revealed. Hypotheses Phase 1 of the study was to test the general ATI hypothesis (Cronbach, 1957; Becker, 1970) for several measures of ability and for two treatments with secondary level subjects. The two treatments, one figural and one symbolic, were roughly the same length. Each took about five hours to complete, and each covered the same mathematical content on absolute value and quadratic inequalities usually taught at the secondary level. The question was, Would these treatments interact with figural, symbolic, or general abilities? Phase 2 of the study was concerned with testing for interactions between different sequences of the two treatments and the same abilities. The rationale was that it may be desirable to teach both methods for conceptualizing such problems, as is often done in the mathematics classroom. Underlying the two theoretical concerns outlined above are the practically important questions of whether ATI research can be conducted in a The authors express their appreciation to the Rutgers University Research Council
An on-chip integrated III–V / CMOS 125MSps 6-bit SAR ADC

This paper presents a fully integrated on-chip III–V HEMT and CMOS hybrid technology, implementing a 6b 125MSps successive approximation register (SAR) ADC. On-chip integration is achieved by using a hybrid PDK that permits direct integration of Au-free III–V devices into a foundry-proven CMOS process. The prototype utilizes an on-chip integrated InGaAs sampling switch and remaining circuits in CMOS. A “more than Moore” design and fabrication methodology has been adopted to overcome CMOS performance limitation. On-chip InGaAs switch integration results in reduction of parasitic elements, enhance the settling speed and superior dynamic performance. In this work the ADC consumes 1.99mW from a 1.8V supply achieving 33.7dB SNDR at nyquist and occupies 0.0225mm2.
Cross entropy based spectrum sensing

In this paper, we propose a spectrum sensing method by exploiting the cross entropy. Spectrum sensing is a critical step for Cognitive Radio (CR) to have a successful communication. Intensive interests have been paid to the improvement of spectrum sensing. However, the relationship of previous and current detected data sets of Primary User (PU) has not been considered by most of the spectrum sensing methods. We propose cross entropy based spectrum sensing to scrutinize this relationship. As cross entropy based spectrum sensing has taken extra information into account, its ability to detect PU signal is strengthened and robust, which leads to a superior spectrum sensing performance. The simulation results have validated this point.
Survival Analysis and Actuarial Parameters of Sternechus subsignatus (Coleoptera: Curculionidae) Adults

Abstract The soybean stalk weevil, Sternechus subsignatus Boheman 1836 (Coleoptera: Curculionidae), is a very serious soybean pest in the Neotropical region. Both adults and larvae feed on soybean, causing significant yield losses. Adult survival was evaluated during three soybean growing seasons under controlled environmental conditions. A survival analysis was performed using a parametric survival fit approach in order to generate survival curves and obtain information that could help optimize integrated management strategies for this weevil pest. Sex of the weevils, crop season, fortnight in which weevils emerged, and their interaction were studied regarding their effect on adult survival. The results showed that females lived longer than males, but both genders were actually long-lived, reaching 224 and 176 d, respectively. Mean lifetime (l50) was 121.88 ± 4.56 d for females and 89.58 ± 2.72 d for males. Although variations were observed in adult longevities among emergence fortnights and soybean seasons, only in December and January fortnights of the 2007–2008 season and December fortnights of 2009–2010 did the statistically longest and shortest longevities occur, respectively. Survivorship data (lx) of adult females and males were fitted to the Weibull frequency distribution model. The survival curve was type I for both sexes, which indicated that mortality corresponded mostly to old individuals.
Chronic Media Worlds: Social Media and the Problem of Pain Communication on Tumblr

This article explores dynamics of pain communication in the social media platform Tumblr. As a device of health communication, the Tumblr platform brings together a network of behaviors, technologies, and media forms through which pain experience is reimaged through and against mainstream biomedical frameworks. The article develops an interpretative approach to analyze how, as social media platforms reorganize affective, emotional, physical, and temporal frames of experience, communication about chronic pain and illness is reimagined in its capacity to create social worlds. Drawing on ethnographic theory to reimagine the relation between politics and poetics in pain communication, the article explores the issue- and world-making capacities of social media.
Language Skills in Prader–Willi Syndrome

• Tolerance and consistency of FM system use: Most of the children readily accepted use of the FM receivers on both ears. Two children complained about the device in their ears, but did not refuse to use the device. • Individual and group speech recognition in noise: All participants exhibited better performance (i.e., lower thresholds) in the FM system condition as compared with the no-FM condition. There was an average difference of 6.1 dB with the FM system, which suggests substantial benefit from the FM system for improving speech recognition thresholds in noise. • The ASD group achieved slightly greater benefit from using the FM system than the ADHD group. The group differences are likely influenced by the small number of participants in the ADHD group and the fact that the child with the least benefit was included in the ADHD group. • In the no-FM condition, the ASD and ADHD groups performed significantly poorer on speech recognition in noise compared with typically functioning peers. When using the FM system, the ASD and ADHD groups performed similarly to their peers. • Observed on-task behaviors without and with FM system. Two independent observers recorded a significant increase in on-task behaviors when the FM system was used by the children with ASD and ADHD, and off-task behaviors relating to inattention and distractibility significantly decreased when the FM system was used. • Teacher questionnaires: Teacher questionnaires revealed that use of the FM system improved teacher ratings related to auditory listening behaviors on the CHAPS questionnaire but did not change ratings related to educational risk on the SIFTER questionnaire. These positive results for improved speech reception and attention/on-task behaviors for children with ASD and ADHD using an FM system warrant further investigation. Determining the need for an FM system for a child with ASD and ADHD should be done on an individualized basis. Children should receive a full hearing evaluation to make certain there is no conductive or sensorineural hearing loss as well as speech recognition in noise testing. Some children may have tactile sensitivities that may preclude the use of an FM system. The authors of this study also recommend a trial with the FM system, teacher questionnaires, a classroom observation to determine onand off-task behaviors with and without the FM system, teacher and student interviews, and a review of the child’s special education file or previous test results.
Licensed under Creative Commons Attribution Cc by Anomalous Events Detection in Frequent Sequence Video via Object Segmentation Using Motion Pixel Compensation and Gradient

The surveillance takes major part in the present world. The collection of data through surveillance is not the thing required by the people but to use it effectively for decision support. Manual processing of huge amount of surveillance data is not feasible; a support to analyse and process all incoming data is needed by the operator of the surveillance system. In this proposal, an approach to intelligent video surveillance is introduced with significance on finding behavioural anomalies. The proposed technique of robust and efficient anomaly detection is capable of dealing with crowded scenes. Initially, the foreground segmentation of input frames is done to analyse the foreground objects and to effectively ignore irrelevant background dynamics. The input frames are split into non overlapping cells, and then the features are extracted based on motion, size and texture from each cell. Each and every feature type is independently analysed to detect the presence of an anomaly. An effective estimate of object motion is obtained by computing the optical flow of only the foreground pixels. To process even large training datasets, the motion and size feature are modelled by an approximated version of kernel density estimation.
Throughput Maximization for Cognitive Radio Users with Energy Constraints in an Underlay Paradigm

In a cognitive radio network (CRN), cognitive radio users (CUs) should be powered by a small battery for their operations. The operations of the CU often include spectrum sensing and data transmission. The spectrum sensing process may help the CU avoid a collision with the primary user (PU) and may save the energy that is wasted in transmitting data when the PU is present. However, in a time-slotted manner, the sensing process consumes energy and reduces the time for transmitting data, which degrades the achieved throughput of the CRN. Subsequently, the sensing process does not always offer an advantage in regards to throughput to the CRN. In this paper, we propose a scheme to ﬁnd an optimal policy (i.e., perform spectrum sensing before transmitting data or transmit data without the sensing process) for maximizing the achieved throughput of the CRN. In the proposed scheme, the data collection period is considered as the main factor effecting on the optimal policy. Simulation results show the advantages of the optimal policy.