Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Privacy-Preserving Decision Support System for Highly Imbalanced Clinical Data

When a medical practitioner encounters a patient with rare symptoms that translates to rare occurrences in the local database, it is quite valuable to draw conclusions collectively from such occurrences in other hospitals. However, for such rare conditions, there will be a huge imbalance in classes among the relevant base population. Due to regulations and privacy concerns, collecting data from other hospitals will be problematic. Consequently, distributed decision support systems that can use just the statistics of data from multiple hospitals are valuable. We present a system that can collectively build a distributed classification model dynamically without the need of patient data from each site in the case of imbalanced data. The system uses a voting ensemble of experts for the decision model. The imbalance condition and number of experts can be determined by the system. Since only statistics of the data and no raw data are required by the system, patient privacy issues are addressed. We demonstrate the outlined principles using the Nationwide Inpatient Sample (NIS) database. Results of experiments conducted on 7,810,762 patients from 1050 hospitals show improvement of 13.68% to 24.46% in balanced prediction accuracy using our model over the baseline model, illustrating the effectiveness of the proposed methodology.
The impact of translocations on neutral and functional genetic diversity within and among populations of the Seychelles warbler

Translocations are an increasingly common tool in conservation. The maintenance of genetic diversity through translocation is critical for both the short‐ and long‐term persistence of populations and species. However, the relative spatio‐temporal impacts of translocations on neutral and functional genetic diversity, and how this affects genetic structure among the conserved populations overall, have received little investigation. We compared the impact of translocating different numbers of founders on both microsatellite and major histocompatibility complex (MHC) class I diversity over a 23‐year period in the Seychelles warbler (Acrocephalus sechellensis). We found low and stable microsatellite and MHC diversity in the source population and evidence for only a limited loss of either type of diversity in the four new populations. However, we found evidence of significant, but low to moderate, genetic differentiation between populations, with those populations established with fewer founders clustering separately. Stochastic genetic capture (as opposed to subsequent drift) was the main determinant of translocated population diversity. Furthermore, a strong correlation between microsatellite and MHC differentiation suggested that neutral processes outweighed selection in shaping MHC diversity in the new populations. These data provide important insights into how to optimize the use of translocation as a conservation tool.
Automatically Generated Prosodic Cues to Lexically Ambiguous Dialog Acts in Multiparty Meetings

We investigate whether automatically extracted prosodic features can serve as cues to dialog acts (DAs) in naturallyoccurring meetings. We focus on the classification of four short DAs, all of which can be conveyed by the same words. DAs were hand-labeled based on the discourse context. Results for classifiers trained on automatically extracted prosodic features show significant associations with DAs in unseen test data. Furthermore, the specific features used depend on the classification task at hand. Results shed light on the relationship between discourse function and prosody, and could be used to aid automatic processing for natural dialog understanding.
Widespread Viral Diseases Endangering Cereal Crops in Ukraine

Aim. Viral diseases are one of the factors governing yield of cereal crops which is a key element to guaranteeing food security in Ukraine. This work assesses spread of most harmful viruses infecting cereals in agriecosystems using different diagnostic approaches. Methods. Viruses were detected in collected samples using double-antibody sandwich enzyme-linked immunosorbent assay with commercial test systems specific to 12 cereal viruses. Transmission electron microscopy was used for direct detection of virus particles. Results. To evaluate the spread of cereal viruses, we have carried out a 15-year monitoring of cereal crops in different regions of Ukraine. For virus screening, we collected plants with typical virus-like symptoms (mosaic, leaf discoloration, etc.). We have identified arthropod-transmitted viruses: Barley yellow dwarf virus-PAV (BYDV-PAV), Barley yellow dwarf virus-MAV (BYDV-MAV), Cereal yellow dwarf virus-RPV (CYDV-RPV), Wheat streak mosaic virus (WSMV) and Wheat dwarf virus (WDV). Brome mosaic virus (BMV) and Barley stripe mosaic virus (BSMV) were sporadically detected in the commercial fields under cereals, as well as several soil-borne viruses, including Wheat spindle streak mosaic virus (WSSMV), Soil-borne cereal mosaic virus (SBCMV), Soil-borne wheat mosaic virus (SBWMV) and Barley mild mosaic virus (BaMMV). Conclusions. Barley yellow dwarf virus-PAV, Wheat streak mosaic virus and Wheat dwarf virus are the most spread viruses endangering cereals’ cultivation in Ukraine. During the last 15 years, these viruses gained not only in terms of spread but also in the context of their economic effect. virus , Barley stripe spindle virus , Soil-borne cereal Barley mild
Automated power model generation method for smartphones

This paper presents an automated power model generation method for smartphones. Having an accurate power model of mobile devices can help identify the sources of fast discharge of the battery and provide opportunities for optimizing battery use. However, current methods rely on either external measurement equipment or internal current sensor support. This study presents a novel method for power modeling that exploits the usage patterns stored on the phone. This method can be applied to any mobile device regardless of the manufacturer and the model of the device. A power consumption analysis tool, and an artificial usage pattern generator tool have been developed for power modeling. The proposed method is able to generate an accurate power model of various smartphones even without detailed knowledge of the phone hardware.
The E-Commerce Model of Health Websites: An Integration of Web Quality, Perceived Interactivity, and Web Outcomes

The study integrates web quality (system quality, information quality, and service quality), perceived interactivity (human-message, human-human), and web outcomes (web usage, web satisfaction, and web loyalty) to explore the e-commerce model of health websites. A survey of 1076 users of health websites was conducted to validate the proposed model. The findings show that web quality has significantly positive effect on perceived interactivity, web usage, and web satisfaction separately, which in turn influence web loyalty. This study also confirms that perceived interactivity is an important mediator between web quality and web outcomes. This study emphasizes the importance of both web quality and perceived interactivity in the progress towards success health websites. The findings may be used as theoretical base for future research and can also offer empirical foresight to executives and managers of hospitals when they initially introduce and upgrade the health websites into their organizations.
Query‐biased summary generation assisted by query expansion

Query‐biased summaries help users to identify which items returned by a search system should be read in full. In this article, we study the generation of query‐biased summaries as a sentence ranking approach, and methods to evaluate their effectiveness. Using sentence‐level relevance assessments from the TREC Novelty track, we gauge the benefits of query expansion to minimize the vocabulary mismatch problem between informational requests and sentence ranking methods. Our results from an intrinsic evaluation show that query expansion significantly improves the selection of short relevant sentences (5–13 words) between 7% and 11%. However, query expansion does not lead to improvements for sentences of medium (14–20 words) and long (21–29 words) lengths. In a separate crowdsourcing study, we analyze whether a summary composed of sentences ranked using query expansion was preferred over summaries not assisted by query expansion, rather than assessing sentences individually. We found that participants chose summaries aided by query expansion around 60% of the time over summaries using an unexpanded query. We conclude that query expansion techniques can benefit the selection of sentences for the construction of query‐biased summaries at the summary level rather than at the sentence ranking level.
Multi-objective placement of virtual network function chains in 5G

In this paper we propose a novel algorithm, namely Multi-Objective Placement (MOP), for the efficient placement of Virtualized Network Function (VNF) chains in future 5G systems. Real datasets are used to evaluate the performance of MOP in terms of acceptance ratio and embedding time when placing the time critical radio access network (RAN) functions as a chain. In addition, we rely on a realistic infrastructure topology to assess the performance of MOP with two main objectives: maximizing the number of base stations that could be embedded in the Cloud and load balancing. The results reveal that the acceptance ratio of embedding RAN functions is only 5% less than the one obtained with the optimal solution for the majority of considered scenarios, with a speedup factor of up to 2000 times.
Exploiting Users' Personal and Public Information for Personal Photo Annotation

Annotation is the core item to enable efficient access to media. Thus, automatic annotation is a highly challenging problem. We present a novel solution to personal digital photograph annotation problem which annotates photo based on users' personal, and public information. We use timestamps of photo together with exact location name as pivots to search for more contextual metadata from users' emails, schedules, web browsing histories and public information repositories such as online encyclopedia and news Web sites. A notable contribution of our approach is that it can effectively leverage the background information of users' daily activities and their ambient environment to semi-automatically annotate their photographs. Experimental results conducted with 10 subjects and a total of 313 photos show that we can reduce 33% of annotation time over manual annotation and we also obtain very encouraging accuracy results.
Positive Predictive Value based dynamic K-Nearest Neighbor

The K Nearest Neighbors classification method assigns to an unclassified observation the class which obtains the best results after a voting criteria is applied among the observation’s K nearest, previously classified points. In a validation process the optimal K is selected for each database and all the cases are classified with this K value. However the optimal K for the database does not have to be the optimal K for all the points. In view of that, we propose a new version where the K value is selected dynamically. The new unclassified case is classified with different K values. And looking for each K how many votes has obtained the winning class, we select the class of the most reliable one. To calculate the reliability, we use the Positive Predictive Value (PPV) that we obtain after a validation process. The new algorithm is tested on several datasets and it is compared with the K-Nearest Neighbor rule.
Sinica Treebank: Design Criteria, Annotation Guidelines, and On-line Interface

This paper describes the design criteria and annotation guidelines of Sinica Treebank. The three design criteria are: Maximal Resource Sharing, Minimal Structural Complexity, and Optimal Semantic Information. One of the important design decisions following these criteria is the encoding of thematic role information. An on-line interface facilitating empirical studies of Chinese phrase structure is also described.
Enhancing extractive summarization using non-negative matrix factorization with semantic aspects and sentence features

The main task in extractive text summarization is to evaluate the important of sentences in a document. This paper aims at improving the quality of an unsupervised summarization method, i.e. non-negative matrix factorization, by using sentence features and considering semantically related words using word embeddings (i.e. word2vec) in sentence scoring. The experiments were carried out with different scenario using the DUC 2007 dataset. Experimental results showed that when NMF was combined three types of sentence features (i.e., surface, content, and relevant features) and word2vec, the system got best performance with 42.34% for Rouge-1 and 10.77% for Rouge-2, increasing 0.67% Rouge-1 and 0.78% Rouge-2 in compared with only NMF.
Scalability of fork/join queueing networks with blocking

This paper investigates how the through put of a general fork-join queueing network with blocking behaves as the number of nodes increases to infinity while the processing speed and buffer space of each node stay unchanged. The problem is motivated by applications arising from distributed systems and computer networks. One example is large-scale distributed stream processing systems where TCP is used as the transport protocol for data transfer in between processing components. Other examples include reliable multicast in overlay networks, and reliable data transfer in ad hoc networks. Using an analytical approach, the paper establishes bounds on the asymptotic throughput of such a network. For a subclass of networks which are balanced, we obtain sufficient conditions under which the network stays scalable in the sense that the throughput is lower bounded by a positive constant as the network size increases. Necessary conditions of throughput scalability are derived for general networks. The special class of series-parallel networks is then studied in greater detail, where the asymptotic behavior of the throughput is characterized.
Implementation of a Cost-effective V2X hardware and software platform

Vehicular ad-hoc networks (VANET) aim at exchanging traffic safety and/or general purpose data by vehicles with fixed devices and other vehicles. The IEEE 802.11p standard defines the physical and medium access control layers for VANETs. High costs of commercial devices make practical experiments quite expensive. This work proposes a low cost, customizable device that supports different VANET standards for 5.9 GHz and 700 MHz frequency bands.
Dimensions of Entrepreneurial Orientation and Marketing Performance: The Moderating Effect of Market Turbulence

Purpose: This study is to understand how the marketing performance of used car showroom SMEs is related to the entrepreneurial orientation of used car showroom SME owners or managers as well as the moderating influence of market turbulence. Theoretical Framework: The key points and emphasizing the importance of understanding the dynamic relationship between entrepreneurial orientation, marketing performance, and market turbulence in the context of used car showroom SMEs. Methods: This study used quantitative research methods through surveys. The used car showroom SMEs analyzed are in the DKI Jakarta, Bogor, Depok, Tangerang and Bekasi Indonesia areas. Data was collected through questionnaires that had been compiled in a structured manner. Of the 290 questionnaires distributed, 289 returned questionnaires were used in data analysis. Analysis is carried out through exploratory factor analysis (EFA) and structural equation modeling (SEM). Results and Conclusion: Research findings show that innovativennes, proactive have an influence on product marketing performance (p < 0.05). Meanwhile, risk taking did not have a significant effect on marketing performance (p > 0.05). In addition, market turbulence moderates negatively the relationship between innovation and marketing performance, but market turbulence is unable to moderate the relationship between proactive and risk taking on marketing performance. The results of this study provide a new understanding for used car SMEs in Indonesia, especially in JABODETABEK. Research implications: The practical implication of this research is that in the used car showroom business, it is important for companies to develop dynamic capabilities that include innovation, proactivity, and readiness in take risks. Originality/value: The ability to understand the entrepreneurial orientation of SMEs will help them deal with uncertain market turmoil be it in competition, technological advancements, ever-changing customer preferences.
Children's perceptions of the personal relationships in their social networks.

Relatively few investigators have compared and contrasted the characteristics of different kinds of relationships in children's social networks. In the present study, 199 fifthand sixth-grade children completed Network of Relationships Inventories, which assessed 10 qualities of their relationships with mothers, fathers, siblings, grandparents, friends, and teachers. Consistent with Weiss's (1974) theory, children reported seeking different provisions from different individuals. Mothers and fathers were turned to most often for affection, enhancement of worth, a sense of reliable aid, and instrumental aid. Next to parents, grandparents were turned to most often for affection and enhancement of worth, and teachers were turned to most often for instrumental aid. Friends were the greatest source of companionship, and friends and mothers received the highest ratings of intimacy. Additionally, children reported having more power in their relationships with other children than in those with adults. Conflict was perceived as occurring most often in sibling relationships. Children reported being most satisfied with their relationships with mothers, and they thought that their relationships with mothers and fathers were most important. Discussion centers around the bases for the children's differentiations of their relationships and the implications for understanding social networks.
Predicting Electron Population Characteristics in 2‐D Using Multispectral Ground‐Based Imaging

Ground‐based imaging and in situ sounding rocket data are compared to electron transport modeling for an active inverted‐V type auroral event. The Ground‐to‐Rocket Electrodynamics‐Electrons Correlative Experiment (GREECE) mission successfully launched from Poker Flat, Alaska, on 3 March 2014 at 11:09:50 UT and reached an apogee of approximately 335 km over the aurora. Multiple ground‐based electron‐multiplying charge‐coupled device (EMCCD) imagers were positioned at Venetie, Alaska, and aimed toward magnetic zenith. The imagers observed the intensity of different auroral emission lines (427.8, 557.7, and 844.6 nm) at the magnetic foot point of the rocket payload. Emission line intensity data are correlated with electron characteristics measured by the GREECE onboard electron spectrometer. A modified version of the GLobal airglOW (GLOW) model is used to estimate precipitating electron characteristics based on optical emissions. GLOW predicted the electron population characteristics with 20% error given the observed spectral intensities within 10° of magnetic zenith. Predictions are within 30% of the actual values within 20° of magnetic zenith for inverted‐V‐type aurora. Therefore, it is argued that this technique can be used, at least in certain types of aurora, such as the inverted‐V type presented here, to derive 2‐D maps of electron characteristics. These can then be used to further derive 2‐D maps of ionospheric parameters as a function of time, based solely on multispectral optical imaging data.
Scale-space processing of point-sampled geometry for efficient 3D object segmentation

In this paper, we present a new framework for analyzing and segmenting point-sampled 3D objects. Our method first computes for each surface point the surface curvature distribution by applying the principal component analysis on local neighborhoods with different sizes. Then we model in the four dimensional space the joint distribution of surface curvature and position features as a mixture of Gaussians using the expectation maximization algorithm. Central to our method is the extension of the scale-space theory from the 2D domain into the three-dimensional space to allow feature analysis and classification at different scales. Our algorithm operates directly on points requiring no vertex connectivity information. We demonstrate and discuss the performance of our framework on a collection of point sampled 3D objects.
Study on image quality assessment under foggy condition

Image quality assessment can provide important prior knowledge for subsequent picture processing, but it may be challenged by foggy distortion under a foggy weather condition, and there is no effective solution for the distorted images. In this paper, an effective image quality assessment method under foggy condition was proposed, with the purpose of giving image quality scores and their changes under different foggy density. Based on 29 reference images in LIVE image database, different image samples in a foggy day were generated. On the basis of analyzing foggy influences on image quality, this paper extracted features representing image scene characteristics in foggy day, used a method based on codebook to encode features, and trained features after using pooling strategy to encode, and, finally acquired image quality scores through the regression mode. Experimental results showed that, the higher accuracy, subjective and objective consistency, and higher identification on image quality under different foggy density can be obtained by the codebook-based method rather than other normal algorithms. The algorithm in this paper can solve the problem of image quality assessment under foggy condition.
Using Experience for Assessing Grasp Reliability

Manipulation skills are a key issue for a humanoid robot. Here, we are interested in a vision-based grasping system able to deal with previously unknown objects in real time and in an intelligent man- ner. Starting from a number of feasible candidate grasps, we focus on the problem of predicting their reliability using the knowledge acquired in previous grasping experiences. A set of visual features which take into account physical properties that can aect the stability and reliability of a grasp are dened. A humanoid robot obtains its grasping experience by repeating a large number of grasping actions on dieren t objects. An experimental protocol is established in order to classify grasps according to their reliability. Two prediction/classication strategies are dened which allow the robot to predict the outcome of a grasp only analizing its visual features. The results indicate that these strategies are adequate to predict the realibility of a grasp and to generalize to dieren t objects.
Robust Image Registration using Mixtures of t-distributions

We propose a pixel similarity-based algorithm enabling accurate rigid registration between single and multimodal images presenting gross dissimilarities due to noise, missing data or outlying measures. The method relies on the partitioning of a reference image by a Student's t-mixture model (SMM). This partition is then projected onto the image to be registered. The main idea is that a t-component in the reference image corresponds to a t-component in the image to be registered. If the images are correctly registered the weighted sum of distances between the corresponding components is minimized. The use of SMM components is justified by the property that they have heavier tails than standard Gaussians, thus providing robustness to outliers. Experimental results indicate that, even in the case of images presenting low SNR or important amount of dissimilarities due to temporal changes, the proposed algorithm compares favorably to the histogram-based mutual information method that is widely used in a variety of applications.