Miracle-dz/UniFAR-FTUs huggingface dataset:
Accuracy-Aware Interference Modeling and Measurement in Wireless Sensor Networks

Wireless sensor networks (WSNs) are increasingly deployed for mission-critical applications such as emergency management and health care, which impose stringent requirements on the communication performance of WSNs. To support these applications, it is crucial to model and measure the effect of wireless interference, which is the major factor that limits WSN performance. Accurate modeling and measurement of interference faces two key challenges. First, as shown in our experimental results, interference yields considerable spatial and temporal variations of WSN performance, which poses a major challenge for measurement at rum-time. Second, in the unlicensed band, the communication of WSN is interfered by coexisting wireless devices such as smartphones and laptops equipped with 802.11 radios, which lead to cross-technology interference that are difficult to characterize due to the heterogeneous PHY. To tackle these challenges, this paper presents a novel accuracy-aware approach to interference modeling and measurement for WSNs. First, we propose a new regression-based interference model and analytically characterize its accuracy based on statistics theory. Second, we develop a novel protocol called accuracy-aware interference measurement for measuring the proposed interference model with assured accuracy at run time. Third, building on interference modeling, we propose an algorithm that accurately forecasts the performance of WSNs in the presence of cross-technology interference. Our extensive experiments on a testbed of 17 TelosB motes show that the proposed approaches achieve high accuracy of interference modeling and WSN performance forecasting with significantly lower overhead than state-of-the-art approaches.
Pervasive and Mobile Computing

In this paper we present textile-based surface pressure mapping as a novel, unobtrusive information source for activity recognition. The concept is motivated by the observation that the vast majority of human activities are associated with certain types of surface contact (walking, running, etc. on the floor; sitting on a chair/sofa; eating, writing, etc. at a table; exercising on a fitness mat, and many others). A key hypothesis which we validate in this paper is: by analysing subtle features of such interaction, various complex activities, often ones that are difficult to distinguish using other unobtrusive sensors, can be well recognised. A core contribution of our work is a sensing and recognition system based on cheap, easy-to-produce textile components. These components can be integrated into matrices with tens of thousands of elements, a spatial pitch as fine as 1 cm 2 , temporal granularity of up to 40 Hz and pressure dynamic range from 0 . 25 × 10 5 to 5 × 10 5 Pa. We present the evaluation of the concept and the technology in five scenarios, through matrix monitoring driver motions at a car seat (32 × 32 sensors on 32 × 32 cm 2 ), a Smart-YogaMat (80 × 80 sensors on 80 × 80 cm 2 ) detecting and counting exercises, to a Smart-Tablecloth (30 × 42 sensors on 30 × 42 cm 2 ) recognising various types of food being eaten. ©
Randomized Pursuit-Evasion with Limited Visibility Volkan Isler

We study the following pursuit-evasion game: One or more hunters are seeking to capture an evading rabbit on a graph. At each round, the rabbit tries to gather information about the location of the hunters but it can see them only if they are located on adjacent nodes. We show that two hunters suffice for catching rabbits with limited visibility with high probability. We distinguish between reactive rabbits who move only when the hunter is visible and general rabbits who can employ more sophisticated strategies. We present polynomial time algorithms that decide whether a graph G is hunter-win, that is, if a single hunter can capture a rabbit of either kind on G. Comments Copyright SIAM, 2004. Published in Proceedings of the 15th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2004), pages 1060-1069. This conference paper is available at ScholarlyCommons: http://repository.upenn. edu/cis_papers/71 Randomized Pursuit-Evasion with Limited Visibility Volkan Isler Sampath Kannan Sanjeev Khanna
BIOINFORMATICS ORIGINAL

Motivation: High-throughput molecular data provide a wealth of information that can be integrated into network analysis. Several approaches exist that identify functional modules in the context of integrated biological networks. The objective of this study is 2-fold: ﬁrst, to assess the accuracy and variability of identiﬁed modules and second, to develop an algorithm for deriving highly robust and accurate solutions. Results: In a comparative simulation study accuracy and robustness of the proposed and established methodologies are validated, considering various sources of variation in the data. To assess this variation, we propose a jackknife resampling procedure resulting in an ensemble of optimal modules. A consensus approach summarizes the ensemble into one ﬁnal module containing maximally robust nodes and edges. The resulting consensus module identiﬁes and visualizes robust and variable regions by assigning support values to nodes and edges. Finally, the proposed approach is exempliﬁed on two large gene expression studies: diffuse large B-cell lymphoma and acute lymphoblastic leukemia.
ScholarWorks@UMass Amherst

Many recognition algorithms depend on careful positioning of an object into a canonical pose, so the position of features relative to a (cid:2)xed coordinate system can be examined. Currently, this positioning is done either manually or by training a class-specialized learning algorithm with samples of the class that have been hand-labeled with parts or poses. In this paper, we describe a novel method to achieve this positioning using poorly aligned examples of a class with no additional labeling. Given a set of unaligned examplars of a class, such as faces, we automatically build an alignment mechanism, without any additional labeling of parts or poses in the data set. Using this alignment mechanism, new members of the class, such as faces resulting from a face detector, can be precisely aligned for the recognition process. Our alignment method improves performance on a face recognition task, both over unaligned images and over images aligned with a face alignment algorithm speci(cid:2)cally developed for and trained on hand-labeled face images. We also demonstrate its use on an entirely different class of objects (cars), again without providing any information about parts or pose to the learning algorithm.
Designing complementary communication systems

We have long assumed that being face to face is the best environment for social interaction. But is "being there" the best we can aspire to? One common approach to improving face-to-face contexts is to add new communication channels — a strategy often described as creating "backchannels. " In my work, I use a series of novel complementary communication systems to show how adding communication platforms to collaborative situations can be useful while also arguing for a new conceptual model of a main stage and a side stage (in the Goffman sense) that contrasts with the traditional model of backchannels. I describe a series of projects that embody this approach and explore its limits. My work covers virtual world meetings and presentations, an audience interaction tool for large groups (backchan.nl), a tablet-based system for small group discussions (Tin Can), and a platform for connecting huge distributed audiences (ROAR). In each of these projects I trace my three major research themes: understanding how conversational grounding operates in these environments, how non-verbal actions complement text-based interaction, and how people make decisions about how to manage their attention in environments with multiple simultaneous communication channels. Thesis Supervisor ! Chris Schmandt ! Title ! Principal Research Scientist
Expert Systems With Applications

Online social lending has facilitated the ability of borrowers to reach lenders for ﬁnancing support. With the increasing number of social lending projects, it is becoming very diﬃcult for lenders to ﬁnd appropriate projects to invest in, and for borrowers to get the funds they need. Project recommendation techniques provide a promising way to solve this problem to some degree, by recommending borrowers’ projects to lenders who are able to invest. Unfortunately, current loan project recommendations only explore some structured information to match borrowers and lenders, so they cannot achieve a satisfactory way to solve the problem very well. In this study, we innovatively mine a huge amount of unstructured data, the text data of borrowers’ and lenders’ motivations, to provide loan project recommendations that solve the problem of mismatches between borrowers and lenders. We present a motivation-based recommendation approach that uses text mining and classiﬁer techniques to identify borrowers’ and lenders’ motivations. Using a dataset from the well-known social lending platform Kiva, our experiment results show that, compared with prior works, the proposed approach improves project recommendations in inactive lender groups and unpopular loan groups, which shows the superiority of the proposed approach in addressing data sparsity and cold start problems in loan project recommendations. This study thus initiates an attempt to solve the information overload problem and improve matching between borrowers and lenders through mining big unstructured text data found in a large number of P2P platforms.
Semantic Privacy Policies for Service Description and Discovery in Service-Oriented Architecture

Privacy preservation in Service-Oriented Architecture (SOA) is an open problem. This paper focuses on the areas of service description and discovery. The problems in these areas are that currently it is not possible to describe how a service provider deals with information received from a service consumer as well as discover a service that satisfies the privacy preferences of a consumer. There is currently no framework which offers a solution that supports a rich description of privacy policies and their integration in the process of service discovery. Thus, the main goal of this paper is to propose a privacy preservation framework for the areas of service description and discovery in SOA. The framework enhances service description and discovery with the specification and intersection of privacy policies using a base and domain-specific privacy ontologies. Moreover, the framework extends SOA to include roles responsible for implementing a privacy registry as well as mediating the interactions between service consumers and providers and the privacy preservation component.
I STITUTO DI A NALISI DEI S ISTEMI ED I NFORMATICA

A number of interesting issues have been addressed on biological networks about their global and local properties. The connection between the topological properties of proteins in Protein-Protein Interaction (PPI) networks and their biological relevance has been investigated focusing on hubs, i.e. proteins with a large number of interacting partners. We will survey the literature trying to answer the following questions: Do hub proteins have special biological properties? Do they tend to be more essential than non-hub proteins? Are they more evolutionarily conserved? Do they play a central role in modular organization of the protein interaction network? Are there structural properties that characterize hub proteins?
Benchmarking state-of-the-art classification algorithms for credit scoring: A ten-year update

Ten years have passed since Baesens et al. published their benchmarking study of classification algorithms in credit scoring [Baesens, B. , Van Gestel, T. , Viaene, S. , Stepanova, M. , Suykens, J., & Vanthienen, J. (2003). Benchmarking state-of-the-art classification algorithms for credit scoring. Journal of the Operational Research Society, 54(6), 627-635. ]. The interest in predictive methods for scorecard construction is unbroken, and the work of Baesens et al. is still held in high regard. However, there have been numerous advancements in predictive modeling, including novel learning methods and techniques to reliably assess the performance of alternative scorecards. Neither Baesens et al. nor other studies in the retail credit scoring literature reflect these developments. The objective of this paper is to close this research gap. To that end, we update the study of Baesens et al. and compare several novel classification algorithms to the state-of-the-art in credit scoring. In addition, we repeat our benchmark for several performance measures and examine the degree to which these differ in their assessment of candidate scorecards. Our study provides valuable insight for professionals and academics in credit scoring. The paper helps practitioners to stay abreast of technical advancements in predictive modeling. From an academic standpoint, the study provides an independent assessment of recent scoring methods and offers a new baseline to which future approaches can be compared. * Corresponding author: Tel. : +49-40-42838-4706, Fax: +49-40-42838-5535. a Institute of Information Systems, University of Hamburg, Von-Melle-Park 5, 20146 Hamburg, Germany; Stefan.Lessmann@uni-hamburg.de b Nottingham University Business School, University of Nottingham-Malaysia Campus, Jalan Broga, 43500Semenyih, Selangor Darul Ehsan, Malaysia; Hsin-Vonn.Seow@nottingham.edu. my c Department of Decision Sciences & Information Management, Catholic University of Leuven, Naamsestraat 69, B3000 Leuven, Belgium; Bart.Baesens@econ.kuleuven. be d School of Management, University of Southampton, Highfield, Southampton, SO17 1BJ, United Kingdom; l.thomas@soton.ac. uk
University of Amsterdam and Renmin University at TRECVID 2017: Searching Video, Detecting Events and Describing Video

In this paper we summarize our TRECVID 2017 [1] video recognition and retrieval experiments. We participated in three tasks: video search, event detection and video description. For both video search and event detection we explore semantic representations based on VideoStory [8] and an ImageNet Shuffle [16], which thrive well in few-example regimes. For the video description task we experiment with a deep network that predicts a visual representation from a natural language description with Word2VisualVec [5], and use this space for the sentence matching. For generative description we enhance a neural image captioning model with Early Embedding and Late Reranking [4]. The 2017 edition of the TRECVID benchmark has been a fruitful participation for our joint-team, resulting in the best overall result for video search and event detection as well as the runner-up position for video description. 1 Video Search and Event Detection The MediaMill approach to ad hoc video search and multimedia event detection is optimized for recognition scenarios when video examples are scarce or even completely absent. The key in such a challenging setting is a semantic video representation [15]. Our experiments focus on exploring such semantic representations for video search. 1.1 Representation I: VideoStory The first representation is based on VideoStory, as detailed in [8, 9]. To summarize, it learns the video representation from freely available web videos and their descriptions using an embedding between video features and term vectors. In the embedding the correlations between the words are utilized to learn a more effective representation by optimizing a joint objective balancing descriptiveness and predictability. We start from a dataset of videos, represented by video features X, and their textual descriptions, represented by binary term vectors Y , indicating which words are present in each video description. Then, our VideoStory representation is learned by minimizing: LV(A,W ) = min S Ld(A,S) + Lp(S,W ), (1) where A is the textual projection matrix, W is the visual projection matrix, and S is the VideoStory embedding. The loss function Ld corresponds to our first objective for learning a descriptive VideoStory, and the loss function Lp corresponds to our second objective for learning a predictable VideoStory. The embedding S interconnects the two loss functions. Descriptiveness. For the Ld function, we use a variant of regularized Latent Semantic Indexing. This objective minimizes the quadratic error between the original video descriptions Y , and the reconstructed translations obtained from A and S:
“ Navigation Among Movable Obstacles : Real-time Reasoning in Complex Environments ”

In this paper, we review the recent publication by Stilman and Kuffner titled “Navigation Among Movable Obstacles: Realtime Reasoning in Complex Environments”. They propose a solution to the problem of navigation in cluttered environments where the robot has the ability to move obstacles to clear previously blocked paths. This problem is particularly challenging because the planner must include both manipulationandnavigationconstraints in the same framework, which causes the problem to rapidly become intractable. They present a resolution-complete planner that takes advantage of the navigational structure of the problem to reduce the solution space dimensions to a manageable size. They also present real-time simulation results on problems of varying size and complexity. In addition to reviewing the paper, we also explore some issues left unanswered that might be the subject of interesting future work.
The Universality of the Fundamental Plane of E and S0 Galaxies: Sample Definition and I-Band Photometric Data

As part of a project to compare the fundamental plane and Tully-Fisher distance scales, we present here I-band CCD photometry for 636 early-type galaxies in eight clusters and groups of galaxies. These are the A262, A1367, Coma (A1656), A2634, Cancer and Pegasus Clusters, and the NGC 383 and NGC 507 Groups. Sample selection, cluster properties, and cluster membership assignment criteria are discussed. We present photometric parameters that are used in the fundamental plane relation, the effective radius re, and the effective surface brightness μe, as derived from a r1/4 fit to the observed radial photometric profile of each galaxy. A comparison with similar data found in the literature for the Coma Cluster shows that large systematic uncertainties can be introduced in the measurement of re and μe by the particular method used to derive those parameters. However, the particular combination of these two parameters that enters in the fundamental plane relation is a quantity that can be measured with high accuracy.
Melody Extraction from Polyphonic Music Signals Using Tandem Filter System

Melody extraction is a key problem in music information retrieval. This paper presents a method to estimate human voice fundamental frequency in polyphonic music signal using tandem filter. There are three-stage filters in this method. At first stage, it's easier to extract human voice fundamental frequency if a singing voice separation preprocessing is obtained. This paper uses the robust principal component analysis to roughly extract the human voice from polyphonic music signal. At the second stage, it is believed that although the human voice spectrogram is reserved and the accompaniment effect is reduced, there is still noise or false fundamental frequency. Thus, a hyper-Fourier Transform is implemented to lower the noise. At the third stage, the STFT spectrogram have the original information of the melody frequency structure. So this paper uses a transverse stripe filter system to eliminate the non-possible fundamental frequency position.
Large-scale social-media analytics on stratosphere Conference Item

The importance of social-media platforms and online communities – in business as well as public context – is more and more acknowledged and appreciated by industry and researchers alike. Consequently, a wide range of analytics has been proposed to understand, steer, and exploit the mechanics and laws driving their functionality and creating the resulting benefits. However, analysts usually face significant problems in scaling existing and novel approaches to match the data volume and size of modern online communities. In this work, we propose and demonstrate the usage of the massively parallel data processing system Stratosphere, based on second order functions as an extended notion of the MapReduce paradigm, to provide a new level of scalability to such social-media analytics. Based on the popular example of role analysis, we present and illustrate how this massively parallel approach can be leveraged to scale out complex data-mining tasks, while providing a programming approach that eases the formulation of complete analytical workflows.
Dual Pyramid of Agents for Image Segmentation

An effective method for the early detection of breast cancer is the mammographic screening. One of the most important signs of early breast cancer is the presence of microcalcifications. For the detection of microcalcification in a mammography image, we propose to conceive a multiagent system based on a dual irregular pyramid. An initial segmentation is obtained by an incremental approach; the result represents level zero of the pyramid. The edge information obtained by application of the Canny filter is taken into account to affine the segmentation. The edge-agents and region-agents cooper level by level of the pyramid by exploiting its various characteristics to provide the segmentation process convergence. Keywords—Dual Pyramid, Image Segmentation, Multi-agent System, Region/Edge Cooperation.
Dual Pyramid of Agents for Image Segmentation

cancer is the mammographic screening. One of the most important signs of early breast cancer is the presence of microcalcifications. For the detection of microcalcification in a mammography image, we propose to conceive a multiagent system based on a dual irregular pyramid. An initial segmentation is obtained by an incremental approach; the result represents level zero of the pyramid. The edge information obtained by application of the Canny filter is taken into account to affine the segmentation. The edge-agents and region-agents cooper level by level of the pyramid by exploiting its various characteristics to provide the segmentation process convergence.
1 2 N ov 2 01 5 Integrating Heterogeneous Information via Flexible Regularization Framework for Recommendation

Recently, there is a surge of social recommendation, which leverages social relations among users to improve recommendation performance. However, in many applications, social relations are absent or very sparse. Meanwhile, the attribute information of users or items may be rich. It is a big challenge to exploit these attribute information for the improvement of recommendation performance. In this paper, we organize objects and relations in recommendation system as a heterogeneous information network, and introduce meta path based similarity measure to evaluate the similarity of users or items. Furthermore, a matrix factorization based dual regularization framework SimMF is proposed to flexibly integrate different types of information through adopting the similarity of users and items as regularization on latent factors of users and items. Extensive experiments not only validate the effectiveness of SimMF but also reveal some interesting findings. We find that attribute information of users and items can significantly improve recommendation accuracy, and their contribution seems more important than that of social relations. The experiments also reveal that different regularization models have obviously different impact on users and items.