Miracle-dz/UniFAR-FTUs huggingface dataset:
No More Chasing Waterfalls: A Measurement Study of the Header Bidding Ad-Ecosystem

In recent years, Header Bidding (HB) has gained popularity among web publishers, challenging the status quo in the ad ecosystem. Contrary to the traditional waterfall standard, HB aims to give back to publishers control of their ad inventory, increase transparency, fairness and competition among advertisers, resulting in higher ad-slot prices. Although promising, little is known about how this ad protocol works: What are HB's possible implementations, who are the major players, and what is its network and UX overhead? To address these questions, we design and implement HBDetector: a novel methodology to detect HB auctions on a website at realtime. By crawling 35,000 top Alexa websites, we collect and analyze a dataset of 800k auctions. We find that: (i) 14.28% of top websites utilize HB. (ii) Publishers prefer to collaborate with a few Demand Partners who also dominate the waterfall market. (iii) HB latency can be significantly higher (up to 3X in median case) than waterfall.
Influence Flowers of Academic Entities

We present the Influence Flower, a new visual metaphor for the influence profile of academic entities, including people, projects, institutions, conferences, and journals. While many tools quantify influence, we aim to expose the flow of influence between entities. The Influence Flower is an ego-centric graph, with a query entity placed in the centre. The petals are styled to reflect the strength of influence to and from other entities of the same or different type. For example, one can break down the incoming and outgoing influences of a research lab by research topics. The Influence Flower uses a recent snapshot of Microsoft Academic Graph, consisting of 212 million authors, their 176 million publications, and 1.2 billion citations. An interactive web app, Influence Map, is constructed around this central metaphor for searching and curating visualisations. We also propose a visual comparison method that highlights change in influence patterns over time. We demonstrate through several case studies that the Influence Flower supports data-driven inquiries about the following: researchers' careers over time; paper(s) and projects, including those with delayed recognition; the interdisciplinary profile of a research institution; and the shifting topical trends in conferences. We also use this tool on influence data beyond academic citations, by contrasting the academic and Twitter activities of a researcher.
Joint Relaying and Spatial Sharing Multicast Scheduling for mmWave Networks

Millimeter-wave (mmWave) communication plays a vital role in disseminating large volumes of data in beyond-5G networks efficiently. Unfortunately, the directionality of mmWave communication significantly complicates efficient data dissemination, particularly in multicasting, which is gaining more and more importance in emerging applications (e.g., V2X, public safety, massive IoT). While multicasting for systems operating at lower frequencies (i.e., sub-6GHz) has been extensively studied, they are sub-optimal for mmWave systems as mmWave has significantly different propagation characteristics, i.e., using the directional transmission to compensate for the high path loss and thus promoting spectrum sharing. In this paper, we propose novel multicast scheduling algorithms by jointly exploiting relaying and spatial sharing gains while aiming to minimize the multicast completion time. We first characterize the problem with a comprehensive model and formulate it with an integer linear program (ILP). We further design a practical and scalable semi-distributed algorithm named mmDiMu, based on gradually maximizing the transmission throughput over time. Finally, we carry out validation through extensive simulations in different scales, and the results show that mmDiMu significantly outperforms conventional algorithms with around 95% reduction on multicast completion time.
i-Berlioz: Towards Interactive Computer-Aided Orchestration with Temporal Control

This paper introduces i-Berlioz, a proof-of-concept interactive Computer-Aided Orchestration (CAO) system that suggests combinations of musical instruments to produce timbres specified by the user by means of verbal descriptors. The system relies on machine learning for timbre classification and generative orchestration, and tools to write and execute interactive scenarios. The paper details the two main parts of i-Berlioz: its generative and exploratory engines, respectively, accompanied with examples. The authors discuss how i-Berlioz can aid the composition of musical form based on timbre.
SkeleMotion: A New Representation of Skeleton Joint Sequences based on Motion Information for 3D Action Recognition

Due to the availability of large-scale skeleton datasets, 3D human action recognition has recently called the attention of computer vision community. Many works have focused on encoding skeleton data as skeleton image representations based on spatial structure of the skeleton joints, in which the temporal dynamics of the sequence is encoded as variations in columns and the spatial structure of each frame is represented as rows of a matrix. To further improve such representations, we introduce a novel skeleton image representation to be used as input of Convolutional Neural Networks (CNNs), named SkeleMotion. The proposed approach encodes the temporal dynamics by explicitly computing the magnitude and orientation values of the skeleton joints. Different temporal scales are employed to compute motion values to aggregate more temporal dynamics to the representation making it able to capture long-range joint interactions involved in actions as well as filtering noisy motion values. Experimental results demonstrate the effectiveness of the proposed representation on 3D action recognition outperforming the state-of-the-art on NTU RGB+D 120 dataset.
Deep Learning architectures for generalized immunofluorescence based nuclear image segmentation

Separating and labeling each instance of a nucleus (instance-aware segmentation) is the key challenge in segmenting single cell nuclei on fluorescence microscopy images. Deep Neural Networks can learn the implicit transformation of a nuclear image into a probability map indicating the class membership of each pixel (nucleus or background), but the use of post-processing steps to turn the probability map into a labeled object mask is error-prone. This especially accounts for nuclear images of tissue sections and nuclear images across varying tissue preparations. In this work, we aim to evaluate the performance of state-of-the-art deep learning architectures to segment nuclei in fluorescence images of various tissue origins and sample preparation types without post-processing. We compare architectures that operate on pixel to pixel translation and an architecture that operates on object detection and subsequent locally applied segmentation. In addition, we propose a novel strategy to create artificial images to extend the training set. We evaluate the influence of ground truth annotation quality, image scale and segmentation complexity on segmentation performance. Results show that three out of four deep learning architectures (U-Net, U-Net with ResNet34 backbone, Mask R-CNN) can segment fluorescent nuclear images on most of the sample preparation types and tissue origins with satisfactory segmentation performance. Mask R-CNN, an architecture designed to address instance aware segmentation tasks, outperforms other architectures. Equal nuclear mean size, consistent nuclear annotations and the use of artificially generated images result in overall acceptable precision and recall across different tissues and sample preparation types.
Clash of the Trackers: Measuring the Evolution of the Online Tracking Ecosystem

Websites are constantly adapting the methods used, and intensity with which they track online visitors. However, the wide-range enforcement of GDPR since one year ago (May 2018) forced websites serving EU-based online visitors to eliminate or at least reduce such tracking activity, given they receive proper user consent. Therefore, it is important to record and analyze the evolution of this tracking activity and assess the overall "privacy health" of the Web ecosystem and if it is better after GDPR enforcement. This work makes a significant step towards this direction. In this paper, we analyze the online ecosystem of 3rd-parties embedded in top websites which amass the majority of online tracking through 6 time snapshots taken every few months apart, in the duration of the last 2 years. We perform this analysis in three ways: 1) by looking into the network activity that 3rd-parties impose on each publisher hosting them, 2) by constructing a bipartite graph of "publisher-to-tracker", connecting 3rd parties with their publishers, 3) by constructing a "tracker-to-tracker" graph connecting 3rd-parties who are commonly found in publishers. We record significant changes through time in number of trackers, traffic induced in publishers (incoming vs. outgoing), embeddedness of trackers in publishers, popularity and mixture of trackers across publishers. We also report how such measures compare with the ranking of publishers based on Alexa. On the last level of our analysis, we dig deeper and look into the connectivity of trackers with each other and how this relates to potential cookie synchronization activity.
Weakly Supervised Body Part Parsing with Pose based Part Priors

Human body part parsing refers to the task of predicting the semantic segmentation mask for each body part. Fully supervised body part parsing methods achieve good performances, but require an enormous amount of effort to annotate part masks for training. In contrast to high annotation costs required for a limited number of part mask annotations, a large number of weak labels such as poses and full body masks already exist and contain relevant information. Motivated by the possibility of using existing weak labels, we propose the first weakly supervised body part parsing framework. The basic idea is to train a parsing network with pose generated part priors that has blank uncertain regions on estimated boundaries, and use an iterative refinement module to generate new supervision and predictions on these regions. When sufficient extra weak supervisions are available, our weakly-supervised results (62.0% mIoU) on Pascal-Person-Part are comparable to the fully supervised state-of-the-art results (63.6% mIoU). Furthermore, in the extended semi-supervised setting, the proposed framework outperforms the state-of-art methods. In addition, we show that the proposed framework can be extended to other keypoint-supervised part parsing tasks such as face parsing.
AUC: Nonparametric Estimators and Their Smoothness

Nonparametric estimation of a statistic, in general, and of the error rate of a classification rule, in particular, from just one available dataset through resampling is well mathematically founded in the literature using several versions of bootstrap and influence function. This article first provides a concise review of this literature to establish the theoretical framework that we use to construct, in a single coherent framework, nonparametric estimators of the AUC (a two-sample statistic) other than the error rate (a one-sample statistic). In addition, the smoothness of some of these estimators is well investigated and explained. Our experiments show that the behavior of the designed AUC estimators confirms the findings of the literature for the behavior of error rate estimators in many aspects including: the weak correlation between the bootstrap-based estimators and the true conditional AUC; and the comparable accuracy of the different versions of the bootstrap estimators in terms of the RMS with little superiority of the . 632+ bootstrap estimator.
Towards Using Data to Inform Decisions in Agile Software Development: Views of Available Data

Software development comprises complex tasks which are performed by humans. It involves problem solving, domain understanding and communication skills as well as knowledge of a broad variety of technologies, architectures, and solution approaches. As such, software development projects include many situations where crucial decisions must be made. Making the appropriate organizational or technical choices for a given software team building a product can make the difference between project success or failure. Software development methods have introduced frameworks and sets of best practices for certain contexts, providing practitioners with established guidelines for these important choices. Current Agile methods employed in modern software development have highlighted the importance of the human factors in software development. These methods rely on short feedback loops and the self-organization of teams to enable collaborative decision making. While Agile methods stress the importance of empirical process control, i.e. relying on data to make decisions, they do not prescribe in detail how this goal should be achieved. In this paper, we describe the types and abstraction levels of data and decisions within modern software development teams and identify the benefits that usage of this data enables. We argue that the principles of data-driven decision making are highly applicable, yet underused, in modern Agile software development.
Wireless Power Transfer: A Reconfigurable Phased Array with Novel Feeding Architecture

Szazynski, Mitchel H. M.S.E.C. E. , Purdue University, May 2018. Wireless Power Transfer: A Reconfigurable Phased Array with Novel Feeding Architecture. Major Professor: Peter J. Schubert. This thesis proposes a reconfigurable phased array of antennas for wireless power transfer. The array finds use in many applications, from drone destruction (for defense) to wireless charging of robots and mobile devices. It utilizes a novel feeding architecture to greatly reduce the number of high cost elements (such as amplifiers and phase shifters) as well as the quantity of unused resources in the system. Upon the instruction of the CPU, the array can separate into any number of subarrays, each of which transmits power to a single receiver, steering its beam as the receiver changes location. Currently dormant elements in the array can be used to provide position information about the receivers, either via Radar, or by listening for beacons pulses from the receiver. All of this is made possible, with only 4 amplifiers and 3 phase shifters, by the proposed 4-Bus Method. The source signal is divided into four buses, which are respectively phase shifted by 270 degrees, 180 degrees, 90 degrees, and 0 degrees (no shifter required) and then amplified. The CPU calculates, based on the number and positions of the receivers / targets, what the amplitude and phase excitation must be at each element. Any phase and amplitude which could be required can be achieved by simply adding together appropriate quantities of the correct two buses. In order to achieve this, the key piece is the variable power divider. These differ from Wilkinson dividers in that the dividing ratio can be changed via an applied DC voltage. Therefore, at each junction, by properly diverting the power levels on each phase bus to their proper location, complete delocalization of both amplifiers and phase shifters can be achieved.
Millimeter-Wave NOMA With User Grouping, Power Allocation and Hybrid Beamforming

This paper investigates the application of non-orthogonal multiple access in millimeter-Wave communications (mmWave-NOMA). Particularly, we consider downlink transmission with a hybrid beamforming structure. A user grouping algorithm is first proposed according to the channel correlations of the users. Whereafter, a joint hybrid beamforming and power allocation problem is formulated to maximize the achievable sum rate, subject to a minimum rate constraint for each user. To solve this non-convex problem with high-dimensional variables, we first obtain the solution of power allocation under arbitrary fixed hybrid beamforming, which is divided into intra-group power allocation and inter-group power allocation. Then, given arbitrary fixed analog beamforming, we utilize the approximate zero-forcing method to design the digital beamforming to minimize the inter-group interference. Finally, the analog beamforming problem with the constant-modulus constraint is solved with a proposed boundary-compressed particle swarm optimization algorithm. The simulation results show that the proposed joint approach, including user grouping, hybrid beamforming and power allocation, outperforms the state-of-the-art schemes and the conventional mmWave orthogonal multiple access system in terms of achievable sum rate, and energy efficiency.
Attention Filtering for Multi-person Spatiotemporal Action Detection on Deep Two-Stream CNN Architectures

Action detection and recognition tasks have been the target of much focus in the computer vision community due to their many applications, namely, security, robotics and recommendation systems. Recently, datasets like AVA, provide multi-person, multi-label, spatiotemporal action detection and recognition challenges. Being unable to discern which portions of the input to use for classification is a limitation of two-stream CNN approaches, once the vision task involves several people with several labels. We address this limitation and improve the state-of-the-art performance of two-stream CNNs. In this paper we present four contributions: our fovea attention filtering that highlights targets for classification without discarding background; a generalized binary loss function designed for the AVA dataset; miniAVA, a partition of AVA that maintains temporal continuity and class distribution with only one tenth of the dataset size; and ablation studies on alternative attention filters. Our method, using fovea attention filtering and our generalized binary loss, achieves a relative video mAP improvement of 20% over the two-stream baseline in AVA, and is competitive with the state-of-the-art in the UCF101-24. We also show a relative video mAP improvement of 12.6% when using our generalized binary loss over the standard sum-of-sigmoids.
LEAF-QA: Locate, Encode & Attend for Figure Question Answering

We introduce LEAF-QA, a comprehensive dataset of 250,000 densely annotated figures/charts, constructed from real-world open data sources, along with 2 million question-answer (QA) pairs querying the structure and semantics of these charts. LEAF-QA highlights the problem of multimodal QA, which is notably different from conventional visual QA (VQA), and has recently gained interest in the community. Furthermore, LEAF-QA is significantly more complex than previous attempts at chart QA, viz. FigureQA and DVQA, which present only limited variations in chart data. LEAF-QA being constructed from real-world sources, requires a novel architecture to enable question answering. To this end, LEAF-Net, a deep architecture involving chart element localization, question and answer encoding in terms of chart elements, and an attention network is proposed. Different experiments are conducted to demonstrate the challenges of QA on LEAF-QA. The proposed architecture, LEAF-Net also considerably advances the current state-of-the-art on FigureQA and DVQA.
Analyzing Learned Molecular Representations for Property Prediction

Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerprints or expert-crafted descriptors and graph convolutional neural networks that construct a learned molecular representation by operating on the graph structure of the molecule. However, recent literature has yet to clearly determine which of these two methods is superior when generalizing to new chemical space. Furthermore, prior research has rarely examined these new models in industry research settings in comparison to existing employed models. In this paper, we benchmark models extensively on 19 public and 16 proprietary industrial data sets spanning a wide variety of chemical end points. In addition, we introduce a graph convolutional model that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary data sets. Our empirical findings indicate that while approaches based on these representations have yet to reach the level of experimental reproducibility, our proposed model nevertheless offers significant improvements over models currently used in industrial workflows.
Extreme High-energy Peaked BL Lac Objects and their TeV Gamma-ray Emission: Are They a Homogeneous Population?

Extreme high-energy peaked BL Lac objects (EHBLs) are an emerging class of blazars with exceptional spectral properties. In blazars, the spectral energy distribution (SED) is dominated by the non-thermal emission of the relativistic jet, and consists of two main broad humps. For the EHBLs, these two components peak in the X-ray and GeV-TeV bands, respectively. Although the number of TeV detected extreme blazars is very limited, recent observations by Imaging Atmospheric Cherenkov Telescopes (IACTs) have revealed that in some of them the energy of the second peak exceeds several TeV (e.g. 1ES 0229+200). Their exceptional hard TeV spectra represent a challenge for the standard leptonic modeling, and a possible hadronic contribution may make these objects high-energy neutrinos producers. Moreover, they are important for the implications on the indirect measurements of the extragalactic background light and of the intergalactic magnetic field. In this contribution, we perform a comparative study of the multi-wavelength spectral energy distributions of a sample of hard X-ray selected EHBL objects. The analysis suggests that the EHBL class is not homogeneous, and a possible sub-classification may be unveiled with TeV gamma-ray observations of the candidates. With the purpose of increasing their number and settle their statistics, we discuss the potential detectability of the currently undetected TeV-emitting EHBLs in our sample by current and next generation of IACTs.