nils-herrmann/clean_scirepeval huggingface dataset:
On Sample Selection Bias and Its Efficient Correction via Model Averaging and Unlabeled Examples

Sample selection bias is a common problem encountered when using data mining algorithms for many real-world applications. Traditionally, it is assumed that training and test data are sampled from the same probability distribution, the so called “stationary or non-biased distribution assumption.” However, this assumption is often violated in reality. Typical examples include marketing solicitation, fraud detection, drug testing, loan approval, school enrollment, etc. For these applications the only labeled data available for training is a biased representation, in various ways, of the future data on which the inductive model will predict. Intuitively, some examples sampled frequently into the training data may actually be infrequent in the testing data, and vice versa. When this happens, an inductive model constructed from biased training set may not be as accurate on unbiased testing data if there had not been any selection bias in the training data. In this paper, we first improve and clarify a previously proposed categorization of sample selection bias. In particular, we show that unless under very restricted conditions, sample selection bias is a common problem for many real-world situations. We then analyze various effects of sample selection bias on inductive modeling, in particular, how the “true” conditional probability P (y|x) to be modeled by inductive learners can be misrepresented in the biased training data, that subsequently misleads a learning algorithm. To solve inaccuracy problems due to sample selection bias, we explore how to use model averaging of (1) conditional probabilities P (y|x), (2) feature probabilities P (x), and (3) joint probabilities, P (x, y), to reduce the influence of sample selection bias on model accuracy. In particular, we explore on how to use unlabeled data in a semi-supervised learning framework to improve the accuracy of descriptive models constructed from biased training samples. IBM T.J.Watson Research Center, Hawthorne, NY 10532, weifan@us.ibm.com Department of Computer Science, University at Albany, State University of New York, Albany, NY 12222, davidson@cs.albany.edu
Sound And Immersion In The First-Person Shooter

One of the aims of modern First-Person Shooter (FPS ) design is to provide an immersive experience to the player. This paper examines the role of sound in enabling s uch immersion and argues that, even in ‘realism’ FPS ga mes, it may be achieved sonically through a focus on carica ture rather than realism. The paper utilizes and develo ps previous work in which both a conceptual framework for the d sign and analysis of run and gun FPS sound is developed and the notion of the relationship between player and FPS soundscape as an acoustic ecology is put forward (G rimshaw and Schott 2007a; Grimshaw and Schott 2007b). Some problems of sound practice and sound reproduction i n the game are highlighted and a conceptual solution is p roposed.
Starburst: A hybrid algorithm for video-based eye tracking combining feature-based and model-based approaches

Knowing the user’s point of gaze has significant potential to enhance current human-computer interfaces, given that eye movements can be used as an indicator of the attentional state of a user. The primary obstacle of integrating eye movements into today’s interfaces is the availability of a reliable, low-cost open-source eye-tracking system. Towards making such a system available to interface designers, we have developed a hybrid eye-tracking algorithm that integrates feature-based and model-based approaches and made it available in an open-source package. We refer to this algorithm as "starburst" because of the novel way in which pupil features are detected. This starburst algorithm is more accurate than pure feature-based approaches yet is signi?cantly less time consuming than pure modelbased approaches. The current implementation is tailored to tracking eye movements in infrared video obtained from an inexpensive head-mounted eye-tracking system. A validation study was conducted and showed that the technique can reliably estimate eye position with an accuracy of approximately one degree of visual angle.
Arithmetic Using Word-wise Homomorphic Encryption

Homomorphic encryption has progressed rapidly in both efficiency and versatility since its emergence in 2009. Meanwhile, a multitude of pressing privacy needs — ranging from cloud computing to healthcare management to the handling of shared databases such as those containing genomics data — call for immediate solutions that apply fully homomorpic encryption (FHE) and somewhat homomorphic encryption (SHE) technologies. Further progress towards these ends requires new ideas for the efficient implementation of algebraic operations on word-based (as opposed to bit-wise) encrypted data. Whereas handling data encrypted at the bit level leads to prohibitively slow algorithms for the arithmetic operations that are essential for cloud computing, the word-based approach hits its bottleneck when operations such as integer comparison are needed. In this work, we tackle this challenging problem, proposing solutions to problems — including comparison and division — in word-based encryption via a leveled FHE scheme. We present concrete performance figures for all proposed primitives.
Design and evaluation of a command recommendation system for software applications

We examine the use of modern recommender system technology to aid command awareness in complex software applications. We first describe our adaptation of traditional recommender system algorithms to meet the unique requirements presented by the domain of software commands. A user study showed that our item-based collaborative filtering algorithm generates 2.1 times as many good suggestions as existing techniques. Motivated by these positive results, we propose a design space framework and its associated algorithms to support both global and contextual recommendations. To evaluate the algorithms, we developed the CommunityCommands plug-in for AutoCAD. This plug-in enabled us to perform a 6-week user study of real-time, within-application command recommendations in actual working environments. We report and visualize command usage behaviors during the study, and discuss how the recommendations affected users behaviors. In particular, we found that the plug-in successfully exposed users to new commands, as unique commands issued significantly increased.
Active Learning of Pareto Fronts

This paper introduces the active learning of Pareto fronts (ALP) algorithm, a novel approach to recover the Pareto front of a multiobjective optimization problem. ALP casts the identification of the Pareto front into a supervised machine learning task. This approach enables an analytical model of the Pareto front to be built. The computational effort in generating the supervised information is reduced by an active learning strategy. In particular, the model is learned from a set of informative training objective vectors. The training objective vectors are approximated Pareto-optimal vectors obtained by solving different scalarized problem instances. The experimental results show that ALP achieves an accurate Pareto front approximation with a lower computational effort than state-of-the-art estimation of distribution algorithms and widely known genetic techniques.
An iBeacon primer for indoor localization: demo abstract

Providing an accurate, low cost estimate of sub-room indoor positioning remains an active area of research with applications including reactive indoor spaces, dynamic temperature control, wireless health, and augmented reality, to name a few. Recently proposed indoor localization solutions have required anywhere from zero additional infrastructure to customized RF hardware and have provided room-level to centimeter-level accuracy, typically in that respective order. One emerging technology that is proving a pragmatic solution for scalable, accurate localization is that of Bluetooth Low Energy beaconing, spearheaded by Apple's recently introduced iBeacon protocol. In this demo, we present a suite of localization tools developed around the iBeacon protocol, providing an in-depth look at Bluetooth Low Energy's viability as an indoor positioning technology. Our system shows an average position estimation error of 0.53 meters.
UTOPIAN: User-Driven Topic Modeling Based on Interactive Nonnegative Matrix Factorization

Topic modeling has been widely used for analyzing text document collections. Recently, there have been significant advancements in various topic modeling techniques, particularly in the form of probabilistic graphical modeling. State-of-the-art techniques such as Latent Dirichlet Allocation (LDA) have been successfully applied in visual text analytics. However, most of the widely-used methods based on probabilistic modeling have drawbacks in terms of consistency from multiple runs and empirical convergence. Furthermore, due to the complicatedness in the formulation and the algorithm, LDA cannot easily incorporate various types of user feedback. To tackle this problem, we propose a reliable and flexible visual analytics system for topic modeling called UTOPIAN (User-driven Topic modeling based on Interactive Nonnegative Matrix Factorization). Centered around its semi-supervised formulation, UTOPIAN enables users to interact with the topic modeling method and steer the result in a user-driven manner. We demonstrate the capability of UTOPIAN via several usage scenarios with real-world document corpuses such as InfoVis/VAST paper data set and product review data sets.
Track-to-track fusion with asynchronous sensors and out-of-sequence tracks using information matrix fusion for advanced driver assistance systems

Future advanced driver assistance systems will contain multiple sensors that are used for several applications, such as highly automated driving on freeways. The problem is that the sensors are usually asynchronous and their data possibly out-of-sequence, making fusion of the sensor data non-trivial. This paper presents a novel approach to track-to-track fusion for automotive applications with asynchronous and out-of-sequence sensors using information matrix fusion. This approach solves the problem of correlation between sensor data due to the common process noise and common track history, which eliminates the need to replace the global track estimate with the fused local estimate at each fusion cycle. The information matrix fusion approach is evaluated in simulation and its performance demonstrated using real sensor data on a test vehicle designed for highly automated driving on freeways.
Max-Margin Deep Generative Models for (Semi-)Supervised Learning

Deep generative models (DGMs) can effectively capture the underlying distributions of complex data by learning multilayered representations and performing inference. However, it is relatively insufficient to boost the discriminative ability of DGMs. This paper presents max-margin deep generative models (mmDGMs) and a class-conditional variant (mmDCGMs), which explore the strongly discriminative principle of max-margin learning to improve the predictive performance of DGMs in both supervised and semi-supervised learning, while retaining the generative capability. In semi-supervised learning, we use the predictions of a max-margin classifier as the missing labels instead of performing full posterior inference for efficiency; we also introduce additional max-margin and label-balance regularization terms of unlabeled data for effectiveness. We develop an efficient doubly stochastic subgradient algorithm for the piecewise linear objectives in different settings. Empirical results on various datasets demonstrate that: (1) max-margin learning can significantly improve the prediction performance of DGMs and meanwhile retain the generative ability; (2) in supervised learning, mmDGMs are competitive to the best fully discriminative networks when employing convolutional neural networks as the generative and recognition models; and (3) in semi-supervised learning, mmDCGMs can perform efficient inference and achieve state-of-the-art classification results on several benchmarks.
Biometric storyboards: visualising game user research data

Player experience is difficult to evaluate and report, especially using quantitative methodologies in addition to observations and interviews. One step towards tying quantitative physiological measures of player arousal to player experience reports are Biometric Storyboards (BioSt). They can visualise meaningful relationships between a player's physiological changes and game events. This paper evaluates the usefulness of BioSt to the game industry. We presented the Biometric Storyboards technique to six game developers and interviewed them about the advantages and disadvantages of this technique.
A 1.2 – 6.4 GHz clock generator with a low-power DCO and programmable multiplier in 40-nm CMOS

This paper presents a clock generator for a MIPI M-PHY serial link transmitter, which includes an ADPLL, a digitally controlled oscillator (DCO), a programmable multiplier, and the actual serial driver. The paper focuses on the design of a DCO and how to enhance the frequency resolution to diminish the quantization noise introduced by the frequency discretization. As a result, a 17-kHz DCO frequency tuning resolution is demonstrated. Furthermore, implementation details of a low-power programmable 1-to-2-or-4 frequency multiplier are elaborated. The design has been implemented in a 40-nm CMOS process. The measurement results verify that the circuit provides the MIPI clock data rates from 1.248 GHz to 5.83 GHz. The DCO and multiplier unit dissipates a maximum of 3.9 mW from a 1.1 V supply and covers a small die area of 0.012 mm2.
Satellite radar interferometry' Two-dimensional phase unwrapping

Interferometric synthetic aperture radar observations provide a means for obtaining high-resolution digital topographic maps from measurements of amplitude and phase of two complex radar images. The phase of the radar echoes may only be measured modulo 2•; however, the whole phase at each point in the image is needed to obtain elevations. We present here our approach to "unwrapping" the 2r• ambiguities in the two-dimensional data set. We find that noise and geometrical radar layover corrupt our measurements locally, and these local errors can propagate to form global phase errors that affect the entire image. We show that the local errors, or residues, can be readily identified and avoided in the global phase estimation. We present a rectified digital topographic map derived from our unwrapped phase values.
Software Defined Networks : The New Norm for Networks

Our paper deals with the Software Defined Networking which is in extensive use in present times due to its programmability that helps in initializing, controlling and managing the network dynamics. It allows the network administrators to work on centralized network configuration and improve data center network efficiency. SDN is basically becoming popular for replacing the static architecture of traditional networks and limited computing and storage of the modern computing environments like data centers. Operations are performed by the controllers with the static switches. Due to imbalance caused due to dynamic traffic controllers are underutilized. On the other hand controllers which are overloaded may cause switches to suffer time delays. Wireless networks involve no cabling, therefore it is cost-effective, efficient, easy-installable, manageable and adaptable. We present how SDN makes it easy to achieve end point security by checking the device's status. Local agents collect device information and send to cloud service to check for vulnerabilities. The results of those checks are sent to the SDN Controller through published Application Program Interfaces (APIs). The SDN Controller instructs Open Flow switches to direct vulnerable devices to a Quarantine Network, thus detecting suspicious traffic. The implementation is done using the data network mathematical model.
Deep Generative Modeling for Scene Synthesis via Hybrid Representations

We present a deep generative scene modeling technique for indoor environments. Our goal is to train a generative model using a feed-forward neural network that maps a prior distribution (e.g., a normal distribution) to the distribution of primary objects in indoor scenes. We introduce a 3D object arrangement representation that models the locations and orientations of objects, based on their size and shape attributes. Moreover, our scene representation is applicable for 3D objects with different multiplicities (repetition counts), selected from a database. We show a principled way to train this model by combining discriminator losses for both a 3D object arrangement representation and a 2D image-based representation. We demonstrate the effectiveness of our scene representation and the deep learning method on benchmark datasets. We also show the applications of this generative model in scene interpolation and scene completion.
Convex Optimization for Big Data: Scalable, randomized, and parallel algorithms for big data analytics

This article reviews recent advances in convex optimization algorithms for big data, which aim to reduce the computational, storage, and communications bottlenecks. We provide an overview of this emerging field, describe contemporary approximation techniques such as first-order methods and randomization for scalability, and survey the important role of parallel and distributed computation. The new big data algorithms are based on surprisingly simple principles and attain staggering accelerations even on classical problems.
Millimeter-Wave Reflective-Type Phase Shifter in CMOS Technology

The design and measurement of a compact, wide-band reflective-type phase shifter in 90 nm CMOS technology in V-band frequency is presented. This phase shifter has a fractional bandwidth of 26% and an average insertion loss of 6 dB over all phase states. The chip area is only 0.08 mm 2. Measurement results show that the developed phase shifter provides 90° continuous phase shift over the frequency range of 50-65 GHz. The measured return loss is greater than 12 dB at 50 GHz. The output power is linear up to at least 4 dBm input power.
Workspace Satisfaction : The privacy-communication trade-off in open-plan offices

Open-plan office layout is commonly assumed to facilitate communication and interaction between co-workers, promoting workplace satisfaction and team-work effectiveness. On the other hand, open-plan layouts are widely acknowledged to be more disruptive due to uncontrollable noise and loss of privacy. Based on the occupant survey database from Center for the Built Environment (CBE), empirical analyses indicated that occupants assessed Indoor Environmental Quality (IEQ) issues in different ways depending on the spatial configuration (classified by the degree of enclosure) of their workspace. Enclosed private offices clearly outperformed open-plan layouts in most aspects of IEQ, particularly in acoustics, privacy and the proxemics issues. Benefits of enhanced ‘ease of interaction’ were smaller than the penalties of increased noise level and decreased privacy resulting from open-plan office configuration.
Initial response of autistic children in human-robot interaction therapy with humanoid robot NAO

The overall context proposed in this paper is part of our long-standing goal to contribute to a group of community that suffers from Autism Spectrum Disorder (ASD); a lifelong developmental disability. The objective of this paper is to present the development of our pilot experiment protocol where children with ASD will be exposed to the humanoid robot NAO. This fully programmable humanoid offers an ideal research platform for human-robot interaction (HRI). This study serves as the platform for fundamental investigation to observe the initial response and behavior of the children in the said environment. The system utilizes external cameras, besides the robot's own visual system. Anticipated results are the real initial response and reaction of ASD children during the HRI with the humanoid robot. This shall leads to adaptation of new procedures in ASD therapy based on HRI, especially for a non-technical-expert person to be involved in the robotics intervention during the therapy session.
AVEC 2017: Real-life Depression, and Affect Recognition Workshop and Challenge

The Audio/Visual Emotion Challenge and Workshop (AVEC 2017) "Real-life depression, and affect" will be the seventh competition event aimed at comparison of multimedia processing and machine learning methods for automatic audiovisual depression and emotion analysis, with all participants competing under strictly the same conditions. The goal of the Challenge is to provide a common benchmark test set for multimodal information processing and to bring together the depression and emotion recognition communities, as well as the audiovisual processing communities, to compare the relative merits of the various approaches to depression and emotion recognition from real-life data. This paper presents the novelties introduced this year, the challenge guidelines, the data used, and the performance of the baseline system on the two proposed tasks: dimensional emotion recognition (time and value-continuous), and dimensional depression estimation (value-continuous).
Intensification of Intimacy in Computer-Mediated Communication From Perception to Behavior : Disclosure Reciprocity and the

This study proposes and tests a novel theoretical mechanism to explain increased selfdisclosure intimacy in text-based computer-mediated communication (CMC) versus face-to-face (FtF) interactions. On the basis of joint effects of perception intensification processes in CMC and the disclosure reciprocity norm, the authors predict a perceptionbehavior intensification effect, according to which people perceive partners’ initial disclosures as more intimate in CMC than FtF and, consequently, reciprocate with more intimate disclosures of their own. An experiment compares disclosure reciprocity in textbased CMC and FtF conversations, in which participants interacted with a confederate who made either intimate or nonintimate disclosures across the two communication media. The utterances generated by the participants are coded for disclosure frequency and intimacy. Consistent with the proposed perception-behavior intensification effect, CMC participants perceive the confederate’s disclosures as more intimate, and, importantly, reciprocate with more intimate disclosures than FtF participants do.
A Tale of Two Graphs: Property Graphs as RDF in Oracle

Graph Databases are gaining popularity, owing to pervasiveness of graph data in social networks, physical sciences, networking, and web applications. A majority of these databases are based on the property graph model, which is characterized as key/value-based, directed, and multi-relational. In this paper, we consider the problem of supporting property graphs as RDF in Oracle Database. We introduce a property graph to RDF transformation scheme. The main challenge lies in representing the key/value properties of property graph edges in RDF. We propose three models: 1) named graph based, 2) subproperty based, and 3) (extended) reification based, all of which can be supported with RDF capabilities in Oracle Database. These models are evaluated with respect to ease of SPARQL query formulation, join complexities, skewness in generated RDF data, query performance, and storage overhead. An experimental study with a real-life Twitter social network dataset on Oracle Database 12c demonstrates the feasibility of representing property graphs as RDF and presents a quantitative performance comparison of the proposed models.