nils-herrmann/clean_scirepeval huggingface dataset:
Conditioning in First-Order Knowledge Compilation and Lifted Probabilistic Inference

Knowledge compilation is a powerful technique for compactly representing and efficiently reasoning about logical knowledge bases. It has been successfully applied to numerous problems in artificial intelligence, such as probabilistic inference and conformant planning. Conditioning, which updates a knowledge base with observed truth values for some propositions, is one of the fundamental operations employed for reasoning. In the propositional setting, conditioning can be efficiently applied in all cases. Recently, people have explored compilation for first-order knowledge bases. The majority of this work has centered around using first-order dDNNF circuits as the target compilation language. However, conditioning has not been studied in this setting. This paper explores how to condition a first-order d-DNNF circuit. We show that it is possible to efficiently condition these circuits on unary relations. However, we prove that conditioning on higher arity relations is #P-hard. We study the implications of these findings on the application of performing lifted inference for first-order probabilistic models. This leads to a better understanding of which types of queries lifted inference can address.
Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework.

The atypical face scanning patterns in individuals with Autism Spectrum Disorder (ASD) has been repeatedly discovered by previous research. The present study examined whether their face scanning patterns could be potentially useful to identify children with ASD by adopting the machine learning algorithm for the classification purpose. Particularly, we applied the machine learning method to analyze an eye movement dataset from a face recognition task [Yi et al., 2016], to classify children with and without ASD. We evaluated the performance of our model in terms of its accuracy, sensitivity, and specificity of classifying ASD. Results indicated promising evidence for applying the machine learning algorithm based on the face scanning patterns to identify children with ASD, with a maximum classification accuracy of 88.51%. Nevertheless, our study is still preliminary with some constraints that may apply in the clinical practice. Future research should shed light on further valuation of our method and contribute to the development of a multitask and multimodel approach to aid the process of early detection and diagnosis of ASD.
A Nonparametric Approach to Modeling Choice with Limited Data

A central push in operations models over the last decade has been the incorporation of models of customer choice. Real world implementations of many of these models face the formidable stumbling block of simply identifying the ‘right’ model of choice to use. Thus motivated, we visit the following problem: For a ‘generic’ model of consumer choice (namely, distributions over preference lists) and a limited amount of data on how consumers actually make decisions (such as marginal information about these distributions), how may one predict revenues from offering a particular assortment of choices? We present a framework to answer such questions and design a number of tractable algorithms from a data and computational standpoint for the same. This paper thus takes a significant step towards ‘automating’ the crucial task of choice model selection in the context of operational decision problems.
Bridging Java and AspectJ through explicit join points

Through AspectJ, aspect-oriented programming (AOP) is becoming of increasing interest and availability to Java programmers as it matures as a methodology for improved software modularity via the separation of cross-cutting concerns. AOP proponents often advocate a development strategy where Java programmers write the main application, ignoring cross-cutting concerns, and then AspectJ programmers, domain experts in their specific concerns, weave in the logic for these more specialized cross-cutting concerns. However, several authors have recently debated the merits of this strategy by empirically showing certain drawbacks. The proposed solutions paint a different development strategy where base code and aspect programmers are aware of each other (to varying degrees) and interactions between cross-cutting concerns are planned for early on.
 Herein we explore new possibilities in the language design space that open up when the base code is aware of cross-cutting aspects. Using our insights from this exploration we concretize these new possibilities by extending AspectJ with concise yet powerful constructs, while maintaining full backwards compatibility. These new constructs allow base code and aspects to cooperate in ways that were previously not possible: arbitrary blocks of code can be advised, advice can be explicitly parameterized, base code can guide aspects in where to apply advice, and aspects can statically enforce new constraints upon the base code that they advise. These new techniques allow aspect modularity and program safety to increase. We illustrate the value of our extensions through an example based on transactions.
Personalized time-aware tweets summarization

We focus on the problem of selecting meaningful tweets given a user's interests; the dynamic nature of user interests, the sheer volume, and the sparseness of individual messages make this an challenging problem. Specifically, we consider the task of time-aware tweets summarization, based on a user's history and collaborative social influences from ``social circles.'' We propose a time-aware user behavior model, the Tweet Propagation Model (TPM), in which we infer dynamic probabilistic distributions over interests and topics. We then explicitly consider novelty, coverage, and diversity to arrive at an iterative optimization algorithm for selecting tweets. Experimental results validate the effectiveness of our personalized time-aware tweets summarization method based on TPM.
Secure Estimation and Control for Cyber-Physical Systems Under Adversarial Attacks

The vast majority of today's critical infrastructure is supported by numerous feedback control loops and an attack on these control loops can have disastrous consequences. This is a major concern since modern control systems are becoming large and decentralized and thus more vulnerable to attacks. This paper is concerned with the estimation and control of linear systems when some of the sensors or actuators are corrupted by an attacker. We give a new simple characterization of the maximum number of attacks that can be detected and corrected as a function of the pair (A,C) of the system and we show in particular that it is impossible to accurately reconstruct the state of a system if more than half the sensors are attacked. In addition, we show how the design of a secure local control loop can improve the resilience of the system. When the number of attacks is smaller than a threshold, we propose an efficient algorithm inspired from techniques in compressed sensing to estimate the state of the plant despite attacks. We give a theoretical characterization of the performance of this algorithm and we show on numerical simulations that the method is promising and allows to reconstruct the state accurately despite attacks. Finally, we consider the problem of designing output-feedback controllers that stabilize the system despite sensor attacks. We show that a principle of separation between estimation and control holds and that the design of resilient output feedback controllers can be reduced to the design of resilient state estimators.
SocialFilter: Introducing social trust to collaborative spam mitigation

We propose SocialFilter, a trust-aware collaborative spam mitigation system. Our proposal enables nodes with no email classification functionality to query the network on whether a host is a spammer. It employs Sybil-resilient trust inference to weigh the reports concerning spamming hosts that collaborating spam-detecting nodes (reporters) submit to the system. It weighs the spam reports according to the trustworthiness of their reporters to derive a measure of the system's belief that a host is a spammer. SocialFilter is the first collaborative unwanted traffic mitigation system that assesses the trustworthiness of spam reporters by both auditing their reports and by leveraging the social network of the reporters' administrators. The design and evaluation of our proposal offers us the following lessons: a) it is plausible to introduce Sybil-resilient Online-Social-Network-based trust inference mechanisms to improve the reliability and the attack-resistance of collaborative spam mitigation; b) using social links to obtain the trustworthiness of reports concerning spammers can result in comparable spam-blocking effectiveness with approaches that use social links to rate-limit spam (e.g., Ostra ; c) unlike Ostra, in the absence of reports that incriminate benign email senders, SocialFilter yields no false positives.
Calibration games: making calibration tasks enjoyable by adding motivating game elements

Interactive systems often require calibration to ensure that input and output are optimally configured. Without calibration, user performance can degrade (e.g., if an input device is not adjusted for the user's abilities), errors can increase (e.g., if color spaces are not matched), and some interactions may not be possible (e.g., use of an eye tracker). The value of calibration is often lost, however, because many calibration processes are tedious and unenjoyable, and many users avoid them altogether. To address this problem, we propose calibration games that gather calibration data in an engaging and entertaining manner. To facilitate the creation of calibration games, we present design guidelines that map common types of calibration to core tasks, and then to well-known game mechanics. To evaluate the approach, we developed three calibration games and compared them to standard procedures. Users found the game versions significantly more enjoyable than regular calibration procedures, without compromising the quality of the data. Calibration games are a novel way to motivate users to carry out calibrations, thereby improving the performance and accuracy of many human-computer systems.
Inverse volume rendering with material dictionaries

Translucent materials are ubiquitous, and simulating their appearance requires accurate physical parameters. However, physically-accurate parameters for scattering materials are difficult to acquire. We introduce an optimization framework for measuring bulk scattering properties of homogeneous materials (phase function, scattering coefficient, and absorption coefficient) that is more accurate, and more applicable to a broad range of materials. The optimization combines stochastic gradient descent with Monte Carlo rendering and a material dictionary to invert the radiative transfer equation. It offers several advantages: (1) it does not require isolating single-scattering events; (2) it allows measuring solids and liquids that are hard to dilute; (3) it returns parameters in physically-meaningful units; and (4) it does not restrict the shape of the phase function using Henyey-Greenstein or any other low-parameter model. We evaluate our approach by creating an acquisition setup that collects images of a material slab under narrow-beam RGB illumination. We validate results by measuring prescribed nano-dispersions and showing that recovered parameters match those predicted by Lorenz-Mie theory. We also provide a table of RGB scattering parameters for some common liquids and solids, which are validated by simulating color images in novel geometric configurations that match the corresponding photographs with less than 5% error.
Guidelines for Internet of Things Deployment Approaches - The Thing Commandments

The Internet of Things (IoT) describes the interconnection of objects (or Things) for various purposes including identification, communication, sensing, and data collection. “Things” in this context range from traditional computing devices like Personal Computers (PC) to general household objects embedded with capabilities for sensing and/or communication through the use of technologies such as Radio Frequency Identification (RFID). This conceptual paper, from a philosophical viewpoint, introduces an initial set of guiding principles also referred to in the paper as commandments that can be applied by all the stakeholders involved in the IoT during its introduction, deployment and thereafter.
Learning Visually Grounded Sentence Representations

We investigate grounded sentence representations, where we train a sentence encoder to predict the image features of a given caption— i.e., we try to “imagine” how a sentence would be depicted visually—and use the resultant features as sentence representations. We examine the quality of the learned representations on a variety of standard sentence representation quality benchmarks, showing improved performance for groundedmodels over non-grounded ones. In addition, we thoroughly analyze the extent to which grounding contributes to improved performance, and show that the system also learns improved word embeddings.
PoseTrack: Joint Multi-person Pose Estimation and Tracking

In this work, we introduce the challenging problem of joint multi-person pose estimation and tracking of an unknown number of persons in unconstrained videos. Existing methods for multi-person pose estimation in images cannot be applied directly to this problem, since it also requires to solve the problem of person association over time in addition to the pose estimation for each person. We therefore propose a novel method that jointly models multi-person pose estimation and tracking in a single formulation. To this end, we represent body joint detections in a video by a spatio-temporal graph and solve an integer linear program to partition the graph into sub-graphs that correspond to plausible body pose trajectories for each person. The proposed approach implicitly handles occlusion and truncation of persons. Since the problem has not been addressed quantitatively in the literature, we introduce a challenging Multi-Person PoseTrack dataset, and also propose a completely unconstrained evaluation protocol that does not make any assumptions about the scale, size, location or the number of persons. Finally, we evaluate the proposed approach and several baseline methods on our new dataset.
Amodal Instance Segmentation

We consider the problem of amodal instance segmentation, the objective of which is to predict the region encompassing both visible and occluded parts of each object. Thus far, the lack of publicly available amodal segmentation annotations has stymied the development of amodal segmentation methods. In this paper, we sidestep this issue by relying solely on standard modal instance segmentation annotations to train our model. The result is a new method for amodal instance segmentation, which represents the first such method to the best of our knowledge. We demonstrate the proposed method’s effectiveness both qualitatively and quantitatively.
Automatic Clutter-Canceler for Microwave Life-Detection Systems

A microprocessor-controlled automatic cluttercancellation subsystem, consisting of a programmable microwave attenuator and a programmable microwave phase-shifter controlled by a microprocessor-based control unit, has been developed for a microwave life-detection system (L-band 2 GHz or X-band 10 GHz) which can remotely sense breathing and heartbeat movements of living subjects. This automatic cluttercancellation subsystem has drastically improved a very slow p~ocess .of manual clutter-cancellation adjustment in our preVIOU.S mlcro~av.e sys~em. ~his is very important for some potential applications mcludmg location of earthquake or avalanche-trapped victims through rubble. A series of experiments have been conducted to demonstrate the applicability of this microwave life-detection system for rescue purposes. The automatic clutter-canceler may also have a potential application in some CW radar systems.
Mirror or Megaphone ? : How relationships between narcissism and social networking site use differ on Facebook and Twitter

As research on the connection between narcissism and social networking site (SNS) use grows, definitions of SNS and measurements of their use continue to vary, leading to conflicting results. To improve understanding of the relationship between narcissism and SNS use, as well as the implications of differences in definition and measurement, we examine two ways of measuring Facebook and Twitter use by testing the hypothesis that SNS use is positively associated with narcissism. We also explore the relation between these types of SNS use and different components of narcissism within college students and general adult samples. Our findings suggest that for college students, posting on Twitter is associated with the Superiority component of narcissistic personality while Facebook posting is associated with the Exhibitionism component. Conversely, adults high in Superiority post on Facebook more rather than Twitter. For adults, Facebook and Twitter are both used more by those focused on their own appearances but not as a means of showing off, as is the case with college students. Given these differences, it is essential for future studies of SNS use and personality traits to distinguish between different types of SNS, different populations, and different types of use.
Alternating proximal gradient method for sparse nonnegative Tucker decomposition

Multi-way data arises in many applications such as electroencephalography classification, face recognition, text mining and hyperspectral data analysis. Tensor decomposition has been commonly used to find the hidden factors and elicit the intrinsic structures of the multi-way data. This paper considers sparse nonnegative Tucker decomposition (NTD), which is to decompose a given tensor into the product of a core tensor and several factor matrices with sparsity and nonnegativity constraints. An alternating proximal gradient method is applied to solve the problem. The algorithm is then modified to sparse NTD with missing values. Per-iteration cost of the algorithm is estimated scalable about the data size, and global convergence is established under fairly loose conditions. Numerical experiments on both synthetic and real world data demonstrate its superiority over a few state-of-the-art methods for (sparse) NTD from partial and/or full observations. The MATLAB code along with demos are accessible from the author’s homepage.
Design and Analysis of a Soft Pneumatic Actuator with Origami Shell Reinforcement

Soft pneumatic actuators (SPAs) are versatile robotic components enabling diverse and complex soft robot hardware design. However, due to inherent material characteristics exhibited by their primary constitutive material, silicone rubber, they often lack robustness and repeatability in performance. In this article, we present a novel SPA-based bending module design with shell reinforcement. The bidirectional soft actuator presented here is enveloped in a Yoshimura patterned origami shell, which acts as an additional protection layer covering the SPA while providing specific bending resilience throughout the actuator’s range of motion. Mechanical tests are performed to characterize several shell folding patterns and their effect on the actuator performance. Details on design decisions and experimental results using the SPA with origami shell modules and performance analysis are presented; the performance of the bending module is significantly enhanced when reinforcement is provided by the shell. With the aid of the shell, the bending module is capable of sustaining higher inflation pressures, delivering larger blocked torques, and generating the targeted motion trajectory.
Dialogue Act Tagging with Transformation-Based Learning

For the task of recognizing dialogue acts, we are applying the Transformation-Based Learning (TBL) machine learning algorithm. To circumvent a sparse data problem, we extract values of well-motivated features of utterances, such as speaker direction, punctuation marks, and a new feature, called dialogue act cues, which we find to be more effective than cue phrases and word n-grams in practice. We present strategies for constructing a set of dialogue act cues automatically by minimizing the entropy of the distribution of dialogue acts in a training corpus, filtering out irrelevant dialogue act cues, and clustering semantically-related words. In addition, to address limitations of TBL, we introduce a Monte Carlo strategy for training efficiently and a committee method for computing confidence measures. These ideas are combined in our working implementation, which labels held-out data as accurately as any other reported system for the dialogue act tagging
MVN : An R Package for Assessing Multivariate Normality

The MVN package contains functions in the S3 class to assess multivariate normality. This package is the updated version of the MVN package . The data to be analyzed should be given in the "data.frame" or "matrix" class. In this example, we will work with the famous Iris data set. These data are from a multivariate data set introduced by Fisher (1936) as an application of linear discriminant analysis . It is also called Anderson’s Iris data set because Edgar Anderson collected the data to measure the morphologic variation of Iris flowers of three related species . First of all, the MVN library should be loaded in order to use related functions.
Switch-controlled multi-octave bandwidth radial power divider/combiner

An improved very wideband radial waveguide-based power divider/combiner is presented, which uses switching to compensate for cavity resonance. The combiner is implemented with broadband probes composed of cylindrical conductors and dielectric spacers, arranged on a rod for mechanical stability. The proposed switch-controlled radial power combiner provides low loss (<1 dB), broad bandwidth (400 MHz∼2000 MHz), and high power capability.
Learning through Dialogue Interactions by Asking Questions

A good dialogue agent should have the ability to interact with users by both responding to questions and by asking questions, and importantly to learn from both types of interaction. In this work, we explore this direction by designing a simulator and a set of synthetic tasks in the movie domain that allow such interactions between a learner and a teacher. We investigate how a learner can benefit from asking questions in both offline and online reinforcement learning settings, and demonstrate that the learner improves when asking questions. Finally, real experiments with Mechanical Turk validate the approach. Our work represents a first step in developing such end-to-end learned interactive dialogue agents.