nils-herrmann/clean_scirepeval huggingface dataset:
Advancing Feature Selection Research − ASU Feature Selection Repository

The rapid advance of computer based high-throughput technique have provided unparalleled opportunities for humans to expand capabilities in production, services, communications, and research. Meanwhile, immense quantities of high-dimensional data are accumulated challenging state-of-the-art data mining techniques. Feature selection is an essential step in successful data mining applications, which can effectively reduce data dimensionality by removing the irrelevant (and the redundant) features. In the past few decades, researchers have developed large amount of feature selection algorithms. These algorithms are designed to serve different purposes, are of different models, and all have their own advantages and disadvantages. Although there have been intensive efforts on surveying existing feature selection algorithms, to the best of our knowledge, there is still not a dedicated repository that collects the representative feature selection algorithms to facilitate their comparison and joint study. To fill this gap, in this work we present a feature selection repository, which is designed to collect the most popular algorithms that have been developed in the feature selection research to serve as a platform for facilitating their application, comparison and joint study. The repository also effectively assists researchers to achieve more reliable evaluation in the process of developing new feature selection algorithms.
A Deep Learning Based 6 Degree-of-Freedom Localization Method for Endoscopic Capsule Robots

We present a robust deep learning based 6 degrees-of-freedom (DoF) localization system for endoscopic capsule robots. Our system mainly focuses on localization of endoscopic capsule robots inside the GI tract using only visual information captured by a mono camera integrated to the robot. The proposed system is a 23-layer deep convolutional neural network (CNN) that is capable to estimate the pose of the robot in real time using a standard CPU. The dataset for the evaluation of the system was recorded inside a surgical human stomach model with realistic surface texture, softness, and surface liquid properties so that the pre-trained CNN architecture can be transferred confidently into a real endoscopic scenario. An average error of 7.1% and 3.4% for translation and rotation has been obtained, respectively. The results accomplished from the experiments demonstrate that a CNN pre-trained with raw 2D endoscopic images performs accurately inside the GI tract and is robust to various challenges posed by reflection distortions, lens imperfections, vignetting, noise, motion blur, low resolution, and lack of unique landmarks to track.
Central Retinal Artery Occlusion and Partial Ophthalmoplegia Caused by Hyaluronic Acid Injection Filler Cosmetic

Central retinal artery occlusion is a rare ocular emergency condition that can lead to a total blindness. Hereby we report a 30-year-old woman with a sudden visual loss of the left eye after being injected by hyaluronic acid filler at the dorsum nasal area for cosmetic purpose. Visual acuity was light perception while anterior segment and intraocular pressure were within normal limit. Partial ophthalmoplegia include restriction to the nasal gaze were found. Funduscopic examination revealed total retinal edema with a cherry red spot at the fovea. Ocular massage was performed as initial management followed by humor aqueous paracentesis. All procedures were done within 90 minutes. The patients discharge with the visual improvement to hand motion and the Partial ophthalmophlegia has been improved after being evaluated less than a month.
Vehicle routing problem with time-windows for perishable food delivery

This study has extended a vehicle routing problem, with time-windows (VRPTW), by considering the randomness of the perishable food delivery process, and constructing a SVRPTW model, to obtain optimal delivery routes, loads, fleet dispatching and departure times for delivering perishable food from a distribution center. Our objective was to minimize not only the fixed costs for dispatching vehicles, but also the transportation, inventory, energy and penalty costs for violating time-windows. We also discussed time-dependent travel and time-varying temperatures, during the day, modifying the objective functions as well as the constraints in the above mathematical programming models. Algorithms were developed to solve the proposed models; results indicated that inventory and energy costs can significantly influence total delivery costs. It was found that our proposed models yielded better results than the traditional VRPTW models.
Face poser: Interactive modeling of 3D facial expressions using facial priors

This article presents an intuitive and easy-to-use system for interactively posing 3D facial expressions. The user can model and edit facial expressions by drawing freeform strokes, by specifying distances between facial points, by incrementally editing curves on the face, or by directly dragging facial points in 2D screen space. Designing such an interface for 3D facial modeling and editing is challenging because many unnatural facial expressions might be consistent with the user's input. We formulate the problem in a maximum a posteriori framework by combining the user's input with priors embedded in a large set of facial expression data. Maximizing the posteriori allows us to generate an optimal and natural facial expression that achieves the goal specified by the user. We evaluate the performance of our system by conducting a thorough comparison of our method with alternative facial modeling techniques. To demonstrate the usability of our system, we also perform a user study of our system and compare with state-of-the-art facial expression modeling software (Poser 7).
The next big one: Detecting earthquakes and other rare events from community-based sensors

Can one use cell phones for earthquake early warning? Detecting rare, disruptive events using community-held sensors is a promising opportunity, but also presents difficult challenges. Rare events are often difficult or impossible to model and characterize a priori, yet we wish to maximize detection performance. Further, heterogeneous, community-operated sensors may differ widely in quality and communication constraints. In this paper, we present a principled approach towards detecting rare events that learns sensor-specific decision thresholds online, in a distributed way. It maximizes anomaly detection performance at a fusion center, under constraints on the false alarm rate and number of messages per sensor. We then present an implementation of our approach in the Community Seismic Network (CSN), a community sensing system with the goal of rapidly detecting earthquakes using cell phone accelerometers, consumer USB devices and cloud-computing based sensor fusion. We experimentally evaluate our approach based on a pilot deployment of the CSN system. Our results, including data from shake table experiments, indicate the effectiveness of our approach in distinguishing seismic motion from accelerations due to normal daily manipulation. They also provide evidence of the feasibility of earthquake early warning using a dense network of cell phones.
Fine-Grain Parallelism with Minimal Hardware Support: A Compiler-Controlled Threaded Abstract Machine

In this paper, we present a relatively primitive execution model for ne-grain parallelism, in which all synchronization, scheduling, and storage management is explicit and under compiler control. This is de ned by a threaded abstract machine (TAM) with a multilevel scheduling hierarchy. Considerable temporal locality of logically related threads is demonstrated, providing an avenue for e ective register use under quasi-dynamic scheduling. A prototype TAM instruction set, TL0, has been developed, along with a translator to a variety of existing sequential and parallel machines. Compilation of Id, an extended functional language requiring ne-grain synchronization, under this model yields performance approaching that of conventional languages on current uniprocessors. Measurements suggest that the net cost of synchronization on conventional multiprocessors can be reduced to within a small factor of that on machines with elaborate hardware support, such as proposed data ow architectures. This brings into question whether tolerance to latency and inexpensive synchronization require speci c hardware support or merely an appropriate compilation strategy and program representation.
A First-Estimates Jacobian EKF for Improving SLAM Consistency

In this work, we study the inconsistency of EKF-based SLAM from the perspective of observability. We analytically prove that when the Jacobians of the system and measurement models are evaluated at the latest state estimates during every time step, the linearized error-state system employed in the EKF has observable subspace of dimension higher than that of the actual, nonlinear, SLAM system. As a result, the covariance estimates of the EKF undergo reduction in directions of the state space where no information is available, which is a primary cause of the inconsistency. Furthermore, a new “First-Estimates Jacobian” (FEJ) EKF is proposed to improve the estimator’s consistency during SLAM. The proposed algorithm performs better in terms of consistency, because when the filter Jacobians are calculated using the first-ever available estimates for each state variable, the error-state system model has an observable subspace of the same dimension as the underlying nonlinear SLAM system. The theoretical analysis is validated through both simulations and experiments.
Stochastic Activation Pruning for Robust Adversarial Defense

Neural networks have been found vulnerable to adversarial examples. Carefully chosen perturbations to real images, while imperceptible to humans, induce misclassification and threaten the reliability of deep learning systems in the wild. To guard against adversarial examples, we take inspiration from game theory. We cast the problem as a minimax zero-sum game between the adversary and the model. For such games, it is in general optimal for players to use a stochastic policy, also known as a mixed strategy. In light of this, we propose a mixed strategy for the model: stochastic activation pruning. SAP prunes a random subset of activations (preferentially pruning activations with smaller magnitude) and scales up the survivors to compensate. SAP can be applied to pre-trained neural networks—even adversarially trained models—without fine-tuning, providing robustness against adversarial examples. Experiments demonstrate that SAP confers robustness in the adversarial setting, increasing accuracy whilst preserving calibration.
Spot the Odd Man Out: Exploring the Associative Power of Lexical Resources

We propose Odd-Man-Out, a novel task which aims to test different properties of word representations. An Odd-Man-Out puzzle is composed of 5 (or more) words, and requires the system to choose the one which does not belong with the others. We show that this simple setup is capable of teasing out various properties of different popular lexical resources (like WordNet and pre-trained word embeddings), while being intuitive enough to annotate on a large scale. In addition, we propose a novel technique for training multi-prototype word representations, based on unsupervised clustering of ELMo embeddings, and show that it surpasses all other representations on all OddMan-Out collections.
Study of Planar Inverted-F Antenna ( PIFA ) for Mobile Devices

Now-a-days more and more radios are being integrated into a single wireless platform to allow maximum connectivity. In this paper a theoretical study of Planar Inverted F Antenna is presented. PIFA as a low profile antenna is widely used in several application areas due to several advantages. Research and experiments done on PIFA structures are discussed and categorized. Some methods that can be used to improve efficiency, bandwidth & reduce volume of an antenna are also discussed which includes effect of dielectric substrate, dimensions of the antenna etc.
Memristor Bridge Synapses

In this paper, we propose a memristor bridge circuit consisting of four identical memristors that is able to perform zero, negative, and positive synaptic weightings. Together with three additional transistors, the memristor bridge weighting circuit is able to perform synaptic operation for neural cells. It is compact as both weighting and weight programming are performed in a memristor bridge synapse. It is power efficient, since the operation is based on pulsed input signals. Its input terminals are utilized commonly for applying both weight programming and weight processing signals via time sharing. In this paper, features of the memristor bridge synapses are investigated using the TiO memristor model via simulations.
Beliefs and biases in web search

People's beliefs, and unconscious biases that arise from those beliefs, influence their judgment, decision making, and actions, as is commonly accepted among psychologists. Biases can be observed in information retrieval in situations where searchers seek or are presented with information that significantly deviates from the truth. There is little understanding of the impact of such biases in search. In this paper we study search-related biases via multiple probes: an exploratory retrospective survey, human labeling of the captions and results returned by a Web search engine, and a large-scale log analysis of search behavior on that engine. Targeting yes-no questions in the critical domain of health search, we show that Web searchers exhibit their own biases and are also subject to bias from the search engine. We clearly observe searchers favoring positive information over negative and more than expected given base rates based on consensus answers from physicians. We also show that search engines strongly favor a particular, usually positive, perspective, irrespective of the truth. Importantly, we show that these biases can be counterproductive and affect search outcomes; in our study, around half of the answers that searchers settled on were actually incorrect. Our findings have implications for search engine design, including the development of ranking algorithms that con-sider the desire to satisfy searchers (by validating their beliefs) and providing accurate answers and properly considering base rates. Incorporating likelihood information into search is particularly important for consequential tasks, such as those with a medical focus.
A High Level Query Language for Big Data Analytics

In this paper, we propose a high level query language for expressing analytic queries over big data  sets. A main contribution of our approach is the clear separation between the conceptual and the  physical level. An analytic query and its answer are defined at the conceptual level independently of  the nature and location of data. The abstract definitions are then mapped to lower level evaluation  mechanisms, taking into account the nature and location of data, as well as other related aspects.  Our overall objective is to have query formulation done on an abstract level, while actual query  evaluation can adapt to the evaluation mechanisms offered in each case. To achieve this objective it  is necessary to raise the level of abstraction, by providing a sound mathematical basis for the  mapping of queries to lower level evaluation mechanisms. However, raising the level of abstraction  offers three advantages: (a) useful insights into the process of data analytics in general, and  MapReduce in particular (b) a formal approach to the rewriting of analytic queries and the  generation of query execution plans and (c) the possibility of leveraging structure and semantics in  data in order to improve performance. We emphasize that, although theoretical in nature, this work  uses only basic and well known mathematical concepts (namely functions and set partitions).  \end{abstract}  Category: H.2.4 {Database Management}{Systems}                                                                                 Terms: MapReduce, Query Language, Data Analytics
The linked data benchmark council: a graph and RDF industry benchmarking effort

The Linked Data Benchmark Council (LDBC) is an EU project that aims to develop industry-strength benchmarks for graph and RDF data management systems. It includes the creation of a non-profit LDBC organization, where industry players and academia come together for managing the development of benchmarks as well as auditing and publishing official results. We present an overview of the project including its goals and organization, and describe its process and design methodology for benchmark development. We introduce so-called "choke-point" based benchmark development through which experts identify key technical challenges, and introduce them in the benchmark workload. Finally, we present the status of two benchmarks currently in development, one targeting graph data management systems using a social network data case, and the other targeting RDF systems using a data publishing case.
Towards Making Systems Forget with Machine Unlearning

Today's systems produce a rapidly exploding amount of data, and the data further derives more data, forming a complex data propagation network that we call the data's lineage. There are many reasons that users want systems to forget certain data including its lineage. From a privacy perspective, users who become concerned with new privacy risks of a system often want the system to forget their data and lineage. From a security perspective, if an attacker pollutes an anomaly detector by injecting manually crafted data into the training data set, the detector must forget the injected data to regain security. From a usability perspective, a user can remove noise and incorrect entries so that a recommendation engine gives useful recommendations. Therefore, we envision forgetting systems, capable of forgetting certain data and their lineages, completely and quickly. This paper focuses on making learning systems forget, the process of which we call machine unlearning, or simply unlearning. We present a general, efficient unlearning approach by transforming learning algorithms used by a system into a summation form. To forget a training data sample, our approach simply updates a small number of summations -- asymptotically faster than retraining from scratch. Our approach is general, because the summation form is from the statistical query learning in which many machine learning algorithms can be implemented. Our approach also applies to all stages of machine learning, including feature selection and modeling. Our evaluation, on four diverse learning systems and real-world workloads, shows that our approach is general, effective, fast, and easy to use.
Sex-dimorphic face shape preference in heterosexual and homosexual men and women.

Studies have used manipulated faces to test the preferences of heterosexual individuals for sexually dimorphic facial cues. In contrast to previous studies, which have generally excluded homosexual participants, we directly compared homosexual and heterosexual male and female preferences for manipulated sexual dimorphism in faces (homosexual males: n = 311; heterosexual males: n = 215; homosexual females: n = 159; heterosexual females: n = 218). Prior studies on sexual orientation and preferences for faces that were paired with masculine and feminine behavioral descriptors suggest that homosexual men prefer more masculine men and that homosexual women demonstrate no preference for either masculinity or femininity in women. In our study, we tested for similarities and differences among heterosexual and homosexual males and females with regard to their preferences for a more specific aspect of faces: sexual dimorphism of face shape. Homosexual men demonstrated stronger preferences for masculinity in male faces than did all of the other groups. Homosexual women demonstrated stronger preferences for masculinity in female faces than did heterosexual women. These results suggest attractiveness judgments of same-sex faces made by homosexual individuals are not a mirror image of those made by heterosexual individuals of the opposite sex. Our data suggest that face preferences of homosexual individuals reflect a system of biologically and socially guided preferences at least as complex as those found among heterosexual individuals.
Distalization pattern of the maxillary arch depending on the number of orthodontic miniscrews.

To determine the effects of linear force vector(s) from interradicular miniscrews on the distalization pattern of the maxillary arch in adult Class II patients.



Twenty-five adult patients with mild to moderate Class II dentition and minimal crowding were collected. Either single (group A, n  =  12) or dual (group B, n  =  13) miniscrews were inserted on the posterior interradicular area to deliver a distalizing force to the main archwire. The displacement patterns of maxillary incisors and molars were measured and compared.


Significant distalization in the molars and incisors was shown in both groups. Significantly greater distalization and intrusion of the first molar and intrusive displacement of the incisor, together with significant reduction of the mandibular plane, were noted in group B, in contrast to the rotation of the occlusal plane in group A.



Interradicular miniscrews predictably induced total arch distalization, leading to the correction of Class II. Additional miniscrews in the premolar area appear to facilitate intrusion and distalization of the entire arch according to the position of the force vectors.
Minemu: The World's Fastest Taint Tracker

Dynamic taint analysis is a powerful technique to detect memory corruption attacks. However, with typical overheads of an order of magnitu de, current implementations are not suitable for most production systems. The r esearch question we address in this paper is whether the slow-down is a fundamenta l speed barrier, or an artifact of bolting information flow tracking on emula tors really not designed for it. In other words, we designed a new type of emulato r from scratch with the goal of removing superfluous instructions to propagate ta in . The results are very promising. The emulator, known as Minemu, incurs a slowdown of 1.5x-3x for real and complex applications and 2.4x for SPEC INT2 006, while tracking taint at byte level granularity. Minemu’s performance is significantly better than that of existing systems, despite the fact that we have not applied s ome of their optimizations yet. We believe that the new design may be suitable for certain classes of applications in production systems.
Tracking down software bugs using automatic anomaly detection

This paper introduces DIDUCE, a practical and effective tool that aids programmers in detecting complex program errors and identifying their root causes. By instrumenting a program and observing its behavior as it runs, DIDUCE dynamically formulates hypotheses of invariants obeyed by the program. DIDUCE hypothesizes the strictest invariants at the beginning, and gradually relaxes the hypothesis as violations are detected to allow for new behavior. The violations reported help users to catch software bugs as soon as they occur. They also give programmers new visibility into the behavior of the programs such as identifying rare corner cases in the program logic or even locating hidden errors that corrupt the program's results.We implemented the DIDUCE system for Java programs and applied it to four programs of significant size and complexity. DIDUCE succeeded in identifying the root causes of programming errors in each of the programs quickly and automatically. In particular, DIDUCE is effective in isolating a timing-dependent bug in a released JSSE ( Extension) library, which would have taken an experienced programmer days to find. Our experience suggests that detecting and checking program invariants dynamically is a simple and effective methodology for debugging many different kinds of program errors across a wide variety of application domains.