nils-herrmann/clean_scirepeval huggingface dataset:
CMOS-MEMS membrane for audio-frequency acoustic actuation

Using CMOS-MEMS micromachining techniques we have constructed a prototype earphone that is audible from 1 to 15 kHz. The fabrication of the acoustic membrane consists of only two steps in addition to the prior post-CMOS micromachining steps developed at CMU. The ability to build a membrane directly on a standard CMOS chip, integrating mechanical structures with signal processing electronics will enable a variety of applications including economical earphones, microphones, hearing aids, high-fidelity earphones, cellular phones and noise cancellation. The large compliance of the CMOS-MEMS membrane also promises application as a sensitive microphone and pressure sensor.
Where Am I? Investigating Map Matching During Self-Localization With Mobile Eye Tracking in an Urban Environment

Self-localization is the process of identifying one’s current position on a map, and it is a crucial part of any wayfinding process. During self-localization the wayfinder matches visually perceptible features of the environment, such as landmarks, with map symbols to constrain potential locations on the map. The success of this visual matching process constitutes an important factor for the success of selflocalization. In this research we aim at observing the visual matching process between environment and map during self-localization with real-world mobile eye tracking. We report on one orientation and one self-localization experiment, both in an outdoor urban environment. The gaze data collected during the experiments show that successful participants put significantly more visual attention to those symbols on the map that were helpful in the given situation than unsuccessful participants. A sequence analysis revealed that they also had significantly more switches of visual attention between map symbols and their corresponding landmarks in the environment, which suggests they were following a more effective self-localization strategy.
Navigation System for Omni-directional Automatic Guided Vehicle with Mecanum Wheel

This paper presents navigation system for an omni-directional AGV (automatic guided vehicle) with Mecanum wheels. The Mecanum wheel, one design for the wheel which can move in any direction, is a conventional wheel with a series of rollers attached to its circumference. The localization techniques for the general mobile robot use basically encoder. Otherwise, they use gyro and electronic compass with encoder. However, it is difficult to use the encoder because in the Mecanum wheel the slip occurs frequently by the rollers attached to conventional wheel's circumference. Hence, we propose the localization of the omnidirectional AGV with the Mecanum wheel. The proposed localization uses encoder, gyro, and accelerometer. In this paper, we ourselves designed and made the AGV with the Mecanum wheels for experiment. And we analyzed the accuracy of the localization when the AGV moves sideways a 20m distance at about 20cm/s and 38cm/s, respectively. In experimental result, we verified that the accuracies of the proposed localization are 27.4944mm and 29.2521mm respectively.
Secure Offloading of Legacy IDSes Using Remote VM Introspection in Semi-trusted Clouds

In Infrastructure-as-a-Service (IaaS) clouds, intrusion detection systems (IDSes) increase their importance. To securely detect attacks against virtual machines (VMs), IDS offloading with VM introspection (VMI) has been proposed. In semi-trusted clouds, however, it is difficult to securely offload IDSes because there may exist insiders such as malicious system administrators. First, secure VM execution cannot coexist with IDS offloading although it has to be enabled to prevent information leakage to insiders. Second, offloaded IDSes can be easily disabled by insiders. To solve these problems, this paper proposes IDS remote offloading with remote VMI. Since IDSes can run at trusted remote hosts outside semi-trusted clouds, they cannot be disabled by insiders in clouds. Remote VMI enables IDSes at remote hosts to introspect VMs via the trusted hypervisor inside semi-trusted clouds. Secure VM execution can be bypassed by performing VMI in the hypervisor. Remote VMI preserves the integrity and confidentiality of introspected data between the hypervisor and remote hosts. The integrity of the hypervisor can be guaranteed by various existing techniques. We have developed RemoteTrans for remotely offloading legacy IDSes and confirmed that RemoteTrans could achieve surprisingly efficient execution of legacy IDSes at remote hosts.
Collaborative Fashion Recommendation: A Functional Tensor Factorization Approach

With the rapid expansion of online shopping for fashion products, effective fashion recommendation has become an increasingly important problem. In this work, we study the problem of personalized outfit recommendation, i.e. automatically suggesting outfits to users that fit their personal fashion preferences. Unlike existing recommendation systems that usually recommend individual items, we suggest sets of items, which interact with each other, to users. We propose a functional tensor factorization method to model the interactions between user and fashion items. To effectively utilize the multi-modal features of the fashion items, we use a gradient boosting based method to learn nonlinear functions to map the feature vectors from the feature space into some low dimensional latent space. The effectiveness of the proposed algorithm is validated through extensive experiments on real world user data from a popular fashion-focused social network.
Generalization and equilibrium in generative adversarial nets (GANs) (invited talk)

Generative Adversarial Networks (GANs) have become one of the dominant methods for fitting generative models to complicated real-life data, and even found unusual uses such as designing good cryptographic primitives. In this talk, we will first introduce the ba- sics of GANs and then discuss the fundamental statistical question about GANs — assuming the training can succeed with polynomial samples, can we have any statistical guarantees for the estimated distributions? In the work with Arora, Ge, Liang, and Zhang, we suggested a dilemma: powerful discriminators cause overfitting, whereas weak discriminators cannot detect mode collapse. Such a conundrum may be solved or alleviated by designing discrimina- tor class with strong distinguishing power against the particular generator class (instead of against all possible generators.
DTW-D: time series semi-supervised learning from a single example

Classification of time series data is an important problem with applications in virtually every scientific endeavor. The large research community working on time series classification has typically used the UCR Archive to test their algorithms. In this work we argue that the availability of this resource has isolated much of the research community from the following reality, labeled time series data is often very difficult to obtain. The obvious solution to this problem is the application of semi-supervised learning; however, as we shall show, direct applications of off-the-shelf semi-supervised learning algorithms do not typically work well for time series. In this work we explain why semi-supervised learning algorithms typically fail for time series problems, and we introduce a simple but very effective fix. We demonstrate our ideas on diverse real word problems.
Development and Evaluation of a UAV-Photogrammetry System for Precise 3D Environmental Modeling

The specific requirements of UAV-photogrammetry necessitate particular solutions for system development, which have mostly been ignored or not assessed adequately in recent studies. Accordingly, this paper presents the methodological and experimental aspects of correctly implementing a UAV-photogrammetry system. The hardware of the system consists of an electric-powered helicopter, a high-resolution digital camera and an inertial navigation system. The software of the system includes the in-house programs specifically designed for camera calibration, platform calibration, system integration, on-board data acquisition, flight planning and on-the-job self-calibration. The detailed features of the system are discussed, and solutions are proposed in order to enhance the system and its photogrammetric outputs. The developed system is extensively tested for precise modeling of the challenging environment of an open-pit gravel mine. The accuracy of the results is evaluated under various mapping conditions, including direct georeferencing and indirect georeferencing with different numbers, distributions and types of ground control points. Additionally, the effects of imaging configuration and network stability on modeling accuracy are assessed. The experiments demonstrated that 1.55 m horizontal and 3.16 m vertical absolute modeling accuracy could be achieved via direct geo-referencing, which was improved to 0.4 cm and 1.7 cm after indirect geo-referencing.
Exploring storage class memory with key value stores

In the near future, new storage-class memory (SCM) technologies -- such as phase-change memory and memristors -- will radically change the nature of long-term storage. These devices will be cheap, non-volatile, byte addressable, and near DRAM density and speed. While SCM offers enormous opportunities, profiting from them will require new storage systems specifically designed for SCM's properties.
 This paper presents Echo, a persistent key-value storage system designed to leverage the advantages and address the challenges of SCM. The goals of Echo include high performance for both small and large data objects, recoverability after failure, and scalability on multicore systems. Echo achieves its goals through the use of a two-level memory design targeted for memory systems containing both DRAM and SCM, exploitation of SCM's byte addressability for fine-grained transactions in non-volatile memory, and the use of snapshot isolation for concurrency, consistency, and versioning. Our evaluation demonstrates that Echo's SCM-centric design achieves the durability guarantees of the best disk-based stores with the performance characteristics approaching the best in-memory key-value stores.
Efficient memory shadowing for 64-bit architectures

Shadow memory is used by dynamic program analysis tools to store metadata for tracking properties of application memory. The efficiency of mapping between application memory and shadow memory has substantial impact on the overall performance of such analysis tools. However, traditional memory mapping schemes that work well on 32-bit architectures cannot easily port to 64-bit architectures due to the much larger 64-bit address space.
 This paper presents EMS64, an efficient memory shadowing scheme for 64-bit architectures. By taking advantage of application reference locality and unused regions in the 64-bit address space, EMS64 provides a fast and flexible memory mapping scheme without relying on any underlying platform features or requiring any specific shadow memory size. Our experiments show that EMS64 is able to reduce the runtime shadow memory translation overhead to 81% on average, which almost halves the overhead of the fastest 64-bit shadow memory system we are aware of.
CapWidgets: tangile widgets versus multi-touch controls on mobile devices

We present CapWidgets, passive tangible controls for capacitive touch screens. CapWidgets bring back physical controls to off-the-shelf multi-touch surfaces as found in mobile phones and tablet computers. While the user touches the widget, the surface detects the capacitive marker on the widget's underside. We study the relative performance of this tangible interaction with direct multi-touch interaction and our experimental results show that user performance and preferences are not automatically in favor of tangible widgets and careful design is necessary to validate their properties.
On the routing in Flying Ad Hoc Networks

The usage of Unmanned Aerial Vehicles (UAVs) is increasing day by day. In recent years, UAVs are being used in increasing number of civil applications, such as policing, fire-fighting, etc in addition to military applications. Instead of using one large UAV, multiple UAVs are nowadays used for higher coverage area and accuracy. Therefore, networking models are required to allow two or more UAV nodes to communicate directly or via relay node(s). Flying Ad-Hoc Networks (FANETs) are formed which is basically an ad hoc network for UAVs. This is relatively a new technology in network family where requirements vary largely from traditional networking model, such as Mobile Ad-hoc Networks and Vehicular Ad-hoc Networks. In this paper, Flying Ad-Hoc Networks are surveyed along with its challenges compared to traditional ad hoc networks. The existing routing protocols for FANETs are then classified into six major categories which are critically analyzed and compared based on various performance criteria. Our comparative analysis will help network engineers in choosing appropriate routing protocols based on the specific scenario where the FANET will be deployed.
Data Noising as Smoothing in Neural Network Language Models

Data noising is an effective technique for regularizing neural network models. While noising is widely adopted in application domains such as vision and speech, commonly used noising primitives have not been developed for discrete sequencelevel settings such as language modeling. In this paper, we derive a connection between input noising in neural network language models and smoothing in ngram models. Using this connection, we draw upon ideas from smoothing to develop effective noising schemes. We demonstrate performance gains when applying the proposed schemes to language modeling and machine translation. Finally, we provide empirical analysis validating the relationship between noising and smoothing.
Device Placement Optimization with Reinforcement Learning

The past few years have witnessed a growth in size and computational requirements for training and inference with neural networks. Currently, a common approach to address these requirements is to use a heterogeneous distributed environment with a mixture of hardware devices such as CPUs and GPUs. Importantly, the decision of placing parts of the neural models on devices is often made by human experts based on simple heuristics and intuitions. In this paper, we propose a method which learns to optimize device placement for TensorFlow computational graphs. Key to our method is the use of a sequence-tosequence model to predict which subsets of operations in a TensorFlow graph should run on which of the available devices. The execution time of the predicted placements is then used as the reward signal to optimize the parameters of the sequence-to-sequence model. Our main result is that on Inception-V3 for ImageNet classification, and on RNN LSTM, for language modeling and neural machine translation, our model finds non-trivial device placements that outperform hand-crafted heuristics and traditional algorithmic methods.
Pixel Objectness

We propose an end-to-end learning framework for foreground object segmentation. Given a single novel image, our approach produces a pixel-level mask for all “object-like” regions—even for object categories never seen during training. We formulate the task as a structured prediction problem of assigning a foreground/background label to each pixel, implemented using a deep fully convolutional network. Key to our idea is training with a mix of image-level object category examples together with relatively few images with boundary-level annotations. Our method substantially improves the state-of-the-art on foreground segmentation for ImageNet and MIT Object Discovery datasets. Furthermore, on over 1 million images, we show that it generalizes well to segment object categories unseen in the foreground maps used for training. Finally, we demonstrate how our approach benefits image retrieval and image retargeting, both of which flourish when given our high-quality foreground maps.
Manifestations of user personality in website choice and behaviour on online social networks

Individual differences in personality affect users’ online activities as much as they do in the offline world. This work, based on a sample of over a third of a million users, examines how users’ behaviour in the online environment, captured by their website choices and Facebook profile features, relates to their personality, as measured by the standard Five Factor Model personality questionnaire. Results show that there are psychologically meaningful links between users’ personalities, their website preferences and Facebook profile features. We show how website audiences differ in terms of their personality, present the relationships between personality and Facebook profile features, and show how an individual’s personality can be predicted from Facebook profile features. We conclude that predicting a user’s personality profile can be applied to personalize content, optimize search results, and improve online advertising.
Sexual hookups among college students: sex differences in emotional reactions.

The purpose of the study was to test four predictions derived from evolutionary (sexual strategies) theory. The central hypothesis was that men and women possess different emotional mechanisms that motivate and evaluate sexual activities. Consequently, even when women express indifference to emotional involvement and commitment and voluntarily engage in casual sexual relations, their goals, their feelings about the experience, and the associations between their sexual behavior and prospects for long-term investment differ significantly from those of men. Women's sexual behavior is associated with their perception of investment potential: long-term, short-term, and partners' ability and willingness to invest. For men,these associations are weaker or inversed. Regression analyses of survey data from 333 male and 363 female college students revealed the following: Greater permissiveness of sexual attitudes was positively associated with number of sex partners; this association was not moderated by sex of subject (Prediction 1); even when women deliberately engaged in casual sexual relations, thoughts that expressed worry and vulnerability crossed their minds; for females, greater number of partners was associated with increased worry-vulnerability whereas for males the trend was the opposite (Prediction 2); with increasing numbers of sex partners, marital thoughts decreased; this finding was not moderated by sex of subject; this finding did not support Prediction 3; for both males and females, greater number of partners was related to larger numbers of one-night stands, partners foreseen in the next 5 years, and deliberately casual sexual relations. This trend was significantly stronger for males than for females (Prediction 4).
Seeing the world behind the image: Spatial layout for 3D scene understanding

When humans look at an image, they see not just a pattern of color and texture, but the world behind the image. In the same way, computer vision algorithms must go beyond the pixels and reason about the underlying scene. In this dissertation, we propose methods to recover the basic spatial layout from a single image and begin to investigate its use as a foundation for scene understanding. Our spatial layout is a description of the 3D scene in terms of surfaces, occlusions, camera viewpoint, and objects. We propose a geometric class representation, a coarse categorization of surfaces according to their 3D orientations, and learn appearance-based models of geometry to identify surfaces in an image. These surface estimates serve as a basis for recovering the boundaries and occlusion relationships of prominent objects. We further show that simple reasoning about camera viewpoint and object size in the image allows accurate inference of the viewpoint and greatly improves object detection. Finally, we demonstrate the potential usefulness of our methods in applications to 3D reconstruction, scene synthesis, and robot navigation. Scene understanding from a single image requires strong assumptions about the world. We show that the necessary assumptions can be modeled statistically and learned from training data. Our work demonstrates the importance of robustness through a wide variety of image cues, multiple segmentations, and a general strategy of soft decisions and gradual inference of image structure. Above all, our work manifests the tremendous amount of 3D information that can be gleaned from a single image. Our hope is that this dissertation will inspire others to further explore how computer vision can go beyond pattern recognition and produce an understanding of the environment.
Proactive Spatiotemporal Resource Allocation and Predictive Visual Analytics for Community Policing and Law Enforcement

In this paper, we present a visual analytics approach that provides decision makers with a proactive and predictive environment in order to assist them in making effective resource allocation and deployment decisions. The challenges involved with such predictive analytics processes include end-users' understanding, and the application of the underlying statistical algorithms at the right spatiotemporal granularity levels so that good prediction estimates can be established. In our approach, we provide analysts with a suite of natural scale templates and methods that enable them to focus and drill down to appropriate geospatial and temporal resolution levels. Our forecasting technique is based on the Seasonal Trend decomposition based on Loess (STL) method, which we apply in a spatiotemporal visual analytics context to provide analysts with predicted levels of future activity. We also present a novel kernel density estimation technique we have developed, in which the prediction process is influenced by the spatial correlation of recent incidents at nearby locations. We demonstrate our techniques by applying our methodology to Criminal, Traffic and Civil (CTC) incident datasets.
Isolated Penile Duplication: Case Report and Literature Review

Penil duplikasyon(diğer adıyla Diphallia veya diphallasparatus) beş milyon da bir görülen nadir bir malformationdur. Sıklıkla anorektal, üriner ve vertebral anomalilerle birliktedir. Hastanemiz üroloji kliniğine penil şekil bozukluğu şikayetiyle başvuran 15 yaşında erkek hasta anomalinin ve eşlik edebilecek diğer patolojilerin görüntülenebilmesi amacıyla radyoloji ünitesine gönderilmişti. MR incelemede tam olmayan psödoduplikasyon ile uyumlu olan ve diğer kanal ile birleşen ikinci bir uretra distalde aksesuar glans düzeyinde künt olarak sonlanmaktaydı. Doppler USG inceleme ile korpus kavernozum ve korpus spongiozum düzeylerinde vasküler yapılar değerlendirildi. Nadir rastlanan penil duplikasyon olgumuzda yapılan MR , sonografi ve indirekt röntgen incelemelerinin sonuçlarını literatürde rastlanan az sayıda benzer olguları da gözden geçirerek sunmayı amaçladık.