nils-herrmann/clean_scirepeval huggingface dataset:
Virtual reality exposure therapy for the treatment of posttraumatic stress disorder following September 11, 2001.

This preliminary study endeavored to evaluate the use of virtual reality (VR) enhanced exposure therapy for the treatment of posttraumatic stress disorder (PTSD) consequent to the World Trade Center attacks of September 11, 2001.



Participants were assigned to a VR treatment (N = 13) or a waitlist control (N = 8) group and were mostly middle-aged, male disaster workers. All participants were diagnosed with PTSD according to DSM-IV-TR criteria using the Clinician-Administered PTSD Scale (CAPS). The study was conducted between February 2002 and August 2005 in offices located in outpatient buildings of a hospital campus.



Analysis of variance showed a significant interaction of time by group (p < .01) on CAPS scores, with a between-groups posttreatment effect size of 1.54. The VR group showed a significant decline in CAPS scores compared with the waitlist group (p < .01).



Our preliminary data suggest that VR is an effective treatment tool for enhancing exposure therapy for both civilians and disaster workers with PTSD and may be especially useful for those patients who cannot engage in imaginal exposure therapy.
Adaptive bidding for display advertising

Motivated by the emergence of auction-based marketplaces for display ads such as the Right Media Exchange, we study the design of a bidding agent that implements a display advertising campaign by bidding in such a marketplace. The bidding agent must acquire a given number of impressions with a given target spend, when the highest external bid in the marketplace is drawn from an unknown distribution P. The quantity and spend constraints arise from the fact that display ads are usually sold on a CPM basis. We consider both the full information setting, where the winning price in each auction is announced publicly, and the partially observable setting where only the winner obtains information about the distribution; these differ in the penalty incurred by the agent while attempting to learn the distribution. We provide algorithms for both settings, and prove performance guarantees using bounds on uniform closeness from statistics, and techniques from online learning. We experimentally evaluate these algorithms: both algorithms perform very well with respect to both target quantity and spend; further, our algorithm for the partially observable case performs nearly as well as that for the fully observable setting despite the higher penalty incurred during learning.
Voltage transient spikes suppression in flyback converter using dissipative voltage snubbers

The Flyback topology results in significant cost and space savings for multiple output power supplies for power levels up to 100 W. Flyback topologies store and transfer energy using a transformer, which due to physical limitations can cause large voltage transient spikes during the switching cycle at the drain of the power switch and at the secondary rectifier. This paper presents an overview of dissipative voltage snubbers and discusses their design guidelines to suppress the voltage transients on both the primary and secondary sides of a flyback converter. In particular, Snubbers used to reduce the stress on the switch and improve efficiency in a flyback topology are discussed.
Image Quality Measures for Fingerprint Image Enhancement

Fingerprint image quality is an important factor in the perf ormance of Automatic Fingerprint Identification Systems(AFIS). It is used to evaluate the system performance, assess enrollment acceptability, and evaluate fingerprint sensors. This paper presents a novel methodology for fingerp rint image quality measurement. We propose limited ring-wedge spectral measu r to estimate the global fingerprint image features, and inhomogeneity with d rectional contrast to estimate local fingerprint image features. Experimental re sults demonstrate the effectiveness of our proposal.
Learning Compact Binary Face Descriptor for Face Recognition

Binary feature descriptors such as local binary patterns (LBP) and its variations have been widely used in many face recognition systems due to their excellent robustness and strong discriminative power. However, most existing binary face descriptors are hand-crafted, which require strong prior knowledge to engineer them by hand. In this paper, we propose a compact binary face descriptor (CBFD) feature learning method for face representation and recognition. Given each face image, we first extract pixel difference vectors (PDVs) in local patches by computing the difference between each pixel and its neighboring pixels. Then, we learn a feature mapping to project these pixel difference vectors into low-dimensional binary vectors in an unsupervised manner, where 1) the variance of all binary codes in the training set is maximized, 2) the loss between the original real-valued codes and the learned binary codes is minimized, and 3) binary codes evenly distribute at each learned bin, so that the redundancy information in PDVs is removed and compact binary codes are obtained. Lastly, we cluster and pool these binary codes into a histogram feature as the final representation for each face image. Moreover, we propose a coupled CBFD (C-CBFD) method by reducing the modality gap of heterogeneous faces at the feature level to make our method applicable to heterogeneous face recognition. Extensive experimental results on five widely used face datasets show that our methods outperform state-of-the-art face descriptors.
"A Game of Thrones": When Human Behavior Models Compete in Repeated Stackelberg Security Games

Several competing human behavior models have been proposed to model and protect against boundedly rational adversaries in repeated Stackelberg security games (SSGs). However, these existing models fail to address three main issues which are extremely detrimental to defender performance. First, while they attempt to learn adversary behavior models from adversaries’ past actions (“attacks on targets”), they fail to take into account adversaries’ future adaptation based on successes or failures of these past actions. Second, they assume that sufficient data in the initial rounds will lead to a reliable model of the adversary. However, our analysis reveals that the issue is not the amount of data, but that there just is not enough of the attack surface exposed to the adversary to learn a reliable model. Third, current leading approaches have failed to include probability weighting functions, even though it is well known that human beings’ weighting of probability is typically nonlinear. The first contribution of this paper is a new human behavior model, SHARP, which mitigates these three limitations as follows: (i) SHARP reasons based on success or failure of the adversary’s past actions on exposed portions of the attack surface to model adversary adaptiveness; (ii) SHARP reasons about similarity between exposed and unexposed areas of the attack surface, and also incorporates a discounting parameter to mitigate adversary’s lack of exposure to enough of the attack surface; and (iii) SHARP integrates a non-linear probability weighting function to capture the adversary’s true weighting of probability. Our second contribution is a first “longitudinal study” – at least in the context of SSGs – of competing models in settings involving repeated interaction between the attacker and the defender. This study, where each experiment lasted a period of multiple weeks with individual sets of human subjects, illustrates the strengths and weaknesses of different models and shows the advantages of SHARP.
Feedback and reflection: teaching methods for clinical settings.

Feedback and reflection are two basic teaching methods used in clinical settings. In this article, the authors explore the distinctions between, and the potential impact of, feedback and reflection in clinical teaching. Feedback is the heart of medical education; different teaching encounters call for different types of feedback. Although most clinicians are familiar with the principles of giving feedback, many clinicians probably do not recognize the many opportunities presented to them for using feedback as a teaching tool. Reflection in medicine-the consideration of the larger context, the meaning, and the implications of an experience and action-allows the assimilation and reordering of concepts, skills, knowledge, and values into pre-existing knowledge structures. When used well, reflection will promote the growth of the individual. While feedback is not used often enough, reflection is probably used even less.
3D Geometry-Aware Semantic Labeling of Outdoor Street Scenes

This paper is concerned with the problem of how to better exploit 3D geometric information for dense semantic image labeling. Existing methods often treat the available 3D geometry information (e.g., 3D depth-map) simply as an additional image channel besides the R-G-B color channels, and apply the same technique for RGB image labeling. In this paper, we demonstrate that directly performing 3D convolution in the framework of a residual connected 3D voxel top-down modulation network can lead to superior results. Specifically, we propose a 3D semantic labeling method to label outdoor street scenes whenever a dense depth map is available. Experiments on the “Synthia” and “Cityscape” datasets show our method outperforms the state-of-the-art methods, suggesting such a simple 3D representation is effective in incorporating 3D geometric information.
COSMOS - A Representation Scheme for 3D Free-Form Objects

We address the problem of representing and recognizing 3D free-form objects when 1) the object viewpoint is arbitrary, 2) the objects may vary in shape and complexity, and 3) no restrictive assumptions are made about the types of surfaces on the object. We assume that a range image of a scene is available, containing a view of a rigid 3D object without occlusion. We propose a new and general surface representation scheme for recognizing objects with free-form (sculpted) surfaces. In this scheme, an object is described concisely in terms of maximal surface patches of constant shape index. The maximal patches that represent the object are mapped onto the unit sphere via their orientations, and aggregated via shape spectral functions. Properties such as surface area, curvedness, and connectivity, which are required to capture local and global information, are also built into the representation. The scheme yields a meaningful and rich description useful for object recognition. A novel concept, the shape spectrum of an object is also introduced within the framework of COSMOS for object view grouping and matching. We demonstrate the generality and the effectiveness of our scheme using real range images of complex objects. Index Terms —Free-form objects, sculpted surfaces, arbitrarily curved objects, 3D object representation, shape index, spherical map, shape spectrum, view matching, object recognition. —————————— ✦ ——————————
Joint multi-person detection and tracking from overlapping cameras

We present a system to track the positions of multiple persons in a scene from overlapping cameras. The distinguishing aspect of our method is a novel, two-step approach that jointly estimates person position and track assignment. The proposed approach keeps solving the assignment problem tractable, while taking into account how different assignments influence feature measurement. In a hypothesis generation stage, the similarity between a person at a particular position and an active track is based on a subset of cues (appearance, motion) that are guaranteed observable in the camera views. This allows for efficient computation of the K-best joint estimates for person position and track assignment under an approximation of the likelihood function. In a subsequent hypothesis verification stage, the known person positions associated with these K-best solutions are used to define a larger set of actually visible cues, which enables a re-ranking of the found assignments using the full likelihood function. We demonstrate that our system outperforms the state-of-the-art on four challenging multi-person datasets (indoor and outdoor), involving 3–5 overlapping cameras and up to 23 persons simultaneously. Two of these datasets are novel: we make the associated images and annotations public to facilitate
Crowdsourcing Multi-Label Classification for Taxonomy Creation

Recent work has introduced CASCADE, an algorithm for creating a globally-consistent taxonomy by crowdsourcing microwork from many individuals, each of whom may see only a tiny fraction of the data (Chilton et al. 2013). While CASCADE needs only unskilled labor and produces taxonomies whose quality approaches that of human experts, it uses significantly more labor than experts. This paper presents DELUGE, an improved workflow that produces taxonomies with comparable quality but demands significantly less crowd labor. Our proposed method for solving the novel problem of crowdsourcing multi-label classification optimizes CASCADE’s categorization step—its most costly step—using less than 10% of the labor required by the original approach. DELUGE’s savings come from the use of decision theory, machine learning, and probabilistic inference, which allow it to pose microtasks that aim to maximize infor-
Bluespec System Verilog: efficient, correct RTL from high level specifications

Bluespec System Verilog is an EDL toolset for ASIC and FPGA design offering significantly higher productivity via a radically different approach to high-level synthesis. Many other attempts at high-level synthesis have tried to move the design language towards a more software-like specification of the behavior of the intended hardware. By means of code samples, demonstrations and measured results, we illustrate how Bluespec System Verilog, in an environment familiar to hardware designers, can significantly improve productivity without compromising generated hardware quality.
NIST GCR 16-010 Survey and New Directions for Physics-Based Attack Detection in Control Systems

Monitoring the “physics” of control systems to detect attacks is a growing area of research. In its basic form a security monitor creates time-series models of sensor readings for an industrial control system and identifies anomalies in these measurements in order to identify potentially false control commands or false sensor readings. In this paper, we review previous work based on a unified taxonomy that allows us to identify limitations, unexplored challenges, and new solutions. In particular, we propose a new adversary model and a way to compare previous work with a new evaluation metric based on the trade-off between false alarms and the negative impact of undetected attacks. We also show the advantages and disadvantages of three experimental scenarios to test the performance of attacks and defenses: real-world network data captured from a large-scale operational facility, a fully-functional testbed that can be used operationally for water treatment, and a simulation of frequency control in the power grid.
PiLoc: A self-calibrating participatory indoor localization system

While location is one of the most important context information in mobile and ubiquitous computing, large-scale deployment of indoor localization system remains elusive.
 In this work, we propose PiLoc, an indoor localization system that utilizes opportunistically sensed data contributed by users. Our system does not require manual calibration, prior knowledge and infrastructure support. The key novelty of PiLoc is that it merges walking segments annotated with displacement and signal strength information from users to derive a map of walking paths annotated with radio signal strengths.
 We evaluate PiLoc over 4 different indoor areas. Evaluation shows that our system can achieve an average localization error of 1.5m.
EMDialog: Bringing Information Visualization into the Museum

Digital information displays are becoming more common in public spaces such as museums, galleries, and libraries. However, the public nature of these locations requires special considerations concerning the design of information visualization in terms of visual representations and interaction techniques. We discuss the potential for, and challenges of, information visualization in the museum context based on our practical experience with EMDialog, an interactive information presentation that was part of the Emily Carr exhibition at the Glenbow Museum in Calgary. EMDialog visualizes the diverse and multi-faceted discourse about this Canadian artist with the goal to both inform and provoke discussion. It provides a visual exploration environment that offers interplay between two integrated visualizations, one for information access along temporal, and the other along contextual dimensions. We describe the results of an observational study we conducted at the museum that revealed the different ways visitors approached and interacted with EMDialog, as well as how they perceived this form of information presentation in the museum context. Our results include the need to present information in a manner sufficiently attractive to draw attention and the importance of rewarding passive observation as well as both short- and longer term information exploration.
How physical text layout affects reading from screen

The primary objective of this paper is to critically evaluate empirical research on some variables relating to the configuration of text on screen to consolidate our current knowledge in these areas. The text layout variables are line length, columns, window size and interlinear spacing, with an emphasis on line length due to the larger number of studies related to this variable. Methodological issues arising from individual studies and from comparisons among studies are identified. A synthesis of results is offered which provides alternative interpretations of some findings and identifies the number of characters per line as the critical variable in looking at line length. Further studies are needed to explore the interactions between characters per line and eye movements, scrolling movements, reading patterns and familiarity with formats.
Browsing and Analyzing the Command-Level Structure of Large Collections of Image Manipulation Tutorials

Many people rely on Web-based tutorials to learn how to use complex software. Yet, it remains difficult for users to systematically explore the set of tutorials available online. We present Sifter, an interface for browsing, comparing and analyzing large collections of image manipulation tutorials based on their command-level structure. Sifter first applies supervised machine learning to identify the commands contained in a collection of 2500 Photoshop tutorials obtained from the Web. It then provides three different views of the tutorial collection based on the extracted command-level structure: (1) A Faceted Browser View allows users to organize, sort and filter the collection based on tutorial category, command names or on frequently used command subsequences, (2) a Tutorial View summarizes and indexes tutorials by the commands they contain, and (3) an Alignment View visualizes the commandlevel similarities and differences between a subset of tutorials. An informal evaluation (n=9) suggests that Sifter enables users to successfully perform a variety of browsing and analysis tasks that are difficult to complete with standard keyword search. We conclude with a meta-analysis of our Photoshop tutorial collection and present several implications for the design of image manipulation software. ACM Classification H5.2 [Information interfaces and presentation]: User Interfaces. Graphical user interfaces.
Auditory stream segregation by musical timbre: effects of static and dynamic acoustic attributes.

Two experiments examined the influence of timbre on auditory stream segregation. In experiment 1, listeners heard sequences of orchestral tones equated for pitch and loudness, and they rated how strongly the instruments segregated. Multidimensional scaling analyses of these ratings revealed that segregation was based on the static and dynamic acoustic attributes that influenced similarity judgements in a previous experiment (P Iverson & CL Krumhansl, 1993). In Experiment 2, listeners heard interleaved melodies and tried to recognize the melodies played by a target timbre. The results extended the findings of Experiment 1 to tones varying pitch. Auditory stream segregation appears to be influenced by gross differences in static spectra and by dynamic attributes, including attack duration and spectral flux. These findings support a gestalt explanation of stream segregation and provide evidence against peripheral channel model.
Biofabrication: an overview of the approaches used for printing of living cells

The development of cell printing is vital for establishing biofabrication approaches as clinically relevant tools. Achieving this requires bio-inks which must not only be easily printable, but also allow controllable and reproducible printing of cells. This review outlines the general principles and current progress and compares the advantages and challenges for the most widely used biofabrication techniques for printing cells: extrusion, laser, microvalve, inkjet and tissue fragment printing. It is expected that significant advances in cell printing will result from synergistic combinations of these techniques and lead to optimised resolution, throughput and the overall complexity of printed constructs.
Comparison deep learning method to traditional methods using for network intrusion detection

Recently, deep learning has gained prominence due to the potential it portends for machine learning. For this reason, deep learning techniques have been applied in many fields, such as recognizing some kinds of patterns or classification. Intrusion detection analyses got data from monitoring security events to get situation assessment of network. Lots of traditional machine learning method has been put forward to intrusion detection, but it is necessary to improvement the detection performance and accuracy. This paper discusses different methods which were used to classify network traffic. We decided to use different methods on open data set and did experiment with these methods to find out a best way to intrusion detection.
An Automated Battery Management System to Enable Persistent Missions With Multiple Aerial Vehicles

This paper presents the development and hardware implementation of an autonomous battery maintenance mechatronic system that significantly extends the operational time of battery powered small-scaled unmanned aerial vehicles (UAVs). A simultaneous change and charge approach is used to overcome the significant downtime experienced by existing charge-only approaches. The automated system quickly swaps a depleted battery of a UAV with a replenished one while simultaneously recharging several other batteries. This results in a battery maintenance system with low UAV downtime, arbitrarily extensible operation time, and a compact footprint. Hence, the system can enable multi-agent UAV missions that require persistent presence. This capability is illustrated by developing and testing in flight a centralized autonomous planning and learning algorithm that incorporates a probabilistic health model dependent on vehicle battery health that is updated during the mission, and replans to improve the performance based on the improved model. Flight test results are presented for a 3-h-long persistent mission with three UAVs that each has an endurance of 8-10 min on a single battery charge (more than 100 battery swaps).