nils-herrmann/clean_scirepeval huggingface dataset:
Heavy Hitter Estimation over Set-Valued Data with Local Differential Privacy

In local differential privacy (LDP), each user perturbs her data locally before sending the noisy data to a data collector. The latter then analyzes the data to obtain useful statistics. Unlike the setting of centralized differential privacy, in LDP the data collector never gains access to the exact values of sensitive data, which protects not only the privacy of data contributors but also the collector itself against the risk of potential data leakage. Existing LDP solutions in the literature are mostly limited to the case that each user possesses a tuple of numeric or categorical values, and the data collector computes basic statistics such as counts or mean values. To the best of our knowledge, no existing work tackles more complex data mining tasks such as heavy hitter discovery over set-valued data. In this paper, we present a systematic study of heavy hitter mining under LDP. We first review existing solutions, extend them to the heavy hitter estimation, and explain why their effectiveness is limited. We then propose LDPMiner, a two-phase mechanism for obtaining accurate heavy hitters with LDP. The main idea is to first gather a candidate set of heavy hitters using a portion of the privacy budget, and focus the remaining budget on refining the candidate set in a second phase, which is much more efficient budget-wise than obtaining the heavy hitters directly from the whole dataset. We provide both in-depth theoretical analysis and extensive experiments to compare LDPMiner against adaptations of previous solutions. The results show that LDPMiner significantly improves over existing methods. More importantly, LDPMiner successfully identifies the majority true heavy hitters in practical settings.
Dealing with betrayal in close relationships: does commitment promote forgiveness?

This work complements existing research regarding the forgiveness process by highlighting the role of commitment in motivating forgiveness. On the basis of an interdependence-theoretic analysis, the authors suggest that (a) victims' self-oriented reactions to betrayal are antithetical to forgiveness, favoring impulses such as grudge and vengeance, and (b) forgiveness rests on prorelationship motivation, one cause of which is strong commitment. A priming experiment, a cross-sectional survey study, and an interaction record study revealed evidence of associations (or causal effects) of commitment with forgiveness. The commitment-forgiveness association appeared to rest on intent to persist rather than long-term orientation or psychological attachment. In addition, the commitment-forgiveness association was mediated by cognitive interpretations of betrayal incidents; evidence for mediation by emotional reactions was inconsistent.
Familial filicide and filicide classification.

Filicide is the killing of a child by his or her parent. Despite the disturbing nature of these crimes, a study of filicide classification can provide insight into their causes. Furthermore, a study of filicide classification provides information essential to accurate death certification. We report a rare case of familial filicide in which twin sisters both attempted to kill their respective children. We then suggest a detailed classification of filicide subtypes that provides a framework of motives and precipitating factors leading to filicide. We identify 16 subtypes of filicide, each of which is sufficiently characteristic to warrant a separate category. We describe in some detail the characteristic features of these subtypes. A knowledge of filicide subtypes contributes to interpretation of difficult cases. Furthermore, to protect potential child homicide victims, it is necessary to know how and why they are killed. Epidemiologic studies using filicide subtypes as their basis could provide information leading to strategies for prevention.
Linear Off-Policy Actor-Critic

This paper presents the first actor-critic algorithm for o↵-policy reinforcement learning. Our algorithm is online and incremental, and its per-time-step complexity scales linearly with the number of learned weights. Previous work on actor-critic algorithms is limited to the on-policy setting and does not take advantage of the recent advances in o↵policy gradient temporal-di↵erence learning. O↵-policy techniques, such as Greedy-GQ, enable a target policy to be learned while following and obtaining data from another (behavior) policy. For many problems, however, actor-critic methods are more practical than action value methods (like Greedy-GQ) because they explicitly represent the policy; consequently, the policy can be stochastic and utilize a large action space. In this paper, we illustrate how to practically combine the generality and learning potential of o↵policy learning with the flexibility in action selection given by actor-critic methods. We derive an incremental, linear time and space complexity algorithm that includes eligibility traces, prove convergence under assumptions similar to previous o↵-policy algorithms, and empirically show better or comparable performance to existing algorithms on standard reinforcement-learning benchmark problems.
DensePose: Dense Human Pose Estimation in the Wild

In this work we establish dense correspondences between an RGB image and a surface-based representation of the human body, a task we refer to as dense human pose estimation. We gather dense correspondences for 50K persons appearing in the COCO dataset by introducing an efficient annotation pipeline. We then use our dataset to train CNN-based systems that deliver dense correspondence 'in the wild', namely in the presence of background, occlusions and scale variations. We improve our training set's effectiveness by training an inpainting network that can fill in missing ground truth values and report improvements with respect to the best results that would be achievable in the past. We experiment with fully-convolutional networks and region-based models and observe a superiority of the latter. We further improve accuracy through cascading, obtaining a system that delivers highly-accurate results at multiple frames per second on a single gpu.
Development of a Texture Sensor Emulating the Tissue Structure and Perceptual Mechanism of Human Fingers

This paper discusses a novel approach in developing a texture sensor emulating the major features of a human finger. The aim of this study is to realize precise and quantitative texture sensing. Three physical properties, roughness, softness, and friction are known to constitute texture perception of humans. The sensor is designed to measure the three specific types of information by adopting the mechanism of human texture perception. First, four features of the human finger that were focused on in designing the novel sensor are introduced. Each feature is considered to play an important role in texture perception; the existence of nails and bone, the multiple layered structure of soft tissue, the distribution of mechanoreceptors, and the deployment of epidermal ridges. Next, detailed design of the texture sensor based on the design concept is explained, followed by evaluating experiments and analysis of the results. Finally, we conducted texture perceptive experiments of actual material using the developed sensor, thus achieving the information expected. Results show the potential of our approach.
A deep learning approach towards pore extraction for high-resolution fingerprint recognition

As high-resolution fingerprint images are becoming more common, the pores have been found to be one of the promising candidates in improving the performance of automated fingerprint identification systems (AFIS). This paper proposes a deep learning approach towards pore extraction. It exploits the feature learning and classification capability of convolutional neural networks (CNNs) to detect pores on fingerprints. Besides, this paper also presents a unique affine Fourier moment-matching (AFMM) method of matching and fusing the scores obtained for three different fingerprint features to deal with both local and global linear distortions. Combining the two aforementioned contributions, an EER of 3.66% can be observed from the experimental results.
Frequent Use of Social Networking Sites Is Associated with Poor Psychological Functioning Among Children and Adolescents

Social networking sites (SNSs) have gained substantial popularity among youth in recent years. However, the relationship between the use of these Web-based platforms and mental health problems in children and adolescents is unclear. This study investigated the association between time spent on SNSs and unmet need for mental health support, poor self-rated mental health, and reports of psychological distress and suicidal ideation in a representative sample of middle and high school children in Ottawa, Canada. Data for this study were based on 753 students (55% female; Mage=14.1 years) in grades 7-12 derived from the 2013 Ontario Student Drug Use and Health Survey. Multinomial logistic regression was used to examine the associations between mental health variables and time spent using SNSs. Overall, 25.2% of students reported using SNSs for more than 2 hours every day, 54.3% reported using SNSs for 2 hours or less every day, and 20.5% reported infrequent or no use of SNSs. Students who reported unmet need for mental health support were more likely to report using SNSs for more than 2 hours every day than those with no identified unmet need for mental health support. Daily SNS use of more than 2 hours was also independently associated with poor self-rating of mental health and experiences of high levels of psychological distress and suicidal ideation. The findings suggest that students with poor mental health may be greater users of SNSs. These results indicate an opportunity to enhance the presence of health service providers on SNSs in order to provide support to youth.
Low-Profile Integrated Microstrip Antenna for GPS-DSRC Application

This letter describes the concept, design, and measurement of a low-profile integrated microstrip antenna for dual-band applications. The antenna operates at both the GPS L1 frequency of 1.575 GHz with circular polarization and 5.88 GHz with a vertical linear polarization for dedicated short-range communication (DSRC) application. The antenna is low profile and meets stringent requirements on pattern/polarization performance in both bands. The design procedure is discussed, and full measured data are presented.
Modeling User Preferences in Recommender Systems: A Classification Framework for Explicit and Implicit User Feedback

Recommender systems are firmly established as a standard technology for assisting users with their choices; however, little attention has been paid to the application of the user model in recommender systems, particularly the variability and noise that are an intrinsic part of human behavior and activity. To enable recommender systems to suggest items that are useful to a particular user, it can be essential to understand the user and his or her interactions with the system. These interactions typically manifest themselves as explicit and implicit user feedback that provides the key indicators for modeling users’ preferences for items and essential information for personalizing recommendations. In this article, we propose a classification framework for the use of explicit and implicit user feedback in recommender systems based on a set of distinct properties that include Cognitive Effort, User Model, Scale of Measurement, and Domain Relevance. We develop a set of comparison criteria for explicit and implicit user feedback to emphasize the key properties. Using our framework, we provide a classification of recommender systems that have addressed questions about user feedback, and we review state-of-the-art techniques to improve such user feedback and thereby improve the performance of the recommender system. Finally, we formulate challenges for future research on improvement of user feedback.
Robust object proposals re-ranking for object detection in autonomous driving using convolutional neural networks

Object proposals have recently emerged as an essential cornerstone for object detection. The current state-of-theart object detectors employ object proposals to detect objects within a modest set of candidate bounding box proposals instead of exhaustively searching across an image using the sliding window approach. However, achieving high recall and good localization with few proposals is still a challenging problem. The challenge becomes even more difficult in the context of autonomous driving, in which small objects, occlusion, shadows, and reflections usually occur. In this paper, we present a robust object proposals re-ranking algorithm that effectivity re-ranks candidates generated from a customized class-independent 3DOP (3D Object Proposals) method using a two-stream convolutional neural network (CNN). The goal is to ensure that those proposals that accurately cover the desired objects are amongst the few top-ranked candidates. The proposed algorithm, which we call DeepStereoOP, exploits not only RGB images as in the conventional CNN architecture, but also depth features including disparity map and distance to the ground. Experiments show that the proposed algorithm outperforms all existing object proposal algorithms on the challenging KITTI benchmark in terms of both recall and localization. Furthermore, the combination of DeepStereoOP and Fast R-CNN achieves one of the best detection results of all three KITTI object classes.
Efficient Video Rectification and Stabilisation for Cell-Phones

This article presents a method for rectifying and stabilising video from cell-phones with rolling shutter (RS) cameras. Due to size constraints, cell-phone cameras have constant, or near constant focal length, making them an ideal application for calibrated projective geometry. In contrast to previous RS rectification attempts that model distortions in the image plane, we model the 3D rotation of the camera. We parameterise the camera rotation as a continuous curve, with knots distributed across a short frame interval. Curve parameters are found using non-linear least squares over inter-frame correspondences from a KLT tracker. By smoothing a sequence of reference rotations from the estimated curve, we can at a small extra cost, obtain a high-quality image stabilisation. Using synthetic RS sequences with associated ground-truth, we demonstrate that our rectification improves over two other methods. We also compare our video stabilisation with the methods in iMovie and Deshaker.
It's About Time: Smartwatches as Public Displays

Current uses of smartwatches are focused solely around the wearer's content, viewed by the wearer alone. When worn on a wrist, however, watches are often visible to many other people, making it easy to quickly glance at their displays. We explore the possibility of extending smartwatch interactions to turn personal wearables into more public displays. We begin opening up this area by investigating fundamental aspects of this interaction form, such as the social acceptability and noticeability of looking at someone else's watch, as well as the likelihood of a watch face being visible to others. We then sketch out interaction dimensions as a design space, evaluating each aspect via a web-based study and a deployment of three potential designs. We conclude with a discussion of the findings, implications of the approach and ways in which designers in this space can approach public wrist-worn wearables.
Cyberbullying Prevention and Intervention Efforts: Current Knowledge and Future Directions.

Bullying is a serious public health concern that is associated with significant negative mental, social, and physical outcomes. Technological advances have increased adolescents' use of social media, and online communication platforms have exposed adolescents to another mode of bullying- cyberbullying. , from websites and tip sheets to classroom curriculum, have been developed to help youth, parents, and teachers address cyberbullying. While youth and parents are willing to disclose their experiences with bullying to their health care providers, these disclosures need to be taken seriously and handled in a caring manner. Health care providers need to include questions about bullying on intake forms to encourage these disclosures. The aim of this article is to examine the current status of cyberbullying prevention and intervention. Research support for several school-based intervention programs is summarised. Recommendations for future research are provided.
Can they hear me now?: a security analysis of law enforcement wiretaps

Although modern communications services are susceptible to third-party eavesdropping via a wide range of possible techniques, law enforcement agencies in the US and other countries generally use one of two technologies when they conduct legally-authorized interception of telephones and other communications traffic. The most common of these, designed to comply with the 1994 Communications Assistance for Law Enforcement Act(CALEA), use a standard interface provided in network switches.
 This paper analyzes the security properties of these interfaces. We demonstrate that the standard CALEA interfaces are vulnerable to a range of unilateral attacks by the intercept target. In particular, because of poor design choices in the interception architecture and protocols, our experiments show it is practical for a CALEA-tapped target to overwhelm the link to law enforcement with spurious signaling messages without degrading her own traffic, effectively preventing call records as well as content from being monitored or recorded. We also identify stop-gap mitigation strategies that partially mitigate some of our identified attacks.
Active-area-segmentation (AAS) technique for compact, ESD robust, fully silicided NMOS design

This paper describes a layout technique to optimize the ESD performance per area for fully silicided NMOS devices by segmenting the active area of drain and source regions. Efficient multi finger triggering is achieved by intrinsic inter-finger-coupling through the bulk enabled by compact finger design. The technique is successfully applied in a 0.13 um and a 0.18 um CMOS technology obtaining HBM ESD capability of up to 8.6 V/um2.
Fast Reflectional Symmetry Detection Using Orientation Histograms

simple and fast reflectional symmetry detection algorithm has been developed in this Apaper. The algorithm employs only the original gray scale image and the gradient information of the image, and it is able to detect multiple reflectional symmetry axes of an object in the image. The directions of the symmetry axes are obtained from the gradient orientation histogram of the input gray scale image by using the Fourier method. Both synthetic and real images have been tested using the proposed algorithm.
Face Recognition Methods & Applications

Face recognition presents a challenging problem in the field of image analysis and computer vision. The security of information is becoming very significant and difficult. Security cameras are presently common in airports, Offices, University, ATM, Bank and in any locations with a security system. Face recognition is a biometric system used to identify or verify a person from a digital image. Face Recognition system is used in security. Face recognition system should be able to automatically detect a face in an image. This involves extracts its features and then recognize it, regardless of lighting, expression, illumination, ageing, transformations (translate, rotate and scale image) and pose, which is a difficult task. This paper contains three sections. The first section describes the common methods like holistic matching method, feature extraction method and hybrid methods. The second section describes applications with examples and finally third section describes the future research directions of face recognition. Face Recognition, Holistic Matching Methods, Feature-based (structural) Methods, Hybrid
Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics

With the rapid growth of the Internet, the ability of users to create and publish content has created active electronic communities that provide a wealth of product information. However, the high volume of reviews that are typically published for a single product makes harder for individuals as well as manufacturers to locate the best reviews and understand the true underlying quality of a product. In this paper, we reexamine the impact of reviews on economic outcomes like product sales and see how different factors affect social outcomes such as their perceived usefulness. Our approach explores multiple aspects of review text, such as subjectivity levels, various measures of readability and extent of spelling errors to identify important text-based features. In addition, we also examine multiple reviewer-level features such as average usefulness of past reviews and the self-disclosed identity measures of reviewers that are displayed next to a review. Our econometric analysis reveals that the extent of subjectivity, informativeness, readability, and linguistic correctness in reviews matters in influencing sales and perceived usefulness. Reviews that have a mixture of objective, and highly subjective sentences are negatively associated with product sales, compared to reviews that tend to include only subjective or only objective information. However, such reviews are rated more informative (or helpful) by other users. By using Random Forest-based classifiers, we show that we can accurately predict the impact of reviews on sales and their perceived usefulness. We examine the relative importance of the three broad feature categories: “reviewer-related” features, “review subjectivity” features, and “review readability” features, and find that using any of the three feature sets results in a statistically equivalent performance as in the case of using all available features. This paper is the first study that integrates econometric, text mining, and predictive modeling techniques toward a more complete analysis of the information captured by user-generated online reviews in order to estimate their helpfulness and economic impact.
A Shallow Varactor-Tuned Cavity-Backed Slot Antenna With a 1.9:1 Tuning Range

A shallow (0.025 wavelengths) microstrip-fed cavity-backed-slot (CBS) antenna has been demonstrated that tunes from 1 to 1.9 GHz with better than -20 dB reflection coefficient using a single varactor diode (0.45-2.5 pF). This is possible because the slot and the cavity combine to form a single resonance, and therefore, do not need to be tuned independently. The cavity is 72 × 72 × 3.18 mm3, and is dielectrically loaded (¿r = 2.1). The impedance match over the tuning range has been achieved by searching for combinations of feed and varactor locations for which the impedance is matched to 50 ¿ by a single series inductor, and the input impedance is nearly identical whether the antenna is in free-space or conformally mounted on a 1.2 × 1.2 m2 ground plane. The cross-polarization is better than -25 dB at 1.0, 1.5, and 1.9 GHz. To the authors' knowledge, this is the first demonstration of a varactor-tuned CBS antenna.