nils-herrmann/clean_scirepeval huggingface dataset:
On the complexity of differentially private data release: efficient algorithms and hardness results

We consider private data analysis in the setting in which a trusted and trustworthy curator, having obtained a large data set containing private information, releases to the public a "sanitization" of the data set that simultaneously protects the privacy of the individual contributors of data and offers utility to the data analyst. The sanitization may be in the form of an arbitrary data structure, accompanied by a computational procedure for determining approximate answers to queries on the original data set, or it may be a "synthetic data set" consisting of data items drawn from the same universe as items in the original data set; queries are carried out as if the synthetic data set were the actual input. In either case the process is non-interactive; once the sanitization has been released the original data and the curator play no further role.
 For the task of sanitizing with a synthetic dataset output, we map the boundary between computational feasibility and infeasibility with respect to a variety of utility measures. For the (potentially easier) task of sanitizing with unrestricted output format, we show a tight qualitative and quantitative connection between hardness of sanitizing and the existence of traitor tracing schemes.
Experiences with Handheld Augmented Reality

In this paper, we present Studierstube ES, a framework for the development of handheld Augmented Reality. The applications run self-contained on handheld computers and smartphones with Windows CE. A detailed description of the performance critical tracking and rendering components are given. We also report on the implementation of a client-server architecture for multi-user applications, and a game engine for location based museum games that has been built on top of this infrastructure. Details on two games that were created, permanently deployed and evaluated in two Austrian museums illustrate the practical value of the framework and lessons learned from using it.
A Survey of Spam Detection Methods on Twitter

Twitter is one of the most popular social media platforms that has 313 million monthly active users which post 500 million tweets per day. This popularity attracts the attention of spammers who use Twitter for their malicious aims such as phishing legitimate users or spreading malicious software and advertises through URLs shared within tweets, aggressively follow/unfollow legitimate users and hijack trending topics to attract their attention, propagating pornography. In August of 2014, Twitter revealed that 8.5% of its monthly active users which equals approximately 23 million users have automatically contacted their servers for regular updates. Thus, detecting and filtering spammers from legitimate users are mandatory in order to provide a spam-free environment in Twitter. In this paper, features of Twitter spam detection presented with discussing their effectiveness. Also, Twitter spam detection methods are categorized and discussed with their pros and cons. The outdated features of Twitter which are commonly used by Twitter spam detection approaches are highlighted. Some new features of Twitter which, to the best of our knowledge, have not been mentioned by any other works are also presented. Twitter spam; spam detection; spam filtering;
Children's moral emotions and moral cognition: towards an integrative perspective.

This chapter presents a brief introduction to the developmental and educational literature linking children's moral emotions to cognitive moral development. A central premise of the chapter is that an integrative developmental perspective on moral emotions and moral cognition provides an important conceptual framework for understanding children's emerging morality and designing developmentally sensitive moral intervention strategies. The subsequent chapters present promising conceptual approaches and empirical evidence linking children's moral emotions to moral cognition. Examples of integrated educational interventions intended to enhance children's moral development are presented and discussed.
Dynamic-Hash-Table Based Public Auditing for Secure Cloud Storage

Cloud storage is an increasingly popular application of cloud computing, which can provide on-demand outsourcing data services for both organizations and individuals. However, users may not fully trust the cloud service providers (CSPs) in that it is difficult to determine whether the CSPs meet their legal expectations for data security. Therefore, it is critical to develop efficient auditing techniques to strengthen data owners’ trust and confidence in cloud storage. In this paper, we present a novel public auditing scheme for secure cloud storage based on dynamic hash table (DHT), which is a new two-dimensional data structure located at a third parity auditor (TPA) to record the data property information for dynamic auditing. Differing from the existing works, the proposed scheme migrates the authorized information from the CSP to the TPA, and thereby significantly reduces the computational cost and communication overhead. Meanwhile, exploiting the structural advantages of the DHT, our scheme can also achieve higher updating efficiency than the state-of-the-art schemes. In addition, we extend our scheme to support privacy preservation by combining the homomorphic authenticator based on the public key with the random masking generated by the TPA, and achieve batch auditing by employing the aggregate BLS signature technique. We formally prove the security of the proposed scheme, and evaluate the auditing performance by detailed experiments and comparisons with the existing ones. The results demonstrate that the proposed scheme can effectively achieve secure auditing for cloud storage, and outperforms the previous schemes in computation complexity, storage costs and communication overhead.
The Evolution of Multiple Memory Systems

The existence of multiple memory systems has been proposed in a number of areas, including cognitive psychology, neuropsychology, and the study of animal learning and memory. We examine whether the existence of such multiple systems seems likely on evolutionary grounds. Multiple systems adapted to serve seemingly similar functions, which differ in important ways, are a common evolutionary outcome. The evolution of multiple memory systems requires memory systems to be specialized to such a degree that the functional problems each system handles cannot be handled by another system. We define this condition as functional incompatibility and show that it occurs for a number of the distinctions that have been proposed between memory systems. The distinction between memory for song and memory for spatial locations in birds, and between incremental habit formation and memory for unique episodes in humans and other primates provide examples. Not all memory systems are highly specialized in function, however, and the conditions under which memory systems could evolve to serve a wide range of functions are also discussed.
Online Learning to Rank for Information Retrieval: SIGIR 2016 Tutorial

During the past 10--15 years offline learning to rank has had a tremendous influence on information retrieval, both scientifically and in practice. Recently, as the limitations of offline learning to rank for information retrieval have become apparent, there is increased attention for online learning to rank methods for information retrieval in the community. Such methods learn from user interactions rather than from a set of labeled data that is fully available for training up front.
 Below we describe why we believe that the time is right for an intermediate-level tutorial on online learning to rank, the objectives of the proposed tutorial, its relevance, as well as more practical details, such as format, schedule and support materials.
Development of lying to conceal a transgression : Children ’ s control of expressive behaviour during verbal deception *

The present study examined lying behaviour in children between 3 and 7 years of age with two experiments. A temptation resistance paradigm was used in which children were left alone in a room with a music-playing toy placed behind their back. The children were told not to peek at the toy. Most children could not resist the temptation and peeked at the toy. When the experimenter asked them whether they had peeked, about half of the 3-year-olds confessed to their transgression, whereas most older children lied. Naṏ ve adult evaluators (undergraduate students and parents) who watched video clips of the children’s responses could not discriminate lie-tellers from nonliars on the basis of their nonverbal expressive behaviours. However, the children were poor at semantic leakage control and adults could correctly identify most of the lie-tellers based on their verbal statements made in the same context as the lie. The combined results regarding children’s verbal and nonverbal leakage control suggest that children under 8 years of age are not fully skilled lie-tellers.
Integrated Internet Marketing

latest CDs; and Quantas makes downloadable software available for itinerary management. These firms are but a fraction of the companies recognizing the Internet as an all-purpose communication medium for interacting with a wide variety of stakeholders. They know they must manage their brands and corporate image in cyberspace . They also know the Internet is not just the Web, but a range of technologies that in combination can be a potent marketing strategy. As organizations stampede to the Internet, they find there is not a systematic way to examine opportunities and relate them to available Internet tools. What is absent, in particular, is a cohesive marketing strategy that applies Internet technologies. Integrated Internet Marketing (IM) is a structured approach that combines marketing strategy with Internet technology. IM promotes creation of a strategy that synergistically exploits the range of Internet technologies (such as text, audio, video, and hyperlinking) to achieve marketing goals. There are two key questions companies should Richard T. Watson, George M. Zinkhan, and Leyland F. Pitt
Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction

CTR prediction in real-world business is a difficult machine learning problem with large scale nonlinear sparse data. In this paper, we introduce an industrial strength solution with model named Large Scale Piece-wise Linear Model (LS-PLM). We formulate the learning problem with L1 and L2,1 regularizers, leading to a non-convex and non-smooth optimization problem. Then, we propose a novel algorithm to solve it efficiently, based on directional derivatives and quasi-Newton method. In addition, we design a distributed system which can run on hundreds of machines parallel and provides us with the industrial scalability. LS-PLM model can capture nonlinear patterns from massive sparse data, saving us from heavy feature engineering jobs. Since 2012, LS-PLM has become the main CTR prediction model in Alibaba’s online display advertising system, serving hundreds of millions users every day.
The Role of Actively Open-Minded Thinking in Information Acquisition , Accuracy , and Calibration

Errors in estimating and forecasting often result from the failure to collect and consider enough relevant information. We examine whether attributes associated with persistence in information acquisition can predict performance in an estimation task. We focus on actively open-minded thinking (AOT), need for cognition, grit, and the tendency to maximize or satisfice when making decisions. In three studies, participants made estimates and predictions of uncertain quantities, with varying levels of control over the amount of information they could collect before estimating. Only AOT predicted performance. This relationship was mediated by information acquisition: AOT predicted the tendency to collect information, and information acquisition predicted performance. To the extent that available information is predictive of future outcomes, actively open-minded thinkers are more likely than others to make accurate forecasts.
Disease Prediction from Electronic Health Records Using Generative Adversarial Networks

Electronic health records (EHRs) have contributed to the computerization of patient records so that it can be used not only for efficient and systematic medical services, but also for research on data science. In this paper, we compared disease prediction performance of generative adversarial networks (GANs) and conventional learning algorithms in combination with missing value prediction methods. As a result, the highest accuracy of 98.05% was obtained using stacked autoencoder as the missing value prediction method and auxiliary classifier GANs (AC-GANs) as the disease predicting method. Results show that the combination of stacked autoencoder and AC-GANs performs significantly greater than existing algorithms at the problem of disease prediction in which missing values and class imbalance exist.
Fuzzy PID Based Trajectory Tracking Control of Mobile Robot and its Simulation in Simulink

The precise control of mobile robot is an important issue in robotics field. In this paper, the motion model of mobile robot is established by mechanism analysis. Then, a fuzzy PID controller is designed for trajectory tracking of mobile robot. The controller consists of a PID controller and a fuzzy inference unit with two inputs and three outputs to tune the parameters of PID controller according to the error and error rate. Finally, the model of a four-wheel mobile robot, fuzzy PID and traditional PID controller are all simulated in Simulink. Simulation experiments are reached in different conditions. The result shows that the mobile robot with the fuzzy PID controller can track the desired trail by about 3 seconds in advance and the overshoot of the system will decrease by 40 percent, comparing to the mobile robot with the traditional PID controller. The advantages of fuzzy PID controller for trajectory tracking control of mobile robot are major in its rapidity, stability, anti-interference and tracking precision.
Automated Attendance Monitoring System using Android Platform

In today’s world, a paper based approach is followed for marking attendance, where the students sign on the attendance sheets. This data is then manually entered into the system. Managing the attendance of the students during lectures is a difficult task and it becomes more difficult during the report generation phase. This is because the process of marking attendance and maintaining the data is not fully automated and manual computation produces errors and also wastes a lot of time. For this reason, the development of Attendance Monitoring System (AMS) using android platform is proposed.
Thin-section CT of the secondary pulmonary lobule: anatomy and the image--the 2004 Fleischner lecture.

The secondary pulmonary lobule is a fundamental unit of lung structure, and it reproduces the lung in miniature. Airways, pulmonary arteries, veins, lymphatics, and the lung interstitium are all represented at the level of the secondary lobule. Several of these components of the secondary lobule are normally visible on thin-section computed tomographic (CT) scans of the lung. The recognition of lung abnormalities relative to the structures of the secondary lobule is fundamental to the interpretation of thin-section CT scans. Pathologic alterations in secondary lobular anatomy visible on thin-section CT scans include interlobular septal thickening and diseases with peripheral lobular distribution, centrilobular abnormalities, and panlobular abnormalities. The differential diagnosis of lobular abnormalities is based on comparisons between lobular anatomy and lung pathology.
Common Elements Wideband MIMO Antenna System for WiFi/LTE Access-Point Applications

A wideband multiple-input-multiple-output (MIMO) antenna system with common elements suitable for WiFi/2.4 GHz and Long Term Evolution (LTE)/2.6 GHz wireless access point (WAP) applications is presented. The proposed MIMO antenna system consists of four wideband microstrip feedline printed monopole antennas with common radiating element and a ring-shaped ground plane. The radiator of the MIMO antenna system is designed as the shape of a modified rectangle with a four-stepped line at the corners to enhance the impedance bandwidth. According to the common elements structure of the MIMO antenna system, isolation between the antennas (ports) can be challenging. Therefore, the ground plane is modified by introducing four slots in each corner to reduce the mutual coupling. For an antenna efficiency of more than 60%, the measured impedance bandwidth for reflection coefficients below -10 dB was observed to be 1100 MHz from 1.8 to 2.9 GHz. Measured isolation is achieved greater than 15 dB by using a modified ground plane. Also, a low envelope correlation coefficient (ECC) less than 0.1 and polarization diversity gain of about 10 dB with the orthogonal mode of linear polarization and quasi-omnidirectional pattern during the analysis of radiation characteristic are achieved. Therefore, the proposed design is a good candidate for indoor WiFi and LTE WAP applications due to the obtained results.
Autonomous indoor navigation of a stock quadcopter with off-board control

We present an enhanced autonomous indoor navigation system for a stock quadcopter drone where all navigation commands are derived off-board on a base station. The base station processes the video stream transmitted from a forward-facing camera on the drone to determine the drone's physical disposition and trajectory in building hallways to derive steering commands that are sent to the drone. Off-board processing and the lack of on-board sensors for localizing the drone permits standard mid-range quadcopters to be used and conserves the limited power source on the quadcopter. We introduce improved and new techniques, compared to our prototype system , to maintain stable flights, estimate distance to hallway intersections and describe algorithms to stop the drone ahead of time and turn correctly at intersections.
Automatic modulation classification based on high order cumulants and hierarchical polynomial classifiers

In this paper, a Hierarchical Polynomial (HP) classifier is proposed to automatically classify M-PSK and M-QAM signals in Additive White Gaussian Noise (AWGN) and slow flat fading environments. The system uses higher order cumulants (HOCs) of the received signal to distinguish between the different modulation types. The proposed system divides the overall modulation classification problem into several hierarchical binary sub-classifications. In each binary sub-classification, the HOCs are expanded into a higher dimensional space in which the two classes are linearly separable. It is shown that there is a significant improvement when using the proposed Hierarchical polynomial structure compared to the conventional polynomial classifier. Moreover, simulation results are shown for different block lengths (number of received symbols) and at different SNR values. The proposed system showed an overall improvement in the probability of correct classification that reaches 100% using only 512 received symbols at 20 dB compared to 98% and 98.33% when using more complicated systems like Genetic Programing with KNN classifier (GP-KNN) and Support Vector Machines (SVM) classifiers, respectively.
Turbo denoising for mobile photographic applications

We propose a new denoising algorithm for camera pipelines and other photographic applications. We aim for a scheme that is (1) fast enough to be practical even for mobile devices, and (2) handles the realistic content dependent noise in real camera captures. Our scheme consists of a simple two-stage non-linear processing. We introduce a new form of boosting/blending which proves to be very effective in restoring the details lost in the first denoising stage. We also employ IIR filtering to significantly reduce the computation time. Further, we incorporate a novel noise model to address the content dependent noise. For realistic camera noise, our results are competitive with BM3D, but with nearly 400 times speedup.
Wounds measured from digital photographs using photodigital planimetry software: validation and rater reliability.

﻿﻿﻿﻿﻿﻿ Traditional wound tracing technique consists of tracing the perimeter of the wound on clear acetate with a fine-tip marker, then placing the tracing on graph paper and counting the grids to calculate the surface area. Standard wound measurement technique for calcu- lating wound surface area (wound tracing) was compared to a new wound measurement method using digital photo-planimetry software ([DPPS], PictZar® Digital Planimetry).



Two hundred wounds of varying etiologies were measured and traced by experienced exam- iners (raters). Simultaneously, digital photographs were also taken of each wound. The digital photographs were downloaded onto a PC, and using DPPS software, the wounds were measured and traced by the same examiners. Accuracy, intra- and interrater reliability of wound measurements obtained from tracings and from DPPS were studied and compared. Both accuracy and rater variability were directly related to wound size when wounds were measured and traced in the tradi- tional manner.



In small (< 4 cm2), regularly shaped (round or oval) wounds, both accuracy and rater reliability was 98% and 95%, respectively. However, in larger, irregularly shaped wounds or wounds with epithelial islands, DPPS was more accurate than traditional mea- suring (3.9% vs. 16.2% [average error]). The mean inter-rater reliabil- ity score was 94% for DPPS and 84% for traditional measuring. The mean intrarater reliability score was 98.3% for DPPS and 89.3% for traditional measuring. In contrast to traditional measurements, DPPS may provide a more objective assessment since it can be done by a technician who is blinded to the treatment plan. Planimetry of digital photographs allows for a closer examination (zoom) of the wound and better visibility of advancing epithelium.



Measurements of wounds performed on digital photographs using planimetry software were simple and convenient. It was more accurate, more objective, and resulted in better correlation within and between examiners.
Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction

Machine understanding of complex images is a key goal of artificial intelligence. One challenge underlying this task is that visual scenes contain multiple interrelated objects, and that global context plays an important role in interpreting the scene. A natural modeling framework for capturing such effects is structured prediction, which optimizes over complex labels, while modeling within-label interactions. However, it is unclear what principles should guide the design of a structured prediction model that utilizes the power of deep learning components. Here we propose a design principle for such architectures that follows from a natural requirement of permutation invariance. We prove a necessary and sufficient characterization for architectures that follow this invariance, and discuss its implication on model design. Finally, we show that the resulting model achieves new state-of-the-art results on the Visual Genome scene-graph labeling benchmark, outperforming all recent approaches.
LATTICE BOLTZMANN METHOD FOR FLUID FLOWS

We present an overview of the lattice Boltzmann method (LBM), a parallel and efficient algorithm for simulating single-phase and multiphase fluid flows and for incorporating additional physical complexities. The LBM is especially useful for modeling complicated boundary conditions and multiphase interfaces. Recent extensions of this method are described, including simulations of fluid turbulence, suspension flows, and reaction diffusion systems.