nils-herrmann/clean_scirepeval huggingface dataset:
Mining Privacy Settings to Find Optimal Privacy-Utility Tradeoffs for Social Network Services

Privacy has been a big concern for users of social network services (SNS). On recent criticism about privacy protection, most SNS now provide fine privacy controls, allowing users to set visibility levels for almost every profile item. However, this also creates a number of difficulties for users. First, SNS providers often set most items by default to the highest visibility to improve the utility of social network, which may conflict with users' intention. It is often formidable for a user to fine-tune tens of privacy settings towards the user desired settings. Second, tuning privacy settings involves an intricate tradeoff between privacy and utility. When you turn off the visibility of one item to protect your privacy, the social utility of that item is turned off as well. It is challenging for users to make a tradeoff between privacy and utility for each privacy setting. We propose a framework for users to conveniently tune the privacy settings towards the user desired privacy level and social utilities. It mines the privacy settings of a large number of users in a SNS, e.g., Facebook, to generate latent trait models for the level of privacy concern and the level of utility preference. A tradeoff algorithm is developed for helping users find the optimal privacy settings for a specified level of privacy concern and a personalized utility preference. We crawl a large number of Facebook accounts and derive the privacy settings with a novel method. These privacy setting data are used to validate and showcase the proposed approach.
A high-performance generalized discontinuous PWM algorithm

In this paper, a generalized discontinuous pulsewidth modulation (GDPWM) method with superior high modulation operating range performance characteristics is developed. An algorithm which employs the conventional space-vector PWM method in the low modulation range, and the GDPWM method in the high modulation range, is established. As a result, the current waveform quality, switching losses, voltage linearity range, and the overmodulation region performance of a PWM voltage-source inverter (PWM-VSI) drive are on-line optimized, as opposed to conventional modulators with fixed characteristics. Due to its compactness, simplicity, and superior performance, the algorithm is suitable for most high-performance PWM-VSI drive applications. This paper provides detailed performance analysis of the method and compares it to the other methods. The experimental results verify the superiority of this algorithm to the conventional PWM methods.
Classification Using Association Rules : Weaknesses and Enhancements

Existing classification and rule learning algorithms in machine learning mainly use heuristic/greedy search to find a subset of regularities (e.g., a decision tree or a set of rules) in data for classification. In the past few years, extensive research was done in the database community on learning rules using exhaustive search under the name of association rule mining. The objective there is to find all rules in data that satisfy the user-specified minimum support and minimum confidence. Although the whole set of rules may not be used directly for accurate classification, effective and efficient classifiers have been built using the rules. This paper aims to improve such an exhaustive search based classification system CBA (Classification Based on Associations). The main strength of this system is that it is able to use the most accurate rules for classification. However, it also has weaknesses. This paper proposes two new techniques to deal with these weaknesses. This results in remarkably accurate classifiers. Experiments on a set of 34 benchmark datasets show that on average the new techniques reduce the error of CBA by 17% and is superior to CBA on 26 of the 34 datasets. They reduce the error of the decision tree classifier C4.5 by 19%, and improve performance on 29 datasets. Similar good results are also achieved against the existing classification systems, RIPPER, LB and a Naïve-Bayes
Convolutional neural networks on irregular domains through approximate translations on inferred graphs

We propose a generalization of convolutional neural networks (CNNs) to irregular domains, through the use of a translation operator on a graph structure. In regular settings such as images, convolutional layers are designed by translating a convolutional kernel over all pixels, thus enforcing translation equivariance. In the case of general graphs however, translation is not a well-defined operation, which makes shifting a convolutional kernel not straightforward. In this article, we introduce a methodology to allow the design of convolutional layers that are adapted to signals evolving on irregular topologies, even in the absence of a natural translation. Using the designed layers, we build a CNN that we train using the initial set of signals. Contrary to other approaches that aim at extending CNNs to irregular domains, we incorporate the classical settings of CNNs for 2D signals as a particular case of our approach. Designing convolutional layers in the vertex domain directly implies weight sharing, which in other approaches is generally estimated a posteriori using heuristics.
Tile Based HEVC Video for Head Mounted Displays

360° Video services with resolutions of UHD and beyond for Virtual Reality head mounted displays are a challenging task due to limits of video decoders in constrained end devices. Adaptivity to the current user viewport is a promising approach but incurs significant encoding overhead when encoding per user or set of viewports. A more efficient way to achieve viewport adaptive streaming is to facilitate motion-constrained HEVC tiles. Original content resolution within the user viewport is preserved while content currently not presented to the user is delivered in lower resolution. A lightweight aggregation of varying resolution tiles into a single HEVC bitstream can be carried out on-the-fly and allows usage of a single decoder instance on the end device.
High-Performance Network Traffic Processing Systems Using Commodity Hardware

The Internet has opened new avenues for information accessing and sharing in a variety of media formats. Such popularity has resulted in an increase of the amount of resources consumed in backbone links, whose capacities have witnessed numerous upgrades to cope with the ever-increasing demand for bandwidth. Consequently, network traffic processing at today’s data transmission rates is a very demanding task, which has been traditionally accomplished by means of specialized hardware tailored to specific tasks. However, such approaches lack either of flexibility or extensibility—or both. As an alternative, the research community has pointed to the utilization of commodity hardware, which may provide flexible and extensible cost-aware solutions, ergo entailing large reductions of the operational and capital expenditure investments. In this chapter, we provide a survey-like introduction to high-performance network traffic processing using commodity hardware. We present the required background to understand the different solutions proposed in the literature to achieve high-speed lossless packet capture, which are reviewed and compared.
Recent Advances and Trends of Cyber-Physical Systems and Big Data Analytics in Industrial Informatics

In today’s competitive business environment, companies are facing challenges in dealing with big data issues for rapid decision making for improved productivity. Many manufacturing systems are not ready to manage big data due to the lack of smart analytics tools. Germany is leading a transformation toward 4th Generation Industrial Revolution (Industry 4.0) based on Cyber-Physical System based manufacturing and service innovation. As more software and embedded intelligence are integrated in industrial products and systems, predictive technologies can further intertwine intelligent algorithms with electronics and tether-free intelligence to predict product performance degradation and autonomously manage and optimize product service needs. This article addresses the trends of industrial transformation in big data environment as well as the readiness of smart predictive informatics tools to manage big data to achieve transparency and productivity. Industry 4.0; Cyber Physical Systems; Prognostics and Health Management; Big Data;
Multiple decision trees

This paper describes experiments, on two domains, to investigate the effect of averaging over predictions of multiple decision trees, instead of using a single tree. Other authors have pointed out theoretical and commonsense reasons for preferring· the multiple tree approach. Ideally, we would like to consider predictions from all trees, weighted by their probability. However, there is a vast·number of different trees, and it is difficult to estimate the probability of each tree. We sidestep the estimation problem by using a modified version of the ID3 algorithm to build good trees, and average over only these trees. Our results are encouraging. For each domain, we managed to produce a small number of good trees. We fmd that it is best to average across sets of trees with different structure; this usually gives better perfonnance than any of the constituent trees, including the ID3 tree.
Semisupervised Learning for Computational Linguistics

Semi-supervised learning is by no means an unfamiliar concept to natural language processing researchers. Labeled data has been used to improve unsupervised parameter estimation procedures such as the EM algorithm and its variants since the beginning of the statistical revolution in NLP (e.g., Pereira and Schabes (1992)). Unlabeled data has also been used to improve supervised learning procedures, the most notable examples being the successful applications of self-training and co-training to word sense disambiguation (Yarowsky 1995) and named entity classification (Collins and Singer 1999). Despite its increasing importance, semi-supervised learning is not a topic that is typically discussed in introductory machine learning texts (e.g., Mitchell (1997), Alpaydin (2004)) or NLP texts (e.g., Manning and Schütze (1999), Jurafsky andMartin (2000)). Consequently, to learn about semi-supervised learning research, one has to consult the machine-learning literature. This can be a daunting task for NLP researchers who have little background in machine learning. Steven Abney’s book Semisupervised Learning for Computational Linguistics is targeted precisely at such researchers, aiming to provide them with a “broad and accessible presentation” of topics in semi-supervised learning. According to the preamble, the reader is assumed to have taken only an introductory course in NLP “that include statistical methods — concretely the material contained in Jurafsky andMartin (2000) andManning and Schütze (1999).”Nonetheless, I agreewith the author that any NLP researcher who has a solid background in machine learning is ready to “tackle the primary literature on semisupervised learning, and will probably not find this book particularly useful” (page 11). As the author promises, the book is self-contained and quite accessible to those who have little background in machine learning. In particular, of the 12 chapters in the book, three are devoted to preparatory material, including: a brief introduction to machine learning, basic unconstrained and constrained optimization techniques (e.g., gradient descent and the method of Lagrange multipliers), and relevant linear-algebra concepts (e.g., eigenvalues, eigenvectors, matrix and vector norms, diagonalization). The remaining chapters focus roughly on six types of semi-supervised learning methods:
Visual simultaneous localization and mapping: a survey

Visual SLAM (simultaneous localization and mapping) refers to the problem of using images, as the only source of external information, in order to establish the position of a robot, a vehicle, or a moving camera in an environment, and at the same time, construct a representation of the explored zone. SLAM is an essential task for the autonomy of a robot. Nowadays, the problem of SLAM is considered solved when range sensors such as lasers or sonar are used to built 2D maps of small static environments. However SLAM for dynamic, complex and large scale environments, using vision as the sole external sensor, is an active area of research. The computer vision techniques employed in visual SLAM, such as detection, description and matching of salient features, image recognition and retrieval, among others, are still susceptible of improvement. The objective of this article is to provide new researchers in the field of visual SLAM a brief and comprehensible review of the state-of-the-art.
A comparison of goodness-of-fit tests for the logistic regression model.

Recent work has shown that there may be disadvantages in the use of the chi-square-like goodness-of-fit tests for the logistic regression model proposed by Hosmer and Lemeshow that use fixed groups of the estimated probabilities. A particular concern with these grouping strategies based on estimated probabilities, fitted values, is that groups may contain subjects with widely different values of the covariates. It is possible to demonstrate situations where one set of fixed groups shows the model fits while the test rejects fit using a different set of fixed groups. We compare the performance by simulation of these tests to tests based on smoothed residuals proposed by le Cessie and Van Houwelingen and Royston, a score test for an extended logistic regression model proposed by Stukel, the Pearson chi-square and the unweighted residual sum-of-squares. These simulations demonstrate that all but one of Royston's tests have the correct size. An examination of the performance of the tests when the correct model has a quadratic term but a model containing only the linear term has been fit shows that the Pearson chi-square, the unweighted sum-of-squares, the Hosmer-Lemeshow decile of risk, the smoothed residual sum-of-squares and Stukel's score test, have power exceeding 50 per cent to detect moderate departures from linearity when the sample size is 100 and have power over 90 per cent for these same alternatives for samples of size 500. All tests had no power when the correct model had an interaction between a dichotomous and continuous covariate but only the continuous covariate model was fit. Power to detect an incorrectly specified link was poor for samples of size 100. For samples of size 500 Stukel's score test had the best power but it only exceeded 50 per cent to detect an asymmetric link function. The power of the unweighted sum-of-squares test to detect an incorrectly specified link function was slightly less than Stukel's score test. We illustrate the tests within the context of a model for factors associated with low birth weight.
Learning from feedback: Spacing and the delay-retention effect.

Most modern research on the effects of feedback during learning has assumed that feedback is an error correction mechanism. Recent studies of feedback-timing effects have suggested that feedback might also strengthen initially correct responses. In an experiment involving cued recall of trivia facts, we directly tested several theories of feedback-timing effects and also examined the effects of restudy and retest trials following immediate and delayed feedback. Results were not consistent with theories assuming that the only function of feedback is to correct initial errors but instead supported a theoretical account assuming that delaying feedback strengthens initially correct responses due to the spacing of encoding opportunities: Delaying feedback increased the probability of correct response perseveration on the final retention test but had minimal effects on error correction or error perseveration probabilities. In a 2nd experiment, the effects of varying the lags between study, test, and feedback trials during learning provided further support for the spacing hypothesis.
Learning to optimize: Training deep neural networks for wireless resource management

For decades, optimization has played a central role in addressing wireless resource management problems such as power control and beamformer design. However, these algorithms often require a considerable number of iterations for convergence, which poses challenges for real-time processing. In this work, we propose a new learning-based approach for wireless resource management. The key idea is to treat the input and output of a resource allocation algorithm as an unknown non-linear mapping and to use a deep neural network (DNN) to approximate it. If the nonlinear mapping can be learned accurately and effectively by a DNN of moderate size, then such DNN can be used for resource allocation in almost real time, since passing the input through a DNN to get the output only requires a small number of simple operations. In this work, we first characterize a class of ‘learnable algorithms’ and then design DNNs to approximate some algorithms of interest in wireless communications. We use extensive numerical simulations to demonstrate the superior ability of DNNs for approximating two considerably complex algorithms that are designed for power allocation in wireless transmit signal design, while giving orders of magnitude speedup in computational time.
Code Obfuscation Literature Survey

In this paper we survey the current literature on code obfuscation and review current practices as well as applications. We analyze the different obfuscation techniques in relation to protection of intellectual property and the hiding of malicious code. Surprisingly, the same techniques used to thwart reverse engineers are used to hide malicious code from virus scanners. Additionally, obfuscation can be used to protect against malicious code injection and attacks. Though obfuscation transformations can protect code, they have limitations in the form of larger code footprints and reduced performance.
Reading handwritten digits: a ZIP code recognition system

A neural network algorithm-based system that reads handwritten ZIP codes appearing on real US mail is described. The system uses a recognition-based segmenter, that is a hybrid of connected-components analysis (CCA), vertical cuts, and a neural network recognizer. Connected components that are single digits are handled by CCA. CCs that are combined or dissected digits are handled by the vertical-cut segmenter. The four main stages of processing are preprocessing, in which noise is removed and the digits are deslanted, CCA segmentation and recognition, vertical-cut-point estimation and segmentation, and directly lookup. The system was trained and tested on approximately 10000 images, five- and nine-digit ZIP code fields taken from real mail.<<ETX>>
Survey of Wireless Indoor Positioning Techniques and Systems

Wireless indoor positioning systems have become very popular in recent years. These systems have been successfully used in many applications such as asset tracking and inventory management. This paper provides an overview of the existing wireless indoor positioning solutions and attempts to classify different techniques and systems. Three typical location estimation schemes of triangulation, scene analysis, and proximity are analyzed. We also discuss location fingerprinting in detail since it is used in most current system or solutions. We then examine a set of properties by which location systems are evaluated, and apply this evaluation method to survey a number of existing systems. Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.
Adductor canal block versus femoral nerve block for analgesia after total knee arthroplasty: a randomized, double-blind study.

Femoral nerve block (FNB), a commonly used postoperative pain treatment after total knee arthroplasty (TKA), reduces quadriceps muscle strength essential for mobilization. In contrast, adductor canal block (ACB) is predominately a sensory nerve block. We hypothesized that ACB preserves quadriceps muscle strength as compared with FNB (primary end point) in patients after TKA. Secondary end points were effects on morphine consumption, pain, adductor muscle strength, morphine-related complications, and mobilization ability.



We performed a double-blind, randomized, controlled study of patients scheduled for TKA with spinal anesthesia. The patients were randomized to receive either a continuous ACB or an FNB via a catheter (30-mL 0.5% ropivacaine given initially, followed by a continuous infusion of 0.2% ropivacaine, 8 mL/h for 24 hours). Muscle strength was assessed with a handheld dynamometer, and we used the percentile change from baseline for comparisons. 
We enrolled 54 patients, of which 48 were analyzed. Quadriceps strength as a percentage of baseline was significantly higher in the ACB group compared with the FNB group: (median [range]) 52% [31-71] versus 18% [4-48], (95% confidence interval, 8-41; P = 0.004). There was no difference between the groups regarding morphine consumption (P = 0.94), pain at rest (P = 0.21), pain during flexion of the knee (P = 0.16), or adductor muscle strength (P = 0.39); neither was there a difference in morphine-related adverse effects or mobilization ability (P > 0.05).



Adductor canal block preserved quadriceps muscle strength better than FNB, without a significant difference in postoperative pain.
A Multi-Horizon Quantile Recurrent Forecaster

We propose a framework for general probabilistic multi-step time series regression. Specifically, we exploit the expressiveness and temporal nature of Sequence-to-Sequence Neural Networks (e.g. recurrent and convolutional structures), the nonparametric nature of Quantile Regression and the efficiency of Direct Multi-Horizon Forecasting. A new training scheme, forking-sequences, is designed for sequential nets to boost stability and performance. We show that the approach accommodates both temporal and static covariates, learning across multiple related series, shifting seasonality, future planned event spikes and coldstarts in real life large-scale forecasting. The performance of the framework is demonstrated in an application to predict the future demand of items sold on Amazon.com, and in a public probabilistic forecasting competition to predict electricity price and load.
Analysis of Oscillator Phase-Noise Effects on Self-Interference Cancellation in Full-Duplex OFDM Radio Transceivers

This paper addresses the analysis of oscillator phase-noise effects on the self-interference cancellation capability of full-duplex direct-conversion radio transceivers. Closed-form solutions are derived for the power of the residual self-interference stemming from phase noise in two alternative cases of having either independent oscillators or the same oscillator at the transmitter and receiver chains of the full-duplex transceiver. The results show that phase noise has a severe effect on self-interference cancellation in both of the considered cases, and that by using the common oscillator in upconversion and downconversion results in clearly lower residual self-interference levels. The results also show that it is in general vital to use high quality oscillators in full-duplex transceivers, or have some means for phase noise estimation and mitigation in order to suppress its effects. One of the main findings is that in practical scenarios the subcarrier-wise phase-noise spread of the multipath components of the self-interference channel causes most of the residual phase-noise effect when high amounts of self-interference cancellation is desired.