nils-herrmann/clean_scirepeval huggingface dataset:
Techniques for Data Hiding

by International Business Machines Corporation. Copying in printed form for private use is permitted without payment of royalty provided that (1) each reproduction is done without alteration and (2) the Journal reference and IBM copyright notice are included on the first page. The title and abstract, but no other portions, of this paper may be copied or distributed royalty free without further permission by computer-based and other information service systems. Permission to republish any other portion of this paper must be obtained from the Editor. Data hiding, a form of steganography, embeds data into digital media for the purpose of identification, annotation, and copyright. Several constraints affect this process: the quantity of data to be hidden, the need for invariance of these data under conditions where a " host " signal is subject to distortions, e.g., lossy compression, and the degree to which the data must be immune to interception, modification, or removal by a third party. We explore both traditional and novel techniques for addressing the data-hiding process and evaluate these techniques in light of three applications: copyright protection, tamper-proofing, and augmentation data embedding. igital representation of media facilitates access and potentially improves the portability, efficiency , and accuracy of the information presented. Undesirable effects of facile data access include an increased opportunity for violation of copyright and tampering with or modification of content. The motivation for this work includes the provision of protection of intellectual property rights, an indication of content manipulation, and a means of annotation. Data hiding represents a class of processes used to embed data, such as copyright information, into various forms of media such as image, audio, or text with a minimum amount of perceivable degradation to the " host " signal; i.e., the embedded data should be invisible and inaudible to a human observer. Note that data hiding, while similar to compression, is distinct from encryption. Its goal is not to restrict or regulate access to the host signal, but rather to ensure that embedded data remain inviolate and recoverable. Two important uses of data hiding in digital media are to provide proof of the copyright, and assurance of content integrity. Therefore, the data should stay hidden in a host signal, even if that signal is subjected to manipulation as degrading as filtering, resampling, cropping, or lossy data compression. Other applications of data hiding, such as the inclusion of augmentation data, need not …
Dialog-to-Action: Conversational Question Answering Over a Large-Scale Knowledge Base

We present an approach to map utterances in conversation to logical forms, which will be executed on a large-scale knowledge base. To handle enormous ellipsis phenomena in conversation, we introduce dialog memory management to manipulate historical entities, predicates, and logical forms when inferring the logical form of current utterances. Dialog memory management is embodied in a generative model, in which a logical form is interpreted in a top-down manner following a small and flexible grammar. We learn the model from denotations without explicit annotation of logical forms, and evaluate it on a large-scale dataset consisting of 200K dialogs over 12.8M entities. Results verify the benefits of modeling dialog memory, and show that our semantic parsing-based approach outperforms a memory network based encoder-decoder model by a huge margin.
Modified Broadband Half Mode Substrate Integrated Waveguide Cruciform Coupler

─ In this paper, a cruciform coupler with Half Mode Substrate Integrated Waveguide (HMSIW) structure is presented. The prototype coupler consists of four half mode SIW structures, which are crossing each other at right angles. Also, two metallic posts are inserted in free section of junction in order to make coupling properties. Compact size and broadband operation are the features of this coupler. The size of coupler is reduced 24% in comparison to previous HMSIW cruciform couplers. A fractional bandwidth of about 35% is obtained. The coupler is designed and simulated by HFSS13. Here, the simulation results are compared with experimental results. Index Terms ─ Cruciform coupler, directional coupler, half mode SIW, millimeter wave, substrate integrated waveguide.
Predicting defects in SAP Java code: An experience report

Which components of a large software system are the most defect-prone? In a study on a large SAP Java system, we evaluated and compared a number of defect predictors, based on code features such as complexity metrics, static error detectors, change frequency, or component imports, thus replicating a number of earlier case studies in an industrial context. We found the overall predictive power to be lower than expected; still, the resulting regression models successfully predicted 50–60% of the 20% most defect-prone components.
Single-stage 6.78 MHz power-amplifier design using high-voltage GaN power ICs for wireless charging applications

Traditional power amplifiers for wireless power transfer require a low voltage dc power supply generated by a power adapter. This multi-stage power conversion solution reduces the overall efficiency. A single-stage “AC-RF” power amplifier architecture using a phase-modulated full-bridge topology and high voltage GaN Power ICs is proposed to directly generate 6.78 MHz wireless power from a rectified ac source. A 50W power amplifier is built and achieves 90% ac-to-coil efficiency, which reduces overall power loss by half, comparing to existing multi-stage solutions. The operating principle of a phase-shifted full bridge power amplifier is discussed, and analytical equations are provided for the passive filter network design. A coupled ZVS-tank scheme is also proposed to improve efficiency.
Epidemiological features of chronic low-back pain

Although the literature is filled with information about the prevalence and incidence of back pain in general, there is less information about chronic back pain, partly because of a lack of agreement about definition. Chronic back pain is sometimes defined as back pain that lasts for longer than 7-12 weeks. Others define it as pain that lasts beyond the expected period of healing, and acknowledge that chronic pain may not have well-defined underlying pathological causes. Others classify frequently recurring back pain as chronic pain since it intermittently affects an individual over a long period. Most national insurance and industrial sources of data include only those individuals in whom symptoms result in loss of days at work or other disability. Thus, even less is known about the epidemiology of chronic low-back pain with no associated work disability or compensation. Chronic low-back pain has also become a diagnosis of convenience for many people who are actually disabled for socioeconomic, work-related, or psychological reasons. In fact, some people argue that chronic disability in back pain is primarily related to a psychosocial dysfunction. Because the validity and reliability of some of the existing data are uncertain, caution is needed in an assessment of the information on this type of pain.
3 DCNN for Lung Nodule Detection And False Positive Reduction

The diagnosis of pulmonary nodules can be roughly divided into three steps: lung area extraction, lung nodule detection, false positive reduction. This article focuses on the nodule detection and false positive reduction. For nodule detection, we design a 3D nodule detector network motivated by the work of Feature Pyramid Networks and the 1st place team of Kaggle Data Science Bowl competition. For the large number of candidates detected by the detection model, we use two 3DCNN classifiers to determine which candidates are false-positive. A 3D network performs better than a 2D network as it also captures the vertical information of nodules. Also, focal loss  function is used to replace the cross-entropy loss function, which has been proved that it can deal with the problem of unbalanced classification. Furthermore, we use Batch Normalization  and weight initialization of convolution kernel by Xavier  and obtained good result.
A digitally compensated 1.5 GHz CMOS/FBAR frequency reference

A temperature-compensated 1.5 GHz film bulk acoustic wave resonator (FBAR)-based frequency reference implemented in a 0.35 ¿m CMOS process is presented. The ultra-small form factor (0.79 mm × 1.72 mm) and low power dissipation (515 ¿A with 2 V supply) of a compensated FBAR oscillator present a promising alternative for the replacement of quartz crystal frequency references. The measured post-compensation frequency drift over a 0-100°C temperature range is <±10 ppm. The measured oscillator phase noise is -133 dBc/ Hz at 100 kHz offset from the 1.5 GHz carrier.
Word of mouth communication within online communities: Conceptualizing the online social network

The authors are grateful for the important contribution of the anonymous reviewers and the Editor of the Journal of Interactive Marketing in improving this manuscript. ord of mouth (WOM) communication is a major part of online consumer interactions, particularly within the environment of online communities. Nevertheless, existing (offline) theory may be inappropriate to describe online WOM and its influence on evaluation and purchase. The authors report the results of a two-stage study aimed at investigating online WOM: a set of in-depth qualitative interviews followed by a social network analysis of a single online community. Combined, the results provide strong evidence that individuals behave as if Web sites themselves are primary “actors” in online social networks and that online communities can act as a social proxy for individual identification. The authors offer a conceptualization of online social networks which takes the Web site into account as an actor, an initial exploration of the concept of a consumer–Web site relationship, and a conceptual model of the online interaction and information evaluation process.
End-to-End Learning of Deterministic Decision Trees

Conventional decision trees have a number of favorable properties, including interpretability, a small computational footprint and the ability to learn from little training data. However, they lack a key quality that has helped fuel the deep learning revolution: that of being end-to-end trainable, and to learn from scratch those features that best allow to solve a given supervised learning problem. Recent work (Kontschieder 2015) has addressed this deficit, but at the cost of losing a main attractive trait of decision trees: the fact that each sample is routed along a small subset of tree nodes only. We here propose a model and ExpectationMaximization training scheme for decision trees that are fully probabilistic at train time, but after a deterministic annealing process become deterministic at test time. We also analyze the learned oblique split parameters on image datasets and show that Neural Networks can be trained at each split node. In summary, we present the first endto-end learning scheme for deterministic decision trees and present results on par with or superior to published standard oblique decision tree algorithms.
TouchLogger: Inferring Keystrokes on Touch Screen from Smartphone Motion

Attacks that use side channels, such as sound and electromagnetic emanation, to infer keystrokes on physical keyboards are ineffective on smartphones without physical keyboards. We describe a new side channel, motion, on touch screen smartphones with only soft keyboards. Since typing on different locations on the screen causes different vibrations, motion data can be used to infer the keys being typed. To demonstrate this attack, we developed TouchLogger, an Android application that extracts features from device orientation data to infer keystrokes. TouchLogger correctly inferred more than 70% of the keys typed on a number-only soft keyboard on a smartphone. We hope to raise the awareness of motion as a significant side channel that may leak confidential data.
The Effect of Tourism Investments on Tourism Income in Turkey : Panel VAR Approach Selahattin Guris

Tourism is rapidly developing around the world and it effects the economic, social and cultural development of the country. In this study based on the effect of tourism on the economic growth, the effect of tourism investments on tourism income was examined using Panel VAR model. The yearly data from the period 2001-2014 on the most popular touristic towns in Turkey, namely, Antalya, Mugla, Izmir, Aydin and Nevsehir, was used. When the results of the analysis are examined, it can be said that tourism investments have stimulating effect on tourism income, but it was noticed that the tourism investments from the last year had a decreasing effect on tourism income.
A classification-based approach to question routing in community question answering

Community-based Question and Answering (CQA) services have brought users to a new era of knowledge dissemination by allowing users to ask questions and to answer other users' questions. However, due to the fast increasing of posted questions and the lack of an effective way to find interesting questions, there is a serious gap between posted questions and potential answerers. This gap may degrade a CQA service's performance as well as reduce users' loyalty to the system. To bridge the gap, we present a new approach to Question Routing, which aims at routing questions to participants who are likely to provide answers. We consider the problem of question routing as a classification task, and develop a variety of local and global features which capture different aspects of questions, users, and their relations. Our experimental results obtained from an evaluation over the Yahoo!~Answers dataset demonstrate high feasibility of question routing. We also perform a systematical comparison on how different types of features contribute to the final results and show that question-user relationship features play a key role in improving the overall performance.
ILP-based joint routing and scheduling for time-triggered networks

Networks in the automotive and aerospace area as well as in production facilities have to support time-critical (i.e., hard real-time) communication. For such applications, time-triggered Ethernet-based networking solutions provide the required timeliness, i.e., reliable packet delivery with deterministic latencies and low jitter. However, the routing and scheduling of the time-triggered traffic is an NP-hard problem. Hence, existing solutions to this problem make certain abstractions to reduce complexity if necessary. Nonetheless, such abstractions exclude feasible routing and scheduling options from the design space. Specifically, it is a typical approach to model routing and scheduling as separate problems, which are solved successively or with heuristic coupling. Therefore, we present a novel ILP formulation that can be used to jointly solve the routing and scheduling problem for time-triggered Ethernet networks. Using this formulation, it is possible to solve various scheduling problems that are infeasible when using a fixed shortest path routing with separate scheduling. Compared to a fixed load balanced routing with separate scheduling, schedules computed with our formulation offer lower communication latencies.
Very High Force Hydraulic McKibben Artificial Muscle with a p-Phenylene-2, 6-benzobisoxazole Cord Sleeve

Small and lightweight actuators that generate high force and high energy are strongly required for realizing powerful robots and tools. By applying ultra-high-strength p-phenylene-2,6-benzobisoxazole fiber sleeves to McKibben artificial muscles, new hydraulic artificial muscles have been developed. While conventional McKibben muscles are driven by a maximum pneumatic pressure of 0.7 MPa, the newly developed muscles are driven by a maximum water hydraulic of pressure of 4 MPa, resulting in very high force capability. This paper presents the materials and structure of the new artificial muscle and the experimental results. The developed muscles are evaluated by four parameters — force density per volume (FDV), force density per mass (FDM), energy density per volume (EDV) and energy density per mass (EDM) — for comparisons with other conventional linear actuators. The prototype artificial muscle, which is 40 mm in diameter and 700 mm in length, can achieve a maximum contracting force of 28 kN, FDV of 32.3 × 10−3 N/mm3, FDM of 9.44 × 103 N/kg, EDV of 2600 × 10−3 J/mm3 and EDM of 762 × 103 J/kg. These values are 1.7 to 33 times larger than those of the typical conventional actuators. As the result, a high force artificial muscle of 40 mm in diameter that generates 28-kN contracting force has been developed successfully.
Comparison between JSON and XML in Applications Based on AJAX

As the core technology of Web 2.0, Ajax has caught more and more attention. Xml, as the traditional data load format, needs to be resolved by DOM (Document Object Model ) both in client-side and server-side, which wastes system resource and makes a great reduction of user-friendliness. In this paper, a light-weightdata-interchanging format-JSON (Java Script Object Notation) will be introduced, which provides a higher level of flexibility and efficiency. We make a comparison between JSON and XML through expriment, then use JSON as data-transfering format in an actual project. Results show that JSON is more suitable as a data-loading tool for Ajax applications.
Plantar fasciitis: evidence-based review of diagnosis and therapy.

Plantar fasciitis causes heel pain in active as well as sedentary adults of all ages. The condition is more likely to occur in persons who are obese or in those who are on their feet most of the day. A diagnosis of plantar fasciitis is based on the patient's history and physical findings. The accuracy of radiologic studies in diagnosing plantar heel pain is unknown. Most interventions used to manage plantar fasciitis have not been studied adequately; however, shoe inserts, stretching exercises, steroid injection, and custom-made night splints may be beneficial. Extracorporeal shock wave therapy may effectively treat runners with chronic heel pain but is ineffective in other patients. Limited evidence suggests that casting or surgery may be beneficial when conservative measures fail.
Double-orientation code and nonlinear matching scheme for palmprint recognition

Many palmprint authentication approaches have been proposed in recent years. Among them, the orientation based coding approach, in which the dominant orientation features of palmprints are extracted and encoded into bitwise codes, is one of the most promising approaches. The distance between codes created from two palmprint images is calculated in the matching stage. Reliable orientation feature extraction and efficient matching are the two most crucial problems in orientation based coding approaches. However, conventional coding based approaches usually extract only one dominant orientation feature by adopting filters with discrete orientations, which is sensitive to the noise and rotation. This paper proposed a novel double-orientation code (DOC) scheme to represent the orientation feature of palmprint and designed an effective nonlinear angular matching score to evaluate the similarity between the DOC. Extensive experiments performed on three types of palmprint databases demonstrate that the proposed approach has excellent performance in comparison with previously proposed state-of-the-art approaches. & 2015
A Spatial-Temporal-Semantic Neural Network Algorithm for Location Prediction on Moving Objects

Location prediction has attracted much attention due to its important role in many location-based services, such as food delivery, taxi-service, real-time bus system, and advertisement posting. Traditional prediction methods often cluster track points into regions and mine movement patterns within the regions. Such methods lose information of points along the road and cannot meet the demand of specific services. Moreover, traditional methods utilizing classic models may not perform well with long location sequences. In this paper, a spatial-temporal-semantic neural network algorithm (STS-LSTM) has been proposed, which includes two steps. First, the spatial-temporal-semantic feature extraction algorithm (STS) is used to convert the trajectory to location sequences with fixed and discrete points in the road networks. The method can take advantage of points along the road and can transform trajectory into model-friendly sequences. Then, a long short-term memory (LSTM)-based model is constructed to make further predictions, which can better deal with long location sequences. Experimental results on two real-world datasets show that STS-LSTM has stable and higher prediction accuracy over traditional feature extraction and model building methods, and the application scenarios of the algorithm are illustrated.
Topological Data Analysis of Biological Aggregation Models

We apply tools from topological data analysis to two mathematical models inspired by biological aggregations such as bird flocks, fish schools, and insect swarms. Our data consists of numerical simulation output from the models of Vicsek and D'Orsogna. These models are dynamical systems describing the movement of agents who interact via alignment, attraction, and/or repulsion. Each simulation time frame is a point cloud in position-velocity space. We analyze the topological structure of these point clouds, interpreting the persistent homology by calculating the first few Betti numbers. These Betti numbers count connected components, topological circles, and trapped volumes present in the data. To interpret our results, we introduce a visualization that displays Betti numbers over simulation time and topological persistence scale. We compare our topological results to order parameters typically used to quantify the global behavior of aggregations, such as polarization and angular momentum. The topological calculations reveal events and structure not captured by the order parameters.
Using speakers' referential intentions to model early cross-situational word learning.

Word learning is a "chicken and egg" problem. If a child could understand speakers' utterances, it would be easy to learn the meanings of individual words, and once a child knows what many words mean, it is easy to infer speakers' intended meanings. To the beginning learner, however, both individual word meanings and speakers' intentions are unknown. We describe a computational model of word learning that solves these two inference problems in parallel, rather than relying exclusively on either the inferred meanings of utterances or cross-situational word-meaning associations. We tested our model using annotated corpus data and found that it inferred pairings between words and object concepts with higher precision than comparison models. Moreover, as the result of making probabilistic inferences about speakers' intentions, our model explains a variety of behavioral phenomena described in the word-learning literature. These phenomena include mutual exclusivity, one-trial learning, cross-situational learning, the role of words in object individuation, and the use of inferred intentions to disambiguate reference.