nils-herrmann/clean_scirepeval huggingface dataset:
High Capacity data hiding using LSB Steganography and Encryption

The network provides a method of communication to distribute information to the masses. With the growth of data communication over computer network, the security of information has become a major issue. Steganography and cryptography are two different data hiding techniques. Steganography hides messages inside some other digital media. Cryptography, on the other hand obscures the content of the message. We propose a high capacity data embedding approach by the combination of Steganography and cryptography. In the process a message is first encrypted using transposition cipher method and then the encrypted message is embedded inside an image using LSB insertion method. The combination of these two methods will enhance the security of the data embedded. This combinational methodology will satisfy the requirements such as capacity, security and robustness for secure data transmission over an open channel. A comparative analysis is made to demonstrate the effectiveness of the proposed method by computing Mean square error (MSE) and Peak Signal to Noise Ratio (PSNR). We analyzed the data hiding technique using the image performance parameters like Entropy, Mean and Standard Deviation. The stego images are tested by transmitting them and the embedded data are successfully extracted by the receiver. The main objective in this paper is to provide resistance against visual and statistical attacks as well as high capacity.
Guideline for a Simplified Differential-Mode EMI Filter Design

The design of electromagnetic interference (EMI) input filters, needed for switched power converters to fulfill the regulatory standards, is typically associated with high development effort. This paper presents a guideline for a simplified differential-mode (DM) filter design. First, a procedure to estimate the required filter attenuation based on the total input rms current using only a few equations is given. Second, a volume optimization of the needed DM filter based on the previously calculated filter attenuation and volumetric component parameters is introduced. It is shown that a minimal volume can be found for a certain optimal number of filter stages. The considerations are exemplified for two single-phase power factor correction converters operated in continuous and discontinuous conduction modes, respectively. Finally, EMI measurements done with a 300-W power converter prototype prove the proposed filter design method.
Active Learning from Data Streams

In this paper, we address a new research problem on active learning from data streams where data volumes grow continuously and labeling all data is considered expensive and impractical. The objective is to label a small portion of stream data from which a model is derived to predict newly arrived instances as accurate as possible. In order to tackle the challenges raised by data streams' dynamic nature, we propose a classifier ensembling based active learning framework which selectively labels instances from data streams to build an accurate classifier. A minimal variance principle is introduced to guide instance labeling from data streams. In addition, a weight updating rule is derived to ensure that our instance labeling process can adaptively adjust to dynamic drifting concepts in the data. Experimental results on synthetic and real-world data demonstrate the performances of the proposed efforts in comparison with other simple approaches.
Conserving self-control strength.

Individuals may be motivated to limit their use of self-control resources, especially when they have depleted some of that resource. Expecting to need self-control strength in the future should heighten the motivation to conserve strength. In 4 experiments, it was found that depleted participants who anticipated exerting self-control in the future performed more poorly in an intervening test of self-control than participants who were not depleted, and more poorly than those who did not expect to exert self-control in the future. Conversely, those who conserved strength performed better on tasks that they conserved the strength for as compared with those who did not conserve. The underlying economic or conservation of resource model sheds some light on the operation of self-control strength.
The reacTable: exploring the synergy between live music performance and tabletop tangible interfaces

In recent years we have seen a proliferation of musical tables. Believing that this is not just the result of a tabletop trend, in this paper we first discuss several of the reasons for which live music performance and HCI in general, and musical instruments and tabletop interfaces in particular, can lead to a fertile two-way cross-pollination that can equally benefit both fields. After that, we present the reac Table, a musical instrument based on a tabletop interface that exemplifies several of these potential achievements.
Deep learning in the automotive industry: Applications and tools

Deep Learning refers to a set of machine learning techniques that utilize neural networks with many hidden layers for tasks, such as image classification, speech recognition, language understanding. Deep learning has been proven to be very effective in these domains and is pervasively used by many Internet services. In this paper, we describe different automotive uses cases for deep learning in particular in the domain of computer vision. We surveys the current state-of-the-art in libraries, tools and infrastructures (e. g. GPUs and clouds) for implementing, training and deploying deep neural networks. We particularly focus on convolutional neural networks and computer vision use cases, such as the visual inspection process in manufacturing plants and the analysis of social media data. To train neural networks, curated and labeled datasets are essential. In particular, both the availability and scope of such datasets is typically very limited. A main contribution of this paper is the creation of an automotive dataset, that allows us to learn and automatically recognize different vehicle properties. We describe an end-to-end deep learning application utilizing a mobile app for data collection and process support, and an Amazon-based cloud backend for storage and training. For training we evaluate the use of cloud and on-premises infrastructures (including multiple GPUs) in conjunction with different neural network architectures and frameworks. We assess both the training times as well as the accuracy of the classifier. Finally, we demonstrate the effectiveness of the trained classifier in a real world setting during manufacturing process.
Sentiment Composition

Sentiment classification of grammatical constituents can be explained in a quasicompositional way. The classification of a complex constituent is derived via the classification of its component constituents and operations on these that resemble the usual methods of compositional semantic analysis. This claim is illustrated with a description of sentiment propagation, polarity reversal, and polarity conflict resolution within various linguistic constituent types at various grammatical levels. We propose a theoretical composition model, evaluate a lexical dependency parsing post-process implementation, and estimate its impact on general NLP pipelines.
Integration of a GFDM secondary system in an OFDM primary system

Concepts of cognitive radio are yet in an early stage of development. They aim at improving the efficiency of spectrum utilization by exploiting locally and temporally vacant parts of the spectrum. When hierarchical spectrum access is considered, secondary users are authorized to use spectrum white spaces on a non-interfering basis, where minimal impact on the primary systems has to be ensured. GFDM is a digital multi-carrier transceiver concept that employs pulse shaping filters to provide control over the transmitted signal's spectral properties, a cyclic prefix that enables an efficient FFT-based frequency domain equalization scheme as well as tail biting as a way to make the prefix independent of the filter length. In this paper, two setups of uncoded AWGN transmission are analyzed through simulation. Both setups have in common that an OFDM primary system is overlaid by a secondary system. For that purpose, resources are made free artificially. First, a non-synchronized OFDM system is inserted into the white space. Then, the results are compared to the case when the secondary system operates with the GFDM scheme. Both setups are reviewed under the aspect of bit error performance in dependence of guard bands and various pulse shaping filter parameters. Conclusions for the primary and secondary system are drawn.
Document cleanup using page frame detection

When a page of a book is scanned or photocopied, textual noise (extraneous symbols from the neighboring page) and/or non-textual noise (black borders, speckles, ...) appear along the border of the document. Existing document analysis methods can handle non-textual noise reasonably well, whereas textual noise still presents a major issue for document analysis systems. Textual noise may result in undesired text in optical character recognition (OCR) output that needs to be removed afterwards. Existing document cleanup methods try to explicitly detect and remove marginal noise. This paper presents a new perspective for document image cleanup by detecting the page frame of the document. The goal of page frame detection is to find the actual page contents area, ignoring marginal noise along the page border. We use a geometric matching algorithm to find the optimal page frame of structured documents (journal articles, books, magazines) by exploiting their text alignment property. We evaluate the algorithm on the UW-III database. The results show that the error rates are below 4% each of the performance measures used. Further tests were run on a dataset of magazine pages and on a set of camera captured document images. To demonstrate the benefits of using page frame detection in practical applications, we choose OCR and layout-based document image retrieval as sample applications. Experiments using a commercial OCR system show that by removing characters outside the computed page frame, the OCR error rate is reduced from 4.3 to 1.7% on the UW-III dataset. The use of page frame detection in layout-based document image retrieval application decreases the retrieval error rates by 30%.
Stylistic Transfer in Natural Language Generation Systems Using Recurrent Neural Networks

Linguistic style conveys the social context in which communication occurs and defines particular ways of using language to engage with the audiences to which the text is accessible. In this work, we are interested in the task of stylistic transfer in natural language generation (NLG) systems, which could have applications in the dissemination of knowledge across styles, automatic summarization and author obfuscation. The main challenges in this task involve the lack of parallel training data and the difficulty in using stylistic features to control generation. To address these challenges, we plan to investigate neural network approaches to NLG to automatically learn and incorporate stylistic features in the process of language generation. We identify several evaluation criteria, and propose manual and automatic evaluation approaches.
Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery

Driven by the prominent thermal signature of humans and following the growing availability of unmanned aerial vehicles (UAVs), more and more research efforts have been focusing on the detection and tracking of pedestrians using thermal infrared images recorded from UAVs. However, pedestrian detection and tracking from the thermal images obtained from UAVs pose many challenges due to the low-resolution of imagery, platform motion, image instability and the relatively small size of the objects. This research tackles these challenges by proposing a pedestrian detection and tracking system. A two-stage blob-based approach is first developed for pedestrian detection. This approach first extracts pedestrian blobs using the regional gradient feature and geometric constraints filtering and then classifies the detected blobs by using a linear Support Vector Machine (SVM) with a hybrid descriptor, which sophisticatedly combines Histogram of Oriented Gradient (HOG) and Discrete Cosine Transform (DCT) features in order to achieve accurate detection. This research further proposes an approach for pedestrian tracking. This approach employs the feature tracker with the update of detected pedestrian location to track pedestrian objects from the registered videos and extracts the motion trajectory data. The proposed detection and tracking approaches have been evaluated by multiple different datasets, and the results illustrate the effectiveness of the proposed methods. This research is expected to significantly benefit many transportation applications, such as the multimodal traffic performance measure, pedestrian behavior study and pedestrian-vehicle crash analysis. Future work will focus on using fused thermal and visual images to further improve the detection efficiency and effectiveness.
K-Pop Genres: A Cross-Cultural Exploration

Current music genre research tends to focus heavily on classical and popular music from Western cultures. Few studies discuss the particular challenges and issues related to non-Western music. The objective of this study is to improve our understanding of how genres are used and perceived in different cultures. In particular, this study attempts to fill gaps in our understanding by examining K-pop music genres used in Korea and comparing them with genres used in North America. We provide background information on K-pop genres by analyzing 602 genre-related labels collected from eight major music distribution websites in Korea. In addition, we report upon a user study in which American and Korean users annotated genre information for 1894 K-pop songs in order to understand how their perceptions might differ or agree. The results show higher consistency among Korean users than American users demonstrated by the difference in Fleiss’ Kappa values and proportion of agreed genre labels. Asymmetric disagreements between Americans and Koreans on specific genres reveal some interesting differences in the perception of genres. Our findings provide some insights into challenges developers may face in creating global music services.
STREAM: The Stanford Data Stream Management System

Traditional database management systems are best equipped to run onetime queries over finite stored data sets. However, many modern applications such as network monitoring, financial analysis, manufacturing, and sensor networks require long-running, or continuous, queries over continuous unbounded streams of data. In the STREAM project at Stanford, we are investigating data management and query processing for this class of applications. As part of the project we are building a general-purpose prototype Data Stream Management System (DSMS), also called STREAM, that supports a large class of declarative continuous queries over continuous streams and traditional stored data sets. The STREAM prototype targets environments where streams may be rapid, stream characteristics and query loads may vary over time, and system resources may be limited. Building a general-purpose DSMS poses many interesting challenges:
Assessment of the Suitability of Fog Computing in the Context of Internet of Things

This work performs a rigorous, comparative analysis of the fog computing paradigm and the conventional cloud computing paradigm in the context of the Internet of Things (IoT), by mathematically formulating the parameters and characteristics of fog computing—one of the first attempts of its kind. With the rapid increase in the number of Internet-connected devices, the increased demand of real-time, low-latency services is proving to be challenging for the traditional cloud computing framework. Also, our irreplaceable dependency on cloud computing demands the cloud data centers (DCs) always to be up and running which exhausts huge amount of power and yield tons of carbon dioxide ( <inline-formula><tex-math notation="LaTeX">$\text{CO}_2$</tex-math><alternatives> <inline-graphic xlink:href="sarkar-ieq1-2485206.gif"/></alternatives></inline-formula>) gas. In this work, we assess the applicability of the newly proposed <italic>fog computing</italic> paradigm to serve the demands of the latency-sensitive applications in the context of IoT. We model the fog computing paradigm by mathematically characterizing the fog computing network in terms of power consumption, service latency, <inline-formula> <tex-math notation="LaTeX">$\text{CO}_2$</tex-math><alternatives> <inline-graphic xlink:href="sarkar-ieq2-2485206.gif"/></alternatives></inline-formula> emission, and cost, and evaluating its performance for an environment with high number of Internet-connected devices demanding real-time service. A case study is performed with traffic generated from the <inline-formula><tex-math notation="LaTeX">$100$</tex-math><alternatives> <inline-graphic xlink:href="sarkar-ieq3-2485206.gif"/></alternatives></inline-formula> highest populated cities being served by eight geographically distributed DCs. Results show that as the number of applications demanding real-time service increases, the fog computing paradigm outperforms traditional cloud computing. For an environment with <inline-formula><tex-math notation="LaTeX">$50$</tex-math><alternatives> <inline-graphic xlink:href="sarkar-ieq4-2485206.gif"/></alternatives></inline-formula> percent applications requesting for instantaneous, real-time services, the overall service latency for fog computing is noted to decrease by <inline-formula><tex-math notation="LaTeX">$50.09$</tex-math><alternatives> <inline-graphic xlink:href="sarkar-ieq5-2485206.gif"/></alternatives></inline-formula> percent. However, it is mentionworthy that for an environment with less percentage of applications demanding for low-latency services, fog computing is observed to be an overhead compared to the traditional cloud computing. Therefore, the work shows that in the context of IoT, with high number of latency-sensitive applications fog computing outperforms cloud computing.
Do you know your brain? A survey on public neuroscience literacy at the closing of the decade of the brain.

What does the public know about the developments offered by brain research? What factors influence public neuroscience literacy? What issues need to be emphasized to the public? To address these questions, a survey was conducted using a questionnaire with 95 assertions, answered by indicating yes, no, or I don't know. The opinions of 35 senior neuroscientists and 2158 members of the public of Rio de Janeiro were heard on issues such as the mind-brain relationship, the senses, learning, and memory. The incidence of "correct" answers among the public improved the most with schooling, followed by reading of popular science magazines and of newspapers. An analysis of the responses to each assertion revealed which themes are well- or poorly known to the public. The results attest for the importance of popular scientific communication and indicate issues on which communication efforts should be concentrated in order to increase public awareness about the brain.
Asphyxial suicide with helium and a plastic bag.

Suicide by helium inhalation inside a plastic bag has recently been publicized by right-to-die proponents in "how to" . This article reports a suicide performed according to this new and highly lethal technique, which is also a potentially undetectable cause of death. Toxicology information could not determine helium inhalation, and drug screening did not reveal data of significance. The cause of death could be determined only by the physical evidence at the scene of death. Helium inhalation can easily be concealed when interested parties remove or alter evidence. To ensure that their deaths are not documented as suicide, some individuals considering assisted suicide may choose helium methods and assistance from helpers. Recent challenges to Oregon's physician-assisted suicide law may increase interest in helium instead of barbiturates for assisted suicide.
A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples

Deep neural networks have been shown to suffer from a surprising weakness: their classification outputs can be changed by small, non-random perturbations of their inputs. This adversarial example phenomenon has been explained as originating from deep networks being “too linear” (Goodfellow et al., 2014). We show here that the linear explanation of adversarial examples presents a number of limitations: the formal argument is not convincing; linear classifiers do not always suffer from the phenomenon, and when they do their adversarial examples are different from the ones affecting deep networks. We propose a new perspective on the phenomenon. We argue that adversarial examples exist when the classification boundary lies close to the submanifold of sampled data, and present a mathematical analysis of this new perspective in the linear case. We define the notion of adversarial strength and show that it can be reduced to the deviation angle between the classifier considered and the nearest centroid classifier. Then, we show that the adversarial strength can be made arbitrarily high independently of the classification performance due to a mechanism that we call boundary tilting. This result leads us to defining a new taxonomy of adversarial examples. Finally, we show that the adversarial strength observed in practice is directly dependent on the level of regularisation used and the strongest adversarial examples, symptomatic of overfitting, can be avoided by using a proper level of regularisation.
Affect , Generalization , and the Perception of Risk

Experimental manipulations of affect induced by a brief newspaper report of a tragic event produced a pervasive increase in subjects' estimates of the frequency of many risks and other undesirable events. Contrary to expectation, the effect was independent of the similarity between the report arid the estimated risk. An account of a fatal stabbing did not increase the frequency estimate of a closely related risk, homicide, more than the estimates of unrelated risks such as natural hazards. An account of a happy event that created positive affect produced a comparable global decrease in judged frequency of risks.
Adding interactive interface to E-Government systems using AIML based chatterbots

This paper proposes a technique to utilize the power of chatterbots to serve as interactive Support systems to enterprise applications which aim to address a huge audience. The need for support systems arises due to inability of computer illiterate audience to utilize the services offered by an enterprise application. Setting up customer support centers works well for small-medium sized businesses but for mass applications (here E-Governance Systems) the audience counts almost all a country has as its population, Setting up support center that can afford such load is irrelevant. This paper proposes a solution by using AIML based chatterbots to implement Artificial Support Entity (ASE) to such Applications.