nils-herrmann/clean_scirepeval huggingface dataset:
Large-Scale Automated Software Diversity—Program Evolution Redux

The software monoculture favors attackers over defenders, since it makes all target environments appear similar. Code-reuse attacks, for example, rely on target hosts running identical software. Attackers use this assumption to their advantage by automating parts of creating an attack. This article presents large-scale automated software diversification as a means to shore up this vulnerability implied by our software monoculture. Besides describing an industrial-strength implementation of automated software diversity, we introduce methods to objectively measure the effectiveness of diversity in general, and its potential to eliminate code-reuse attacks in particular.
Using Inquiry-based Bluetooth RSSI Probability Distributions for Indoor Positioning

Fingerprinting is a common technique for indoor positioning using short range Radio Frequency (RF) technologies such as Wireless Location Area Network (WLAN) and Bluetooth (BT). It works in two phases: The first phase is a data training phase in which a radio map for the targeted area is generated in advance, while the second phase is the real-time location determination phase using the radio map. Considering the work amount for generating the radio map, only a few samples of the Radio Signal Strength Indicator (RSSI) are typically collected at each reference point. The limited samples are not able to represent the real signal distribution well in the conventional fingerprint approach such as in an occurrence-based solution. This paper presents a new solution using the Weibull function for approximating the Bluetooth signal strength distribution in the data training phase. This approach requires only a few RSSI samples to estimate the parameters of the Weibull distribution. Compared to the occurrence-based solution, the Weibull function utilizes the shape, shift, and scale parameters to describe the distribution over the entire RSSI domain. This study indicates that the reliability and accuracy of the fingerprint database is improved with the Weibull function approach. A Histogram Maximum Likelihood position estimation based on Bayesian theory is utilized in the positioning phase. The test results show that the fingerprinting solution using the Weibull probability distribution performs better than the occurrence-based fingerprint approach.
Robot reinforcement learning using EEG-based reward signals

Reinforcement learning algorithms have been successfully applied in robotics to learn how to solve tasks based on reward signals obtained during task execution. These reward signals are usually modeled by the programmer or provided by supervision. However, there are situations in which this reward is hard to encode, and so would require a supervised approach of reinforcement learning, where a user directly types the reward on each trial. This paper proposes to use brain activity recorded by an EEG-based BCI system as reward signals. The idea is to obtain the reward from the activity generated while observing the robot solving the task. This process does not require an explicit model of the reward signal. Moreover, it is possible to capture subjective aspects which are specific to each user. To achieve this, we designed a new protocol to use brain activity related to the correct or wrong execution of the task. We showed that it is possible to detect and classify different levels of error in single trials. We also showed that it is possible to apply reinforcement learning algorithms to learn new similar tasks using the rewards obtained from brain activity.
Group Touch: Distinguishing Tabletop Users in Group Settings via Statistical Modeling of Touch Pairs

We present Group Touch, a method for distinguishing among multiple users simultaneously interacting with a tabletop computer using only the touch information supplied by the device. Rather than tracking individual users for the duration of an activity, Group Touch distinguishes users from each other by modeling whether an interaction with the tabletop corresponds to either: (1) a new user, or (2) a change in users currently interacting with the tabletop. This reframing of the challenge as distinguishing users rather than tracking and identifying them allows Group Touch to support multi-user collaboration in real-world settings without custom instrumentation. Specifically, Group Touch examines pairs of touches and uses the difference in orientation, distance, and time between two touches to determine whether the same person performed both touches in the pair. Validated with field data from high-school students in a classroom setting, Group Touch distinguishes among users "in the wild" with a mean accuracy of 92.92% (SD=3.94%). Group Touch can imbue collaborative touch applications in real-world settings with the ability to distinguish among multiple users.
Investigating User Privacy in Android Ad Libraries

Recent years have witnessed incredible growth in the popularity and prevalence of smart phones. A flourishing mobile application market has evolved to provide users with additional functionality such as interacting with social networks, games, and more. Mobile applications may have a direct purchasing cost or be free but ad-supported. Unlike in-browser ads, the privacy implications of ads in Android applications has not been thoroughly explored. We start by comparing the similarities and differences of in-browser ads and in-app ads. We examine the effect on user privacy of thirteen popular Android ad providers by reviewing their use of permissions. Worryingly, several ad libraries checked for permissions beyond the required and optional ones listed in their documentation, including dangerous permissions like CAMERA, WRITE CALENDAR and WRITE CONTACTS. Further, we discover the insecure use of Android’s JavaScript extension mechanism in several ad libraries. We identify fields in ad requests for private user information and confirm their presence in network data obtained from a tier-1 network provider. We also show that users can be tracked by a network sniffer across ad providers and by an ad provider across applications. Finally, we discuss several possible solutions to the privacy issues identified above.
Induction motor tests using MATLAB/Simulink and their integration into undergraduate electric machinery courses

This work describes MATLAB/Simulink implementation of three induction motor tests, namely dc, no-load, and blocked-rotor tests performed to identify equivalent circuit parameters. These simulation models are developed to support and enhance electric machinery education at the undergraduate level. The proposed tests have been successfully integrated into electric machinery courses at Drexel University, Philadelphia, PA, and Nigde University, Nigde, Turkey.
A fast, invariant representation for human action in the visual system.

Humans can effortlessly recognize others' actions in the presence of complex transformations, such as changes in viewpoint. Several studies have located the regions in the brain involved in invariant action recognition; however, the underlying neural computations remain poorly understood. We use magnetoencephalography decoding and a data set of well-controlled, naturalistic videos of five actions (run, walk, jump, eat, drink) performed by different actors at different viewpoints to study the computational steps used to recognize actions across complex transformations. In particular, we ask when the brain discriminates between different actions, and when it does so in a manner that is invariant to changes in 3D viewpoint. We measure the latency difference between invariant and noninvariant action decoding when subjects view full videos as well as form-depleted and motion-depleted stimuli. We were unable to detect a difference in decoding latency or temporal profile between invariant and noninvariant action recognition in full videos. However, when either form or motion information is removed from the stimulus set, we observe a decrease and delay in invariant action decoding. Our results suggest that the brain recognizes actions and builds invariance to complex transformations at the same time and that both form and motion information are crucial for fast, invariant action recognition. NEW & NOTEWORTHY The human brain can quickly recognize actions despite transformations that change their visual appearance. We use neural timing data to uncover the computations underlying this ability. We find that within 200 ms action can be read out of magnetoencephalography data and that this representation is invariant to changes in viewpoint. We find form and motion are needed for this fast action decoding, suggesting that the brain quickly integrates complex spatiotemporal features to form invariant action representations.
Sensor Reduction for Driver-Automation Shared Steering Control via an Adaptive Authority Allocation Strategy

This paper presents a new shared control method for lane keeping assist (LKA) systems of intelligent vehicles. The proposed method allows the LKA system to effectively share the control authority with a human driver by avoiding or minimizing the conflict situations between these two driving actors. To realize the shared control scheme, the unpredictable driver-automation interaction is explicitly taken into account in the control design via a fictive driver activity variable. This latter is judiciously introduced into the driver–road–vehicle system to represent the driver's need for assistance in accordance with his/her real-time driving activity. Using Lyapunov stability arguments, Takagi–Sugeno fuzzy model-based design conditions are derived to handle not only the time-varying driver activity variable, but also a large variation range of vehicle speed. Both simulation and hardware experiments are presented to demonstrate that the proposed control strategy together with a linear matrix inequality design formulation provide an effective tool to deal with the challenging shared steering control issue. In particular, a fuzzy output feedback control scheme is exploited to achieve the shared control goal without at least two important vehicle sensors. These physical sensors are widely employed in previous works to measure the lateral speed and the steering rate for the control design and real-time implementation. The successful results of this idea of sensor-reduction control has an obvious interest from practical viewpoint.
A deep learning method for classifying mammographic breast density categories.

Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI-RADS) breast density categories. It is particularly difficult for radiologists to consistently distinguish the two most common and most variably assigned BI-RADS categories, i.e., "scattered density" and "heterogeneously dense". The aim of this work was to investigate a deep learning-based breast density classifier to consistently distinguish these two categories, aiming at providing a potential computerized tool to assist radiologists in assigning a BI-RADS category in current clinical workflow.



In this study, we constructed a convolutional neural network (CNN)-based model coupled with a large (i.e., 22,000 images) digital mammogram imaging dataset to evaluate the classification performance between the two aforementioned breast density categories. All images were collected from a cohort of 1,427 women who underwent standard digital mammography screening from 2005 to 2016 at our institution. The truths of the density categories were based on standard clinical assessment made by board-certified breast imaging radiologists. Effects of direct training from scratch solely using digital mammogram images and transfer learning of a pretrained model on a large nonmedical imaging dataset were evaluated for the specific task of breast density classification. In order to measure the classification performance, the CNN classifier was also tested on a refined version of the mammogram image dataset by removing some potentially inaccurately labeled images. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to measure the accuracy of the classifier.



The AUC was 0.9421 when the CNN-model was trained from scratch on our own mammogram images, and the accuracy increased gradually along with an increased size of training samples. Using the pretrained model followed by a fine-tuning process with as few as 500 mammogram images led to an AUC of 0.9265. After removing the potentially inaccurately labeled images, AUC was increased to 0.9882 and 0.9857 for without and with the pretrained model, respectively, both significantly higher (P < 0.001) than when using the full imaging dataset.



Our study demonstrated high classification accuracies between two difficult to distinguish breast density categories that are routinely assessed by radiologists. We anticipate that our approach will help enhance current clinical assessment of breast density and better support consistent density notification to patients in breast cancer screening.
Panoramic Image Stitching

This thesis investigates the panoramic image stitching problem in computer vision area. The panoramic image stitching algorithms are divided into image alignment and image stitching approaches. The former can find the correspondence relationships among images with certain degrees of overlap, while the latter takes the correspondence relationships estimated by the registration algorithm and uses blending method to blend images in a seamless manner. Furthermore, image stitching algorithm is able to solve blurring or ghosting effects resulted from scene movements and exposure differences. This paper adopts the feature-based alignment algorithms and blending algorithms to produce high-quality image mosaics.
Image Segmentation and Classification of MRI Brain Tumor Based on Cellular Automata and Neural Networks

This paper proposes segmentation of MRI brain tumor using cellular automata and classification of tumors using Gray level Co-occurrence matrix features and artificial neural network. In this technique, cellular automata (CA) based seeded tumor segmentation method on magnetic resonance (MR) images, which uses volume of interest (VOI) and seed selection. Seed based segmentation is performed in the image for detecting the tumor region and then highlighting the region with help of level set method. The brain images are classified into three stages that are normal, benign and malignant. For this non knowledge based automatic image classification, image texture features and Artificial Neural Network are employed. The conventional method for medical resonance brain images classification and tumors detection is by human inspection. Decision making was performed in two stages: feature extraction using Gray level Co-occurrence matrix and the classification using Radial basis function which is the type of ANN. The performance of the ANN classifier was evaluated in terms of training performance and classification accuracies. Artificial Neural Network gives fast and accurate classification than other neural networks and it is a promising tool for classification of the tumors.
Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm

In this paper, a hybrid Taguchi-genetic algorithm (HTGA) is applied to solve the problem of tuning both network structure and parameters of a feedforward neural network. The HTGA approach is a method of combining the traditional genetic algorithm (TGA), which has a powerful global exploration capability, with the Taguchi method, which can exploit the optimum offspring. The Taguchi method is inserted between crossover and mutation operations of a TGA. Then, the systematic reasoning ability of the Taguchi method is incorporated in the crossover operations to select the better genes to achieve crossover, and consequently enhance the genetic algorithms. Therefore, the HTGA approach can be more robust, statistically sound, and quickly convergent. First, the authors evaluate the performance of the presented HTGA approach by studying some global numerical optimization problems. Then, the presented HTGA approach is effectively applied to solve three examples on forecasting the sunspot numbers, tuning the associative memory, and solving the XOR problem. The numbers of hidden nodes and the links of the feedforward neural network are chosen by increasing them from small numbers until the learning performance is good enough. As a result, a partially connected feedforward neural network can be obtained after tuning. This implies that the cost of implementation of the neural network can be reduced. In these studied problems of tuning both network structure and parameters of a feedforward neural network, there are many parameters and numerous local optima so that these studied problems are challenging enough for evaluating the performances of any proposed GA-based approaches. The computational experiments show that the presented HTGA approach can obtain better results than the existing method reported recently in the literature.
Conceptual Impact-Based Recommender System for CiteSeerx

CiteSeer is a digital library for scientific publications written by Computer Science researchers. Users are able to retrieve relevant documents from the database by searching by author name and/or keyword queries. Users may also receive recommendations of papers they might want to read provided by an existing conceptual recommender system. This system recommends documents based on an automaticallyconstructed user profile. Unlike traditional content-based recommender systems, the documents and the user profile are represented as concepts vectors rather than keyword vectors and papers are recommended based on conceptual matches rather than keyword matches between the profile and the documents. Although the current system provides recommendations that are on-topic, they are not necessarily high quality papers. In this work, we introduce the Conceptual Impact-Based Recommender (CIBR), a hybrid recommender system that extends the existing conceptual recommender system in CiteSeer by including an explicit quality factor as part of the recommendation criteria. To measure quality, our system considers the impact factor of each paper’s authors as measured by the authors’ h-index. Experiments to evaluate the effectiveness of our hybrid system show that the CIBR system recommends more relevant papers as compared to the conceptual recommender system.
From Characters to Understanding Natural Language ( C 2 NLU ) : Robust End-to-End Deep Learning for NLP Organized by

This report documents the program and the outcomes of Dagstuhl Seminar 17042 “From Characters to Understanding Natural Language (C2NLU): Robust End-to-End Deep Learning for NLP”. The seminar brought together researchers from different fields, including natural language processing, computational linguistics, deep learning and general machine learning. 31 participants from 22 academic and industrial institutions discussed advantages and challenges of using characters, i.e., “raw text”, as input for deep learning models instead of language-specific tokens. Eight talks provided overviews of different topics, approaches and challenges in current natural language processing research. In five working groups, the participants discussed current natural language processing/understanding topics in the context of character-based modeling, namely, morphology, machine translation, representation learning, end-to-end systems and dialogue. In most of the discussions, the need for a more detailed model analysis was pointed out. Especially for character-based input, it is important to analyze what a deep learning model is able to learn about language – about tokens, morphology or syntax in general. For an efficient and effective understanding of language, it might furthermore be beneficial to share representations learned from multiple objectives to enable the models to focus on their specific understanding task instead of needing to learn syntactic regularities of language first. Therefore, benefits and challenges of transfer learning were an important topic of the working groups as well as of the panel discussion and the final plenary discussion. Seminar January 22–27, 2017 – http://www.dagstuhl.de/17042 1998 ACM Subject Classification I.2 Artificial Intelligence, I.2.7 Natural Language Processing
Person Transfer GAN to Bridge Domain Gap for Person Re-identification

Although the performance of person Re-Identification (ReID) has been significantly boosted, many challenging issues in real scenarios have not been fully investigated, e.g., the complex scenes and lighting variations, viewpoint and pose changes, and the large number of identities in a camera network. To facilitate the research towards conquering those issues, this paper contributes a new dataset called MSMT171 with many important features, e.g., 1) the raw videos are taken by an 15-camera network deployed in both indoor and outdoor scenes, 2) the videos cover a long period of time and present complex lighting variations, and 3) it contains currently the largest number of annotated identities, i.e., 4,101 identities and 126,441 bounding boxes. We also observe that, domain gap commonly exists between datasets, which essentially causes severe performance drop when training and testing on different datasets. This results in that available training data cannot be effectively leveraged for new testing domains. To relieve the expensive costs of annotating new training samples, we propose a Person Transfer Generative Adversarial Network (PTGAN) to bridge the domain gap. Comprehensive experiments show that the domain gap could be substantially narrowed-down by the PTGAN.
Brain tumor detection by using a modified FCM and Level set algorithms

Segmentation is a vital role in medical image processing, where clustering technique widely used in medical application particularly for brain tumor detection in MRI. The aim of this research is to carry out a new technique for detecting a brain tumor from MRI images in the goal to design a new cooperative approach for the biomedical framework. First, we introduce the notion of FCM which incorporates the spatial information to get a better estimation of the clusters centers then the results obtained are considered as an initialization of the active edge for the Level set. During the application of this method, multiple initializations have been carried out to set the initial parameters. The results found after the implementation are satisfactory, which allows us to say that the use of a combined method of several segmentation algorithms allows to have better results and also a high diagnosis accuracy.