nils-herrmann/clean_scirepeval huggingface dataset:
Privacy protection issues in social networking sites

Social Networking Sites (SNS) have become very popular during the past few years, as they allow users to both express their individuality and meet people with similar interests. Nonetheless, there are also many potential threats to privacy associated with these SNS such as identity theft and disclosure of sensitive information. However, many users still are not aware of these threats and the privacy settings provided by SNS are not flexible enough to protect user data. In addition, users do not have any control over what others reveal about them. As such, we conduct a preliminary study which examines the privacy protection issues on Social Networking Sites (SNS) such as MySpace, Facebook and LinkedIn. Based on this study, we identify three privacy problems in SNS and propose a Privacy Framework as a foundation to cope with these problems.
AnatomyNet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy.

Radiation therapy (RT) is a common treatment option for head and neck (HaN) cancer. An important step involved in RT planning is the delineation of organs-at-risks (OARs) based on HaN computed tomography (CT). However, manually delineating OARs is time-consuming as each slice of CT images needs to be individually examined and a typical CT consists of hundreds of slices. Automating OARs segmentation has the benefit of both reducing the time and improving the quality of RT planning. Existing anatomy autosegmentation algorithms use primarily atlas-based methods, which require sophisticated atlas creation and cannot adequately account for anatomy variations among patients. In this work, we propose an end-to-end, atlas-free three-dimensional (3D) convolutional deep learning framework for fast and fully automated whole-volume HaN anatomy segmentation.



Our deep learning model, called AnatomyNet, segments OARs from head and neck CT images in an end-to-end fashion, receiving whole-volume HaN CT images as input and generating masks of all OARs of interest in one shot. AnatomyNet is built upon the popular 3D U-net architecture, but extends it in three important ways: (a) a new encoding scheme to allow autosegmentation on whole-volume CT images instead of local patches or subsets of slices, (b) incorporating 3D squeeze-and-excitation residual blocks in encoding layers for better feature representation, and (c) a new loss function combining Dice scores and focal loss to facilitate the training of the neural model. These features are designed to address two main challenges in deep learning-based HaN segmentation: (a) segmenting small anatomies (i.e., optic chiasm and optic nerves) occupying only a few slices, and (b) training with inconsistent data annotations with missing ground truth for some anatomical structures.



We collected 261 HaN CT images to train AnatomyNet and used MICCAI Head and Neck Auto Segmentation Challenge 2015 as a benchmark dataset to evaluate the performance of AnatomyNet. The objective is to segment nine anatomies: brain stem, chiasm, mandible, optic nerve left, optic nerve right, parotid gland left, parotid gland right, submandibular gland left, and submandibular gland right. Compared to previous state-of-the-art results from the MICCAI 2015 competition, AnatomyNet increases Dice similarity coefficient by 3.3% on average. AnatomyNet takes about 0.12 s to fully segment a head and neck CT image of dimension 178 × 302 × 225, significantly faster than previous methods. In addition, the model is able to process whole-volume CT images and delineate all OARs in one pass, requiring little pre- or postprocessing.



Deep learning models offer a feasible solution to the problem of delineating OARs from CT images. We demonstrate that our proposed model can improve segmentation accuracy and simplify the autosegmentation pipeline. With this method, it is possible to delineate OARs of a head and neck CT within a fraction of a second.
Introduction: Hybrid intelligent adaptive systems

This issue of International Journal of Intelligent Systems includes extended versions of selected papers from the 4th International Conference on Soft Computing, held in Iizuka, Japan, September 30]October 5, 1996. The topic of the special issue is ‘‘Hybrid Intelligent Adaptive Systems.’’ Research on hybrid systems is one of the key issues of developing intelligent systems and it can apply a wide range of tools, including artificial neural networks, fuzzy logic, knowledge-based systems, genetic algorithms, evolutionary computation, and chaos models. The papers in this issue have been carefully reviewed and modified to give the readers a comprehensive overview of theoretical aspects, design, and implementation issues of hybrid intelligent adaptive systems. In the first paper by Kasabov and Kozma, a general framework of developing hybrid, intelligent, and adaptive systems is given. This work develops multimodular, fuzzy neural network systems and applies it to phoneme-based speech recognition. The second paper by Miyata, Furuhashi, and Uchikawa proposes fuzzy abductive inference with degrees of manifestations. This method infers irredundant combinations of candidates with degrees of belief for the manifestations. It is also demonstrated that the results of the inference method are applicable to medical diagnosis and system fault detection. Cho adopts ideas of artificial life in his work to develop evolutionary neural networks. The introduced modular neural network can evole its structure autonomously using a structural genetic code. The effectiveness of the method is demonstrated on the example of handwritten digit recognition. Feuring and Lippe study theoretical aspects of fuzzy neural networks. They propose a training algorithm for fuzzy neural networks that satisfies a certain goodness criterion. The second part of the special issue contains articles related to time series analysis and systems control. Yamazaki, Kang, and Ochiai introduce a hierarchical neural network system for adaptive, intelligent control, based on the analogy with the human thinking process. The optimum parameter space of the neural network system is found by a self-controllable algorithm, which can lead to either equilibrium or to nonequilibrium, chaotic behavior. The results of this study are applied, e.g., to laser beam analysis, semiconductor design, and design of magnetic devices. The work by Kozma, Kasabov, Kim, and Cohen presents a chaotic neuro-fuzzy method for time series analysis and process control.
A Submicrowatt 1.1-MHz CMOS Relaxation Oscillator With Temperature Compensation

A 1.1-MHz submicrowatt current-mode relaxation oscillator with temperature compensation is presented. In this oscillator, the current-starving inverters are biased by using the current sources with positive and negative temperature coefficients. It relaxes the temperature variations. This oscillator is fabricated in a 0.18- μm CMOS process, and its area is 0.075 mm2. The power consumption is 0.859 μW, with a supply voltage of 1.8 V, and the calculated figure of merit is 0.78 nW/kHz. The measured output relative frequency variation is less than 3%/V for the supply voltage of 1.2-2.4 V and ±0.5% for the temperature of -20°C-80°C. The average temperature coefficient is 64.3 ppm/°C.
Can Google nowcast the market trend of Iranian mobile games?

The Internet has become an integral part of everyday life. In this paper, we investigate if the Internet behavior of consumers correlates with their actual behavior in computer games market. In particular, we aim to investigate to what extent Web search data can be exploited to predict the ranking of mobile games in the world. Based on our findings that show the existence of such correlations, we use web search data (from Google) about mobile games in Iran to nowcast1 (predict the present status of) the ranking of Iranian mobile games.
Relationship of personality to performance motivation: a meta-analytic review.

This article provides a meta-analysis of the relationship between the five-factor model of personality and 3 central theories of performance motivation (goal-setting, expectancy, and self-efficacy motivation). The quantitative review includes 150 correlations from 65 studies. Traits were organized according to the five-factor model of personality. Results indicated that Neuroticism (average validity = -.31) and Conscientiousness (average validity = .24) were the strongest and most consistent correlates of performance motivation across the 3 theoretical perspectives. Results further indicated that the validity of 3 of the Big Five traits--Neuroticism, Extraversion, and Conscientiousness--generalized across studies. As a set, the Big Five traits had an average multiple correlation of .49 with the motivational criteria, suggesting that the Big Five traits are an important source of performance motivation.
Partial Multi-Modal Sparse Coding via Adaptive Similarity Structure Regularization

Multi-modal sparse coding has played an important role in many multimedia applications, where data are usually with multiple modalities. Recently, various multi-modal sparse coding approaches have been proposed to learn sparse codes of multi-modal data, which assume that data appear in all modalities, or at least there is one modality containing all data. However, in real applications, it is often the case that some modalities of the data may suffer from missing information and thus result in partial multi-modality data. In this paper, we propose to solve the partial multi-modal sparse coding problem via multi-modal similarity structure regularization. Specifically, we propose a partial multi-modal sparse coding framework termed Adaptive Partial Multi-Modal Similarity Structure Regularization for Sparse Coding (AdaPM2SC), which preserves the similarity structure within the same modality and between different modalities. Experimental results conducted on two real-world datasets demonstrate that AdaPM2SC significantly outperforms the state-of-the-art methods under partial multi-modality scenario.
Knowledge tracing: Modeling the acquisition of procedural knowledge

This paper describes an effort to model students' changing knowledge state during skill acquisition. Students in this research are learning to write short programs with the ACT Programming Tutor (APT). APT is constructed around a production rule cognitive model of programming knowledge, called theideal student model. This model allows the tutor to solve exercises along with the student and provide assistance as necessary. As the student works, the tutor also maintains an estimate of the probability that the student has learned each of the rules in the ideal model, in a process calledknowledge tracing. The tutor presents an individualized sequence of exercises to the student based on these probability estimates until the student has ‘mastered’ each rule. The programming tutor, cognitive model and learning and performance assumptions are described. A series of studies is reviewed that examine the empirical validity of knowledge tracing and has led to modifications in the process. Currently the model is quite successful in predicting test performance. Further modifications in the modeling process are discussed that may improve performance levels.
Soldier suicide rates continue to rise: military, scientists work to stem the tide.

SUICIDES AMONG ACTIVE-DUTY SOLdiers in the US Army reached a 28year high in 2008, continuing a 4-year trend thathaspersisteddespiteongoing military efforts to curb such deaths. In the face of these grim statistics, Army leaders are redoubling their efforts to address this problem and have recruited scientists at the National Institute of Mental Health (NIMH) to probe the underlying causes of suicide in this population and identify effective interventions. The annual number of suicides among soldiersonactiveduty in theArmy,Army Reserve, and Army National Guard has risen steadily from 67 in 2004 to at least 128 in 2008. The 2008 figure is likely to top 140 because another 15 cases are under investigation; about 90% of such cases ultimately are ruled to be suicides, said Master Sergeant Marshall Bradshaw, program manager of the Army Suicide Prevention Program, according to a transcript from a January 29 roundtable discussion on the 2008 suicide rates. This revised total would raise the suicide rate among active-duty Army personnel to 20.2 per 100 000 in 2008, surpassing the suicide rate among civilians with similar demographics, which was 19.5 per 100 000 in 2005 (the most recent year for which data are available from the national Centers for Disease Control and Prevention [CDC]). Suicides among non–active-duty ArmyReserveandNational Guard troops also have risen substantially since 2004, when 15 cases were confirmed. However, the number of suicides among these non–active-duty forces actually dropped from 61 in 2007 to 43 in 2008. Suicide rates among veterans also have risen. In 2004, there were 35.4 suicides per 100 000 male users of Department of Veterans Affairs (VA) medical care and 12.3 suicides per 100 000 female users. These rates rose to 37.2 suicides per
COPSS-lite: Lightweight ICN Based Pub/Sub for IoT Environments

Information Centric Networking (ICN) is a new networking paradigm that treats content as the first class entity. It provides content to users without regards to the current location or source of the content. The publish/subscribe (pub/sub) systems have gained popularity in Internet. Pub/sub systems dismisses the need for users to request every content of their interest. Instead, the content is supplied to the interested users (subscribers) as and when it is published. CCN/NDN are popular ICN proposals widely accepted in the ICN community however, they do not provide an efficient pub/sub mechanism. COPSS enhances CCN/NDN with an efficient pub/sub capability. Internet of Things (IoT) is a growing topic of interest in both Academia and Industry. The current designs for IoT relies on IP. However, the IoT devices are constrained in their available resources and IP is heavy for their operation.We observed that IoT’s are information centric in nature and hence ICN is a more suitable candidate to support IoT environments. Although NDN and COPSS work well for the Internet, their current full fledged implementations cannot be used by the resource constrained IoT devices. CCN-lite is a light weight, inter-operable version of the CCNx protocol for supporting the IoT devices. However, CCN-lite like its ancestors lacks the support for an efficient pub/sub mechanism. In this paper, we developed COPSS-lite, an efficient and light weight implementation of pub/sub for IoT. COPSS-lite is developed to enhance CCN-lite and also support multi-hop connection by incorporating the famous RPL protocol for low power and lossy networks. We provide a preliminary evaluation to show proof of operability with real world sensor devices in IoT lab. Our results show that COPSS-lite is compact, operates on all platforms that support CCN-lite and we observe significant performance benefits with COPSS-lite in IoT environments.
Holonomic Control of a robot with an omni-directional drive .

This paper shows how to control a robot with omnidirectional wheels, using as example robots with four motors, and generalizing to n motors. More than three wheels provide redundancy: many combinations of motors speeds can provide the same Euclidean movement. Since the system is over-determined, we show how to compute a set of consistent and optimal motor forces and speeds using the pseudoinverse of coupling matrices. This approach allows us also to perform a consistency check to determine whether a wheel is slipping on the floor or not. We show that it is possible to avoid wheel slippage by driving the robot with a motor torque under a certain threshold or handle it and make high accelerations possible.
Performance modeling for systematic performance tuning

The performance of parallel scientific applications depends on many factors which are determined by the execution environment and the parallel application. Especially on large parallel systems, it is too expensive to explore the solution space with series of experiments. Deriving analytical models for applications and platforms allow estimating and extrapolating their execution performance, bottlenecks, and the potential impact of optimization options. We propose to use such "performance modeling" techniques beginning from the application design process throughout the whole software development cycle and also during the lifetime of supercomputer systems. Such models help to guide supercomputer system design and re-engineering efforts to adopt applications to changing platforms and allow users to estimate costs to solve a particular problem. Models can often be built with the help of well-known performance profiling tools. We discuss how we successfully used modeling throughout the proposal, initial testing, and beginning deployment phase of the Blue Waters supercomputer system.
Towards Query Efficient Black-box Attacks: An Input-free Perspective

Recent studies have highlighted that deep neural networks (DNNs) are vulnerable to adversarial attacks, even in a black-box scenario. However, most of the existing black-box attack algorithms need to make a huge amount of queries to perform attacks, which is not practical in the real world. We note one of the main reasons for the massive queries is that the adversarial example is required to be visually similar to the original image, but in many cases, how adversarial examples look like does not matter much. It inspires us to introduce a new attack called input-free attack, under which an adversary can choose an arbitrary image to start with and is allowed to add perceptible perturbations on it. Following this approach, we propose two techniques to significantly reduce the query complexity. First, we initialize an adversarial example with a gray color image on which every pixel has roughly the same importance for the target model. Then we shrink the dimension of the attack space by perturbing a small region and tiling it to cover the input image. To make our algorithm more effective, we stabilize a projected gradient ascent algorithm with momentum, and also propose a heuristic approach for region size selection. Recent studies have highlighted that deep neural networks (DNNs) are vulnerable to adversarial attacks, even in a black-box scenario. However, most of the existing black-box attack algorithms need to make a huge amount of queries to perform attacks, which is not practical in the real world. We note one of the main reasons for the massive queries is that the adversarial example is required to be visually similar to the original image, but in many cases, how adversarial examples look like does not matter much. It inspires us to introduce a new attack called input-free attack, under which an adversary can choose an arbitrary image to start with and is allowed to add perceptible perturbations on it. Following this approach, we propose two techniques to significantly reduce the query complexity. First, we initialize an adversarial example with a gray color image on which every pixel has roughly the same importance for the target model. Then we shrink the dimension of the attack space by perturbing a small region and tiling it to cover the input image. To make our algorithm more effective, we stabilize a projected gradient ascent algorithm with momentum, and also propose a heuristic approach for region size selection. Through extensive experiments, we show that with only 1,701 queries on average, we can perturb a gray image to any target class of ImageNet with a 100% success rate on InceptionV3. Besides, our algorithm has successfully defeated two real-world systems, the Clarifai food detection API and the Baidu Animal Identification API.
Engineering Automotive Software

The amount of software in cars grows exponentially. Driving forces of this development are the availability of cheaper and more powerful hardware, as well as the demand for innovation through new functionality. The rapidly growing significance of software and software-based functionality is at the root of various challenges in the automotive industries. These concern their organization, definition of key competencies, processes, methods, tools, models, product structures, division of labor, logistics, maintenance, and long-term strategies. This paper pinpoints the idiosyncrasies of the domain, characterizes the essentials of automotive software, and discusses the challenges of automotive software engineering
Learning and Teaching Programming: A Review and Discussion

In this paper we review the literature relating to the psychological/educational study of programming. We identify general trends comparing novice and expert programmers, programming knowledge and strategies, program generation and comprehension, and objectoriented versus procedural programming. We do not cover research relating specifically to other programming styles.) The main focus of the review is on novice programming and topics relating to novice teaching and learning. Various problems experienced by novices are identified, including issues relating to basic program design, to algorithmic complexity in certain language features, to the ‘‘fragility’’ of novice knowledge, and so on. We summarise this material and suggest some practical implications for teachers. We suggest that a key issue that emerges is the distinction between effective and ineffective novices. What characterises effective novices? Is it possible to identify the specific deficits of ineffective novices and help them to become effective learners of programming?
Broadcast and Weight: An Integrated Network For Scalable Photonic Spike Processing

We propose an on-chip optical architecture to support massive parallel communication among high-performance spiking laser neurons. Designs for a network protocol, computational element, and waveguide medium are described, and novel methods are considered in relation to prior research in optical on-chip networking, neural networking, and computing. Broadcast-and-weight is a new approach for combining neuromorphic processing and optoelectronic physics, a pairing that is found to yield a variety of advantageous features. We discuss properties and design considerations for architectures for scalable wavelength reuse and biologically relevant organizational capabilities, in addition to aspects of practical feasibility. Given recent developments commercial photonic systems integration and neuromorphic computing, we suggest that a novel approach to photonic spike processing represents a promising opportunity in unconventional computing.
Software transformations to improve malware detection

Malware is code designed for a malicious purpose, such as obtaining root privilege on a host. A malware detector identifies malware and thus prevents it from adversely affecting a host. In order to evade detection, malware writers use various obfuscation techniques to transform their malware. There is strong evidence that commercial malware detectors are susceptible to these evasion tactics. In this paper, we describe the design and implementation of a malware transformer that reverses the obfuscations performed by a malware writer. Our experimental evaluation demonstrates that this malware transformer can drastically improve the detection rates of commercial malware detectors.