nils-herrmann/clean_scirepeval huggingface dataset:
Homing Socialbots: Intrusion on a specific organization's employee using Socialbots

One dimension on the Internet, which has gained great popularity in recent years are the online social networks (OSNs). Users all over the globe write, share, and publish personal information about themselves, their friends, and their workplace. In this study we present a method for infiltrating specific users in targeted organizations by using organizational social networks topologies and Socialbots. The targeted organizations, which have been chosen by us, were technology-oriented organizations. Employees from this kind of organization should be more aware of the dangers of exposing private information. An infiltration is defined as accepting a Socialbot's friend request. Upon accepting a Socialbot's friend request, users unknowingly expose information about themselves and their workplace. To infiltrate this we had to use our Socialbots in a sophisticated manner. First, we had to gather information and recognize Facebook users who work in targeted organizations. Afterwards, we chose ten Facebook users from every targeted organization randomly. These ten users were chosen to be the specific users from targeted organizations of which we would like to infiltrate. The Socialbots sent friend requests to all specific users' mutual friends who worked or work in the same targeted organization. The rationale behind this idea was to gain as many mutual friends as possible and through this act increase the probability that our friend requests will be accepted by the targeted users. We tested the proposed method on targeted users from two different organizations. Our method was able to gain a successful percentage of 50% and 70% respectively. The results demonstrate how easily adversaries can infiltrate users they do not know and get full access to personal and valuable information. These results are more surprising when we emphasize the fact that we chose oriented users who should be more aware to the dangers of information leakage for this study on purpose. Moreover, the results indicate once again that users who are interested in protecting themselves should not disclose information in OSNs and should be cautious of accepting friendship requests from unknown persons.
Non-orthogonal multiple access for 5G: solutions, challenges, opportunities, and future research trends

The increasing demand of mobile Internet and the Internet of Things poses challenging requirements for 5G wireless communications, such as high spectral efficiency and massive connectivity. In this article, a promising technology, non-orthogonal multiple access (NOMA), is discussed, which can address some of these challenges for 5G. Different from conventional orthogonal multiple access technologies, NOMA can accommodate much more users via nonorthogonal resource allocation. We divide existing dominant NOMA schemes into two categories: power-domain multiplexing and code-domain multiplexing, and the corresponding schemes include power-domain NOMA, multiple access with low-density spreading, sparse code multiple access, multi-user shared access, pattern division multiple access, and so on. We discuss their principles, key features, and pros/cons, and then provide a comprehensive comparison of these solutions from the perspective of spectral efficiency, system performance, receiver complexity, and so on. In addition, challenges, opportunities, and future research trends for NOMA design are highlighted to provide some insight on the potential future work for researchers in this field. Finally, to leverage different multiple access schemes including both conventional OMA and new NOMA, we propose the concept of software defined multiple access (SoDeMA), which enables adaptive configuration of available multiple access schemes to support diverse services and applications in future 5G networks.
Denial of Service Attacks in Wireless Networks: The Case of Jammers

The shared nature of the medium in wireless networks makes it easy for an adversary to launch a Wireless Denial of Service (WDoS) attack. Recent studies, demonstrate that such attacks can be very easily accomplished using off-the-shelf equipment. To give a simple example, a malicious node can continually transmit a radio signal in order to block any legitimate access to the medium and/or interfere with reception. This act is called jamming and the malicious nodes are referred to as jammers. Jamming techniques vary from simple ones based on the continual transmission of interference signals, to more sophisticated attacks that aim at exploiting vulnerabilities of the particular protocol used. In this survey, we present a detailed up-to-date discussion on the jamming attacks recorded in the literature. We also describe various techniques proposed for detecting the presence of jammers. Finally, we survey numerous mechanisms which attempt to protect the network from jamming attacks. We conclude with a summary and by suggesting future directions.
Large-scale, AST-based API-usage analysis of open-source Java projects

Research on API migration and language conversion can be informed by empirical data about API usage. For instance, such data may help with designing and defending mapping rules for API migration in terms of relevance and applicability. We describe an approach to large-scale API-usage analysis of open-source Java projects, which we also instantiate for the Source-Forge open-source repository in a certain way. Our approach covers checkout, building, tagging with metadata, fact extraction, analysis, and synthesis with a large degree of automation. Fact extraction relies on resolved (type-checked) ASTs. We describe a few examples of API-usage analysis; they are motivated by API migration. These examples are concerned with analysing API footprint (such as the numbers of distinct APIs used in a project), API coverage (such as the percentage of methods of an API used in a corpus), and framework-like vs. class-library-like usage.
Analyzing the Contributor Activity of a Volunteered Geographic Information Project - The Case of OpenStreetMap

The OpenStreetMap (OSM) project, founded in 2004, has gathered an exceptional amount of interest in recent years and counts as one of the most impressive sources of Volunteered Geographic Information (VGI) on the Internet. In total, more than half a million members had registered for the project by the end of 2011. However, while this number of contributors seems impressive, questions remain about the individual contributions that have been made by the project members. This research article contains several studies regarding the contributions by the community of the project. The results show that only 38% (192,000) of the registered members carried out at least one edit in the OSM database and that only 5% (24,000) of all members actively contributed to the project in a more productive way. The majority of the members are located in Europe (72%) and each member has an activity area whose size may range from one soccer field up to more than 50 km 2 . In addition to several more analyses conducted for this article, predictions will be made about how this newly acquired knowledge can be used for future research.
Breaking Web Applications Built On Top of Encrypted Data

We develop a systematic approach for analyzing client-server applications that aim to hide sensitive user data from untrusted servers. We then apply it to Mylar, a framework that uses multi-key searchable encryption (MKSE) to build Web applications on top of encrypted data.
 We demonstrate that (1) the Popa-Zeldovich model for MKSE does not imply security against either passive or active attacks; (2) Mylar-based Web applications reveal users' data and queries to passive and active adversarial servers; and (3) Mylar is generically insecure against active attacks due to system design flaws. Our results show that the problem of securing client-server applications against actively malicious servers is challenging and still unsolved.
 We conclude with general lessons for the designers of systems that rely on property-preserving or searchable encryption to protect data from untrusted servers.
Frictionless Commerce ? A Comparison of Internet and Conventional Retailers

There have been many claims that the Internet represents a new nearly “frictionless market.” Our research empirically analyzes the characteristics of the Internet as a channel for two categories of homogeneous products—books and CDs. Using a data set of over 8,500 price observations collected over a period of 15 months, we compare pricing behavior at 41 Internet and conventional retail outlets. We find that prices on the Internet are 9–16% lower than prices in conventional outlets, depending on whether taxes, shipping, and shopping costs are included in the price. Additionally, we find that Internet retailers’ price adjustments over time are up to 100 times smaller than conventional retailers’ price adjustments—presumably reflecting lower menu costs in Internet channels. We also find that levels of price dispersion depend importantly on the measures employed. When we compare the prices posted by different Internet retailers we find substantial dispersion. Internet retailer prices differ by an average of 33% for books and 25% for CDs. However, when we weight these prices by proxies for market share, we find dispersion is lower in Internet channels than in conventional channels, reflecting the dominance of certain heavily branded retailers. We conclude that while there is lower friction in many dimensions of Internet competition, branding, awareness, and trust remain important sources of heterogeneity among Internet retailers. (Search; Competition; Internet; Price Dispersion; Menu Costs; Pricing; Intermediaries)
Neural GPUs Learn Algorithms

Learning an algorithm from examples is a fundamental problem that has been widely studied. It has been addressed using neural networks too, in particular by Neural Turing Machines (NTMs). These are fully differentiable computers that use backpropagation to learn their own programming. Despite their appeal NTMs have a weakness that is caused by their sequential nature: they are not parallel and are are hard to train due to their large depth when unfolded. We present a neural network architecture to address this problem: the Neural GPU. It is based on a type of convolutional gated recurrent unit and, like the NTM, is computationally universal. Unlike the NTM, the Neural GPU is highly parallel which makes it easier to train and efficient to run. An essential property of algorithms is their ability to handle inputs of arbitrary size. We show that the Neural GPU can be trained on short instances of an al-gorithmic task and successfully generalize to long instances. We verified it on a number of tasks including long addition and long multiplication of numbers represented in binary. We train the Neural GPU on numbers with up-to 20 bits and observe no errors whatsoever while testing it, even on much longer numbers. To achieve these results we introduce a technique for training deep recurrent networks: parameter sharing relaxation. We also found a small amount of dropout and gradient noise to have a large positive effect on learning and generalization.
Shooting a moving target: Motion-prediction-based transmission for 360-degree videos

Enabled by the rapid development of virtual reality hardware and software, 360-degree video content has proliferated. From the network perspective, 360-degree video transmission imposes significant challenges because it consumes 4 6χ the bandwidth of a regular video with the same resolution. To address these challenges, in this paper, we propose a motion-prediction-based transmission mechanism that matches network video transmission to viewer needs. Ideally, if viewer motion is perfectly known in advance, we could reduce bandwidth consumption by 80%. Practically, however, to guarantee the quality of viewing experience, we have to address the random nature of viewer motion. Based on our experimental study of viewer motion (comprising 16 video clips and over 150 subjects), we found the viewer motion can be well predicted in 100∼500ms. We propose a machine learning mechanism that predicts not only viewer motion but also prediction deviation itself. The latter is important because it provides valuable input on the amount of redundancy to be transmitted. Based on such predictions, we propose a targeted transmission mechanism that minimizes overall bandwidth consumption while providing probabilistic performance guarantees. Real-data-based evaluations show that the proposed scheme significantly reduces bandwidth consumption while minimizing performance degradation, typically a 45% bandwidth reduction with less than 0.1% failure ratio.
FEDD: Feature Extraction for Explicit Concept Drift Detection in time series

A time series is a sequence of observations collected over fixed sampling intervals. Several real-world dynamic processes can be modeled as a time series, such as stock price movements, exchange rates, temperatures, among others. As a special kind of data stream, a time series may present concept drift, which affects negatively time series analysis and forecasting. Explicit drift detection methods based on monitoring the time series features may provide a better understanding of how concepts evolve over time than methods based on monitoring the forecasting error of a base predictor. In this paper, we propose an online explicit drift detection method that identifies concept drifts in time series by monitoring time series features, called Feature Extraction for Explicit Concept Drift Detection (FEDD). Computational experiments showed that FEDD performed better than error-based approaches in several linear and nonlinear artificial time series with abrupt and gradual concept drifts.
Robust Adaptive Segmentation of Range Images

We propose a novel image segmentation technique using the robust, adaptive least k-th order squares (ALKS) estimator which minimizes the k-th order statistics of the squared of residuals. The optimal value of k is determined from the data and the procedure detects the homogeneous surface patch representing the relative majority of the pixels. The ALKS shows a better tolerance to structured outliers than other recently proposed similar techniques: Minimize the Probability of Randomness (MINPRAN) and Residual Consensus (RESC). The performance of the new, fully autonomous, range image segmentation algorithm is compared to several other methods. Index Terms|robust methods, range image segmentation, surface tting
Learning to Interpret and Describe Abstract Scenes

Given a (static) scene, a human can effortlessly describe what is going on (who is doing what to whom, how, and why). The process requires knowledge about the world, how it is perceived, and described. In this paper we study the problem of interpreting and verbalizing visual information using abstract scenes created from collections of clip art images. We propose a model inspired by machine translation operating over a large parallel corpus of visual relations and linguistic descriptions. We demonstrate that this approach produces human-like scene descriptions which are both fluent and relevant, outperforming a number of competitive alternatives based on templates, sentence-based retrieval, and a multimodal neural language model.
Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization

We introduce an efficient algorithm for the problem of online linear optimization in the bandit setting which achieves the optimal O*(√T)regret. The setting is a natural generalization of the nonstochastic multiarmed bandit problem, and the existence of an efficient optimal algorithm has been posed as an open problem in a number of recent papers. We show how the difficulties encountered by previous approaches are overcome by the use of a self-concordant potential function. Our approach presents a novel connection between online learning and interior point methods. Disciplines Statistics and Probability Comments At the time of publication, author Alexander Rakhlin was affiliated with the University of California, Berkeley. Currently, he is a faculty member at the Statistics Department at the University of Pennsylvania. This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/statistics_papers/110 Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization Jacob Abernethy Computer Science Division UC Berkeley jake@cs.berkeley.edu (eligible for best student paper award) Elad Hazan IBM Almaden hazan@us.ibm.com Alexander Rakhlin Computer Science Division UC Berkeley rakhlin@cs.berkeley.edu
Yield estimation in vineyards by visual grape detection

The harvest yield in vineyards can vary significantly from year to year and also spatially within plots due to variations in climate, soil conditions and pests. Fine grained knowledge of crop yields can allow viticulturists to better manage their vineyards. The current industry practice for yield prediction is destructive, expensive and spatially sparse - during the growing season sparse samples are taken and extrapolated to determine overall yield. We present an automated method that uses computer vision to detect and count grape berries. The method could potentially be deployed across large vineyards taking measurements at every vine in a non-destructive manner. Our berry detection uses both shape and visual texture and we can demonstrate detection of green berries against a green leaf background. Berry detections are counted and the eventual harvest yield is predicted. Results are presented for 224 vines (over 450 meters) of two different grape varieties and compared against the actual harvest yield as groundtruth. We calibrate our berry count to yield and find that we can predict yield of individual vineyard rows to within 9.8% of actual crop weight.
Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers

The use of on-body wearable sensors is widespread in several academic and industrial domains. Of great interest are their applications in ambulatory monitoring and pervasive computing systems; here, some quantitative analysis of human motion and its automatic classification are the main computational tasks to be pursued. In this paper, we discuss how human physical activity can be classified using on-body accelerometers, with a major emphasis devoted to the computational algorithms employed for this purpose. In particular, we motivate our current interest for classifiers based on Hidden Markov Models (HMMs). An example is illustrated and discussed by analysing a dataset of accelerometer time series.
Stochastic Park-and-Charge Balancing for Fully Electric and Plug-in Hybrid Vehicles

Motivated by the need to provide services to alleviate range anxiety of electric vehicles, we consider the problem of balancing charging demand across a network of charging stations. Our objective is to reduce the potential for excessively long queues to build up at some charging stations, although other charging stations are underutilized. A stochastic balancing algorithm is presented to achieve these goals. A further feature of this algorithm is that it is fully decentralized and facilitates a plug-and-play type of behavior. Using our system, the charging stations can join and leave the network without any changes to, or communication with, a centralized infrastructure. Analysis and simulations are presented to illustrate the efficacy of our algorithm.
Public Key Broadcast Encryption for Stateless Receivers

A broadcast encryption scheme allows the sender to securely distribute data to a dynamically changing set of users over an insecure channel. It has numerous applications including pay-TV systems, distribution of copyrighted material, streaming audio/video and many others. One of the most challenging settings for this problem is that of stateless receivers, where each user is given a fixed set of keys which cannot be updated through the lifetime of the system. This setting was considered by Naor, Naor and Lotspiech [NNL01], who also present a very efficient “subset difference” (SD) method for solving this problem. The efficiency of this method (which also enjoys efficient traitor tracing mechanism and several other useful features) was recently improved by Halevi and Shamir [HS02], who called their refinement the “Layered SD” (LSD) method. Both of the above methods were originally designed to work in the centralized (symmetric key) setting, where only the trusted designer of the system can encrypt messages to users. On the other hand, in many applications it is desirable not to store the secret keys “on-line”, or to allow untrusted users to broadcast information. This leads to the question of building a public key broadcast encryption scheme for stateless receivers; in particular, of extending the elegant SD/LSD methods to the public key setting. Unfortunately, Naor et al. [NNL01] notice that the natural technique for doing so will result in an enormous public key and very large storage for every user. In fact, [NNL01] pose this question of reducing the public key size and user’s storage as the first open problem of their paper. We resolve this question in the affirmative, by demonstrating that an O(1) size public key can be achieved for both of SD/LSD methods, in addition to the same (small) user’s storage and ciphertext size as in the symmetric key setting. Courant Institute of Mathematical Sciences, New York University.
ICT in Precision Agriculture – diffusion of technology

Precision Agriculture (PA) is the management of spatial and temporal variability of the fields. This management concept incorporates a range of management as well as ICT tools to assess and treat the variability within the field. The adoption and diffusion of PA is discussed in this chapter. PA has been practiced over the last 15 years mostly in North America and Northern Europe. Despite its promises, PA has not yet managed to be adopted widely by farmers. This chapter’s results are based on the findings of six mail surveys, focus groups and personal interviews with PA practitioners in the UK, Denmark and the USA over six years (1998-2003). The information related to ICT adoption in PA is presented, including software and hardware aspects, data ownership, data handling, data interpretation, internet and e-mail use, as well as information preferences to invest and practice in PA. It is a common belief that the use of PA technologies has been tremendously improved over the years. However, findings from these studies showed that lack of agronomic and technical skills are key problems for adopting PA practices and there is an urgent need for holistic decision support systems. Moreover, compatibility between hardware and software, as well as user friendliness and particularly time consumption are for many users a serious impediment for PA adoption. An overview of Precision Agriculture Precision agriculture applications and trends The recent advances in information and telecommunication technologies have allowed farmers to acquire vast amounts of site-specific data for their fields, with the ultimate aim being to reduce uncertainty in decision-making (National Research Council, 1997; Blackmore, 2000). Precision Agriculture (PA) or site-specific crop management can be defined as the management of spatial and temporal variability at a sub-field level to improve economic returns and reduce environmental impact (Blackmore et al., 2003). Within the concept of PA the main activities are data collection and processing and variable rate applications of inputs. The tools available consist of a wide range of techniques and technologies from information and communication technology as well as sensor and application technologies, farm management and economics.