nils-herrmann/clean_scirepeval huggingface dataset:
AccessMiner: using system-centric models for malware protection

Models based on system calls are a popular and common approach to characterize the run-time behavior of programs. For example, system calls are used by intrusion detection systems to detect software exploits. As another example, policies based on system calls are used to sandbox applications or to enforce access control. Given that malware represents a significant security threat for today's computing infrastructure, it is not surprising that system calls were also proposed to distinguish between benign processes and malicious code.
 Most proposed malware detectors that use system calls follows program-centric analysis approach. That is, they build models based on specific behaviors of individual applications. Unfortunately, it is not clear how well these models generalize, especially when exposed to a diverse set of previously-unseen, real-world applications that operate on realistic inputs. This is particularly problematic as most previous work has used only a small set of programs to measure their technique's false positive rate. Moreover, these programs were run for a short time, often by the authors themselves.
 In this paper, we study the diversity of system calls by performing a large-scale collection (compared to previous efforts) of system calls on hosts that run applications for regular users on actual inputs. Our analysis of the data demonstrates that simple malware detectors, such as those based on system call sequences, face significant challenges in such environments. To address the limitations of program-centric approaches, we propose an alternative detection model that characterizes the general interactions between benign programs and the operating system (OS). More precisely, our system-centric approach models the way in which benign programs access OS resources (such as files and registry entries). Our experiments demonstrate that this approach captures well the behavior of benign programs and raises very few (even zero) false positives while being able to detect a significant fraction of today's malware.
A Printed, Broadband Luneburg Lens Antenna

The design of a 2D broadband, Luneburg lens antenna implemented using printed circuit board techniques is detailed. The refractive index of the lens is controlled through a combination of meandering crossed microstrip lines and varying their widths. The 12.4λ° diameter lens is designed to operate in the transverse electromagnetic (TEM) mode at 13 GHz. The lens antenna was designed, fabricated, and measured. The measured half power beamwidth of the experimental antenna is 4.34°.
Estimating True and False Positive Rates in Higher Dimensional Problems and Its Data Mining Applications

If we can estimate the accuracy of our observations then we can estimate the true and false positive rates over a series of samples in high dimensional data mining problems. To date such issues have been largely neglected and previously no algorithm has been provided to facilitate the computations involved. In high dimensional data mining tasks, increasing sparsity leads to decreasing true positive rates. Estimating this effect allows the estimation of the true size of membership of a class or cluster allowing us to identify the top candidates for these false negatives, while tracking the likelihood of false positives. These estimates of true and false positive rates can also help researchers avoid unnecessary costs by collecting only the number of samples that are really needed. We propose an algorithm for these computations designated the statistical error rate algorithm (SERA) and give an example of its use.
Techniques to Detect Spammers in Twitter- A Survey

With the rapid growth of social networking sites for communicating, sharing, storing and managing significant information, it is attracting cybercriminals who misuse the Web to exploit vulnerabilities for their illicit benefits. Forged online accounts crack up every day. Impersonators, phishers, scammers and spammers crop up all the time in Online Social Networks (OSNs), and are harder to identify. Spammers are the users who send unsolicited messages to a large audience with the intention of advertising some product or to lure victims to click on malicious links or infecting user&apos;s system just for the purpose of making money. A lot of research has been done to detect spam profiles in OSNs. In this paper we have reviewed the existing techniques for detecting spam users in Twitter social network. Features for the detection of spammers could be user based or content based or both. Current study provides an overview of the methods, features used, detection rate and their limitations (if any) for detecting spam profiles mainly in Twitter.
Corpus-Guided Sentence Generation of Natural Images

We propose a sentence generation strategy that describes images by predicting the most likely nouns, verbs, scenes and prepositions that make up the core sentence structure. The input are initial noisy estimates of the objects and scenes detected in the image using state of the art trained detectors. As predicting actions from still images directly is unreliable, we use a language model trained from the English Gigaword corpus to obtain their estimates; together with probabilities of co-located nouns, scenes and prepositions. We use these estimates as parameters on a HMM that models the sentence generation process, with hidden nodes as sentence components and image detections as the emissions. Experimental results show that our strategy of combining vision and language produces readable and descriptive sentences compared to naive strategies that use vision alone.
Primary generalized and localized hypertrichosis in children.

To review the causes, presentation, and therapy of primary generalized and localized symmetrical hypertrichosis in children.



Retrospective medical record review.



Academic specialty referral clinic for pediatric dermatological disorders.



Case series of 11 prepubertal male and female patients who had idiopathic hypertrichosis between July 1, 1990, and November 30, 1999.



None.



Clinical distribution of increased hair growth and types of hair removal methods used.



Seven girls and 4 boys, ranging in age from 4 months to 11 years, were evaluated. Four patients showed generalized hypertrichosis. The other 7 patients had localized symmetrical hypertrichosis, representing the subsets of hypertrichosis cubiti, anterior cervical hypertrichosis, posterior cervical hypertrichosis, and faun tail deformity. All patients with generalized hypertrichosis manifested the condition at birth; the age of onset in children with localized symmetrical primary hypertrichosis ranged from birth to 4 years. One girl with generalized hypertrichosis had gingival hyperplasia and the girl with faun tail deformity had bony diastematomyelia with spina bifida occulta. The medical histories and physical examination findings of all of the children were otherwise unremarkable. All patients were referred for diagnostic and therapeutic considerations.



Primary hypertrichotic conditions, whether localized or generalized, are rare in pediatric patients and of unknown origin. Although otherwise benign, these disorders may result in cosmetic disfigurement and psychosocial trauma for patients and families. Patients and their families should be adequately advised of the available treatment methods for both temporary and permanent hair removal.
Data Collection in Wireless Sensor Networks with Mobile Elements: A Survey

Wireless sensor networks (WSNs) have emerged as an effective solution for a wide range of applications. Most of the traditional WSN architectures consist of static nodes which are densely deployed over a sensing area. Recently, several WSN architectures based on mobile elements (MEs) have been proposed. Most of them exploit mobility to address the problem of data collection in WSNs. In this article we first define WSNs with MEs and provide a comprehensive taxonomy of their architectures, based on the role of the MEs. Then we present an overview of the data collection process in such a scenario, and identify the corresponding issues and challenges. On the basis of these issues, we provide an extensive survey of the related literature. Finally, we compare the underlying approaches and solutions, with hints to open problems and future research directions.
Characterizing concept drift

Most machine learning models are static, but the world is dynamic, and increasing online deployment of learned models gives increasing urgency to the development of efficient and effective mechanisms to address learning in the context of non-stationary distributions, or as it is commonly called concept drift. However, the key issue of characterizing the different types of drift that can occur has not previously been subjected to rigorous definition and analysis. In particular, while some qualitative drift categorizations have been proposed, few have been formally defined, and the quantitative descriptions required for precise and objective understanding of learner performance have not existed. We present the first comprehensive framework for quantitative analysis of drift. This supports the development of the first comprehensive set of formal definitions of types of concept drift. The formal definitions clarify ambiguities and identify gaps in previous definitions, giving rise to a new comprehensive taxonomy of concept drift types and a solid foundation for research into mechanisms to detect and address concept drift.
CloudNet: A Platform for Optimized WAN Migration of Virtual Machines

Cloud computing platforms are growing from clusters of machines within a data center to networks of data centers with resources spread across the globe. Virtual machine migration within the LAN has changed the scale of resource management from allocating resources on a single server to manipulating pools of resources within a data center. We expect WAN migration to likewise transform the scope of provisioning from a single data center to multiple data centers spread across the country or around the world. In this paper we propose a cloud computing platform linked with a VPN based network infrastructure that provides seamless connectivity between enterprise and data center sites, as well as support for live WAN migration of virtual machines. We describe a set of optimizations that minimize the cost of transferring persistent storage and moving virtual machine memory during migrations over low bandwidth, high latency Internet links. Our evaluation on both a local testbed and across two real data centers demonstrates that these improvements can reduce total migration and pause time by over 30%. During simultaneous migrations of four VMs between Texas and Illinois, CloudNet’s optimizations reduce memory migration time by 65% and lower bandwidth consumption for the storage and memory transfer by 20GB, a 57% reduction.
Implementation of deep-learning based image classification on single board computer

In this paper, a deep-learning agorithm based on convolutional neural-network is implemented using python and tflearn for image classification. A large number of different images which contains two types of animals, namely cat and dog are used for classification. Two different structures of CNN are used, namely with two and five layers. It is shown that the CNN with higher layer performs classification process with much higher accuracy. The best CNN model with high accuracy and small loss function deployed in single board computer.
Securing web application code by static analysis and runtime protection

Security remains a major roadblock to universal acceptance of the Web for many kinds of transactions, especially since the recent sharp increase in remotely exploitable vulnerabilities have been attributed to Web application bugs. Many verification tools are discovering previously unknown vulnerabilities in legacy C programs, raising hopes that the same success can be achieved with Web applications. In this paper, we describe a sound and holistic approach to ensuring Web application security. Viewing Web application vulnerabilities as a secure information flow problem, we created a lattice-based static analysis algorithm derived from type systems and typestate, and addressed its soundness. During the analysis, sections of code considered vulnerable are instrumented with runtime guards, thus securing Web applications in the absence of user intervention. With sufficient annotations, runtime overhead can be reduced to zero. We also created a tool named.WebSSARI (Web application Security by Static Analysis and Runtime Inspection) to test our algorithm, and used it to verify 230 open-source Web application projects on SourceForge.net, which were selected to represent projects of different maturity, popularity, and scale. 69 contained vulnerabilities. After notifying the developers, 38 acknowledged our findings and stated their plans to provide patches. Our statistics also show that static analysis reduced potential runtime overhead by 98.4%.
Multi-Imager Compatible, MR Safe, Remote Center of Motion Needle-Guide Robot

We report the development of a new robotic system for direct image-guided interventions (DIGI; images acquired at the time of the intervention). The manipulator uses our previously reported pneumatic step motors and is entirely made of electrically nonconductive, nonmetallic, and nonmagnetic materials. It orients a needle-guide with two degrees of freedom (DoF) about a fulcrum point located below the guide using an innovative remote center of motion parallelogram type mechanism. The depth of manual needle insertion is preset with a third DoF, located remotely of the manipulator. Special consideration was given to the kinematic accuracy and the structural stiffness. The manipulator includes registration markers for image-to-robot registration. Based on the images, it may guide needles, drills, or other slender instruments to a target (OD < 10 mm). Comprehensive preclinical tests were performed. The manipulator is MR safe (ASTM F2503-13). Electromagnetic compatibility (EMC) testing (IEC 60601-1-2) of the system shows that it does not conduct or radiate EM emissions. The change in the signal to noise ratio of the MRI due to the presence and motion of the robot in the scanner is below 1%. The structural stiffness at the needle-guide is 33 N/mm. The angular accuracy and precision of the manipulator itself are 0.177° and 0.077°. MRI-guided targeting accuracy and precision in vitro were 1.71 mm and 0.51 mm, at an average target depth of ∼38 mm, with no adjustments. The system may be suitable for DIGI where ${\text{1.07 + 0.0031}} \cdot \boldsymbol{d}$ [mm] accuracy lateral to the needle (2D) or  ${\text{1.59 + 0.0031}} \cdot \boldsymbol{d}$ [mm] in 3D is acceptable. The system is also multi-imager compatible and could be used with other imaging modalities.
Harnessing the Influence of Social Proof in Online Shopping: The Effect of Electronic Word of Mouth on Sales of Digital Microproducts

Social commerce has taken the e‐tailing world by storm. Business‐to‐consumer sites and, more important, intermediaries that facilitate shopping experience, continue to offer more and more innovative technologies to support social interaction among like‐ minded community members or friends who share the same shopping interests. Among these technologies, reviews, ratings, and recommendation systems have become some of the most popular social shopping platforms due to their ease of use and simplicity in sharing buying experience and aggregating evaluations. This paper studies the effect of electronic word of mouth (eWOM) communication among a closed community of book readers. We studied the entire market of Amazon Shorts e‐books, which are digital mi‐ croproducts sold at a low and uniform price. With the minimal role of price in the buying decision, social discussion via eWOM becomes a collective signal of reputation, and ultimately a significant demand driver. Our empirical study suggests that eWOM can be used to convey the reputation of the product (e.g., the book), the reputation of the brand (i.e., the author), and the reputation of complementary goods (e.g., books in the same category). Until newer social shopping technologies gain acceptance, eWOM technolo‐ gies should be considered by both e‐tailers and shoppers as the first and perhaps primary source of social buying experience.
JackIn: integrating first-person view with out-of-body vision generation for human-human augmentation

JackIn is a new human-human communication framework for connecting two or more people. With first-person view video streaming from a person (called Body) wearing a transparent head-mounted display and a head-mounted camera, the other person (called Ghost) participates in the shared first-person view. With JackIn, people's activities can be shared and assistance or guidance can be given through other peoples expertise. This can be applied to daily activities such as cooking lessons, shopping navigation, education in craft-work or electrical work, and sharing experiences of sporting and live events. For a better viewing experience with frist-person view, we developed the out-of-body view in which first-person images are integrated to construct a scene around a Body, and a Ghost can virtually control the viewpoint to look around the space surrounding the Body. We also developed a tele-pointing gesture interface. We conducted an experiment to evaluate how effective this framework is and found that Ghosts can understand the spatial situation of the Body.
WordNet Atlas : a web application for visualizing WordNet as a zoomable map

The English WordNet is a lexical database containing more than 206,900 wordconcept pairs and more than 377,500 semantic and lexical links. Trying to visualize them all at once as a node-link diagram results in a representation which can be too big and complex for the user to grasp, and which cannot be easily processed by current web technologies. We propose a visualization technique based on the concept of semantic zooming, usually found in popular web map applications. This technique makes it feasible to handle large and complex graphs, and display them in an interactive representation. By zooming, it is possible to switch from an overview visualization, quickly and clearly presenting the global characteristics of the structure, to a detailed one, displaying a classic node-link diagram. WordNet Atlas is a web application that leverages this method to aid the user in the exploration of the WordNet data set, from its global taxonomy structure to the detail of words and synonym sets.
The CMA Evolution Strategy : A Comparing Review

Derived from the concept of self-adaptation in evolution strategies, the CMA (Covariance Matrix Adaptation) adapts the covariance matrix of a multi-variate normal search distribution. The CMA was originally designed to perform well with small populations. In this review, the argument starts out with large population sizes, reflecting recent extensions of the CMA algorithm. Commonalities and differences to continuous Estimation of Distribution Algorithms are analyzed. The aspects of reliability of the estimation, overall step size control, and independence from the coordinate system (invariance) become particularly important in small populations sizes. Consequently, performing the adaptation task with small populations is more intricate.
A Wideband Dual Circularly Polarized Full-Corporate Waveguide Array Antenna Fed by Triple-Resonant Cavities

A dual circularly polarized (CP) waveguide array antenna is presented for Ka-band wireless communication. A novel triple-resonant cavity is proposed to implement mode conversion and impedance matching in the <inline-formula> <tex-math notation="LaTeX">$2 \times 2$ </tex-math></inline-formula>-element subarray. A ridge waveguide polarizer is integrated to form the left-hand circular polarization (LHCP) and the right-hand circular polarization (RHCP). A flyover-shaped full-corporate feeding network is designed to accommodate the dual polarizations and keep the wideband characteristic for a large-scale array. Subarray spacing is used to optimize the port-to-port isolation and decrease the sidelobe level at some definite angles. Computer numerically controlled machining technology is applied to fabricating a <inline-formula> <tex-math notation="LaTeX">$16 \times 16$ </tex-math></inline-formula>-element prototype. Measured results demonstrate a bandwidth of 16% from 27.6 to 32.4 GHz for the dual polarizations, in terms of good reflection coefficient and axial ratio. A maximum gain of 32.8 dBic is achieved at 28.8 GHz. A total efficiency over 60% is obtained throughout the above band for both LHCP and RHCP ports.
PURE-LET Image Deconvolution

We propose a non-iterative image deconvolution algorithm for data corrupted by Poisson or mixed Poisson-Gaussian noise. Many applications involve such a problem, ranging from astronomical to biological imaging. We parameterize the deconvolution process as a linear combination of elementary functions, termed as linear expansion of thresholds. This parameterization is then optimized by minimizing a robust estimate of the true mean squared error, the Poisson unbiased risk estimate. Each elementary function consists of a Wiener filtering followed by a pointwise thresholding of undecimated Haar wavelet coefficients. In contrast to existing approaches, the proposed algorithm merely amounts to solving a linear system of equations, which has a fast and exact solution. Simulation experiments over different types of convolution kernels and various noise levels indicate that the proposed method outperforms the state-of-the-art techniques, in terms of both restoration quality and computational complexity. Finally, we present some results on real confocal fluorescence microscopy images and demonstrate the potential applicability of the proposed method for improving the quality of these images.
A High-Power $Ka$-Band (31–36 GHz) Solid-State Amplifier Based on Low-Loss Corporate Waveguide Combining

A method of using low-loss waveguide septum combiners is developed into a high-power -band (31-36 GHz) amplifier producing 50 W at 33 GHz (Ka-band) using 32 low-power (>2 W) solid-state amplifier modules. By using low-loss waveguide combining and a packaged monolithic microwave integrated circuit with a low-loss microstrip-to-waveguide launcher, the output loss is minimized, allowing for the overall power-combining efficiency to remain high, 80% (average insertion loss of combiner < 0.7 dB and average insertion loss of launcher <0.3 dB) over 31-36 GHz. In the past, lower power-combining efficiencies have limited the number of modules that can be combined at -band, and hence, have limited the power output. The approach demonstrated in this paper, with high power-combining efficiency, allows a very large number (32) of solid-state amplifier modules to be combined to produce high powers. Greater than 50 W was demonstrated with low power modules, but even higher powers 120 W are possible. The current approach is based on corporate combining, using low-loss waveguide septum combiners that provide isolation, maintaining the true graceful degradation of a modular solid-state amplifier system.