nils-herrmann/clean_scirepeval huggingface dataset:
ClassBench: A Packet Classification Benchmark

Packet classification is an enabling technology for next generation network services and often a performance bottleneck in high-performance routers. The performance and capacity of many classification algorithms and devices, including TCAMs, depend upon properties of filter sets and query patterns. Despite the pressing need, no standard performance evaluation tools or filter sets are publicly available. In response to this problem, we present ClassBench, a suite of tools for benchmarking packet classification algorithms and devices. ClassBench includes a Filter Set Generator that produces synthetic filter sets that accurately model the characteristics of real filter sets. Along with varying the size of the filter sets, we provide high-level control over the composition of the filters in the resulting filter set. The tool suite also includes a Trace Generator that produces a sequence of packet headers to exercise packet classification algorithms with respect to a given filter set. Along with specifying the relative size of the trace, we provide a simple mechanism for controlling locality of reference. While we have already found ClassBench to be very useful in our own research, we seek to eliminate the significant access barriers to realistic test vectors for researchers and initiate a broader discussion to guide the refinement of the tools and codification of a formal benchmarking methodology. (The ClassBench tools are publicly available at the following site: http://www.arl.wustl.edu/~det3/ClassBench/.)
Thin Structures in Image Based Rendering

We propose a novel method to handle thin structures in Image-Based Rendering (IBR), and specifically structures supported by simple geometric shapes such as planes, cylinders, etc. These structures, e.g. railings, fences, oven grills etc, are present in many man-made environments and are extremely challenging for multi-view 3D reconstruction, representing a major limitation of existing IBR methods. Our key insight is to exploit multi-view information. After a handful of user clicks to specify the supporting geometry, we compute multi-view and multi-layer alpha mattes to extract the thin structures. We use two multi-view terms in a graph-cut segmentation, the first based on multi-view foreground color prediction and the second ensuring multiview consistency of labels. Occlusion of the background can challenge reprojection error calculation and we use multiview median images and variance, with multiple layers of thin structures. Our end-to-end solution uses the multi-layer segmentation to create per-view mattes and the median colors and variance to create a clean background. We introduce a new multi-pass IBR algorithm based on depth-peeling to allow free-viewpoint navigation of multi-layer semi-transparent thin structures. Our results show significant improvement in rendering quality for thin structures compared to previous image-based rendering solutions.
Estimating mutual information and multi-information in large networks

We address the practical problems of estimating the information relations that characterize large networks. Building on methods developed for analysis of the neural code, we show that reliable estimates of mutual information can be obtained with manageable computational effort. The same methods allow estimation of higher order, multi–information terms. These ideas are illustrated by analyses of gene expression, financial markets, and consumer preferences. In each case, information theoretic measures correlate with independent, intuitive measures of the underlying structures in the system.
Teaching evaluation using data mining on moodle LMS forum

Recently, teaching evaluation is defined the main part of quality in education. The students normally make answers on questionnaire that are divided into types; close-end question and open-end question. The close-end question is simple answer as multi-choices that are easily processed by statistical evaluation. On the other hand, open-end question gives the person answering in phrases or statements that are recommended their teacher. The problem is mostly LMS ignored these open-end questions to overall analysis. Therefore, analysis and processing of these open-end questions are very importance and determined teaching. This research presents analysis model for teaching evaluation from answering and posting a comment to discussion in form of open-end question obtained from moodle LMS forum using data mining techniques. The techniques extract classification of attitudes that are defined positive and negative attitude from students to instructor for improvement of learning and teaching. These classification models are compared three algorithms; ID3, BFTree and Naïve Bayes. The experimental results, the decision tree is achieved correctly classifier 80% compared with others.
CYPRESS: Combining Static and Dynamic Analysis for Top-Down Communication Trace Compression

Communication traces are increasingly important, both for parallel applications' performance analysis/optimization, and for designing next-generation HPC systems. Meanwhile, the problem size and the execution scale on supercomputers keep growing, producing prohibitive volume of communication traces. To reduce the size of communication traces, existing dynamic compression methods introduce large compression overhead with the job scale. We propose a hybrid static-dynamic method that leverages information acquired from static analysis to facilitate more effective and efficient dynamic trace compression. Our proposed scheme, Cypress, extracts a program communication structure tree at compile time using inter-procedural analysis. This tree naturally contains crucial iterative computing features such as the loop structure, allowing subsequent runtime compression to "fill in", in a "top-down" manner, event details into the known communication template. Results show that Cypress reduces intra-process and inter-process compression overhead up to 5× and 9× respectively over state-of-the-art dynamic methods, while only introducing very low compiling overhead.
Application-driven dynamic vertical scaling of virtual machines in resource pools

Most modern hypervisors offer powerful resource control primitives such as reservations, limits, and shares for individual virtual machines (VMs). These primitives provide a means to dynamic vertical scaling of VMs in order for the virtual applications to meet their respective service level objectives (SLOs). VMware DRS offers an additional resource abstraction of a resource pool (RP) as a logical container representing an aggregate resource allocation for a collection of VMs. In spite of the abundant research on translating application performance goals to resource requirements, the implementation of VM vertical scaling techniques in commercial products remains limited. In addition, no prior research has studied automatic adjustment of resource control settings at the resource pool level. In this paper, we present AppRM, a tool that automatically sets resource controls for both virtual machines and resource pools to meet application SLOs. AppRM contains a hierarchy of virtual application managers and resource pool managers. At the application level, AppRM translates performance objectives into the appropriate resource control settings for the individual VMs running that application. At the resource pool level, AppRM ensures that all important applications within the resource pool can meet their performance targets by adjusting controls at the resource pool level. Experimental results under a variety of dynamically changing workloads composed by multi-tiered applications demonstrate the effectiveness of AppRM. In all cases, AppRM is able to deliver application performance satisfaction without manual intervention.
Android botnets for multi-targeted attacks

Today, mobile Botnets are well known in the IT security field. Whenever we talk about Botnets on mobile phones, we mostly deal with denial of service attacks (Kifayat and Wilson http://www.cms.livjm.ac.uk/pgnet2012/Proceedings/Papers/1569607737.pdf , 2012). This is due to the fact that we refer to classical Botnets on computers. But mobile phones are “mobiles” by definition. Indeed, they offer a lot of information not present on personal computers. They have a lot of sensors which are interesting for attackers. Most of the time, we used to think that targeted attacks have a single target. But with mobile phones, targeting a group of people does make sense. Coupled with data collected by the Sat Nav, we could so be able to localize with a certain probability meeting points in a criminal organization. By this way of attacking, we can deduce lots of things by cross-checking information obtained on devices. Thereby, this paper will aim to show the potential offered by such attacks. Firstly, this paper will focus on localization data. Furthermore, an implementation of an Android botnet and its server side part will be presented for illustrative purposes. Besides, the major part of the source code used will be included step by step in this paper. This paper aims to be technical because the author does not want to show any theory without trying some practicals tests with real and technical constraints.
Plant disease detection using CNNs and GANs as an augmentative approach

Almost 40% of the world's crop yield is lost to diseases and pest infestations. According to a 2012 survey, Maharashtra has the highest rate of farmer suicides and one of the major reasons for this is the failure of crops. This paper presents an image-based classification system for identification of plant diseases. Since existing datasets have diluted focus across several countries and there are none that pertain to India specifically, there is a need for establishing a local dataset to be of use to Indian farmers. It uses Generative Adversarial Networks (GANs) to augment the limited number of local images available. The classification is done by a Convolutional Neural Network (CNN) model deployed in a smart phone app.
TFET-based cellular neural network architectures

It is well known that CMOS scaling trends are now accompanied by less desirable byproducts such as increased energy dissipation. To combat the aforementioned challenges, solutions are sought at both the device and architectural levels. With this context, this work focuses on embedding a low voltage device, a Tunneling Field Effect Transistor (TFET) within a Cellular Neural Network (CNN) -- a low power analog computing architecture. Our study shows that TFET-based CNN systems, aside from being fully functional, also provide significant power savings when compared to the conventional resistor-based CNN. Our initial studies suggest that power savings are possible by carefully engineering lower voltage, lower current TFET devices without sacrificing performance. Moreover, TFET-based CNN reduces implementation footprints by eliminating the hardware required to realize output transfer functions. Application dynamics are verified through simulations. We conclude the paper with a discussion of desired device characteristics for CNN architectures with enhanced functionality.
Enthesitis: from pathophysiology to treatment

Entheses are the insertion sites of tendons and ligaments to the bone surface and are essential structures for locomotion. Inflammation of the entheses (enthesitis) is a key feature of psoriatic arthritis and spondyloarthritis. To date, our conceptual understanding of enthesitis remains limited. This Review provides an insight into the pathophysiology of enthesitis, addressing the role of biomechanics, prostaglandin E2-mediated vasodilation and the activation of innate immune cells in the initiation phase of enthesitis, as well as the role of entheseal IL-23-responsive cells that augment inflammation by producing pro-inflammatory mediators such as IL-17A, IL-22 and TNF. In addition, the molecular steps that translate inflammation into resident tissue responses, resulting in new bone formation, are discussed. The second part of the article summarizes the clinical features of enthesitis, and the role of clinical and imaging instruments in detecting enthesitis are discussed together with their challenges and limitations. Finally, the Review summarizes the current treatment possibilities for enthesitis based on the aforementioned pathophysiological concepts, focusing on the role of cytokine-blocking agents.
Exploiting Cross-Sentence Context for Neural Machine Translation

In translation, considering the document as a whole can help to resolve ambiguities and inconsistencies. In this paper, we propose a cross-sentence context-aware approach and investigate the influence of historical contextual information on the performance of neural machine translation (NMT). First, this history is summarized in a hierarchical way. We then integrate the historical representation into NMT in two strategies: 1) a warm-start of encoder and decoder states, and 2) an auxiliary context source for updating decoder states. Experimental results on a large Chinese-English translation task show that our approach significantly improves upon a strong attention-based NMT system by up to +2.1 BLEU points.
Elite swimmers with and without unilateral shoulder pain: mechanical hyperalgesia and active/latent muscle trigger points in neck-shoulder muscles.

Our aim was to investigate the presence of mechanical hypersensitivity and active trigger points (TrPs) in the neck-shoulder muscles in elite swimmers with/without unilateral shoulder pain. Seventeen elite swimmers with shoulder pain; 18 swimmers without shoulder pain; and 15 elite athletes matched controls were recruited. Pressure pain thresholds (PPT) were assessed over the levator scapulae, sternocleidomastoid, upper trapezius, infraspinatus, scalene, subscapularis and tibialis anterior muscles. TrPs in the levator scapulae, upper trapezius, infraspinatus, scalene, sternocleidomastoid and subscapularis muscles were also explored. Swimmers with shoulder pain showed significant lower PPT in all muscles compared with controls (P<0.01). No differences in PPT were found between swimmers with and without shoulder pain, underlining widespread mechanical hypersensitivity. The mean number of TrPs for elite swimmer with and without shoulder pain was, respectively, 4.7 ± 1 (2.1 ± 1.5 active; 2.6 ± 1.4 latent) and 4.7 ± 1.3 (1.3 ± 1.3 active; 3.4 ± 1.5 latent), whereas healthy athletes only showed latent TrPs (2.4 ± 1.2). Elite swimmers with shoulder pain showed higher number of active TrPs than swimmers without pain, whereas it was the opposite for the number of latent muscle TrP (P<0.05). The reported mechanical hypersensitivity suggests that active TrPs play a role in the development of shoulder pain in elite swimmers.
Combinators for bi-directional tree transformations: a linguistic approach to the view update problem

We propose a novel approach to the well-known view update problem for the case of tree-structured data: a domain-specific programming language in which all expressions denote bi-directional transformations on trees. In one direction, these transformations---dubbed lenses---map a "concrete" tree into a simplified "abstract view"; in the other, they map a modified abstract view, together with the original concrete tree, to a correspondingly modified concrete tree. Our design emphasizes both robustness and ease of use, guaranteeing strong well-behavedness and totality properties for well-typed lenses.We identify a natural space of well-behaved bi-directional transformations over arbitrary structures, study definedness and continuity in this setting, and state a precise connection with the classical theory of "update translation under a constant complement" from databases. We then instantiate this semantic framework in the form of a collection of lens combinators that can be assembled to describe transformations on trees. These combinators include familiar constructs from functional programming (composition, mapping, projection, conditionals, recursion) together with some novel primitives for manipulating trees (splitting, pruning, copying, merging, etc.). We illustrate the expressiveness of these combinators by developing a number of bi-directional list-processing transformations as derived forms.
Security Analysis of Smartphone and Cloud Computing Authentication Frameworks and Protocols

We live in a digital world where every detail of our information is being transferred from one smart device to another via cross-platform, third-party cloud services. Smart technologies, such as, smartphones are playing dynamic roles in order to successfully complete our daily routines and official tasks that require access to all types of critical data. Before the advent of these smart technologies, securing critical information was quite a challenge. However, after the advent and global adoption of such technologies, information security has become one of the primary and most fundamental task for security professionals. The integration of social media has made this task even more challenging to undertake successfully. To this day, there are plentiful studies in which numerous authentication and security techniques were proposed and developed for smartphone and cloud computing technologies. These studies have successfully addressed multiple authentication threats and other related issues in existing the smartphone and cloud computing technologies. However, to the best of our understanding and knowledge, these studies lack many aspects in terms of authentication attacks, logical authentication analysis, and the absence of authentication implementation scenarios. Due to these authentication anomalies and ambiguities, such studies cannot be fully considered for successful implementation. Therefore, in this paper, we have performed a comprehensive security analysis and review of various smartphone and cloud computing authentication frameworks and protocols to outline up-to-date authentication threats and issues in the literature. These authentication challenges are further summarized and presented in the form of different graphs to illustrate where the research is currently heading. Finally, based on those outcomes, we identify the latest and existing authentication uncertainties, threats, and other related issues to address future directions and open research issues in the domain of the smartphone-and cloud-computing authentication.
Once-daily valacyclovir to reduce the risk of transmission of genital herpes.

Nucleoside analogues against herpes simplex virus (HSV) have been shown to suppress shedding of HSV type 2 (HSV-2) on genital mucosal surfaces and may prevent sexual transmission of HSV.



We followed 1484 immunocompetent, heterosexual, monogamous couples: one with clinically symptomatic genital HSV-2 and one susceptible to HSV-2. The partners with HSV-2 infection were randomly assigned to receive either 500 mg of valacyclovir once daily or placebo for eight months. The susceptible partner was evaluated monthly for clinical signs and symptoms of genital herpes. Source partners were followed for recurrences of genital herpes; 89 were enrolled in a substudy of HSV-2 mucosal shedding. Both partners were counseled on safer sex and were offered condoms at each visit. The predefined primary end point was the reduction in transmission of symptomatic genital herpes.



Clinically symptomatic HSV-2 infection developed in 4 of 743 susceptible partners who were given valacyclovir, as compared with 16 of 741 who were given placebo (hazard ratio, 0.25; 95 percent confidence interval, 0.08 to 0.75; P=0.008). Overall, acquisition of HSV-2 was observed in 14 of the susceptible partners who received valacyclovir (1.9 percent), as compared with 27 (3.6 percent) who received placebo (hazard ratio, 0.52; 95 percent confidence interval, 0.27 to 0.99; P=0.04). HSV DNA was detected in samples of genital secretions on 2.9 percent of the days among the HSV-2-infected (source) partners who received valacyclovir, as compared with 10.8 percent of the days among those who received placebo (P<0.001). The mean rates of recurrence were 0.11 per month and 0.40 per month, respectively (P<0.001).



Once-daily suppressive therapy with valacyclovir significantly reduces the risk of transmission of genital herpes among heterosexual, HSV-2-discordant couples.
Blur invariant pattern recognition and registration in the Fourier domain

Pattern recognition and registration are integral elements of computer vision, which considers image patterns. This thesis presents novel blur, and combined blur and geometric invariant features for pattern recognition and registration related to images. These global or local features are based on the Fourier transform phase, and are invariant or insensitive to image blurring with a centrally symmetric point spread function which can result, for example, from linear motion or out of focus. The global features are based on the even powers of the phase-only discrete Fourier spectrum or bispectrum of an image and are invariant to centrally symmetric blur. These global features are used for object recognition and image registration. The features are extended for geometrical invariances up to similarity transformation: shift invariance is obtained using bispectrum, and rotation-scale invariance using log-polar mapping of bispectrum slices. Affine invariance can be achieved as well using rotated sets of the log-log mapped bispectrum slices. The novel invariants are shown to be more robust to additive noise than the earlier blur, and combined blur and geometric invariants based on image moments. The local features are computed using the short term Fourier transform in local windows around the points of interest. Only the lowest horizontal, vertical, and diagonal frequency coefficients are used, the phase of which is insensitive to centrally symmetric blur. The phases of these four frequency coefficients are quantized and used to form a descriptor code for the local region. When these local descriptors are used for texture classification, they are computed for every pixel, and added up to a histogram which describes the local pattern. There are no earlier textures features which have been claimed to be invariant to blur. The proposed descriptors were superior in the classification of blurred textures compared to a few non-blur invariant state of the art texture classification methods.
Analysis of dynamic power management on multi-core processors

Power management of multi-core processors is extremely important because it allows power/energy savings when all cores are not used. OS directed power management according to ACPI (Advanced Power and Configurations Interface) specifications is the common approach that industry has adopted for this purpose. While operating systems are capable of such power management, heuristics for effectively managing the power are still evolving. The granularity at which the cores are slowed down/turned off should be designed considering the phase behavior of the workloads. Using 3-D, video creation, office and e-learning applications from the SYSmark benchmark suite, we study the challenges in power management of a multi-core processor such as the AMD Quad-Core Opteron" and Phenom". We unveil effects of the idle core frequency on the performance and power of the active cores. We adjust the idle core frequency to have the least detrimental effect on the active core performance. We present optimized hardware and operating system configurations that reduce average active power by 30% while reducing performance by an average of less than 3%. We also present complete system measurements and power breakdown between the various systems components using the SYSmark and SPEC CPU workloads. It is observed that the processor core and the disk consume the most power, with core having the highest variability.