nils-herrmann/clean_scirepeval huggingface dataset:
Dummynet: a simple approach to the evaluation of network protocols

Network protocols are usually tested in operational networks or in simulated environments. With the former approach it is not easy to set and control the various operational parameters such as bandwidth, delays, queue sizes. Simulators are easier to control, but they are often only an approximate model of the desired setting, especially for what regards the various traffic generators (both producers and consumers) and their interaction with the protocol itself.In this paper we show how a simple, yet flexible and accurate network simulator - dummynet - can be built with minimal modifications to an existing protocol stack, allowing experiments to be run on a standalone system. dummynet works by intercepting communications of the protocol layer under test and simulating the effects of finite queues, bandwidth limitations and communication delays. It runs in a fully operational system, hence allowing the use of real traffic generators and protocol implementations, while solving the problem of simulating unusual environments. With our tool, doing experiments with network protocols is as simple as running the desired set of applications on a workstation.A FreeBSD implementation of dummynet, targeted to TCP, is available from the author. This implementation is highly portable and compatible with other BSD-derived systems, and takes less than 300 lines of kernel code.
Mask-Specific Inpainting with Deep Neural Networks

Most inpainting approaches require a good image model to infer the unknown pixels. In this work, we directly learn a mapping from image patches, corrupted by missing pixels, onto complete image patches. This mapping is represented as a deep neural network that is automatically trained on a large image data set. In particular, we are interested in the question whether it is helpful to exploit the shape information of the missing regions, i.e. the masks, which is something commonly ignored by other approaches. In comprehensive experiments on various images, we demonstrate that our learning-based approach is able to use this extra information and can achieve state-of-the-art inpainting results. Furthermore, we show that training with such extra information is useful for blind inpainting, where the exact shape of the missing region might be uncertain, for instance due to aliasing effects.
Clustering Algorithms for Content-Based Publication-Subscription Systems

We consider efficient communication schemes based on both network-supported and application-level multicast techn iques for content-based publication-subscription systems. We show t at the communication costs depend heavily on the network configura tions, distribution of publications and subscriptions. We devise new algorithms and adapt existing partitional data clustering al orithms. These algorithms can be used to determine multicast groups w ith as much commonality as possible, based on the totality of subsc ribers’ interests. They perform well in the context of highly hetero g neous subscriptions, and they also scale well. An efficiency of 60% to 80% with respect to the ideal solution can be achieved with a smal l number of multicast groups (less than 100 in our experiments). S ome of these same concepts can be applied to match publications to s ubscribers in real-time, and also to determine dynamically wh ether to unicast, multicast or broadcast information about the even ts over the network to the matched subscribers. We demonstrate the q uality of our algorithms via simulation experiments.
Automatic extraction and recognition of shoe logos with a wide variety of appearance

A logo is a symbolic presentation that is designed not only to identify a product manufacturer but also to attract the attention of shoppers. Shoe logos are a challenging subject for automatic extraction and recognition using image analysis techniques because they have characteristics that distinguish them from those of other products, that is, there is much variation in the appearance of shoe logos. In this paper, we propose an automatic extraction and recognition method for shoe logos with a wide variety of appearanee using a limited number training samples. The proposed method employs maximally stable extremal regions (MSERs) for the initial region extraction, an iterative algorithm for region grouping, and gradient features and a support vector machine for logo recognition. The results of performance evaluation experiments using a logo dataset that consists of a wide variety of appearance show that the proposed method achieves promising performance for both logo extraction and recognition.
Who Benefits From Humor-Based Positive Psychology Interventions? The Moderating Effects of Personality Traits and Sense of Humor

The evidence for the effectiveness of humor-based positive psychology interventions (PPIs; i.e., interventions aimed at enhancing happiness and lowering depressive symptoms) is steadily increasing. However, little is known about who benefits most from them. We aim at narrowing this gap by examining whether personality traits and sense of humor moderate the long-term effects of humor-based interventions on happiness and depressive symptoms. We conducted two placebo-controlled online-intervention studies testing for moderation effects. In Study 1 (N = 104) we tested for moderation effects of basic personality traits (i.e., psychoticism, extraversion, and neuroticism) in the three funny things intervention, a humor-based PPI. In Study 2 (N = 632) we tested for moderation effects of the sense of humor in five different humor-based interventions. Happiness and depressive symptoms were assessed before and after the intervention, as well as after 1, 3, and 6 months. In Study 2, we assessed sense of humor before and 1 month after the intervention to investigate if changes in sense of humor go along with changes in happiness and depressive symptoms. We found moderating effects only for extraversion. Extraverts benefitted more from the three funny things intervention than introverts. For neuroticism and psychoticism no moderation effects were found. For sense of humor, no moderating effects were found for the effectiveness of the five humor-based interventions tested in Study 2. However, changes in sense of humor from pretest to the 1-month follow-up predicted changes in happiness and depressive symptoms. Taking a closer look, the playful attitude- and sense of humor-subscales predicted changes in happiness and depression for up to 6 months. Overall, moderating effects for personality (i.e., extraversion) were found, but none for sense of humor at baseline. However, increases in sense of humor during and after the intervention were associated with the interventions' effectiveness. Thus, we found humor-based interventions to be equally suited for humorous and non-humorous people, but increases in the sense of humor during the intervention phase could serve as an indicator whether it is worth continuing the intervention in the long-term.
OPEM: A Static-Dynamic Approach for Machine-Learning-Based Malware Detection

Malware is any computer software potentially harmful to both computers and networks. The amount of malware is growing every year and poses a serious global security threat. Signature-based detection is the most extended method in commercial antivirus software, however, it consistently fails to detect new malware. Supervised machine learning has been adopted to solve this issue. There are two types of features that supervised malware detectors use: (i) static features and (ii) dynamic features. Static features are extracted without executing the sample whereas dynamic ones requires an execution. Both approaches have their advantages and disadvantages. In this paper, we propose for the first time, OPEM, an hybrid unknown malware detector which combines the frequency of occurrence of operational codes (statically obtained) with the information of the execution trace of an executable (dynamically obtained). We show that this hybrid approach enhances the performance of both approaches when run separately.
Photorealistic facial expression synthesis by the conditional difference adversarial autoencoder

Photorealistic facial expression synthesis from single face image can be widely applied to face recognition, data augmentation for emotion recognition or entertainment. This problem is challenging, in part due to a paucity of labeled facial expression data, making it difficult for algorithms to disambiguate changes due to identity and changes due to expression. In this paper, we propose the conditional difference adversarial autoencoder (CDAAE) for facial expression synthesis. The CDAAE takes a facial image of a previously unseen person and generates an image of that person's face with a target emotion or facial action unit (AU) label. The CDAAE adds a feedforward path to an autoencoder structure connecting low level features at the encoder to features at the corresponding level at the decoder. It handles the problem of disambiguating changes due to identity and changes due to facial expression by learning to generate the difference between low-level features of images of the same person but with different facial expressions. The CDAAE structure can be used to generate novel expressions by combining and interpolating between facial expressions/action units within the training set. Our experimental results demonstrate that the CDAAE can preserve identity information when generating facial expression for unseen subjects more faithfully than previous approaches. This is especially advantageous when training with small databases.
On the benefits of explaining herd immunity in vaccine advocacy

Most vaccines protect both the vaccinated individual and the community at large by building up herd immunity. Even though reaching disease-specific herd immunity thresholds is crucial for eliminating or eradicating certain diseases1,2, explanation of this concept remains rare in vaccine advocacy3. An awareness of this social benefit makes vaccination not only an individual but also a social decision. Although knowledge of herd immunity can induce prosocial vaccination in order to protect others, it can also invite free-riding, in which individuals profit from the protection provided by a well-vaccinated society without contributing to herd immunity themselves. This cross-cultural experiment assesses whether people will be more or less likely to be vaccinated when they know more about herd immunity. Results show that in cultures that focus on collective benefits, vaccination willingness is generally higher. Communicating the concept of herd immunity improved willingness to vaccinate, especially in cultures lacking this prosocial cultural background. Prosocial nudges can thus help to close these immunity gaps.
Location Privacy in Pervasive Computing

M any countries recognize privacy as a right and have attempted to codify it in law. The first known piece of privacy legislation was England's 1361 Justices of the Peace Act, which legislated for the arrest of eaves-droppers and stalkers. The Fourth Amendment to the US Constitution proclaims citizens' right to privacy , and in 1890 US Supreme Court Justice Louis Brandeis stated that " the right to be left alone " is one of the fundamental rights of a democracy. 1 The 1948 Universal Declaration of Human Rights 2 declares that everyone has a right to privacy at home, with family, and in correspondence. Other pieces of more recent legislation follow this principle. Although many people clearly consider their privacy a fundamental right, comparatively few can give a precise definition of the term. The Global Internet Liberty Campaign 3 has produced an extensive report that discusses personal privacy at length and identifies four broad categories: information privacy, bodily privacy, privacy of communications, and territorial privacy. This article concentrates on location privacy, a particular type of information privacy that we define as the ability to prevent other parties from learning one's current or past location. Until recently, the very concept of location privacy was unknown: people did not usually have access to reliable and timely information about the exact location of others, and therefore most people could see no privacy implications in revealing their location, except in special circumstances. With pervasive computing, though, the scale of the problem changes completely. You probably do not care if someone finds out where you were yesterday at 4:30 p.m., but if this someone could inspect the history of all your past movements, recorded every second with submeter accuracy, you might start to see things differently. A change of scale of several orders of magnitude is often qualitative as well as quantitative—a recurring problem in pervasive computing. 4 We shall focus on the privacy aspects of using location information in pervasive computing applications. When location systems track users automatically on an ongoing basis, they generate an enormous amount of potentially sensitive information. Privacy of location information is about controlling access to this information. We do not necessarily want to stop all access—because some applications can use this information to provide useful services—but we want to be in control. Some goals are clearly mutually exclusive and cannot be simultaneously satisfied: for example, wanting to keep …
Rule-based detection of design patterns in program code

The process of understanding and reusing software is often time-consuming, especially in legacy code and open-source libraries. While some core code of open-source libraries may be well-documented, it is frequently the case that open-source libraries lack informative API documentation and reliable design information. As a result, the source code itself is often the sole reliable source of information for program understanding activities. In this article, we propose a reverse-engineering approach that can provide assistance during the process of understanding software through the automatic recovery of hidden design patterns in software libraries. Specifically, we use ontology formalism to represent the conceptual knowledge of the source code and semantic rules to capture the structures and behaviors of the design patterns in the libraries. Several software libraries were examined with this approach and the evaluation results show that effective and flexible detection of design patterns can be achieved without using hard-coded heuristics.
Security in Cloud Computing Using Cryptographic Algorithms Miss .

Cloud computing is the concept implemented to decipher the Daily Computing Problems. Cloud computing is basically virtual pool of resources and it provides these resources to users via internet. Cloud computing is the internet based development and used in computer technology. The prevalent problem associated with cloud computing is data privacy, security, anonymity and reliability etc. But the most important between them is security and how cloud provider assures it. To secure the Cloud means secure the treatments (calculations) and storage (databases hosted by the Cloud provider). In this paper we analyses different security issues to cloud and different cryptographic algorithms adoptable to better security for the cloud.  Cloud Computing, Cryptographic Algorithm, Internet, Security Algorithms, Security Attacks, Security Issue
Incremental LSTM-based dialog state tracker

A dialog state tracker is an important component in modern spoken dialog systems. We present an incremental dialog state tracker, based on LSTM networks. It directly uses automatic speech recognition hypotheses to track the state. We also present the key non-standard aspects of the model that bring its performance close to the state-of-the-art and experimentally analyze their contribution: including the ASR confidence scores, abstracting scarcely represented values, including transcriptions in the training data, and model averaging.
The efficiency frontier approach to economic evaluation of health-care interventions.

IQWiG commissioned an international panel of experts to develop methods for the assessment of the relation of benefits to costs in the German statutory health-care system.



The panel recommended that IQWiG inform German decision makers of the net costs and value of additional benefits of an intervention in the context of relevant other interventions in that indication. To facilitate guidance regarding maximum reimbursement, this information is presented in an efficiency plot with costs on the horizontal axis and value of benefits on the vertical. The efficiency frontier links the interventions that are not dominated and provides guidance. A technology that places on the frontier or to the left is reasonably efficient, while one falling to the right requires further justification for reimbursement at that price. This information does not automatically give the maximum reimbursement, as other considerations may be relevant. Given that the estimates are for a specific indication, they do not address priority setting across the health-care system.



This approach informs decision makers about efficiency of interventions, conforms to the mandate and is consistent with basic economic principles. Empirical testing of its feasibility and usefulness is required.
SOME PHILOSOPHICAL PROBLEMS FROM THE STANDPOINT OF ARTI CIAL INTELLIGENCE

A computer program capable of acting intelligently in the world must have a general representation of the world in terms of which its inputs are interpreted. Designing such a program requires commitments about what knowledge is and how it is obtained. Thus, some of the major traditional problems of philosophy arise in artificial intelligence. More specifically, we want a computer program that decides what to do by inferring in a formal language that a certain strategy will achieve its assigned goal. This requires formalizing concepts of causality, ability, and knowledge. Such formalisms are also considered in philosophical logic. The first part of the paper begins with a philosophical point of view that seems to arise naturally once we take seriously the idea of actually making an intelligent machine. We go on to the notions of metaphysically and epistemo-logically adequate representations of Purchase Export
HF outphasing transmitter using class-E power amplifiers

Chireix outphasing systems have been used successfully with power amplifiers operating in class-B and F. However, when class-E PAs are used in these systems, either the efficiency or the dynamic range is poor. The asymmetric combining technique described here uses transmission lines or equivalent networks of different electrical lengths to position the impedance loci to provide good dynamic range while maintaining high efficiency. This in turn allows linear power amplification of amplitude-modulated signals with high average efficiency. The concept is verified by a 28-W PEP transmitter that operates at 1.82 MHz and achieves an efficiency of 85% or better over an amplitude range of 10 dB.
Taxonomy of intrusion risk assessment and response system

In recent years, we have seen notable changes in the way attackers infiltrate computer systems compromising their functionality. Research in intrusion detection systems aims to reduce the impact of these attacks. In this paper, we present a taxonomy of intrusion response systems (IRS) and Intrusion Risk Assessment (IRA), two important components of an intrusion detection solution. We achieve this by classifying a number of studies published during the last two decades . We discuss the key features of existing IRS and IRA. We show how characterizing security risks and choosing the right countermeasures are an important and challenging part of designing an IRS and an IRA. Poorly designed IRS and IRA may reduce network performance and wrongly disconnect users from a network. We propose techniques on how to address these challenges and highlight the need for a comprehensive defense mechanism approach. We believe that this taxonomy will open up interesting areas for future research in the growing field of intrusion risk assessment and response systems.
Automatic assessment of expressive oral reading

We investigated the automatic assessment of expressive children’s oral reading of grade level text passages using a standardized rubric. After a careful review of the reading literature and a close examination of the rubric, we designed a novel set of prosodic and lexical features to characterize fluent expressive reading. A number of complementary sources of information were used to design the features, each of them motivated by research on different components of reading fluency. Features are connected to the child’s reading rate, to the presence and number of pauses, filled-pauses and word-repetitions, the correlation between punctuation marks and pauses, the length of word groupings, syllable stress and duration and the location of pitch peaks and contours. The proposed features were evaluated on a corpus of 783 one-minute reading sessions from 313 students reading grade-leveled passages without assistance (cold unassisted reading). Experimental results show that the proposed lexical and prosodic features provide complementary information and are able to capture the characteristics of expressive reading. The results showed that on both the 2-point and the 4-point expressiveness scales, computer-generated ratings of expressiveness agreed with human raters better than the human raters agreed with each other. The results of the study suggest that automatic assessment of expressive oral reading can be combined with automatic measures of word accuracy and reading rate to produce an accurate multidimensional estimate of children’s oral reading
Closed-Loop Deep Brain Stimulation Is Superior in Ameliorating Parkinsonism

Continuous high-frequency deep brain stimulation (DBS) is a widely used therapy for advanced Parkinson's disease (PD) management. However, the mechanisms underlying DBS effects remain enigmatic and are the subject of an ongoing debate. Here, we present and test a closed-loop stimulation strategy for PD in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) primate model of PD. Application of pallidal closed-loop stimulation leads to dissociation between changes in basal ganglia (BG) discharge rates and patterns, providing insights into PD pathophysiology. Furthermore, cortico-pallidal closed-loop stimulation has a significantly greater effect on akinesia and on cortical and pallidal discharge patterns than standard open-loop DBS and matched control stimulation paradigms. Thus, closed-loop DBS paradigms, by modulating pathological oscillatory activity rather than the discharge rate of the BG-cortical networks, may afford more effective management of advanced PD. Such strategies have the potential to be effective in additional brain disorders in which a pathological neuronal discharge pattern can be recognized.
Log Your Car: The Non-invasive Vehicle Forensics

Digital forensics is becoming an important feature for many embedded devices. In automotive systems, digital forensics involves multiple electronic control units (ECUs) used to support the connected and intelligent vehicle's technology. Digital evidence from these ECUs can be used in forensics investigation and analysis. Such a mechanism can potentially facilitate crash investigation, insurance claims and crime investigation. Issues related to forensics include the authenticity, integrity and privacy of the data. In this paper, the security of the forensic process and data in automotive systems is analysed. We propose an efficient, secure, privacy-preserving and reliable mechanism to provide a forensics data collection and storage process. A diagnostic application for smart phones, DiaLOG, is incorporated in the proposed process that uses a secure protocol to communicate the collected forensic data to a secure cloud storage. The proposed protocol for communicating forensic data is implemented to measure performance results and formally analysed using Scyther and CasperFDR with no known attack found.