nils-herrmann/clean_scirepeval huggingface dataset:
Proxy Caching Mechanism for Multimedia Playback Streams in the Internet

Despite the success of proxy caching in the Web, proxy servers have not been used effectively for caching of Internet multimedia streams such as audio and video. Explosive growth in demand for web-based streaming applications justifies the need for caching popular streams at a proxy server close to the interested clients. Because of the need for congestion control in the Internet, multimedia streams should be quality adaptive. This implies that on a cache-hit, a proxy must replay a variable-quality cached stream whose quality is determined by the bandwidth of the first session. This paper addresses the implications of congestion control and quality adaptation on proxy caching mechanisms. We present a fine-grain replacement algorithm for layered-encoded multimedia streams at Internet proxy servers, and describe a pre-fetching scheme to smooth out the variations in quality of a cached stream during subsequent playbacks. This enables the proxy to perform quality adaptation more effectively and maximizes the delivered quality. We also extend the semantics of popularity and introduce the idea of weighted hit to capture both the level of interest and the usefulness of a layer for a cached stream. Finally, we present our replacement algorithm and show that its interaction with prefetching results in the state of the cache converging to the optimal state such that the quality of a cached stream is proportional to its popularity, and the variations in quality of a cached stream are inversely proportional to its popularity. This implies that after serving several requests for a stream, the proxy can effectively hide low bandwidth paths to the original server from interested clients.
Tango: distributed data structures over a shared log

Distributed systems are easier to build than ever with the emergence of new, data-centric abstractions for storing and computing over massive datasets. However, similar abstractions do not exist for storing and accessing meta-data. To fill this gap, Tango provides developers with the abstraction of a replicated, in-memory data structure (such as a map or a tree) backed by a shared log. Tango objects are easy to build and use, replicating state via simple append and read operations on the shared log instead of complex distributed protocols; in the process, they obtain properties such as linearizability, persistence and high availability from the shared log. Tango also leverages the shared log to enable fast transactions across different objects, allowing applications to partition state across machines and scale to the limits of the underlying log without sacrificing consistency.
Combining ontologies for requirements elicitation

A variety of ontologies are used to define and represent knowledge in many domains. Many ontological approaches have been successfully applied in the field of Requirements Engineering. In order to successfully harness the disparate ontologies, researchers have focused on various ontology merging techniques. However, no serious attempts have been made in the area of Requirements Elicitation where ontology merging has the potential to be quite effective in generating requirements specifications quickly through the means of reasoning based on combined ontologies. This paper attempts to define an approach needed to effectively combine ontologies to enhance the Requirements Elicitation process. A methodology is proposed whereby domain knowledge encapsulated in existing ontologies is combined with an ontology being developed to capture the requirements. Using this, requirements engineers would be able to create more refined Requirements Deliverables.
Semi-supervised Deep Learning for Fully Convolutional Networks

Deep learning usually requires large amounts of labeled training data, but annotating data is costly and tedious. The framework of semi-supervised learning provides the means to use both labeled data and arbitrary amounts of unlabeled data for training. Recently, semisupervised deep learning has been intensively studied for standard CNN architectures. However, Fully Convolutional Networks (FCNs) set the state-of-the-art for many image segmentation tasks. To the best of our knowledge, there is no existing semi-supervised learning method for such FCNs yet. We lift the concept of auxiliary manifold embedding for semisupervised learning to FCNs with the help of Random Feature Embedding. In our experiments on the challenging task of MS Lesion Segmentation, we leverage the proposed framework for the purpose of domain adaptation and report substantial improvements over the baseline model.
Resolution Recommendation for Event Tickets in Service Management

In recent years, IT service providers have rapidly achieved an automated service delivery model. Software monitoring systems are designed to actively collect and signal event occurrences and, when necessary, automatically generate incident tickets. Repeating events generate similar tickets, which in turn have a vast number of repeated problem resolutions likely to be found in earlier tickets. In this paper, we develop techniques to recommend appropriate resolution for incoming events by making use of similarities between the events and historical resolutions of similar events. Built on the traditional k-nearest neighbor algorithm (KNN), our proposed algorithms take into account false positives often generated by monitoring systems. An additional penalty is incorporated into the algorithms to control the number of misleading resolutions in the recommendation results. Moreover, as the effectiveness of the KNN heavily relies on the underlying similarity measurement, we proposed two other approaches to significantly improve our recommendation with respect to resolution relevance. One approach uses topic-level features to incorporate resolution information into the similarity measurement; the other uses metric learning to learn a more effective similarity measure. Extensive empirical evaluations on three ticket data sets demonstrate the effectiveness and efficiency of our proposed methods.
Model Reference Adaptive System ( MRAS ) Based Speed Sensorless Vector Control of Induction Motor Drive

This paper presents a Model Reference Adaptive System (MRAS) based speed sensorless estimation of vector controlled Induction Motor Drive. MRAS based techniques are one of the best methods to estimate the rotor speed due to its performance and straightforward stability approach. Depending on the type of tuning signal driving the adaptation mechanism, MRAS estimators are classified into rotor flux based MRAS, back e.m.f based MRAS, reactive power based MRAS and artificial neural network based MRAS. In this paper, the performance of the rotor flux based MRAS for estimating the rotor speed was studied. Overview on the IM mathematical model is briefly summarized to establish a physical basis for the sensorless scheme used. Further, the theoretical basis of indirect field oriented vector control is explained in detail and it is implemented in MATLAB/SIMULINK.
Security against hardware Trojan through a novel application of design obfuscation

Malicious hardware Trojan circuitry inserted in safety-critical applications is a major threat to national security. In this work, we propose a novel application of a key-based obfuscation technique to achieve security against hardware Trojans. The obfuscation scheme is based on modifying the state transition function of a given circuit by expanding its reachable state space and enabling it to operate in two distinct modes -- the normal mode and the obfuscated mode. Such a modification obfuscates the rareness of the internal circuit nodes, thus making it difficult for an adversary to insert hard-to-detect Trojans. It also makes some inserted Trojans benign by making them activate only in the obfuscated mode. The combined effect leads to higher Trojan detectability and higher level of protection against such attack. Simulation results for a set of benchmark circuits show that the scheme is capable of achieving high levels of security at modest design overhead.
An Integrated Architecture for Demand Response Communications and Control

In the competitive electricity structure, demand response programs enable customers to react dynamically to changes in electricity prices. The implementation of such programs may reduce energy costs and increase reliability. To fully harness such benefits, existing load controllers and appliances need around-the-clock price information. Advances in the development and deployment of advanced meter infrastructures (AMIs), building automation systems (BASs), and various dedicated embedded control systems provide the capability to effectively address this requirement. In this paper we introduce a meter gateway architecture (MGA) to serve as a foundation for integrated control of loads by energy aggregators, facility hubs, and intelligent appliances. We discuss the requirements that motivate the architecture, describe its design, and illustrate its application to a small system with an intelligent appliance and a legacy appliance using a prototype implementation of an intelligent hub for the MGA and ZigBee wireless communications.
OBJECTIVE VIDEO QUALITY ASSESSMENT

Digital video data, stored in video databases and distributed through communication networks, is subject to various kinds of distortions during acquisition, compression, processing, transmission, and reproduction. For example, lossy video compression techniques, which are almost always used to reduce the bandwidth needed to store or transmit video data, may degrade the quality during the quantization process. For another instance, the digital video bitstreams delivered over error-prone channels, such as wireless channels, may be received imperfectly due to the impairment occurred during transmission. Package-switched communication networks, such as the Internet, can cause loss or severe delay of received data packages, depending on the network conditions and the quality of services. All these transmission errors may result in distortions in the received video data. It is therefore imperative for a video service system to be able to realize and quantify the video quality degradations that occur in the system, so that it can maintain, control and possibly enhance the quality of the video data. An effective image and video quality metric is crucial for this purpose.
Coevolution of Role-Based Cooperation in Multiagent Systems

In tasks such as pursuit and evasion, multiple agents need to coordinate their behavior to achieve a common goal. An interesting question is, how can such behavior be best evolved? A powerful approach is to control the agents with neural networks, coevolve them in separate subpopulations, and test them together in the common task. In this paper, such a method, called multiagent enforced subpopulations (multiagent ESP), is proposed and demonstrated in a prey-capture task. First, the approach is shown to be more efficient than evolving a single central controller for all agents. Second, cooperation is found to be most efficient through stigmergy, i.e., through role-based responses to the environment, rather than communication between the agents. Together these results suggest that role-based cooperation is an effective strategy in certain multiagent tasks.
PRISM: A Framework for Authoring Interactive Narratives

The advances in computing technologies enable the computer users to create and share their own stories to the community at large. However, it is still regarded as complicated and laborious to author interactive narratives, where a story adapts as the user interacts with it. In authoring interactive narratives, two main approaches—branching graphs and AI planning—have been significantly used to augment interactivity into conventional linear narratives. Although each approach offers its own possibilities and limitations, few efforts have been made to blend these approaches. This paper describes a framework for authoring interactive narratives that employs an adapted branching narrative structure that also uses planning formalism to enable automated association between nodes. We expect that our work is valuable for non-expert users as well as AI researchers in interactive storytelling who need to create a large quantity of story contents for varied endings for a story.
Parallel algorithms for Burrows-Wheeler compression and decompression

We present work-optimal PRAM algorithms for Burrows-Wheeler compression and decompression of strings over a constant alphabet. For a string of length n, the depth of the compression algorithm is O(log n), and the depth of the the corresponding decompression algorithm is O(log n). These appear to be the first polylogarithmic-time work-optimal parallel algorithms for any standard lossless compression scheme. The algorithms for the individual stages of compression and decompression may also be of independent interest: 1. a novel O(log n)-time, O(n)-work PRAM algorithm for Huffman decoding; 2. original insights into the stages of the BW compression and decompression problems, bringing out parallelism that was not readily apparent, allowing them to be mapped to elementary parallel routines that have O(log n)-time, O(n)-work solutions, such as: (i) prefix-sums problems with an appropriately-defined associative binary operator for several stages, and (ii) list ranking for the final stage of decompression.
Modeling of a stair-climbing wheelchair mechanism with high single-step capability

In the field of providing mobility for the elderly and disabled, the aspect of dealing with stairs continues largely unresolved. This paper focuses on presenting the development of a stair-climbing wheelchair mechanism with high single-step capability. The mechanism is based on front and rear wheel clusters connected to the base (chair) via powered linkages so as to permit both autonomous stair ascent and descent in the forward direction, and high single-step functionality for such as direct entry to and from a van. Primary considerations were inherent stability, provision of a mechanism that is physically no larger than a standard powered wheelchair, aesthetics, and being based on readily available low-cost components.
A mutation in a case of early onset narcolepsy and a generalized absence of hypocretin peptides in human narcoleptic brains

We explored the role of hypocretins in human narcolepsy through histopathology of six narcolepsy brains and mutation screening of Hcrt, Hcrtr1 and Hcrtr2 in 74 patients of various human leukocyte antigen and family history status. One Hcrt mutation, impairing peptide trafficking and processing, was found in a single case with early onset narcolepsy. In situ hybridization of the perifornical area and peptide radioimmunoassays indicated global loss of hypocretins, without gliosis or signs of inflammation in all human cases examined. Although hypocretin loci do not contribute significantly to genetic predisposition, most cases of human narcolepsy are associated with a deficient hypocretin system.
LTE-Advanced and the evolution to Beyond 4G (B4G) systems

Cellular networks have been undergoing an extraordinarily fast evolution in the past years. With commercial deployments of Release 8 (Rel-8) Long Term Evolution (LTE) already being carried out worldwide, a significant effort is being put forth by the research and standardization communities on the development and specification of LTE-Advanced. The work started in Rel-10 by the Third Generation Partnership Project (3GPP) had the initial objective of meeting the International Mobile Telecommunications-Advanced (IMTAdvanced) requirements set by the International Telecommunications Union (ITU) which defined fourth generation (4G) systems. However, predictions based on the wireless traffic explosion in recent years indicate a need for more advanced technologies and higher performance. Hence, 3GPP’s efforts have continued through Rel-11 and now Rel-12. This paper provides a state-of-the-art comprehensive view on the key enabling technologies for LTE-Advanced systems. Already consolidated technologies developed for Rel-10 and Rel11 are reviewed while novel approaches and enhancements currently under consideration for Rel-12 are also discussed. Technical challenges for each of the main areas of study are pointed out as an encouragement for the research community to participate in this
Mobile-Sandbox: combining static and dynamic analysis with machine-learning techniques

Smartphones in general and Android in particular are increasingly shifting into the focus of cyber criminals. For understanding the threat to security and privacy, it is important for security researchers to analyze malicious software written for these systems. The exploding number of Android malware calls for automation in the analysis. In this paper, we present Mobile-Sandbox, a system designed to automatically analyze Android applications in novel ways: First, it combines static and dynamic analysis, i.e., results of static analysis are used to guide dynamic analysis and extend coverage of executed code. Additionally, it uses specific techniques to log calls to native (i.e., “non-Java”) APIs, and last but not least it combines these results with machine-learning techniques to cluster the analyzed samples into benign and malicious ones. We evaluated the system on more than 69,000 applications from Asian third-party mobile markets and found that about 21 % of them actually use native calls in their code.
Rx: treating bugs as allergies - a safe method to survive software failures

Many applications demand availability. Unfortunately, software failures greatly reduce system availability. Prior work on surviving software failures suffers from one or more of the following limitations: Required application restructuring, inability to address deterministic software bugs, unsafe speculation on program execution, and long recovery time.This paper proposes an innovative safe technique, called Rx, which can quickly recover programs from many types of software bugs, both deterministic and non-deterministic. Our idea, inspired from allergy treatment in real life, is to rollback the program to a recent checkpoint upon a software failure, and then to re-execute the program in a modified environment. We base this idea on the observation that many bugs are correlated with the execution environment, and therefore can be avoided by removing the "allergen" from the environment. Rx requires few to no modifications to applications and provides programmers with additional feedback for bug diagnosis.We have implemented RX on Linux. Our experiments with four server applications that contain six bugs of various types show that RX can survive all the six software failures and provide transparent fast recovery within 0.017-0.16 seconds, 21-53 times faster than the whole program restart approach for all but one case (CVS). In contrast, the two tested alternatives, a whole program restart approach and a simple rollback and re-execution without environmental changes, cannot successfully recover the three servers (Squid, Apache, and CVS) that contain deterministic bugs, and have only a 40% recovery rate for the server (MySQL) that contains a non-deterministic concurrency bug. Additionally, RX's checkpointing system is lightweight, imposing small time and space overheads.
Gaining insights into support vector machine pattern classifiers using projection-based tour methods

This paper discusses visual methods that can be used to understand and interpret the results of classification using support vector machines (SVM) on data with continuous real-valued variables. SVM induction algorithms build pattern classifiers by identifying a maximal margin separating hyperplane from training examples in high dimensional pattern spaces or spaces induced by suitable nonlinear kernel transformations over pattern spaces. SVM have been demonstrated to be quite effective in a number of practical pattern classification tasks. Since the separating hyperplane is defined in terms of more than two variables it is necessary to use visual techniques that can navigate the viewer through high-dimensional spaces. We demonstrate the use of projection-based tour methods to gain useful insights into SVM classifiers with linear kernels on 8-dimensional data.
Zero-Rate Output and Quadrature Compensation in Vibratory MEMS Gyroscopes

In this paper, issues related to the zero-rate output (ZRO) of a vibratory microgyroscope are studied. Different sources of the ZRO are discussed and how the effect of each source can be minimized and their stability improved is described. The effects of synchronous demodulation and electrostatic quadrature compensation performed with a dc voltage on the final ZRO are analyzed. Ways to implement the control loop for electrostatic quadrature compensation performed with a dc voltage are described, concentrating on a case where the compensation voltage is generated with a digital-to-analog converter and the controller is digital. In particular, extending the resolution with SigmaDelta techniques is studied. The experimental work shows the feasibility of the implemented quadrature compensation loop and analyzes the ZRO sources of one particular gyroscope implementation. How to perform the ZRO measurements in such a way that the various sources can be distinguished from each other is also described.
Data Mining of Online Genealogy Datasets for Revealing Lifespan Patterns in Human Population

Online genealogy datasets contain extensive information about millions of people and their past and present family connections. This vast amount of data can help identify various patterns in the human population. In this study, we present methods and algorithms that can assist in identifying variations in lifespan distributions of the human population in the past centuries, in detecting social and genetic features that correlate with the human lifespan, and in constructing predictive models of human lifespan based on various features that can easily be extracted from genealogy datasets.
 We have evaluated the presented methods and algorithms on a large online genealogy dataset with over a million profiles and over 9 million connections, all of which were collected from the WikiTree website. Our findings indicate that significant but small positive correlations exist between the parents’ lifespan and their children’s lifespan. Additionally, we found slightly higher and significant correlations between the lifespans of spouses. We also discovered a very small positive and significant correlation between longevity and reproductive success in males, and a small and significant negative correlation between longevity and reproductive success in females. Moreover, our predictive models presented results with a Mean Absolute Error as low as 13.18 in predicting the lifespans of individuals who outlived the age of 10, and our classification models presented better than random classification results in predicting which people who outlive the age of 50 will also outlive the age of 80.
 We believe that this study will be the first of many studies to utilize the wealth of data on human populations, existing in online genealogy datasets, to better understand factors that influence the human lifespan. Understanding these factors can assist scientists in providing solutions for successful aging.
Literature Survey on Interplay of Topics, Information Diffusion and Connections on Social Networks

Researchers have attempted to model information diffusion and topic trends and lifecycle on online social networks. They have investigated the role of content, social connections and communities, familiarity and behavioral similarity in this context. The current article presents a survey of representative models that perform topic analysis, capture information diffusion, and explore the properties of social connections in the context of online social networks. The article concludes with a set of outlines of open problems and possible directions of future research interest. This article is intended for researchers to identify the current literature, and explore possibilities to improve the art.