nils-herrmann/clean_scirepeval huggingface dataset:
A Game Theoretic Approach to Strategy Generation for Moving Target Defense in Web Applications

The present complexity in designing web applications makes software security a difficult goal to achieve. An attacker can explore a deployed service on the web and attack at his/her own leisure. Moving Target Defense (MTD) in web applications is an effective mechanism to nullify this advantage of their reconnaissance but the framework demands a good switching strategy when switching between multiple configurations for its web-stack. To address this issue, we propose the modeling of a real world MTD web application as a repeated Bayesian game. We formulate an optimization problem that generates an effective switching strategy while considering the cost of switching between different web-stack configurations. To use this model for a developed MTD system, we develop an automated system for generating attack sets of Common Vulnerabilities and Exposures (CVEs) for input attacker types with predefined capabilities. Our framework obtains realistic reward values for the players (defenders and attackers) in this game by using security domain expertise on CVEs obtained from the National Vulnerability Database (NVD). We also address the issue of prioritizing vulnerabilities that when fixed, improves the security of the MTD system. Lastly, we demonstrate the robustness of our proposed model by evaluating its performance when there is uncertainty about input attacker information.
Reduced Order Modeling of Triple Link Inverted Pendulum Using Particle Swarm Optimization Algorithm [

In recent years, Particle Swarm Optimization has evolved as an effective global optimization algorithm whose dynamics has been inspired from swarming or collaborative behavior of biological populations. In this paper, PSO has been applied to Triple Link Inverted Pendulum model to find its reduced order model by minimization of error between the step responses of higher and reduced order model. Model Order Reduction using PSO algorithm is advantageous due to ease in implementation, higher accuracy and decreased time of computation. The second and third order reduced transfer functions of Triple Link Inverted Pendulum have been computed for comparison. Particle Swarm Optimization,  Pole Placement technique.
Quasicyclic low-density parity-check codes from circulant permutation matrices

In this correspondence, the construction of low-density parity-check (LDPC) codes from circulant permutation matrices is investigated. It is shown that such codes cannot have a Tanner graph representation with girth larger than 12, and a relatively mild necessary and sufficient condition for the code to have a girth of 6, 8,10, or 12 is derived. These results suggest that families of LDPC codes with such girth values are relatively easy to obtain and, consequently, additional parameters such as the minimum distance or the number of redundant check sums should be considered. To this end, a necessary condition for the codes investigated to reach their maximum possible minimum Hamming distance is proposed.
Spelling Correction as an Iterative Process that Exploits the Collective Knowledge of Web Users

Logs of user queries to an internet search engine p rovide a large amount of implicit and explicit inform ation about language. In this paper, we investigate their use in spelling correction of search queries, a task which poses many additional challenges beyond the traditional spelling correction problem. We pre sent an approach that uses an iterative transformat ion of the input query strings into other strings that correspond to more and more likely queries according to statistics extracted from internet search query log s.
Pose Embeddings: A Deep Architecture for Learning to Match Human Poses

We present a method for learning an embedding that places images of humans in similar poses nearby. This embedding can be used as a direct method of comparing images based on human pose, avoiding potential challenges of estimating body joint positions. Pose embedding learning is formulated under a triplet-based distance criterion. A deep architecture is used to allow learning of a representation capable of making distinctions between different poses. Experiments on human pose matching and retrieval from video data demonstrate the potential of the method.
CMEE-IL: Code Mix Entity Extraction in Indian Languages from Social Media Text @ FIRE 2016 - An Overview

The penetration of smart devices such as mobile phones, tabs has significantly changed the way people communicate. This has led to the growth of usage of social media tools such as twitter, facebook chats for communication. This has led to development of new challenges and perspectives in the language technologies research. Automatic processing of such texts requires us to develop new methodologies. Thus there is great need to develop various automatic systems such as information extraction, retrieval and summarization. Entity recognition is a very important sub task of Information extraction and finds its applications in information retrieval, machine translation and other higher Natural Language Processing (NLP) applications such as co-reference resolution. Some of the main issues in handling of such social media texts are i) Spelling errors ii) Abbreviated new language vocabulary such as “gr8” for great iii) use of symbols such as emoticons/emojis iv) use of meta tags and hash tags v) Code mixing. Entity recognition and extraction has gained increased attention in Indian research community. However there is no benchmark data available where all these systems could be compared on same data for respective languages in this new generation user generated text. Towards this we have organized the Code Mix Entity Extraction in social media text track for Indian languages (CMEE-IL) in the Forum for Information Retrieval Evaluation (FIRE). We present the overview of CMEE-IL 2016 track. This paper describes the corpus created for Hindi-English and Tamil-English. Here we also present overview of the approaches used by the participants. CCS Concepts • Computing methodologies ~ Artificial intelligence • Computing methodologies ~ Natural language processing • Information systems ~ Information extraction
Beyond Lambert: reconstructing specular surfaces using color

We present a photometric stereo method for non-diffuse materials that does not require an explicit reflectance model or reference object. By computing a data-dependent rotation of RGB color space, we show that the specular reflection effects can be separated from the much simpler, diffuse (approximately Lambertian) reflection effects for surfaces that can be modeled with dichromatic reflectance. Images in this transformed color space are used to obtain photometric reconstructions that are independent of the specular reflectance. In contrast to other methods for highlight removal based on dichromatic color separation (e.g., color histogram analysis and/or polarization), we do not explicitly recover the specular and diffuse components of an image. Instead, we simply find a transformation of color space that yields more direct access to shape information. The method is purely local and is able to handle surfaces with arbitrary texture.
LDA-based keyword selection in text categorization

Text categorization is the task of automatically assigning unlabeled text documents to some predefined category labels by means of an induction algorithm. Since the data in text categorization are high-dimensional, feature selection is broadly used in text categorization systems for reducing the dimensionality. In the literature, there are some widely known metrics such as information gain and document frequency thresholding. Recently, a generative graphical model called latent dirichlet allocation (LDA) that can be used to model and discover the underlying topic structures of textual data, was proposed. In this paper, we use the hidden topic analysis of LDA for feature selection and compare it with the classical feature selection metrics in text categorization. For the experiments, we use SVM as the classifier and tf∗idf weighting for weighting the terms. We observed that almost in all metrics, information gain performs best at all keyword numbers while the LDA-based metrics perform similar to chi-square and document frequency thresholding.
A Unified Framework for Multi-view Multi-class Object Pose Estimation

One core challenge in object pose estimation is to ensure accurate and robust performance for large numbers of diverse foreground objects amidst complex background clutter. In this work, we present a scalable framework for accurately inferring six Degree-of-Freedom (6-DoF) pose for a large number of object classes from single or multiple views. To learn discriminative pose features, we integrate three new capabilities into a deep Convolutional Neural Network (CNN): an inference scheme that combines both classification and pose regression based on a uniform tessellation of the Special Euclidean group in three dimensions (SE(3)), the fusion of class priors into the training process via a tiled class map, and an additional regularization using deep supervision with an object mask. Further, an efficient multi-view framework is formulated to address single-view ambiguity. We show that this framework consistently improves the performance of the single-view network. We evaluate our method on three large-scale benchmarks: YCB-Video, JHUScene-50 and ObjectNet-3D. Our approach achieves competitive or superior performance over the current state-of-the-art methods.
Failure and reliability analysis of STT-MRAM

Spin Transfer Torque Magnetic RAM (STT-MRAM) promises low power, great miniaturization prospective (e.g. 22 nm) and easy integration with CMOS process. It becomes actually a strong non-volatile memory candidate for both embedded and standalone applications. However STT-MRAM suffers from important failure and reliability issues compared with the conventional solutions based on magnetic field switching. For example, a read current could write erroneously the stored data, the variability of ultra-thin oxide barrier drives high resistance variation and the injected current in the nanopillar induces lower lifetime etc. This paper classifies firstly all the possible failures of STT-MRAM into ‘‘soft errors’’ and ‘‘hard errors’’, and analyzes their impact on the memory reliability. Based on this work, we can find some efficient design solutions to address respectively these two types of errors and improve the reliability of STTMRAM.
Instagram: Motives for its use and relationship to narcissism and contextual age

Instagram is the fastest growing social network site globally. This study investigates motives for its use, and its relationship to contextual age and narcissism. A survey of 239 college students revealed that the main reasons for Instagram use are “Surveillance/Knowledge about others,” “Documentation,” “Coolness,” and “Creativity.” The next significant finding was a positive relationship between those who scored high in interpersonal interaction and using Instagram for coolness, creative purposes, and surveillance. Another interesting finding shows that there is a positive relationship between high levels of social activity (traveling, going to sporting events, visiting friends, etc.) and being motivated to use Instagram as a means of documentation. In reference to narcissism, there was a positive relationship between using Instagram to be cool and for surveillance. Theoretical contributions of this study relate to our understanding of uses and gratifications theory. This study uncovers new motives for social media use not identified in previous literature.
Mathematical problems in image processing - partial differential equations and the calculus of variations, 2nd Edition

It's coming again, the new collection that this site has. To complete your curiosity, we offer the favorite mathematical problems in image processing partial differential equations and the calculus of variations book as the choice today. This is a book that will show you even new to old thing. Forget it; it will be right for you. Well, when you are really dying of mathematical problems in image processing partial differential equations and the calculus of variations, just pick it. You know, this book is always making the fans to be dizzy if not to find.
Initial trust, perceived risk, and the adoption of internet banking

Studies on the adoption of business-to-consumer e-commerce have not simultaneously considered trust and risk as important determinants of adoption behavior. Further, trust in information technology has not been addressed to a great extent in the context of e-commerce. This research explicitly encompasses the electronic channel and the firm as objects to be trusted in e-commerce. Our conceptual model leads us to believe that trust in the electronic channel and perceived risks of e-commerce are the major determinants of the adoption behavior. Based on the social network theory and the trust theory, determinants of trust in the electronic channel are included in the research model. This research is expected to provide both theoretical explanations and empirical validation on the adoption of e-commerce. We will also be able to offer specific recommendations on marketing strategies for practitioners, regarding the adoption of Internet banking.
Electronically Tunable Filters

In this article, a brief introduction to the development of tunable filters was given. A classical design technique based on a combline filter approach was shown, where minimum degradation in passband performance could be obtained across a broad-tuning range. The fundamental disadvantages associated with the conventional resonator tuning approaches were also discussed, recognizing the importance of developing new techniques for realizing tunable microwave filters. It was shown that there is a possibility in realizing an electronically reconfigurable microwave filter based on parallel- coupled switched-delay lines, which possesses the important property of maintaining constant absolute bandwidth over almost an octave of tuning bandwidth. Furthermore, the filter has the ability to incorporate active switching elements in the filter circuit, without sacrificing its loss and linearity performance. With the exceptional linearity performance and power handling capability, the filter is readily adapted to poor environments. Although the use of p-i-n diodes as switching elements would result in large dc consumption, the approach could also be readily adapted for use with any switches, such as pHEMT or RF MEMS switches, to achieve extremely low power consumption. The integration of switchable couplings to enable both bandwidth and center frequency to be reconfigurable would be an enhancement.
Inverse reinforcement learning with leveraged Gaussian processes

In this paper, we propose a novel inverse reinforcement learning algorithm with leveraged Gaussian processes that can learn from both positive and negative demonstrations. While most existing inverse reinforcement learning (IRL) methods suffer from the lack of information near low reward regions, the proposed method alleviates this issue by incorporating (negative) demonstrations of what not to do. To mathematically formulate negative demonstrations, we introduce a novel generative model which can generate both positive and negative demonstrations using a parameter, called proficiency. Moreover, since we represent a reward function using a leveraged Gaussian process which can model a nonlinear function, the proposed method can effectively estimate the structure of a nonlinear reward function.
AdaMS: Adaptive MLC/SLC phase-change memory design for file storage

Phase-change memory (PCM) is an emerging memory technology that has made rapid progress in the recent years, and surpasses other technologies such as FeRAM and MRAM in terms of scalability. Recently, the feasibility of multi-level cell (MLC) for PCM, which enables a cell to store more than one bit of digital data, has also been shown. This new property makes PCM more competitive and considered as the successor of the NAND flash technology, which also has the MLC capability but does not have an easy scaling path to reach higher densities. However, the MLC capability of PCM comes with the penalty of longer programming time and shortened cell lifetime compared to its single-level cell (SLC) mode. Therefore, it suggests an adaptive MLC/SLC reconfigurable PCM design that can exploit the fast SLC access speed and the large MLC capacity with the awareness of workload characteristics and lifetime requirements. In this work, a circuit-level adaptive MLC/SLC PCM array is designed at first, the management policy of MLC/SLC mode is proposed, and finally the performance and lifetime of a novel PCM-based SSD with run-time MLC/SLC reconfiguration ability is evaluated.
PageRank Beyond the Web

Google’s PageRank method was developed to evaluate the importance of web-pages via their link structure. The mathematics of PageRank, however, are entirely general and apply to any graph or network in any domain. Thus, PageRank is now regularly used in bibliometrics, social and information network analysis, and for link prediction and recommendation. It’s even used for systems analysis of road networks, as well as biology, chemistry, neuroscience, and physics. We’ll see the mathematics and ideas that unite these diverse applications.
A systematic literature review on Health Recommender Systems

Health Information Systems are becoming an important platform for healthcare services. In this context, Health Recommender Systems (HRS) are presented as complementary tools in decision making processes in healthcare services. Health Recommender Systems increase usability of technologies and reduce information overload in processes. In this paper, a literature review was conducted by following a review procedure. Major approaches in HRS were outlined and findings were discussed. The paper presented current developments in the market, challenges and opportunities regarding to HRS and emerging approaches. It is believed that this study is an illuminating start-up point for HRS literature review.
Estimating sentiment orientation in social media for intelligence monitoring and analysis

This paper presents a computational approach to inferring the sentiment orientation of “social media” content (e.g., blog posts) which focuses on the challenges associated with Web-based analysis. The proposed methodology formulates the task as one of text classification, models the data as a bipartite graph of documents and words, and uses this framework to develop a semi-supervised sentiment classifier that is well-suited for social media domains. In particular, the proposed algorithm is capable of combining prior knowledge regarding the sentiment orientation of a few documents and words with information present in unlabeled data, which is abundant online. We demonstrate the utility of the approach by showing it outperforms several standard methods for the task of inferring the sentiment of online movie reviews, and illustrate its potential for security informatics through a case study involving the estimation of Indonesian public sentiment regarding the July 2009 Jakarta hotel bombings.
DepSpace: a byzantine fault-tolerant coordination service

The tuple space coordination model is one of the most interesting coordination models for open distributed systems due to its space and time decoupling and its synchronization power. Several works have tried to improve the dependability of tuple spaces through the use of replication for fault tolerance and access control for security. However, many practical applications in the Internet require both fault tolerance and security. This paper describes the design and implementation of DepSpace, a Byzantine fault-tolerant coordination service that provides a tuple space abstraction. The service offered by DepSpace is secure, reliable and available as long as less than a third of service replicas are faulty. Moreover, the content-addressable confidentiality scheme developed for DepSpace bridges the gap between Byzantine fault-tolerant replication and confidentiality of replicated data and can be used in other systems that store critical data.
Prefrontal cortical contributions to working memory: evidence from event-related fMRI studies

Working memory refers to the short-term retention of information that is no longer accessible in the environment, and the manipulation of this information, for subsequent use in guiding behavior. In this review, we will present data from a series of event-related functional magnetic-resonance-imaging (fMRI) studies of delayed-response tasks that were designed to investigate the role of different regions of the prefrontal cortex (PFC) during different working-memory component processes. From these data, we conclude that: (1) lateral PFC is anatomically organized according to the types of cognitive operations that one performs when attempting to temporarily maintain and manipulate information; and (2) consistent with the picture that has emerged from the monkey electrophysiological literature, human lateral PFC is involved in several encoding- and response-related processes as well as mnemonic and nonmnemonic processes that are engaged during the temporary maintenance of information. Thus, lateral PFC activity cannot be ascribed to the function of a single, unitary cognitive operation.
Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning

The literature on short-term traffic flow forecasting has undergone great development recently. Many works, describing a wide variety of different approaches, which very often share similar features and ideas, have been published. However, publications presenting new prediction algorithms usually employ different settings, data sets, and performance measurements, making it difficult to infer a clear picture of the advantages and limitations of each model. The aim of this paper is twofold. First, we review existing approaches to short-term traffic flow forecasting methods under the common view of probabilistic graphical models, presenting an extensive experimental comparison, which proposes a common baseline for their performance analysis and provides the infrastructure to operate on a publicly available data set. Second, we present two new support vector regression models, which are specifically devised to benefit from typical traffic flow seasonality and are shown to represent an interesting compromise between prediction accuracy and computational efficiency. The SARIMA model coupled with a Kalman filter is the most accurate model; however, the proposed seasonal support vector regressor turns out to be highly competitive when performing forecasts during the most congested periods.
A Logic for Default Reasoning

The need to make default assumptions is frequently encountered in reasoning'about incompletely specified worlds. Inferences sanctioned by default are best viewed as beliefs which may well be modified or rejected by subsequent observations. It is this property which leads to the non.monotonJcity of any logic of defaults. In this paper we propose a logic for default reasoning. We then specialize our treatment to a very large class of commonly occurring defaults. For this class we develop a complete proof theory and show how to interface it with a top down resolution theorem prover. Finally, we provide criteria under which the revision of derived beliefs must be effected. The gods did not reveal, from the beginning, All things to us, but in the course of time Through seeking we may learn and know things better. But as for certain truth, no man has known it, Nor shall he know it, neither of the gods Nor yet of all the things of which I speak. For even if by chance he were to utter The final truth, he would himself not know it: For all is but a woven web of guesses.