nils-herrmann/clean_scirepeval huggingface dataset:
R2N2: Residual Recurrent Neural Networks for Multivariate Time Series Forecasting

Multivariate time-series modeling and forecasting is an important problem with numerous applications. Traditional approaches such as VAR (vector auto-regressive) models and more recent approaches such as RNNs (recurrent neural networks) are indispensable tools in modeling time-series data. In many multivariate time series modeling problems, there is usually a significant linear dependency component, for which VARs are suitable, and a nonlinear component, for which RNNs are suitable. Modeling such times series with only VAR or only RNNs can lead to poor predictive performance or complex models with large training times. In this work, we propose a hybrid model called R2N2 (Residual RNN), which first models the time series with a simple linear model (like VAR) and then models its residual errors using RNNs. R2N2s can be trained using existing algorithms for VARs and RNNs. Through an extensive empirical evaluation on two real world datasets (aviation and climate domains), we show that R2N2 is competitive, usually better than VAR or RNN, used alone. We also show that R2N2 is faster to train as compared to an RNN, while requiring less number of hidden units.
Guide to Vulnerability Analysis for Computer Networks and Systems

Vulnerability assessment is the essential and well-established process of probing security flaws, weaknesses and inadequacies in a computing infrastructure. The process helps organisations to eliminate security issues before attackers can exploit them formonetary gains or othermalicious purposes. The significant advancements in desktop, Web and mobile computing technologies have widened the range of security-related complications. It has become an increasingly crucial challenge for security analysts to devise comprehensive security evaluation and mitigation tools that can protect the business-critical operations. Researchers have proposed a variety of methods for vulnerability assessment, which can be broadly categorised into manual, assistive and fully automated.Manual vulnerability assessment is performed by a human expert, based on a specific set of instructions that are aimed at finding the security vulnerability. This method requires a large amount of time, effort and resources, and it is heavily reliant on expert knowledge, something that is widely attributed to being in short supply. The assistive vulnerability assessment is conducted with the help of scanning tools or frameworks that are usually up-to-date and look for the most relevant security weakness. However, the lack of flexibility, compatibility and regular maintenance of tools, as they contain static knowledge, renders them outdated and does not provide the beneficial information (in terms of depth and scope of tests) about the state of security. Fully automated vulnerability assessment leverages artificial intelligence techniques to produce expert-like decisionswithout human assistance and is by far considered as the most desirable (due to time and financial reduction for the end-user) method of evaluating a systems’ security. Although being highly desirable, such techniques require additional research in improving automated knowledge acquisition, representation and learning mechanisms. Further research is also needed to develop automated vulnerability mitigation techniques that are capable of actually securing the computing platform. The volume of research being S. Khan (B) · S. Parkinson Department of Computer Science, University of Huddersfield, Huddersfield, UK e-mail: saad.khan@hud.ac.uk S. Parkinson  2018 S. Parkinson et al. (eds.), Guide to Vulnerability Analysis for Computer Networks and Systems, Computer Communications and Networks, https://doi.org/10.1007/978-3-319-92624-7_1 3 4 S. Khan and S. Parkinson performed into the use of artificial intelligence techniques in vulnerability assessment is increasing, and there is a need to provide a survey into the state of the art.
Drone networks: Communications, coordination, and sensing

Small drones are being utilized in monitoring, transport, safety and disaster management, and other domains. Envisioning that drones form autonomous networks incorporated into the air traffic, we describe a high-level architecture for the design of a collaborative aerial system consisting of drones with on-board sensors and embedded processing, sensing, coordination, and networking capabilities. We implement a multi-drone system consisting of quadcopters and demonstrate its potential in disaster assistance, search and rescue, and aerial monitoring. Furthermore, we illustrate design challenges and present potential solutions based on the lessons learned so far.
Product Quantization for Nearest Neighbor Search

This paper introduces a product quantization-based approach for approximate nearest neighbor search. The idea is to decompose the space into a Cartesian product of low-dimensional subspaces and to quantize each subspace separately. A vector is represented by a short code composed of its subspace quantization indices. The euclidean distance between two vectors can be efficiently estimated from their codes. An asymmetric version increases precision, as it computes the approximate distance between a vector and a code. Experimental results show that our approach searches for nearest neighbors efficiently, in particular in combination with an inverted file system. Results for SIFT and GIST image descriptors show excellent search accuracy, outperforming three state-of-the-art approaches. The scalability of our approach is validated on a data set of two billion vectors.
Space-Time Representation of People Based on 3D Skeletal Data: A Review

Spatiotemporal human representation based on 3D visual perception data is a rapidly growing research area. Based on the information sources, these representations can be broadly categorized into two groups based on RGB-D information or 3D skeleton data. Recently, skeleton-based human representations have been intensively studied and kept attracting an increasing attention, due to their robustness to variations of viewpoint, human body scale and motion speed as well as the realtime, online performance. This paper presents a comprehensive survey of existing space-time representations of people based on 3D skeletal data, and provides an informative categorization and analysis of these methods from the perspectives, including information modality, representation encoding, structure and transition, and feature engineering. We also provide a brief overview of skeleton acquisition devices and construction methods, enlist a number of public benchmark datasets with skeleton data, and discuss potential future research
Sustainable Balanced Scorecard Model for Chilean Wineries

The purpose of this article is to propose a Sustainable Balanced Scorecard model for Chilean wineries (SBSC). This system, which is based on the Balanced Scorecard (BSC), one of the most widespread management systems nowadays in the world, Rigby, and Bilodeau (2011), will allow the wine companies to manage the business in two dimensions: sustainability, which will measure how sustainable is the business and the temporal dimension, linking the measurement of strategic performance with the day to day. To achieve the target previously raised, a research on sustainability will be developed, along with strategic performance measurement systems and a diagnosis of the Chilean wine industry, based on in-depth interviews to 42 companies in the central zone of Chile. On the basis of the assessment of the wine industry carried out, it is concluded that the bases for a future design and implementation of the SBSC system are in place since it was found that 83% of the vineyards have a strategic plan formally in place, which corresponds to the input of the proposed system.
A user browsing model to predict search engine click data from past observations

Search engine click logs provide an invaluable source of relevance information but this information is biased because we ignore which documents from the result list the users have actually seen before and after they clicked. Otherwise, we could estimate document relevance by simple counting. In this paper, we propose a set of assumptions on user browsing behavior that allows the estimation of the probability that a document is seen, thereby providing an unbiased estimate of document relevance. To train, test and compare our model to the best alternatives described in the Literature, we gather a large set of real data and proceed to an extensive cross-validation experiment. Our solution outperforms very significantly all previous models. As a side effect, we gain insight into the browsing behavior of users and we can compare it to the conclusions of an eye-tracking experiments by Joachims et al. . In particular, our findings confirm that a user almost always see the document directly after a clicked document. They also explain why documents situated just after a very relevant document are clicked more often.
Circuit ORAM: On Tightness of the Goldreich-Ostrovsky Lower Bound

We propose a new tree-based ORAM scheme called Circuit ORAM. Circuit ORAM makes both theoretical and practical contributions. From a theoretical perspective, Circuit ORAM shows that the well-known Goldreich-Ostrovsky logarithmic ORAM lower bound is tight under certain parameter ranges, for several performance metrics. Therefore, we are the first to give an answer to a theoretical challenge that remained open for the past twenty-seven years. Second, Circuit ORAM earns its name because it achieves (almost) optimal circuit size both in theory and in practice for realistic choices of block sizes. We demonstrate compelling practical performance and show that Circuit ORAM is an ideal candidate for secure multi-party computation applications.
Efficient cryptographic techniques for securing storage systems

The growth of outsourced storage in the form of storage service providers underlines the importance of developing efficient security mechanisms to protect the data stored in a networked storage system. For securing the data stored remotely, we consider an architecture in which clients have access to a small amount of trusted storage, which could either be local to each client or, alternatively, could be provided by a client’s organization through a dedicated server. In this thesis, we propose new approaches for various mechanisms that are currently employed in implementations of secure networked storage systems. In designing the new algorithms for securing storage systems, we set three main goals. First, security should be added by clients transparently for the storage servers so that the storage interface does not change; second, the amount of trusted storage used by clients should be minimized; and, third, the performance overhead of the security algorithms should not be prohibitive. The first contribution of this dissertation is the construction of novel space-efficient integrity algorithms for both block-level storage systems and cryptographic file systems. These constructions are based on the observation that block contents typically written to disks feature low entropy, and as such are efficiently distinguishable from uniformly random blocks. We provide a rigorous analysis of security of the new integrity algorithms and demonstrate that they maintain the same security properties as existing algorithms (e.g., Merkle tree). We implement the new algorithms for integrity checking of files in the EncFS cryptographic file system and measure their performance cost, as well as the amount of storage needed for integrity and the integrity bandwidth (i.e., the amount of information needed to update or check the integrity of a file block) used. We evaluate the block-level integrity algorithms using a disk trace we collected, and the integrity algorithms for file systems using NFS traces collected at Harvard university. We also construct efficient key management schemes for cryptographic file systems in which the re-encryption of a file following a user revocation is delayed until the next write to that file, a model called lazy revocation. The encryption key evolves at each revocation and we devise an efficient algorithm to recover previous encryption keys with only logarithmic cost in the number of revocations supported. The novel key management scheme is based on a binary tree to derive the keys and improves existing techniques by several orders of magnitude, as shown by our experiments. Our final contribution is to analyze theoretically the consistency of encrypted shared file objects used to implement cryptographic file systems. We provide sufficient conditions for the realization of a given level of consistency, when concurrent writes to both the file and encryption key objects are possible. We show that the consistency of both the key
Solros: a data-centric operating system architecture for heterogeneous computing

We propose Solros---a new operating system architecture for heterogeneous systems that comprises fast host processors, slow but massively parallel co-processors, and fast I/O devices. A general consensus to fully drive such a hardware system is to have a tight integration among processors and I/O devices. Thus, in the Solros architecture, a co-processor OS (data-plane OS) delegates its services, specifically I/O stacks, to the host OS (control-plane OS). Our observation for such a design is that global coordination with system-wide knowledge (e.g., PCIe topology, a load of each co-processor) and the best use of heterogeneous processors is critical to achieving high performance. Hence, we fully harness these specialized processors by delegating complex I/O stacks on fast host processors, which leads to an efficient global coordination at the level of the control-plane OS.
 We developed Solros with Xeon Phi co-processors and implemented three core OS services: transport, file system, and network services. Our experimental results show significant performance improvement compared with the stock Xeon Phi running the Linux kernel. For example, Solros improves the throughput of file system and network operations by 19x and 7x, respectively. Moreover, it improves the performance of two realistic applications: 19x for text indexing and 2x for image search.
Text mining: Generating hypotheses from MEDLINE

Hypothesis generation, a crucial initial step for making scientific discoveries, relies on prior knowledge, experience and intuition. Chance connections made between seemingly distinct subareas sometimes turn out to be fruitful. The goal in text mining is to assist in this process by automatically discovering a small set of interesting hypotheses from a suitable text collection. In this paper we present open and closed text mining algorithms that are built within the discovery framework established by Swanson and Smalheiser. Our algorithms represent topics using metadata profiles. When applied to MEDLINE these are MeSH based profiles. We present experiments that demonstrate the effectiveness of our algorithms. Specifically, our algorithms generate ranked term lists where the key terms representing novel relationships between topics are ranked high.
International Financial Liberalization and Economic Growth

This paper pulls together existing theory and evidence to assess whether international financial liberalization, by improving the functioning of domestic financial markets and banks, accelerates economic growth. The analysis suggests that the answer is yes. First, liberalizing restrictions on international portfolio flows tends to enhance stock market liquidity. In turn, enhanced stock market liquidity accelerates economic growth primarily by boosting productivity growth. Second, allowing greater foreign bank presence tends to enhance the efficiency of the domestic banking system. In turn, better-developed banks spur economic growth primarily by accelerating productivity growth. Thus, international financial integration can promote economic development by encouraging improvements in the domestic financial system.  Finance Department, Carlson School of Management, University of Minnesota, 321 19 Avenue South, Minneapolis, MN 55455. Tel: 612-624-9551, Fax: 612-626-1335,  rlevine@csom.umn.edu. I thank, without implicating, Maria Carkovic and two anonymous referees for very helpful comments.  0  2 Date: September 5, 2000  Ross Levine, Finance Department, Carlson School of Management, University of Minnesota, 321 19 Avenue South, Minneapolis, MN 55455. Tel: 612-624-9551, Fax: 612-626-1335,
An artificial immune system architecture for computer security applications

With increased global interconnectivity, reliance on e-commerce, network services, and Internet communication, computer security has become a necessity. Organizations must protect their systems from intrusion and computer-virus attacks. Such protection must detect anomalous patterns by exploiting known signatures while monitoring normal computer programs and network usage for abnormalities. Current antivirus and network intrusion detection (ID) solutions can become overwhelmed by the burden of capturing and classifying new viral stains and intrusion patterns. To overcome this problem, a self-adaptive distributed agent-based defense immune system based on biological strategies is developed within a hierarchical layered architecture. A prototype interactive system is designed, implemented in Java, and tested. The results validate the use of a distributed-agent biological-system approach toward the computer-security problems of virus elimination and ID.
Green leaf area index estimation in maize and soybean: Combining vegetation indices to achieve maximal sensitivity

(2012) Posted online 29 June 2012 doi:10.2134/agronj2012.0065  No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. T leaf area index (LAI), the ratio of leaf area to ground area, typically reported as square meters per square meter, is a commonly used biophysical characteristic of vegetation (Watson, 1947). The LAI can be subdivided into photosynthetically active and photosynthetically inactive components. The former, the gLAI, is a metric commonly used in climate (e.g., Buermann et al., 2001), ecological (e.g., Bulcock and Jewitt, 2010), and crop yield (e.g., Fang et al., 2011) models. Because of its wide use and applicability to modeling, there is a need for a nondestructive remote estimation of gLAI across large geographic areas. Various techniques based on remotely sensed data have been utilized for assessing gLAI (see reviews by Pinter et al., 2003; Hatfield et al., 2004, 2008; Doraiswamy et al., 2003; le Maire et al., 2008, and references therein). Vegetation indices, particularly the NDVI (Rouse et al., 1974) and SR (Jordan, 1969), are the most widely used. The NDVI, however, is prone to saturation at moderate to high gLAI values (Kanemasu, 1974; Curran and Steven, 1983; Asrar et al., 1984; Huete et al., 2002; Gitelson, 2004; Wu et al., 2007; González-Sanpedro et al., 2008) and requires reparameterization for different crops and species. The saturation of NDVI has been attributed to insensitivity of reflectance in the red region at moderate to high gLAI values due to the high absorption coefficient of chlorophyll. For gLAI below 3 m2/m2, total absorption by a canopy in the red range reaches 90 to 95%, and further increases in gLAI do not bring additional changes in absorption and reflectance (Hatfield et al., 2008; Gitelson, 2011). Another reason for the decrease in the sensitivity of NDVI to moderate to high gLAI values is the mathematical formulation of that index. At moderate to high gLAI, the NDVI is dominated by nearinfrared (NIR) reflectance. Because scattering by the cellular or leaf structure causes the NIR reflectance to be high and the absorption by chlorophyll causes the red reflectance to be low, NIR reflectance is considerably greater than red reflectance: e.g., for gLAI >3 m2/m2, NIR reflectance is >40% while red reflectance is <5%. Thus, NDVI becomes insensitive to changes in both red and NIR reflectance. Other commonly used VIs include the Enhanced Vegetation Index, EVI (Liu and Huete, 1995; Huete et al., 1997, 2002), its ABStrAct
Binary Tree Slotted ALOHA for Passive RFID Tag Anticollision

In order to enhance the efficiency of radio frequency identification (RFID) and lower system computational complexity, this paper proposes three novel tag anticollision protocols for passive RFID systems. The three proposed protocols are based on a binary tree slotted ALOHA (BTSA) algorithm. In BTSA, tags are randomly assigned to slots of a frame and if some tags collide in a slot, the collided tags in the slot will be resolved by binary tree splitting while the other tags in the subsequent slots will wait. The three protocols utilize a dynamic, an adaptive, and a splitting method to adjust the frame length to a value close to the number of tags, respectively. For BTSA, the identification efficiency can achieve an optimal value only when the frame length is close to the number of tags. Therefore, the proposed protocols efficiency is close to the optimal value. The advantages of the protocols are that, they do not need the estimation of the number of tags, and their efficiency is not affected by the variance of the number of tags. Computer simulation results show that splitting BTSA's efficiency can achieve 0.425, and the other two protocols efficiencies are about 0.40. Also, the results show that the protocols efficiency curves are nearly horizontal when the number of tags increases from 20 to 4,000.
Hyperbolically Discounted Temporal Difference Learning

Hyperbolic discounting of future outcomes is widely observed to underlie choice behavior in animals. Additionally, recent studies (Kobayashi & Schultz, 2008) have reported that hyperbolic discounting is observed even in neural systems underlying choice. However, the most prevalent models of temporal discounting, such as temporal difference learning, assume that future outcomes are discounted exponentially. Exponential discounting has been preferred largely because it can be expressed recursively, whereas hyperbolic discounting has heretofore been thought not to have a recursive definition. In this letter, we define a learning algorithm, hyperbolically discounted temporal difference (HDTD) learning, which constitutes a recursive formulation of the hyperbolic model.
Emotion, gender, and gender typical identity in autobiographical memory.

Gender differences in the emotional intensity and content of autobiographical memory (AM) are inconsistent across studies, and may be influenced as much by gender identity as by categorical gender. To explore this question, data were collected from 196 participants (age 18-40), split evenly between men and women. Participants narrated four memories, a neutral event, high point event, low point event, and self-defining memory, completed ratings of emotional intensity for each event, and completed four measures of gender typical identity. For self-reported emotional intensity, gender differences in AM were mediated by identification with stereotypical feminine gender norms. For narrative use of affect terms, both gender and gender typical identity predicted affective expression. The results confirm contextual models of gender identity (e.g., Diamond, 2012 . The desire disorder in research on sexual orientation in women: Contributions of dynamical systems theory. Archives of Sexual Behavior, 41, 73-83) and underscore the dynamic interplay between gender and gender identity in the emotional expression of autobiographical memories.