nils-herrmann/clean_scirepeval huggingface dataset:
Compact routing messages in self healing trees
Venue: ICDCN

Existing compact routing schemes, e.g. Thorup and Zwick [SPAA 2001] and Chechik [PODC 2013] often have no means to tolerate failures, once the system has been setup and started. This paper presents, to our knowledge, the first self healing compact routing scheme. Besides, our schemes are developed for low memory nodes, i.e. nodes need only O(log2 n) memory, and are thus, compact schemes. We introduce two algorithms of independent interest: The first is CompactFT, a novel compact version (using only O(log n) local memory) of the self healing algorithm Forgiving Tree of Hayes et al. [PODC 2008] The second algorithm (CompactFTZ) combines CompactFT with Thorup Zwick's tree based compact routing scheme [SPAA 2001] to produce a fully compact self healing routing scheme. In the self healing model, the adversary deletes nodes one at a time with the affected nodes self healing locally by adding few edges. CompactFT recovers from each attack in only O(1) time and D messages, with only +3 degree increase and O(logD) graph diameter increase, over any sequence of deletions (D is the initial maximum degree) Additionally, CompactFTZ guarantees delivery of a packet sent from sender s as long as the receiver t has not been deleted, with only an additional O(y logD) latency, where y is the number of nodes that have been deleted on the path between s and t. If t has been deleted, s gets informed and the packet removed from the network.
Quota Restrictions on Land Use for Decelerating Urban Sprawl of Mega City: A Case Study of Shanghai, China

The mega cities of China are faced with great pressure from conflict between huge land demand for construction use and limited resources. To improve this situation, it is necessary to slow the increasing rate of built up land and even reduce the existing built up land by improving land use efficiency. Herein, land use planning with urban sprawl deceleration is proposed. Land quotas are important tools for the control of land use planning in China, but the current quotas are mainly in the form of increment, thus cannot support land use planning with urban sprawl deceleration. To address these issues, we developed a model named "current plan requirement response (CPRR)" to optimize the land quotas. With this model, we investigated the current status of land use, the process of land use planning, and the requirements of land use planning with urban sprawl deceleration in Shanghai. It was found that the current land quotas have provided somewhat inhabited conversion of arable land to built up land, but scarcely any regulation of land use efficiency or protection of natural environment. Then, the land quotas were optimized and quota management recommendations were made. The optimized land quotas included four aspects of quotas: arable land protection, ecological conservation, scale constraint on built up land and economical and intensive land use. These new land quotas could be used to guide reduction of built up land and increase ecological land in China's mega cities. It might also be beneficial for easing conflict between supply and demand of built up land, for building an eco friendly city land use structure.
Improving Opinion Aspect Extraction Using Semantic Similarity and Aspect Associations
Venue: AAAI

Aspect extraction is a key task of fine grained opinion mining. Although it has been studied by many researchers, it remains to be highly challenging. This paper proposes a novel unsupervised approach to make a major improvement. The approach is based on the framework of lifelong learning and is implemented with two forms of recommendations that are based on semantic similarity and aspect associations respectively. Experimental results using eight review datasets show the effectiveness of the proposed approach.
Mining the Opinionated Web: Classification and Detection of Aspect Contexts for Aspect Based Sentiment Analysis
Venue: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)

Aspect Based Sentiment Analysis (ABSA) provides further insight into the analysis of social media. Understanding user opinion about different aspects of products, services or policies can be used for improving and innovating in an effective way. Thus, it is becoming an increasingly important task in the Natural Language Processing (NLP) realm. The standard pipeline of aspect based sentiment analysis consists of three phases: aspect category detection, Opinion Target Extraction (OTE) and sentiment polarity classification. In this article, we propose an alternative pipeline: OTE, aspect classification, aspect context detection and sentiment classification. As it can be observed, the opinionated words are first detected and then are classified into aspects. In addition, the opinionated fragment of every aspect is delimited before performing the sentiment analysis. This paper is focused on the aspect classification and aspect context detection phases and proposes a twofold contribution. First, we propose a hybrid model consisting of a word embeddings model used in conjunction with semantic similarity measures in order to develop an aspect classifier module. Second, we extend the context detection algorithm by Mukherjee et al. to improve its performance. The system has been evaluated using the SemEval2016 datasets. The evaluation shows through several experiments that the use of hybrid techniques that aggregate different sources of information improves the classification performance.
Transparency Overlays and Applications
Venue: IACR Cryptol. ePrint Arch.

In this paper, we initiate a formal study of transparency, which in recent years has become an increasingly critical requirement for the systems in which people place trust. We present the abstract concept of a transparency overlay, which can be used in conjunction with any system to give it provable transparency guarantees, and then apply the overlay to two settings: Certificate Transparency and Bitcoin. In the latter setting, we show that the usage of our transparency overlay eliminates the need to engage in mining and allows users to store a single small value rather than the entire blockchain. Our transparency overlay is generically constructed from a signature scheme and a new primitive we call a dynamic list commitment, which in practice can be instantiated using a collision resistant hash function.
Investigation of market efficiency and Financial Stability between S&P 500 and London Stock Exchange: Monthly and yearly Forecasting of Time Series Stock Returns using ARMA model

We investigated the presence and changes in, long memory features in the returns and volatility dynamics of S&P 500 and London Stock Exchange using ARMA model. Recently, multifractal analysis has been evolved as an important way to explain the complexity of financial markets which can hardly be described by linear methods of efficient market theory. In financial markets, the weak form of the efficient market hypothesis implies that price returns are serially uncorrelated sequences. In other words, prices should follow a random walk behavior. The random walk hypothesis is evaluated against alternatives accommodating either unifractality or multifractality. Several studies find that the return volatility of stocks tends to exhibit long range dependence, heavy tails, and clustering. Because stochastic processes with self similarity possess long range dependence and heavy tails, it has been suggested that self similar processes be employed to capture these characteristics in return volatility modeling. The present study applies monthly and yearly forecasting of Time Series Stock Returns in S&P 500 and London Stock Exchange using ARMA model. The statistical analysis of S&P 500 shows that the ARMA model for S&P 500 outperforms the London stock exchange and it is capable for predicting medium or long horizons using real known values. The statistical analysis in London Stock Exchange shows that the ARMA model for monthly stock returns outperforms the yearly. A comparison between S&P 500 and London Stock Exchange shows that both markets are efficient and have Financial Stability during periods of boom and bust.
Financial time series forecasting using rough sets with time weighted rule voting
Venue: Expert Syst. Appl.

We propose a trend prediction model for financial assets with a rough sets approach.For the rough set model an adaptive time weighted rule voting method is proposed.We do experiments with real life stock data, the results are compared with SVM.We show that our model outperforms other models, including baseline buy and hold.Time weighted rule voting stabilizes classification accuracy of the model. In the paper we investigate experimentally the feasibility of rough sets in building profitable trend prediction models for financial time series. In order to improve the decision process for long time series, a novel time weighted rule voting method, which accounts for information aging, is proposed. The experiments have been performed using market data of multiple stock market indices. The classification efficiency and financial performance of the proposed rough sets models was verified and compared with that of support vector machines models and reference financial indices. The results showed that the rough sets approach with time weighted rule voting outperforms the classical rough sets and support vector machines decision systems and is profitable as compared to the buy and hold strategy. In addition, with the use of variable precision rough sets, the effectiveness of generated trading signals was further improved.
Forest health in a changing world: Effects of globalization and climate change on forest insect and pathogen impacts

Forests and trees throughout the world are increasingly affected by factors related to global change. Expanding international trade has facilitated invasions of numerous insects and pathogens into new regions. Many of these invasions have caused substantial forest damage, economic impacts and losses of ecosystem goods and services provided by trees. Climate change is already affecting the geographic distribution of host trees and their associated insects and pathogens, with anticipated increases in pest impacts by both native and invasive pests. Although climate change will benefit many forest insects, changes in thermal conditions may disrupt evolved life history traits and cause phenological mismatches. Individually, the threats posed to forest ecosystems by invasive pests and climate change are serious. Although interactions between these two drivers and their outcomes are poorly understood and hence difficult to predict, it is clear that the cumulative impacts on forest ecosystems will be exacerbated. Herewe introduce and synthesize the information in this special issue of Forestry with articles that illustrate the impacts of invasions of insects and pathogens, climate change, forest management and their interactions, as well as methods to predict, assess and mitigate these impacts. Most of these contributions were presented at the XXIV IUFROWorld Congress in 2014.
Phenotypic interactions between tree hosts and invasive forest pathogens in the light of globalization and climate change
Venue: Philosophical Transactions of the Royal Society B: Biological Sciences

Invasive pathogens can cause considerable damage to forest ecosystems. Lack of coevolution is generally thought to enable invasive pathogens to bypass the defence and/or recognition systems in the host. Although mostly true, this argument fails to predict intermittent outcomes in space and time, underlining the need to include the roles of the environment and the phenotype in host pathogen interactions when predicting disease impacts. We emphasize the need to consider host tree imbalances from a phenotypic perspective, considering the lack of coevolutionary and evolutionary history with the pathogen and the environment, respectively. We describe how phenotypic plasticity and plastic responses to environmental shifts may become maladaptive when hosts are faced with novel pathogens. The lack of host pathogen and environmental coevolution are aligned with two global processes currently driving forest damage: globalization and climate change, respectively. We suggest that globalization and climate change act synergistically, increasing the chances of both genotypic and phenotypic imbalances. Short moves on the same continent are more likely to be in balance than if the move is from another part of the world. We use Gremmeniella abietina outbreaks in Sweden to exemplify how host pathogen phenotypic interactions can help to predict the impacts of specific invasive and emergent diseases. This article is part of the themed issue 'Tackling emerging fungal threats to animal health, food security and ecosystem resilience'
Modifying effect of XmnI, BCL11A, and HBS1L MYB on clinical appearances: A study on b thalassemia and hemoglobin E/b thalassemia patients in Indonesia.
Venue: Hematology/oncology and stem cell therapy

Thalassemia is a monogenic hematologic disease that has the highest prevalence globally. In addition, there is complexity of the genetic background associated with a variety of phenotypes presented among patients. Genetic heterogeneity related to fetal hemoglobin (HbF) production has been reported as an influencing phenotypic factor of b thalassemia (b thal) Therefore, this study aimed to find the effect of these genetic modifiers, especially in the XmnI locus, rs11886868, rs766432 (BCL11A) and rs9399137 (HBS1L MYB) among b thal and HbE/b thal patients in Indonesia, according to laboratory and clinical outcomes, including HbF levels and clinical scores. This study was also designed to compare these modifying effects among b thal and HbE/b thal patients in Indonesia.  A total of 189 patients with genotyping of b thal and HbE/b thal were included in this study. The erythrocytes index and Hb electrophoresis measurements were calculated using appropriate methods. The severity of b thal and HbE/b thal was classified based on the Mahidol score. Polymorphism of the XmnI locus, rs11886868, rs766432 (BCL11A) and rs9399137 (HBS1L MYB) was determined using polymerase chain reaction restriction fragment length polymorphism (PCR RFLP) and amplification refractory mutation system (ARMS) methods.  The distributions of minor allele in the XmnI locus, rs11886868, rs766432, and rs9399137 were 14% 22% 19% and 18% respectively. The variation allele in the XmnI locus, rs11886868, and rs766432 showed a significant value for modifying HbF and clinical score in HbE/b thal patients, but rs9399137 did not demonstrate such features. In b thal patients, however, no correlation was found for any single nucleotide polymorphisms and clinical appearance.  The XmnI locus, rs11886868, and rs766432 have a modifying effect on HbF and clinical score in HbE/b thal patients in Indonesia, but not in b thal patients.
Readers' Advisory: Do You Remember that Moment You, You Know, Became a Reader?

As editor of this column, I enjoy the rich experiences, anecdotes, and research that professionals share within these columns. They provide a source of affirmation, statistical evidence, data and "feel good" stories that add value to our knowledge about readers' services. For many of us, it's refreshing to read thoughts from like minded professionals, as we find ourselves nodding in agreement or opposition against what other professionals are sharing with us. What we don't always write about are the moments we share with colleagues over a cup of coffee or tea. Those stolen moments when you just want to share a heart warming conversation you had with a reader, or the small moments littered throughout a day, week, month, or year that make you fall in love with reading all over again. In particular, those moments readers tell you when they became readers and how powerful those stories are when told.
300 Faces In The Wild Challenge: database and results
Venue: Image Vis. Comput.

Computer Vision has recently witnessed great research advance towards automatic facial points detection. Numerous methodologies have been proposed during the last few years that achieve accurate and efficient performance. However, fair comparison between these methodologies is infeasible mainly due to two issues. (a) Most existing databases, captured under both constrained and unconstrained (in the wild) conditions have been annotated using different mark ups and, in most cases, the accuracy of the annotations is low. (b) Most published works report experimental results using different training/testing sets, different error metrics and, of course, landmark points with semantically different locations. In this paper, we aim to overcome the aforementioned problems by (a) proposing a semi automatic annotation technique that was employed to re annotate most existing facial databases under a unified protocol, and (b) presenting the 300 Faces In The Wild Challenge (300 W) the first facial landmark localization challenge that was organized twice, in 2013 and 2015. To the best of our knowledge, this is the first effort towards a unified annotation scheme of massive databases and a fair experimental comparison of existing facial landmark localization systems. The images and annotations of the new testing database that was used in the 300 W challenge are available from http:/ibug.doc.ic.ac.uk/resources/300 W_IMAVIS/
Faces InThe Wild Challenge database and results

Computer Vision has recently witnessed great research advance towards automatic facial points detection. Numerous methodologies have been proposed during the last few years that achieve accurate and efficient performance. However, fair comparison between these methodologies is infeasible mainly due to two issues. (a) Most existing databases, captured under both constrained and unconstrained (in the wild) conditions have been annotated using different mark ups and, in most cases, the accuracy of the annotations is low. (b) Most published works report experimental results using different training/testing sets, different error metrics and, of course, landmark points with semantically different locations. In this paper, we aim to overcome the aforementioned problems by (a) proposing a semi automatic annotation technique that was employed to re annotate most existing facial databases under a unified protocol, and (b) presenting the 300 Faces InThe Wild Challenge (300 W) the first facial landmark localization challenge that was organized twice, in 2013 and 2015. To the best of our knowledge, this is the first effort towards a unified annotation scheme of massive databases and a fair experimental comparison of existing facial landmark localization systems. The images and annotations of the new testing database that was used in the 300 W challenge are available from http:/ibug.doc.ic.ac.uk/resources/300 W_IMAVIS/ (c) 2016 Elsevier B.V. All rights reserved.
3D Face Alignment in the Wild: A Landmark Free, Nose Based Approach
Venue: ECCV Workshops

We present a methodology for 3D face alignment in the wild, such that only the nose is required as input for assessing the position of the landmarks. Our approach works by first detecting the nose region, which is used for estimating the head pose. After that, a generic face landmark model, obtained by averaging all training images, is rotated, translated and scaled based on the size and localization of the nose. Because little information is needed and there are no refinement steps, our method is able to find suitable landmarks even in challenging poses. While not taking into account facial expressions and specific facial traits, our algorithm achieved competitive scores on the 3D Face Alignment in the Wild (3DFAW) challenge. The obtained results have the potential to be used as rough estimation of the position of the 3D face landmarks in the wild images, which can be further refined by specially designed algorithms.
Impact assessment of international anti dumping events on synchronization and comovement of the Chinese photovoltaic stocks

This study investigated the effects of international anti dumping events on the stock prices of the photovoltaic (PV) sector using sample data from China. Nine international anti dumping events were tested using the event study methodology, and we found that most of them had significant impact on the stock returns of PV companies. Then, we chose a threshold value to construct stock networks for the different phases of the event development. We studied the changes in the scale and strength of the stock price synchronization, the evolution of the stock price co movement and the stability of the co movement between adjacent stocks by analyzing the changes in the topological structure of the stock networks. We found that the international anti dumping events had different effects on the evolution of the synchronization and co movement of the prices of Chinese PV stocks. In addition, the stock market of the PV sector exhibits high sensitivity to certain events. This study provides a novel approach that combines the event study methodology and stock network analysis to help us to better understand the effects of anti dumping on the PV stocks.
Technological innovation and renewable energy development: evidence based on patent counts

Technological innovation plays an important role in the process of renewable energy development. This paper mainly discussed the influenced mechanism of the technological innovation on renewable energy development in six major developed countries during 1980 2010 employing panel cointegration and panel error correction models. The empirical results indicate that renewable energy consumption has a long run equilibrium relationship with technological innovation and other external driving factors. The results from the panel error correction model indicate that bidirectional causality exists between technological innovation and renewable energy consumption in the long run. This shows that the manifestation of the promoting effect of technological innovation on renewable energy development requires a certain amount of time. Moreover, there is significant unidirectional causality from other external driving factors including GDP per capita, carbon dioxide emissions per capita and international crude oil price to renewable energy technological innovation.
Underpricing, Faktor Robust Ipo Pada Bursa Efek Indonesia

This study proves that underpricing is a robust factor on the Indonesia Stock Exchange. This research examines the pricing of IPO, the allocation of shares to groups of institutional investors, the underwriter reputation toward underpricing, price stabilization and excess returns in three regression models and some sub models. Regression results provide evidence that the IPO pricing and allocation of shares to groups of institutional investors affect the underpricing. This evidence supports book building models that developed by Benveniste and Spindt (1989) This study provides evidence that underpricing has a positive effect on price stabilization. Underpricing is also a positive effect on excess returns. Expected proceeds are strong support factor explains underpricing as a central factor. Age and expected proceeds are the support factors, together with underpricing explain price stabilization. Partial price adjustement is support factor together with underpricing explain excess returns.