nils-herrmann/clean_scirepeval huggingface dataset:
Fashion Style Generator

In this paper, we focus on a new problem: applying artificial intelligence to automatically generate fashion style images. Given a basic clothing image and a fashion style image (e.g., leopard print), we generate a clothing image with the certain style in real time with a neural fashion style generator. Fashion style generation is related to recent artistic style transfer works, but has its own challenges. The synthetic image should preserve the similar design as the basic clothing, and meanwhile blend the new style pattern on the clothing. Neither existing global nor patch based neural style transfer methods could well solve these challenges. In this paper, we propose an end-to-end feed-forward neural network which consists of a fashion style generator and a discriminator. The global and patch based style and content losses calculated by the discriminator alternatively back-propagate the generator network and optimize it. The global optimization stage preserves the clothing form and design and the local optimization stage preserves the detailed style pattern. Extensive experiments show that our method outperforms the state-of-the-arts.
Anomaly Detection System in Cloud Environment Using Fuzzy Clustering Based ANN

Cloud computing affords lot of resources and computing facilities through Internet. Cloud systems attract many users with its desirable features. In spite of them, Cloud systems may experience severe security issues. Thus, it is essential to create an Intrusion Detection System (IDS) to detect both insider and outsider attacks with high detection accuracy in cloud environment. This work proposes an anomaly detection system at the hypervisor layer named Hypervisor Detector that uses a hybrid algorithmwhich is a mixture of Fuzzy CMeans clustering algorithm and Artificial Neural Network (FCM-ANN) to improve the accuracy of the detection system. The proposed system is implemented and compared with Naïve Bayes classifier and Classic ANN algorithm. The DARPA’s KDD cup dataset 1999 is used for experiments. Based on extensive theoretical and performance analysis, it is evident that the proposed system is able to detect the anomalies with high detection accuracy and low false alarm rate even for low frequent attacks thereby outperforming Naïve Bayes classifier and Classic ANN.
Smoke detection using spatial and temporal analyses

Video-based fire detection is currently a fairly common application with the growth in the number of installed surveillance video systems. Moreover, the related processing units are becoming more powerful. Smoke is an early sign of most fires; therefore, selecting an appropriate smoke-detection method is essential. However, detecting smoke without creating a false alarm remains a challenging problem for open or large spaces with the disturbances of common moving objects, such as pedestrians and vehicles. This study proposes a novel video-based smoke-detection method that can be incorporated into a surveillance system to provide early alerts. In this study, the process of extracting smoke features from candidate regions was accomplished by analyzing the spatial and temporal characteristics of video sequences for three important features: edge blurring, gradual energy changes, and gradual chromatic configuration changes. The proposed spatialtemporal analysis technique improves the feature extraction of gradual energy changes. In order to make the video smoke-detection results more reliable, these three features were combined using a support vector machine (SVM) technique and a temporal-based alarm decision unit (ADU) was also introduced. The effectiveness of the proposed algorithm was evaluated on a PC with an Intel R © Core2 Duo CPU (2.2 GHz) and 2 GB RAM. The average processing time was 32.27 ms per frame; i.e., the proposed algorithm can process 30.98 frames per second. Experimental results showed that the proposed system can detect smoke effectively with a low false-alarm rate and a short reaction time in many real-world scenarios.
Credibility and trust of information in online environments : The use of cognitive heuristics

Networked digital media present new challenges for people to locate information that they can trust. At the same time, societal reliance on information that is available solely or primarily via the Internet is increasing. This article discusses how and why digitally networked communication environments alter traditional notions of trust, and presents research that examines how information consumers make judgments about the credibility and accuracy of information they encounter online. Based on this research, the article focuses on the use of cognitive heuristics in credibility evaluation. Findings from recent studies are used to illustrate the types of cognitive heuristics that information consumers employ when determining what sources and information to trust online. The article concludes with an agenda for future research that is needed to better understand the role and influence of cognitive heuristics in credibility evaluation in computer-mediated communication contexts.
SUMMAC: a text summarization evaluation

The TIPSTER Text Summarization Evaluation (SUMMAC) has developed several new extrinsic and intrinsic methods for evaluating summaries. It has established definitively that automatic text summarization is very effective in relevance assessment tasks on news articles. Summaries as short as 17% of full text length sped up decision-making by almost a factor of 2 with no statistically significant degradation in accuracy. Analysis of feedback forms filled in after each decision indicated that the intelligibility of present-day machine-generated summaries is high. Systems that performed most accurately in the production of indicative and informative topic-related summaries used term frequency and co-occurrence statistics, and vocabulary overlap comparisons between text passages. However, in the absence of a topic, these statistical methods do not appear to provide any additional leverage: in the case of generic summaries, the systems were indistinguishable in accuracy. The paper discusses some of the tradeoffs and challenges faced by the evaluation, and also lists some of the lessons learned, impacts, and possible future directions. The evaluation methods used in the SUMMAC evaluation are of interest to both summarization evaluation as well as evaluation of other 'output-related' NLP technologies, where there may be many potentially acceptable outputs, with no automatic way to compare them.
Corruption in America

We use a data set of federal corruption convictions in the U.S. to investigate the causes and consequences of corruption. More educated states, and to a smaller degree richer states, have less corruption. This relationship holds even when we use historical factors like Congregationalism in 1890 as an instrument for the level of schooling today. The level of corruption is also correlated with the level of income inequality and racial fractionalization, and uncorrelated with the size of government. There is a weak negative relationship between corruption and economic development in a state. These results echo the cross-country findings, and support the view that the correlation between development and good political outcomes occurs because education improves political institutions. D 2005 Elsevier B.V. All rights reserved.
An Exploratory Study on Drivers and Deterrents of Collaborative Consumption in Travel

Due to the rise of businesses utilizing the sharing economy concept, it is important to better understand the motivational factors that drive and hinder collaborative consumption in the travel and tourism marketplace. Based on responses from 754 adult travellers residing in the US, drivers and deterrents of the use of peer-to-peer accommodation rental services were identified. Factors that deter the use of peer-to-peer accommodation rental services include lack of trust, lack of efficacy with regards to technology, and lack of economic benefits. The motivations that drive the use of peer-to-peer accommodation include the societal aspects of sustainability and community, as well as economic benefits. Based on the empirical evidence, this study suggests several propositions for future studies and implications for tourism destinations and hospitality businesses on how to manage collaborative consumption.
Predicting Short-Term Traffic Flow by Long Short-Term Memory Recurrent Neural Network

Intelligent Transportation System (ITS) is a significant part of smart city, and short-term traffic flow prediction plays an important role in intelligent transportation management and route guidance. A number of models and algorithms based on time series prediction and machine learning were applied to short-term traffic flow prediction and achieved good results. However, most of the models require the length of the input historical data to be predefined and static, which cannot automatically determine the optimal time lags. To overcome this shortage, a model called Long Short-Term Memory Recurrent Neural Network (LSTM RNN) is proposed in this paper, which takes advantages of the three multiplicative units in the memory block to determine the optimal time lags dynamically. The dataset from Caltrans Performance Measurement System (PeMS) is used for building the model and comparing LSTM RNN with several well-known models, such as random walk(RW), support vector machine(SVM), single layer feed forward neural network(FFNN) and stacked autoencoder(SAE). The results show that the proposed prediction model achieves higher accuracy and generalizes well.
The Life Course Development of Human Sexual Orientation : An Integrative Paradigm

Through the application of life course theory to the study of sexual orientation, this paper specifies a new paradigm for research on human sexual orientation that seeks to reconcile divisions among biological, social science, and humanistic paradigms. Recognizing the historical, social, and cultural relativity of human development, this paradigm argues for a moderate stance between essentialism and constructionism, identifying (a) the history of sexual orientation as an identity category emerging from the medical model of homosexuality in the late 1800s; (b) the presence of same-sex desire across species, history, and cultures, revealing its normality; (c) an underlying affective motivational force which organizes sexual desire within individuals, and (d) the assumption of a sexual identity in response to the identity and behavioral possibilities of a culture. This framework considers the biology of sexual desire while simultaneously acknowledging the socially constructed nature of identity and the historical foundations of sexual orientation as a meaningful index of human identity. The study of human sexual orientation is currently confronted with two significant problems. First, research on sexual orientation continues to be intellectually fragmented along disciplinary lines, primarily due to divergent epistemological, methodological, and metatheoretical perspectives. Second, as societal transformations fundamentally alter the life course of individuals who identify as gay, lesbian, or bisexual [Cohler & Hammack, in press], it becomes increasingly apparent
PREPARATION METHODS AND PROPERTIES OF HYDROGEL : A

Hydrogels are three-dimensional cross-linked polymer network that can respond to the fluctuations of the environmental stimuli. These biomaterials can incorporate large quantum of biological fluids and swell. When swelled, they are soft & rubbery and resemble the living tissue, exhibiting excellent biocompatibility. Today, drug delivery experience several challenges where hydrogel could be one potential answer to those. Thanks to the unique properties of hydrogel for which they are widely exposed to different biomedical fields. Hence the preparation techniques of hydrogel biomaterial and the evaluation of the properties are of utmost significance. Literature reveals that this three dimensional architecture could be homo-polymeric, co-polymeric, semiinterpenetrating and interpenetrating polymer networks (IPN) based on preparation methods. Polymeric blends like semi-IPN have also been investigated to satisfy the specific needs of biomedical field. Such blends have shown superior performances over individual polymers. Unique biocompatibility, flexible methods of synthesis and tailor able physical properties have made the hydrogels to be used as a drug delivery device to tissue engineering scaffolds. As scaffolds they should provide structural integrity like tissue constructs and as a drug carrier it should have sufficient mechanical strength to control and protect the drug and proteins until they are delivered to the specific sites of the biological system. Hence, the evaluation of swelling, mechanical and biocompatible properties consider more attention before the hydrogels are applied. In this review article an attempt has been made to describe the available methods of hydrogel synthesis along with their inevitable properties.
Airport Gate Assignment A Hybrid Model and Implementation

With the rapid development of airlines, airports today become much busier and more complicated than previous days. During airlines daily operations, assigning the available gates to the arriving aircrafts based on the fixed schedule is a very important issue, which motivates researchers to study and solve Airport Gate Assignment Problems (AGAP) with all kinds of state-of-the-art combinatorial optimization techniques. In this paper, we study the AGAP and propose a novel hybrid mathematical model based on the method of constraint programming and 0 1 mixed-integer programming. With the objective to minimize the number of gate conflicts of any two adjacent aircrafts assigned to the same gate, we build a mathematical model with logical constraints and the binary constraints. For practical considerations, the potential objective of the model is also to minimize the number of gates that airlines must lease or purchase in order to run their business smoothly. We implement the model in the Optimization Programming Language (OPL) and carry out empirical studies with the data obtained from online timetable of Continental Airlines, Houston Gorge Bush Intercontinental Airport IAH, which demonstrate that our model can provide an efficient evaluation criteria for the airline companies to estimate the efficiency of their current gate assignments.
Development of Torque Sensor with High Sensitivity for Joint of Robot Manipulator Using 4-Bar Linkage Shape

The torque sensor is used to measure the joint torque of a robot manipulator. Previous research showed that the sensitivity and the stiffness of torque sensors have trade-off characteristics. Stiffness has to be sacrificed to increase the sensitivity of the sensor. In this research, a new torque sensor with high sensitivity (TSHS) is proposed in order to resolve this problem. The key idea of the TSHS comes from its 4-bar linkage shape in which the angular displacement of a short link is larger than that of a long link. The sensitivity of the torque sensor with a 4-bar link shape is improved without decreasing stiffness. Optimization techniques are applied to maximize the sensitivity of the sensor. An actual TSHS is constructed to verify the validity of the proposed mechanism. Experimental results show that the sensitivity of TSHS can be increased 3.5 times without sacrificing stiffness.
Evolution of bitcoin and security risk in bitcoin wallets

This paper identifies trust factor and rewarding nature of bitcoin system, and analyzes bitcoin features which may facilitate bitcoin to emerge as a universal currency. Paper presents the gap between proposed theoretical-architecture and current practical-implementation of bitcoin system in terms of achieving decentralization, anonymity of users, and consensus. Paper presents three different ways in which a user can manage bitcoins. We attempt to identify the security risk and feasible attacks on these configurations of bitcoin management. We have shown that not all bitcoin wallets are safe against all possible types of attacks. Bitcoin core is only safest mode of operating bitcoin till date as it is secure against all feasible attacks, and is vulnerable only against block-chain rewriting.
Comparison of Several Cloud Computing Platforms

Cloud computing is the development of parallel computing, distributed computing and grid computing. It has been one of the most hot research topics. Now many corporations have involved in the cloud computing related techniques and many cloud computing platforms have been put forward. This is a favorable situation to study and application of cloud computing related techniques. Though interesting, there are also some problems for so many flatforms. For to a novice or user with little knowledge about cloud computing, it is still very hard to make a reasonable choice. What differences are there for different cloud computing platforms and what characteristics and advantages each has? To answer these problems, the characteristics, architectures and applications of several popular cloud computing platforms are analyzed and discussed in detail. From the comparison of these platforms, users can better understand the different cloud platforms and more reasonablely choose what they want.
Variable Neighborhood Search: Methods and Applications

Variable neighborhood search (VNS) is a metaheuristic, or framework for building heuristics, based upon systematic change of neighborhoods both in a decent phase to find a local minimum, and in a perturbation phase to get out of the corresponding valley. It was first proposed in 1997 and since has rapidly developed both in its methods and its applications. Both of these aspects are thouroughly reviewed and a large bibliography is given.
In vivo loads on a vertebral body replacement during different lifting techniques.

The repeated lifting of heavy weights has been identified as a risk factor for low back pain (LBP). Whether squat lifting leads to lower spinal loads than stoop lifting and whether lifting a weight laterally results in smaller forces than lifting the same weight in front of the body remain matters of debate. Instrumented vertebral body replacements (VBRs) were used to measure the in vivo load in the lumbar spine in three patients at level L1 and in one patient at level L3. Stoop lifting and squat lifting were compared in 17 measuring sessions, in which both techniques were performed a total of 104 times. The trunk inclination and amount of knee bending were simultaneously estimated from recorded images. Compared with the aforementioned lifting tasks, the patients additionally lifted a weight laterally with one hand 26 times. Only a small difference (4%) in the measured resultant force was observed between stoop lifting and squat lifting, although the knee-bending angle (stoop 10°, squat 45°) and trunk inclination (stoop 52°, squat 39°) differed considerably at the time points of maximal resultant forces. Lifting a weight laterally caused 14% less implant force on average than lifting the same weight in front of the body. The current in vivo biomechanical study does not provide evidence that spinal loads differ substantially between stoop and squat lifting. The anterior-posterior position of the lifted weight relative to the spine appears to be crucial for spinal loading.
A Sales Forecast Model for the German Automobile Market Based on Time Series Analysis and Data Mining Methods

In this contribution, various sales forecast models for the German automobile market are developed and tested. Our most important criteria for the assessment of these models are the quality of the prediction as well as an easy explicability. Yearly, quarterly and monthly data for newly registered automobiles from 1992 to 2007 serve as the basis for the tests of these models. The time series model used consists of additive components: trend, seasonal, calendar and error component. The three latter components are estimated univariately while the trend component is estimated multivariately by Multiple Linear Regression as well as by a Support Vector Machine. Possible influences which are considered include macro-economic and market-specific factors. These influences are analysed by a feature selection. We found the non-linear model to be superior. Furthermore, the quarterly data provided the most accurate results.
Dynamic author name disambiguation for growing digital libraries

When a digital library user searches for publications by an author name, she often sees a mixture of publications by different authors who have the same name. With the growth of digital libraries and involvement of more authors, this author ambiguity problem is becoming critical. Author disambiguation (AD) often tries to solve this problem by leveraging metadata such as coauthors, research topics, publication venues and citation information, since more personal information such as the contact details is often restricted or missing. In this paper, we study the problem of how to efficiently disambiguate author names given an incessant stream of published papers. To this end, we propose a “BatchAD+IncAD” framework for dynamic author disambiguation. First, we perform batch author disambiguation (BatchAD) to disambiguate all author names at a given time by grouping all records (each record refers to a paper with one of its author names) into disjoint clusters. This establishes a one-to-one mapping between the clusters and real-world authors. Then, for newly added papers, we periodically perform incremental author disambiguation (IncAD), which determines whether each new record can be assigned to an existing cluster, or to a new cluster not yet included in the previous data. Based on the new data, IncAD also tries to correct previous AD results. Our main contributions are: (1) We demonstrate with real data that a small number of new papers often have overlapping author names with a large portion of existing papers, so it is challenging for IncAD to effectively leverage previous AD results. (2) We propose a novel IncAD model which aggregates metadata from a cluster of records to estimate the author’s profile such as her coauthor distributions and keyword distributions, in order to predict how likely it is that a new record is “produced” by the author. (3) Using two labeled datasets and one large-scale raw dataset, we show that the proposed method is much more efficient than state-of-the-art methods while ensuring high accuracy.
Transform Domain Techniques for Image

Steganography is the powerful tool for hiding information inside useful cover medium in ways such that the hidden message is undetectable. In Greek language, stego means covered or secret and graphy means to write. Hence, steganography means covered writing. Transform domain steganography is one of the techniques used for hidden exchange of information in frequency domain and it can be defined as the study of invisible communication that deals with the ways of hiding the existence of the communicated message. In this way, if successfully achieved, the message does not get attention of attackers and eavesdroppers. In steganography, information can be hidden in different cover carriers. Cover media can be a text, image, audio or video files.