nils-herrmann/clean_scirepeval huggingface dataset:
Automatically Annotated Turkish Corpus for Named Entity Recognition and Text Categorization using Large-Scale Gazetteers

Turkish Wikipedia Named-Entity Recognition and Text Categorization (TWNERTC) dataset is a collection of automatically categorized and annotated sentences obtained from Wikipedia. We constructed large-scale gazetteers by using a graph crawler algorithm to extract relevant entity and domain information from a semantic knowledge base, Freebase1. The constructed gazetteers contains approximately 300K entities with thousands of fine-grained entity types under 77 different domains. Since automated processes are prone to ambiguity, we also introduce two new content specific noise reduction methodologies. Moreover, we map fine-grained entity types to the equivalent four coarse-grained types, person, loc, org, misc. Eventually, we construct six different dataset versions and evaluate the quality of annotations by comparing ground truths from human annotators. We make these datasets publicly available to support studies on Turkish named-entity recognition (NER) and text categorization (TC).
Table Cell Search for Question Answering

Tables are pervasive on the Web. Informative web tables range across a large variety of topics, which can naturally serve as a significant resource to satisfy user information needs. Driven by such observations, in this paper, we investigate an important yet largely under-addressed problem: Given millions of tables, how to precisely retrieve table cells to answer a user question. This work proposes a novel table cell search framework to attack this problem. We first formulate the concept of a relational chain which connects two cells in a table and represents the semantic relation between them. With the help of search engine snippets, our framework generates a set of relational chains pointing to potentially correct answer cells. We further employ deep neural networks to conduct more fine-grained inference on which relational chains best match the input question and finally extract the corresponding answer cells. Based on millions of tables crawled from the Web, we evaluate our framework in the open-domain question answering (QA) setting, using both the well-known WebQuestions dataset and user queries mined from Bing search engine logs. On WebQuestions, our framework is comparable to state-of-the-art QA systems based on knowledge bases (KBs), while on Bing queries, it outperforms other systems with a 56.7% relative gain. Moreover, when combined with results from our framework, KB-based QA performance can obtain a relative improvement of 28.1% to 66.7%, demonstrating that web tables supply rich knowledge that might not exist or is difficult to be identified in existing KBs.
Learning nonstationary models of normal network traffic for detecting novel attacks

Traditional intrusion detection systems (IDS) detect attacks by comparing current behavior to signatures of known attacks. One main drawback is the inability of detecting new attacks which do not have known signatures. In this paper we propose a learning algorithm that constructs models of normal behavior from attack-free network traffic. Behavior that deviates from the learned normal model signals possible novel attacks. Our IDS is unique in two respects. First, it is nonstationary, modeling probabilities based on the time since the last event rather than on average rate. This prevents alarm floods. Second, the IDS learns protocol vocabularies (at the data link through application layers) in order to detect unknown attacks that attempt to exploit implementation errors in poorly tested features of the target software. On the 1999 DARPA IDS evaluation data set , we detect 70 of 180 attacks (with 100 false alarms), about evenly divided between user behavioral anomalies (IP addresses and ports, as modeled by most other systems) and protocol anomalies. Because our methods are unconventional there is a significant non-overlap of our IDS with the original DARPA participants, which implies that they could be combined to increase coverage.
DDoS Attacks in Cloud Computing: Issues, Taxonomy, and Future Directions

Security issues related to the cloud computing are relevant to various stakeholders for an informed cloud adoption decision. Apart from data breaches, the cyber security research community is revisiting the attack space for cloud-specific solutions as these issues affect budget, resource management, and service quality. Distributed Denial of Service (DDoS) attack is one such serious attack in the cloud space. In this paper, we present developments related to DDoS attack mitigation solutions in the cloud. In particular, we present a comprehensive survey with a detailed insight into the characterization, prevention, detection, and mitigation mechanisms of these attacks. Additionally, we present a comprehensive solution taxonomy to classify DDoS attack solutions. We also provide a comprehensive discussion on important metrics to evaluate various solutions. This survey concludes that there is a strong requirement of solutions, which are designed keeping utility computing models in mind. Accurate auto-scaling decisions, multi-layer mitigation, and defense using profound resources in the cloud, are some of the key requirements of the desired solutions. In the end, we provide a definite guideline on effective solution building and detailed solution requirements to help the cyber security research community in designing defense mechanisms. To the best of our knowledge, this work is a novel attempt to identify the need of DDoS mitigation solutions involving multi-level information flow and effective resource management during the attack.
Style Transfer as Unsupervised Machine Translation

Language style transferring rephrases text with specific stylistic attributes while preserving the original attributeindependent content. One main challenge in learning a style transfer system is a lack of parallel data where the source sentence is in one style and the target sentence in another style. With this constraint, in this paper, we adapt unsupervised machine translation methods for the task of automatic style transfer. We first take advantage of style-preference information and word embedding similarity to produce pseudoparallel data with a statistical machine translation (SMT) framework. Then the iterative back-translation approach is employed to jointly train two neural machine translation (NMT) based transfer systems. To control the noise generated during joint training, a style classifier is introduced to guarantee the accuracy of style transfer and penalize bad candidates in the generated pseudo data. Experiments on benchmark datasets show that our proposed method outperforms previous state-of-the-art models in terms of both accuracy of style transfer and quality of input-output correspondence.
The Invention of Enigma and How the Polish Broke It Before the Start of WWII

From the early days of distance-based communications, people have naturally tried to disguise messages being sent from one place to another to avoid their intentions being revealed to other parties whether they were friends or enemies. For example, Julius Caesar sent confidential letters to Cicero in secret writing, Mary, Queen of Scots, sent secret messages to her followers from her prison, and Jefferson invented an encryption device that was even used by the Americans in World War II (Kippenhahn, 1999). However, with the advent of secret messages came also attempts at deciphering them. It happened that Mary Stuart’s secret messages were intercepted by Britain’s then secret police and decoded. In one of the messages she approved of a conspiracy against Elisabeth I, which sealed her well-known fate.
Using and validating the strategic alignment model

The literature suggests that firms cannot be competitive if their business and information technology strategies are not aligned. Yet achieving strategic alignment continues to be a major concern for business executives. A number of alignment models have been offered in the literature, primary among them the strategic alignment model (SAM). However, there is little published research that attempts to validate SAM or describe its use in practice. This paper reports on the use of SAM in a financial services firm. Data from completed projects are applied to the model to determine whether SAM is useful as a management tool to create, assess and sustain strategic alignment between information technology and the business. The paper demonstrates that SAM has conceptual and practical value. The paper also proposes a practical framework that allows management, particularly technology management, to determine current alignment levels and to monitor and change future alignment as required. Through the use of this framework, alignment is more likely to be achieved in practice.
Understanding Malicious Behavior in Crowdsourcing Platforms: The Case of Online Surveys

Crowdsourcing is increasingly being used as a means to tackle problems requiring human intelligence. With the ever-growing worker base that aims to complete microtasks on crowdsourcing platforms in exchange for financial gains, there is a need for stringent mechanisms to prevent exploitation of deployed tasks. Quality control mechanisms need to accommodate a diverse pool of workers, exhibiting a wide range of behavior. A pivotal step towards fraud-proof task design is understanding the behavioral patterns of microtask workers. In this paper, we analyze the prevalent malicious activity on crowdsourcing platforms and study the behavior exhibited by trustworthy and untrustworthy workers, particularly on crowdsourced surveys. Based on our analysis of the typical malicious activity, we define and identify different types of workers in the crowd, propose a method to measure malicious activity, and finally present guidelines for the efficient design of crowdsourced surveys.
Resistance integrons: class 1, 2 and 3 integrons

As recently indiscriminate abuse of existing antibiotics in both clinical and veterinary treatment leads to proliferation of antibiotic resistance in microbes and poses a dilemma for the future treatment of such bacterial infection, antimicrobial resistance has been considered to be one of the currently leading concerns in global public health, and reported to widely spread and extended to a large variety of microorganisms. In China, as one of the currently worst areas for antibiotics abuse, the annual prescription of antibiotics, including both clinical and veterinary treatment, has approaching 140 gram per person and been roughly estimated to be 10 times higher than that in the United Kingdom, which is considered to be a potential area for the emergence of “Super Bugs”. Based on the integrons surveillance in Guangzhou, China in the past decade, this review thus aimed at summarizing the role of integrons in the perspective of both clinical setting and environment, with the focus on the occurrence and prevalence of class 1, 2 and 3 integrons.
End to End Learning for Self-Driving Cars

We trained a convolutional neural network (CNN) to map raw pixels from a single front-facing camera directly to steering commands. This end-to-end approach proved surprisingly powerful. With minimum training data from humans the system learns to drive in traffic on local roads with or without lane markings and on highways. It also operates in areas with unclear visual guidance such as in parking lots and on unpaved roads. The system automatically learns internal representations of the necessary processing steps such as detecting useful road features with only the human steering angle as the training signal. We never explicitly trained it to detect, for example, the outline of roads. Compared to explicit decomposition of the problem, such as lane marking detection, path planning, and control, our end-to-end system optimizes all processing steps simultaneously. We argue that this will eventually lead to better performance and smaller systems. Better performance will result because the internal components self-optimize to maximize overall system performance, instead of optimizing human-selected intermediate criteria, e. g., lane detection. Such criteria understandably are selected for ease of human interpretation which doesn’t automatically guarantee maximum system performance. Smaller networks are possible because the system learns to solve the problem with the minimal number of processing steps. We used an NVIDIA DevBox and Torch 7 for training and an NVIDIA DRIVETM PX self-driving car computer also running Torch 7 for determining where to drive. The system operates at 30 frames per second (FPS). 1 ar X iv :1 60 4. 07 31 6v 1 [ cs .C V ] 2 5 A pr 2 01 6
Factors Affecting Consumer ’ s Continuance Online Purchase Intention in Malaysia

This paper attempts to explore the continuance purchase intention among Malaysian consumers on the internet, through the conceptual framework as modified from the Technology Acceptance Model.  The research data has been collected through the use of an internet-based survey method, with a convenience sample of 300 Malaysian respondents in June 2014; whereas, data analysis is further conducted using the SPSS software, through the measurements of a simple linear regression test, mediation test and moderation test.  Results show a positive relationship between perceived usefulness and trust; while the continuance intention for online purchase is positively significant to perceived usefulness. The partial mediating role of subjective norm is further identified, between perceived usefulness and continuance intention. It is also understood that both male and female perceive the usefulness of website as an important factor toward their intention to online re-purchase. Novelty – Most of the researches are conducted solely upon the factors or purchase intention, limited studies have been done to investigate the continuance purchase intention among fellow consumers.  Empirical
TWO FACTOR AUTHENTICATION

In the present digital day with remarkable development in Computer sector, Single factor authentication, e.g. passwords, is no more examined as secure in the World Wide Web. It has never been less difficult in Securing the system and remote access. Simple, obvious and easy-to-guess passwords, such as names and age, are effortlessly found via computerized secret key gathering programs. The security and privacy threats through malware are always constantly growing both in quantity as well as quality. Expanded access to information increases weakness to hacking, cracking of passwords and online frauds. In this association the conventional login/password authentication is taken into account inadequately secure for several security-critical applications such as login to Mailing Accounts, Social Networks, Gadgets, Financial accounts, official secured networks, commercial websites online etc. Obliging more than one independent factor increases the difficulty of providing false credentials. Two-factor authentication proposal guarantee a higher protection level by extending the single authentication factor. This paper focuses on the implementation of two-factor authentication methods by using both users friendly traditional Alphanumeric Password and graphical Password as gateway for authentication. An attempt has been made by using two factor Authentication, and in this paper we describe the two factor Authentication system design and design implementation. Thus affording an additional password adds an extra layer of security.
Learning and Transferring Social and Item Visibilities for Personalized Recommendation

User feedback in the form of movie-watching history, item ratings, or product consumption is very helpful in training recommender systems. However, relatively few interactions between items and users can be observed. Instances of missing user--item entries are caused by the user not seeing the item (although the actual preference to the item could still be positive) or the user seeing the item but not liking it. Separating these two cases enables missing interactions to be modeled with finer granularity, and thus reflects user preferences more accurately. However, most previous studies on the modeling of missing instances have not fully considered the case where the user has not seen the item. Social connections are known to be helpful for modeling users' potential preferences more extensively, although a similar visibility problem exists in accurately identifying social relationships. That is, when two users are unaware of each other's existence, they have no opportunity to connect. In this paper, we propose a novel user preference model for recommender systems that considers the visibility of both items and social relationships. Furthermore, the two kinds of information are coordinated in a unified model inspired by the idea of transfer learning. Extensive experiments have been conducted on three real-world datasets in comparison with five state-of-the-art approaches. The encouraging performance of the proposed system verifies the effectiveness of social knowledge transfer and the modeling of both item and social visibilities.
A planning framework for community empowerment goals within health promotion.

Health promotion often comprises a tension between 'bottom-up' and 'top-down' programming. The former, more associated with concepts of community empowerment, begins on issues of concern to particular groups or individuals, and regards some improvement in their overall power or capacity as the important health outcome. The latter, more associated with disease prevention efforts, begins by seeking to involve particular groups or individuals in issues and activities largely defined by health agencies, and regards improvement in particular behaviours as the important health outcome. Community empowerment is viewed more instrumentally as a means to the end of health behaviour change. The tension between these two approaches is not unresolvable, but this requires a different orientation on the part of those responsible for planning more conventional, top-down programmes. This article presents a framework intended to assist planners, implementers and evaluators to systematically consider community empowerment goals within top-down health promotion programming. The framework 'unpacks' the tensions in health promotion at each stage of the more conventional, top-down programme cycle, by presenting a parallel 'empowerment' track. The framework also presents a new technology for the assessment and strategic planning of nine identified 'domains' that represent the organizational influences on the process of community empowerment. Future papers analyze the design of this assessment and planning methodology, and discuss the findings of its field-testing in rural communities in Fiji.
Advertising Spending and Market Capitalization

Marketing decision makers are increasingly aware of the importance of shareholder value maximization, which calls for an evaluation of the long-run effects of their actions on product-market response as well as investor response. However, the marketing literature to date has focused on the sales or profit response of marketing actions such as advertising spending and new-product development, and the goals of marketing have traditionally been formulated from a customer perspective. There have been no studies of the long-term investor response to marketing actions, in particular the stock price of publicly traded firms. Our research investigates one important aspect of this impact, the long-run relationship between advertising spending and market capitalization. We hypothesize that advertising can have a direct effect on valuation, i.e., an effect over and above its indirect effect via revenue and profit response. Our empirical test is based on 10 years of monthly data for several PC manufacturers. We use multivariate time-series methods that disentangle the long-run effects and short-run effects, as well as the direct and the indirect effects of advertising on firm valuation. The results are categorized as customer response effects (the impact of advertising and R&D expenditures on sales and profits) and investor response effects (the net effect on the value of the firm). The empirical results provide support for our hypothesis that advertising spending has a positive and long-run impact on firms’ market capitalization. Thus, even if productmarket response to the advertising is demonstrably weak, investors are willing to pay a premium for aggressive advertisers. We quantify the magnitude of this investor response effect and discuss its implications for future research.
Bogus currency authorization using HSV techniques

In present scenario, the Indian government has announced the demonetization of all Rs 500 and Rs 1000, in reserve bank notes of Mahatma Gandhi series. Indian government has introduced a new Rs 500 and Rs 2000, to reduce fund illegal activity in India. Even then the new notes of fake or bogus currency are circulated in the society. The main objective of this work is used to identify fake currencies among the real. From the currency, the strip lines or continuous lines are detected from real and fake note by using edge detection techniques. HSV techniques are used to saturate the value of an input image. To achieve the enhance reliability and dynamic way in detecting the counterfeit currency.
DeepMove: Predicting Human Mobility with Attentional Recurrent Networks

Human mobility prediction is of great importance for a wide spectrum of location-based applications. However, predicting mobility is not trivial because of three challenges: 1) the complex sequential transition regularities exhibited with time-dependent and highorder nature; 2) the multi-level periodicity of human mobility; and 3) the heterogeneity and sparsity of the collected trajectory data. In this paper, we propose DeepMove, an attentional recurrent network for mobility prediction from lengthy and sparse trajectories. In DeepMove, we first design a multi-modal embedding recurrent neural network to capture the complicated sequential transitions by jointly embedding the multiple factors that govern the human mobility. Then, we propose a historical attention model with two mechanisms to capture the multi-level periodicity in a principle way, which effectively utilizes the periodicity nature to augment the recurrent neural network for mobility prediction. We perform experiments on three representative real-life mobility datasets, and extensive evaluation results demonstrate that our model outperforms the state-of-the-art models by more than 10%. Moreover, compared with the state-of-the-art neural network models, DeepMove provides intuitive explanations into the prediction and sheds light on interpretable mobility prediction.
Generation of High Dynamic Range Illumination from a Single Image for the Enhancement of Undesirably Illuminated Images

This paper presents an algorithm that enhances undesirably illuminated images by generating and fusing multi-level illuminations from a single image. The input image is first decomposed into illumination and reflectance components by using an edge-preserving smoothing filter. Then the reflectance component is scaled up to improve the image details in bright areas. The illumination component is scaled up and down to generate several illumination images that correspond to certain camera exposure values different from the original. The virtual multi-exposure illuminations are blended into an enhanced illumination, where we also propose a method to generate appropriate weight maps for the tone fusion. Finally, an enhanced image is obtained by multiplying the equalized illumination and enhanced reflectance. Experiments show that the proposed algorithm produces visually pleasing output and also yields comparable objective results to the conventional enhancement methods, while requiring modest computational loads.