nils-herrmann/clean_scirepeval huggingface dataset:
Stay On-Topic: Generating Context-Specific Fake Restaurant Reviews

Automatically generated fake restaurant reviews are a threat to online review systems. Recent research has shown that users have difficulties in detecting machine-generated fake reviews hiding among real restaurant reviews. The method used in this work (char-LSTM ) has one drawback: it has difficulties staying in context, i.e. when it generates a review for specific target entity, the resulting review may contain phrases that are unrelated to the target, thus increasing its detectability. In this work, we present and evaluate a more sophisticated technique based on neural machine translation (NMT) with which we can generate reviews that stay on-topic. We test multiple variants of our technique using native English speakers on Amazon Mechanical Turk. We demonstrate that reviews generated by the best variant have almost optimal undetectability (class-averaged F-score 47%). We conduct a user study with experienced users and show that our method evades detection more frequently compared to the state-of-the-art (average evasion 3.2/4 vs 1.5/4) with statistical significance, at level α = 1% (Section 4.3). We develop very effective detection tools and reach average F-score of 97% in classifying these. Although fake reviews are very effective in fooling people, effective automatic detection is still feasible.
Mining the Web's Link Structure

60 Computer T he Web is a hypertext body of approximately 300 million pages that continues to grow at roughly a million pages per day. Page variation is more prodigious than the data's raw scale: Taken as a whole, the set of Web pages lacks a unifying structure and shows far more author-ing style and content variation than that seen in traditional text-document collections. This level of complexity makes an " off-the-shelf " database-management and information-retrieval solution impossible. To date, index-based search engines for the Web have been the primary tool by which users search for information. The largest such search engines exploit technology's ability to store and index much of the Web. Such engines can therefore build giant indices that let you quickly retrieve the set of all Web pages containing a given word or string. Experienced users can make effective use of such engines for tasks that can be solved by searching for tightly constrained keywords and phrases. These search engines are, however, unsuited for a wide range of equally important tasks. In particular, a topic of any breadth will typically contain several thousand or million relevant Web pages. Yet a user will be willing, typically , to look at only a few of these pages. How then, from this sea of pages, should a search engine select the correct ones—those of most value to the user? AUTHORITATIVE WEB PAGES First, to distill a large Web search topic to a size that makes sense to a human user, we need a means of identifying the topic's most definitive or authoritative Web pages. The notion of authority adds a crucial second dimension to the concept of relevance: We wish to locate not only a set of relevant pages, but also those relevant pages of the highest quality. Second, the Web consists not only of pages, but hyperlinks that connect one page to another. This hyperlink structure contains an enormous amount of latent human annotation that can help automatically infer notions of authority. Specifically, the creation of a hyperlink by the author of a Web page represents an implicit endorsement of the page being pointed to; by mining the collective judgment contained in the set of such endorsements, we can gain a richer understanding of the relevance and quality of the Web's contents. To address both these parameters, we began development of the Clever system 1-3 three years ago. Clever …
Natural Steganography: cover-source switching for better steganography

This paper proposes a new steganographic scheme relying on the principle of “cover-source switching”, the key idea being that the embedding should switch from one coversource to another. The proposed implementation, called Natural Steganography, considers the sensor noise naturally present in the raw images and uses the principle that, by the addition of a specific noise the steganographic embedding tries to mimic a change of ISO sensitivity. The embedding methodology consists in 1) perturbing the image in the raw domain, 2) modeling the perturbation in the processed domain, 3) embedding the payload in the processed domain. We show that this methodology is easily tractable whenever the processes are known and enables to embed large and undetectable payloads. We also show that already used heuristics such as synchronization of embedding changes or detectability after rescaling can be respectively explained by operations such as color demosaicing and down-scaling kernels.
Crowdfunding : Why People Are Motivated to Post and Fund Projects on Crowdfunding Platforms

Computer-mediated crowdfunding is an emerging paradigm used by individuals to solicit funds from other individuals to realize projects. We are interested in how and why these platforms work and the impact they can have on what projects are realized and how they are disseminated in the world. In this paper, we report preliminary findings from a qualitative exploratory study of creators and funders on three popular crowdfunding platforms. In addition to anticipated extrinsic motivators, such as securing funding (creators) and consuming products and experiences (funders), our initial findings suggest that people are also motivated to participate because of social interactions realized through crowdfunding platforms, such as strengthening commitment to an idea through feedback (creators) and feelings of connectedness to a community with similar interests and ideals (funders). We present this research in the context of what we are calling motivational crowdwork, the investigation of motivation as it relates to online task outsourcing, and discuss ideas for ongoing work in this area.
Learning to Reconstruct Shapes from Unseen Classes

From a single image, humans are able to perceive the full 3D shape of an object by exploiting learned shape priors from everyday life. Contemporary single-image 3D reconstruction algorithms aim to solve this task in a similar fashion, but often end up with priors that are highly biased by training classes. Here we present an algorithm, Generalizable Reconstruction (GenRe), designed to capture more generic, class-agnostic shape priors. We achieve this with an inference network and training procedure that combine 2.5D representations of visible surfaces (depth and silhouette), spherical shape representations of both visible and non-visible surfaces, and 3D voxel-based representations, in a principled manner that exploits the causal structure of how 3D shapes give rise to 2D images. Experiments demonstrate that GenRe performs well on single-view shape reconstruction, and generalizes to diverse novel objects from categories not seen during training.
A Look at the Recent Wireless Positioning Techniques With a Focus on Algorithms for Moving Receivers

Employment of ground-based positioning systems has been consistently growing over the past decades due to the growing number of applications that require location information where the conventional satellite-based systems have limitations. Such systems have been successfully adopted in the context of wireless emergency services, tactical military operations, and various other applications offering location-based services. In current and previous generation of cellular systems, i.e., 3G, 4G, and LTE, the base stations, which have known locations, have been assumed to be stationary and fixed. However, with the possibility of having mobile relays in 5G networks, there is a demand for novel algorithms that address the challenges that did not exist in the previous generations of localization systems. This paper includes a review of various fundamental techniques, current trends, and state-of-the-art systems and algorithms employed in wireless position estimation using moving receivers. Subsequently, performance criteria comparisons are given for the aforementioned techniques and systems. Moreover, a discussion addressing potential research directions when dealing with moving receivers, e.g., receiver's movement pattern for efficient and accurate localization, non-line-of-sight problem, sensor fusion, and cooperative localization, is briefly given.
Language Processing Value Network Natural Language Goal G State S t Value for actions " Go to the blue torch " Vision Processing Gated Attention Goal Embedding Attention Text Image Representation

Sparse reward is one of the most challenging problems in reinforcement learning (RL). Hindsight Experience Replay (HER) attempts to address this issue by converting a failure experience to a successful one by relabeling the goals. Despite its effectiveness, HER has limited applicability because it lacks a compact and universal goal representation. We present Augmenting experienCe via TeacheR’s adviCE (ACTRCE), an efficient reinforcement learning technique that extends the HER framework using natural language as the goal representation. We first analyze the differences among goal representation, and show that ACTRCE can efficiently solve difficult reinforcement learning problems in challenging 3D navigation tasks, whereas HER with non-language goal representation failed to learn. We also show that with language goal representations, the agent can generalize to unseen instructions, and even generalize to instructions with unseen lexicons. We further demonstrate it is crucial to use hindsight advice to solve challenging tasks, but we also found that little amount of hindsight advice is sufficient for the learning to take off, showing the practical aspect of the method.
Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review

Convolutional neural networks (CNNs) have been applied to visual tasks since the late 1980s. However, despite a few scattered applications, they were dormant until the mid-2000s when developments in computing power and the advent of large amounts of labeled data, supplemented by improved algorithms, contributed to their advancement and brought them to the forefront of a neural network renaissance that has seen rapid progression since 2012. In this review, which focuses on the application of CNNs to image classification tasks, we cover their development, from their predecessors up to recent state-of-the-art deep learning systems. Along the way, we analyze (1) their early successes, (2) their role in the deep learning renaissance, (3) selected symbolic works that have contributed to their recent popularity, and (4) several improvement attempts by reviewing contributions and challenges of over 300 publications. We also introduce some of their current trends and remaining challenges.
Context-aware Natural Language Generation for Spoken Dialogue Systems

Natural language generation (NLG) is an important component of question answering(QA) systems which has a significant impact on system quality. Most tranditional QA systems based on templates or rules tend to generate rigid and stylised responses without the natural variation of human language. Furthermore, such methods need an amount of work to generate the templates or rules. To address this problem, we propose a Context-Aware LSTM model for NLG. The model is completely driven by data without manual designed templates or rules. In addition, the context information, including the question to be answered, semantic values to be addressed in the response, and the dialogue act type during interaction, are well approached in the neural network model, which enables the model to produce variant and informative responses. The quantitative evaluation and human evaluation show that CA-LSTM obtains state-of-the-art performance.
Developing Consumers' Brand Loyalty in Companies' Microblogs: The Roles of Social- and Self- Factors

This paper aims to explore how socialand self-factors may affect consumers’ brand loyalty while they follow companies’ microblogs. Drawing upon the commitment-trust theory, social influence theory, and self-congruence theory, we propose that network externalities, social norms, and self-congruence are the key determinants in the research model. The impacts of these factors on brand loyalty will be mediated by brand trust and brand commitment. We empirically test the model through an online survey on an existing microblogging site. The findings illustrate that network externalities and self-congruence can positively affect brand trust, which subsequently leads to brand commitment and brand loyalty. Meanwhile, social norms, together with self-congruence, directly posit influence on brand commitment. Brand commitment is then positively associated with brand loyalty. We believe that the findings of this research can contribute to the literature. We offer new insights regarding how consumers’ brand loyalty develops from the two social-factors and their self-congruence with the brand. Company managers could also apply our findings to strengthen their relationship marketing with consumers on microblogging sites.
Beyond Bitcoin: A Critical Look at Blockchain-Based Systems

After more than eight years since the launch of Bitcoin, the decentralized transaction ledger functionality implemented through the blockchain technology is being used not only for cryptocurrencies, but to register, confirm and transfer any kind of contract and property. In this work, we analyze the most relevant functionalities and known issues of this technology, with the intent of pointing out the possible behaviours that are not as efficient and reliable as they should be when thinking with a broader outlook.
Who Is This " We " ? Levels of Collective Identity and Self Representations

Cross-cultural perspectives have brought renewed interest in the social aspects of the self and the extent to which individuals define themselves in terms of their relationships to others and to social groups. This article provides a conceptual review of research and theory of the social self, arguing that the personal, relational, and collective levels of self-definition represent distinct forms of selfrepresentation with different origins, sources of self-worth, and social motivations. A set of 3 experiments illustrates haw priming of the interpersonal or collective "we" can alter spontaneous judgments of similarity and self-descriptions.
Big Data – BigData 2018

Traditional time series similarity search, based on relevance feedback, combines initial, positive and negative relevant series directly to create new query sequence for the next search; it can’t make full use of the negative relevant sequence, even results in inaccurate query results due to excessive adjustment of the query sequence in some cases. In this paper, time series similarity search based on separate relevance feedback is proposed, each round of query includes positive query and negative query, and combines the results of them to generate the query results of each round. For one data sequence, positive query evaluates its similarity to the initial and positive relevant sequences, and negative query evaluates it’s similarity to the negative relevant sequences. The final similar sequences should be not only close to positive relevant series but also far away from negative relevant series. The experiments on UCR data sets showed that, compared with the retrieval method without feedback and the commonly used feedback algorithm the proposed method can improve accuracy of similarity search on some data sets.
Benchmarking Deep Sequential Models on Volatility Predictions for Financial Time Series

Volatility is a quantity of measurement for the price movements of stocks or options which indicates the uncertainty within financial markets. As an indicator of the level of risk or the degree of variation, volatility is important to analyse the financial market, and it is taken into consideration in various decision-making processes in financial activities. On the other hand, recent advancement in deep learning techniques has shown strong capabilities in modelling sequential data, such as speech and natural language. In this paper, we empirically study the applicability of the latest deep structures with respect to the volatility modelling problem, through which we aim to provide an empirical guidance for the theoretical analysis of the marriage between deep learning techniques and financial applications in the future. We examine both the traditional approaches and the deep sequential models on the task of volatility prediction, including the most recent variants of convolutional and recurrent networks, such as the dilated architecture. Accordingly, experiments with real-world stock price datasets are performed on a set of 1314 daily stock series for 2018 days of transaction. The evaluation and comparison are based on the negative log likelihood (NLL) of real-world stock price time series. The result shows that the dilated neural models, including dilated CNN and Dilated RNN, produce most accurate estimation and prediction, outperforming various widely-used deterministic models in the GARCH family and several recently proposed stochastic models. In addition, the high flexibility and rich expressive power are validated in this study.
A survey of Commit Protocols in Distributed Real Time database systems

The commit processing in a Distributed Real Time Database (DRTDBS) can significantly increase execution time of a transaction. Therefore, designing a good commit protocol is important for the DRTDBS; the main challenge is the adaptation of standard commit protocol into the real time database system and so, decreasing the number of missed transaction in the systems. In these papers we review the basic commit protocols and the other protocols depend on it, for enhancing the transaction performance in DRTDBS. We propose a new commit protocol for reducing the number of transaction that missing their deadline.  DRTDBS, Commit protocols, Commit processing, 2PC protocol, 3PC protocol, Missed Transaction, Abort Transaction.
Evolution of Convolutional Highway Networks

Convolutional highways are deep networks based on multiple stacked convolutional layers for feature preprocessing. We introduce an evolutionary algorithm (EA) for optimization of the structure and hyperparameters of convolutional highways and demonstrate the potential of this optimization setting on the well-known MNIST data set. The (1+1)-EA employs Rechenberg’s mutation rate control and a niching mechanism to overcome local optima adapts the optimization approach. An experimental study shows that the EA is capable of improving the state-of-the-art network contribution and of evolving highway networks from scratch.
Support Vector Machine Based Fingerprint Identification

This work is released in biometric field and has as goal, development of a full automatic fingerprint identification system based on support vector machine. Promising Results of first experiences pushed us to develop codification and recognition algorithms which are specifically associated to this system. In this context, works were consecrated on algorithm developing of the original image processing, minutiae and singular points localization; Gabor filters coding and testing these algorithms on well known databases which are: FVC2004 databases & FingerCell database. Performance Evaluating has proved that SVM achieved a good recognition rate in comparing with results obtained using a classic neural network RBF. Biometry, Core and Delta points Detection, Gabor filters coding, Image processing and Support vector machine.
An Image Steganography Scheme Using 3 D-Sudoku

Steganography, an information-hiding technique, is that embedding secret information into a cover-media to generate a meaningful stego-media. This paper proposes a novel image steganography scheme using 3D-Sudoku. In this paper, a cyclically moving algorithm is used to construct a 3D-Sudoku. The data-embedding phase is that the pixels of the cover-image as the coordinate of 3D-Sudoku are modified in the rule of a minimal distortion to indicate the position of a given secret data. And in the dataextraction phase, the modified pixels of the stego-image as the coordinate of 3D-Sudoku are applied to directly extract the embedded secret data. The experimental results show that the visual quality of stego-image in the proposed scheme is slightly less than that in the steganography scheme using 2D-Sudoku, but the embedding capacity in the proposed scheme is higher than that in the steganography scheme using 2D-Sudoku.
Content-based author co-citation analysis

Author Co-citation Analysis (ACA) has long been used as an effective method for identifying the intellectual structure of a research domain, but it relies on simple co-citation counting, which does not take the citation content into consideration. The present study proposes a new method for measuring the similarity between co-cited authors by considering author's citation content. We collected the full-text journal articles in the information science domain and extracted the citing sentences to calculate their similarity distances. We compared our method with traditional ACA and found out that our approach, while displaying a similar intellectual structure for the information science domain as the other baseline methods, also provides more details about the sub-disciplines in the domain than with traditional ACA.
Business ethics : the state of the art

The purpose of this paper is to give an account of some of the current areas of scholarship in business ethics and to suggest how these areasmay be relevant for scholars working in other business disciplines.We endeavor to paint a picture of a healthy discipline full of controversy, rich intellectual discussions, and the beginnings of several research traditions. To begin, we examine how it is common practice to think of `business' and `ethics' as separate entities, and suggest how such a `separation thesis' can be used to diagnose problems in a host of business disciplines.We next examine the literature on corporate agency and responsibility that questionswhether or not a corporation can be said to be normally accountable, in the sameway that individual moral agents can be held accountable, andwe look at an emerging research tradition of `stakeholder theory' that cuts across the disciplines of business.We then explore two contemporary issues in business ethics: (1) International Business Ethics; and, (2) Environmental Ethics and Business, andwe concludewith suggestions for some additional research questions.