nils-herrmann/clean_scirepeval huggingface dataset:
Semantic Image Synthesis via Adversarial Learning

In this paper, we propose a way of synthesizing realistic images directly with natural language description, which has many useful applications, e.g. intelligent image manipulation. We attempt to accomplish such synthesis: given a source image and a target text description, our model synthesizes images to meet two requirements: 1) being realistic while matching the target text description; 2) maintaining other image features that are irrelevant to the text description. The model should be able to disentangle the semantic information from the two modalities (image and text), and generate new images from the combined semantics. To achieve this, we proposed an end-to-end neural architecture that leverages adversarial learning to automatically learn implicit loss functions, which are optimized to fulfill the aforementioned two requirements. We have evaluated our model by conducting experiments on Caltech-200 bird dataset and Oxford-102 flower dataset, and have demonstrated that our model is capable of synthesizing realistic images that match the given descriptions, while still maintain other features of original images.
Monetary Policy Rules Based on Real-Time Data

This paper examines the magnitude of informational problems associated with the implementation and interpretation of simple monetary policy rules. Using Taylor’s rule as an example, I demonstrate that real-time policy recommendations differ considerably from those obtained with ex post revised data. Further, estimated policy reaction functions based on ex post revised data provide misleading descriptions of historical policy and obscure the behavior suggested by information available to the Federal Reserve in real time. These results indicate that reliance on the information actually available to policy makers in real time is essential for the analysis of monetary policy rules.
Estimation of the dietary nutrient profile of free-roaming feral cats: possible implications for nutrition of domestic cats.

Cats are strict carnivores and in the wild rely on a diet solely based on animal tissues to meet their specific and unique nutritional requirements. Although the feeding ecology of cats in the wild has been well documented in the literature, there is no information on the precise nutrient profile to which the cat's metabolism has adapted. The present study aimed to derive the dietary nutrient profile of free-living cats. Studies reporting the feeding habits of cats in the wild were reviewed and data on the nutrient composition of the consumed prey items obtained from the literature. Fifty-five studies reported feeding strategy data of cats in the wild. After specific exclusion criteria, twenty-seven studies were used to derive thirty individual dietary nutrient profiles. The results show that feral cats are obligatory carnivores, with their daily energy intake from crude protein being 52 %, from crude fat 46 % and from N-free extract only 2 %. Minerals and trace elements are consumed in relatively high concentrations compared with recommended allowances determined using empirical methods. The calculated nutrient profile may be considered the nutrient intake to which the cat's metabolic system has adapted. The present study provides insight into the nutritive, as well as possible non-nutritive aspects of a natural diet of whole prey for cats and provides novel ways to further improve feline diets to increase health and longevity.
Adaptive Cluster Ensemble Selection

Cluster ensembles generate a large number of different clustering solutions and combine them into a more robust and accurate consensus clustering. On forming the ensembles, the literature has suggested that higher diversity among ensemble members produces higher performance gain. In contrast, some studies also indicated that medium diversity leads to the best performing ensembles. Such contradicting observations suggest that different data, with varying characteristics, may require different treatments. We empirically investigate this issue by examining the behavior of cluster ensembles on benchmark data sets. This leads to a novel framework that selects ensemble members for each data set based on its own characteristics. Our framework first generates a diverse set of solutions and combines them into a consensus partition P*. Based on the diversity between the ensemble members and P*, a subset of ensemble members is selected and combined to obtain the final output. We evaluate the proposed method on benchmark data sets and the results show that the proposed method can significantly improve the clustering performance, often by a substantial margin. In some cases, we were able to produce final solutions that significantly outperform even the best ensemble members.
JEDI: Joint Entity and Relation Detection using Type Inference

FREEBASE contains entities and relation information but is highly incomplete. Relevant information is ubiquitous in web text, but extraction deems challenging. We present JEDI, an automated system to jointly extract typed named entities and FREEBASE relations using dependency pattern from text. An innovative method for constraint solving on entity types of multiple relations is used to disambiguate pattern. The high precision in the evaluation supports our claim that we can detect entities and relations together, alleviating the need to train a custom classifier for an entity type1.
How to Architect a Query Compiler, Revisited

To leverage modern hardware platforms to their fullest, more and more database systems embrace compilation of query plans to native code. In the research community, there is an ongoing debate about the best way to architect such query compilers. This is perceived to be a difficult task, requiring techniques fundamentally different from traditional interpreted query execution. 
 We aim to contribute to this discussion by drawing attention to an old but underappreciated idea known as Futamura projections, which fundamentally link interpreters and compilers. Guided by this idea, we demonstrate that efficient query compilation can actually be very simple, using techniques that are no more difficult than writing a query interpreter in a high-level language. Moreover, we demonstrate how intricate compilation patterns that were previously used to justify multiple compiler passes can be realized in one single, straightforward, generation pass. Key examples are injection of specialized index structures, data representation changes such as string dictionaries, and various kinds of code motion to reduce the amount of work on the critical path.
 We present LB2: a high-level query compiler developed in this style that performs on par with, and sometimes beats, the best compiled query engines on the standard TPC-H benchmark.
Findel: Secure Derivative Contracts for Ethereum

Blockchain-based smart contracts are considered a promising technology for handling financial agreements securely. In order to realize this vision, we need a formal language to unambiguously describe contract clauses. We introduce Findel – a purely declarative financial domain-specific language (DSL) well suited for implementation in blockchain networks. We implement an Ethereum smart contract that acts as a marketplace for Findel contracts and measure the cost of its operation. We analyze challenges in modeling financial agreements in decentralized networks and outline directions for future work.
Quasi-Recurrent Neural Networks

Recurrent neural networks are a powerful tool for modeling sequential data, but the dependence of each timestep’s computation on the previous timestep’s output limits parallelism and makes RNNs unwieldy for very long sequences. We introduce quasi-recurrent neural networks (QRNNs), an approach to neural sequence modeling that alternates convolutional layers, which apply in parallel across timesteps, and a minimalist recurrent pooling function that applies in parallel across channels. Despite lacking trainable recurrent layers, stacked QRNNs have better predictive accuracy than stacked LSTMs of the same hidden size. Due to their increased parallelism, they are up to 16 times faster at train and test time. Experiments on language modeling, sentiment classification, and character-level neural machine translation demonstrate these advantages and underline the viability of QRNNs as a basic building block for a variety of sequence tasks.
Motivations for Play in Online Games

An empirical model of player motivations in online games provides the foundation to understand and assess how players differ from one another and how motivations of play relate to age, gender, usage patterns, and in-game behaviors. In the current study, a factor analytic approach was used to create an empirical model of player motivations. The analysis revealed 10 motivation subcomponents that grouped into three overarching components (achievement, social, and immersion). Relationships between motivations and demographic variables (age, gender, and usage patterns) are also presented.
Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg?

Twitter is a new web application playing dual roles of online social networking and microblogging. Users communicate with each other by publishing text-based posts. The popularity and open structure of Twitter have attracted a large number of automated programs, known as bots, which appear to be a double-edged sword to Twitter. Legitimate bots generate a large amount of benign tweets delivering news and updating feeds, while malicious bots spread spam or malicious contents. More interestingly, in the middle between human and bot, there has emerged cyborg referred to either bot-assisted human or human-assisted bot. To assist human users in identifying who they are interacting with, this paper focuses on the classification of human, bot, and cyborg accounts on Twitter. We first conduct a set of large-scale measurements with a collection of over 500,000 accounts. We observe the difference among human, bot, and cyborg in terms of tweeting behavior, tweet content, and account properties. Based on the measurement results, we propose a classification system that includes the following four parts: 1) an entropy-based component, 2) a spam detection component, 3) an account properties component, and 4) a decision maker. It uses the combination of features extracted from an unknown user to determine the likelihood of being a human, bot, or cyborg. Our experimental evaluation demonstrates the efficacy of the proposed classification system.
Stance Detection for the Fake News Challenge : Identifying Textual Relationships with Deep Neural Nets

The issue of “fake news” has arisen recently as a potential threat to high-quality journalism and well-informed public discourse. The Fake News Challenge was organized in early 2017 to encourage development of machine learning-based classification systems that perform “stance detection” -i.e. identifying whether a particular news headline “agrees” with, “disagrees” with, “discusses,” or is unrelated to a particular news article -in order to allow journalists and others to more easily find and investigate possible instances of “fake news.” We developed several deep neural network-based models to tackle the stance detection problem, ranging from relatively simple feed-forward networks to elaborate recurrent models featuring attention and multiple vocabularies. We ultimately found that an LSTM-based bidirectional conditional encoding model using pre-trained GloVe word embeddings delivered the best performance: greater than 97% classification accuracy on the dev set.
Benchmarking Classic and Learned Navigation in Complex 3D Environments

Navigation research is attracting renewed interest with the advent of learning-based methods. However, this new line of work is largely disconnected from well-established classic navigation approaches. In this paper, we take a step towards coordinating these two directions of research. We set up classic and learning-based navigation systems in common simulated environments and thoroughly evaluate them in indoor spaces of varying complexity, with access to different sensory modalities. Additionally, we measure human performance in the same environments. We find that a classic pipeline, when properly tuned, can perform very well in complex cluttered environments. On the other hand, learned systems can operate more robustly with a limited sensor suite. Both approaches are still far from human-level performance.
SMS-Based One-Time Passwords: Attacks and Defense - (Short Paper)

SMS-based One-Time Passwords (SMS OTP) were introduced to counter phishing and other attacks against Internet services such as online banking. Today, SMS OTPs are commonly used for authentication and authorization for many different applications. Recently, SMS OTPs have come under heavy attack, especially by smartphone trojans. In this paper, we analyze the security architecture of SMS OTP systems and study attacks that pose a threat to Internet-based authentication and authorization services. We determined that the two foundations SMS OTP is built on, cellular networks and mobile handsets, were completely different at the time when SMS OTP was designed and introduced. Throughout this work, we show why SMS OTP systems cannot be considered secure anymore. Based on our findings, we propose mechanisms to secure SMS OTPs against common attacks and specifically against smartphone trojans.
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

In this paper we compare different types of recurrent units in recurrent neural networks (RNNs). Especially, we focus on more sophisticated units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU). We evaluate these recurrent units on the tasks of polyphonic music modeling and speech signal modeling. Our experiments revealed that these advanced recurrent units are indeed better than more traditional recurrent units such as tanh units. Also, we found GRU to be comparable to LSTM.
An Improved Relative Self-Attention Mechanism for Transformer with Application to Music Generation

Music relies heavily on repetition to build structure and meaning. Self-reference occurs on multiple timescales, from motifs to phrases to reusing of entire sections of music, such as in pieces with ABA structure. e he Transformer (Vaswani et al., 2017), a sequence model based on self-attention, has achieved compelling results in many generation tasks that require maintaining long-range coherence. This suggests that self-attention might also be well-suited to modeling music. In musical composition and performance, however, relative timing is critically important. Existing approaches for representing relative positional information in the Transformer modulate attention based on pairwise distance (Shaw et al., 2018). This is impractical for long sequences such as musical compositions since their memory complexity is quadratic in the sequence length. We propose an algorithm that reduces the intermediate memory requirements to linear in the sequence length. This enables us to demonstrate that a Transformer with our modified relative attention mechanism can generate minute-long (thousands of steps) compositions with compelling structure, generate continuations that coherently elaborate on a given motif, and in a seq2seq setup generate accompaniments conditioned on melodies1. We evaluate the Transformer with our relative attention mechanism on two datasets, JSB Chorales and Piano-e-Competition, and obtain state-of-the-art results on the latter.
Where Has All the Education Gone ?

Cross-national data show no association between increases in human capital attributable to the rising educational attainment of the labor force and the rate of growth of output per worker. This implies that the association of educational capital growth with conventional measures of total factor production is large, strongly statistically significant, and negative. These are “on average” results, derived from imposing a constant coefficient. However, the development impact of education varied widely across countries and has fallen short of expectations for three possible reasons. First, the institutional/ governance environment could have been sufficiently perverse that the accumulation of educational capital lowered economic growth. Second, marginal returns to education could have fallen rapidly as the supply of educated labor expanded while demand remained stagnant. Third, educational quality could have been so low that years of schooling created no human capital. The extent and mix of these three phenomena vary from country to country in explaining the actual economic impact of education, or the lack thereof.
The Process of Knowledge Discovery in A First Sketch Databases

The general idea of discovering knowledge in large amounts of data is both appealing and intuitive. Typically we focus our attention on learning algorithms, which provide the core capability of generalizing from large numbers of small, very specific facts to useful high-level rules; these learning techniques seem to hold the most excitement and perhaps the most substantive scientific content in the knowledge discovery in databases (KDD) enterprise. However, when we engage in real-world discovery tasks, we find that they can be extremely complex, and that induction of rules is only one small part of the overall process. While others have written overviews of "the concept of KDD, and even provided block diagrams for "knowledge discovery systems," no one has begun to identify all of the building blocks in a realistic KDD process. This is what we attempt to do here. Besides bringing into the discussion several parts of the process that have received inadequate attention in the KDD community, a careful elucidation of the steps in a realistic knowledge discovery process can provide a framework for comparison of different technologies and tools that are almost impossible to compare without a clean model.
Defining the price of hospitality: networked hospitality exchange via Airbnb

This study examines how money mediates and structures social exchange in a hospitality exchange service, and how social and economic factors guiding exchange get intertwined in this context. We present a qualitative study on the experiences of people who offer to rent out their homes, or parts of them, via the online peer-to-peer renting service Airbnb. Our study suggests that the frame monetary transactions set to exchange relationships contributes to the hosts' sense of control and ease in the exchange. We identified two behavioral patterns that highlight the importance of reputation and trust: (1) hosts divert their accumulated reputational capital into the rental price and (2) they may price their property below "the market price", so that they can choose their exchange partners form a wider pool of candidates.
Using Virtual Reality in Experimental Psychology

The major aim of the present study is to emphasize the potential offered by Virtual Reality (VR) to develop new tools for research in experimental psychology. Despite several works have addressed cognitive, clinical and methodological issues concerning the application of this technology in psychological and neuro-psychological assessment and rehabilitation, there is a lack of discussion focusing on the role played by Virtual Reality and 3D computer graphics in experimental behaviour research. This chapter provides an introduction to the basic concepts and the historical background of experimental psychology along with a rationale for the application of Virtual Reality in this scientific discipline. In particular, the historical framework aims at emphasizing that the application of VR in experimental psychology represents the leading edge of the revolution that informatics has operated into the traditional psychology laboratory. We point out that the use of VR and Virtual Environments (VEs) as research tool might discover new methodological horizons for experimental psychology and that it has the potential to raise important questions concerning the nature of many psychological phenomena. In order to put the discussion on a concrete basis, we review the relevant literature regarding the application of VR to the main areas of psychological research, such as perception, memory, problem solving, mental imagery and attention. Finally, fundamental issues having important implications for the feasibility of a VR approach applied to psychological research are discussed.
Temporal people-to-people recommendation on social networks with sentiment-based matrix factorization

Nowadays, the exponential advancement of social networks is creating new application areas for recommender systems (RSs). People-to-people RSs aim to exploit user’s interests for suggesting relevant people to follow. However, traditional recommenders do not consider that people may share similar interests but might have different feelings or opinions about them. In this paper we propose a novel recommendation engine which relies on the identification of semantic attitudes, that is, sentiment, volume, and objectivity extracted from user-generated content. In order to do this at large-scale on traditional social networks, we devise a three-dimensional matrix factorization, one for each attitude. Potential temporal alterations of users’ attitudes are also taken into consideration in the factorization model. Extensive offline experiments on different real world datasets, reveal the benefits of the proposed approach compared with some state-of-the-art techniques.
Neural Cross-lingual Named Entity Recognition with Minimal Resources

For languages with no annotated resources, unsupervised transfer of natural language processing models such as named-entity recognition (NER) from resource-rich languages would be an appealing capability. However, differences in words and word order across languages make it a challenging problem. To improve mapping of lexical items across languages, we propose a method that finds translations based on bilingual word embeddings. To improve robustness to word order differences, we propose to use self-attention, which allows for a degree of flexibility with respect to word order. We demonstrate that these methods achieve state-of-the-art or competitive NER performance on commonly tested languages under a cross-lingual setting, with much lower resource requirements than past approaches. We also evaluate the challenges of applying these methods to Uyghur, a lowresource
Harmonised digital forensic investigation process model

Digital forensics gained significant importance over the past decade, due to the increase in the number of information security incidents over this time period, but also due to the fact that our society is becoming more dependent on information technology. Performing a digital forensic investigation requires a standardised and formalised process to be followed. There is currently no international standard formalising the digital forensic investigation process, nor does a harmonised digital forensic investigation process exist that is acceptable in this field. This paper proposes a harmonised digital forensic investigation process model. The proposed model is an iterative and multi-tier model. The authors introduce the term “parallel actions”, defined as the principles which should be translated into actions within the digital forensic investigation process (i.e. principle that evidence's integrity must be preserved through the process and that chain of evidence must be preserved). The authors believe that the proposed model is comprehensive and that it harmonises existing state-of-the-art digital forensic investigation process models. Furthermore, we believe that the proposed model can lead to the standardisation of the digital forensic investigation process.