nils-herrmann/clean_scirepeval huggingface dataset:
Model Ensemble for Click Prediction in Bing Search Ads

Accurate estimation of the click-through rate (CTR) in sponsored ads significantly impacts the user search experience and businesses’ revenue, even 0.1% of accuracy improvement would yield greater earnings in the hundreds of millions of dollars. CTR prediction is generally formulated as a supervised classification problem. In this paper, we share our experience and learning on model ensemble design and our innovation. Specifically, we present 8 ensemble methods and evaluate them on our production data. Boosting neural networks with gradient boosting decision trees turns out to be the best. With larger training data, there is a nearly 0.9% AUC improvement in offline testing and significant click yield gains in online traffic. In addition, we share our experience and learning on improving the quality of training.
Sustainable Supply Chain Management Capability Maturity: Framework Development and initial Evaluation

Owing to global importance given to sustainability, commercial and government organizations alike are becoming aware about the need for them to implement sustainable supply chain management (SSCM) practices. These practices are mostly inter-organizational in nature, which introduce complexity due to the need for managing activities and information across the supply chain. However, currently there is still a lack of studies assessing the required organizational capability for implementing SSCM practices. Therefore, in this research-in-progress paper, we propose a SSCM capability maturity framework and provide an initial evaluation of the proposed framework through an in-depth case study with a large Australian city council. The framework together with the supportive empirical evidence represents a contribution to theory and practice. For practice, it helps organizations to understand their current SSCM maturity level and establish an appropriate strategy to progress in their maturity level. For theory, the study contributes to the on-going conceptual development related to SSCM and has clear implications for understanding the roles of IS/IT in building the required SSCM capability.
Smart city and the applications

Based on the “digital city”, “smart city” is widely used in daily livelihood, environmental protection, public security, city services and other fields. In this paper, we mainly focus on recent research and the concept of “smart city”, summarizing the relationship between "smart city" and "digital city", putting forward the main content of application systems as well as the importance and difficulty of the construction of “smart city”, and making a brief statement of the influence of developing smart city in China.
A Measurement Study of Hate Speech in Social Media

Social media platforms provide an inexpensive communication medium that allows anyone to quickly reach millions of users. Consequently, in these platforms anyone can publish content and anyone interested in the content can obtain it, representing a transformative revolution in our society. However, this same potential of social media systems brings together an important challenge---these systems provide space for discourses that are harmful to certain groups of people. This challenge manifests itself with a number of variations, including bullying, offensive content, and hate speech. Specifically, authorities of many countries today are rapidly recognizing hate speech as a serious problem, specially because it is hard to create barriers on the Internet to prevent the dissemination of hate across countries or minorities. In this paper, we provide the first of a kind systematic large scale measurement and analysis study of hate speech in online social media. We aim to understand the abundance of hate speech in online social media, the most common hate expressions, the effect of anonymity on hate speech and the most hated groups across regions. In order to achieve our objectives, we gather traces from two social media systems: Whisper and Twitter. We then develop and validate a methodology to identify hate speech on both of these systems. Our results identify hate speech forms and unveil a set of important patterns, providing not only a broader understanding of online hate speech, but also offering directions for detection and prevention approaches.
Functional Logic Programming: From Theory to Curry

Functional logic programming languages combine the most important declarative programming paradigms, and attempts to combine these paradigms have a long history. The declarative multi-paradigm language Curry is influenced by recent advances in the foundations and implementation of functional logic languages. The development of Curry is an international initiative intended to provide a common platform for the research, teaching, and application of integrated functional logic languages. This paper surveys the foundations of functional logic programming that are relevant for Curry, the main features of Curry, and extensions and applications of Curry and functional logic programming.
I2T2I: Learning text to image synthesis with textual data augmentation

Translating information between text and image is a fundamental problem in artificial intelligence that connects natural language processing and computer vision. In the past few years, performance in image caption generation has seen significant improvement through the adoption of recurrent neural networks (RNN). Meanwhile, text-to-image generation begun to generate plausible images using datasets of specific categories like birds and flowers. We've even seen image generation from multi-category datasets such as the Microsoft Common Objects in Context (MSCOCO) through the use of generative adversarial networks (GANs). Synthesizing objects with a complex shape, however, is still challenging. For example, animals and humans have many degrees of freedom, which means that they can take on many complex shapes. We propose a new training method called Image-Text-Image (I2T2I) which integrates text-to-image and image-to-text (image captioning) synthesis to improve the performance of text-to-image synthesis. We demonstrate that I2T2I can generate better multi-categories images using MSCOCO than the state-of-the-art. We also demonstrate that I2T2I can achieve transfer learning by using a pre-trained image captioning module to generate human images on the MPII Human Pose dataset (MHP) without using sentence annotation.
Learning Bayesian networks from data : an information theory based approach

This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop efficient algorithms that can effectively learn Bayesian networks, requiring only polynomial numbers of conditional independence (CI) tests in typical cases. We provide precise conditions that specify when these algorithms are guaranteed to be correct as well as empirical evidence (from real world applications and simulation tests) that demonstrates that these systems work efficiently and reliably in practice.
Back to the Past: Source Identification in Diffusion Networks from Partially Observed Cascades

When a piece of malicious information becomes rampant in an information diffusion network, can we identify the source node that originally introduced the piece into the network and infer the time when it initiated this? Being able to do so is critical for curtailing the spread of malicious information, and reducing the potential losses incurred. This is a very challenging problem since typically only incomplete traces are observed and we need to unroll the incomplete traces into the past in order to pinpoint the source. In this paper, we tackle this problem by developing a twostage framework, which first learns a continuoustime diffusion network model based on historical diffusion traces and then identifies the source of an incomplete diffusion trace by maximizing the likelihood of the trace under the learned model. Experiments on both large synthetic and realworld data show that our framework can effectively “go back to the past”, and pinpoint the source node and its initiation time significantly more accurately than previous state-of-the-arts.
Noise2Void - Learning Denoising from Single Noisy Images

The field of image denoising is currently dominated by discriminative deep learning methods that are trained on pairs of noisy input and clean target images. Recently it has been shown that such methods can also be trained without clean targets. Instead, independent pairs of noisy images can be used, in an approach known as NOISE2NOISE (N2N). Here, we introduce NOISE2VOID (N2V), a training scheme that takes this idea one step further. It does not require noisy image pairs, nor clean target images. Consequently, N2V allows us to train directly on the body of data to be denoised and can therefore be applied when other methods cannot. Especially interesting is the application to biomedical image data, where the acquisition of training targets, clean or noisy, is frequently not possible. We compare the performance of N2V to approaches that have either clean target images and/or noisy image pairs available. Intuitively, N2V cannot be expected to outperform methods that have more information available during training. Still, we observe that the denoising performance of NOISE2VOID drops in moderation and compares favorably to training-free denoising methods.
Challenges in Information Systems Procurement in the Norwegian Public Sector

Public procurement of information systems (IS) and IS services provides several challenges to the stakeholders involved in the procurement processes. This paper reports initial results from a Delphi study, which involved 46 experienced procurement managers, chief information officers, and vendor representatives in the Norwegian public sector. The participants identified altogether 98 challenges related to IS procurement, divided further into 13 categories: requirements specification, change management, cooperation among stakeholders, competence, competition, contracting, inter-municipal cooperation, governmental management, procurement process, rules and regulations, technology and infrastructure, vendors, and IT governance. The results contribute by supporting a few previous findings from conceptual and case-based studies, and by suggesting additional issues which deserve both further research and managerial and governmental attention. As such, the results provide altogether a rich overview of the IS procurement challenges in the Norwegian public context.
Sharing Means Renting?: An Entire-marketplace Analysis of Airbnb

Airbnb, an online marketplace for accommodations, has experienced a staggering growth accompanied by intense debates and scattered regulations around the world. Current discourses, however, are largely focused on opinions rather than empirical evidences. Here, we aim to bridge this gap by presenting the first large-scale measurement study on Airbnb, using a crawled data set containing 2.3 million listings, 1.3 million hosts, and 19.3 million reviews. We measure several key characteristics at the heart of the ongoing debate and the sharing economy. Among others, we find that Airbnb has reached a global yet heterogeneous coverage. The majority of its listings across many countries are entire homes, suggesting that Airbnb is actually more like a rental marketplace rather than a spare-room sharing platform. Analysis on star-ratings reveals that there is a bias toward positive ratings, amplified by a bias toward using positive words in reviews. The extent of such bias is greater than Yelp reviews, which were already shown to exhibit a positive bias. We investigate a key issue - commercial hosts who own multiple listings on Airbnb - repeatedly discussed in the current debate. We find that their existence is prevalent, they are early movers towards joining Airbnb, and their listings are disproportionately entire homes and located in the US. Our work advances the current understanding of how Airbnb is being used and may serve as an independent and empirical reference to inform the debate.
Daily caffeine intake among Osijek High School students: questionnaire study.

To assess caffeine intake habits of Osijek high school students and identify the most important sources of caffeine intake.



Adjusted Wisconsin University Caffeine Consumption Questionnaire was administered to 571 high school students (371 boys and 200 girls in the ninth grade) from Osijek, the largest town in eastern Croatia. The level of caffeine in soft drinks was determined by the high pressure liquid chromatography method, and in chocolate and coffee from the literature data.



Only 10% of our participants did not use foodstuffs containing caffeine. The intake of caffeine originated from soft drinks (50%), coffee (37%), and chocolate (13%). The mean caffeine concentration in soft drinks was 100-/+26.9 mg/L. The mean estimated caffeine intake was 62.8-/+59.8 mg/day. There was no statistically significant difference between boys and girls in caffeine consumption (1.0-/+0.9 mg/kg bw for boys vs 1.1-/+1.4 mg/kg bw for girls). Daily caffeine intake of 50-100 mg was recorded in 32% of girls and 29% of boys, whereas intake greater than 100 mg/day was recorded in 18% of girls and 25% of boys.



Soft drinks containing caffeine were the major source of caffeine intake in high school students. Large-scale public health measures are needed to inform the public on health issues related to excessive intake of caffeine-containing foodstuffs by children and adolescents.
Discourse Representation Theory

Discourse Representation Theory is a specific name for the work of Hans Kamp in the area of dynamic interpretation of natural language. Also, it has gradually become a generic term for proposals for dynamic interpretation of natural language in the same spirit. These proposals have in common that each new sentence is interpreted in terms of the contribution it makes to an existing piece of interpreted discourse. The interpretation conditions for sentences are given as instructions for updating the representation of the discourse. This article first introduces the problem that discourse representation theory, in its specific sense, sets out to solve. Then the basic ideas of the theory are listed, various extensions of the basic theory are discussed, the relation to partial interpretation of language is sketched, and proof theory for discourse representation structures is presented. The paper ends with a brief account of the use of ‘unresolved’ discourse representation structures for the representation of ambiguities.
Secure Attribute-Based Signature Scheme With Multiple Authorities for Blockchain in Electronic Health Records Systems

Electronic Health Records (EHRs) are entirely controlled by hospitals instead of patients, which complicates seeking medical advices from different hospitals. Patients face a critical need to focus on the details of their own healthcare and restore management of their own medical data. The rapid development of blockchain technology promotes population healthcare, including medical records as well as patient-related data. This technology provides patients with comprehensive, immutable records, and access to EHRs free from service providers and treatment websites. In this paper, to guarantee the validity of EHRs encapsulated in blockchain, we present an attribute-based signature scheme with multiple authorities, in which a patient endorses a message according to the attribute while disclosing no information other than the evidence that he has attested to it. Furthermore, there are multiple authorities without a trusted single or central one to generate and distribute public/private keys of the patient, which avoids the escrow problem and conforms to the mode of distributed data storage in the blockchain. By sharing the secret pseudorandom function seeds among authorities, this protocol resists collusion attack out of  $N$  from  $N-1$  corrupted authorities. Under the assumption of the computational bilinear Diffie-Hellman, we also formally demonstrate that, in terms of the unforgeability and perfect privacy of the attribute-signer, this attribute-based signature scheme is secure in the random oracle model. The comparison shows the efficiency and properties between the proposed method and methods proposed in other studies.
BotSniffer: Detecting Botnet Command and Control Channels in Network Traffic

Botnets are now recognized as one of the most serious security threats. In contrast to previous malware, botnets have the characteristic of a command and control (C&C) channel. Botnets also often use existing common protocols, e.g., IRC, HTTP, and in protocol-conforming manners. This makes the detection of botnet C&C a challenging problem. In this paper, we propose an approach that uses network-based anomaly detection to identify botnet C&C channels in a local area network without any prior knowledge of signatures or C&C server addresses. This detection approach can identify both the C&C servers and infected hosts in the network. Our approach is based on the observation that, because of the pre-programmed activities related to C&C, bots within the same botnet will likely demonstrate spatial-temporal correlation and similarity. For example, they engage in coordinated communication, propagation, and attack and fraudulent activities. Our prototype system, BotSniffer, can capture this spatial-temporal correlation in network traffic and utilize statistical algorithms to detect botnets with theoretical bounds on the false positive and false negative rates. We evaluated BotSniffer using many real-world network traces. The results show that BotSniffer can detect real-world botnets with high accuracy and has a very low false positive rate.
From Big data platforms to smarter solution, with intelligent learning: [PAV] 4 - Pave the way for Intelligence

In today's time when data is generating by everyone at every moment, and the word is moving so fast with exponential growth of new technologies and innovations in all science and engineering domains, the age of big data is coming, and the potential of learning from this huge amount of data and from different sources is undoubtedly significant to uncover underlying structure and facilitate the development of more intelligent solution. Intelligence is around us, and the concept of big data and learning from it has existed since the emergence of the human being. In this article we focus on data from; sensors, images, and text, and we incorporate the principles of human intelligence; brain - body - environment, as a source of inspiration that allows us to put a new concept based on big data - machine learning--domain and pave the way for intelligent platform.
Dual-Polarization Slot Antenna With High Cross-Polarization Discrimination for Indoor Small-Cell MIMO Systems

We demonstrate a dual-polarization slot antenna for indoor small-cell multiple-input-multiple-output (MIMO) systems. The symmetric structure and differential feeding promotes destructive interference of cross-polarized radiation in the far field to achieve high cross-polarization discrimination (XPD) in all directions. In addition, a very similar radiation pattern is observed not only between the major planes of each polarization but also between the two polarization orientations. Therefore, the proposed antenna can be considered as a stronger candidate for indoor small-cell MIMO systems. With an average XPD of 26.4 dB in all directions, the 3-D ray-tracing simulation results show a more than 22% increase in the system throughput compared to previous dual-polarization antennas for a single-user MIMO system in a typical indoor environment.
ARITHMETIC PROPERTIES OF COEFFICIENTS OF HALF-INTEGRAL WEIGHT MAASS-POINCARÉ SERIES

Let j(z) be the usual modular function for SL2(Z) j(z) = q−1 + 744 + 196884q + 21493760q + · · · , where q = e . The values of modular functions such as j(z) at imaginary quadratic arguments in H, the upper half of the complex plane, are known as singular moduli. Singular moduli are algebraic integers which play many roles in number theory. For example, they generate class fields of imaginary quadratic fields, and they parameterize isomorphism classes of elliptic curves with complex multiplication. We shall slightly abuse terminology by referring to the value of any modular invariant at an imaginary quadratic argument as a singular modulus. In an important paper [23], Zagier gave a new proof of Borcherds’ famous theorem on the infinite product expansions of integer weight modular forms on SL2(Z) with Heegner divisor. This proof, as well as all of the results of [23], are connected to his beautiful observation that the generating functions for traces of singular moduli are essentially weight 3/2 weakly holomorphic modular forms.
Learning Robust Representations for Computer Vision

Unsupervised learning techniques in computer vision of ten require learning latent representations, such as low-dimensional linear and non-linear subspaces. Noise and outliers in the data can frustrate these approaches by obscuring the latent spaces. Our main goal is deeper understanding and new development of robust approaches for representation learning. We provide a new interpretation for existing robust approaches and present two specific contributions: a new robust PCA approach, which can separate foreground features from dynamic background, and a novel robust spectral clustering method, that can cluster facial images with high accuracy. Both contributions show superior performance to standard methods on real-world test sets.
Deep Active Learning for Named Entity Recognition

Deep learning has yielded state-of-the-art performance on many natural language processing tasks including named entity recognition (NER). However, this typically requires large amounts of labeled data. In this work, we demonstrate that the amount of labeled training data can be drastically reduced when deep learning is combined with active learning. While active learning is sample-efficient, it can be computationally expensive since it requires iterative retraining. To speed this up, we introduce a lightweight architecture for NER, viz., the CNN-CNN-LSTM model consisting of convolutional character and word encoders and a long short term memory (LSTM) tag decoder. The model achieves nearly state-of-the-art performance on standard datasets for the task while being computationally much more efficient than best performing models. We carry out incremental active learning, during the training process, and are able to nearly match state-of-the-art performance with just 25% of the original training data.
Hierarchical Recurrent Neural Encoder for Video Representation with Application to Captioning

Recently, deep learning approach, especially deep Convolutional Neural Networks (ConvNets), have achieved overwhelming accuracy with fast processing speed for image classification. Incorporating temporal structure with deep ConvNets for video representation becomes a fundamental problem for video content analysis. In this paper, we propose a new approach, namely Hierarchical Recurrent Neural Encoder (HRNE), to exploit temporal information of videos. Compared to recent video representation inference approaches, this paper makes the following three contributions. First, our HRNE is able to efficiently exploit video temporal structure in a longer range by reducing the length of input information flow, and compositing multiple consecutive inputs at a higher level. Second, computation operations are significantly lessened while attaining more non-linearity. Third, HRNE is able to uncover temporal tran-sitions between frame chunks with different granularities, i.e. it can model the temporal transitions between frames as well as the transitions between segments. We apply the new method to video captioning where temporal information plays a crucial role. Experiments demonstrate that our method outperforms the state-of-the-art on video captioning benchmarks.