nils-herrmann/clean_scirepeval huggingface dataset:
Adversarial Training for Cross-Domain Universal Dependency Parsing

We describe our submission to the CoNLL 2017 shared task, which exploits the shared common knowledge of a language across different domains via a domain adaptation technique. Our approach is an extension to the recently proposed adversarial training technique for domain adaptation, which we apply on top of a graph-based neural dependency parsing model on bidirectional LSTMs. In our experiments, we find our baseline graphbased parser already outperforms the official baseline model (UDPipe) by a large margin. Further, by applying our technique to the treebanks of the same language with different domains, we observe an additional gain in the performance, in particular for the domains with less training data.
Long-lasting complete response status of advanced stage IV gall bladder cancer and colon cancer after combined treatment including autologous formalin-fixed tumor vaccine: two case reports

The prognosis of advanced (stage IV) cancer of the digestive organs is very poor. We have previously reported a case of advanced breast cancer with bone metastasis that was successfully treated with combined treatments including autologous formalin-fixed tumor vaccine (AFTV). Herein, we report the success of this approach in advanced stage IV (heavily metastasized) cases of gall bladder cancer and colon cancer.



 A 61-year-old woman with stage IV gall bladder cancer (liver metastasis and lymph node metastasis) underwent surgery in May 2011, including partial resection of the liver. She was treated with AFTV as the first-line adjuvant therapy, followed by conventional chemotherapy. This patient is still alive without any recurrence, as confirmed with computed tomography, for more than 5 years.  A 64-year-old man with stage IV colon cancer (multiple para-aortic lymph node metastases and direct abdominal wall invasion) underwent non-curative surgery in May 2006. Following conventional chemotherapy, two courses of AFTV and radiation therapy were administered sequentially. This patient has had no recurrence for more than 5 years.



We report the success of combination therapy including AFTV in cases of liver-metastasized gall bladder cancer and abdominal wall-metastasized colon cancer. Both patients experienced long-lasting, complete remission. Therefore, combination therapies including AFTV should be considered in patients with advanced cancer of the digestive organs.
The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training

Whereas theoretical work suggests that deep architectures might be more efficient at representing highly-varying functions, training deep architectures was unsuccessful until the recent advent of algorithms based on unsupervised pretraining. Even though these new algorithms have enabled training deep models, many questions remain as to the nature of this difficult learning problem. Answering these questions is important if learning in deep architectures is to be further improved. We attempt to shed some light on these questions through extensive simulations. The experiments confirm and clarify the advantage of unsupervised pre-training. They demonstrate the robustness of the training procedure with respect to the random initialization, the positive effect of pre-training in terms of optimization and its role as a regularizer. We empirically show the influence of pre-training with respect to architecture depth, model capacity, and number of training examples.
Context-Aware Synthesis for Video Frame Interpolation

Video frame interpolation algorithms typically estimate optical flow or its variations and then use it to guide the synthesis of an intermediate frame between two consecutive original frames. To handle challenges like occlusion, bidirectional flow between the two input frames is often estimated and used to warp and blend the input frames. However, how to effectively blend the two warped frames still remains a challenging problem. This paper presents a context-aware synthesis approach that warps not only the input frames but also their pixel-wise contextual information and uses them to interpolate a high-quality intermediate frame. Specifically, we first use a pre-trained neural network to extract per-pixel contextual information for input frames. We then employ a state-of-the-art optical flow algorithm to estimate bidirectional flow between them and pre-warp both input frames and their context maps. Finally, unlike common approaches that blend the pre-warped frames, our method feeds them and their context maps to a video frame synthesis neural network to produce the interpolated frame in a context-aware fashion. Our neural network is fully convolutional and is trained end to end. Our experiments show that our method can handle challenging scenarios such as occlusion and large motion and outperforms representative state-of-the-art approaches.
The impact of food prices on consumption: a systematic review of research on the price elasticity of demand for food.

In light of proposals to improve diets by shifting food prices, it is important to understand how price changes affect demand for various foods. We reviewed 160 studies on the price elasticity of demand for major food categories to assess mean elasticities by food category and variations in estimates by study design. Price elasticities for foods and nonalcoholic beverages ranged from 0.27 to 0.81 (absolute values), with food away from home, soft drinks, juice, and meats being most responsive to price changes (0.7-0.8). As an example, a 10% increase in soft drink prices should reduce consumption by 8% to 10%. Studies estimating price effects on substitutions from unhealthy to healthy food and price responsiveness among at-risk populations are particularly needed.
Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.
Effect of IoT botnets on Cryptocurrency

As technology advances, more and more devices have Internet access. This gives rise to the Internet of Things. With all these new devices connected to the Internet, cybercriminals are undoubtedly trying to take advantage of these devices, especially when they have poor protection. These botnets will have a large amount of processing power in the near future. This paper will elaborate on how much processing power these IoT botnets can gain and to what extend cryptocurrencies will be influenced by it. This will be done through a literature study which is validated through an experiment.
Stein Points

An important task in computational statistics and machine learning is to approximate a posterior distribution p(x) with an empirical measure supported on a set of representative points  This paper focuses on methods where the selection of points is essentially deterministic, with an emphasis on achieving accurate approximation when n is small. To this end, we present Stein Points. The idea is to exploit either a greedy or a conditional gradient method to iteratively minimise a kernel Stein discrepancy between the empirical measure and p(x). Our empirical results demonstrate that Stein Points enable accurate approximation of the posterior at modest computational cost. In addition, theoretical results are provided to establish convergence of the method.
Recursive partitioning for heterogeneous causal effects.

In this paper we propose methods for estimating heterogeneity in causal effects in experimental and observational studies and for conducting hypothesis tests about the magnitude of differences in treatment effects across subsets of the population. We provide a data-driven approach to partition the data into subpopulations that differ in the magnitude of their treatment effects. The approach enables the construction of valid confidence intervals for treatment effects, even with many covariates relative to the sample size, and without "sparsity" assumptions. We propose an "honest" approach to estimation, whereby one sample is used to construct the partition and another to estimate treatment effects for each subpopulation. Our approach builds on regression tree methods, modified to optimize for goodness of fit in treatment effects and to account for honest estimation. Our model selection criterion anticipates that bias will be eliminated by honest estimation and also accounts for the effect of making additional splits on the variance of treatment effect estimates within each subpopulation. We address the challenge that the "ground truth" for a causal effect is not observed for any individual unit, so that standard approaches to cross-validation must be modified. Through a simulation study, we show that for our preferred method honest estimation results in nominal coverage for 90% confidence intervals, whereas coverage ranges between 74% and 84% for nonhonest approaches. Honest estimation requires estimating the model with a smaller sample size; the cost in terms of mean squared error of treatment effects for our preferred method ranges between 7-22%.
Guiding the Use of Grounded Theory in Doctoral Studies - An Example from the Australian Film Industry

Selecting the most appropriate research method is one of the most difficult problems facing a doctoral researcher. Grounded Theory is presented here as a method of choice as it is detailed, rigorous, and systematic, yet it also permits flexibility and freedom. Grounded Theory offers many benefits to research in Information Systems as it is suitable for the investigation of complex multifaceted phenomena. It is also well equipped to explore socially related issues. Despite existing criticism, it is a rigorous and methodical research approach capable of broadening the perceptions of those in the research community. This paper provides detailed and practical guidelines that illustrate the techniques, utility, and ease of use of grounded theory, especially as these apply to information systems based research. This paper tracks a Grounded Theory research project undertaken to study the phenomena of collaboration and knowledge sharing in the Australian Film Industry. It uses this to illustrate and emphasize salient points to assist potential users in applying the method. The very practical approach shared in this paper provides a focused critique rendering it a valuable contribution to the discussion of methods of analysis in the IS sphere, particularly grounded theory.
A dataset and baselines for sequential open-domain question answering

Previous work on question-answering systems has mainly focused on answering individual questions, assuming they are independent and devoid of context. Instead, we investigate sequential question answering, in which multiple related questions are asked sequentially. We introduce a new dataset of fully humanauthored questions. We extend existing strong question answering frameworks to include information about previous asked questions to improve the overall question-answering accuracy in open-domain question answering. The dataset is publicly available at http:// sequential.qanta.org.
Summarization with Attention-based Deep Recurrent Neural Networks

This paper focuses on approaches to building a text automatic summarization model for news articles, generating a one-sentence summarization that mimics the style of a news title given some paragraphs. We managed to build and train two relatively complex deep learning models that outperformed our baseline model, which is a simple feed forward neural network. We explored Recurrent Neural Network models with encoder-decoder using LSTM and GRU cells, and with/without attention. We obtained some results that we then measured by calculating their respective ROUGE scores with respect to the actual references. For future work, we believe abstractive method of text summarization is a power way of summarizing texts, and we will continue with this approach. We think that the deficiencies currently embedded in our language model can be improved by better fine-tuning the model, more deep-learning method exploration, as well as larger training dataset.
Word meaning , sentence meaning , and syntactic meaning

The lexicon has long been assumed to be the source of all conceptual content expressed by sentences. Syntactic structures have corr spondingly been seen only as providing instructions for the assembly of the concepts expressed by words. Under this view, sentences have meaning, but the syntactic structures which sentences instantiate do not. This paper challenges this view: it uses the phenomenon of i mplicit type-shifting to demonstrate that constructions have meanings distinct from those of words and that, in cases of conflict, construction meaning overrides word meaning; and it argues that such overrides are predictable by-products of the ge neral mechanism of constru ction-word integration. This mechanism will be described with respect to three different kinds of constructions: argument-structure constructions, which specify linkings of thematic roles to grammatical functions; aspectual co nstructions, which encode the situation type denoted by the verb or verb phrase; and sentence types, which pair a discourse function with a clausal structure. On the basis of these three short case studies, I will argue that appeal to constructional meaning greatly enhances the descriptive power of a theory of se n-
Social commerce constructs and consumer's intention to buy

Social commerce is a new development in e-commerce generated by the use of social media to empower customers to interact on the Internet. The recent advancements in ICTs and the emergence of Web 2.0 technologies along with the popularity of social media and social networking sites have seen the development of new social platforms. These platforms facilitate the use of social commerce. Drawing on literature from marketing and information systems (IS) the author proposes a new model to develop our underocial media ocial networking site rust LS-SEM standing of social commerce using a PLS-SEM methodology to test the model. Results show that Web 2.0 applications are attracting individuals to have interactions as well as generate content on the Internet. Consumers use social commerce constructs for these activities, which in turn increase the level of trust and intention to buy. Implications, limitations, discussion, and future research directions are discussed at the end of the paper.
The Social Regulation of Emotion: An Integrative, Cross-Disciplinary Model

Research in emotion regulation has largely focused on how people manage their own emotions, but there is a growing recognition that the ways in which we regulate the emotions of others also are important. Drawing on work from diverse disciplines, we propose an integrative model of the psychological and neural processes supporting the social regulation of emotion. This organizing framework, the 'social regulatory cycle', specifies at multiple levels of description the act of regulating another person's emotions as well as the experience of being a target of regulation. The cycle describes the processing stages that lead regulators to attempt to change the emotions of a target person, the impact of regulation on the processes that generate emotions in the target, and the underlying neural systems.
The Effects of the Social Structure of Digital Networks on Viral Marketing Performance

V marketing is a form of peer-to-peer communication in which individuals are encouraged to pass on promotional messages within their social networks. Conventional wisdom holds that the viral marketing process is both random and unmanageable. In this paper, we deconstruct the process and investigate the formation of the activated digital network as distinct from the underlying social network. We then consider the impact of the social structure of digital networks (random, scale free, and small world) and of the transmission behavior of individuals on campaign performance. Specifically, we identify alternative social network models to understand the mediating effects of the social structures of these models on viral marketing campaigns. Next, we analyse an actual viral marketing campaign and use the empirical data to develop and validate a computer simulation model for viral marketing. Finally, we conduct a number of simulation experiments to predict the spread of a viral message within different types of social network structures under different assumptions and scenarios. Our findings confirm that the social structure of digital networks play a critical role in the spread of a viral message. Managers seeking to optimize campaign performance should give consideration to these findings before designing and implementing viral marketing campaigns. We also demonstrate how a simulation model is used to quantify the impact of campaign management inputs and how these learnings can support managerial decision making.
Distributed denial of service (DDoS) resilience in cloud: Review and conceptual cloud DDoS mitigation framework

Despite the increasing popularity of cloud services, ensuring the security and availability of data, resources and services remains an ongoing research challenge. Distributed denial of service (DDoS) attacks are not a new threat, but remain a major security challenge and are a topic of ongoing research interest. Mitigating DDoS attack in cloud presents a new dimension to solutions proffered in traditional computing due to its architecture and features. This paper reviews 96 publications on DDoS attack and defense approaches in cloud computing published between January 2009 and December 2015, and discusses existing research trends. A taxonomy and a conceptual cloud DDoS mitigation framework based on change point detection are presented. Future research directions are also outlined.
An occupational therapy program for adults with developmental disabilities.

This paper describes an occupational therapy independent living skills programs for adults with developmental disabilities living in group homes. Four clients have participated in this program for 1 year. Verbal reports from house and workshop staffs and written documentation in the clients' records were examined to see if the clients' behaviors changed over the course of their first year in the program. These reports indicate that the clients have moved toward increased independence by showing greater initiative in directing their own care. Treatment issues in group home systems are also discussed.
Attachment Styles and Relationship Quality among Young Couples

Attachment theory was first proposed by Bowlby (1969). According to John Bowlby’s theory, the caregiver would have a direct effect on an infant’s attachment styles (Fraley, 2010). The caregiver’s behaviour and their responses towards the infants would determine his or her internal working model. This working model would be used by the infants when they interact with their peers in later relationship formation (Fraley, 2010).
An Empirical Study of Application of Data Mining Techniques in Library System

Few years ago, the information flow in library was relatively simple and the application of technology was limited. However, as we progress into a more integrated world where technology has become an integral part of the business processes, the process of transfer of information has become more complicated. Today, one of the biggest challenges that libraries face is the explosive growth of library data and to use this data to improve the quality of managerial decisions. Data mining techniques are analytical tools that can be used to extract meaningful knowledge from large data sets. This paper addresses the applications of data mining in library to extract useful information from the huge data sets and providing analytical tool to view and use this information for decision making processes by taking real life examples.
A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications

Fog/edge computing has been proposed to be integrated with Internet of Things (IoT) to enable computing services devices deployed at network edge, aiming to improve the user’s experience and resilience of the services in case of failures. With the advantage of distributed architecture and close to end-users, fog/edge computing can provide faster response and greater quality of service for IoT applications. Thus, fog/edge computing-based IoT becomes future infrastructure on IoT development. To develop fog/edge computing-based IoT infrastructure, the architecture, enabling techniques, and issues related to IoT should be investigated first, and then the integration of fog/edge computing and IoT should be explored. To this end, this paper conducts a comprehensive overview of IoT with respect to system architecture, enabling technologies, security and privacy issues, and present the integration of fog/edge computing and IoT, and applications. Particularly, this paper first explores the relationship between cyber-physical systems and IoT, both of which play important roles in realizing an intelligent cyber-physical world. Then, existing architectures, enabling technologies, and security and privacy issues in IoT are presented to enhance the understanding of the state of the art IoT development. To investigate the fog/edge computing-based IoT, this paper also investigate the relationship between IoT and fog/edge computing, and discuss issues in fog/edge computing-based IoT. Finally, several applications, including the smart grid, smart transportation, and smart cities, are presented to demonstrate how fog/edge computing-based IoT to be implemented in real-world applications.