nils-herrmann/clean_scirepeval huggingface dataset:
Mining term association patterns from search logs for effective query reformulation

Search engine logs are an emerging new type of data that offers interesting opportunities for data mining. Existing work on mining such data has mostly attempted to discover knowledge at the level of queries (e.g., query clusters). In this paper, we propose to mine search engine logs for patterns at the level of terms through analyzing the relations of terms inside a query. We define two novel term association patterns (i.e., context-sensitive term substitutions and term additions) and propose new methods for mining such patterns from search engine logs. These two patterns can be used to address the mis-specification and under-specification problems of ineffective queries. Experiment results on real search engine logs show that the mined context-sensitive term substitutions can be used to effectively reword queries and improve their accuracy, while the mined context-sensitive term addition patterns can be used to support query refinement in a more effective way.
Yelp Restaurant Photo Classification

Everyday, millions of users upload numerous photos of different businesses to Yelp. Though most of them are tagged with simple information such as business names, the deep-level information typically are not revealed until looked by humans. Such deep-level information contains things like whether the restaurant is good for lunch or dinner, whether it has outdoor seating, etc. This information can be crucial in helping potential customers to determine whether its the ideal restaurant they want to dine in. Hand labeling these restaurants can be not only costly but also tedious since photos are uploaded almost every second to Yelp. In this project, we used machine learning techniques, specifically image classification methods, to identify the context and rich information of different photos on Yelp. We used three different models to train and test the given dataset. The dataset is provided by Yelp on Kaggle.com. In the training set, 234842 arbitrary-sized images submitted by Yelp users are correlated to one of 2000 Yelp businesses. The number of corresponding photos for each business ranges from 2 to 2974, with mean of 117. The image count distribution is shown by Fig1. Every business is tagged with multiple descriptive labels in 9 aspects. The labels includes: 0: good for lunch 1: good for dinner 2: takes reservations 3: outdoor seating 4: restaurant is expensive 5: has alcohol 6: has table service 7: ambience is classy 8: good for kids As an example, Fig2 illustrates the structure of the dataset. Our goal is to predict the labels of a business in these 9 aspects given its photos. A notable challenge is that the labels are not directly correlated to each image. Individual photos can possibly be indicative in one aspect, yet it is tagged with all the labels of the business. As a result, the dataset might exhibit high noise level. The fraction of each label across the training set is shown in Fig3. Though assumed independent, some label pairs exhibit high covariance, such as ”has alcohol” and ”has table service” (Fig4).
MonetDB: Two Decades of Research in Column-oriented Database Architectures

MonetDB is a state-of-the-art open-source column-store database management system targeting applications in need for analytics over large collections of data. MonetDB is actively used nowadays in health care, in telecommunications as well as in scientific databases and in data management research, accumulating on average more than 10,000 downloads on a monthly basis. This paper gives a brief overview of the MonetDB technology as it developed over the past two decades and the main research highlights which drive the current MonetDB design and form the basis for its future evolution.
Molding CNNs for text: non-linear, non-consecutive convolutions

The success of deep learning often derives from well-chosen operational building blocks. In this work, we revise the temporal convolution operation in CNNs to better adapt it to text processing. Instead of concatenating word representations, we appeal to tensor algebra and use low-rank n-gram tensors to directly exploit interactions between words already at the convolution stage. Moreover, we extend the n-gram convolution to non-consecutive words to recognize patterns with intervening words. Through a combination of lowrank tensors, and pattern weighting, we can efficiently evaluate the resulting convolution operation via dynamic programming. We test the resulting architecture on standard sentiment classification and news categorization tasks. Our model achieves state-of-the-art performance both in terms of accuracy and training speed. For instance, we obtain 51.2% accuracy on the fine-grained sentiment classification
Permutation invariant training of deep models for speaker-independent multi-talker speech separation

We propose a novel deep learning training criterion, named permutation invariant training (PIT), for speaker independent multi-talker speech separation, commonly known as the cocktail-party problem. Different from the multi-class regression technique and the deep clustering (DPCL) technique, our novel approach minimizes the separation error directly. This strategy effectively solves the long-lasting label permutation problem, that has prevented progress on deep learning based techniques for speech separation. We evaluated PIT on the WSJ0 and Danish mixed-speech separation tasks and found that it compares favorably to non-negative matrix factorization (NMF), computational auditory scene analysis (CASA), and DPCL and generalizes well over unseen speakers and languages. Since PIT is simple to implement and can be easily integrated and combined with other advanced techniques, we believe improvements built upon PIT can eventually solve the cocktail-party problem.
Applied Text Generation

While we were able to exploit existing research for many of the design issues, it turned out that we needed to develop our own approach to text planning (Rambow 1990). This paper will present the system and a t tempt to show how these design objectives led to particular design decisions. The structure of the paper is as follows. In Section 2, we will present the underlying application and give examples of the output of the System. In Section 3, we will discuss the overall s tructure of Joyce. We then discuss the three main components in turn: the text planner in Section 4, the sentence planner in Section 5 and the realizer in Section 6. We will discuss the text planner in some detail since it represents a new approach to the problem. Section 7 traces the generation of a short text. In Section 8, we address the problem of portability, and wind up by discussing some shortcomings of Joyce in the conclusion.
Image inpainting

Inpainting, the technique of modifying an image in an undetectable form, is as ancient as art itself. The goals and applications of inpainting are numerous, from the restoration of damaged paintings and photographs to the removal/replacement of selected objects. In this paper, we introduce a novel algorithm for digital inpainting of still images that attempts to replicate the basic techniques used by professional restorators. After the user selects the regions to be restored, the algorithm automatically fills-in these regions with information surrounding them. The fill-in is done in such a way that isophote lines arriving at the regions' boundaries are completed inside. In contrast with previous approaches, the technique here introduced does not require the user to specify where the novel information comes from. This is automatically done (and in a fast way), thereby allowing to simultaneously fill-in numerous regions containing completely different structures and surrounding backgrounds. In addition, no limitations are imposed on the topology of the region to be inpainted. Applications of this technique include the restoration of old photographs and damaged film; removal of superimposed text like dates, subtitles, or publicity; and the removal of entire objects from the image like microphones or wires in special effects.
Enablers and Barriers to the Organizational Adoption of Sustainable Business Practices

Individuals, organizations, and governments are increasingly becoming aware of the necessity of sustainability in living, organizing, performing, and managing work. In this context, “green IS” has become an established colloquial term, acknowledging that information technology, corporate information systems, and the surrounding practices are both a contributor to the sustainability challenge and a potential enabler for green and sustainable practices. To date, however, there are few reported studies on the role of information systems for the challenge, and solution, of sustainability. This paper presents results from a case study of a world-wide operating IT software solution provider that is engaged in the development and adoption of sustainable practices. Our study suggests that the adoption of sustainable practices comes along with a number of particularities. We found information technology to be a key enabler of transparency about the progress of sustainability operations. We further found personal, motivator factors as well as organizational factors such as business inclusion, strategy definition, and a dialectic top-management and bottom-up support, to play a role in enabling a company to manage their sustainability. We describe a set of conjectures forthcoming from our case analysis, and detail some implications for further research in this area.
Answer Selection in Community Question Answering via Attentive Neural Networks

Answer selection in community question answering (cQA) is a challenging task in natural language processing. The difficulty lies in that it not only needs the consideration of semantic matching between question answer pairs but also requires a serious modeling of contextual factors. In this letter, we propose an attentive deep neural network architecture so as to learn the deterministic information for answer selection. The architecture can support various input formats through the organization of convolutional neural networks, attention-based long short-term memory, and conditional random fields. Experiments are carried out on the SemEval-2015 cQA dataset. We attain 58.35% on macroaveraged $\mathrm{F_1}$, which outperforms the Top-1 system in the shared task by 1.16% and improves the state-of-the-art deep-neural-network-based method by 2.21%.
eSports: collaborative and synchronous video annotation system in grid computing environment

We designed eSports - a collaborative and synchronous video annotation platform, which is to be used in Internet scale cross-platform grid computing environment to facilitate computer supported cooperative work (CSCW) in education settings such as distance sport coaching, distance classroom etc. Different from traditional multimedia annotation systems, eSports provides the capabilities to collaboratively and synchronously play and archive real time live video, to take snapshots, to annotate video snapshots using whiteboard and to play back the video annotations synchronized with original video streams. eSports is designed based on the grid based collaboration paradigm $the shared event model using NaradaBrokering, which is a publish/subscribe based distributed message passing and event notification system. In addition to elaborate the design and implementation of eSports, we analyze the potential use cases of eSports under different education settings. We believed that eSports is very useful to improve the online collaborative coaching and education.
Quantifying and Predicting Mental Illness Severity in Online Pro-Eating Disorder Communities

Social media sites have struggled with the presence of emotional and physical self-injury content. Individuals who share such content are often challenged with severe mental illnesses like eating disorders. We present the first study quantifying levels of mental illness severity (MIS) in social media. We examine a set of users on Instagram who post content on pro-eating disorder tags (26M posts from 100K users). Our novel statistical methodology combines topic modeling and novice/clinician annotations to infer MIS in a userâs content. Alarmingly, we find that proportion of users whose content expresses high MIS have been on the rise since 2012 (13%/year increase). Previous MIS in a userâs content over seven months can predict future risk with 81% accuracy. Our model can also forecast MIS levels up to eight months in the future with performance better than baseline. We discuss the health outcomes and design implications as well as ethical considerations of this line of research.
Working Memory: A View from Neuroimaging

We have used neuroimaging techniques, mainly positron emission tomography (PET), to study cognitively driven issues about working memory. Two kinds of experiments are described. In the first kind, we employ standard subtraction logic to uncover the basic components of working memory. These studies indicate that: (a) there are different working-memory systems for spatial, object, and verbal information (with the spatial system localized more in the right hemisphere, and the verbal system more in the left hemisphere); (b) within at least the spatial and verbal systems, separable components seem to be responsible for the passive storage of information and the active maintenance of information (with the storage component being localized more in the back of the brain, and the maintenance component in the front); and (c) there may be separate components responsible for processing the contents of working memory (localized in prefrontal cortex). In our second kind of experiment we have focused on verbal working memory and incrementally varied one task parameter-memory load-in an effort to obtain a more fine-grained analysis of the system's operations. The results indicate that all relevant components of the system show some increase in activity with increasing memory load (e.g., the frontal regions responsible for verbal rehearsal show incremental increases in activation with increasing memory load). In contrast, brain regions that are not part of the working-memory system show no effect of memory load. Furthermore, the time courses of activation may differ for regions that are sensitive to load versus those that are not. Taken together, our results provide support for certain cognitive models of working memory (e.g., Baddeley, 1992) and also suggest some distinctions that these models have not emphasized. And more fundamentally, the results provide a neural base for cognitive models of working memory.
Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification

We present an analysis of three possible strategies for exploiting the power of existing convolutional neural networks (ConvNets) in different scenarios from the ones they were trained: full training, fine tuning, and using ConvNets as feature extractors. In many applications, especially including remote sensing, it is not feasible to fully design and train a new ConvNet, as this usually requires a considerable amount of labeled data and demands high computational costs. Therefore, it is important to understand how to obtain the best profit from existing ConvNets. We perform experiments with six popular ConvNets using three remote sensing datasets. We also compare ConvNets in each strategy with existing descriptors and with state-of-the-art baselines. Results point that fine tuning tends to be the best performing strategy. In fact, using the features from the fine-tuned ConvNet with linear SVM obtains the best results. We also achieved state-of-the-art results for the three datasets used.
Extracting Data from NoSQL Databases

Businesses and organizations today generate increasing volumes of data. Being able to analyze and visualize this data to find trends that can be used as input when making business decisions is an important factor for competitive advantage. Spotfire is a software platform for doing this. Spotfire uses a tabular data model similar to the relational model used in relational database management systems (RDBMSs), which are commonly used by companies for storing data. Extraction and import of data from RDBMSs to Spotfire is generally a simple task. In recent years, because of changing application requirements, new types of databases under the general term NoSQL have become popular. NoSQL databases differ from RDBMSs mainly in that they use non-relational data models, lack explicit schemas and scale horizontally. Some of these features cause problems for applications like Spotfire when extracting and importing data. This thesis investigates how these problems can be solved, thus enabling support for NoSQL databases in Spotfire. The approach and conclusions are valid for any application that interacts with databases in a similar way as Spotfire. General solutions for supporting NoSQL databases are suggested. Also, two concrete tools for importing data from Cassandra and Neo4j that have been implemented in the Spotfire platform are described. The presented solutions comprise a data model mapping from the NoSQL system to Spotfire tables, sampling and possibly clustering for finding schemas, and an extraction mechanism tailored to the particular system’s query interface. The suggested solutions are not claimed to be complete. Rather, the work in this thesis can serve as a starting point for more thorough investigations or as a basis for something that can be extended.
The Bilingual's Language Modes.

GROSJEAN 2 Bilinguals who have reflected on their bilingualism will often report that they change their way of speaking when they are with monolinguals and when they are with bilinguals. Whereas they avoid using their other language with the former, they may call on it for a word or a sentence with the latter or even change over to it completely. In addition, bilinguals will also report that, as listeners, they are sometimes taken by surprise when they are spoken to in a language that they did not expect. Although these reports are quite anecdotal, they do point to an important phenomenon, language mode, which researchers have been alluding to over the years. For example, Weinreich (1966) writes that when speaking to a monolingual, the bilingual is subject to interlocutory constraint which requires that he or she limit interferences (Weinreich uses this as a cover term for any element of the other language) but when speaking to another bilingual, there is hardly any limit to interferences; forms can be transferred freely from one language to the other and often used in an unadapted way. A few years later, Hasselmo (1970) refers to three sets of "norms" or "modes of speaking" among Swedish-English bilinguals in the United States: English only for contact with English monolinguals, American Swedish with some bilinguals (the main language used is Swedish), and Swedish American with other bilinguals (here the main language is English). In the latter two cases, code-switching can take place in the other language. The author also notes that there exists two extremes in the behavior of certain bilinguals, one extreme involves minimal and the other maximal code-switching. A couple of years later, Clyne (1972) talks of three communication possibilities in bilingual discourse: in the first, both codes are used by both speakers; in the second, each one uses a different code but the two understand both codes; and, in the third, only one of the two speakers uses and understands both codes whereas the other speaker is monolingual in one of the codes. Finally, Baetens Beardsmore (1982) echoes these views when he writes that bilinguals in communication with other bilinguals may feel free to use both of their language repertoires. However, the same bilingual speakers in conversation with monoglots may not feel the same liberty and may well attempt to maximize alignment on monoglot norms by consciously reducing any formal "interference" features to …
gHull: A GPU algorithm for 3D convex hull

A novel algorithm is presented to compute the convex hull of a point set in ℝ3 using the graphics processing unit (GPU). By exploiting the relationship between the Voronoi diagram and the convex hull, the algorithm derives the approximation of the convex hull from the former. The other extreme vertices of the convex hull are then found by using a two-round checking in the digital and the continuous space successively. The algorithm does not need explicit locking or any other concurrency control mechanism, thus it can maximize the parallelism available on the modern GPU.
 The implementation using the CUDA programming model on NVIDIA GPUs is exact and efficient. The experiments show that it is up to an order of magnitude faster than other sequential convex hull implementations running on the CPU for inputs of millions of points. The works demonstrate that the GPU can be used to solve nontrivial computational geometry problems with significant performance benefit.
Learning and development in neural networks: the importance of starting small

It is a striking fact that in humans the greatest learning occurs precisely at that point in time--childhood--when the most dramatic maturational changes also occur. This report describes possible synergistic interactions between maturational change and the ability to learn a complex domain (language), as investigated in connectionist networks. The networks are trained to process complex sentences involving relative clauses, number agreement, and several types of verb argument structure. Training fails in the case of networks which are fully formed and 'adultlike' in their capacity. Training succeeds only when networks begin with limited working memory and gradually 'mature' to the adult state. This result suggests that rather than being a limitation, developmental restrictions on resources may constitute a necessary prerequisite for mastering certain complex domains. Specifically, successful learning may depend on starting small.
An Efficient Privacy-Preserving Ranked Keyword Search Method

Cloud data owners prefer to outsource documents in an encrypted form for the purpose of privacy preserving. Therefore it is essential to develop efficient and reliable ciphertext search techniques. One challenge is that the relationship between documents will be normally concealed in the process of encryption, which will lead to significant search accuracy performance degradation. Also the volume of data in data centers has experienced a dramatic growth. This will make it even more challenging to design ciphertext search schemes that can provide efficient and reliable online information retrieval on large volume of encrypted data. In this paper, a hierarchical clustering method is proposed to support more search semantics and also to meet the demand for fast ciphertext search within a big data environment. The proposed hierarchical approach clusters the documents based on the minimum relevance threshold, and then partitions the resulting clusters into sub-clusters until the constraint on the maximum size of cluster is reached. In the search phase, this approach can reach a linear computational complexity against an exponential size increase of document collection. In order to verify the authenticity of search results, a structure called minimum hash sub-tree is designed in this paper. Experiments have been conducted using the collection set built from the IEEE Xplore. The results show that with a sharp increase of documents in the dataset the search time of the proposed method increases linearly whereas the search time of the traditional method increases exponentially. Furthermore, the proposed method has an advantage over the traditional method in the rank privacy and relevance of retrieved documents.