nils-herrmann/clean_scirepeval huggingface dataset:
Question Type Classification Using a Part-of-Speech Hierarchy

Question type (or answer type) classification is the task of determining the correct type of the answer expected to a given query. This is often done by defining or discovering syntactic patterns that represent the structure of typical queries of each type, and classify a given query according to which pattern they satisfy. In this paper, we combine the idea of using informer spans as patterns with our own part-of-speech hierarchy in order to propose both a new approach to pattern-based question type classification and a new way of discovering the informers to be used as patterns. We show experimentally that using our part-of-speech hierarchy greatly improves type classification results, and allows our system to learn valid new informers.
Perceived interactivity leading to e-loyalty: Development of a model for cognitive-affective user responses

Novel applications of website interactivity are important to attract and retain online users. In this empirical study five designs for interactivity are examined using different web-poll interfaces. The goal of the investigation is to examine perceived interactivity in a model which includes most commonly tested cognitive elements such as efficiency and effectiveness, but augments this model with the inclusion of a cognitive–affective element for trust, and an affective element of enjoyment. More specifically, a model is created to validate the relationship of perceived interactivity (comprised of user control, user connectedness, and responsiveness of the web-poll application) to efficiency, effectiveness, trust and enjoyment, of the website. In turn, efficiency, effectiveness, trust, and enjoyment are tested for their influence on user behavioral intentions for e-loyalty. All relationships in the model are supported. In addition, exploratory evaluation of qualitative comments is conducted to investigate additional insights between the five web-poll treatments in this investigation. The research confirms the complexity of a model in which cognitive, cognitive–affective and affective elements are present, and advances knowledge on the consequences of perceived interactivity. In additional to theoretical advancements, the research has merit for web designers and online marketers regarding how to enhance interactive online web applications.
Construction site navigation for the autonomous excavator Thor

The paper at hand is part of the autonomous excavator project Thor (Terraforming Heavy Outdoor Robot) who's goal is the development of a construction machine which performs landscaping on a construction site without an operator. So far the project mainly focused on the local excavation on one position. Due to the high digging forces the machine permanently changes its position during excavation. Furthermore, the global goal is to shape the complete construction site. Therefore, a final test platform needs to permanently reposition itself on the site. Within this paper the construction site navigation function is described which guarantees safe traveling from one pose to another one. It is based on an extended A* path planning algorithm, executed on a 2D gridmap including region growing for obstacles, including forward and backward movement. In combination with an intelligent path following algorithm the machine proved to safely reach its position with the desired orientation.
Does visual attention select objects or locations?

Much research supports location-based attentional selection, but J. Duncan (1984) presented data favoring object-based selection in a shape discrimination task. Does attention select objects or locations? We confirmed that Duncan's task elicits selection from spatially invariant object representations rather than from a grouped location-based representation. We next asked whether this finding was due to location-based filtering; the results again supported object-based selection. Finally, we demonstrated that when Duncan's objects were used in a cued detection task the results were consistent with location-based selection. These results suggest that there may not be a single attention mechanism, consistent with Duncan's original claim that object-based and location-based attentional selection are not mutually exclusive. Rather, attentional limitations may depend on the type of stimulus representation used in performing a given task.
Robust Structured Light Coding for 3D Reconstruction

In this paper we present a new monochromatic pattern for a robust structured light coding based on the spatial neighborhood scheme using the M-array approach. The proposed pattern is robust as it allows a high error rate characterized by an average Hamming distance higher than 6. We tackle the design problem with the definition of a small set of symbols associated to simple geometrical primitives. One of these primitives embeds the local orientation of the pattern. This is helpful while performing the search for the relevant neighborhood during the decoding process. The aim of this work is to use this pattern for the real-time 3-D reconstruction of dynamic scenes, particularly in endoscopic surgery, with fast and reliable detection and decoding stages. Ongoing results are presented to assess both the capabilities of the proposed pattern and the reliable decoding algorithm with projections onto simple 3-D scenes and onto internal structures of a pig abdomen.
LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better

Language exhibits hierarchical structure, but recent work using a subject-verb agreement diagnostic argued that state-ofthe-art language models, LSTMs, fail to learn long-range syntax-sensitive dependencies. Using the same diagnostic, we show that, in fact, LSTMs do succeed in learning such dependencies—provided they have enough capacity. We then explore whether models that have access to explicit syntactic information learn agreement more effectively, and how the way in which this structural information is incorporated into the model impacts performance. We find that the mere presence of syntactic information does not improve accuracy, but when model architecture is determined by syntax, number agreement is improved. Further, we find that the choice of how syntactic structure is built affects how well number agreement is learned: top-down construction outperforms leftcorner and bottom-up variants in capturing long-distance structural dependencies.
TUCH: Turning Cross-view Hashing into Single-view Hashing via Generative Adversarial Nets

Cross-view retrieval, which focuses on searching images as response to text queries or vice versa, has received increasing attention recently. Crossview hashing is to efficiently solve the cross-view retrieval problem with binary hash codes. Most existing works on cross-view hashing exploit multiview embedding method to tackle this problem, which inevitably causes the information loss in both image and text domains. Inspired by the Generative Adversarial Nets (GANs), this paper presents a new model that is able to Turn Crossview Hashing into single-view hashing (TUCH), thus enabling the information of image to be preserved as much as possible. TUCH is a novel deep architecture that integrates a language model network T for text feature extraction, a generator network G to generate fake images from text feature and a hashing network H for learning hashing functions to generate compact binary codes. Our architecture effectively unifies joint generative adversarial learning and cross-view hashing. Extensive empirical evidence shows that our TUCH approach achieves state-of-the-art results, especially on text to image retrieval, based on imagesentences datasets, i.e. standard IAPRTC-12 and large-scale Microsoft COCO.
Clustering of contingency table and mixture model

Basing cluster analysis on mixture models has become a classical and powerful approach. It enables some classical criteria such as the well-known k-means criterion to be explained. To classify the rows or the columns of a contingency table, an adapted version of k-means known as Mndki2, which uses the chi-square distance, can be used. Unfortunately, this simple, effective method which can be used jointly with correspondence analysis based on the same representation of the data, cannot be associated with a mixture model in the same way as the classical k-means algorithm. In this paper we show that the Mndki2 algorithm can be viewed as an approximation of a classifying version of the EM algorithm for a mixture of multinomial distributions. A comparison of the algorithms belonging in this context are experimentally investigated using Monte Carlo simulations.
Distant Supervision for Relation Extraction with an Incomplete Knowledge Base

Distant supervision, heuristically labeling a corpus using a knowledge base, has emerged as a popular choice for training relation extractors. In this paper, we show that a significant number of “negative“ examples generated by the labeling process are false negatives because the knowledge base is incomplete. Therefore the heuristic for generating negative examples has a serious flaw. Building on a state-of-the-art distantly-supervised extraction algorithm, we proposed an algorithm that learns from only positive and unlabeled labels at the pair-of-entity level. Experimental results demonstrate its advantage over existing algorithms.
Assessing the Validity of Online Drug Forums as a Source for Estimating Demographic and Temporal Trends in Drug Use.

Addiction researchers have begun monitoring online forums to uncover self-reported details about use and effects of emerging drugs. The use of such online data sources has not been validated against data from large epidemiological surveys. This study aimed to characterize and compare the demographic and temporal trends associated with drug use as reported in online forums and in a large epidemiological survey.



Data were collected from the Web site, drugs-forum.com, from January 2007 through August 2012 (143,416 messages posted by 8087 members) and from the US National Survey on Drug Use and Health (NSDUH) from 2007 to 2012. Measures of forum participation levels were compared with and validated against 2 measures from the NSDUH survey data: percentage of people using the drug in past 30 days and percentage using the drug more than 100 times in the past year.



For established drugs (eg, cannabis), significant correlations were found across demographic groups between drugs-forum.com and the NSDUH survey data, whereas weaker, nonsignificant correlations were found with temporal trends. Emerging drugs (eg, Salvia divinorum) were strongly associated with male users in the forum, in agreement with survey-derived data, and had temporal patterns that increased in synchrony with poison control reports.



These results offer the first assessment of online drug forums as a valid source for estimating demographic and temporal trends in drug use. The analyses suggest that online forums are a reliable source for estimation of demographic associations and early identification of emerging drugs, but a less reliable source for measurement of long-term temporal trends.
Performance Analysis and Application of Mobile Blockchain

Mobile security has become more and more important due to the boom of mobile commerce (m-commerce). However, the development of m-commerce is facing many challenges regarding data security problems. Recently, blockchain has been introduced as an effective security solution deployed successfully in many applications in practice, such as, Bitcoin, cloud computing, and Internet-of-Things. In this paper, we introduce a new m-commerce application using blockchain technology, namely, MobiChain, to secure transactions in the m-commerce. Especially, in the MobiChain application, the mining processes can be executed efficiently on mobile devices using our proposed Android core module. Through real experiments, we evaluate the performance of the proposed model and show that blockchain will be an efficient security solution for future m-commerce.
Designing Energy-Efficient Convolutional Neural Networks Using Energy-Aware Pruning

Deep convolutional neural networks (CNNs) are indispensable to state-of-the-art computer vision algorithms. However, they are still rarely deployed on battery-powered mobile devices, such as smartphones and wearable gadgets, where vision algorithms can enable many revolutionary real-world applications. The key limiting factor is the high energy consumption of CNN processing due to its high computational complexity. While there are many previous efforts that try to reduce the CNN model size or the amount of computation, we find that they do not necessarily result in lower energy consumption. Therefore, these targets do not serve as a good metric for energy cost estimation. To close the gap between CNN design and energy consumption optimization, we propose an energy-aware pruning algorithm for CNNs that directly uses the energy consumption of a CNN to guide the pruning process. The energy estimation methodology uses parameters extrapolated from actual hardware measurements. The proposed layer-by-layer pruning algorithm also prunes more aggressively than previously proposed pruning methods by minimizing the error in the output feature maps instead of the filter weights. For each layer, the weights are first pruned and then locally fine-tuned with a closed-form least-square solution to quickly restore the accuracy. After all layers are pruned, the entire network is globally fine-tuned using back-propagation. With the proposed pruning method, the energy consumption of AlexNet and GoogLeNet is reduced by 3.7x and 1.6x, respectively, with less than 1% top-5 accuracy loss. We also show that reducing the number of target classes in AlexNet greatly decreases the number of weights, but has a limited impact on energy consumption.
Color-Ciratefi : A color-based RST-invariant template matching algorithm

Template matching is a technique widely used for finding patterns in digital images. An efficient template matching algorithm should be able to detect template instances that have undergone geometric transformations. Similarly, a color template matching should be able to deal with color constancy problem. Recently we have proposed a new grayscale template matching algorithm named Ciratefi, invariant to rotation, scale, translation, brightness and contrast. In this paper we introduce the Color-Ciratefi that takes into account the color information. We use a new similarity metric in the CIELAB space to obtain invariance to brightness and contrast changes. Experiments show that Color-Ciratefi is more accurate than C-color-SIFT, the well-known SIFT algorithm that uses a set of color invariants. In conventional computers, Color-Ciratefi is slower than C-color-SIFT. However Color-Ciratefi is more suitable than C-color-SIFT to be implemented in highly parallel architectures like FPGA, because it repeats exactly the same set of operations for each pixel.
Collaboratively crowdsourcing workflows with turkomatic

Preparing complex jobs for crowdsourcing marketplaces requires careful attention to workflow design, the process of decomposing jobs into multiple tasks, which are solved by multiple workers. Can the crowd help design such workflows? This paper presents Turkomatic, a tool that recruits crowd workers to aid requesters in planning and solving complex jobs. While workers decompose and solve tasks, requesters can view the status of worker-designed workflows in real time; intervene to change tasks and solutions; and request new solutions to subtasks from the crowd. These features lower the threshold for crowd employers to request complex work. During two evaluations, we found that allowing the crowd to plan without requester supervision is partially successful, but that requester intervention during workflow planning and execution improves quality substantially. We argue that Turkomatic's collaborative approach can be more successful than the conventional workflow design process and discuss implications for the design of collaborative crowd planning systems.
Advanced Metamorphic Techniques in Computer Viruses

Nowadays viruses use polymorphic techniques to mutate their code on each replication, thus evading detection by antiviruses. However detection by emulation can defeat simpl e polymorphism: thus metamorphic techniques are used which thoro ughly change the viral code, even after decryption. We briefly deta il this evolution of virus protection techniques against detectio n and then study the METAPHOR virus, today’s most advanced metamorphic virus. Computer virus, Viral mutation, Polymorphism, Metamorphism, MetaPHOR, Virus history, Obfuscation, Viral gen etic techniques
Computing With Words for Hierarchical Decision Making Applied to Evaluating a Weapon System

The perceptual computer (Per-C) is an architecture that makes subjective judgments by computing with words (CWWs). This paper applies the Per-C to hierarchical decision making, which means decision making based on comparing the performance of competing alternatives, where each alternative is first evaluated based on hierarchical criteria and subcriteria, and then, these alternatives are compared to arrive at either a single winner or a subset of winners. What can make this challenging is that the inputs to the subcriteria and criteria can be numbers, intervals, type-1 fuzzy sets, or even words modeled by interval type-2 fuzzy sets. Novel weighted averages are proposed in this paper as a CWW engine in the Per-C to aggregate these diverse inputs. A missile-evaluation problem is used to illustrate it. The main advantages of our approaches are that diverse inputs can be aggregated, and uncertainties associated with these inputs can be preserved and are propagated into the final evaluation.
Faster reinforcement learning after pretraining deep networks to predict state dynamics

Deep learning algorithms have recently appeared that pretrain hidden layers of neural networks in unsupervised ways, leading to state-of-the-art performance on large classification problems. These methods can also pretrain networks used for reinforcement learning. However, this ignores the additional information that exists in a reinforcement learning paradigm via the ongoing sequence of state, action, new state tuples. This paper demonstrates that learning a predictive model of state dynamics can result in a pretrained hidden layer structure that reduces the time needed to solve reinforcement learning problems.
Enhancements in UltraScale CLB Architecture

Each generation of FPGA architecture benefits from optimizations around its technology node and target usage. In this paper, we discuss some of the changes made to the CLB for Xilinx's 20nm UltraScale product family. We motivate those changes and demonstrate better results than previous CLB architectures on a variety of metrics. We show that, in demanding scenarios, logic placed in an UltraScale device requires 16% less wirelength than 7-series. Designs mapped to UltraScale devices also require fewer logic tiles. In this paper, we demonstrate the utilization benefits of the UltraScale CLB attributed to certain CLB enhancements. The enhancements described herein result in an average packing improvement of 3% for the example design suite. We also show that the UltraScale architecture handles aggressive, tighter packing more gracefully than previous generations of FPGA. These significant reductions in wirelength and CLB counts translate directly into power, performance and ease-of-use benefits.
Natural language processing and information retrieval methods for intellectual property analysis

More intellectual property information is generated now than ever before. The accumulation of intellectual property data, further complicated by this continued increase in production, makes it imperative to develop better methods for archiving and more importantly for accessing this information. Information retrieval (IR) is a standard technique used for efficiently accessing information in such large collections. The most prominent example comprising a vast amount of data is the World Wide Web, where current search engines already satisfy user queries by immediately providing an accurate list of relevant documents. However, IR for intellectual property is neither as fast nor as accurate as what we expect from an Internet search engine. In this thesis, we explore how to improve information access in intellectual property collections by combining previously mentioned IR techniques with advanced natural language processing (NLP) techniques. The information in intellectual property is encoded in text (i.e., language), and we expect that by adding better language processing to IR we can better understand and access the data. NLP is a quite varied field encompassing a number of solutions for improving the understanding of language input. We concentrate more specifically on the NLP tasks of statistical machine translation, information extraction, named entity recognition (NER), sentiment analysis, relation extraction, and text classification. Searching for intellectual property, specifically patents, is a difficult retrieval task where standard IR techniques have had only moderate success. The difficulty of this task only increases when presented with multilingual collections as is the case with patents. We present an approach for improving retrieval performance on a multilingual patent collection by using machine translation (an active research area in NLP) to translate patent queries before concatenating these parallel translations into a multilingual query. Even after retrieving an intellectual property document however, we still face the problem of extracting the relevant information needed. We would like to improve our understanding of the complex intellectual property data by uncovering latent information in the text. We do this by identifying citations in a collection of scientific literature and classifying them by their citation function. This classification is successfully carried out by exploiting some characteristics of the citation text, including features extracted via sentiment analysis, NER, and relation extraction. By assigning labels to citations we can better understand the relationships between intellectual property documents, which can be valuable information for IR or other applications.
Demand forecast sharing in a dual-channel supply chain

We assess the benefits of sharing demand forecast information in a manufacturer–retailer supply chain, consisting of a traditional retail channel and a direct channel. The demand is a linear function of price with a Gaussian primary demand (i.e., zero-price market potential). Both the manufacturer and the retailer set their price based on their forecast of the primary demand. In this setting, we investigate the value of sharing demand forecasts. We analyze the make-toorder scenario, in which prices are set before and production takes place after the primary demand is known, and the make-to-stock scenario, in which production takes place and prices are set before the primary demand is known. We also compare the supply chain performance with and without the direct channel under some assumptions (production cost is zero, and each demand function has the same slope of price). We find that the direct channel has a negative impact on the retailer s performance, and, under some conditions, the manufacturer and the whole supply chain are better off. Our research extends and complements prior research that has investigated only the inventory and replenishment-related benefits of information sharing.