nils-herrmann/clean_scirepeval huggingface dataset:
Multi-Domain Neural Machine Translation

In this thesis we present an approach to neural machine translation (NMT) that supports multiple domains in a single model and allows switching between the domains when translating. The core idea is to treat text domains as distinct languages and use multilingual NMT methods to create multi-domain translation systems; we show that this approach results in significant translation quality gains over fine-tuning. We also propose approach of unsupervised domain assignment and explore whether the knowledge of pre-specified text domains is necessary; turns out that it is after all, but also that when it is not known quite high translation quality can be reached, and even higher than with known domains in some cases. Additionally, we explore the possibility of intra-language style adaptation through zero shot translation. We show that this approach is able to style adapt, however, with unresolved text deterioration issues.
A machine-learning classifier implemented in a standard 6T SRAM array

This paper presents a machine-learning classifier where the computation is performed within a standard 6T SRAM array. This eliminates explicit memory operations, which otherwise pose energy/performance bottlenecks, especially for emerging algorithms (e.g., from machine learning) that result in high ratio of memory accesses. We present an algorithm and prototype IC (in 130nm CMOS), where a 128×128 SRAM array performs storage of classifier models and complete classifier computations. We demonstrate a real application, namely digit recognition from MNIST-database images. The accuracy is equal to a conventional (ideal) digital/SRAM system, yet with 113× lower energy. The approach achieves accuracy >95% with a full feature set (i.e., 28×28=784 image pixels), and 90% when reduced to 82 features (as demonstrated on the IC due to area limitations). The energy per 10-way digit classification is 633pJ at a speed of 50MHz.
Regular Expression Learning for Information Extraction

Regular expressions have served as the dominant workhorse of practical information extraction for several years. However, there has been little work on reducing the manual effort involved in building high-quality, complex regular expressions for information extraction tasks. In this paper, we propose ReLIE, a novel transformation-based algorithm for learning such complex regular expressions. We evaluate the performance of our algorithm on multiple datasets and compare it against the CRF algorithm. We show that ReLIE, in addition to being an order of magnitude faster, outperforms CRF under conditions of limited training data and cross-domain data. Finally, we show how the accuracy of CRF can be improved by using features extracted by ReLIE.
Outdoor SLAM using visual appearance and laser ranging

This paper describes a 3D SLAM system using information from an actuated laser scanner and camera installed on a mobile robot. The laser samples the local geometry of the environment and is used to incrementally build a 3D point-cloud map of the workspace. Sequences of images from the camera are used to detect loop closure events (without reference to the internal estimates of vehicle location) using a novel appearance-based retrieval system. The loop closure detection is robust to repetitive visual structure and provides a probabilistic measure of confidence. The images suggesting loop closure are then further processed with their corresponding local laser scans to yield putative Euclidean image-image transformations. We show how naive application of this transformation to effect the loop closure can lead to catastrophic linearization errors and go on to describe a way in which gross, pre-loop closing errors can be successfully annulled. We demonstrate our system working in a challenging, outdoor setting containing substantial loops and beguiling, gently curving traversals. The results are overlaid on an aerial image to provide a ground truth comparison with the estimated map. The paper concludes with an extension into the multi-robot domain in which 3D maps resulting from distinct SLAM sessions (no common reference frame) are combined without recourse to mutual observation
Driver behaviour with adaptive cruise control.

This paper reports on the evaluation of adaptive cruise control (ACC) from a psychological perspective. It was anticipated that ACC would have an effect upon the psychology of driving, i.e. make the driver feel like they have less control, reduce the level of trust in the vehicle, make drivers less situationally aware, but workload might be reduced and driving might be less stressful. Drivers were asked to drive in a driving simulator under manual and ACC conditions. Analysis of variance techniques were used to determine the effects of workload (i.e. amount of traffic) and feedback (i.e. degree of information from the ACC system) on the psychological variables measured (i.e. locus of control, trust, workload, stress, mental models and situation awareness). The results showed that: locus of control and trust were unaffected by ACC, whereas situation awareness, workload and stress were reduced by ACC. Ways of improving situation awareness could include cues to help the driver predict vehicle trajectory and identify conflicts.
Balancing Exploration and Exploitation in Sampling-Based Motion Planning

We present the exploring/exploiting tree (EET) algorithm for motion planning. The EET planner deliberately trades probabilistic completeness for computational efficiency. This tradeoff enables the EET planner to outperform state-of-the-art sampling-based planners by up to three orders of magnitude. We show that these considerable speedups apply for a variety of challenging real-world motion planning problems. The performance improvements are achieved by leveraging work space information to continuously adjust the sampling behavior of the planner. When the available information captures the planning problem's inherent structure, the planner's sampler becomes increasingly exploitative. When the available information is less accurate, the planner automatically compensates by increasing local configuration space exploration. We show that active balancing of exploration and exploitation based on workspace information can be a key ingredient to enabling highly efficient motion planning in practical scenarios.
Multimodal Interfaces for Dynamic Interactive Maps

Dynamic interactive maps with transparent but powerful human interface capabilities are beginning to emerge for a variety of geographical information systems, including ones situated on portables for travelers, students, business and service people, and others working in field settings. In the present research, interfaces supporting spoken, pen-based, and multimodal input were analyze for their potential effectiveness in interacting with this new generation of map systems. Input modality (speech, writing, multimodal) and map display format (highly versus minimally structured) were varied in a within-subject factorial design as people completed realistic tasks with a simulated map system. The results identified a constellation of performance difficulties associated with speech-only map interactions, including elevated performance errors, spontaneous disfluencies, and lengthier task completion t ime-problems that declined substantially when people could interact multimodally with the map. These performance advantages also mirrored a strong user preference to interact multimodally. The error-proneness and unacceptability of speech-only input to maps was attributed in large part to people's difficulty generating spoken descriptions of spatial location. Analyses also indicated that map display format can be used to minimize performance errors and disfluencies, and map interfaces that guide users' speech toward brevity can nearly eliminate disfiuencies. Implications of this research are discussed for the design of high-performance multimodal interfaces for future map
Generic behaviour similarity measures for evolutionary swarm robotics

Novelty search has shown to be a promising approach for the evolution of controllers for swarms of robots. In existing studies, however, the experimenter had to craft a task-specific behaviour similarity measure. The reliance on hand-crafted similarity measures places an additional burden to the experimenter and introduces a bias in the evolutionary process. In this paper, we propose and compare two generic behaviour similarity measures: combined state count and sampled average state. The proposed measures are based on the values of sensors and effectors recorded for each individual robot of the swarm. The characterisation of the group-level behaviour is then obtained by combining the sensor-effector values from all the robots. We evaluate the proposed measures in an aggregation task and in a resource sharing task. We show that the generic measures match the performance of task-specific measures in terms of solution quality. Our results indicate that the proposed generic measures operate as effective behaviour similarity measures, and that it is possible to leverage the benefits of novelty search without having to craft task-specific similarity measures.
Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration

We present a bottom-up aggregation approach to image segmentation. Beginning with an image, we execute a sequence of steps in which pixels are gradually merged to produce larger and larger regions. In each step, we consider pairs of adjacent regions and provide a probability measure to assess whether or not they should be included in the same segment. Our probabilistic formulation takes into account intensity and texture distributions in a local area around each region. It further incorporates priors based on the geometry of the regions. Finally, posteriors based on intensity and texture cues are combined using “ a mixture of experts” formulation. This probabilistic approach is integrated into a graph coarsening scheme, providing a complete hierarchical segmentation of the image. The algorithm complexity is linear in the number of the image pixels and it requires almost no user-tuned parameters. In addition, we provide a novel evaluation scheme for image segmentation algorithms, attempting to avoid human semantic considerations that are out of scope for segmentation algorithms. Using this novel evaluation scheme, we test our method and provide a comparison to several existing segmentation algorithms.
Beyond keyword search: discovering relevant scientific literature

In scientific research, it is often difficult to express information needs as simple keyword queries. We present a more natural way of searching for relevant scientific literature. Rather than a string of keywords, we define a query as a small set of papers deemed relevant to the research task at hand. By optimizing an objective function based on a fine-grained notion of influence between documents, our approach efficiently selects a set of highly relevant articles. Moreover, as scientists trust some authors more than others, results are personalized to individual preferences. In a user study, researchers found the papers recommended by our method to be more useful, trustworthy and diverse than those selected by popular alternatives, such as Google Scholar and a state-of-the-art topic modeling approach.
A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae


RNA-seq, has recently become an attractive method of choice in the studies of transcriptomes, promising several advantages compared with microarrays. In this study, we sought to assess the contribution of the different analytical steps involved in the analysis of RNA-seq data generated with the Illumina platform, and to perform a cross-platform comparison based on the results obtained through Affymetrix microarray. As a case study for our work we, used the Saccharomyces cerevisiae strain CEN.PK 113-7D, grown under two different conditions (batch and chemostat). Here, we asses the influence of genetic variation on the estimation of gene expression level using three different aligners for read-mapping (Gsnap, Stampy and TopHat) on S288c genome, the capabilities of five different statistical methods to detect differential gene expression (baySeq, Cuffdiff, DESeq, edgeR and NOISeq) and we explored the consistency between RNA-seq analysis using reference genome and de novo assembly approach. High reproducibility among biological replicates (correlation≥0.99) and high consistency between the two platforms for analysis of gene expression levels (correlation≥0.91) are reported. The results from differential gene expression identification derived from the different statistical methods, as well as their integrated analysis results based on gene ontology annotation are in good agreement. Overall, our study provides a useful and comprehensive comparison between the two platforms (RNA-seq and microrrays) for gene expression analysis and addresses the contribution of the different steps involved in the analysis of RNA-seq data.
Vessel Bend-Based Cup Segmentation in Retinal Images

In this paper, we present a method for cup boundary detection from monocular colour fundus image to help quantify cup changes. The method is based on anatomical evidence such as vessel bends at cup boundary, considered relevant by glaucoma experts. Vessels are modeled and detected in a curvature space to better handle inter-image variations. Bends in a vessel are robustly detected using a region of support concept, which automatically selects the right scale for analysis. A reliable subset called r-bends is derived using a multi-stage strategy and a local splinetting is used to obtain the desired cup boundary. The method has been successfully tested on 133 images comprising 32 normal and 101 glaucomatous images against three glaucoma experts. The proposed method shows high sensitivity in cup to disk ratio-based glaucoma detection and local assessment of the detected cup boundary shows good consensus with the expert markings.
Image morphing: a survey

Image morphing has received much attention in recent years. It has proven to be a powerful tool for visual effects in film and television, enabling the fluid transformation of one digital image into another. This paper surveys the growth of this field and describes recent advances in image morphing in terms of feature specification, warp generation methods, and transition control. These areas relate to the ease of use and quality of results. We describe the role of radial basis functions, thin plate splines, energy minimization, and multilevel free-form deformations in advancing the state-of-the-art in image morphing. Recent work on a generalized framework for morphing among multiple images is described.
A Binning Scheme for Fast Hard Drive Based Image Search

In this paper we investigate how to scale a content based image retrieval approach beyond the RAM limits of a single computer and to make use of its hard drive to store the feature database. The feature vectors describing the images in the database are binned in multiple independent ways. Each bin contains images similar to a representative prototype. Each binning is considered through two stages of processing. First, the prototype closest to the query is found. Second, the bin corresponding to the closest prototype is fetched from disk and searched completely. The query process is repeatedly performing these two stages, each time with a binning independent of the previous ones. The scheme cuts down the hard drive access significantly and results in a major speed up. An experimental comparison between the binning scheme and a raw search shows competitive retrieval quality.
A flexible and dynamic mission planning architecture for UAV swarm coordination

In this paper a scalable and flexible Architecture for real-time mission planning and dynamic agent-to-task assignment for a swarm of Unmanned Aerial Vehicles (UAV) is presented. The proposed mission planning architecture consists of a Global Mission Planner (GMP) which is responsible of assigning and monitoring different high-level missions through an Agent Mission Planner (AMP), which is in charge of providing and monitoring each task of the mission to each UAV in the swarm. The objective of the proposed architecture is to carry out high-level missions such as autonomous multi-agent exploration, automatic target detection and recognition, search and rescue, and other different missions with the ability of dynamically re-adapt the mission in real-time. The proposed architecture has been evaluated in simulation and real indoor flights demonstrating its robustness in different scenarios and its flexibility for real-time mission re-planning and dynamic agent-to-task assignment.
A method for automatic stock trading combining technical analysis and nearest neighbor classification

In this paper we propose and analyze a novel method for automatic stock trading which combines technical analysis and the nearest neighbor classification. Our first and foremost objective is to study the feasibility of the practical use of an intelligent prediction system exclusively based on the history of daily stock closing prices and volumes. To this end we propose a technique that consists of a combination of a nearest neighbor classifier and some well known tools of technical analysis, namely, stop loss, stop gain and RSI filter. For assessing the potential use of the proposed method in practice we compared the results obtained to the results that would be obtained by adopting a buy-and-hold strategy. The key performance measure in this comparison was profitability. The proposed method was shown to generate considerable higher profits than buy-and-hold for most of the companies, with few buy operations generated and, consequently, minimizing the risk of market exposure.
Beyond the Words: Predicting User Personality from Heterogeneous Information

An incisive understanding of user personality is not only essential to many scientific disciplines, but also has a profound business impact on practical applications such as digital marketing, personalized recommendation, mental diagnosis, and human resources management. Previous studies have demonstrated that language usage in social media is effective in personality prediction. However, except for single language features, a less researched direction is how to leverage the heterogeneous information on social media to have a better understanding of user personality. In this paper, we propose a Heterogeneous Information Ensemble framework, called HIE, to predict users' personality traits by integrating heterogeneous information including self-language usage, avatar, emoticon, and responsive patterns. In our framework, to improve the performance of personality prediction, we have designed different strategies extracting semantic representations to fully leverage heterogeneous information on social media. We evaluate our methods with extensive experiments based on a real-world data covering both personality survey results and social media usage from thousands of volunteers. The results reveal that our approaches significantly outperform several widely adopted state-of-the-art baseline methods. To figure out the utility of HIE in a real-world interactive setting, we also present DiPsy, a personalized chatbot to predict user personality through heterogeneous information in digital traces and conversation logs.
Activity recognition from on-body sensors by classifier fusion: sensor scalability and robustness

Activity recognition from on-body sensors is affected by sensor degradation, interconnections failures, and jitter in sensor placement and orientation. We investigate how this may be balanced by exploiting redundant sensors distributed on the body. We recognize activities by a meta-classifier that fuses the information of simple classifiers operating on individual sensors. We investigate the robustness to faults and sensor scalability which follows from classifier fusion. We compare a reference majority voting and a naive Bayesian fusion scheme. We validate this approach by recognizing a set of 10 activities carried out by workers in the quality assurance checkpoint of a car assembly line. Results show that classification accuracy greatly increases with additional sensors (50% with 1 sensor, 80% and 98% with 3 and 57 sensors), and that sensor fusion implicitly allows to compensate for typical faults up to high fault rates. These results highlight the benefit of large on- body sensor network rather than a minimum set of sensors for activity recognition and prompts further investigation.
Power mean SVM for large scale visual classification

PmSVM (Power Mean SVM), a classifier that trains significantly faster than state-of-the-art linear and non-linear SVM solvers in large scale visual classification tasks, is presented. PmSVM also achieves higher accuracies. A scalable learning method for large vision problems, e.g., with millions of examples or dimensions, is a key component in many current vision systems. Recent progresses have enabled linear classifiers to efficiently process such large scale problems. Linear classifiers, however, usually have inferior accuracies in vision tasks. Non-linear classifiers, on the other hand, may take weeks or even years to train. We propose a power mean kernel and present an efficient learning algorithm through gradient approximation. The power mean kernel family include as special cases many popular additive kernels. Empirically, PmSVM is up to 5 times faster than LIBLINEAR, and two times faster than state-of-the-art additive kernel classifiers. In terms of accuracy, it outperforms state-of-the-art additive kernel implementations, and has major advantages over linear SVM.
Context-Aware Sequential Recommendation

Since sequential information plays an important role in modeling user behaviors, various sequential recommendation methods have been proposed. Methods based on Markov assumption are widely-used, but independently combine several most recent components. Recently, Recurrent Neural Networks (RNN) based methods have been successfully applied in several sequential modeling tasks. However, for real-world applications, these methods have difficulty in modeling the contextual information, which has been proved to be very important for behavior modeling. In this paper, we propose a novel model, named Context-Aware Recurrent Neural Networks (CA-RNN). Instead of using the constant input matrix and transition matrix in conventional RNN models, CA-RNN employs adaptive context-specific input matrices and adaptive context-specific transition matrices. The adaptive context-specific input matrices capture external situations where user behaviors happen, such as time, location, weather and so on. And the adaptive context-specific transition matrices capture how lengths of time intervals between adjacent behaviors in historical sequences affect the transition of global sequential features. Experimental results show that the proposed CA-RNN model yields significant improvements over state-of-the-art sequential recommendation methods and context-aware recommendation methods on two public datasets, i.e., the Taobao dataset and the Movielens-1M dataset.