nils-herrmann/clean_scirepeval huggingface dataset:
Tree Memory Networks for modelling long-term temporal dependencies

In the domain of sequence modelling, Recurrent Neural Networks (RNN) have been capable of achieving impressive results in a variety of application areas including visual question answering, part-of-speech tagging and machine translation. However this success in modelling short term dependencies has not successfully transitioned to application areas such as trajectory prediction, which require capturing both short term and long term relationships. In this paper, we propose a Tree Memory Network (TMN) for modelling long term and short term relationships in sequence-to-sequence mapping problems. The proposed network architecture is composed of an input module, controller and a memory module. In contrast to related literature, which models the memory as a sequence of historical states, we model the memory as a recursive tree structure. This structure more effectively captures temporal dependencies across both short term and long term sequences using its hierarchical structure. We demonstrate the effectiveness and flexibility of the proposed TMN in two practical problems, aircraft trajectory modelling and pedestrian trajectory modelling in a surveillance setting, and in both cases we outperform the current state-of-the-art. Furthermore, we perform an in depth analysis on the evolution of the memory module content over time and provide visual evidence on how the proposed TMN is able to map both long term and short term relationships efficiently via a hierarchi1 ar X iv :1 70 3. 04 70 6v 1 [ cs .L G ] 1 2 M ar 2 01 7
3D Shape Segmentation via Shape Fully Convolutional Networks

We propose a novel fully convolutional network architecture for shapes, denoted by Shape Fully Convolutional Networks (SFCN). 3D shapes are represented as graph structures in the SFCN architecture, based on novel graph convolution and pooling operations, which are similar to convolution and pooling operations used on images. Meanwhile, to build our SFCN architecture in the original image segmentation fully convolutional network (FCN) architecture, we also design and implement a generating operation with bridging function. This ensures that the convolution and pooling operation we have designed can be successfully applied in the original FCN architecture. In this paper, we also present a new shape segmentation approach based on SFCN. Furthermore, we allow more general and challenging input, such as mixed datasets of different categories of shapes which can prove the ability of our generalisation. In our approach, SFCNs are trained triangles-to-triangles by using three low-level geometric features as input. Finally, the feature voting-based multi-label graph cuts is adopted to optimise the segmentation results obtained by SFCN prediction. The experiment results show that our method can effectively learn and predict mixed shape datasets of either similar or different characteristics, and achieve excellent segmentation results.
WATER MAIN BURST EVENT DETECTION AND LOCALIZATION

In this paper we present a technique for detecting and locating burst events in pipelines. The proposed method uses wavelet analysis of the high-rate pressure data to detect pipe burst events. Multiscale wavelet analysis of the pressure signal will be shown to be robust to impulsive noise encountered in the physical phenomena under observation. The wavelet coefficients also allow us to obtain additional information about the nature of the signal feature detected, which can used for further feature classification. A local search method is also proposed to accurately determine the arrival time of the pressure front associated with the burst event. The detection performance of these algorithms is verified through leak-off experiments performed on the WaterWiSe@SG test bed deployed on the water distribution system in Singapore. We also propose a graph-based search algorithm which uses the arrival times of the pressure front at different locations within the water distribution system to determine the actual location of the pipe burst event.
Gamified Digital Services: How Gameful Experiences Drive Continued Service Usage

Recently, many digital service providers started to gamify their services to promote continued service usage. Although gamification has drawn attention in both practice and research, it remains unclear how users experience gamified services and how these gameful experiences may increase service usage. This research adopts a user-centered perspective to reveal the underlying gameful experience dimensions during gamified service usage and how they drive continued service usage. Findings from Study 1 – a survey with 148 app-users – reveal four essential gameful experience dimensions (skill development, social comparison, social connectedness, and expressive freedom) and how they relate to game mechanics. Study 2, which is based on a survey among 821 app-users, shows that gameful experiences trigger continued service usage through two different types of motivation, namely autonomous and controlled motivation.
Support vector machine techniques for nonlinear equalization

The emerging machine learning technique called support vector machines is proposed as a method for performing nonlinear equalization in communication systems. The support vector machine has the advantage that a smaller number of parameters for the model can be identified in a manner that does not require the extent of prior information or heuristic assumptions that some previous techniques require. Furthermore, the optimization method of a support vector machine is quadratic programming, which is a well-studied and understood mathematical programming technique. Support vector machine simulations are carried out on nonlinear problems previously studied by other researchers using neural networks. This allows initial comparison against other techniques to determine the feasibility of using the proposed method for nonlinear detection. Results show that support vector machines perform as well as neural networks on the nonlinear problems investigated. A method is then proposed to introduce decision feedback processing to support vector machines to address the fact that intersymbol interference (ISI) data generates input vectors having temporal correlation, whereas a standard support vector machine assumes independent input vectors. Presenting the problem from the viewpoint of the pattern space illustrates the utility of a bank of support vector machines. This approach yields a nonlinear processing method that is somewhat different than the nonlinear decision feedback method whereby the linear feedback filter of the decision feedback equalizer is replaced by a Volterra filter. A simulation using a linear system shows that the proposed method performs equally to a conventional decision feedback equalizer for this problem.
An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field

This paper presents an approach to analyze the thematic evolution of a given research field. This approach combines performance analysis and science mapping for detecting and visualizing conceptual subdomains (particular themes or general thematic areas). It allows us to quantify and visualize the thematic evolution of a given research field. To do this, coword analysis is used in a longitudinal framework in order to detect the different themes treated by the research field across the given time period. The performance analysis uses different bibliometric measures, including the h-index, with the purpose of measuring the impact of both the detected themes and thematic areas. The presented approach includes a visualization method for showing the thematic evolution of the studied field. Then, as an example, the thematic evolution of the Fuzzy Sets Theory field is analyzed using the two most important journals in the topic: Fuzzy Sets and Systems and
Temporal Convolutional Networks for Action Segmentation and Detection

The ability to identify and temporally segment fine-grained human actions throughout a video is crucial for robotics, surveillance, education, and beyond. Typical approaches decouple this problem by first extracting local spatiotemporal features from video frames and then feeding them into a temporal classifier that captures high-level temporal patterns. We describe a class of temporal models, which we call Temporal Convolutional Networks (TCNs), that use a hierarchy of temporal convolutions to perform fine-grained action segmentation or detection. Our Encoder-Decoder TCN uses pooling and upsampling to efficiently capture long-range temporal patterns whereas our Dilated TCN uses dilated convolutions. We show that TCNs are capable of capturing action compositions, segment durations, and long-range dependencies, and are over a magnitude faster to train than competing LSTM-based Recurrent Neural Networks. We apply these models to three challenging fine-grained datasets and show large improvements over the state of the art.
Making sense of ethnography and medical education.

This paper aims to locate the ethnographic tradition in a socio-historical context.



In this paper we chart the history of the ethnographic tradition, explaining its roots and highlighting its value in enabling the ethnographic researcher to explore and make sense of the otherwise invisible aspects of cultural norms and practices. We discuss a number of studies that have provided detailed and context-sensitive accounts of the everyday life of medical schools, medical practitioners and medical students. We demonstrate how the methods of ethnographic fieldwork offer "other ways of knowing" that can have a significant impact on medical education.



The ethnographic research tradition in sociological and anthropological studies of educational settings is a significant one. Ethnographic research in higher education institutions is less common, but is itself a growing research strategy.
Indoor distance estimated from Bluetooth Low Energy signal strength: Comparison of regression models

Bluetooth Low Energy (BLE) is a wireless technology for exchanging data, over short distances, designed for the Internet-of-Things era. As widely supported by wearable devices, BLE has the potential to become an alternative for indoor-localization and proximity sensing. The aim of this work was to perform a thorough characterization of the RSSI-distance relationship under controlled conditions using two BLE devices. Four calibration models underwent to a comparative evaluation analysis. The best results were obtained using a polynomial model with a mean distance percentage error equal to 25.7% (0.4 m) in the range 0-3 m. An overall improvement of 14.3% (0.24 m) in the distance estimate compared to the exponential model commonly adopted in the literature was reported.
A DIVINE CONSISTENCY PROOF FOR MATHEMATICS by

We present familiar principles involving objects and classes (of objects), pairing (on objects), choice (selecting elements from classes), positive classes (elements of an ultrafilter), and definable classes (definable using the preceding notions). We also postulate the existence of a divine object in the formalized sense that it lies in every definable positive class. ZFC (even extended with certain hypotheses just shy of the existence of a measurable cardinal) is interpretable in the resulting system. This establishes the consistency of mathematics relative to the consistency of these systems. Measurable cardinals are used to interpret and prove the consistency of the system. Positive classes and various kinds of divine objects have played significant roles in theology. 1. T1: Objects, classes, pairing. 2. T2: Extensionality, choice operator. 3.  Positive classes. 4. T4: Definable classes. 5. T5: Divine objects. 6. Interpreting ZFC in T5. 7. Interpreting a strong extension of ZFC in T5. 8. Without Extensionality.
EE-Grad: Exploration and Exploitation for Cost-Efficient Mini-Batch SGD

We present a generic framework for trading off fidelity and cost in computing stochastic gradients when the costs of acquiring stochastic gradients of different quality are not known a priori. We consider a mini-batch oracle that distributes a limited query budget over a number of stochastic gradients and aggregates them to estimate the true gradient. Since the optimal mini-batch size depends on the unknown cost-fidelity function, we propose an algorithm, EE-Grad, that sequentially explores the performance of mini-batch oracles and exploits the accumulated knowledge to estimate the one achieving the best performance in terms of cost-efficiency. We provide performance guarantees for EE-Grad with respect to the optimal mini-batch oracle, and illustrate these results in the case of strongly convex objectives. We also provide a simple numerical example that corroborates our theoretical findings.
ASTR NOMICAL ALIGN ENTS AT THE TE PLO MAYOR OF TENOC TITLAN, MEXICO

archaeoastronomical re earch carried out during he last few decades has revealed t at architectural orientati ns in Mesoamerica exhi it a clearly non-random distributio and that civic and cer monial buildings were ostly oriented on the basis of a tronomical considerati ns, particularly to the un's positions on the horizon 0 certain dates of the trop'cal year.! While the ali nments to sunrises and sunsets on the solstices and equin xes have been found on various archaeological sites, the ost frequent orientati nal groups correspond to other dates whose significan e is less obvious. Acc rding to various hypot eses put forward thus far, the solar dates recorded by t e orientations can be i terpreted in terms of their relevance in the agricultural cycle and in the comp tations related to the calendrica system. It has been su gested, for example, t at the dates indicated by the ali nments are separated b calendrically significa t intervals. The most elaborate odel of this type has een proposed by Tich ,2 who contends that these date mark intervals of 13 a d 20 days and multiple thereof; on the other hand, he Iso suggests that the rientations are spaced in accordance with a geometric 1 system based on a 45° angular measureme t unit. Some authors reconstruc ed possible horizon ca endars for particular si s, on the assumption that promi ent peaks of the local horizon served as natur I markers of sunrises and sunset on relevant dates.3 In order to test such hypotheses, undertook precise meas rements of alignments at 37 Prec assic, Classic and Post'lassic archaeological si es in central Mexico. This invol ed measuring not only the orientations of civi -ceremonial structures but also th alignments to promine t mountains on the local horizon, placed within the angle f annual movement of t e sun. The analyses of e data obtained show that the d tes of sunrises and su sets both along the arc itectural orientations and above the prominent hills on the local horizon exhi it consistent patterns, being sep ated by intervals that e predominantly multi les of 13 and 20 days and are, t erefore, significant in t rms of the Mesoameric n calendrical system. Furthermo e, the most frequently r current dates, registere at a number of sites, apparently marked crucial moment of a ritual agricultural ycle. The regularities detected st ongly suggest that the i portant ceremonial stru tures were constructed on careful! selected places, in orde to employ certain surro nding peaks as natural markers 0 horizon calendars. Bot the orientations embod ed in the monumental architectur of a particular site oc asionally dominating th entire urban layoutand the pr minent features of the 10 al horizon allowed the se of an observational
A COMPERATIVE STUDY OF TEXT CLASSIFICATION MODELS ON INVOICES

Text classification for companies is becoming more important in a world where an increasing amount of digital data are made available. The aim is to research whether five different machine learning algorithms can be used to automate the process of classification of invoice data and see which one gets the highest accuracy. Algorithms are in a later stage combined for an attempt to achieve higher results. N-grams are used, and results are compared in form of total accuracy of classification for each algorithm. A library in Python, called scikit-learn, implementing the chosen algorithms, was used. Data is collected and generated to represent data present on a real invoice where data has been extracted. Results from this thesis show that it is possible to use machine learning for this type of problem. The highest scoring algorithm (LinearSVC from scikit-learn) classifies 86% of all samples correctly. This is a margin of 16% above the acceptable level of 70%.
Human Body Detection and Geolocalization for UAV Search and Rescue Missions Using Color and Thermal Imagery

Recent advances in the field of Unmanned Aerial Vehicles (UAVs) make flying robots suitable platforms for carrying sensors and computer systems capable of performing advanced tasks. This paper presents a technique which allows detecting humans at a high frame rate on standard hardware onboard an autonomous UAV in a real-world outdoor environment using thermal and color imagery. Detected human positions are geolocated and a map of points of interest is built. Such a saliency map can, for example, be used to plan medical supply delivery during a disaster relief effort. The technique has been implemented and tested on-board the UAVTech1 autonomous unmanned helicopter platform as a part of a complete autonomous mission. The results of flight- tests are presented and performance and limitations of the technique are discussed.
Unsupervised Person Image Synthesis in Arbitrary Poses

We present a novel approach for synthesizing photorealistic images of people in arbitrary poses using generative adversarial learning. Given an input image of a person and a desired pose represented by a 2D skeleton, our model renders the image of the same person under the new pose, synthesizing novel views of the parts visible in the input image and hallucinating those that are not seen. This problem has recently been addressed in a supervised manner [16, 35], i.e., during training the ground truth images under the new poses are given to the network. We go beyond these approaches by proposing a fully unsupervised strategy. We tackle this challenging scenario by splitting the problem into two principal subtasks. First, we consider a pose conditioned bidirectional generator that maps back the initially rendered image to the original pose, hence being directly comparable to the input image without the need to resort to any training image. Second, we devise a novel loss function that incorporates content and style terms, and aims at producing images of high perceptual quality. Extensive experiments conducted on the DeepFashion dataset demonstrate that the images rendered by our model are very close in appearance to those obtained by fully supervised approaches.
Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering

End-to-end neural models have made significant progress in question answering, however recent studies show that these models implicitly assume that the answer and evidence appear close together in a single document. In this work, we propose the Coarse-grain Fine-grain Coattention Network (CFC), a new question answering model that combines information from evidence across multiple documents. The CFC consists of a coarse-grain module that interprets documents with respect to the query then finds a relevant answer, and a fine-grain module which scores each candidate answer by comparing its occurrences across all of the documents with the query. We design these modules using hierarchies of coattention and selfattention, which learn to emphasize different parts of the input. On the Qangaroo WikiHop multi-evidence question answering task, the CFC obtains a new stateof-the-art result of 70.6% on the blind test set, outperforming the previous best by 3% accuracy despite not using pretrained contextual encoders.
A multi-agent optimization algorithm for resource constrained project scheduling problem

In this paper, a multi-agent optimization algorithm (MAOA) is proposed for solving the resourceconstrained project scheduling problem (RCPSP). In the MAOA, multiple agents work in a grouped environment where each agent represents a feasible solution. The evolution of agents is achieved by using four main elements in the MAOA, including social behavior, autonomous behavior, self-learning, and environment adjustment. The social behavior includes the global one and the local one for performing exploration. Through the global social behavior, the leader agent in every group is guided by the global best leader. Through the local social behavior, each agent is guided by its own leader agent. Through the autonomous behavior, each agent exploits its own neighborhood. Through the self-learning, the best agent performs an intensified search to further exploit the promising region. Meanwhile, some agents perform migration among groups to adjust the environment dynamically for information sharing. The implementation of the MAOA for solving the RCPSP is presented in detail, and the effect of key parameters of the MAOA is investigated based on the Taguchi method of design of experiment. Numerical testing results are provided by using three sets of benchmarking instances. The comparisons to the existing algorithms demonstrate the effectiveness of the proposed MAOA for solving the RCPSP.
E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures

Activity monitoring in home environments has become increasingly important and has the potential to support a broad array of applications including elder care, well-being management, and latchkey child safety. Traditional approaches involve wearable sensors and specialized hardware installations. This paper presents device-free location-oriented activity identification at home through the use of existing WiFi access points and WiFi devices (e.g., desktops, thermostats, refrigerators, smartTVs, laptops). Our low-cost system takes advantage of the ever more complex web of WiFi links between such devices and the increasingly fine-grained channel state information that can be extracted from such links. It examines channel features and can uniquely identify both in-place activities and walking movements across a home by comparing them against signal profiles. Signal profiles construction can be semi-supervised and the profiles can be adaptively updated to accommodate the movement of the mobile devices and day-to-day signal calibration. Our experimental evaluation in two apartments of different size demonstrates that our approach can achieve over 96% average true positive rate and less than 1% average false positive rate to distinguish a set of in-place and walking activities with only a single WiFi access point. Our prototype also shows that our system can work with wider signal band (802.11ac) with even higher accuracy.
Waveform Design for MIMO Radars

We consider the problem of waveform design for multiple input/multiple output (MIMO) radars, where the transmit waveforms are adjusted based on target and clutter statistics. A model for the radar returns which incorporates the transmit waveforms is developed. The target detection problem is formulated for that model. Optimal and suboptimal algorithms are derived for designing the transmit waveforms under different assumptions regarding the statistical information available to the detector. The performance of these algorithms is illustrated by computer simulation.