nils-herrmann/clean_scirepeval huggingface dataset:
Media segmentation using self-similarity decomposition

We present a framework for analyzing the structure of digital media streams. Though our methods work for video, text, and audio, we concentrate on detecting the structure of digital music files. In the first step, spectral data is used to construct a similarity matrix calculated from inter-frame spectral similarity. The digital audio can be robustly segmented by correlating a kernel along the diagonal of the similarity matrix. Once segmented, spectral statistics of each segment are computed. In the second step, segments are clustered based on the selfsimilarity of their statistics. This reveals the structure of the digital music in a set of segment boundaries and labels. Finally, the music can be summarized by selecting clusters with repeated segments throughout the piece. The summaries can be customized for various applications based on the structure of the original music.
A comparative evaluation of personality estimation algorithms for the twin recommender system

The appearance of the so-called recommender systems has led to the possibility of reducing the information overload experienced by individuals searching among online resources. One of the areas of application of recommender systems is the online tourism domain where sites like TripAdvisor allow people to post reviews of various hotels to help others make a good choice when planning their trip. As the number of such reviews grows in size every day, clearly it is impractical for the individual to go through all of them. We propose the TWIN ("Tell me What I Need") Personality-based Recommender System that analyzes the textual content of the reviews and estimates the personality of the user according to the Big Five model to suggest the reviews written by "twin-minded" people. In this paper we compare a number of algorithms to select the better option for personality estimation in the task of user profile construction.
PatchBatch: A Batch Augmented Loss for Optical Flow

We propose a new pipeline for optical flow computation, based on Deep Learning techniques. We suggest using a Siamese CNN to independently, and in parallel, compute the descriptors of both images. The learned descriptors are then compared efficiently using the L2 norm and do not require network processing of patch pairs. The success of the method is based on an innovative loss function that computes higher moments of the loss distributions for each training batch. Combined with an Approximate Nearest Neighbor patch matching method and a flow interpolation technique, state of the art performance is obtained on the most challenging and competitive optical flow benchmarks.
Joint Distant and Direct Supervision for Relation Extraction

Supervised approaches to Relation Extraction (RE) are characterized by higher accuracy than unsupervised models. Unfortunately, their applicability is limited by the need of training data for each relation type. Automatic creation of such data using Distant Supervision (DS) provides a promising solution to the problem. In this paper, we study DS for designing endto-end systems of sentence-level RE. In particular, we propose a joint model between Web data derived with DS and manually annotated data from ACE. The results show (i) an improvement on the previous state-of-the-art in ACE, which provides important evidence of the benefit of DS; and (ii) a rather good accuracy on extracting 52 types of relations from Web data, which suggests the applicability of DS for general RE.
3D-odometry for rough terrain - towards real 3D navigation

Up to recently autonomous mobile robots were mostly designed to run within an indoor, yet partly structured and flat, environment. In rough terrain many problems arise and position tracking becomes more difficult. The robot has to deal with wheel slippage and large orientation changes. In this paper we will first present the recent developments on the off-road rover Shrimp. Then a new method, called 3D-Odometry, which extends the standard 2D odometry to the 3D space will be developed. Since it accounts for transitions, the 3D-Odometry provides better position estimates. It will certainly help to go towards real 3D navigation for outdoor robots.
Online gaming motivations scale: development and validation

Understanding gaming motivations is important given the growing trend of incorporating game-based mechanisms in non-gaming applications. In this paper, we describe the development and validation of an online gaming motivations scale based on a 3-factor model. Data from 2,071 US participants and 645 Hong Kong and Taiwan participants is used to provide a cross-cultural validation of the developed scale. Analysis of actual in-game behavioral metrics is also provided to demonstrate predictive validity of the scale.
Efficient design of a novel wide-illumination-angle Ku/Ka-band feed for reflector antennas

A novel wide-illumination-angle feed for satellite communication which could operate simultaneously in Ku/Ka uplink and downlink bands is proposed in this paper, based on a highly efficient design routine. Excellent radiation performances and high reflection loss are achieved by employing multi scalar rings, hybrid mode launcher and coaxial structure. Cylindrical modal matching method is employed for generalized scattering matrix calculation inside the feed, while spherical mode expansion is adopted for the feed aperture discontinuity. Key specific parameters are finally compared with the full wave simulation results. Good consistency confirms the validity of our numerical approach and the proposed antenna.
The table lens: merging graphical and symbolic representations in an interactive focus + context visualization for tabular information

We present a new visualization, called the Table Lens, for visualizing and making sense of large tables. The visualization uses a focus+context (fisheye) technique that works effectively on tabular information because it allows display of crucial label information and multiple distal focal areas. In addition, a graphical mapping scheme for depicting table contents has been developed for the most widespread kind of tables, the cases-by-variables table. The Table Lens fuses symbolic and graphical representations into a single coherent view that can be fluidly adjusted by the user. This fusion and interactivity enables an extremely rich and natural style of direct manipulation exploratory data analysis.
Tracking Interacting Objects Optimally Using Integer Programming

In this paper, we show that tracking different kinds of interacting objects can be formulated as a network-flow Mixed Integer Program. This is made possible by tracking all objects simultaneously and expressing the fact that one object can appear or disappear at locations where another is in terms of linear flow constraints. We demonstrate the power of our approach on scenes involving cars and pedestrians, bags being carried and dropped by people, and balls being passed from one player to the next in a basketball game. In particular, we show that by estimating jointly and globally the trajectories of different types of objects, the presence of the ones which were not initially detected based solely on image evidence can be inferred from the detections of the others.
UJIIndoorLoc: A new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems

Although indoor localization is a key topic for mobile computing, it is still very difficult for the mobile sensing community to compare state-of-art localization algorithms due to the scarcity of databases. Thus, a multi-building and multi-floor localization database based on WLAN fingerprinting is presented in this work, being its public access granted for the research community. The here proposed database not only is the biggest database in the literature but it is also the first publicly available database. Among other comprehensively described features, full raw information taken by more than 20 users and by means of 25 devices is provided.
Dynamic Local Search for the Maximum Clique Problem

In this paper, we introduce DLS-MC, a new stochastic local search algorithm for the maximum clique problem. DLS-MC alternates between phases of iterative improvement, during which suitable vertices are added to the current clique, and plateau search, during which vertices of the current clique are swapped with vertices not contained in the current clique. The selection of vertices is solely based on vertex penalties that are dynamically adjusted during the search, and a perturbation mechanism is used to overcome search stagnation. The behaviour of DLS-MC is controlled by a single parameter, penalty delay, which controls the frequency at which vertex penalties are reduced. We show empirically that DLSMC achieves substantial performance improvements over state-of-the-art algorithms for the maximum clique problem over a large range of the commonly used DIMACS benchmark instances.
The Why and How of Nonnegative Matrix Factorization

Nonnegative matrix factorization (NMF) has become a widely used tool for the analysis of high-dimensional data as it automatically extracts sparse and meaningful features from a set of nonnegative data vectors. We first illustrate this property of NMF on three applications, in image processing, text mining and hyperspectral imaging –this is the why. Then we address the problem of solving NMF, which is NP-hard in general. We review some standard NMF algorithms, and also present a recent subclass of NMF problems, referred to as near-separable NMF, that can be solved efficiently (that is, in polynomial time), even in the presence of noise –this is the how. Finally, we briefly describe some problems in mathematics and computer science closely related to NMF via the nonnegative rank.
Retricoin: Bitcoin based on compact proofs of retrievability

Bitcoin [24] is a fully decentralized electronic cash system. The generation of the proof-of-work in Bitcoin requires large amount of computing resources. However, this huge amount of energy is wasted as one cannot make something useful out of it. In this paper, we propose a scheme called Retricoin which replaces the heavy computational proof-of-work of Bitcoin by proofs of retrievability that have practical benefits. To guarantee the availability of an important but large file, we distribute the segments of the file among the users in the Bitcoin network. Every user who wants to mine Bitcoins must store a considerable portion of this file and prove her storage to other peers in the network using proofs of retrievability. The file can be constructed at any point of time from the users storing their respective segments untampered. Retricoin is more efficient than the existing Permacoin scheme  in terms of storage overhead and network bandwidth required to broadcast the proof to the Bitcoin network. The verification time in our scheme is comparable to that of Permacoin and reasonable for all practical purposes. We also design an algorithm to let the miners in a group (or pool) mine collectively.
Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns

We present a novel method for classifying emotions from static facial images. Our approach leverages on the recent success of Convolutional Neural Networks (CNN) on face recognition problems. Unlike the settings often assumed there, far less labeled data is typically available for training emotion classification systems. Our method is therefore designed with the goal of simplifying the problem domain by removing confounding factors from the input images, with an emphasis on image illumination variations. This, in an effort to reduce the amount of data required to effectively train deep CNN models. To this end, we propose novel transformations of image intensities to 3D spaces, designed to be invariant to monotonic photometric transformations. These are applied to CASIA Webface images which are then used to train an ensemble of multiple architecture CNNs on multiple representations. Each model is then fine-tuned with limited emotion labeled training data to obtain final classification models. Our method was tested on the Emotion Recognition in the Wild Challenge (EmotiW 2015), Static Facial Expression Recognition sub-challenge (SFEW) and shown to provide a substantial, 15.36% improvement over baseline results (40% gain in performance).
Extending UTAUT2 To Explore Consumer Adoption Of Mobile Payments

There is a growing interest in studying the adoption of m-payments but literature on the subject is still in its infancy and no empirical research relating to this has been conducted in the context of the UK to date. The aim of this study is to unveil the current situation in m-payment adoption research and provide future research direction through the development of a research model for the examination of factors affecting m-payment adoption in the UK context. Following an extensive search of the literature, this study finds that 186 relationships between independent and dependent variables have been analysed by 32 existing empirical m-payment and m-banking adoption studies. From analysis of these relationships the most significant factors found to influence adoption are uncovered and an extension of UTAUT2 with the addition of perceived risk and trust is proposed to increase the applicability of UTAUT2 to the m-payment context.
Multiscale Features for Approximate Alignment of Point-based Surfaces

We introduce a novel method for approximate alignment of point-based surfaces. Our approach is based on detecting a set of salient feature points using a scale-space representation. For each feature point we compute a signature vector that is approximately invariant under rigid transformations. We use the extracted signed feature set in order to obtain approximate alignment of two surfaces. We apply our method for the automatic alignment of multiple scans using both scan-to-scan and scan-to-model matching capabilities.
Mining Duplicate Questions of Stack Overflow

Stack Overflow is a popular question answering site that is focused on programming problems. Despite efforts to prevent asking questions that have already been answered, the site contains duplicate questions. This may cause developers to unnecessarily wait for a question to be answered when it has already been asked and answered. The site currently depends on its moderators and users with high reputation to manually mark those questions as duplicates, which not only results in delayed responses but also requires additional efforts. In this paper, we first perform a manual investigation to understand why users submit duplicate questions in Stack Overflow. Based on our manual investigation we propose a classification technique that uses a number of carefully chosen features to identify duplicate questions. Evaluation using a large number of questions shows that our technique can detect duplicate questions with reasonable accuracy. We also compare our technique with DupPredictor, a state-of-the-art technique for detecting duplicate questions, and we found that our proposed technique has a better recall-rate than that technique.
Outsourcing Multi-Party Computation

We initiate the study of secure multi-party computation (MPC) in a server-aided setting, where the parties have access to a single server that (1) does not have any input to the computation; (2) does not receive any output from the computation; but (3) has a vast (but bounded) amount of computational resources. In this setting, we are concerned with designing protocols that minimize the computation of the parties at the expense of the server. We develop new definitions of security for this server-aided setting that generalize the standard simulation-based definitions for MPC and allow us to formally capture the existence of dishonest but non-colluding participants. This requires us to introduce a formal characterization of non-colluding adversaries that may be of independent interest. We then design general and special-purpose server-aided MPC protocols that are more efficient (in terms of computation and communication) for the parties than the alternative of running a standard MPC protocol (i.e., without the server). Our main general-purpose protocol provides security when there is at least one honest party with input. We also construct a new and efficient server-aided protocol for private set intersection and give a general transformation from any secure delegated computation scheme to a server-aided two-party protocol. ∗Microsoft Research. senyk@microsoft.com. †University of Calgary. pmohassel@cspc.ucalgary.ca. Work done while visiting Microsoft Research. ‡Columbia University. mariana@cs.columbia.edu. Work done as an intern at Microsoft Research.
“Resistance is futile”: reading science fiction alongside ubiquitous computing

Design-oriented research is an act of collective imagining—a way in which we work together to bring about a future that lies slightly out of our grasp. In this paper, we examine the collective imagining of ubiquitous computing by bringing it into alignment with a related phenomenon, science fiction, in particular as imagined by a series of television shows that form part of the cultural backdrop for many members of the research community. A comparative reading of these fictional narratives highlights a series of themes that are also implicit in the research literature. We argue both that these themes are important considerations in the shaping of technological design and that an attention to the tropes of popular culture holds methodological value for ubiquitous computing.
Citation count prediction: learning to estimate future citations for literature

In most of the cases, scientists depend on previous literature which is relevant to their research fields for developing new ideas. However, it is not wise, nor possible, to track all existed publications because the volume of literature collection grows extremely fast. Therefore, researchers generally follow, or cite merely a small proportion of publications which they are interested in. For such a large collection, it is rather interesting to forecast which kind of literature is more likely to attract scientists' response. In this paper, we use the citations as a measurement for the popularity among researchers and study the interesting problem of Citation Count Prediction (CCP) to examine the characteristics for popularity. Estimation of possible popularity is of great significance and is quite challenging. We have utilized several features of fundamental characteristics for those papers that are highly cited and have predicted the popularity degree of each literature in the future. We have implemented a system which takes a series of features of a particular publication as input and produces as output the estimated citation counts of that article after a given time period. We consider several regression models to formulate the learning process and evaluate their performance based on the coefficient of determination (R-square). Experimental results on a real-large data set show that the best predictive model achieves a mean average predictive performance of 0.740 measured in R-square, which significantly outperforms several alternative algorithms.
Towards an "Internet of Food": Food Ontologies for the Internet of Things

Automated food and drink recognition methods connect to cloud-based lookup databases (e.g., food item barcodes, previously identified food images, or previously classified NIR (Near Infrared) spectra of food and drink items databases) to match and identify a scanned food or drink item, and report the results back to the user. However, these methods remain of limited value if we cannot further reason with the identified food and drink items, ingredients and quantities/portion sizes in a proposed meal in various contexts; i.e., understand from a semantic perspective their types, properties, and interrelationships in the context of a given user’s health condition and preferences. In this paper, we review a number of “food ontologies”, such as the Food Products Ontology/FOODpedia (by Kolchin and Zamula), Open Food Facts (by Gigandet et al.), FoodWiki (Ontology-driven Mobile Safe Food Consumption System by Celik), FOODS-Diabetes Edition (A Food-Oriented Ontology-Driven System by Snae Namahoot and Bruckner), and AGROVOC multilingual agricultural thesaurus (by the UN Food and Agriculture Organization—FAO). These food ontologies, with appropriate modifications (or as a basis, to be added to and further OPEN ACCESS Future Internet 2015, 7 373 expanded) and together with other relevant non-food ontologies (e.g., about diet-sensitive disease conditions), can supplement the aforementioned lookup databases to enable progression from the mere automated identification of food and drinks in our meals to a more useful application whereby we can automatically reason with the identified food and drink items and their details (quantities and ingredients/bromatological composition) in order to better assist users in making the correct, healthy food and drink choices for their particular health condition, age, body weight/BMI (Body Mass Index), lifestyle and preferences, etc.
Energy Harvesting Circuit on a One-Sided Directional Flexible Antenna

The present letter describes the design of an energy harvesting circuit on a one-sided directional flexible planar antenna. The circuit is composed of a flexible antenna with an impedance matching circuit, a resonant circuit, and a booster circuit for converting and boosting radio frequency power into a dc voltage. The proposed one-sided directional flexible antenna has a bottom floating metal layer that enables one-sided radiation and easy connection of the booster circuit to the metal layer. The simulated output dc voltage is 2.89 V for an input of 100 mV and a 50 Ω power source at 900 MHz, and power efficiency is 58.7% for 1.0 × 107 Ω load resistance.
Waste collection vehicle routing problem with time windows

In this paper, we address a real life waste collection vehicle routing problem with time windows (VRPTW) with consideration of multiple disposal trips and drivers’ lunch breaks. Solomon’s well-known insertion algorithm is extended for the problem. While minimizing the number of vehicles and total traveling time is the major objective of vehicle routing problems in the literature, here we also consider the route compactness and workload balancing of a solution since they are very important aspects in practical applications. In order to improve the route compactness and workload balancing, a capacitated clustering-based waste collection VRPTW algorithm is developed. The proposed algorithms have been successfully implemented and deployed for the real life waste collection problems at Waste Management, Inc. A set of waste collection VRPTW benchmark problems is also presented in this paper. Waste collection problems are frequently considered as arc routing problems without time windows. However, that point of view can be applied only to residential waste collection problems. In the waste collection industry, there are three major areas: commercial waste collection, residential waste collection and roll-on-roll-off. In this paper, we mainly focus on the commercial waste collection problem. The problem can be characterized as a variant of VRPTW since commercial waste collection stops may have time windows. The major variation from a standard VRPTW is due to disposal operations and driver’s lunch break. When a vehicle is full, it needs to go to one of the disposal facilities (landfill or transfer station). Each vehicle can, and typically does, make multiple disposal trips per day. The purpose of this paper is to introduce the waste collection VRPTW, benchmark problem sets, and a solution approach for the problem. The proposed algorithms have been successfully implemented and deployed for the real life waste collection problems of Waste Management, the leading provider of comprehensive waste management services in North America with nearly 26,000 collection and transfer vehicles.