nils-herrmann/clean_scirepeval huggingface dataset:
3D Shape Reconstruction from a Single 2D Image via 2D-3D Self-Consistency

Aiming at inferring 3D shapes from 2D images, 3D shape reconstruction has drawn huge attention from researchers in computer vision and deep learning communities. However, it is not practical to assume that 2D input images and their associated ground truth 3D shapes are always available during training. In this paper, we propose a framework for semi-supervised 3D reconstruction. This is realized by our introduced 2D-3D self-consistency, which aligns the predicted 3D models and the projected 2D foreground segmentation masks. Moreover, our model not only enables recovering 3D shapes with the corresponding 2D masks, camera pose information can be jointly disentangled and predicted, even such supervision is never available during training. In the experiments, we qualitatively and quantitatively demonstrate the effectiveness of our model, which performs favorably against state-of-the-art approaches in either supervised or semi-supervised settings.
User acceptance of smart home services: an extension of the theory of planned behavior

This study develops a comprehensive research model that can explain potential customers’ behavioral intentions to adopt and use smart home services.  This study proposes and validates a new theoretical model that extends the theory of planned behavior (TPB). Partial least squares analysis (PLS) is employed to test the research model and corresponding hypotheses on data collected from 216 survey samples.  Mobility, security/privacy risk, and trust in the service provider are important factors affecting the adoption of smart home services.  To increase potential users’ adoption rate, service providers should focus on developing mobility-related services that enable people to access smart home services while on the move using mobile devices via control and monitoring functions.  This study is the first empirical attempt to examine user acceptance of smart home services, as most of the prior literature has concerned technical features.
SafeRoute: Learning to Navigate Streets Safely in an Urban Environment

Recent studies show that 85% of women have changed their traveled route to avoid harassment and assault. Despite this, current mapping tools do not empower users with information to take charge of their personal safety. We propose SafeRoute, a novel solution to the problem of navigating cities and avoiding street harassment and crime. Unlike other street navigation applications, SafeRoute introduces a new type of path generation via deep reinforcement learning. is enables us to successfully optimize for multi-criteria path-nding and incorporate representation learning within our framework. Our agent learns to pick favorable streets to create a safe and short path with a reward function that incorporates safety and eciency. Given access to recent crime reports in many urban cities, we train our model for experiments in Boston, New York, and San Francisco. We test our model on areas of these cities, specically the populated downtown regions where tourists and those unfamiliar with the streets walk. We evaluate SafeRoute and successfully improve over state-of-the-art methods by up to 17% in local average distance from crimes while decreasing path length by up to 7%.
Coordinated Scheduling and Dynamic Performance Analysis in Multiprocessor Systems

to design and build computing systems capable of running themselves, adjusting to varying circumstances, and preparing their resources to handle most efficiently the workloads we put upon them. These autonomic systems must anticipate needs and allow users to concentrate on what they want to accomplish rather than figuring how to rig the computing systems to get them there.  The performance of current shared-memory multiprocessor systems depends on both the efficient utilization of all the architectural elements in the system (processors, memory, etc), and the workload characteristics. This Thesis has the main goal of improving the execution of workloads of parallel applications in shared-memory multiprocessor systems by using real performance information in the processor scheduling. In multiprocessor systems, users request for resources (processors) to execute their parallel applications. The Operating System is responsible to distribute the available physical resources among parallel applications in the more convenient way for both the system and the application performance. It is a typical practice of users in multiprocessor systems to request for a high number of processors assuming that the higher the processor request, the higher the number of processors allocated, and the higher the speedup achieved by their applications. However, this is not true. Parallel applications have different characteristics with respect to their scalability. Their speedup also depends on run-time parameters such as the influence of the rest of running applications. This Thesis proposes that the system should not base its decisions on the users requests only, but the system must decide, or adjust, its decisions based on real performance information calculated at run-time. The performance of parallel applications is an information that the system can dynamically measure without introducing a significant penalty in the application execution time. Using this information, the processor allocation can be decided, or modified, being robust to incorrect processor requests given by users. We also propose that the system use a target efficiency to ensure the efficient use of processors. This target efficiency is a system parameter and can be dynamically decided as a function of the characteristics of running applications or the number of queued applications. We also propose to coordinate the different scheduling levels that operate in the processor scheduling: the run-time scheduler, the processor scheduler, and the queueing system. We propose to establish an interface between levels to send and receive information, and to take scheduling decisions considering the information provided by the rest of …
Greenhouse gas emissions-do carbon taxes work ?

During the last decade, Norway has carried out an ambitious climate policy by implementing a relatively high carbon tax already in 1991. The Norwegian carbon taxes are among the highest in the world. Data for the development in CO2 emissions provide a unique opportunity to evaluate carbon taxes as a policy tool for CO2 abatement. We combine a divisia index decomposition method and applied general equilibrium simulations to decompose the emission changes, with and without the carbon taxes, in the period 1990-1999. We find that despite significant price increases for some fueltypes, the carbon tax effect on emissions was modest. The taxes contributed to a reduction in onshore emissions of only 1.5 percent and total emissions of 2.3 percent. With zero tax, the total emissions would have increased by 21.1 percent over the period 1990-1999, as opposed to the observed growth of 18.7 percent. This surprisingly small effect relates to the extensive tax exemptions and relatively inelastic demand in the sectors in which the tax is actually implemented. The tax does not work on the levied sources, and is exempted in sectors where it could have worked.
Field Experiments on 5G Radio Access Using Multi-Point Transmission

This paper presents outdoor field experimental results to clarify the 4x4 MIMO throughput performance from applying multi-point transmission in the 15 GHz frequency band in the downlink of 5G cellular radio access system. The experimental results in large-cell scenario shows that up to 30 % throughput gain compared to non-multi-point transmission is achieved although the difference for the RSRP of two TPs is over 10 dB, so that the improvement for the antenna correlation is achievable and important aspect for the multi-point transmission in the 15 GHz frequency band as well as the improvement of the RSRP. Furthermore in small-cell scenario, the throughput gain of 70% and over 5 Gbps are achieved applying multi-point transmission in the condition of two different MIMO streams transmission from a single TP as distributed MIMO instead of four MIMO streams transmission from a single TP.
ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters

To bridge the gap between the capabilities of the state-of-the-art in factoid question answering (QA) and what real users ask, we need large datasets of real user questions that capture the various question phenomena users are interested in, and the diverse ways in which these questions are formulated. We introduce ComQA, a large dataset of real user questions that exhibit different challenging aspects such as temporal reasoning, compositionality, etc. ComQA questions come from the WikiAnswers community QA platform1. Through a large crowdsourcing effort, we clean the question dataset, group questions into paraphrase clusters, and annotate clusters with their answers. ComQA contains 11,214 questions grouped into 4,834 paraphrase clusters. We detail the process of constructing ComQA, including the measures taken to ensure its high quality while making effective use of crowdsourcing. We also present an extensive analysis of the dataset and the results achieved by state-of-the-art systems on ComQA, demonstrating that our dataset can be a driver of future research on QA.
essy Genetic Algorithms : Motivation , Analysis , and First Results

This paper defines and explores a somewhat different type of genetic algorithm (GA) a messy genetic algorithm (mGA). Messy GAs process variable-length strings that may be either underor overspecified with respect to the problem being solved . As nature has formed its genotypes by progressing from simple to more complex life forms, messy GAs solve problems by combining relatively short, well-tested building blocks to form longer, more complex strings that increasingly cover all features of a problem. This approach stands in contrast to the usual fixed-length, fixed-coding genetic algorithm, where the existence of the requisite tight linkage is taken for granted or ignored altogether. To compare the two approaches, a 3D-bit, orderthree-deceptive problem is searched using a simple GA and a messy GA. Using a random but fixed ordering of the bits, the simple GA makes errors at roughly three-quarters of its positions; under a worstcase ordering, the simple GA errs at all positions. In contrast to the simple GA results, the messy GA repeatedly solves the same problem to optimality. Prior to this time, no GA had ever solved a provably difficult problem to optimality without prior knowledge of good string arrangements. The mGA presented herein repeatedly achieves globally optimal results without such knowledge, and it does so at the very first generation in which strings are long enough to cover the problem. The solution of a difficult nonlinear problem to optimality suggests that messy GAs can solve more difficult problems than has been possible to date with other genetic algorithms. The ramifications of these techniques in search and machine learning are explored, including the possibility of messy floating-point codes, messy permutations, and messy classifiers.
Applying Basic Features from Sentiment Analysis for Automatic Irony Detection

People use social media to express their opinions. Often linguistic devices such as irony are used. From the sentiment analysis perspective such utterances represent a challenge being a polarity reversor (usually from positive to negative). This paper presents an approach to address irony detection from a machine learning perspective. Our model considers structural features as well as, for the first time, sentiment analysis features such as the overall sentiment of a tweet and a score of its polarity. The approach has been evaluated over a set classifiers such as: Näıve Bayes, Decision Tree, Maximum Entropy, Support Vector Machine, and for the first time in irony detection task: Multilayer Perceptron. The results obtained showed the ability of our model to distinguish between potentially ironic and non-ironic sentences.
Clustering Data of Mixed Categorical and Numerical Type With Unsupervised Feature Learning

Mixed-type categorical and numerical data are a challenge in many applications. This general area of mixed-type data is among the frontier areas, where computational intelligence approaches are often brittle compared with the capabilities of living creatures. In this paper, unsupervised feature learning (UFL) is applied to the mixed-type data to achieve a sparse representation, which makes it easier for clustering algorithms to separate the data. Unlike other UFL methods that work with homogeneous data, such as image and video data, the presented UFL works with the mixed-type data using fuzzy adaptive resonance theory (ART). UFL with fuzzy ART (UFLA) obtains a better clustering result by removing the differences in treating categorical and numeric features. The advantages of doing this are demonstrated with several real-world data sets with ground truth, including heart disease, teaching assistant evaluation, and credit approval. The approach is also demonstrated on noisy, mixed-type petroleum industry data. UFLA is compared with several alternative methods. To the best of our knowledge, this is the first time UFL has been extended to accomplish the fusion of mixed data types.
Resection of the vaginal vault for vaginal recurrence of cervical cancer after hysterectomy and brachytherapy

We describe our experiences with vaginal vault resection for vaginal recurrence of cervical cancer after hysterectomy and radiotherapy. After operative treatment, the rate of vaginal vault recurrence of uterine cervical cancer is reported to be about 5%. There is no consensus regarding the treatment for these cases.



Between 2004 and 2012, eight patients with vaginal vault recurrence underwent removal of the vaginal wall via laparotomy after hysterectomy and radiotherapy.



The median patient age was 45 years (range 35 to 70 years). The median operation time was 244.5 min (range 172 to 590 min), the median estimated blood loss was 362.5 mL (range 49 to 1,890 mL), and the median duration of hospitalization was 24.5 days (range 11 to 50 days). Two patients had intraoperative complications: a grade 1 bowel injury and a grade 1 bladder injury. The following postoperative complications were observed: one patient had vaginal vault bleeding, three patients developed vesicovaginal fistulae, and one patient had repeated ileus. Two patients needed clean intermittent catheterization. Local control was achieved in five of the eight cases.



Vaginal vault resection is an effective treatment for vaginal recurrence of cervical cancer after hysterectomy and radiotherapy. However, complications of this procedure can be expected to reduce quality of life. Therefore, this operation should be selected with great care.
Physiology-Based Face Recognition in the Thermal Infrared Spectrum

The current dominant approaches to face recognition rely on facial characteristics that are on or over the skin. Some of these characteristics have low permanency can be altered, and their phenomenology varies significantly with environmental factors (e.g., lighting). Many methodologies have been developed to address these problems to various degrees. However, the current framework of face recognition research has a potential weakness due to its very nature. We present a novel framework for face recognition based on physiological information. The motivation behind this effort is to capitalize on the permanency of innate characteristics that are under the skin. To establish feasibility, we propose a specific methodology to capture facial physiological patterns using the bioheat information contained in thermal imagery. First, the algorithm delineates the human face from the background using the Bayesian framework. Then, it localizes the superficial blood vessel network using image morphology. The extracted vascular network produces contour shapes that are characteristic to each individual. The branching points of the skeletonized vascular network are referred to as thermal minutia points (TMPs) and constitute the feature database. To render the method robust to facial pose variations, we collect for each subject to be stored in the database five different pose images (center, midleft profile, left profile, midright profile, and right profile). During the classification stage, the algorithm first estimates the pose of the test image. Then, it matches the local and global TMP structures extracted from the test image with those of the corresponding pose images in the database. We have conducted experiments on a multipose database of thermal facial images collected in our laboratory, as well as on the time-gap database of the University of Notre Dame. The good experimental results show that the proposed methodology has merit, especially with respect to the problem of low permanence over time. More importantly, the results demonstrate the feasibility of the physiological framework in face recognition and open the way for further methodological and experimental research in the area
Camera Self-Calibration with Known Camera Orientation

support for the camera interfaces. My colleagues Felix Woelk and Kevin Köser I would like to thank for many fruitful discussions. I thank our system administrator Torge Storm for always fixing my machine and providing enough data space for all my sequences which was really a hard job. Of course I also would like to thank the other members of the group Jan Woetzel, Daniel Grest, Birger Streckel and Renate Staecker for their help, the discussions and providing the exciting working environment. Last but not least, I would like to express my gratitude to my wife Miriam for always supporting me and my work. I also want to thank my sons Joshua and Noah for suffering under my paper writing. Finally I thank my parents for always supporting my education and my work.
Control your game-self: effects of controller type on enjoyment, motivation, and personality in game

Whether they are made to entertain you, or to educate you, good video games engage you. Significant research has tried to understand engagement in games by measuring player experience (PX). Traditionally, PX evaluation has focused on the enjoyment of game, or the motivation of players; these factors no doubt contribute to engagement, but do decisions regarding play environment (e.g., the choice of game controller) affect the player more deeply than that? We apply self-determination theory (specifically satisfaction of needs and self-discrepancy represented using the five factors model of personality) to explain PX in an experiment with controller type as the manipulation. Our study shows that there are a number of effects of controller on PX and in-game player personality. These findings provide both a lens with which to view controller effects in games and a guide for controller choice in the design of new games. Our research demonstrates that including self-characteristics assessment in the PX evaluation toolbox is valuable and useful for understanding player experience.
Improving learning of computational thinking using computational creativity exercises in a college CSI computer science course for engineers

Promoting computational thinking is a priority in CS education and other STEM and non-STEM disciplines. Our innovative, NSF-funded IC2Think project blends computational and creative thinking. In Spring 2013, we deployed Computational Creativity Exercises (CCE) designed to engage creative competencies (Surrounding, Capturing, Challenging and Broadening) in an introductory CSI course for engineering students. We compared this CCE implementation semester (80 students, 95% completing 3 or 4 CCEs) to the Fall 2013 semester of the same course (55 students) without CCEs. CCE implementation students had significantly higher scores on a CS concepts and skills knowledge test (F(1, 132) = 7.72, p <; 01, partial Eta2 = .055; M=7.47 to M=6.13; 13 items) and significantly higher self-efficacy for applying CS knowledge in their field (F(1, 153) = 12.22, p <; .01, partial Eta2 = .074; M=70.64 to M=61.47; 100-point scale). CCE implementation students had significantly higher study time (t(1, 136) = 2.08, p = .04; M=3.88 to M=3.29; 7-point scale) and significantly lower lack of regulation, which measures difficulties with studying (t(1, 136) = 2.82, p = .006; M=2.80 to M=3.21; 5-point scale). The addition of computational creativity exercises to CS courses may improve computational thinking and learning of CS knowledge and skills.
Telerehabilitation robotics: bright lights, big future?

The potential for remote diagnosis and treatment over the Internet using robotics is now a reality. The state of the art is exemplified by several Internet applications, and we explore the current trends in developing new systems. We review the technical challenges that lie ahead, along with some potential solutions. Some promising results for a new bilateral system involving two InMotion2 robots are presented. Finally, we discuss the future direction and commercial outlook for rehabilitation robots over the next 15 years.
Program Synthesis Without Full Specifications for Novel Applications

Associate Professor Daniel Grossman Computer Science & Engineering Affiliate Professor Sumit Gulwani Computer Science & Engineering Program synthesis is a family of techniques that generate programs from a description of what the program should do but not how it should do it. By designing a program synthesis algorithm together with the user interaction model we show that by accepting small increases in user effort, it is easier to write the synthesizer and the need for specialization of the synthesizer to a given domain is reduced without losing performance. In this work, we target three tasks to show the breadth of our methodology: code completion, end-user programming-by-example for data transformations, and feedback for introductory programming assignments. For each of these tasks, we develop an interaction model and program synthesis algorithm together to best support the user. In the first, we use partial expressions to allow programmers to express exactly what they don’t know and want the completion system to fill in. In the second, we use the sequence of examples to inform building up larger programs iteratively. In the last, we use attempts from other students on the same assignment to mine corrections.
Design of high gain microstrip Yagi array antenna for avalanche radar

This paper presents the design and construction of a microstrip Yagi array antenna operating at 5.3 GHz, to be used with an avalanche sensor in avalanche measurement. The advantage of the antenna is it can achieve a high gain of 15.2 dB with bandwidth of 8% in compact size. The gain enhancement is achieved by using a compact microstrip Yagi antenna as the array element; separating the feed network from the main radiating elements; and increasing the antenna height by installing the feed layer at the back of the patch layer, sharing the same ground plane. In order to ensure the power is transferred smoothly from the main input port to the radiating elements, the corporate feed is also design and tested. The fabricated antenna shows an agreeable performance with the simulated version.