nils-herrmann/clean_scirepeval huggingface dataset:
Highly Automatic Approach to Architectural Floorplan Image Understanding & Model Generation

Authoring of a 3D model using computers typically involves tedious manual efforts. In this paper, we propose a highly automated approach to generate the 3D model of a building from its 2D floorplan image. The proposed system treats the floorplan as a vector image in that the lines in the plan are converted into vector outlines. Building wall identification is thus reduced to a matching problem of the outlines in the plan. A number of advantages are resulted from using this approach. For example, special constructs such as doors can be recognized through the geometric characteristics of the line signatures. The concept of interior space can also be explored through examining the line loop within the plan. Our system allows the generation of building interior as well as the exterior model. The presented algorithm is useful for data generation in architectural walkthrough of city scenes as well as 3D games development.
MAUI: making smartphones last longer with code offload

This paper presents MAUI, a system that enables fine-grained energy-aware offload of mobile code to the infrastructure. Previous approaches to these problems either relied heavily on programmer support to partition an application, or they were coarse-grained requiring full process (or full VM) migration. MAUI uses the benefits of a managed code environment to offer the best of both worlds: it supports fine-grained code offload to maximize energy savings with minimal burden on the programmer. MAUI decides at run-time which methods should be remotely executed, driven by an optimization engine that achieves the best energy savings possible under the mobile device's current connectivity constrains. In our evaluation, we show that MAUI enables: 1) a resource-intensive face recognition application that consumes an order of magnitude less energy, 2) a latency-sensitive arcade game application that doubles its refresh rate, and 3) a voice-based language translation application that bypasses the limitations of the smartphone environment by executing unsupported components remotely.
Surpassing Humans in Boundary Detection using Deep Learning

In this work we show that Deep Convolutional Neural Networks can outperform humans on the task of boundary detection, as measured on the standard Berkeley Segmentation Dataset. Our detector is fully integrated in the popular Caffe framework and processes a 320x420 image in less than a second. Our contributions consist firstly in combining a careful design of the loss for boundary detection training, a multi-resolution architecture and training with external data to improve the detection accuracy of the current state of the art, from an optimal dataset scale F-measure of 0.780 to 0.808 while human performance is at 0.803. We further improve performance to 0.813 by combining deep learning with grouping, integrating the Normalized Cuts technique within a deep network. We also examine the potential of our boundary detector in conjunction with the higher level tasks of object proposal generation and semantic segmentation for both tasks our detector yields clear improvements over state-of-the-art systems.
NEAT-o-Games: blending physical activity and fun in the daily routine

This article describes research that aims to encourage physical activity through a novel pervasive gaming paradigm. Data from a wearable accelerometer are logged wirelessly to a cell phone and control the animation of an avatar that represents the player in a virtual race game with other players over the cellular network. Winners are declared every day and players with an excess of activity points can spend some to get hints in mental games of the suite, like Sudoku. The racing game runs in the background throughout the day and every little move counts. As the gaming platform is embedded in the daily routine of players, it may act as a strong behavioral modifier and increase everyday physical activity other than volitional sporting exercise. Such physical activity (e.g., taking the stairs), is termed NEAT and was shown to play a major role in obesity prevention and intervention. A pilot experiment demonstrates that players are engaged in NEAT-o-Games and become more physically active while having a good dosage of fun.
TouchIn: Sightless two-factor authentication on multi-touch mobile devices

Mobile authentication is indispensable for preventing unauthorized access to multi-touch mobile devices. Existing mobile authentication techniques are often cumbersome to use and also vulnerable to shoulder-surfing and smudge attacks. This paper focuses on designing, implementing, and evaluating TouchIn, a two-factor authentication system on multi-touch mobile devices. TouchIn works by letting a user draw on the touchscreen with one or multiple fingers to unlock his mobile device, and the user is authenticated based on the geometric properties of his drawn curves as well as his behavioral and physiological characteristics. TouchIn allows the user to draw on arbitrary regions on the touchscreen without looking at it. This nice sightless feature makes TouchIn very easy to use and also robust to shoulder-surfing and smudge attacks. Comprehensive experiments on Android devices confirm the high security and usability of TouchIn.
Image Colorization Using Generative Adversarial Networks

Over the last decade, the process of automatic image colorization has been of significant interest for several application areas including restoration of aged or degraded images. This problem is highly ill-posed due to the large degrees of freedom during the assignment of color information. Many of the recent developments in automatic colorization involve images that contain a common theme or require highly processed data such as semantic maps as input. In our approach, we attempt to fully generalize the colorization procedure using a conditional Deep Convolutional Generative Adversarial Network (DCGAN). The network is trained over datasets that are publicly available such as CIFAR-10 and Places365. The results between the generative model and traditional deep neural networks are compared.
A New Concave Hull Algorithm and Concaveness Measure for n-dimensional Datasets

Convex and concave hulls are useful concepts for a wide variety of application areas, such as pattern recognition, image processing, statistics, and classification tasks. Concave hull performs better than convex hull, but it is difficult to formulate and few algorithms are suggested. Especially, an n-dimensional concave hull is more difficult than a 2or 3dimensional one. In this paper, we propose a new concave hull algorithm for n-dimensional datasets. It is simple but creative. We show its application to dataset analysis. We also suggest a concaveness measure and a graph that captures geometric shape of an n-dimensional dataset. Proposed concave hull algorithm and concaveness measure/graph are implemented using java, and are posted to http://user.dankook.ac.kr/ ~bitl/dkuCH.
Closing the Gap Between Short and Long XORs for Model Counting

Many recent algorithms for approximate model counting are based on a reduction to combinatorial searches over random subsets of the space defined by parity or XOR constraints. Long parity constraints (involving many variables) provide strong theoretical guarantees but are computationally difficult. Short parity constraints are easier to solve but have weaker statistical properties. It is currently not known how long these parity constraints need to be. We close the gap by providing matching necessary and sufficient conditions on the required asymptotic length of the parity constraints. Further, we provide a new family of lower bounds and the first non-trivial upper bounds on the model count that are valid for arbitrarily short XORs. We empirically demonstrate the effectiveness of these bounds on model counting benchmarks and in a Satisfiability Modulo Theory (SMT) application motivated by the analysis of contingency tables in statistics.
Dynamic Units of Visual Speech

We present a new method for generating a dynamic, concatenative, unit of visual speech that can generate realistic visual speech animation. We redefine visemes as temporal units that describe distinctive speech movements of the visual speech articulators. Traditionally visemes have been surmized as the set of static mouth shapes representing clusters of contrastive phonemes (e.g. /p, b, m/, and /f, v/). In this work, the motion of the visual speech articulators are used to generate discrete, dynamic visual speech gestures. These gestures are clustered, providing a finite set of movements that describe visual speech, the visemes. Dynamic visemes are applied to speech animation by simply concatenating viseme units. We compare to static visemes using subjective evaluation. We find that dynamic visemes are able to produce more accurate and visually pleasing speech animation given phonetically annotated audio, reducing the amount of time that an animator needs to spend manually refining the animation.
A geographic mobility prediction routing protocol for Ad Hoc UAV Network

Unmanned Aerial Vehicles (UAVs) play more and more important roles in modern warfare. However, the data routing for communication among UAVs faces several challenges, such as packet loss or routing path failure etc. The main problem of UAVs data routing is caused by the high mobility of UAVs. In this paper, an effective geographic mobility prediction routing protocol is proposed to improve the performance of routing among UAVs. First, a Gaussian distribution of UAVs movement probability density function is deduced to reduce the impact of high mobility. Then, two-hop perimeter forwarding is proposed to reduce the impact of routing void. The experiment results show that the proposed approach can provide effective and reliable data routing with acceptable communication overhead in the highly dynamic environment of Ad Hoc UAV Network.
How schema and novelty augment memory formation

Information that is congruent with existing knowledge (a schema) is usually better remembered than less congruent information. Only recently, however, has the role of schemas in memory been studied from a systems neuroscience perspective. Moreover, incongruent (novel) information is also sometimes better remembered. Here, we review lesion and neuroimaging findings in animals and humans that relate to this apparent paradoxical relationship between schema and novelty. In addition, we sketch a framework relating key brain regions in medial temporal lobe (MTL) and medial prefrontal cortex (mPFC) during encoding, consolidation and retrieval of information as a function of its congruency with existing information represented in neocortex. An important aspect of this framework is the efficiency of learning enabled by congruency-dependent MTL-mPFC interactions.
outputs Semantic patterns for sentiment analysis of Twitter Conference Item

Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly expressed through affective words. Nevertheless, sentiment is often implicitly expressed via latent semantic relations, patterns and dependencies among words in tweets. In this paper, we propose a novel approach that automatically captures patterns of words of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment pattern extraction, our proposed approach does not rely on external and fixed sets of syntactical templates/patterns, nor requires deep analyses of the syntactic structure of sentences in tweets. We evaluate our approach with tweetand entity-level sentiment analysis tasks by using the extracted semantic patterns as classification features in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance of our patterns against 6 state-of-the-art baselines. Results show that our patterns consistently outperform all other baselines on all datasets by 2.19% at the tweet-level and 7.5% at the entity-level in average F-measure.
CPR: Composable performance regression for scalable multiprocessor models

Uniprocessor simulators track resource utilization cycle by cycle to estimate performance. Multiprocessor simulators, however, must account for synchronization events that increase the cost of every cycle simulated and shared resource contention that increases the total number of cycles simulated. These effects cause multiprocessor simulation times to scale superlinearly with the number of cores. Composable performance regression (CPR) fundamentally addresses these intractable multiprocessor simulation times, estimating multiprocessor performance with a combination of uniprocessor, contention, and penalty models. The uniprocessor model predicts baseline performance of each core while the contention models predict interfering accesses from other cores. Uniprocessor and contention model outputs are composed by a penalty model to produce the final multiprocessor performance estimate. Trained with a production quality simulator, CPR is accurate with median errors of 6.63, 4.83 percent for dual-, quad-core multiprocessors. Furthermore, composable regression is scalable, requiring 0.33× the simulations required by prior regression strategies.
Giraffe: Using Deep Reinforcement Learning to Play Chess

This report presents Giraffe, a chess engine that uses self-play to discover all its domain-specific knowledge, with minimal hand-crafted knowledge given by the programmer. Unlike previous attempts using machine learning only to perform parametertuning on hand-crafted evaluation functions, Giraffe’s learning system also performs automatic feature extraction and pattern recognition. The trained evaluation function performs comparably to the evaluation functions of state-of-the-art chess engines all of which containing thousands of lines of carefully hand-crafted pattern recognizers, tuned over many years by both computer chess experts and human chess masters. Giraffe is the most successful attempt thus far at using end-to-end machine learning to play chess. We also investigated the possibility of using probability thresholds instead of depth to shape search trees. Depth-based searches form the backbone of virtually all chess engines in existence today, and is an algorithm that has become well-established over the past half century. Preliminary comparisons between a basic implementation of probability-based search and a basic implementation of depth-based search showed that our new probability-based approach performs moderately better than the established approach. There are also evidences suggesting that many successful ad-hoc add-ons to depth-based searches are generalized by switching to a probability-based search. We believe the probability-based search to be a more fundamentally correct way to perform minimax. Finally, we designed another machine learning system to shape search trees within the probability-based search framework. Given any position, this system estimates the probability of each of the moves being the best move without looking ahead. The system is highly effective the actual best move is within the top 3 ranked moves 70% of the time, out of an average of approximately 35 legal moves from each position. This also resulted in a significant increase in playing strength. With the move evaluator guiding a probability-based search using the learned evaluator, Giraffe plays at approximately the level of an FIDE International Master (top 2.2% of tournament chess players with an official rating)12. Fédération Internationale des Échecs, or the World Chess Federation, is the international organisation that governs all major international chess competitions.  A for a description of the Elo rating system.
Exome sequencing in sporadic autism spectrum disorders identifies severe de novo mutations

Evidence for the etiology of autism spectrum disorders (ASDs) has consistently pointed to a strong genetic component complicated by substantial locus heterogeneity. We sequenced the exomes of 20 individuals with sporadic ASD (cases) and their parents, reasoning that these families would be enriched for de novo mutations of major effect. We identified 21 de novo mutations, 11 of which were protein altering. Protein-altering mutations were significantly enriched for changes at highly conserved residues. We identified potentially causative de novo events in 4 out of 20 probands, particularly among more severely affected individuals, in FOXP1, GRIN2B, SCN1A and LAMC3. In the FOXP1 mutation carrier, we also observed a rare inherited CNTNAP2 missense variant, and we provide functional support for a multi-hit model for disease risk. Our results show that trio-based exome sequencing is a powerful approach for identifying new candidate genes for ASDs and suggest that de novo mutations may contribute substantially to the genetic etiology of ASDs.
Multi-task assignment for crowdsensing in mobile social networks

Mobile crowdsensing is a new paradigm in which a crowd of mobile users exploit their carried smart devices to conduct complex computation and sensing tasks in mobile social networks (MSNs). In this paper, we focus on the task assignment problem in mobile crowdsensing. Unlike traditional task scheduling problems, the task assignment in mobile crowdsensing must follow the mobility model of users in MSNs. To solve this problem, we propose an oFfline Task Assignment (FTA) algorithm and an oNline Task Assignment (NTA) algorithm. Both FTA and NTA adopt a greedy task assignment strategy. Moreover, we prove that the FTA algorithm is an optimal offline task assignment algorithm, and give a competitive ratio of the NTA algorithm. In addition, we demonstrate the significant performance of our algorithms through extensive simulations, based on four real MSN traces and a synthetic MSN trace.
From cognitive to neural models of working memory.

Working memory refers to the temporary retention of information that was just experienced or just retrieved from long-term memory but no longer exists in the external environment. These internal representations are short-lived, but can be stored for longer periods of time through active maintenance or rehearsal strategies, and can be subjected to various operations that manipulate the information in such a way that makes it useful for goal-directed behaviour. Empirical studies of working memory using neuroscientific techniques, such as neuronal recordings in monkeys or functional neuroimaging in humans, have advanced our knowledge of the underlying neural mechanisms of working memory. This rich dataset can be reconciled with behavioural findings derived from investigating the cognitive mechanisms underlying working memory. In this paper, I review the progress that has been made towards this effort by illustrating how investigations of the neural mechanisms underlying working memory can be influenced by cognitive models and, in turn, how cognitive models can be shaped and modified by neuroscientific data. One conclusion that arises from this research is that working memory can be viewed as neither a unitary nor a dedicated system. A network of brain regions, including the prefrontal cortex (PFC), is critical for the active maintenance of internal representations that are necessary for goal-directed behaviour. Thus, working memory is not localized to a single brain region but probably is an emergent property of the functional interactions between the PFC and the rest of the brain.
Efficient Large-Scale Graph Processing on Hybrid CPU and GPU Systems

The increasing scale and wealth of inter-connected data, such as those accrued by social network applications, demand the design of new techniques and platforms to efficiently derive actionable knowledge from large-scale graphs. Large real-world graphs, however, are famously difficult to process efficiently. Not only do they have a large memory footprint, but most graph algorithms also entail memory access patterns with poor locality, data-dependent parallelism, and a low compute-to-memory access ratio. To complicate matters further, many real-world graphs have a highly heterogeneous node degree distribution (i.e., they are scale-free), hence partitioning these graphs for parallel processing and simultaneously achieving access locality and load-balancing is difficult if not impossible.
Statistical Syntax-Directed Translation with Extended Domain of Locality

In syntax-directed translation, the sourcelanguage input is first parsed into a parsetree, which is then recursively converted into a string in the target-language. We model this conversion by an extended treeto-string transducer that has multi-level trees on the source-side, which gives our system more expressive power and flexibility. We also define a direct probability model and use a linear-time dynamic programming algorithm to search for the best derivation. The model is then extended to the general log-linear framework in order to incorporate other features like n-gram language models. We devise a simple-yet-effective algorithm to generate non-duplicate k-best translations for ngram rescoring. Preliminary experiments on English-to-Chinese translation show a significant improvement in terms of translation quality compared to a state-of-theart phrase-based system.
Air Quality Monitoring with SensorMap

The Mobile Air Quality Monitoring Network (MAQUMON) is presented. The system consists of a number of car-mounted sensor nodes measuring different pollutants in the air. The data points are tagged with location and time utilizing an on-board GPS. Periodically, the measurements are uploaded to a server, processed and then published on the SensorMap portal. Given a sufficient number of nodes and diverse mobility patterns, a detailed picture of the air quality in a large area will be obtained at a low cost.
Visualizing the non-visual: spatial analysis and interaction with information from text documents

The paper describes an approach to IV that involves spatializing text content for enhanced visual browsing and analysis. The application arena is large text document corpora such as digital libraries, regulations and procedures, archived reports, etc. The basic idea is that text content from these sources may be transformed to a spatial representation that preserves informational characteristics from the documents. The spatial representation may then be visually browsed and analyzed in ways that avoid language processing and that reduce the analysts mental workload. The result is an interaction with text that more nearly resembles perception and action with the natural world than with the abstractions of written language.
Normalized Cuts and Image Segmentation

ÐWe propose a novel approach for solving the perceptual grouping problem in vision. Rather than focusing on local features and their consistencies in the image data, our approach aims at extracting the global impression of an image. We treat image segmentation as a graph partitioning problem and propose a novel global criterion, the normalized cut, for segmenting the graph. The normalized cut criterion measures both the total dissimilarity between the different groups as well as the total similarity within the groups. We show that an efficient computational technique based on a generalized eigenvalue problem can be used to optimize this criterion. We have applied this approach to segmenting static images, as well as motion sequences, and found the results to be very encouraging. Index TermsÐGrouping, image segmentation, graph partitioning.