nils-herrmann/clean_scirepeval huggingface dataset:
Introducing instant messaging and chat in the workplace

We report on our experiences of introducing an instant messaging and group chat application into geographically distributed workgroups. We describe a number of issues we encountered, including privacy concerns, individual versus group training, and focusing on teams or individuals. The perception of the tool's utility was a complex issue, depending both on users' views of the importance of informal communication, and their perceptions of the nature of cross-site communication issues. Finally, we conclude with a discussion of critical mass, which is related to the features each user actually uses. More generally, we encountered a dilemma that imposes serious challenges for user-centered design of groupware systems
Dynamically Testing GUIs Using Ant Colony Optimization (T)

In this paper we introduce a dynamic GUI test generator that incorporates ant colony optimization. We created two ant systems for generating tests. Our first ant system implements the normal ant colony optimization algorithm in order to traverse the GUI and find good event sequences. Our second ant system, called AntQ, implements the antq algorithm that incorporates Q-Learning, which is a behavioral reinforcement learning technique. Both systems use the same fitness function in order to determine good paths through the GUI. Our fitness function looks at the amount of change in the GUI state that each event causes. Events that have a larger impact on the GUI state will be favored in future tests. We compared our two ant systems to random selection. We ran experiments on six subject applications and report on the code coverage and fault finding abilities of all three algorithms.
Vision for Looking at Traffic Lights: Issues, Survey, and Perspectives

This paper presents the challenges that researchers must overcome in traffic light recognition (TLR) research and provides an overview of ongoing work. The aim is to elucidate which areas have been thoroughly researched and which have not, thereby uncovering opportunities for further improvement. An overview of the applied methods and noteworthy contributions from a wide range of recent papers is presented, along with the corresponding evaluation results. The evaluation of TLR systems is studied and discussed in depth, and we propose a common evaluation procedure, which will strengthen evaluation and ease comparison. To provide a shared basis for comparing TLR systems, we publish an extensive public data set based on footage from U.S. roads. The data set contains annotated video sequences, captured under varying light and weather conditions using a stereo camera. The data set, with its variety, size, and continuous sequences, should challenge current and future TLR systems.
Internet of things enabled real time water quality monitoring system

Smart solutions for water quality monitoring are gaining importance with advancement in communication technology. This paper presents a detailed overview of recent works carried out in the field of smart water quality monitoring. Also, a power efficient, simpler solution for in-pipe water quality monitoring based on Internet of Things technology is presented. The model developed is used for testing water samples and the data uploaded over the Internet are analyzed. The system also provides an alert to a remote user, when there is a deviation of water quality parameters from the pre-defined set of standard values.
Byzantine fault-tolerant state machine replication with twin virtual machines

The reliability and availability of distributed services can be ensured using replication. We present an architecture and an algorithm for Byzantine fault-tolerant state machine replication. We explore the benefits of virtualization to reliably detect and tolerate faulty replicas, allowing the transformation of Byzantine faults into omission faults. Our approach reduces the total number of physical replicas from 3f+1 to 2f+1. It is based on the concept of twin virtual machines, which involves having two virtual machines in each physical host, each one acting as failure detector of the other.
Effort-related functions of nucleus accumbens dopamine and associated forebrain circuits

Over the last several years, it has become apparent that there are critical problems with the hypothesis that brain dopamine (DA) systems, particularly in the nucleus accumbens, directly mediate the rewarding or primary motivational characteristics of natural stimuli such as food. Hypotheses related to DA function are undergoing a substantial restructuring, such that the classic emphasis on hedonia and primary reward is giving way to diverse lines of research that focus on aspects of instrumental learning, reward prediction, incentive motivation, and behavioral activation. The present review discusses dopaminergic involvement in behavioral activation and, in particular, emphasizes the effort-related functions of nucleus accumbens DA and associated forebrain circuitry. The effects of accumbens DA depletions on food-seeking behavior are critically dependent upon the work requirements of the task. Lever pressing schedules that have minimal work requirements are largely unaffected by accumbens DA depletions, whereas reinforcement schedules that have high work (e.g., ratio) requirements are substantially impaired by accumbens DA depletions. Moreover, interference with accumbens DA transmission exerts a powerful influence over effort-related decision making. Rats with accumbens DA depletions reallocate their instrumental behavior away from food-reinforced tasks that have high response requirements, and instead, these rats select a less-effortful type of food-seeking behavior. Along with prefrontal cortex and the amygdala, nucleus accumbens is a component of the brain circuitry regulating effort-related functions. Studies of the brain systems regulating effort-based processes may have implications for understanding drug abuse, as well as energy-related disorders such as psychomotor slowing, fatigue, or anergia in depression.
Discriminative Multidimensional Scaling for Low-Resolution Face Recognition

Face images captured by surveillance videos usually have limited resolution. Due to resolution mismatch, it is hard to match high-resolution (HR) faces with low-resolution (LR) faces directly. Recently, multidimensional scaling (MDS) has been employed to solve the problem. In this letter, we proposed a more discriminative MDS method to learn a mapping matrix, which projects the HR images and LR images to a common subspace. Our method is discriminative since both interclass distances and intraclass distances are taken into consideration. We add an interclass constraint to enlarge the distances of different subjects in the subspace to ensure discriminability. Besides, we consider not only the relationship of HR–LR images, but also the relationship of HR–HR images and LR–LR images in order to preserve local consistency. Experimental results on FERET, Multi-PIE, and SCface databases demonstrate the effectiveness of our proposed approach.
Evaluating FrameNet-style semantic parsing: the role of coverage gaps in FrameNet

Supervised semantic role labeling (SRL) systems are generally claimed to have accuracies in the range of 80% and higher (Erk and Padó, 2006). These numbers, though, are the result of highly-restricted evaluations, i.e., typically evaluating on hand-picked lemmas for which training data is available. In this paper we consider performance of such systems when we evaluate at the document level rather than on the lemma level. While it is wellknown that coverage gaps exist in the resources available for training supervised SRL systems, what we have been lacking until now is an understanding of the precise nature of this coverage problem and its impact on the performance of SRL systems. We present a typology of five different types of coverage gaps in . We then analyze the impact of the coverage gaps on performance of a supervised semantic role labeling system on full texts, showing an average oracle upper bound of 46.8%.
Shared-Memory Parallelism Can Be Simple, Fast, and Scalable

Parallelism is the key to achieving high performance in computing. However, writing efficient and scalable parallel programs is notoriously difficult, and often requires significant expertise. To address this challenge, it is crucial to provide programmers with high-level tools to enable them to develop solutions efficiently, and at the same time emphasize the theoretical and practical aspects of algorithm design to allow the solutions developed to run efficiently under all possible settings. This thesis addresses this challenge using a three-pronged approach consisting of the design of shared-memory programming techniques, frameworks, and algorithms for important problems in computing. The thesis provides evidence that with appropriate programming techniques, frameworks, and algorithms, shared-memory programs can be simple, fast, and scalable, both in theory and in practice. The results developed in this thesis serve to ease the transition into the multicore era. The first part of this thesis introduces tools and techniques for deterministic parallel programming, including means for encapsulating nondeterminism via powerful commutative building blocks, as well as a novel framework for executing sequential iterative loops in parallel, which lead to deterministic parallel algorithms that are efficient both in theory and in practice. The second part of this thesis introduces Ligra, the first high-level sharedmemory framework for parallel graph traversal algorithms. The framework allows programmers to express graph traversal algorithms using very short and concise code, delivers performance competitive with that of highly-optimized code, and is up to orders of magnitude faster than existing systems designed for distributed memory. This part of the thesis also introduces Ligra+, which extends Ligra with graph compression techniques to reduce space usage and improve parallel performance at the same time, and is also the first graph processing system to support in-memory graph compression. The third and fourth parts of this thesis bridge the gap between theory and practice in parallel algorithm design by introducing the first algorithms for a variety of important problems on graphs and strings that are efficient both in theory and in practice. For example, the thesis develops the first linear-work and polylogarithmic-depth algorithms for suffix tree construction and graph connectivity that are also practical, as well as a work-efficient, polylogarithmicdepth, and cache-efficient shared-memory algorithm for triangle computations that achieves a 2–5x speedup over the best existing algorithms on 40 cores.
RefineNet : MultiPath Refinement Networks with Identity Mappings for High-Resolution Semantic Segmentation

Recently, very deep convolutional neural networks (CNNs) have shown outstanding performance in object recognition and have also been the first choice for dense classification problems such as semantic segmentation. However, repeated subsampling operations like pooling or convolution striding in deep CNNs lead to a significant decrease in the initial image resolution. Here, we present RefineNet, a generic multi-path refinement network that explicitly exploits all the information available along the down-sampling process to enable high-resolution prediction using long-range residual connections. In this way, the deeper layers that capture high-level semantic features can be directly refined using fine-grained features from earlier convolutions. The individual components of RefineNet employ residual connections following the identity mapping mindset, which allows for effective end-to-end training. Further, we introduce chained residual pooling, which captures rich background context in an efficient manner. We carry out comprehensive experiments and set new stateof-the-art results on seven public datasets. In particular, we achieve an intersection-over-union score of 83.4 on the challenging PASCAL VOC 2012 dataset, which is the best reported result to date.
Algorithms for congruent sphere packing and applications

The problem of packing congruent spheres (i.e., copies of the same sph ere) in a bounded domain arises in many applications. In this paper, we present a new pack-and-shake scheme for packing congruent spheres in various bounded 2-D domains. Our packing scheme is based on a number of interesting ideas, such as a trimming and packing approach, optimal lattice packing under translation and/or rotation, shaking procedures, etc. Our packing algorithms have fairly low time complexities. In certain cases, they even run in nearly linear time. Our techniques can be easily generalized to congruent packing of other shapes of objects, and are readily extended to higher dimensional spaces. Applications of our packing algorithms to treatment planning of radiosurgery are discussed. Experimental results suggest that our algorithms produce reasonably dense packings.
TextProposals: a Text-specific Selective Search Algorithm for Word Spotting in the Wild

Motivated by the success of powerful while expensive techniques to recognize words in a holistic way [1, 2, 3], object proposals techniques emerge as an alternative to the traditional text detectors. In this paper we introduce a novel object proposals method that is specifically designed for text. We rely on a similarity based region grouping algorithm that generates a hierarchy of word hypotheses. Over the nodes of this hierarchy it is possible to apply a holistic word recognition method in an efficient way. Our experiments demonstrate that the presented method is superior in its ability of producing good quality word proposals when compared with class-independent algorithms. We show impressive recall rates with a few thousand proposals in different standard benchmarks, including focused or incidental text datasets, and multi-language scenarios. Moreover, the combination of our object proposals with existing whole-word recognizers [2, 3] shows competitive performance in end-to-end word spotting, and, in some benchmarks, outperforms previously published results. Concretely, in the challenging ICDAR2015 Incidental Text dataset, we overcome in more than 10 percent f-score the best-performing method in the last ICDAR Robust Preprint submitted to Pattern Recognition February 2, 2017 ar X iv :1 60 4. 02 61 9v 3 [ cs .C V ] 1 F eb 2 01 7 Reading Competition . Source code of the complete end-to-end system is available at https://github.com/lluisgomez/TextProposals.
A simulation model to improve warehouse operations

Warehouse or distribution centre managers have to decide how to collect the products to fulfill customers requests but also where to locate the products (SKUs) and how much space to allocate to each of them. Moreover, they have to deploy replenishment strategies to guarantee the reliability of their own stocks. These are challenging decisions because of their level of complexity and their high impact on the centre performance in terms of both its throughput and the operation costs. In particular, the goal of this work is to evaluate whether specific strategies to share the storage space could lead to reduce the operation costs while keeping the service level as high as possible. To this end, this paper develops a discrete event simulation model of the logistic operations at a real high throughput warehouse which handles more than 12 millions of cases annually. Preliminary results show that potential economies may be achieved by reducing the number of stock-outs at the picking area where customer orders are collected.
Aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision

We address one of the main challenges towards autonomous quadrotor flight in complex environments, which is flight through narrow gaps. While previous works relied on off-board localization systems or on accurate prior knowledge of the gap position and orientation in the world reference frame, we rely solely on onboard sensing and computing and estimate the full state by fusing gap detection from a single onboard camera with an IMU. This problem is challenging for two reasons: (i) the quadrotor pose uncertainty with respect to the gap increases quadratically with the distance from the gap; (ii) the quadrotor has to actively control its orientation towards the gap to enable state estimation (i.e., active vision). We solve this problem by generating a trajectory that considers geometric, dynamic, and perception constraints: during the approach maneuver, the quadrotor always faces the gap to allow state estimation, while respecting the vehicle dynamics; during the traverse through the gap, the distance of the quadrotor to the edges of the gap is maximized. Furthermore, we replan the trajectory during its execution to cope with the varying uncertainty of the state estimate. We successfully evaluate and demonstrate the proposed approach in many real experiments, achieving a success rate of 80% and gap orientations up to 45°. To the best of our knowledge, this is the first work that addresses and achieves autonomous, aggressive flight through narrow gaps using only onboard sensing and computing and without prior knowledge of the pose of the gap.
Grounded Semantic Parsing for Complex Knowledge Extraction

Recently, there has been increasing interest in learning semantic parsers with indirect supervision, but existing work focuses almost exclusively on question answering. Separately, there have been active pursuits in leveraging databases for distant supervision in information extraction, yet such methods are often limited to binary relations and none can handle nested events. In this paper, we generalize distant supervision to complex knowledge extraction, by proposing the first approach to learn a semantic parser for extracting nested event structures without annotated examples, using only a database of such complex events and unannotated text. The key idea is to model the annotations as latent variables, and incorporate a prior that favors semantic parses containing known events. Experiments on the GENIA event extraction dataset show that our approach can learn from and extract complex biological pathway events. Moreover, when supplied with just five example words per event type, it becomes competitive even among supervised systems, outperforming 19 out of 24 teams that participated in the original shared task.
Real-time creation of bitmap indexes on streaming network data

High-speed archival and indexing solutions of streaming traffic are growing in importance for applications such as monitoring, forensic analysis, and auditing. Many large institutions require fast solutions to support expedient analysis of historical network data, particularly in case of security breaches. However, “turning back the clock” is not a trivial task. The first major challenge is that such a technology needs to support data archiving under extremely high-speed insertion rates. Moreover, the archives created have to be stored in a compressed format that is still amenable to indexing and search. The above requirements make general-purpose databases unsuitable for this task and dedicated solutions are required. This work describes a solution for high-speed archival storage, indexing, and data querying on network flow information. We make the two following important contributions: (a) we propose a novel compressed bitmap index approach that significantly reduces both CPU load and disk consumption and, (b) we introduce an online stream reordering mechanism that further reduces space requirements and improves the time for data retrieval. The reordering methodology is based on the principles of locality-sensitive hashing (LSH) and also of interest for other bitmap creation techniques. Because of the synergy of these two components, our solution can sustain data insertion rates that reach 500,000–1 million records per second. To put these numbers into perspective, typical commercial network flow solutions can currently process 20,000–60,000 flows per second. In addition, our system offers interactive query response times that enable administrators to perform complex analysis tasks on the fly. Our technique is directly amenable to parallel execution, allowing its application in domains that are challenged by large volumes of historical measurement data, such as network auditing, traffic behavior analysis, and large-scale data visualization in service provider networks.
Man-In-The-Middle attacks on bluetooth: a comparative analysis, a novel attack, and countermeasures

We provide a comparative analysis of the existing MITM (Man-In-The-Middle) attacks on Bluetooth. In addition, we propose a novel Bluetooth MITM attack against Bluetooth- enabled printers that support SSP (Secure Simple Pairing). Our attack is based on the fact that the security of the protocol is likely to be limited by the capabilities of the least powerful or the least secure device type. Moreover, we propose improvements to the existing Bluetooth SSP in order to make it more secure.
Boosting Structured Prediction for Imitation Learning

The Maximum Margin Planning (MMP) (Ratliff et al., 2006) algorithm solves imitation learning problems by learning linear mappings from features to cost functions in a planning domain. The learned policy is the result of minimum-cost planning using these cost functions. These mappings are chosen so that example policies (or trajectories) given by a teacher appear to be lower cost (with a lossscaled margin) than any other policy for a given planning domain. We provide a novel approach, MMPBOOST , based on the functional gradient descent view of boosting (Mason et al., 1999; Friedman, 1999a) that extends MMP by “boosting” in new features. This approach uses simple binary classification or regression to improve performance of MMP imitation learning, and naturally extends to the class of structured maximum margin prediction problems.
Cone carving for surface reconstruction

We present cone carving, a novel space carving technique supporting topologically correct surface reconstruction from an incomplete scanned point cloud. The technique utilizes the point samples not only for local surface position estimation but also to obtain global visibility information under the assumption that each acquired point is visible from a point lying outside the shape. This enables associating each point with a generalized cone, called the visibility cone, that carves a portion of the outside ambient space of the shape from the inside out. These cones collectively provide a means to better approximate the signed distances to the shape specifically near regions containing large holes in the scan, allowing one to infer the correct surface topology. Combining the new distance measure with conventional RBF, we define an implicit function whose zero level set defines the surface of the shape. We demonstrate the utility of cone carving in coping with significant missing data and raw scans from a commercial 3D scanner as well as synthetic input.