nils-herrmann/clean_scirepeval huggingface dataset:
A High-Quality Video Denoising Algorithm Based on Reliable Motion Estimation

Although the recent advances in the sparse representations of images have achieved outstanding denosing results, removing real, structured noise in digital videos remains a challenging problem. We show the utility of reliable motion estimation to establish temporal correspondence across frames in order to achieve high-quality video denoising. In this paper, we propose an adaptive video denosing framework that integrates robust optical flow into a non-local means (NLM) framework with noise level estimation. The spatial regularization in optical flow is the key to ensure temporal coherence in removing structured noise. Furthermore, we introduce approximate K-nearest neighbor matching to significantly reduce the complexity of classical NLM methods. Experimental results show that our system is comparable with the state of the art in removing AWGN, and significantly outperforms the state of the art in removing real, structured noise.
An Intelligent Personalized Fashion Recommendation System

In this paper, we propose a novel system-Intelligent Personalized Fashion Recommendation System, which creates a new space in web multimedia mining and recommendation. The proposed system significantly helps customers find their most suitable fashion choices in mass fashion information in the virtual space based on multimedia mining. There are three stand-alone models developed in this paper to optimize the analysis of fashion features in mass fashion trend: (i). Interaction and recommender model, which associated clients' personalized demand with the current fashion trend, and helps clients find the most favorable fashion factors in trend. (ii). Evolutionary hierachical fashion multimedia mining model, which creates a hierachical structure to filer the key components of fashion multimedia information in the virtual space, and it proves to be more efficient for web mass multimedia mining in an evolutionary way. (iii). Color tone analysis model, a relevant and straightforward approach for analysis of main color tone as to the skin and clothing is used. In this model, a refined contour extraction of the fashion model method is also developed to solve the dilemma that the accuracy and efficiency of contour extraction in the dynamic and complex video scene. As evidenced by the experiment, the proposed system outperforms in effectiveness on mass fashion information in the virtual space compared with human, and thus developing a personalized and diversified way for fashion recommendation.
Predictive control approach to autonomous vehicle steering

A model predictive control (MPC) approach to active steering is presented for autonomous vehicle systems. The controller is designed to stabilize a vehicle along a desired path while rejecting wind gusts and fulfilling its physical constraints. Simulation results of a side wind rejection scenario and a double lane change maneuver on slippery surfaces show the benefits of the systematic control methodology used. A trade-off between the vehicle speed and the required preview on the desired path for vehicle stabilization is highlighted
3D Printing Your Wireless Coverage

Directing wireless signals and customizing wireless coverage is of great importance in residential, commercial, and industrial environments. It can improve the wireless reception quality, reduce the energy consumption, and achieve better security and privacy. To this end, we propose \name, a new computational approach to control wireless coverage by mounting signal reflectors in carefully optimized shapes on wireless routers. Leveraging 3D reconstruction, fast-wave simulations in acoustics, computational optimization, and 3D fabrication, our method is low-cost, adapts to different wireless routers and physical environments, and has a far-reaching impact by interweaving computational techniques to solve key problems in wireless communication.
Cohesion-based Online Actor-Critic Reinforcement Learning for mHealth Intervention

In the wake of the vast population of smart device users worldwide, mobile health (mHealth) technologies are hopeful to generate positive and wide influence on people’s health. They are able to provide flexible, affordable and portable health guides to device users. Current online decision-making methods for mHealth assume that the users are completely heterogeneous. They share no information among users and learn a separate policy for each user. However, data for each user is very limited in size to support the separate online learning, leading to unstable policies that contain lots of variances. Besides, we find the truth that a user may be similar with some, but not all, users, and connected users tend to have similar behaviors. In this paper, we propose a network cohesion constrained (actor-critic) Reinforcement Learning (RL) method for mHealth. The goal is to explore how to share information among similar users to better convert the limited user information into sharper learned policies. To the best of our knowledge, this is the first online actor-critic RL for mHealth and first network cohesion constrained (actor-critic) RL method in all applications. The network cohesion is important to derive effective policies. We come up with a novel method to learn the network by using the warm start trajectory, which directly reflects the users’ property. The optimization of our model is difficult and very different from the general supervised learning due to the indirect observation of values. As a contribution, we propose two algorithms for the proposed online RLs. Apart from mHealth, the proposed methods can be easily applied or adapted to other health-related tasks. Extensive experiment results on the HeartSteps dataset demonstrates that in a variety of parameter settings, the proposed two methods obtain obvious improvements over the state-of-the-art methods.
Agile requirements engineering practices and challenges: an empirical study

This paper describes empirical research into agile requirements engineering (RE) practices. Based on an analysis of data collected in 16 US software development organizations, we identify six agile practices. We also identify seven challenges that are created by the use of these practices. We further analyse how this collection of practices helps mitigate some, while exacerbating other risks in RE. We provide a framework for evaluating the impact and appropriateness of agile RE practices by relating them to RE risks. Two risks that are intractable by agile RE practices emerge from the analysis. First, problems with customer inability and a lack of concurrence among customers significantly impact agile development. Second, risks associated with the neglecting non-functional requirements such as security and scalability are a serious concern. Developers should carefully evaluate the risk factors in their project environment to understand whether the benefits of agile RE practices outweigh the costs imposed by the challenges.
Using singular value decomposition approximation for collaborative filtering

Singular value decomposition (SVD), together with the expectation-maximization (EM) procedure, can be used to find a low-dimension model that maximizes the log-likelihood of observed ratings in recommendation systems. However, the computational cost of this approach is a major concern, since each iteration of the EM algorithm requires a new SVD computation. We present a novel algorithm that incorporates SVD approximation into the EM procedure to reduce the overall computational cost while maintaining accurate predictions. Furthermore, we propose a new framework for collaborating filtering in distributed recommendation systems that allows users to maintain their own rating profiles for privacy. A server periodically collects aggregate information from those users that are online to provide predictions for all users. Both theoretical analysis and experimental results show that this framework is effective and achieves almost the same prediction performance as that of centralized systems.
Research Directions in Requirements Engineering

In this paper, we review current requirements engineering (RE) research and identify future research directions suggested by emerging software needs. First, we overview the state of the art in RE research. The research is considered with respect to technologies developed to address specific requirements tasks, such as elicitation, modeling, and analysis. Such a review enables us to identify mature areas of research, as well as areas that warrant further investigation. Next, we review several strategies for performing and extending RE research results, to help delineate the scope of future research directions. Finally, we highlight what we consider to be the "hot" current and future research topics, which aim to address RE needs for emerging systems of the future.
ARGUS: An Automated Multi-Agent Visitor Identification System

ARGUS is a multi-agent visitor identification system distributed over several workstations. Human faces are extracted from security camera images by a neuralnetwork-based face detector, and identified as frequent visitors by ARENA, a memory-based face recognition system. ARGUS then uses a messaging system to notify hosts that their guests have arrived. An interface agent enables users to submit feedback, which is immediately incorporated by ARENA to improve its face recognition performance. The ARGUS components were rapidly developed using JGram, an agent framework that is also detailed in this paper. JGram automatically converts high-level agent specifications into Java source code, and assembles complex tasks by composing individual agent services into a JGram pipeline. ARGUS has been operating successfully in an outdoor environment for several months.
A Formal Model of the Process of Wayfinding in Built Environments

Previous recent research on human wayfinding has focused primarily on mental representations rather than processes of wayfinding. This paper presents a formal model of some aspects of the process of wayfinding, where appropriate elements of human perception and cognition are formally realized using image schemata and affordances. The goal-driven reasoning chain that leads to action begins with incomplete and imprecise knowledge derived from imperfect observations of space. Actions result in further observations, derived knowledge and, recursively, further actions, until the goal is achieved or the wayfinder gives up. This paper gives a formalization of this process, using a modal extension to classical propositional logic to represent incomplete knowledge. Both knowledge and action are represented through a wayfinding graph. A special case of wayfinding in a building, that is finding one’s way through an airport, is used to demonstrate the formal model.
Detecting stress during real-world driving tasks using physiological sensors

This paper presents methods for collecting and analyzing physiological data during real-world driving tasks to determine a driver's relative stress level. Electrocardiogram, electromyogram, skin conductance, and respiration were recorded continuously while drivers followed a set route through open roads in the greater Boston area. Data from 24 drives of at least 50-min duration were collected for analysis. The data were analyzed in two ways. Analysis I used features from 5-min intervals of data during the rest, highway, and city driving conditions to distinguish three levels of driver stress with an accuracy of over 97% across multiple drivers and driving days. Analysis II compared continuous features, calculated at 1-s intervals throughout the entire drive, with a metric of observable stressors created by independent coders from videotapes. The results show that for most drivers studied, skin conductivity and heart rate metrics are most closely correlated with driver stress level. These findings indicate that physiological signals can provide a metric of driver stress in future cars capable of physiological monitoring. Such a metric could be used to help manage noncritical in-vehicle information systems and could also provide a continuous measure of how different road and traffic conditions affect drivers.
Visual Analytics Methodology for Eye Movement Studies

Eye movement analysis is gaining popularity as a tool for evaluation of visual displays and interfaces. However, the existing methods and tools for analyzing eye movements and scanpaths are limited in terms of the tasks they can support and effectiveness for large data and data with high variation. We have performed an extensive empirical evaluation of a broad range of visual analytics methods used in analysis of geographic movement data. The methods have been tested for the applicability to eye tracking data and the capability to extract useful knowledge about users' viewing behaviors. This allowed us to select the suitable methods and match them to possible analysis tasks they can support. The paper describes how the methods work in application to eye tracking data and provides guidelines for method selection depending on the analysis tasks.
IT Project Management: Infamous Failures, Classic Mistakes, and Best Practices

In recent years, IT project failures have received a great deal of attention in the press as well as the boardroom. In an attempt to avoid disasters going forward, many organizations are now learning from the past by conducting retrospectives—that is, project postmortems or post-implementation reviews. While each individual retrospective tells a unique story and contributes to organizational learning, even more insight can be gained by examining multiple retrospectives across a variety of organizations over time. This research aggregates the knowledge gained from 99 retrospectives conducted in 74 organizations over the past seven years. It uses the findings to reveal the most common mistakes and suggest best practices for more effective project management.2
Puberty suppression in gender identity disorder: the Amsterdam experience

The use of gonadotropin-releasing hormone analogs (GnRHa) to suppress puberty in adolescents with gender dysphoria is a fairly new intervention in the field of gender identity disorders or transsexualism. GnRHa are used to give adolescents time to make balanced decisions on any further treatment steps, and to obtain improved results in the physical appearance of those who opt to continue with sex reassignment. The effects of GnRHa are reversible. However, concerns have been raised about the risk of making the wrong treatment decisions, as gender identity could fluctuate during adolescence, adolescents in general might have poor decision-making abilities, and there are potential adverse effects on health and on psychological and psychosexual functioning. Proponents of puberty suppression emphasize the beneficial effects of GnRHa on the adolescents' mental health, quality of life and of having a physical appearance that makes it possible for the patients to live unobtrusively in their desired gender role. In this Review, we discuss the evidence pertaining to the debate on the effects of GnRHa treatment. From the studies that have been published thus far, it seems that the benefits outweigh the risks. However, more systematic research in this area is needed to determine the safety of this approach.
A comparison of named entity recognition tools applied to biographical texts

Named entity recognition (NER) is a popular domain of natural language processing. For this reason, many tools exist to perform this task. Amongst other points, they differ in the processing method they rely upon, the entity types they can detect, the nature of the text they can handle, and their input/output formats. This makes it difficult for a user to select an appropriate NER tool for a specific situation. In this article, we try to answer this question in the context of biographic texts. For this matter, we first constitute a new corpus by annotating 247 Wikipedia articles. We then select 4 publicly available, well known and free for research NER tools for comparison: Stanford NER, Illinois NET, OpenCalais NER WS and Alias-i LingPipe. We apply them to our corpus, assess their performances and compare them. When considering overall performances, a clear hierarchy emerges: Stanford has the best results, followed by LingPipe, Illionois and OpenCalais. However, a more detailed evaluation performed relatively to entity types and article categories highlights the fact their performances are diversely influenced by those factors. This complementarity opens an interesting perspective regarding the combination of these individual tools in order to improve performance.
Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate

Finding objective and effective thresholds for voxelwise statistics derived from neuroimaging data has been a long-standing problem. With at least one test performed for every voxel in an image, some correction of the thresholds is needed to control the error rates, but standard procedures for multiple hypothesis testing (e.g., Bonferroni) tend to not be sensitive enough to be useful in this context. This paper introduces to the neuroscience literature statistical procedures for controlling the false discovery rate (FDR). Recent theoretical work in statistics suggests that FDR-controlling procedures will be effective for the analysis of neuroimaging data. These procedures operate simultaneously on all voxelwise test statistics to determine which tests should be considered statistically significant. The innovation of the procedures is that they control the expected proportion of the rejected hypotheses that are falsely rejected. We demonstrate this approach using both simulations and functional magnetic resonance imaging data from two simple experiments.
Multi-resolution-Tract CNN with Hybrid Pretrained and Skin-Lesion Trained Layers

Correctly classifying a skin lesion is one of the first steps towards treatment. We propose a novel convolutional neural network (CNN) architecture for skin lesion classification designed to learn based on information from multiple image resolutions while leveraging pretrained CNNs. While traditional CNNs are generally trained on a single resolution image, our CNN is composed of multiple tracts, where each tract analyzes the image at a different resolution simultaneously and learns interactions across multiple image resolutions using the same field-of-view. We convert a CNN, pretrained on a single resolution, to work for multi-resolution input. The entire network is fine-tuned in a fully learned end-to-end optimization with auxiliary loss functions. We show how our proposed novel multi-tract network yields higher classification accuracy, outperforming state-of-the-art multi-scale approaches when compared over a public skin lesion dataset.
Load Balancing in Structured P2P Systems

Most P2P systems that provide a DHT abstraction distribute objects among “peer nodes” by choosing random identifiers for the objects. This could result in an O(log N) imbalance. Besides, P2P systems can be highly heterogeneous, i.e. they may consist of peers that range from old desktops behind modem lines to powerful servers connected to the Internet through high-bandwidth lines. In this paper, we address the problem of load balancing in such P2P systems. We explore the space of designing load-balancing algorithms that uses the notion of “virtual servers”. We present three schemes that differ primarily in the amount of information used to decide how to re-arrange load. Our simulation results show that even the simplest scheme is able to balance the load within 80% of the optimal value, while the most complex scheme is able to balance the load within 95% of the optimal value.
Curiosity-Driven Reinforcement Learning with Homeostatic Regulation

We propose a curiosity reward based on information theory principles and consistent with the animal instinct to maintain certain critical parameters within a bounded range. Our experimental validation shows the added value of the additional homeostatic drive to enhance the overall information gain of a reinforcement learning agent interacting with a complex environment using continuous actions. Our method builds upon two ideas: i) To take advantage of a new Bellman-like equation of information gain and ii) to simplify the computation of the local rewards by avoiding the approximation of complex distributions over continuous states and actions.
Quasi-cyclic LDPC codes for fast encoding

In this correspondence we present a special class of quasi-cyclic low-density parity-check (QC-LDPC) codes, called block-type LDPC (B-LDPC) codes, which have an efficient encoding algorithm due to the simple structure of their parity-check matrices. Since the parity-check matrix of a QC-LDPC code consists of circulant permutation matrices or the zero matrix, the required memory for storing it can be significantly reduced, as compared with randomly constructed LDPC codes. We show that the girth of a QC-LDPC code is upper-bounded by a certain number which is determined by the positions of circulant permutation matrices. The B-LDPC codes are constructed as irregular QC-LDPC codes with parity-check matrices of an almost lower triangular form so that they have an efficient encoding algorithm, good noise threshold, and low error floor. Their encoding complexity is linearly scaled regardless of the size of circulant permutation matrices.
System level analysis of fast, per-core DVFS using on-chip switching regulators

Portable, embedded systems place ever-increasing demands on high-performance, low-power microprocessor design. Dynamic voltage and frequency scaling (DVFS) is a well-known technique to reduce energy in digital systems, but the effectiveness of DVFS is hampered by slow voltage transitions that occur on the order of tens of microseconds. In addition, the recent trend towards chip-multiprocessors (CMP) executing multi-threaded workloads with heterogeneous behavior motivates the need for per-core DVFS control mechanisms. Voltage regulators that are integrated onto the same chip as the microprocessor core provide the benefit of both nanosecond-scale voltage switching and per-core voltage control. We show that these characteristics provide significant energy-saving opportunities compared to traditional off-chip regulators. However, the implementation of on-chip regulators presents many challenges including regulator efficiency and output voltage transient characteristics, which are significantly impacted by the system-level application of the regulator. In this paper, we describe and model these costs, and perform a comprehensive analysis of a CMP system with on-chip integrated regulators. We conclude that on-chip regulators can significantly improve DVFS effectiveness and lead to overall system energy savings in a CMP, but architects must carefully account for overheads and costs when designing next-generation DVFS systems and algorithms.