nils-herrmann/clean_scirepeval huggingface dataset:
Geodesic Saliency Using Background Priors

Generic object level saliency detection is important for many vision tasks. Previous approaches are mostly built on the prior that “appearance contrast between objects and backgrounds is high”. Although various computational models have been developed, the problem remains challenging and huge behavioral discrepancies between previous approaches can be observed. This suggest that the problem may still be highly ill-posed by using this prior only. In this work, we tackle the problem from a different viewpoint: we focus more on the background instead of the object. We exploit two common priors about backgrounds in natural images, namely boundary and connectivity priors, to provide more clues for the problem. Accordingly, we propose a novel saliency measure called geodesic saliency. It is intuitive, easy to interpret and allows fast implementation. Furthermore, it is complementary to previous approaches, because it benefits more from background priors while previous approaches do not. Evaluation on two databases validates that geodesic saliency achieves superior results and outperforms previous approaches by a large margin, in both accuracy and speed (2 ms per image). This illustrates that appropriate prior exploitation is helpful for the ill-posed saliency detection problem.
From recurrent choice to skill learning: a reinforcement-learning model.

The authors propose a reinforcement-learning mechanism as a model for recurrent choice and extend it to account for skill learning. The model was inspired by recent research in neurophysiological studies of the basal ganglia and provides an integrated explanation of recurrent choice behavior and skill learning. The behavior includes effects of differential probabilities, magnitudes, variabilities, and delay of reinforcement. The model can also produce the violation of independence, preference reversals, and the goal gradient of reinforcement in maze learning. An experiment was conducted to study learning of action sequences in a multistep task. The fit of the model to the data demonstrated its ability to account for complex skill learning. The advantages of incorporating the mechanism into a larger cognitive architecture are discussed.
Joint entity and relation extraction based on a hybrid neural network

Entity and relation extraction is a task that combines detecting entity mentions and recognizing entities’ semantic relationships from unstructured text. We propose a hybrid neural network model to extract entities and their relationships without any handcrafted features. The hybrid neural network contains a novel bidirectional encoder-decoder L STM module (BiL STM-ED) for entity extraction and a CNN module for relation classification. The contextual information of entities obtained in BiLSTM-ED further pass though to CNN module to improve the relation classification. We conduct experiments on the public dataset ACE05 (Automatic Content Extraction program) to verify the effectiveness of our method. The method we proposed achieves the state-of-the-art results on entity and relation extraction task.
Learning to relate images: Mapping units, complex cells and simultaneous eigenspaces

A fundamental operation in many vision tasks, including motion understanding, stereopsis, visual odometry, or invariant recognition, is establishing correspondences between images or between images and data from other modalities. We present an analysis of the role that multiplicative interactions play in learning such correspondences, and we show how learning and inferring relationships between images can be viewed as detecting rotations in the eigenspaces shared among a set of orthogonal matrices. We review a variety of recent multiplicative sparse coding methods in light of this observation. We also review how the squaring operation performed by energy models and by models of complex cells can be thought of as a way to implement multiplicative interactions. This suggests that the main utility of including complex cells in computational models of vision may be that they can encode relations not invariances.
Semi-automatic selection of primary studies in systematic literature reviews: is it reasonable?

The systematic review (SR) is a methodology used to find and aggregate all relevant existing evidence about a specific research question of interest. One of the activities associated with the SR process is the selection of primary studies, which is a time consuming manual task. The quality of primary study selection impacts the overall quality of SR. The goal of this paper is to propose a strategy named “Score Citation Automatic Selection” (SCAS), to automate part of the primary study selection activity. The SCAS strategy combines two different features, content and citation relationships between the studies, to make the selection activity as automated as possible. Aiming to evaluate the feasibility of our strategy, we conducted an exploratory case study to compare the accuracy of selecting primary studies manually and using the SCAS strategy. The case study shows that for three SRs published in the literature and previously conducted in a manual implementation, the average effort reduction was 58.2 % when applying the SCAS strategy to automate part of the initial selection of primary studies, and the percentage error was 12.98 %. Our case study provided confidence in our strategy, and suggested that it can reduce the effort required to select the primary studies without adversely affecting the overall results of SR.
Particle swarm optimization method in multiobjective problems

This paper constitutes a first study of the Particle Swarm Optimization (PSO) method in Multiobjective Optimization (MO) problems. The ability of PSO to detect Pareto Optimal points and capture the shape of the Pareto Front is studied through experiments on well-known non-trivial test functions. The Weighted Aggregation technique with fixed or adaptive weights is considered. Furthermore, critical aspects of the VEGA approach for Multiobjective Optimization using Genetic Algorithms are adapted to the PSO framework in order to develop a multi-swarm PSO that can cope effectively with MO problems. Conclusions are derived and ideas for further research are proposed.
Beyond Left-to-Right: Multiple Decomposition Structures for SMT

Standard phrase-based translation models do not explicitly model context dependence between translation units. As a result, they rely on large phrase pairs and target language models to recover contextual effects in translation. In this work, we explore n-gram models over Minimal Translation Units (MTUs) to explicitly capture contextual dependencies across phrase boundaries in the channel model. As there is no single best direction in which contextual information should flow, we explore multiple decomposition structures as well as dynamic bidirectional decomposition. The resulting models are evaluated in an intrinsic task of lexical selection for MT as well as a full MT system, through n-best reranking. These experiments demonstrate that additional contextual modeling does indeed benefit a phrase-based system and that the direction of conditioning is important. Integrating multiple conditioning orders provides consistent benefit, and the most important directions differ by language pair.
Large-scale Multi-label Text Classification - Revisiting Neural Networks

Large-scale datasets with multi-labels are becoming readily available, and the demand for large-scale multi-label classification algorithm is also increasing. In this work, we investigate limitations of a neural network (NN) architecture that aims at minimizing pairwise ranking error, and propose to utilize a rather simple NN approach in large-scale multi-label text classification tasks with recently proposed learning techniques. Additionally, we present a simple threshold predictor in order to make real-valued NN outputs binary. Our experimental results show that the simple NN models equipped with recent advanced techniques such as rectified linear units, dropout, and adagrad performs as well as or even outperforms the previous state-of-the-art approaches on six large-scale textual datasets with diverse characteristics.
A hazard based approach to user return time prediction

In the competitive environment of the internet, retaining and growing one's user base is of major concern to most web services. Furthermore, the economic model of many web services is allowing free access to most content, and generating revenue through advertising. This unique model requires securing user time on a site rather than the purchase of good which makes it crucially important to create new kinds of metrics and solutions for growth and retention efforts for web services. In this work, we address this problem by proposing a new retention metric for web services by concentrating on the rate of user return. We further apply predictive analysis to the proposed retention metric on a service, as a means for characterizing lost customers. Finally, we set up a simple yet effective framework to evaluate a multitude of factors that contribute to user return. Specifically, we define the problem of return time prediction for free web services. Our solution is based on the Cox's proportional hazard model from survival analysis. The hazard based approach offers several benefits including the ability to work with censored data, to model the dynamics in user return rates, and to easily incorporate different types of covariates in the model. We compare the performance of our hazard based model in predicting the user return time and in categorizing users into buckets based on their predicted return time, against several baseline regression and classification methods and find the hazard based approach to be superior.
Just Say NO to Paxos Overhead: Replacing Consensus with Network Ordering

Distributed applications use replication, implemented by protocols like Paxos, to ensure data availability and transparently mask server failures. This paper presents a new approach to achieving replication in the data center without the performance cost of traditional methods. Our work carefully divides replication responsibility between the network and protocol layers. The network orders requests but does not ensure reliable delivery – using a new primitive we call ordered unreliable multicast (OUM). Implementing this primitive can be achieved with near-zero-cost in the data center. Our new replication protocol, NetworkOrdered Paxos (NOPaxos), exploits network ordering to provide strongly consistent replication without coordination. The resulting system not only outperforms both latencyand throughput-optimized protocols on their respective metrics, but also yields throughput within 2% and latency within 16 μs of an unreplicated system – providing replication without the performance cost.
Objective measures for predicting speech intelligibility in noisy conditions based on new band-importance functions.

The articulation index (AI), speech-transmission index (STI), and coherence-based intelligibility metrics have been evaluated primarily in steady-state noisy conditions and have not been tested extensively in fluctuating noise conditions. The aim of the present work is to evaluate the performance of new speech-based STI measures, modified coherence-based measures, and AI-based measures operating on short-term (30 ms) intervals in realistic noisy conditions. Much emphasis is placed on the design of new band-importance weighting functions which can be used in situations wherein speech is corrupted by fluctuating maskers. The proposed measures were evaluated with intelligibility scores obtained by normal-hearing listeners in 72 noisy conditions involving noise-suppressed speech (consonants and sentences) corrupted by four different maskers (car, babble, train, and street interferences). Of all the measures considered, the modified coherence-based measures and speech-based STI measures incorporating signal-specific band-importance functions yielded the highest correlations (r=0.89-0.94). The modified coherence measure, in particular, that only included vowel/consonant transitions and weak consonant information yielded the highest correlation (r=0.94) with sentence recognition scores. The results from this study clearly suggest that the traditional AI and STI indices could benefit from the use of the proposed signal- and segment-dependent band-importance functions.
A Geometrically Exact Model for Externally Loaded Concentric-Tube Continuum Robots

Continuum robots, which are composed of multiple concentric, precurved elastic tubes, can provide dexterity at diameters equivalent to standard surgical needles. Recent mechanics-based models of these “active cannulas” are able to accurately describe the curve of the robot in free space, given the preformed tube curves and the linear and angular positions of the tube bases. However, in practical applications, where the active cannula must interact with its environment or apply controlled forces, a model that accounts for deformation under external loading is required. In this paper, we apply geometrically exact rod theory to produce a forward kinematic model that accurately describes large deflections due to a general collection of externally applied point and/or distributed wrench loads. This model accommodates arbitrarily many tubes, with each having a general preshaped curve. It also describes the independent torsional deformation of the individual tubes. Experimental results are provided for both point and distributed loads. Average tip error under load was 2.91 mm (1.5% - 3% of total robot length), which is similar to the accuracy of existing free-space models.
Modeling relationship strength in online social networks

Previous work analyzing social networks has mainly focused on binary friendship relations. However, in online social networks the low cost of link formation can lead to networks with heterogeneous relationship strengths (e.g., acquaintances and best friends mixed together). In this case, the binary friendship indicator provides only a coarse representation of relationship information. In this work, we develop an unsupervised model to estimate relationship strength from interaction activity (e.g., communication, tagging) and user similarity. More specifically, we formulate a link-based latent variable model, along with a coordinate ascent optimization procedure for the inference. We evaluate our approach on real-world data from Facebook and LinkedIn, showing that the estimated link weights result in higher autocorrelation and lead to improved classification accuracy.
Finding the weakest link in person detectors

Detecting people remains a popular and challenging problem in computer vision. In this paper, we analyze parts-based models for person detection to determine which components of their pipeline could benefit the most if improved. We accomplish this task by studying numerous detectors formed from combinations of components performed by human subjects and machines. The parts-based model we study can be roughly broken into four components: feature detection, part detection, spatial part scoring and contextual reasoning including non-maximal suppression. Our experiments conclude that part detection is the weakest link for challenging person detection datasets. Non-maximal suppression and context can also significantly boost performance. However, the use of human or machine spatial models does not significantly or consistently affect detection accuracy.
Design and Analysis of a Reconfi gurable Hierarchical Temporal Memory Architecture

Self-learning hardware systems, with high-degree of plasticity, are critical in performing spatio-temporal tasks in next-generation computing systems. To this end, hierarchical temporal memory (HTM) offers time-based online-learning algorithms that store and recall temporal and spatial patterns. In this work, a reconfigurable and scalable HTM architecture is designed with unique pooling realizations. Virtual synapse design is proposed to address the dynamic interconnections occurring in the learning process. The architecture is interweaved with parallel cells and columns that enable high processing speed for the cortical learning algorithm. HTM has two core operations, spatial and temporal pooling. These operations are verified for two different datasets: MNIST and European number plate font. The spatial pooling operation is independently verified for classification with and without the presence of noise. The temporal pooling is verified for simple prediction. The spatial pooler architecture is ported onto an Altera cyclone II fabric and the entire architecture is synthesized for Xilinx Virtex IV. The results show that ≈ 91% classification accuracy is achieved with MNIST database and ≈ 90% accuracy for the European number plate font numbers with the presence of Gaussian and Salt & Pepper noise. For the prediction, first and second order predictions are observed for a 5-number long sequence generated from European number plate font and ≈ 95% accuracy is obtained. Moreover, the proposed hardware architecture offers 3902X speedup over the software realization. These results indicate that the proposed architecture can serve as a core to build the HTM in hardware and eventually as a standalone self-learning hardware system.
Fiber Optic Sensors in Structural Health Monitoring

Structural Health Monitoring (SHM) can be understood as the integration of sensing and intelligence to enable the structure loading and damage-provoking conditions to be recorded, analyzed, localized, and predicted in such a way that nondestructive testing becomes an integral part of them. In addition, SHM systems can include actuation devices to take proper reaction or correction actions. SHM sensing requirements are very well suited for the application of optical fiber sensors (OFS), in particular, to provide integrated, quasi-distributed or fully distributed technologies. In this tutorial, after a brief introduction of the basic SHM concepts, the main fiber optic techniques available for this application are reviewed, emphasizing the four most successful ones. Then, several examples of the use of OFS in real structures are also addressed, including those from the renewable energy, transportation, civil engineering and the oil and gas industry sectors. Finally, the most relevant current technical challenges and the key sector markets are identified. This paper provides a tutorial introduction, a comprehensive background on this subject and also a forecast of the future of OFS for SHM. In addition, some of the challenges to be faced in the near future are addressed.
Building Knowledge through Families of Experiments

Experimentation in software engineering is necessar y but difficult. One reason is that there are a lar ge number of context variables, and so creating a cohesive under standing of experimental results requires a mechani sm for motivating studies and integrating results. It requ ires a community of researchers that can replicate studies, vary context variables, and build models that represent the common observations about the discipline. This paper discusses the experience of the authors, based upon a c llection of experiments, in terms of a framewo rk f r organizing sets of related studies. With such a fra mework, experiments can be viewed as part of common families of studies, rather than being isolated events. Common families of studies can contribute to important and relevant hypotheses that may not be suggested by individual experiments. A framework also facilitates building knowledge in an incremental manner through the replication of experiments within families of studies. To support the framework, this paper discusses the exp riences of the authors in carrying out empirica l studies, with specific emphasis on persistent problems encountere d in xperimental design, threats to validity, crit eria for evaluation, and execution of experiments in the dom ain of software engineering.
Battery management system and control strategy for hybrid and electric vehicle

Battery management system (BMS) is an integral part of any electrical vehicle, which ensures that the batteries are not subjected to conditions outside their specified safe operating conditions. Thus the safety of the battery as well as of the passengers depend on the design of the BMS. In the present work a preliminary work is carried out to simulate a typical BMS for hybrid electrical vehicle. The various functional blocks of the BMS are implemented in SIMULINK toolbox of MATLAB. The BMS proposed is equipped with a battery model in which SOC is used as one of the states to overcome the limitation of stand-alone coulomb counting method for SOC estimation. The parameters of the battery are extracted from experimental results and incorporated in the model. The simulation results are validated by experimental results.
Sampling Algorithms in a Stream Operator

Complex queries over high speed data streams often need to rely on approximations to keep up with their input. The research community has developed a rich literature on approximate streaming algorithms for this application. Many of these algorithms produce samples of the input stream, providing better properties than conventional random sampling. In this paper, we abstract the stream sampling process and design a new stream sample operator. We show how it can be used to implement a wide variety of algorithms that perform sampling and sampling-based aggregations. Also, we show how to implement the operator in Gigascope - a high speed stream database specialized for IP network monitoring applications. As an example study, we apply the operator within such an enhanced Gigascope to perform subset-sum sampling which is of great interest for IP network management. We evaluate this implemention on a live, high speed internet traffic data stream and find that (a) the operator is a flexible, versatile addition to Gigascope suitable for tuning and algorithm engineering, and (b) the operator imposes only a small evaluation overhead. This is the first operational implementation we know of, for a wide variety of stream sampling algorithms at line speed within a data stream management system.
Embedded systems and software challenges in electric vehicles

The design of electric vehicles require a complete paradigm shift in terms of embedded systems architectures and software design techniques that are followed within the conventional automotive systems domain. It is increasingly being realized that the evolutionary approach of replacing the engine of a car by an electric engine will not be able to address issues like acceptable vehicle range, battery lifetime performance, battery management techniques, costs and weight, which are the core issues for the success of electric vehicles. While battery technology has crucial importance in the domain of electric vehicles, how these batteries are used and managed pose new problems in the area of embedded systems architecture and software for electric vehicles. At the same time, the communication and computation design challenges in electric vehicles also have to be addressed appropriately. This paper discusses some of these research challenges.
Capturing the visual language of social media

With the rapid growth in the usage of social networks worldwide, uploading and sharing of user-generated content, both text and visual, has become increasingly prevalent. An analysis of the content a user shares and engages with can provide valuable insights into an individual's preferences and lifestyle. In this paper, we present a system to automatically infer a user's interests by analysing the content of the photos they share online. We propose a way to leverage web image search engines for detecting high-level semantic concepts, such as interests, in images, without relying on a large set of labeled images. We demonstrate the effectiveness of our system through quantitative and qualitative results on data collected from Instagram.