nils-herrmann/clean_scirepeval huggingface dataset:
A Novel Digital Control Technique for Brushless DC Motor Drives

Brushless DC (BLDC) motor drives are continually gaining popularity in motion control applications. Therefore, it is necessary to have a low cost, but effective BLDC motor speed/torque regulator. This paper introduces a novel concept for digital control of trapezoidal BLDC motors. The digital controller was implemented via two different methods, namely conduction-angle control and current-mode control. Motor operation is allowed only at two operating points or states. Alternating between the two operating points results in an average operating point that produces an average operating speed. The controller design equations are derived from Newton's second law. The novel controller is verified via computer simulations and an experimental demonstration is carried out with the rapid prototyping and real-time interface system dSPACE.
Effort estimation in agile software development: a survey on the state of the practice

There are numerous studies on effort estimation in Agile Software Development (ASD) and the state of the art in this area has been recently documented in a Systematic Literature Review (SLR). However, to date there are no studies on the state of the practice in this area, focusing on similar issues to those investigated in the above-mentioned SLR.  The aim of this paper is to report on the state of the practice on effort estimation in ASD, focusing on a wide range of aspects such as the estimation techniques and effort predictors used, to name a few.  A survey was carried out using as instrument an on-line questionnaire answered by agile practitioners who have experience in effort estimation.  Data was collected from 60 agile practitioners from 16 different countries, and the main findings are: 1) Planning poker (63%), analogy (47%) and expert judgment (38%) are frequently practiced estimation techniques in ASD; 2) Story points is the most frequently (62%) employed size metric, used solo or in combination with other metrics (e.g., function points); 3) Team's expertise level and prior experience are most commonly used cost drivers; 4) 52% of the respondents believe that their effort estimates on average are under/over estimated by an error of 25% or more; 5) Most agile teams take into account implementation and testing activities during effort estimation; and 6) Estimation is mostly performed at sprint and release planning levels in ASD.  Estimation techniques that rely on experts' subjective assessment are the ones used the most in ASD, with effort underestimation being the dominant trend. Further, the use of multiple techniques in combination and story points seem to present a positive association with estimation accuracy, and team-related cost drivers are the ones used by most agile teams. Finally, requirements and management related issues are perceived as the main reasons for inaccurate estimates.
Policy Networks with Two-Stage Training for Dialogue Systems

In this paper, we propose to use deep policy networks which are trained with an advantage actor-critic method for statistically optimised dialogue systems. First, we show that, on summary state and action spaces, deep Reinforcement Learning (RL) outperforms Gaussian Processes methods. Summary state and action spaces lead to good performance but require pre-engineering effort, RL knowledge, and domain expertise. In order to remove the need to define such summary spaces, we show that deep RL can also be trained efficiently on the original state and action spaces. Dialogue systems based on partially observable Markov decision processes are known to require many dialogues to train, which makes them unappealing for practical deployment. We show that a deep RL method based on an actor-critic architecture can exploit a small amount of data very efficiently. Indeed, with only a few hundred dialogues collected with a handcrafted policy, the actorcritic deep learner is considerably bootstrapped from a combination of supervised and batch RL. In addition, convergence to an optimal policy is significantly sped up compared to other deep RL methods initialized on the data with batch RL. All experiments are performed on a restaurant domain derived from the Dialogue State Tracking Challenge 2 (DSTC2) dataset.
A Bayesian Framework for Multi-cue 3D Object Tracking

This paper presents a Bayesian framework for multi-cue 3D object tracking of deformable objects. The proposed spatio-temporal object representation involves a set of distinct linear subspace models or Dynamic Point Distribution Models (DPDMs), which can deal with both continuous and discontinuous appearance changes; the representation is learned fully automatically from training data. The representation is enriched with texture information by means of intensity histograms, which are compared using the Bhattacharyya coefficient. Direct 3D measurement is furthermore provided by a stereo system. State propagation is achieved by a particle filter which combines the three cues shape, texture and depth, in its observation density function. The tracking framework integrates an independently operating object detection system by means of importance sampling. We illustrate the benefit of our integrated multi-cue tracking approach on pedestrian tracking from a moving vehicle.
Context-aware wireless sensor networks for assisted living and residential monitoring

Improving the quality of healthcare and the prospects of "aging in place" using wireless sensor technology requires solving difficult problems in scale, energy management, data access, security, and privacy. We present AlarmNet, a novel system for assisted living and residential monitoring that uses a two-way flow of data and analysis between the front- and back-ends to enable context-aware protocols that are tailored to residents' individual patterns of living. AlarmNet integrates environmental, physiological, and activity sensors in a scalable heterogeneous architecture. The SenQ query protocol provides real-time access to data and lightweight in-network processing. Circadian activity rhythm analysis learns resident activity patterns and feeds them back into the network to aid context-aware power management and dynamic privacy policies.
Developing an open-source, rule-based proofreading tool

In this paper, we show how an open-source, language-independent proofreading tool has been built. Many languages lack contextual proofreading tools; for many others, only partial solutions are available. Using existing, largely language-independent tools and collaborative processes it is possible to develop a practical style and grammar checker and to fight the digital divide in countries where commercial linguistic application software is unavailable or too expensive for average users. The described solution depends on relatively easily available language resources and does not require a fully formalized grammar nor a deep parser, yet it can detect many frequent context-dependent spelling mistakes, as well as grammatical, punctuation, usage, and stylistic errors.
A comprehensive taxonomy for multi-robot task allocation

Task allocation is an important aspect of many multi-robot systems. The features and complexity of multi-robot task allocation (MRTA) problems are dictated by the requirements of the particular domain under consideration. These problems can range from those involving instantaneous distribution of simple, independent tasks among members of a homogenous team, to those requiring the time-extended scheduling of complex interrelated multi-step tasks for a members of a heterogenous team related by several constraints. The existing widely-used taxonomy for task allocation in multi-robot systems addresses only problems with independent tasks and does not deal with problems with interrelated utilities and constraints. A survey of recent work in multi-robot task allocation reveals that this is a significant deficiency with respect to realistic multi-robot task allocation problems. Thus, in this paper, we present a new, comprehensive taxonomy, iTax, that explicitly takes into consideration the issues of interrelated utilities and constraints. Our taxonomy maps categories of MRTA problems to existing mathematical models from combinatorial optimization and operations research, and hence draws important parallels between robotics and these fields.
SFC-Checker: Checking the correct forwarding behavior of Service Function chaining

Network middleboxes are difficult to manage and troubleshoot, due to their proprietary monolithic design. Moving towards Network Functions Virtualization (NFV), virtualized middlebox appliances can be more flexibly instantiated and dynamically chained, making troubleshooting even more difficult. To guarantee carrier-grade availability and minimize outages, operators need ways to automatically verify that the deployed network and middlebox configurations obey higher level network policies. In this paper, we first define and identify the key challenges for checking the correct forwarding behavior of Service Function Chains (SFC). We then design and develop a network diagnosis framework that aids network administrators in verifying the correctness of SFC policy enforcement. Our prototype - SFC-Checker can verify stateful service chains efficiently, by analyzing the switches' forwarding rules and the middleboxes' stateful forwarding behavior. Built on top of the network function models we proposed, we develop a diagnosis algorithm that is able to check the stateful forwarding behavior of a chain of network service functions.
SPADE: Support for Provenance Auditing in Distributed Environments

software infrastructure for data provenance collection and management. The underlying data model used throughout the system is graph-based, consisting of vertices and directed edges that are modeled after the node and relationship types described in the Open Provenance Model. The system has been designed to decouple the collection, storage, and querying of provenance metadata. At its core is a novel provenance kernel that mediates between the producers and consumers of provenance information, and handles the persistent storage of records. It operates as a service, peering with remote instances to enable distributed provenance queries. The provenance kernel on each host handles the buffering, filtering, and multiplexing of incoming metadata from multiple sources, including the operating system, applications, and manual curation. Provenance elements can be located locally with queries that use wildcard, fuzzy, proximity, range, and Boolean operators. Ancestor and descendant queries are transparently propagated across hosts until a terminating expression is satisfied, while distributed path queries are accelerated with provenance sketches.
Quality Estimation for Automatic Speech Recognition

We address the problem of estimating the quality of Automatic Speech Recognition (ASR) output at utterance level, without recourse to manual reference transcriptions and when information about system’s confidence is not accessible. Given a source signal and its automatic transcription, we approach this problem as a regression task where the word error rate of the transcribed utterance has to be predicted. To this aim, we explore the contribution of different feature sets and the potential of different algorithms in testing conditions of increasing complexity. Results show that our automatic quality estimates closely approximate the word error rate scores calculated over reference transcripts, outperforming a strong baseline in all the testing conditions.
: A Cognitive-Affective Model of Organizational Communication for Designing

There are several theories available to describe how managers choose a medium for communication. However, current technology can affect not only how we communicate but also what we communicate. As a result, the issue for designers of communication support systems has become broader: how should technology be designed to make communication more effective by changing the medium and the attributes of the message itself? The answer to this question requires a shift from current preoccupations with the medium of 1Richard Watson was the accepting senior editor for this paper. 2MISQ Review articles survey, conceptualize, and synthesize prior MIS research and set directions for future research. For more details see http://www.misq.org/misreview/announce.html The associated web site for this paper is located at http://misq.org/misreview/teeni.shtml communication to a view that assesses the balance between medium and message form. There is also a need to look more closely at the process of communication in order to identify more precisely any potential areas of computer
Building strong e-democracy: the role of technology in developing democracy for the information age

The idea of democracy had come a long way before it was given its first modern forms in the liberal ideas of the 17th and 18th centuries. Now the premises of this hierarchical and representative political system are crumbling, and we must seriously consider the need to revitalize democracy. This article aims at clarifying the overall preconditions for the revitalization of democracy, and demonstrates how to build a comprehensive framework for a multidimensional institutional design in which the potentials of ICTs are made to serve relevant democratic purposes. What conditions the functioning of any contemporary democratic system includes such contextual factors as increased global interdependency, extended use of market-based mechanisms, significant impacts of media and ICTs, new forms of governance, and individualism in its various forms. One of the most burning issues is how to develop new democracy in such a complex setting so that it accords with people’s ways of thinking and acting. To ensure this, citizens with all their collective actions and willingness to influence public affairs must be placed in the overall framework of e-transformation in politics [11]. This implies that we go beyond the dichotomous discourse that suggests that we have a choice to make between democracy-as-usual and direct e-democracy [9].
Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds

Sparsity-based representations have recently led to notable results in various visual recognition tasks. In a separate line of research, Riemannian manifolds have been shown useful for dealing with features and models that do not lie in Euclidean spaces. With the aim of building a bridge between the two realms, we address the problem of sparse coding and dictionary learning in Grassmann manifolds, i.e., the space of linear subspaces. To this end, we propose to embed Grassmann manifolds into the space of symmetric matrices by an isometric mapping. This in turn enables us to extend two sparse coding schemes to Grassmann manifolds. Furthermore, we propose an algorithm for learning a Grassmann dictionary, atom by atom. Lastly, to handle non-linearity in data, we extend the proposed Grassmann sparse coding and dictionary learning algorithms through embedding into higher dimensional Hilbert spaces. Experiments on several classification tasks (gender recognition, gesture classification, scene analysis, face recognition, action recognition and dynamic texture classification) show that the proposed approaches achieve considerable improvements in discrimination accuracy, in comparison to state-of-the-art methods such as kernelized Affine Hull Method and graph-embedding Grassmann discriminant analysis.
Recursive Recurrent Nets with Attention Modeling for OCR in the Wild

We present recursive recurrent neural networks with attention modeling (R2AM) for lexicon-free optical character recognition in natural scene images. The primary advantages of the proposed method are: (1) use of recursive convolutional neural networks (CNNs), which allow for parametrically efficient and effective image feature extraction, (2) an implicitly learned character-level language model, embodied in a recurrent neural network which avoids the need to use N-grams, and (3) the use of a soft-attention mechanism, allowing the model to selectively exploit image features in a coordinated way, and allowing for end-to-end training within a standard backpropagation framework. We validate our method with state-of-the-art performance on challenging benchmark datasets: Street View Text, IIIT5k, ICDAR and Synth90k.
What happened in my network: mining network events from router syslogs

Router syslogs are messages that a router logs to describe a wide range of events observed by it. They are considered one of the most valuable data sources for monitoring network health and for trou- bleshooting network faults and performance anomalies. However, router syslog messages are essentially free-form text with only a minimal structure, and their formats vary among different vendors and router OSes. Furthermore, since router syslogs are aimed for tracking and debugging router software/hardware problems, they are often too low-level from network service management perspectives. Due to their sheer volume (e.g., millions per day in a large ISP network), detailed router syslog messages are typically examined only when required by an on-going troubleshooting investigation or when given a narrow time range and a specific router under suspicion. Automated systems based on router syslogs on the other hand tend to focus on a subset of the mission critical messages (e.g., relating to network fault) to avoid dealing with the full diversity and complexity of syslog messages. In this project, we design a Sys-logDigest system that can automatically transform and compress such low-level minimally-structured syslog messages into meaningful and prioritized high-level network events, using powerful data mining techniques tailored to our problem domain. These events are three orders of magnitude fewer in number and have much better usability than raw syslog messages. We demonstrate that they provide critical input to network troubleshooting, and net- work health monitoring and visualization.
Deep Convolutional Neural Network Using Triplets of Faces, Deep Ensemble, and Score-Level Fusion for Face Recognition

This paper proposes a new face verification method that uses multiple deep convolutional neural networks (DCNNs) and a deep ensemble, that extracts two types of low dimensional but discriminative and high-level abstracted features from each DCNN, then combines them as a descriptor for face verification. Our DCNNs are built from stacked multi-scale convolutional layer blocks to present multi-scale abstraction. To train our DCNNs, we use different resolutions of triplets that consist of reference images, positive images, and negative images, and triplet-based loss function that maximize the ratio of distances between negative pairs and positive pairs and minimize the absolute distances between positive face images. A deep ensemble is generated from features extracted by each DCNN, and used as a descriptor to train the joint Bayesian learning and its transfer learning method. On the LFW, although we use only 198,018 images and only four different types of networks, the proposed method with the joint Bayesian learning and its transfer learning method achieved 98.33% accuracy. In addition to further increase the accuracy, we combine the proposed method and high dimensional LBP based joint Bayesian method, and achieved 99.08% accuracy on the LFW. Therefore, the proposed method helps to improve the accuracy of face verification when training data is insufficient to train DCNNs.
Location-Based Services: Back to the Future

Gainesville, Florida, 10 March 2 012. Today, the Mobile Location- Based Services Summit hosted a panel entitled "What Was Wrong with First-Generation Location-Based Services?" The panel chair, Sumi Helal of the University of Florida, invited two world-class experts in LBS history and technology to discuss the topic: Paolo Bellavista of the University of Bologna and Axel Kupper of the University of Munich. The panel discussed the popularity of today's LBSs and analyzed their distinguishing aspects in comparison with first-generation LBSs. The panel was anything but controversial, with all panelists in total agreement on what initially went wrong and why today's LBSs work. They analyzed how the failure unfolded to set the stage for a major paradigm shift in LBS business and technology and noted the milestones that shaped today's LBSs.
Adoption and use of technology in early education: The interplay of extrinsic barriers and teacher attitudes

The increased access to, but continued under-use of, technology in education makes it imperative to understand the barriers teachers face when integrating technology into their classrooms. While prior research suggests teachers encounter both first-order extrinsic barriers and second-order personal barriers, much of this research has focused on K-12 teachers, not early childhood educators. Applying the Unified Theory of Acceptance and Use of Technology to early childhood education, the current study examines predictors of early childhood educators’ access to and use of traditional technologies and newer mobile devices. Findings from 1329 teachers of 0–4-year-olds reveal that while extrinsic barriers influence access to a range of technologies, positive beliefs in children’s learning from technology significantly predicted actual use of technology. Overall, the study provides new insight into factors influencing technology integration specifically for early childhood educators, a subgroup that has not been represented in much of the literature on technology integration in formal education.
An on-line learning algorithm for energy efficient delay constrained scheduling over a fading channel

In this paper, we consider the problem of energy efficient scheduling under average delay constraint for a single user fading channel. We propose a new approach for on-line implementation of the optimal packet scheduling algorithm. This approach is based on reformulating the value iteration equation by introducing a virtual state called post-decision state. The resultant value iteration equation becomes amenable to online implementation based on stochastic approximation. This approach has an advantage that an explicit knowledge of the probability distribution of the channel state as well as the arrivals is not required for the implementation. We prove that the on-line algorithm indeed converges to the optimal policy.
A Bayesian sampling approach to in-door localization of wireless devices using received signal strength indication

This paper describes a probabilistic approach to global localization within an in-door environment with minimum infrastructure requirements. Global localization is a flavor of localization in which the device is unaware of its initial position and has to determine the same from scratch. Localization is performed based on the received signal strength indication (RSSI) as the only sensor reading, which is provided by most off-the-shelf wireless network interface cards. Location and orientation estimates are computed using Bayesian filtering on a sample set derived using Monte-Carlo sampling. Research leading to the proposed method is outlined along with results and conclusions from simulations and real life experiments.