nils-herrmann/clean_scirepeval huggingface dataset:
BT-Nets: Simplifying Deep Neural Networks via Block Term Decomposition

Recently, deep neural networks (DNNs) have been regarded as the state-of-the-art classification methods in a wide range of applications, especially in image classification. Despite the success, the huge number of parameters blocks its deployment to situations with light computing resources. Researchers resort to the redundancy in the weights of DNNs and attempt to find how fewer parameters can be chosen while preserving the accuracy at the same time. Although several promising results have been shown along this research line, most existing methods either fail to significantly compress a well-trained deep network or require a heavy fine-tuning process for the compressed network to regain the original performance. In this paper, we propose the Block Term networks (BT-nets) in which the commonly used fully-connected layers (FC-layers) are replaced with block term layers (BT-layers). In BT-layers, the inputs and the outputs are reshaped into two low-dimensional high-order tensors, then block-term decomposition is applied as tensor operators to connect them. We conduct extensive experiments on benchmark datasets to demonstrate that BT-layers can achieve a very large compression ratio on the number of parameters while preserving the representation power of the original FC-layers as much as possible. Specifically, we can get a higher performance while requiring fewer parameters compared with the tensor train method.
Geometric flight control of a hovering robotic hummingbird

Controlled hovering of motor driven flapping wing micro aerial vehicles (FWMAVs) is challenging due to its limited control authority, large inertia, vibration produced by wing strokes, and limited components accuracy due to fabrication methods. In this work, we present a hummingbird inspired FWMAV with 12 grams of weight and 20 grams of maximum lift. We present its full non-linear dynamic model including the full inertia tensor, non-linear input mapping, and damping effect from flapping counter torques (FCTs) and flapping counter forces (FCFs). We also present a geometric flight controller to ensure exponentially stable and globally exponential attractive properties. We experimentally demonstrated the vehicle lifting off and hover with attitude stabilization.
Path Planning Using Lazy PRM

This paper describes a new approach t o probabilistic roadmap planners (PRMs). The overall theme of the algorithm, called Lazy PRM, i s to minimize the number of collision checks performed during planning and hence minimize the running t ime of the planner. Our algorithm builds a roadmap in the configuration space, whose nodes are the user-defined initial and goal configurations and a number of randomly generated nodes. Neighboring nodes are connected by edges representing paths between the nodes. In contrast with PRMs, our planner initially assumes that all nodes and edges in the roadmap are collision-free, and searches the roadmap at hand for a shortest path between the initial and the goal node. The nodes and edges along the path are then checked for collision. If a collision with the obstacles occurs, the corresponding nodes and edges are removed fFom the roadmap. Our planner either finds a new shortest path, or first updates the roadmap with new nodes and edges, and then searches for a shortest path. The above process i s repeated until a collision-free path is returned. Lazy P R M is tailored to eficiently answer single planning queries, but can also be used for multiple queries. Experimental results presented in this paper show that our lazy method i s very eficient in practice.
LICENSE PLATE RECOGNITION SYSTEM FOR INDIAN VEHICLES

License Plate Recognition (LPR) has been intensively studied in many countries. Due to the different types of number plates being used, the requirements of an automatic number plate recognition system are different for each country. In general, objective of any LPR system is to localize and recognize potential license plate region(s) of the vehicle images captured from handheld device/digital camera/mobile phone camera. The proposed method for License Plate Recognition (LPR) system works in three modules: localization of license plate, segmentation of the characters and recognition of the characters from the license plate. Localization of the license plate is done using morphological operations, horizontal & vertical edge processing. Character segmentation is carried out using connected component labeling. Character recognition is done by using Neural Network classifier. The method is tested on 100 samples of Indian vehicle images. The system achieves 86% accuracy in localization, 81% accuracy in segmentation and 80% accuracy in character recognition.
Bootstrapped two-electrode biosignal amplifier

Portable biomedical instrumentation has become an important part of diagnostic and treatment instrumentation. Low-voltage and low-power tendencies prevail. A two-electrode biopotential amplifier, designed for low-supply voltage (2.7–5.5 V), is presented. This biomedical amplifier design has high differential and sufficiently low common mode input impedances achieved by means of positive feedback, implemented with an original interface stage. The presented circuit makes use of passive components of popular values and tolerances. The amplifier is intended for use in various two-electrode applications, such as Holter monitors, external defibrillators, ECG monitors and other heart beat sensing biomedical devices.
Modeling Human Plan Recognition using Bayesian Theory of Mind

The human brain is the most powerful plan recognition system we know. Central to the brain’s remarkable plan recognition capacity is a theory of mind (ToM): our intuitive conception of other agents’ mental states – chiefly, beliefs and desires – and how they cause behavior. We present a Bayesian framework for ToM-based plan recognition, expressing generative models of beliefand desire-dependent planning in terms of partially observable Markov decision processes (POMDPs), and reconstructing an agent’s joint belief state and reward function using Bayesian inference, conditioned on observations of the agent’s behavior in the context of its environment. We show that the framework predicts human judgments with surprising accuracy, and substantially better than alternative accounts. We propose that “reverse engineering” human ToM by quantitatively evaluating the performance of computational cognitive models against data from human behavioral experiments provides a promising avenue for building plan recognition systems.
Music Boundary Detection Using Neural Networks on Combined Features and Two-Level Annotations

The determination of structural boundaries is a key task for understanding the structure of a musical piece, but it is also highly ambiguous. Recently, Convolutional Neural Networks (CNN) trained on spectrogram features and human annotations have been successfully used to tackle the problem, but still fall clearly behind human performance. We expand on the CNN approach by combining spectrograms with self-similarity lag matrices as audio features, thereby capturing more facets of the underlying structural information. Furthermore, in order to consider the hierarchical nature of structural organization, we explore different strategies to learn from the two-level annotations of main and secondary boundaries available in the SALAMI structural annotation dataset. We show that both measures improve boundary recognition performance, resulting in a significant improvement over the previous state of the art. As a side-effect, our algorithm can predict boundaries on two different structural levels, equivalent to the training data.
New technology in dietary assessment: a review of digital methods in improving food record accuracy.

Methods for conducting dietary assessment in the United States date back to the early twentieth century. Methods of assessment encompassed dietary records, written and spoken dietary recalls, FFQ using pencil and paper and more recently computer and internet applications. Emerging innovations involve camera and mobile telephone technology to capture food and meal images. This paper describes six projects sponsored by the United States National Institutes of Health that use digital methods to improve food records and two mobile phone applications using crowdsourcing. The techniques under development show promise for improving accuracy of food records.
How do users evaluate the credibility of Web sites?: a study with over 2,500 participants

In this study 2,684 people evaluated the credibility of two live Web sites on a similar topic (such as health sites). We gathered the comments people wrote about each siteís credibility and analyzed the comments to find out what features of a Web site get noticed when people evaluate credibility. We found that the ìdesign lookî of the site was mentioned most frequently, being present in 46.1% of the comments. Next most common were comments about information structure and information focus. In this paper we share sample participant comments in the top 18 areas that people noticed when evaluating Web site credibility. We discuss reasons for the prominence of design look, point out how future studies can build on what we have learned in this new line of research, and outline six design implications for human-computer interaction professionals.
Design of Circularly Polarized Waveguide Slot Linear Arrays

A design procedure for circularly polarized waveguide slot linear arrays is presented. The array element, a circularly polarized radiator, consists of two closely spaced inclined radiating slots. Both the characterization of the isolated element and the evaluation of the mutual coupling between the array elements are performed by using a method of moments procedure. A number of traveling wave arrays with equiphase excitations are designed and then analyzed using a finite element method commercial software. A good circular polarization is achieved, the design goals on the far field pattern are fulfilled and high antenna efficiency can be obtained
A Large and Diverse Dataset for Improved Vehicle Make and Model Recognition

Vehicle Make and Model Recognition (VMMR) has evolved into a significant subject of study due to its importance in numerous Intelligent Transportation Systems (ITS) and corresponding components such as Automated Vehicular Surveillance (AVS). A highly accurate and real-time VMMR system significantly reduces the overhead cost of resources otherwise required. The VMMR problem is a multiclass classification task with a peculiar set of issues and challenges like multiplicity, inter- and intra-make ambiguity among various vehicle makes and models, which need to be solved in an efficient and reliable manner to achieve a highly robust VMMR system.,,,,,, In this paper, facing the growing importance of make and model recognition of vehicles, we present an image dataset1 with 9; 170 different classes of vehicles to advance the corresponding tasks. Extensive experiments conducted using baseline approaches yield superior results for images that were occluded, under low illumination, partial or nonfrontal camera views, available in our VMMR dataset. The approaches presented herewith provide a robust VMMR system for applications in realistic environments.
A survey of table recognition

Table characteristics vary widely. Consequently, a great variety of computational approaches have been applied to table recognition. In this survey, the table recognition literature is presented as an interaction of table models, observations, transformations, and inferences. A table model defines the physical and logical structure of tables; the model is used to detect tables and to analyze and decompose the detected tables. Observations perform feature measurements and data lookup, transformations alter or restructure data, and inferences generate and test hypotheses. This presentation clarifies both the decisions made by a table recognizer and the assumptions and inferencing techniques that underlie these decisions.
Evaluation of a hierarchical reinforcement learning spoken dialogue system

We describe an evaluation of spoken dialogue strategies designed using hierarchical reinforcement learning agents. The dialogue strategies were learnt in a simulated environment and tested in a laboratory setting with 32 users. These dialogues were used to evaluate three types of machine dialogue behaviour: hand-coded, fully-learnt and semi-learnt. These experiments also served to evaluate the realism of simulated dialogues using two proposed metrics contrasted with ‘PrecisionRecall’. The learnt dialogue behaviours used the Semi-Markov Decision Process (SMDP) model, and we report the first evaluation of this model in a realistic conversational environment. Experimental results in the travel planning domain provide evidence to support the following claims: (a) hierarchical semi-learnt dialogue agents are a better alternative (with higher overall performance) than deterministic or fully-learnt behaviour; (b) spoken dialogue strategies learnt with highly coherent user behaviour and conservative recognition error rates (keyword error rate of 20%) can outperform a reasonable hand-coded strategy; and (c) hierarchical reinforcement learning dialogue agents are feasible and promising for the (semi) automatic design of optimized dialogue behaviours in larger-scale systems.
Practical Implications of Traceability

Traceability defined as the ability to trace dependent items within a model and the ability to trace correspondent items in other models is advocated as a desirable property of a software development process. Potential benefits of good traceability are clearer documentation, more focussed development, increased ease of system understanding, and more precise impact analysis of proposed changes. An industry-scale project applying the analysis and design method Objectory has been examined and documented with a number of traceability examples generated from the perspective of a maintainer attempting to understand the system. Four representative examples and a categorization of traceability are presented in this paper in order to provide a concrete empirical basis for the application of traceability to systems development.
Compress and Control

This paper describes a new information-theoretic policy evaluation technique for reinforcement learning. This technique converts any compression or density model into a corresponding estimate of value. Under appropriate stationarity and ergodicity conditions, we show that the use of a sufficiently powerful model gives rise to a consistent value function estimator. We also study the behavior of this technique when applied to various Atari 2600 video games, where the use of suboptimal modeling techniques is unavoidable. We consider three fundamentally different models, all too limited to perfectly model the dynamics of the system. Remarkably, we find that our technique provides sufficiently accurate value estimates for effective on-policy control. We conclude with a suggestive study highlighting the potential of our technique to scale to large problems.
An Alternating Direction Algorithm for Matrix Completion with Nonnegative Factors

This paper introduces a novel algorithm for the nonnegative matrix factorization and completion problem, which aims to find nonnegative matrices X and Y from a subset of entries of a nonnegative matrix M so that XY approximates M . This problem is closely related to the two existing problems: nonnegative matrix factorization and low-rank matrix completion, in the sense that it kills two birds with one stone. As it takes advantages of both nonnegativity and low rank, its results can be superior than those of the two problems alone. Our algorithm is applied to minimizing a non-convex constrained least-squares formulation and is based on the classic alternating direction augmented Lagrangian method. Preliminary convergence properties and numerical simulation results are presented. Compared to a recent algorithm for nonnegative random matrix factorization, the proposed algorithm yields comparable factorization through accessing only half of the matrix entries. On tasks of recovering incomplete grayscale and hyperspectral images, the results of the proposed algorithm have overall better qualities than those of two recent algorithms for matrix completion.
Out of fear or desire? Toward a better understanding of employees' motivation to follow IS security policies

Given the significant role of people in the management of security, attention has recently been paid to the issue of how to motivate employees to improve security performance of organizations. However, past work has been dependent on deterrence theory rooted in an extrinsic motivation model to help understand why employees do or do not follow security rules in their organization. We postulated that we could better explain employees’ security-related rule-following behavior with an approach rooted in an intrinsic motivation model. We therefore developed a model of employees’ motivation to comply with IS security policies which incorporated both extrinsic and intrinsic models of human behavior. It was tested with data collected through a survey of 602 employees in the United States. We found that variables rooted in the intrinsic motivation model contributed significantly more to the explained variance of employees’ compliance than did those rooted in the extrinsic motivation model.
Reactors: A Case for Predictable, Virtualized OLTP Actor Database Systems

The requirements for OLTP database systems are becoming ever more demanding. New OLTP applications require high degrees of scalability with controlled transaction latencies in in-memory databases. Deployments of these applications require low-level control of database system overhead and program-to-data affinity to maximize resource utilization in modern machines. Unfortunately, current solutions fail to meet these requirements. First, existing database solutions fail to expose a high-level programming abstraction in which latency of transactions can be reasoned about by application developers. Second, these solutions limit infrastructure engineers in exercising low-level control on the deployment of the system on a target infrastructure, further impacting performance. In this paper, we propose a relational actor programming model for in-memory databases. Conceptually, relational actors, or reactors for short, are application-defined, isolated logical actors encapsulating relations that process function calls asynchronously. Reactors ease reasoning about correctness by guaranteeing serializability of application-level function calls. In contrast to classic transactional models, however, reactors allow developers to take advantage of intra-transaction parallelism to reduce latency and improve performance. Moreover, reactors enable a new degree of flexibility in database deployment. We present REACTDB, a novel system design exposing reactors that allows for flexible virtualization of database architecture between the extremes of shared-nothing and shared-everything without changes to application code. Our experiments with REACTDB illustrate performance predictability, multi-core scalability, and low overhead in OLTP benchmarks.
Context-Aware Single-Shot Detector

SSD (Single Shot Detector) is one of the state-of-the-art object detection algorithms, and it combines high detection accuracy with real-time speed. However, it is widely recognized that SSD is less accurate in detecting small objects compared to large objects, because it ignores the context from outside the proposal boxes. In this paper, we present CSSD–a shorthand for context-aware single-shot multibox object detector. CSSD is built on top of SSD, with additional layers modeling multi-scale contexts. We describe two variants of CSSD, which differ in their context layers, using dilated convolution layers (DiCSSD) and deconvolution layers (DeCSSD) respectively. The experimental results show that the multi-scale context modeling significantly improves the detection accuracy. In addition, we study the relationship between effective receptive fields (ERFs) and the theoretical receptive fields (TRFs), particularly on a VGGNet. The empirical results further strengthen our conclusion that SSD coupled with context layers achieves better detection results especially for small objects (+3.2%AP@0.5 on MSCOCO compared to the newest SSD), while maintaining comparable runtime performance.
The internet and social life.

The Internet is the latest in a series of technological breakthroughs in interpersonal communication, following the telegraph, telephone, radio, and television. It combines innovative features of its predecessors, such as bridging great distances and reaching a mass audience. However, the Internet has novel features as well, most critically the relative anonymity afforded to users and the provision of group venues in which to meet others with similar interests and values. We place the Internet in its historical context, and then examine the effects of Internet use on the user's psychological well-being, the formation and maintenance of personal relationships, group memberships and social identity, the workplace, and community involvement. The evidence suggests that while these effects are largely dependent on the particular goals that users bring to the interaction-such as self-expression, affiliation, or competition-they also interact in important ways with the unique qualities of the Internet communication situation.
Third-Generation Pleated Pneumatic Artificial Muscles for Robotic Applications: Development and Comparison with McKibben Muscle

10  This paper introduces the third generation of Pleated Pneumatic Artificial Muscles (PPAM), which has been developed to simplify the production over the first and second prototype. This type of artificial muscle was developed to overcome dry friction and material deformation, which is present in the widely used McKibben muscle. The essence of the PPAM is its pleated membrane structure which enables the 15 muscle to work at low pressures and at large contractions. In order to validate the new PPAM generation, it has been compared with the mathematical model and the previous generation. The new production process and the use of new materials introduce improvements such as 55% reduction in the actuator’s weight, a higher reliability, a 75% reduction in the production time and PPAMs can now be produced in all sizes from 4 to 50 cm. This opens the possibility to commercialize this type of muscles 20 so others can implement it. Furthermore, a comparison with experiments between PPAM and Festo McKibben muscles is discussed. Small PPAMs present similar force ranges and larger contractions than commercially available McKibben-like muscles. The use of series arrangements of PPAMs allows for large strokes and relatively small diameters at the same time and, since PPAM 3.0 is much more lightweight than the commong McKibben models made by Festo, it presents better force-to-mass and energy 25 to mass ratios than Festo models. 2012 Taylor & Francis and The Robotics Society of Japan