nils-herrmann/clean_scirepeval huggingface dataset:
A contractor muscle based continuum trunk robot

We present a new and novel continuum robot, built from contracting pneumatic muscles. The robot has a continuous compliant backbone, achieved via three independently controlled serially connected three degree of freedom sections, for a total of nine degrees of freedom. We detail the design, construction, and initial testing of the robot. The use of contracting muscles, in contrast to previous comparable designs featuring expanding muscles, is well-suited to use of the robot as an active hook in dynamic manipulation tasks. We describe experiments using the robot in this novel manipulation mode.
Combining human and machine intelligence in large-scale crowdsourcing

We show how machine learning and inference can be harnessed to leverage the complementary strengths of humans and computational agents to solve crowdsourcing tasks. We construct a set of Bayesian predictive models from data and describe how the models operate within an overall crowdsourcing architecture that combines the efforts of people and machine vision on the task of classifying celestial bodies defined within a citizens’ science project named Galaxy Zoo. We show how learned probabilistic models can be used to fuse human and machine contributions and to predict the behaviors of workers. We employ multiple inferences in concert to guide decisions on hiring and routing workers to tasks so as to maximize the efficiency of large-scale crowdsourcing processes based on expected utility.
A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation

We propose a weighted variational model to estimate both the reflectance and the illumination from an observed image. We show that, though it is widely adopted for ease of modeling, the log-transformed image for this task is not ideal. Based on the previous investigation of the logarithmic transformation, a new weighted variational model is proposed for better prior representation, which is imposed in the regularization terms. Different from conventional variational models, the proposed model can preserve the estimated reflectance with more details. Moreover, the proposed model can suppress noise to some extent. An alternating minimization scheme is adopted to solve the proposed model. Experimental results demonstrate the effectiveness of the proposed model with its algorithm. Compared with other variational methods, the proposed method yields comparable or better results on both subjective and objective assessments.
Walking from thought

Online analysis and classification of single electroencephalogram (EEG) trials during motor imagery were used for navigation in the virtual environment (VE). The EEG was recorded bipolarly with electrode placement over the hand and foot representation areas. The aim of the study was to demonstrate for the first time that it is possible to move through a virtual street without muscular activity when the participant only imagines feet movements. This is achieved by exploiting a brain-computer interface (BCI) which transforms thought-modulated EEG signals into an output signal that controls events within the VE. The experiments were carried out in an immersive projection environment, commonly referred to as a "Cave" (Cruz-Neira, C., Sandin, D.J., DeFanti, T.A., Surround-screen projection-based virtual reality: the design and implementation of the CAVE. Proceedings of the 20th annual conference on Computer graphics and interactive techniques, ACM Press, 1993, pp. 135-142) where participants were able to move through a virtual street by foot imagery only. Prior to the final experiments in the Cave, the participants underwent an extensive BCI training.
Generating realistic test models for model processing tools

Test models are needed to evaluate and benchmark algorithms and tools in model driven development. Most model generators randomly apply graph operations on graph representations of models. This approach leads to test models of poor quality. Some approaches do not guarantee the basic syntactic correctness of the created models. Even if so, it is almost impossible to guarantee, or even control, the creation of complex structures, e.g. a subgraph which implements an association between two classes. Such a subgraph consists of an association node, two association end nodes, and several edges, and is normally created by one user command. This paper presents the SiDiff Model Generator, which can generate models, or sets of models, which are syntactically correct, contain complex structures, and exhibit defined statistical characteristics.
Detection of Faults and Attacks Including False Data Injection Attack in Smart Grid Using Kalman Filter

By exploiting the communication infrastructure among the sensors, actuators, and control systems, attackers may compromise the security of smart-grid systems, with techniques such as denial-of-service (DoS) attack, random attack, and data-injection attack. In this paper, we present a mathematical model of the system to study these pitfalls and propose a robust security framework for the smart grid. Our framework adopts the Kalman filter to estimate the variables of a wide range of state processes in the model. The estimates from the Kalman filter and the system readings are then fed into the χ2-detector or the proposed Euclidean detector. The χ2-detector is a proven effective exploratory method used with the Kalman filter for the measurement of the relationship between dependent variables and a series of predictor variables. The χ2-detector can detect system faults/attacks, such as DoS attack, short-term, and long-term random attacks. However, the studies show that the χ2-detector is unable to detect the statistically derived false data-injection attack. To overcome this limitation, we prove that the Euclidean detector can effectively detect such a sophisticated injection attack.
Online Human Action Detection using Joint Classification-Regression Recurrent Neural Networks

Human action recognition from well-segmented 3D skeleton data has been intensively studied and attracting an increasing attention. Online action detection goes one step further and is more challenging, which identifies the action type and localizes the action positions on the fly from the untrimmed stream. In this paper, we study the problem of online action detection from the streaming skeleton data. We propose a multi-task end-to-end Joint Classification-Regression Recurrent Neural Network to better explore the action type and temporal localization information. By employing a joint classification and regression optimization objective, this network is capable of automatically localizing the start and end points of actions more accurately. Specifically, by leveraging the merits of the deep Long Short-Term Memory (LSTM) subnetwork, the proposed model automatically captures the complex long-range temporal dynamics, which naturally avoids the typical sliding window design and thus ensures high computational efficiency. Furthermore, the subtask of regression optimization provides the ability to forecast the action prior to its occurrence. To evaluate our proposed model, we build a large streaming video dataset with annotations. Experimental results on our dataset and the public G3D dataset both demonstrate very promising performance of our scheme.
Weakly Supervised Object Detection with Posterior Regularization

This paper focuses on the problem of object detection when the annotation at training time is restricted to presence or absence of object instances at image level. We present a method based on features extracted from a Convolutional Neural Network and latent SVM that can represent and exploit the presence of multiple object instances in an image. Moreover, the detection of the object instances in the image is improved by incorporating in the learning procedure additional constraints that represent domain-specific knowledge such as symmetry and mutual exclusion. We show that the proposed method outperforms the state-of-the-art in weakly-supervised object detection and object classification on the Pascal VOC 2007 dataset.
Innovative soft robots based on electro-rheological fluids

Electro-rheological (ER) fluids are smart fluids which can transform into solid-like phase by applying an electric field. This process is reversible and can be strategically used to build fluidic components for innovative soft robots capable of soft locomotion. In this work, we show the potential applications of ER fluids to build valves that simplify design of fluidic based soft robots. We propose the design and development of a composite ER valve, aimed at controlling the flexibility of soft robots bodies by controlling the ER fluid flow. We present how an ad hoc number of such soft components can be embodied in a simple crawling soft robot (Wormbot); in a locomotion mechanism capable of forward motion through rotation; and, in a tendon driven continuum arm. All these embodiments show how simplification of the hydraulic circuits relies on the simple structure of ER valves. Finally, we address preliminary experiments to characterize the behavior of Wormbot in terms of actuation forces.
Combining fraud and intrusion detection-meeting new requirements -

This paper studies the area of fraud detection in the light of existing intrusion detection research. Fraud detection and intrusion detection have traditionally been two almost completely separate research areas. Fraud detection has long been used by such businesses as telecom companies, banks and insurance companies. Intrusion detection has recently become a popular means to protect computer systems and computer based services. Many of the services offered by businesses using fraud detection are now computer based, thus opening new ways of committing fraud not covered by traditional fraud detection systems. Merging fraud detection with intrusion detection may be a solution for protecting new computer based services. An IP based telecom service is used as an example to illustrate these new problems and the use of a suggested fraud model.
Vocabulary independent spoken term detection

We are interested in retrieving information from speech data like broadcast news, telephone conversations and roundtable meetings. Today, most systems use large vocabulary continuous speech recognition tools to produce word transcripts; the transcripts are indexed and query terms are retrieved from the index. However, query terms that are not part of the recognizer's vocabulary cannot be retrieved, and the recall of the search is affected. In addition to the output word transcript, advanced systems provide also phonetic transcripts, against which query terms can be matched phonetically. Such phonetic transcripts suffer from lower accuracy and cannot be an alternative to word transcripts.We present a vocabulary independent system that can handle arbitrary queries, exploiting the information provided by having both word transcripts and phonetic transcripts. A speech recognizer generates word confusion networks and phonetic lattices. The transcripts are indexed for query processing and ranking purpose.The value of the proposed method is demonstrated by the relative high performance ofour system, which received the highest overall ranking for US English speech data in the recent NIST Spoken Term Detection evaluation.
An Omnidirectional Circularly Polarized Antenna Array

An omnidirectional circularly polarized (CP) antenna array is proposed. The antenna array is composed of four identical CP antenna elements and one parallel strip-line feeding network. Each of CP antenna elements comprises a dipole and a zero-phase-shift (ZPS) line loop. The in-phase fed dipole and the ZPS line loop generate vertically and horizontally polarized omnidirectional radiation, respectively. Furthermore, the vertically polarized dipole is positioned in the center of the horizontally polarized ZPS line loop. The size of the loop is designed such that a 90° phase difference is realized between the two orthogonal components because of the spatial difference and, therefore, generates CP omnidirectional radiation. A 1 × 4 antenna array at 900 MHz is prototyped and targeted to ultra-high frequency (UHF) radio frequency identification (RFID) applications. The measurement results show that the antenna array achieves a 10-dB return loss over a frequency range of 900-935 MHz and 3-dB axial-ratio (AR) from 890 to 930 MHz. At the frequency of 915 MHz, the measured maximum AR of 1.53 dB, maximum gain of 5.4 dBic, and an omnidirectionality of ±1 dB are achieved.
Real-time Bidding for Online Advertising: Measurement and Analysis

The real-time bidding (RTB), aka programmatic buying, has recently become the fastest growing area in online advertising. Instead of bulking buying and inventory-centric buying, RTB mimics stock exchanges and utilises computer algorithms to automatically buy and sell ads in real-time; It uses per impression context and targets the ads to specific people based on data about them, and hence dramatically increases the effectiveness of display advertising. In this paper, we provide an empirical analysis and measurement of a production ad exchange. Using the data sampled from both demand and supply side, we aim to provide first-hand insights into the emerging new impression selling infrastructure and its bidding behaviours, and help identifying research and design issues in such systems. From our study, we observed that periodic patterns occur in various statistics including impressions, clicks, bids, and conversion rates (both post-view and post-click), which suggest time-dependent models would be appropriate for capturing the repeated patterns in RTB. We also found that despite the claimed second price auction, the first price payment in fact is accounted for 55.4% of total cost due to the arrangement of the soft floor price. As such, we argue that the setting of soft floor price in the current RTB systems puts advertisers in a less favourable position. Furthermore, our analysis on the conversation rates shows that the current bidding strategy is far less optimal, indicating the significant needs for optimisation algorithms incorporating the facts such as the temporal behaviours, the frequency and recency of the ad displays, which have not been well considered in the past.
Graph Regularized Restricted Boltzmann Machine

The restricted Boltzmann machine (RBM) has received an increasing amount of interest in recent years. It determines good mapping weights that capture useful latent features in an unsupervised manner. The RBM and its generalizations have been successfully applied to a variety of image classification and speech recognition tasks. However, most of the existing RBM-based models disregard the preservation of the data manifold structure. In many real applications, the data generally reside on a low-dimensional manifold embedded in high-dimensional ambient space. In this brief, we propose a novel graph regularized RBM to capture features and learning representations, explicitly considering the local manifold structure of the data. By imposing manifold-based locality that preserves constraints on the hidden layer of the RBM, the model ultimately learns sparse and discriminative representations. The representations can reflect data distributions while simultaneously preserving the local manifold structure of data. We test our model using several benchmark image data sets for unsupervised clustering and supervised classification problem. The results demonstrate that the performance of our method exceeds the state-of-the-art alternatives.
Tracking objects outside the line of sight using 2D intensity images

The observation of objects located in inaccessible regions is a recurring challenge in a wide variety of important applications. Recent work has shown that using rare and expensive optical setups, indirect diffuse light reflections can be used to reconstruct objects and two-dimensional (2D) patterns around a corner. Here we show that occluded objects can be tracked in real time using much simpler means, namely a standard 2D camera and a laser pointer. Our method fundamentally differs from previous solutions by approaching the problem in an analysis-by-synthesis sense. By repeatedly simulating light transport through the scene, we determine the set of object parameters that most closely fits the measured intensity distribution. We experimentally demonstrate that this approach is capable of following the translation of unknown objects, and translation and orientation of a known object, in real time.
Petabit Optical Switch for Data Center Networks

As critical infrastructures in the Internet, data centers have evolved to include hundreds of thousands of servers in a single facility to support dataand/or computing-intensive applications. For such large-scale systems, it becomes a great challenge to design an interconnection network that provides high capacity, low complexity, low latency and low power consumption. The traditional approach is to build a hierarchical packet network using switches and routers. This approach suffers from limited scalability in the aspects of power consumption, wiring and control complexity, and delay caused by multi-hop store-andforwarding. In this paper we tackle the challenge by designing a novel switch architecture that supports direct interconnection of huge number of server racks and provides switching capacity at the level of Petabit/s. Our design combines the best features of electronics and optics. Exploiting recent advances in optics, we propose to build a bufferless optical switch fabric that includes interconnected arrayed waveguide grating routers (AWGRs) and tunable wavelength converters (TWCs). The optical fabric is integrated with electronic buffering and control to perform highspeed switching with nanosecond-level reconfiguration overhead. In particular, our architecture reduces the wiring complexity from O(N) to O(sqrt(N)). We design a practical and scalable scheduling algorithm to achieve high throughput under various traffic load. We also discuss implementation issues to justify the feasibility of this design. Simulation shows that our design achieves good throughput and delay performance.
The development of the web-based Attendance Register System ( ARS ) for higher academic institution : From feasibility study to the design phase

This study focuses on the development of a web-based Attendance Register System or formerly known as ARS. The development of this system is motivated due to the fact that the students’ attendance records are one of the important elements that reflect their academic achievements in the higher academic institutions. However, the current practice implemented in most of the higher academic institutions in Malaysia is becoming more prone to human errors and frauds. Assisted by the System Development Life Cycle (SDLC) methodology, the ARS has been built using the web-based applications such as PHP, MySQL and Apache to cater the recording and reporting of the students’ attendances. The development of this prototype system is inspired by the feasibility study done in Universiti Teknologi MARA, Malaysia where 550 respondents have taken part in answering the questionnaires. From the analysis done, it has revealed that a more systematic and revolutionary system is indeed needed to be reinforced in order to improve the process of recording and reporting the attendances in the higher academic institution. ARS can be easily accessed by the lecturers via the Web and most importantly, the reports can be generated in realtime processing, thus, providing invaluable information about the students’ commitments in attending the classes. This paper will discuss in details the development of ARS from the feasibility study until the design phase.
MIRPLib - A library of maritime inventory routing problem instances: Survey, core model, and benchmark results

This paper presents a detailed description of a particular class of deterministic single product maritime inventory routing problems (MIRPs), which we call deep-sea MIRPs with inventory tracking at every port. This class involves vessel travel times between ports that are significantly longer than the time spent in port and require inventory levels at all ports to be monitored throughout the planning horizon. After providing a comprehensive literature survey of this class, we introduce a core model for it cast as a mixed-integer linear program. This formulation is quite general and incorporates assumptions and families of constraints that are most prevalent in practice. We also discuss other modeling features commonly found in the literature and how they can be incorporated into the core model. We then offer a unified discussion of some of the most common advanced techniques used for improving the bounds of these problems. Finally, we present a library, called MIRPLib, of publicly available test problem instances for MIRPs with inventory tracking at every port. Despite a growing interest in combined routing and inventory management problems in a maritime setting, no data sets are publicly available, which represents a significant “barrier to entry” for those interested in related research. Our main goal for MIRPLib is to help maritime inventory routing gain maturity as an important and interesting class of planning problems. As a means to this end, we (1) make available benchmark instances for this particular class of MIRPs; (2) provide the mixed-integer linear programming community with a set of optimization problem instances from the maritime transportation domain in LP and MPS format; and (3) provide a template for other researchers when specifying characteristics of MIRPs arising in other settings. Best known computational results are reported for each instance.
PROW: A Step Toward Automatic Program Writing

This paper aescriDes a program, ca l led "PRUW", which w r i t es programs. PROW accepts the s p e c i f i ­ ca t i on of the program in the language of predicate ca l cu lus , decides the a lgor i thm f o r the program and then produces a LISP program which is an im­ plementat ion of the a lgor i thm. Since the construc­ t i o n of the a lgor i thm is obtained by formal theorem-proving techniques, the programs tha t PROW wr i t es are f ree from l o g i c a l er rors and do not have to be debugged. The user of PROW can make PROW w r i t e programs in languages other than LISP by modifying the par t of PROW tha t t rans la tes an a lgor i thm to a LISP program. Thus PROW can be modi f ied to w r i t e programs in any language. In the end of t h i s paper, it is shown tha t PROW can a lso be used as a quest ion-answering program.
An Origami-Inspired Approach to Worm Robots

This paper presents an origami-inspired technique which allows the application of 2-D fabrication methods to build 3-D robotic systems. The ability to design robots as origami structures introduces a fast and low-cost fabrication method to modern, real-world robotic applications. We employ laser-machined origami patterns to build a new class of robotic systems for mobility and manipulation. Origami robots use only a flat sheet as the base structure for building complicated bodies. An arbitrarily complex folding pattern can be used to yield an array of functionalities, in the form of actuated hinges or active spring elements. For actuation, we use compact NiTi coil actuators placed on the body to move parts of the structure on-demand. We demonstrate, as a proof-of-concept case study, the end-to-end fabrication and assembly of a simple mobile robot that can undergo worm-like peristaltic locomotion.