nils-herrmann/clean_scirepeval huggingface dataset:
Ultrasound Indoor Positioning System Based on a Low-Power Wireless Sensor Network Providing Sub-Centimeter Accuracy

This paper describes the TELIAMADE system, a new indoor positioning system based on time-of-flight (TOF) of ultrasonic signal to estimate the distance between a receiver node and a transmitter node. TELIAMADE system consists of a set of wireless nodes equipped with a radio module for communication and a module for the transmission and reception of ultrasound. The access to the ultrasonic channel is managed by applying a synchronization algorithm based on a time-division multiplexing (TDMA) scheme. The ultrasonic signal is transmitted using a carrier frequency of 40 kHz and the TOF measurement is estimated by applying a quadrature detector to the signal obtained at the A/D converter output. Low sampling frequencies of 17.78 kHz or even 12.31 kHz are possible using quadrature sampling in order to optimize memory requirements and to reduce the computational cost in signal processing. The distance is calculated from the TOF taking into account the speed of sound. An excellent accuracy in the estimation of the TOF is achieved using parabolic interpolation to detect of maximum of the signal envelope at the matched filter output. The signal phase information is also used for enhancing the TOF measurement accuracy. Experimental results show a root mean square error (rmse) less than 2 mm and a standard deviation less than 0.3 mm for pseudorange measurements in the range of distances between 2 and 6 m. The system location accuracy is also evaluated by applying multilateration. A sub-centimeter location accuracy is achieved with an average rmse of 9.6 mm.
Sentence Level Discourse Parsing using Syntactic and Lexical Information

We introduce two probabilistic models that can be used to identify elementary discourse units and build sentence-level discourse parse trees. The models use syntactic and lexical features. A discourse parsing algorithm that implements these models derives discourse parse trees with an error reduction of 18.8% over a state-ofthe-art decision-based discourse parser. A set of empirical evaluations shows that our discourse parsing model is sophisticated enough to yield discourse trees at an accuracy level that matches near-human levels of performance.
Binary Rewriting of an Operating System Kernel ∗

This paper deals with some of the issues that arise in the context of binary rewriting and instrumentation of an operatin g system kernel. OS kernels are very different from ordinary application code in many ways, e.g., they contain a significant amount of hand-written assembly code. Binary rewriting is an attractive approach for processing OS kernel code for several reasons, e.g., it provides a uniform way to handl e heterogeneity in code due to a combination of source code, assembly code and legacy code such as in device drivers. However, because of the many differences between ordinary application code and OS kernel code, binary rewriting techniques that work for application code do not always carry over directly to kernel code. This paper describes some of the issues that arise in this context, and the approaches we have taken to address them. A key goal when developing our system was to deal in a systematic manner with the various peculiarities seen in low-level systems code, and reason about the safety and correctness of code transformation s, without requiring significant deviations from the regular d evelopmental path. For example, a precondition we assumed was that no compiler or linker modifications should be required to use it and the tool should be able to process kernel binaries in the same way as it does ordinary applications.
Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems

Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact both on usability and perceived quality. Most NLG systems in common use employ rules and heuristics and tend to generate rigid and stylised responses without the natural variation of human language. They are also not easily scaled to systems covering multiple domains and languages. This paper presents a statistical language generator based on a semantically controlled Long Short-term Memory (LSTM) structure. The LSTM generator can learn from unaligned data by jointly optimising sentence planning and surface realisation using a simple cross entropy training criterion, and language variation can be easily achieved by sampling from output candidates. With fewer heuristics, an objective evaluation in two differing test domains showed the proposed method improved performance compared to previous methods. Human judges scored the LSTM system higher on informativeness and naturalness and overall preferred it to the other systems.
An algorithm to determine peer-reviewers

The peer-review process is the most widely accepted certification mechanism for officially accepting the written results of researchers within the scientific community. An essential component of peer-review is the identification of competent referees to review a submitted manuscript. This article presents an algorithm to automatically determine the most appropriate reviewers for a manuscript by way of a co-authorship network data structure and a relative-rank particle-swarm algorithm. This approach is novel in that it is not limited to a pre-selected set of referees, is computationally efficient, requires no human-intervention, and, in some instances, can automatically identify conflict of interest situations. A useful application of this algorithm would be to open commentary peer-review systems because it provides a weighting for each referee with respects to their expertise in the domain of a manuscript. The algorithm is validated using referee bid data from the 2005 Joint Conference on Digital Libraries.
Long Short-Term Memory

Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.
REPAIR OPTIMIZATION OF HIGHWAY BRIDGES USING SYSTEM RELIABILITY APPROACH

As reliability based methods gain increased acceptance, there is greater opportunity to use scarce resources more efficiently while maintaining a prescribed level of reliability of a structure throughout its service life. The goal is to provide management decisions that will balance lifetime system reliability and expected lifecycle cost in an optimal manner. This study proposes a system reliability approach for optimizing the lifetime repair strategy for highway bridges. The approach is demonstrated using an existing Colorado State highway bridge. The bridge is modeled as a series-parallel combination of failure modes, and the reliability of the overall bridge system is computed using time-dependent deterioration models and live load models. Based on an estab­ lished repair criterion, available repair options, repair costs, and updating, the optimum lifetime repair strategy is developed. The sensitivity of the optimum strategy to changes in various problem parameters including the prescribed service life, system failure criterion, and net discount rate is studied. Finally, the conclusions reveal that the proposed approach demonstrates real potential for practical applications, needs frequent updates through inspection, and requires considerable research effort to develop accurate input data.
Towards Future THz Communications Systems

Carrier frequencies beyond 300 GHz, belonging to the so-called THz range, have received attention for considering this frequency band for future multi-gigabit short-range communication systems. This review paper gives an overview of current issues in the emerging field of THz communications targeting to deliver wireless 100 Gbps over short distances. The paper will start by introducing scenarios and applications requiring such high data rates followed by a discussion on the radio channel characteristics. In the 300 GHz frequency band, the path loss is even more significant than at 60 GHz and appropriate measures to mitigate effects in none-line-of-sight (NLOS) cases, caused e. g. by the influence of moving people, are required. Advanced antenna techniques like beam forming or beam switching are a pre-requisite to guarantee seamless service. In order to consider such techniques in the standards development, the propagation channel operating at these mmand sub-mm wave bands in realistic environments must be well understood. Therefore, intensive channel measurement and modeling activities have been done at 300 GHz, which started a couple of years ago at Terahertz Communications Lab (TCL). Due to the short wave length, the influence of surface roughness of typical building materials plays an important role. Hence, the modeling of rough surface scattering has been one of the main areas in these investigations. In this contribution, the main results of the propagation research activities at TCL are summarized. In the last part of the paper, an overview of the state-of-the-art in technology development and successful demonstrations of data transmission will be given together with a report on the status quo of ongoing activities in standardization at IEEE 802.15 IG THz and the regulation of the spectrum beyond 300 GHz.
Improved seam carving for video retargeting

Video, like images, should support content aware resizing. We present video retargeting using an improved seam carving operator. Instead of removing 1D seams from 2D images we remove 2D seam manifolds from 3D space-time volumes. To achieve this we replace the dynamic programming method of seam carving with graph cuts that are suitable for 3D volumes. In the new formulation, a seam is given by a minimal cut in the graph and we show how to construct a graph such that the resulting cut is a valid seam. That is, the cut is monotonic and connected. In addition, we present a novel energy criterion that improves the visual quality of the retargeted images and videos. The original seam carving operator is focused on removing seams with the least amount of energy, ignoring energy that is introduced into the images and video by applying the operator. To counter this, the new criterion is looking forward in time - removing seams that introduce the least amount of energy into the retargeted result. We show how to encode the improved criterion into graph cuts (for images and video) as well as dynamic programming (for images). We apply our technique to images and videos and present results of various applications.
Data Clustering: 50 Years Beyond K-means

Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into a system of ranked taxa: domain, kingdom, phylum, class, etc. Cluster analysis is the formal study of methods and algorithms for grouping, or clustering, objects according to measured or perceived intrinsic characteristics or similarity. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes data clustering (unsupervised learning) from classification or discriminant analysis (supervised learning). The aim of clustering is to find structure in data and is therefore exploratory in nature. Clustering has a long and rich history in a variety of scientific fields. One of the most popular and simple clustering algorithms, K-means, was first published in 1955. In spite of the fact that K-means was proposed over 50 years ago and thousands of clustering algorithms have been published since then, K-means is still widely used. This speaks to the difficulty in designing a general purpose clustering algorithm and the ill-posed problem of clustering. We provide a brief overview of clustering, summarize well known clustering methods, discuss the major challenges and key issues in designing clustering algorithms, and point out some of the emerging and useful research directions, including semi-supervised clustering, ensemble clustering, simultaneous feature selection during data clustering, and large scale data clustering.
Muscle simulation for facial animation in Kong: Skull Island

For Kong: Skull Island, Industrial Light & Magic created an anatomically motivated facial simulation model for Kong that includes the facial skeleton and musculature. We applied a muscle simulation framework that allowed us to target facial shapes while maintaining desirable physical properties to ensure that the simulations stayed on-model. This allowed muscle simulations to be used as a powerful tool for adding physical detail to and improving the anatomical validity of both blendshapes and blendshape animations in order to achieve more realistic facial animation with less hand sculpting.
Neuro-fuzzy control of a robotic exoskeleton with EMG signals

We have been developing robotic exoskeletons to assist motion of physically weak persons such as elderly, disabled, and injured persons. The robotic exoskeleton is controlled basically based on the electromyogram (EMG) signals, since the EMG signals of human muscles are important signals to understand how the user intends to move. Even though the EMG signals contain very important information, however, it is not very easy to predict the user's upper-limb motion (elbow and shoulder motion) based on the EMG signals in real-time because of the difficulty in using the EMG signals as the controller input signals. In this paper, we propose a robotic exoskeleton for human upper-limb motion assist, a hierarchical neuro-fuzzy controller for the robotic exoskeleton, and its adaptation method.
Building a Large Annotated Corpus of English: The Penn Treebank

In this paper, we review our experience with constructing one such large annotated corpus--the Penn Treebank, a corpus consisting of over 4.5 million words of American English. During the first three-year phase of the Penn Treebank Project (1989-1992), this corpus has been annotated for part-of-speech (POS) information. In addition, over half of it has been annotated for skeletal syntactic structure. Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-93-87. This technical report is available at ScholarlyCommons: http://repository.upenn.edu/cis_reports/237 Building A Large Annotated Corpus of English: The Penn Treebank MS-CIS-93-87 LINC LAB 260 Mitchell P. Marcus Beatrice Santorini Mary Ann Marcinkiewicz University of Pennsylvania School of Engineering and Applied Science Computer and Information Science Department Philadelphia, PA 19104-6389
Cyber security in the Smart Grid: Survey and challenges

The Smart Grid, generally referred to as the next-generation power system, is considered as a revolutionary and evolutionary regime of existing power grids. More importantly, with the integration of advanced computing and communication technologies, the Smart Grid is expected to greatly enhance efficiency and reliability of future power systems with renewable energy resources, as well as distributed intelligence and demand response. Along with the silent features of the Smart Grid, cyber security emerges to be a critical issue because millions of electronic devices are inter-connected via communication networks throughout critical power facilities, which has an immediate impact on reliability of such a widespread infrastructure. In this paper, we present a comprehensive survey of cyber security issues for the Smart Grid. Specifically, we focus on reviewing and discussing security requirements, network vulnerabilities, attack countermeasures, secure communication protocols and architectures in the Smart Grid. We aim to provide a deep understanding of security vulnerabilities and solutions in the Smart Grid and shed light on future research directions for Smart Grid security.
Distributional Clustering of English Words

We describe and experimentally evaluate a method for automatically clustering words according to their distribution in particular syntactic contexts. Deterministic annealing is used to find lowest distortion sets of clusters. As the annealing parameter increases, existing clusters become unstable and subdivide, yielding a hierarchical “soft” clustering of the data. Clusters are used as the basis for class models of word coocurrence, and the models evaluated with respect to held-out test data.
We're in it together: interpersonal management of disclosure in social network services

The workload needed for managing privacy and publicness in current social network services (SNSs) is placed on individuals, yet people have few means to control what others disclose about them. This paper considers SNS-users' concerns in relation to online disclosure and the ways in which they cope with these both individually and collaboratively. While previous work has focused mainly on individual coping strategies, our findings from a qualitative study with 27 participants suggest that collaborative strategies in boundary regulation are of additional importance. We present a framework of strategies for boundary regulation that informs both theoretical work and design practice related to management of disclosure in SNSs. The framework considers disclosure as an interpersonal process of boundary regulation, in which people are dependent on what others choose to disclose about them. The paper concludes by proposing design solutions supportive of collaborative and preventive strategies in boundary regulation that facilitate the management of disclosure online.
Temporal Ensembling for Semi-Supervised Learning

In this paper, we present a simple and efficient method for training deep neural networks in a semi-supervised setting where only a small portion of training data is labeled. We introduce self-ensembling, where we form a consensus prediction of the unknown labels using the outputs of the network-in-training on different epochs, and most importantly, under different regularization and input augmentation conditions. This ensemble prediction can be expected to be a better predictor for the unknown labels than the output of the network at the most recent training epoch, and can thus be used as a target for training. Using our method, we set new records for two standard semi-supervised learning benchmarks, reducing the (non-augmented) classification error rate from 18.44% to 7.05% in SVHN with 500 labels and from 18.63% to 16.55% in CIFAR-10 with 4000 labels, and further to 5.12% and 12.16% by enabling the standard augmentations. We additionally obtain a clear improvement in CIFAR-100 classification accuracy by using random images from the Tiny Images dataset as unlabeled extra inputs during training. Finally, we demonstrate good tolerance to incorrect labels.
Program Synthesis Using Natural Language

Interacting with computers is a ubiquitous activity for millions of people. Repetitive or specialized tasks often require creation of small, often one-off, programs. End-users struggle with learning and using the myriad of domain-specific languages (DSLs) to effectively accomplish these tasks.
 We present a general framework for constructing program synthesizers that take natural language (NL) inputs and produce expressions in a target DSL. The framework takes as input a DSL definition and training data consisting of NL/DSL pairs. From these it constructs a synthesizer by learning optimal weights and classifiers (using NLP features) that rank the outputs of a keyword-programming based translation. We applied our framework to three domains: repetitive text editing, an intelligent tutoring system, and flight information queries. On 1200+ English descriptions, the respective synthesizers rank the desired program as the top-1 and top-3 for 80% and 90% descriptions respectively.
A parallel and efficient approach to large scale clone detection

Over the past few years, researchers have implemented various algorithms to improve the scalability of clone detection. Most of these algorithms focus on scaling vertically on a single machine, and require complex intermediate data structures (e.g., suffix tree, etc.). However, several new use-cases of clone detection have emerged, which are beyond the computational capacity of a single machine. Moreover, for some of these use-cases it may be expensive to invest upfront in the cost of building these data structures.
 In this paper, we propose a technique to horizontally scale clone detection across multiple machines using the popular MapReduce framework. The technique does not require building any complex intermediate data structures. Moreover, in order to increase the efficiency, the technique uses a filtering heuristic to prune the number of code block comparisons. The filtering heuristic is independent of our approach and it can be used in conjunction with other approaches to increase their efficiency. In our experiments, we found that: (i) the computation time to detect clones decreases by almost half every time we double the number of nodes; and (ii) the scaleup is linear, with a decline of not more than 70% compared to the ideal case, on a cluster of 2-32 nodes for 150-2800 projects.
Learning and Refining of Privileged Information-Based RNNs for Action Recognition from Depth Sequences

Existing RNN-based approaches for action recognition from depth sequences require either skeleton joints or hand-crafted depth features as inputs. An end-to-end manner, mapping from raw depth maps to action classes, is non-trivial to design due to the fact that: 1) single channel map lacks texture thus weakens the discriminative power, 2) relatively small set of depth training data. To address these challenges, we propose to learn an RNN driven by privileged information (PI) in three-steps: An encoder is pre-trained to learn a joint embedding of depth appearance and PI (i.e. skeleton joints). The learned embedding layers are then tuned in the learning step, aiming to optimize the network by exploiting PI in a form of multi-task loss. However, exploiting PI as a secondary task provides little help to improve the performance of a primary task (i.e. classification) due to the gap between them. Finally, a bridging matrix is defined to connect two tasks by discovering latent PI in the refining step. Our PI-based classification loss maintains a consistency between latent PI and predicted distribution. The latent PI and network are iteratively estimated and updated in an expectation-maximization procedure. The proposed learning process provides greater discriminative power to model subtle depth difference, while helping avoid overfitting the scarcer training data. Our experiments show significant performance gains over state-of-the-art methods on three public benchmark datasets and our newly collected Blanket dataset.