nils-herrmann/clean_scirepeval huggingface dataset:
GPS-free Positioning in Mobile Ad Hoc Networks

We consider the problem of node positioning in ad hoc networks. We propose a distributed, infrastructure-free positioning algorithm that does not rely on GPS (Global Positioning System). Instead, the algorithm uses the distances between the nodes to build a relative coordinate system in which the node positions are computed in two dimensions. Despite the distance measurement errors and the motion of the nodes, the algorithm provides sufficient location information and accuracy to support basic network functions. Examples of applications where this algorithm can be used include Location Aided Routing [10] and Geodesic Packet Forwarding . Another example are sensor networks, where mobility is less of a problem. The main contribution of this work is to define and compute relative positions of the nodes in an ad hoc network without using GPS. We further explain how the proposed approach can be applied to wide area ad hoc networks.
A Rigorous Design Methodology for Compact Planar Branch-Line and Rat-Race Couplers With Asymmetrical T-Structures

In this paper, a rigorous design methodology is developed to design compact planar branch-line and rat-race couplers using asymmetrical T-structures. The quarter-wave transmission line, namely the basic element for realizing the coupler, can be replaced by the asymmetrical T-structure, which is composed of a low-impedance shunt stub and two series high-impedance lines with unequal electrical lengths. As compared with the use of the conventional symmetrical T-structure, employing the asymmetrical one to implement the coupler not only has the advantage of flexibly interleaving the shunt stubs to achieve a more compact circuit size, but also provides a wider return loss bandwidth. Based on the proposed designed methodology, the asymmetrical T-structure can be exactly synthesized and then applied to implement the compact planar couplers. The developed planar branch-line coupler occupies 12.2% of the conventional structure and has a 35.5% 10-dB return loss bandwidth. On the other hand, the rat-race coupler is miniaturized to a 5% circuit size and developed with a 29.5% 20-dB return loss bandwidth.
Natural language processing based features for sarcasm detection: An investigation using bilingual social media texts

The presence of sarcasm in text can hamper the performance of sentiment analysis. The challenge is to detect the existence of sarcasm in texts. This challenge is compounded when bilingual texts are considered, for example using Malay social media data. In this paper a feature extraction process is proposed to detect sarcasm using bilingual texts; more specifically public comments on economic related posts on Facebook. Four categories of feature that can be extracted using natural language processing are considered; lexical, pragmatic, prosodic and syntactic. We also investigated the use of idiosyncratic feature to capture the peculiar and odd comments found in a text. To determine the effectiveness of the proposed process, a non-linear Support Vector Machine was used to classify texts, in terms of the identified features, according to whether they included sarcastic content or not. The results obtained demonstrate that a combination of syntactic, pragmatic and prosodic features produced the best performance with an F-measure score of 0.852.
Revisions to the JDL Data Fusion Model

Alan N. Steinberg Technical Director, Data Fusion ERIM International, Inc. 1101 Wilson Blvd Arlington, VA 22209 (703)528-5250 x4109 steinberg@erim-int.com Christopher L. Bowman Data Fusion and Neural Networks 1643 Hemlock Way Broomfield, CO 80020 (303)469-9828 cbowman@indra.com Franklin E. White Director, Program Development SPAWAR Systems Center San Diego, CA 92152 Chair, Data Fusion Group (619) 553-4036 whitefe@spawar.navy.mil
AFEW-VA database for valence and arousal estimation in-the-wild

Continuous dimensional models of human affect, such as those based on valence and arousal, have been shown to be more accurate in describing a broad range of spontaneous, everyday emotions than the more traditional models of discrete stereotypical emotion categories (e.g. happiness, surprise). However, most prior work on estimating valence and arousal considered only laboratory settings and acted data. It is unclear whether the findings of these studies also hold when the methodologies proposed in these works are tested on data collected in-the-wild. In this paper we investigate this. We propose a new dataset of highly accurate per-frame annotations of valence and arousal for 600 challenging video clips extracted from feature films (also used in part for the AFEW dataset). For each video clip, we further provide per-frame annotations of 68 facial landmarks. We subsequently evaluate a number of common baseline and state-of-the-art methods on both a commonly used laboratory recording dataset (Semaine database) and the newly proposed recording set (AFEW-VA). Our results show that geometric features perform well independently of the settings. However, as expected, methods that perform well on constrained data do not necessarily generalise to uncontrolled data and vice-versa.
Compile-time optimized and statically scheduled N-D convnet primitives for multi-core and many-core (Xeon Phi) CPUs

Convolutional networks (ConvNets), largely running on GPUs, have become the most popular approach to computer vision. Now that CPUs are closing the FLOPS gap with GPUs, efficient CPU algorithms are becoming more important. We propose a novel parallel and vectorized algorithm for N-D convolutional layers. Our goal is to achieve high utilization of available FLOPS, independent of ConvNet architecture and CPU properties (e.g. vector units, number of cores, cache sizes). Our approach is to rely on the compiler to optimize code, thereby removing the need for hand-tuning. We assume that the network architecture is known at compile-time. Our serial algorithm divides the computation into small sub-tasks designed to be easily optimized by the compiler for a specific CPU. Sub-tasks are executed in an order that maximizes cache reuse. We parallelize the algorithm by statically scheduling tasks to be executed by each core. Our novel compile-time recursive scheduling algorithm is capable of dividing the computation evenly between an arbitrary number of cores, regardless of ConvNet architecture. It introduces zero runtime overhead and minimal synchronization overhead. We demonstrate that our serial primitives efficiently utilize available FLOPS (75--95%), while our parallel algorithm attains 50--90% utilization on 64+ core machines. Our algorithm is competitive with the fastest CPU implementation to date (MKL2017) for 2D object recognition, and performs much better for image segmentation. For 3D ConvNets we demonstrate comparable performance to the latest GPU hardware and software even though the CPU is only capable of half the FLOPS of the GPU.
The fundamentals of software aging

Since the notion of software aging was introduced thirteen years ago, the interest in this phenomenon has been increasing from both academia and industry. The majority of the research efforts in studying software aging have focused on understanding its effects theoretically and empirically. However, conceptual aspects related to the foundation of this phenomenon have not been covered in the literature. This paper discusses foundational aspects of the software aging phenomenon, introducing new concepts and interconnecting them with the current body of knowledge, in order to compose a base taxonomy for the software aging research. Three real case studies are presented with the purpose of exemplifying many of the concepts discussed.
Evolution of Planning for Wireless Communication Systems

In this paper we provide a detailed and comprehensive survey of proposed approaches for network design, charting the evolution of models and techniques for the automatic planning of cellular wireless services. These problems present themselves as a trade-off between commitment to infrastructure and quality of service, and have become increasingly complex with the advent of more sophisticated protocols and wireless architectures. Consequently these problems are receiving increased attention from researchers in a variety of fields who adopt a wide range of models, assumptions and methodologies for problem solution. We seek to unify this dispersed and fragmented literature by charting the evolution of centralised planning for cellular systems.
Driving Process Improvement via Comparative Agility Assessment

Rather than striving to be “perfectly agile, ” some organizations desire to be more agile than their competition and/or the industry. The Comparative Agility™ (CA) assessment tool can be used to aid organizations in determining their relative agility compared with other teams who responded to CA. The results of CA can be used by a team to guide process improvement related to the use of agile software development practices. This paper provides an overview of industry trends in agility based upon 1, 235 CA respondents in a range of domains and geographical locations. Additionally, the paper goes further in depth on the results of four industrial teams who responded to the CA, explaining why their results were relatively high or low based upon experiences with the teams. The paper also discusses the resultant process improvement reactions and plans of these teams subsequent to reviewing their CA results.
ST-DBSCAN: An algorithm for clustering spatial-temporal data

This paper presents a new density-based clustering algorithm, ST-DBSCAN, which is based on DBSCAN. We propose three marginal extensions to DBSCAN related with the identification of (i) core objects, (ii) noise objects, and (iii) adjacent clusters. In contrast to the existing density-based clustering algorithms, our algorithm has the ability of discovering clusters according to non-spatial, spatial and temporal values of the objects. In this paper, we also present a spatial–temporal data warehouse system designed for storing and clustering a wide range of spatial–temporal data. We show an implementation of our algorithm by using this data warehouse and present the data mining results.
Blockchain-based traceability in Agri-Food supply chain management: A practical implementation

The recent, exponential rise in adoption of the most disparate Internet of Things (IoT) devices and technologies has reached also Agriculture and Food (Agri-Food) supply chains, drumming up substantial research and innovation interest towards developing reliable, auditable and transparent traceability systems. Current IoT-based traceability and provenance systems for Agri-Food supply chains are built on top of centralized infrastructures and this leaves room for unsolved issues and major concerns, including data integrity, tampering and single points of failure. Blockchains, the distributed ledger technology underpinning cryptocurrencies such as Bitcoin, represent a new and innovative technological approach to realizing decentralized trustless systems. Indeed, the inherent properties of this digital technology provide fault-tolerance, immutability, transparency and full traceability of the stored transaction records, as well as coherent digital representations of physical assets and autonomous transaction executions. This paper presents AgriBlockIoT, a fully decentralized, blockchain-based traceability solution for Agri-Food supply chain management, able to seamless integrate IoT devices producing and consuming digital data along the chain. To effectively assess AgriBlockIoT, first, we defined a classical use-case within the given vertical domain, namely from-farm-to-fork. Then, we developed and deployed such use-case, achieving traceability using two different blockchain implementations, namely Ethereum and Hyperledger Sawtooth. Finally, we evaluated and compared the performance of both the deployments, in terms of latency, CPU, and network usage, also highlighting their main pros and cons.
Fast Network Embedding Enhancement via High Order Proximity Approximation

Many Network Representation Learning (NRL) methods have been proposed to learn vector representations for vertices in a network recently. In this paper, we summarize most existing NRL methods into a unified two-step framework, including proximity matrix construction and dimension reduction. We focus on the analysis of proximity matrix construction step and conclude that an NRL method can be improved by exploring higher order proximities when building the proximity matrix. We propose Network Embedding Update (NEU) algorithm which implicitly approximates higher order proximities with theoretical approximation bound and can be applied on any NRL methods to enhance their performances. We conduct experiments on multi-label classification and link prediction tasks. Experimental results show that NEU can make a consistent and significant improvement over a number of NRL methods with almost negligible running time on all three publicly available datasets. The source code of this paper can be obtained from https://github.com/thunlp/NEU.
Suppressing acoustic echo in a spectral envelope space

Full-duplex hands-free telecommunication systems employ an acoustic echo canceler (AEC) to remove the undesired echoes that result from the coupling between a loudspeaker and a microphone. Traditionally, the removal is achieved by modeling the echo path impulse response with an adaptive finite impulse response (FIR) filter and subtracting an echo estimate from the microphone signal. It is not uncommon that an adaptive filter with a length of 50-300 ms needs to be considered, which makes an AEC highly computationally expensive. In this paper, we propose an echo suppression algorithm to eliminate the echo effect. Instead of identifying the echo path impulse response, the proposed method estimates the spectral envelope of the echo signal. The suppression is done by spectral modification-a technique originally proposed for noise reduction. It is shown that this new approach has several advantages over the traditional AEC. Properties of human auditory perception are considered, by estimating spectral envelopes according to the frequency selectivity of the auditory system, resulting in improved perceptual quality. A conventional AEC is often combined with a post-processor to reduce the residual echoes due to minor echo path changes. It is shown that the proposed algorithm is insensitive to such changes. Therefore, no post-processor is necessary. Furthermore, the new scheme is computationally much more efficient than a conventional AEC.
Dynamic Programming Treatment of the Travelling Salesman Problem

The well-known travelling salesman problem is the following: " A salesman is required ~,o visit once and only once each of n different cities starting from a base city, and returning to this city. What path minimizes the to ta l distance travelled by the salesman?" The problem has been treated by a number of different people using a var ie ty of techniques; el. Dantzig, Fulkerson, Johnson , where a combination of ingemtity and linear programming is used, and Miller, Tucker and Zemlin , whose experiments using an all-integer program of Gomory did not produce results i~ cases with ten cities although some success was achieved in eases of simply four cities. The purpose of this note is to show tha t this problem can easily be formulated in dynamic programming terms , and resolved computationally for up to 17 cities. For larger numbers, the method presented below, combined with various simple manipulations, may be used to obtain quick approximate solutions. Results of this nature were independently obtained by M. Held and R. M. Karp, who are in the process of publishing some extensions and computat ional results.
Sentiment analysis of Thai children stories

Sentiment Text Tagging System (STTS) with Thai sentiment resource has been developed and used to tag emotions directly to words and sentences in Thai children stories. The Thai sentiment resource, developed from SenticNet2 resource, groups emotions into four independent but concomitant dimensions: pleasantness, attention, sensitivity and aptitude. The measure of each dimension is called a sentic value of that dimension. Thai sentiment resource stores each word’s sentic value and polarity value, a value calculated from the sentic value, in the form of floating point number. The resource was constructed from bi-directional translation of 14,244 English terms in SenticNet2 into 16,584 Thai terms. The main purpose of this study was to implement a sentiment analysis of Thai children stories system with support vector machine using a set of proposed discriminating features for classifying emotions. It was found that the system can achieve 75.67 % of accuracy.
Why are deep nets reversible: A simple theory, with implications for training

Generative models for deep learning are promising both to improve understanding of the model, and yield training methods requiring fewer labeled samples. Recent works use generative model approaches to produce the deep net’s input given the value of a hidden layer several levels above. However, there is no accompanying “proof of correctness” for the generative model, showing that the feedforward deep net is the correct inference method for recovering the hidden layer given the input. Furthermore, these models are complicated. The current paper takes a more theoretical tack. It presents a very simple generative model for ReLU deep nets, with the following characteristics: (i) The generative model is just the reverse of the feedforward net: if the forward transformation at a layer is A then the reverse transformation is A . (This can be seen as an explanation of the old weight tying idea for denoising autoencoders.) (ii) Its correctness can be proven under a clean theoretical assumption: the edge weights in real-life deep nets behave like random numbers. Under this assumption —which is experimentally tested on real-life nets like AlexNet— it is formally proved that feed forward net is a correct inference method for recovering the hidden layer. The generative model suggests a simple modification for training: use the generative model to produce synthetic data with labels and include it in the training set. Experiments are shown to support this theory of random-like deep nets; and that it helps the training.
Real-time image-based 6-DOF localization in large-scale environments

We present a real-time approach for image-based localization within large scenes that have been reconstructed offline using structure from motion (Sfm). From monocular video, our method continuously computes a precise 6-DOF camera pose, by efficiently tracking natural features and matching them to 3D points in the Sfm point cloud. Our main contribution lies in efficiently interleaving a fast keypoint tracker that uses inexpensive binary feature descriptors with a new approach for direct 2D-to-3D matching. The 2D-to-3D matching avoids the need for online extraction of scale-invariant features. Instead, offline we construct an indexed database containing multiple DAISY descriptors per 3D point extracted at multiple scales. The key to the efficiency of our method lies in invoking DAISY descriptor extraction and matching sparingly during localization, and in distributing this computation over a window of successive frames. This enables the algorithm to run in real-time, without fluctuations in the latency over long durations. We evaluate the method in large indoor and outdoor scenes. Our algorithm runs at over 30 Hz on a laptop and at 12 Hz on a low-power, mobile computer suitable for onboard computation on a quadrotor micro aerial vehicle.
Efficient Learning of Mahalanobis Metrics for Ranking

We develop an efficient algorithm to learn a Mahalanobis distance metric by directly optimizing a ranking loss. Our approach focuses on optimizing the top of the induced rankings, which is desirable in tasks such as visualization and nearestneighbor retrieval. We further develop and justify a simple technique to reduce training time significantly with minimal impact on performance. Our proposed method significantly outperforms alternative methods on several real-world tasks, and can scale to large and high-dimensional data.
Radiological evaluation by magnetic resonance of the ‘new anatomy’ of transsexual patients undergoing male to female sex reassignment surgery

Magnetic resonance (MR) is the best way to assess the new anatomy of the pelvis after male to female (MtF) sex reassignment surgery. The aim of the study was to evaluate the radiological appearance of the small pelvis after MtF surgery and to compare it with the normal women's anatomy. Fifteen patients who underwent MtF surgery were subjected to pelvic MR at least 6 months after surgery. The anthropometric parameters of the small pelvis were measured and compared with those of ten healthy women (control group). Our personal technique (creation of the mons Veneris under the pubic skin) was performed in all patients. In patients who underwent MtF surgery, the mean neovaginal depth was slightly superior than in women (P=0.009). The length of the inferior pelvic aperture and of the inlet of pelvis was higher in the control group (P<0.005). The inclination between the axis of the neovagina and the inferior pelvis aperture, the thickness of the mons Veneris and the thickness of the rectovaginal septum were comparable between the two study groups. MR consents a detailed assessment of the new pelvic anatomy after MtF surgery. The anthropometric parameters measured in our patients were comparable with those of women.
Anatomically distinct dopamine release during anticipation and experience of peak emotion to music

Music, an abstract stimulus, can arouse feelings of euphoria and craving, similar to tangible rewards that involve the striatal dopaminergic system. Using the neurochemical specificity of [11C]raclopride positron emission tomography scanning, combined with psychophysiological measures of autonomic nervous system activity, we found endogenous dopamine release in the striatum at peak emotional arousal during music listening. To examine the time course of dopamine release, we used functional magnetic resonance imaging with the same stimuli and listeners, and found a functional dissociation: the caudate was more involved during the anticipation and the nucleus accumbens was more involved during the experience of peak emotional responses to music. These results indicate that intense pleasure in response to music can lead to dopamine release in the striatal system. Notably, the anticipation of an abstract reward can result in dopamine release in an anatomical pathway distinct from that associated with the peak pleasure itself. Our results help to explain why music is of such high value across all human societies.