nils-herrmann/clean_scirepeval huggingface dataset:
Unsupervised Learning of Local Features for Music Classification

In this work we investigate the applicability of unsupervised feature learning methods to the task of automatic genre prediction of music pieces. More specifically we evaluate a framework that recently has been successfully used to recognize objects in images. We first extract local patches from the time-frequency transformed audio signal, which are then pre-processed and used for unsupervised learning of an overcomplete dictionary of local features. For learning we either use a bootstrapped k-means clustering approach or select features randomly. We further extract feature responses in a convolutional manner and train a linear SVM for classification. We extensively evaluate the approach on the GTZAN dataset, emphasizing the influence of important design choices such as dimensionality reduction, pooling and patch dimension on the classification accuracy. We show that convolutional extraction of local feature responses is crucial to reach high performance. Furthermore we find that using this approach, simple and fast learning techniques such as k-means or randomly selected features are competitive with previously published results which also learn features from audio signals.
FlowRanger: A request prioritizing algorithm for controller DoS attacks in Software Defined Networks

Software Defined Networking (SDN) introduces a new communication network management paradigm and has gained much attention from academia and industry. However, the centralized nature of SDN is a potential vulnerability to the system since attackers may launch denial of services (DoS) attacks against the controller. Existing solutions limit requests rate to the controller by dropping overflowed requests, but they also drop legitimate requests to the controller. To address this problem, we propose FlowRanger, a buffer prioritizing solution for controllers to handle routing requests based on their likelihood to be attacking requests, which derives the trust values of the requesting sources. Based on their trust values, FlowRanger classifies routing requests into multiple buffer queues with different priorities. Thus, attacking requests are served with a lower priority than regular requests. Our simulation results demonstrates that FlowRanger can significantly enhance the request serving rate of regular users under DoS attacks against the controller. To the best of our knowledge, our work is the first solution to battle against controller DoS attacks on the controller side.
Arbitrary Size Benes Networks

The Benes network is a rearrangeable nonblocking network which can realize any arbitrary permutation. Overall, the r-dimensional Benes network connects 2 r inputs to 2 r outputs through 2r ? 1 levels of 2 2 switches. Each level of switches consists of 2 r?1 switches, and hence the size of the network has to be a power of two. In this paper, we extend Benes networks to arbitrary sizes. We also show that the looping routing algorithm used in Benes networks can be slightly modiied and applied to arbitrary size Benes networks.
A traffic sign detection pipeline based on interest region extraction

In this paper we present a pipeline for automatic detection of traffic signs in images. The proposed system can deal with high appearance variations, which typically occur in traffic sign recognition applications, especially with strong illumination changes and dramatic scale changes. Unlike most existing systems, our pipeline is based on interest regions extraction rather than a sliding window detection scheme. The proposed approach has been specialized and tested in three variants, each aimed at detecting one of the three categories of Mandatory, Prohibitory and Danger traffic signs. Our proposal has been evaluated experimentally within the German Traffic Sign Detection Benchmark competition.
An integrated ECC and redundancy repair scheme for memory reliability enhancement

With the fast development pace of deep submicron technology, the size and density of semiconductor memory grows rapidly. However, keeping a high level of yield and reliability for memory products is more and more difficult. Both the redundancy repair and ECC techniques have been widely used for enhancing the yield and reliability of memory chips. Specifically, the redundancy repair and ECC techniques are conventionally used to repair or correct the hard faults and soft errors, respectively. In this paper, we propose an integrated ECC and redundancy repair scheme for memory reliability enhancement. Our approach can identify the hard faults and soft errors during the memory normal operation mode, and repair the hard faults during the memory idle time as long as there are unused redundant elements. We also develop a method for evaluating the memory reliability. Experimental results show that the proposed approach is effective, e.g., the MTTF of a 32K /spl times/ 64 memory is improved by 1.412 hours (7.1%) with our integrated ECC and repair scheme.
Under-Foliage Object Imaging Using SAR Tomography and Polarimetric Spectral Estimators

This paper addresses the imaging of objects located under a forest cover using polarimetric synthetic aperture radar tomography (POLTOMSAR) at L-band. High-resolution spectral estimators, able to accurately discriminate multiple scattering centers in the vertical direction, are used to separate the response of objects and vehicles embedded in a volumetric background. A new polarimetric spectral analysis technique is introduced and is shown to improve the estimation accuracy of the vertical position of both artificial scatterers and natural environments. This approach provides optimal polarimetric features that may be used to further characterize the objects under analysis. The effectiveness of this novel technique for POLTOMSAR is demonstrated using fully polarimetric L-band airborne data sets acquired by the German Aerospace Center (DLR)'s E-SAR system over the test site in Dornstetten, Germany.
Supply chain ontology: Review, analysis and synthesis

In an environment where supply chains compete against supply chains, information sharing among supply chain partners using information systems is a competitive tool. Supply chain ontology has been proposed as an important medium for attaining information systems interoperability. Ontology has its origin in philosophy, and the computing community has adopted ontology in its language. This paper presents a study of state of the art research in supply chain ontology and identifies the outstanding research gaps. Six supply chain ontology models were identified from a systematic review of literature. A seven point comparison framework was developed to consider the underlying concepts as well as application of the ontology models. The comparison results were then synthesised into nine gaps to inform future supply chain ontology research. This work is a rigorous and systematic attempt to identify and synthesise the research in supply chain ontology.
Social Media Analytics - An Interdisciplinary Approach and Its Implications for Information Systems

Social Media Analytics is an emerging interdisciplinary research field that aims on combining, extending, and adapting methods for analysis of social media data. On the one hand it can support IS and other research disciplines to answer their research questions and on the other hand it helps to provide architectural designs as well as solution frameworks for new social media-based applications and information systems. The authors suggest that IS should contribute to this field and help to develop and process an interdisciplinary research agenda.
Deep video portraits

We present a novel approach that enables photo-realistic re-animation of portrait videos using only an input video. In contrast to existing approaches that are restricted to manipulations of facial expressions only, we are the first to transfer the full 3D head position, head rotation, face expression, eye gaze, and eye blinking from a source actor to a portrait video of a target actor. The core of our approach is a generative neural network with a novel space-time architecture. The network takes as input synthetic renderings of a parametric face model, based on which it predicts photo-realistic video frames for a given target actor. The realism in this rendering-to-video transfer is achieved by careful adversarial training, and as a result, we can create modified target videos that mimic the behavior of the synthetically-created input. In order to enable source-to-target video re-animation, we render a synthetic target video with the reconstructed head animation parameters from a source video, and feed it into the trained network - thus taking full control of the target. With the ability to freely recombine source and target parameters, we are able to demonstrate a large variety of video rewrite applications without explicitly modeling hair, body or background. For instance, we can reenact the full head using interactive user-controlled editing, and realize high-fidelity visual dubbing. To demonstrate the high quality of our output, we conduct an extensive series of experiments and evaluations, where for instance a user study shows that our video edits are hard to detect.
Compact UWB Filter With Double Notch-Bands Using Multilayer LCP Technology

A novel ultra wideband (UWB) bandpass filter with double notch-bands is presented in this paper. Multilayer schematic is adopted to achieve compact size. Stepped impedance resonators (SIRs), which can also suppress harmonic response, are designed on top and second layers, respectively, and broadside coupling technique is used to achieve tight couplings for a wide passband. Folded SIRs that can provide desired notch-bands are designed on the third layer and coupled underneath the second layer SIRs. The designed prototype is fabricated using multilayer liquid crystal polymer (LCP) technology. Good agreement between simulated and measured response is observed. The fabricated filter has dual notch-bands with center frequencies of 6.4/8.0 GHz with 3 dB bandwidths of 9.5%/13.4% and high rejection levels up to 26.4 dB and 43.7 dB at 6.4/8.0 GHz are observed, respectively. It also has low-insertion losses and flat group delay in passbands, and excellent stopband rejection level higher than 30.0 dB from 11.4 GHz to 18.0 GHz.
Resource Sharing Optimization for Device-to-Device Communication Underlaying Cellular Networks

We consider Device-to-Device (D2D) communication underlaying cellular networks to improve local services. The system aims to optimize the throughput over the shared resources while fulfilling prioritized cellular service constraints. Optimum resource allocation and power control between the cellular and D2D connections that share the same resources are analyzed for different resource sharing modes. Optimality is discussed under practical constraints such as minimum and maximum spectral efficiency restrictions, and maximum transmit power or energy limitation. It is found that in most of the considered cases, optimum power control and resource allocation for the considered resource sharing modes can either be solved in closed form or searched from a finite set. The performance of the D2D underlay system is evaluated in both a single-cell scenario, and a Manhattan grid environment with multiple WINNER II A1 office buildings. The results show that by proper resource management, D2D communication can effectively improve the total throughput without generating harmful interference to cellular networks.
Vision-based bin-picking: recognition and localization of multiple complex objects using simple visual cues

Vision based bin picking is increasingly more di cult as the complexity of target objects increases We propose an e cient solution where complex objects are su ciently represented by simple features cues thus invariance to object complexity is established The re gion extraction algorithm utilized in our approach is capable of providing the focus of attention to the simple cues as a trigger toward recognition and pose estima tion Successful bin picking experiments of industrial objects using stereo vision tools are presented
An ecological valence theory of human color preference.

Color preference is an important aspect of visual experience, but little is known about why people in general like some colors more than others. Previous research suggested explanations based on biological adaptations [Hurlbert AC, Ling YL (2007) ] and color-emotions [Ou L-C, Luo MR, Woodcock A, Wright A (2004) Color Res Appl 29:381-389]. In this article we articulate an ecological valence theory in which color preferences arise from people's average affective responses to color-associated objects. An empirical test provides strong support for this theory: People like colors strongly associated with objects they like (e.g., blues with clear skies and clean water) and dislike colors strongly associated with objects they dislike (e.g., browns with feces and rotten food). Relative to alternative theories, the ecological valence theory both fits the data better (even with fewer free parameters) and provides a more plausible, comprehensive causal explanation of color preferences.
Neural Belief Tracker: Data-Driven Dialogue State Tracking

Belief tracking is a core component of modern spoken dialogue system pipelines. However, most current approaches would have difficulty scaling to larger, more complex dialogue domains. This is due to their dependency on either: a) Spoken Language Understanding models that require large amounts of annotated training data; or b) hand-crafted semantic lexicons that capture the lexical variation in users’ language. We propose a novel Neural Belief Tracking (NBT) framework which aims to overcome these problems by building on recent advances in semantic representation learning. The NBT models reason over continuous distributed representations of words, utterances and dialogue context. Our evaluation on two datasets shows that this approach overcomes both limitations, matching the performance of state-of-the-art models that have greater resource requirements.
Urban mobility study using taxi traces

In this work, we analyze taxi-GPS traces collected in Lisbon, Portugal. We perform an exploratory analysis to visualize the spatiotemporal variation of taxi services; explore the relationships between pick-up and drop-off locations; and analyze the behavior in downtime (between the previous drop-off and the following pick-up). We also carry out the analysis of predictability of taxi trips for the next pick-up area type given history of taxi flow in time and space.
Sentiment classification for Indonesian message in social media

Nowadays, classifying sentiment from social media has been a strategic thing since people can express their feeling about something in an easy way and short text. Mining opinion from social media has become important because people are usually honest with their feeling on something. In our research, we tried to identify the problems of classifying sentiment from Indonesian social media. We identified that people tend to express their opinion in text while the emoticon is rarely used and sometimes misleading. We also identified that the Indonesian social media opinion can be classified not only to positive, negative, neutral and question but also to a special mix case between negative and question type. Basically there are two levels of problem: word level and sentence level. Word level problems include the usage of punctuation mark, the number usage to replace letter, misspelled word and the usage of nonstandard abbreviation. In sentence level, the problem is related with the sentiment type such as mentioned before. In our research, we built a sentiment classification system which includes several steps such as text preprocessing, feature extraction, and classification. The text preprocessing aims to transform the informal text into formal text. The word formalization method in that we use is the deletion of punctuation mark, the tokenization, conversion of number to letter, the reduction of repetition letter, and using corpus with Levensthein to formalize abbreviation. The sentence formalization method that we use is negation handling, sentiment relative, and affixes handling. Rule-based, SVM and Maximum Entropy are used as the classification algorithms with features of count of positive, negative, and question word in sentence and bigram. From our experimental result, the best classification method is SVM that yields 83.5% accuracy.
Accurate Resource Prediction for Hybrid IaaS Clouds Using Workload-Tailored Elastic Compute Units

Cloud computing's pay-per-use model greatly reduces upfront cost and also enables on-demand scalability as service demand grows or shrinks. Hybrid clouds are an attractive option in terms of cost benefit, however, without proper elastic resource management, computational resources could be over-provisioned or under-provisioned, resulting in wasting money or failing to satisfy service demand. In this paper, to accomplish accurate performance prediction and cost-optimal resource management for hybrid clouds, we introduce Workload-tailored Elastic Compute Units (WECU) as a measure of computing resources analogous to Amazon EC2's ECUs, but customized for a specific workload. We present a dynamic programming-based scheduling algorithm to select a combination of private and public resources which satisfy a desired throughput. Using a loosely-coupled benchmark, we confirmed WECUs have 24 (J% better runtime prediction ability than ECUs on average. Moreover, simulation results with a real workload distribution of web service requests show that our WECU-based algorithm reduces costs by 8-31% compared to a fixed provisioning approach.
Inferring 3D Object Pose in RGB-D Images

The goal of this work is to replace objects in an RGB-D scene with corresponding 3D models from a library. We approach this problem by first detecting and segmenting object instances in the scene using the approach from Gupta et al. . We use a convolutional neural network (CNN) to predict the pose of the object. This CNN is trained using pixel normals in images containing rendered synthetic objects. When tested on real data, it outperforms alternative algorithms trained on real data. We then use this coarse pose estimate along with the inferred pixel support to align a small number of prototypical models to the data, and place the model that fits the best into the scene. We observe a 48% relative improvement in performance at the task of 3D detection over the current state-of-the-art , while being an order of magnitude faster at the same time.
How the Weak Win Wars: A Theory of Asymmetric Conflict

No one had given Muhammad Ali a chance against George Foreman in the World Heavyweight Championship ght of October 30, 1974. Foreman, none of whose opponents had lasted more than three rounds in the ring, was the strongest, hardest hitting boxer of his generation. Ali, though not as powerful as Foreman, had a slightly faster punch and was lighter on his feet. In the weeks leading up to the ght, however, Foreman had practiced against nimble sparring partners. He was ready. But when the bell rang just after 4:00 a.m. in Kinshasa, something completely unexpected happened. In round two, instead of moving into the ring to meet Foreman, Ali appeared to cower against the ropes. Foreman, now condent of victory, pounded him again and again, while Ali whispered hoarse taunts: “George, you’re not hittin’,” “George, you disappoint me.” Foreman lost his temper, and his punches became a furious blur. To spectators, unaware that the elastic ring ropes were absorbing much of the force of Foreman’s blows, it looked as if Ali would surely fall. By the fth round, however, Foreman was worn out. And in round eight, as stunned commentators and a delirious crowd looked on, Muhammad Ali knocked George Foreman to the canvas, and the ght was over. The outcome of that now-famous “rumble in the jungle” was completely unexpected. The two ghters were equally motivated to win: Both had boasted of victory, and both had enormous egos. Yet in the end, a ght that should have been over in three rounds went eight, and Foreman’s prodigious punches proved useless against Ali’s rope-a-dope strategy. This ght illustrates an important yet relatively unexplored feature of interstate conict: how a weak actor’s strategy can make a strong actor’s power irHow the Weak Win Wars Ivan Arreguín-Toft
Speech enhancement with missing data techniques using recurrent neural networks

This paper presents an application of missing data techniques in speech enhancement. The enhancement system consists of two stages: the first stage uses a recurrent neural network, which is supplied with noisy speech and produces enhanced speech; whereas the second stage uses missing data techniques to further improve the quality of enhanced speech. The results suggest that combining missing data technique with RNN enhancement is an effective enhancement scheme resulting in a 16 dB background noise reduction for all input signal to noise ratio (SNR) conditions from -5 to 20 dB, improved spectral quality and robust automatic speech recognition performance.
Metric and Relevance Mismatch in Retrieval Evaluation

Recent investigations of search performance have shown that, even when presented with two systems that are superior and inferior based on a Cranfield-style batch experiment, real users may perform equally well with either system. In this paper, we explore how these evaluation paradigms may be reconciled. First, we investigate the DCG@1 and P@1 metrics, and their relationship with user performance on a common web search task. Our results show that batch experiment predictions based on P@1 or DCG@1 translate directly to user search effectiveness. However, marginally relevant documents are not strongly differentiable from non-relevant documents. Therefore, when folding multiple relevance levels into a binary scale, marginally relevant documents should be grouped with non-relevant documents, rather than with highly relevant documents, as is currently done in standard IR evaluations. We then investigate relevance mismatch, classifying users based on relevance profiles, the likelihood with which they will judge documents of different relevance levels to be useful. When relevance profiles can be estimated well, this classification scheme can offer further insight into the transferability of batch results to real user search tasks.
Cognitive Status and Form of Reference in Multimodal Human-Computer Interaction

We analyzea corpusof referringexpressionscollected from userinteractionswith a multimodal travel guide application.Theanalysissuggeststhat,in dramaticcontrastto normalmodesof human-humaninteraction,the interpretationof referringexpressionscanbecomputed with very high accuracy usinga modelwhich pairsan impoverishednotionof discoursestatewith asimpleset of rulesthatareinsensiti ve to the type of referringexpressionused. We attribute this result to the implicit mannerin which theinterfaceconveys thesystem’ s beliefs abouttheoperati ve discoursestate,to which users tailor their choiceof referringexpressions.This result offersnew insightinto thewaycomputerinterfacescan shapea user’ s languagebehavior, insightswhich can be exploited to bring otherwisedifficult interpretation problemsinto therealmof tractability.