nils-herrmann/clean_scirepeval huggingface dataset:
Aérgia: exploiting packet latency slack in on-chip networks

Traditional Network-on-Chips (NoCs) employ simple arbitration strategies, such as round-robin or oldest-first, to decide which packets should be prioritized in the network. This is counter-intuitive since different packets can have very different effects on system performance due to, e.g., different level of memory-level parallelism (MLP) of applications. Certain packets may be performance-critical because they cause the processor to stall, whereas others may be delayed for a number of cycles with no effect on application-level performance as their latencies are hidden by other outstanding packets'latencies. In this paper, we define slack as a key measure that characterizes the relative importance of a packet. Specifically, the slack of a packet is the number of cycles the packet can be delayed in the network with no effect on execution time. This paper proposes new router prioritization policies that exploit the available slack of interfering packets in order to accelerate performance-critical packets and thus improve overall system performance. When two packets interfere with each other in a router, the packet with the lower slack value is prioritized. We describe mechanisms to estimate slack, prevent starvation, and combine slack-based prioritization with other recently proposed application-aware prioritization mechanisms.
 We evaluate slack-based prioritization policies on a 64-core CMP with an 8x8 mesh NoC using a suite of 35 diverse applications. For a representative set of case studies, our proposed policy increases average system throughput by 21.0% over the commonlyused round-robin policy. Averaged over 56 randomly-generated multiprogrammed workload mixes, the proposed policy improves system throughput by 10.3%, while also reducing application-level unfairness by 30.8%.
A Comparison of ISO 9001 and the Capability Maturity Model for Software

The Capability Maturity Model for Software (CMM), developed by the Software Engineering Institute, and the ISO 9000 series of standards, developed by the International Standards Organization, share a common concern with quality and process management. The two are driven by similar concerns and intuitively correlated. The purpose of this report is to contrast the CMM and ISO 9001, showing both their differences and their similarities. The results of the analysis indicate that, although an ISO 9001-compliant organization would not necessarily satisfy all of the level 2 key process areas, it would satisfy most of the level 2 goals and many of the level 3 goals. Because there are practices in the CMM that are not addressed in ISO 9000, it is possible for a level 1 organization to receive ISO 9001 registration; similarly, there are areas addressed by ISO 9001 that are not addressed in the CMM. A level 3 organization would have little difficulty in obtaining ISO 9001 certification, and a level 2 organization would have significant advantages in obtaining certification.
Structured Dictionary Learning for Classification

Sparsity driven signal processing has gained tremendous popularity in the last decade. At its core, the assumption is that the signal of interest is sparse with respect to either a fixed transformation or a signal dependent dictionary. To better capture the data characteristics, various dictionary learning methods have been proposed for both reconstruction and classification tasks. For classification particularly, most approaches proposed so far have focused on designing explicit constraints on the sparse code to improve classification accuracy while simply adopting l0-norm or l1-norm for sparsity regularization. Motivated by the success of structured sparsity in the area of Compressed Sensing, we propose a structured dictionary learning framework (StructDL) that incorporates the structure information on both group and task levels in the learning process. Its benefits are two-fold: (i) the label consistency between dictionary atoms and training data are implicitly enforced; and (ii) the classification performance is more robust in the cases of a small dictionary size or limited training data than other techniques. Using the subspace model, we derive the conditions for StructDL to guarantee the performance and show theoretically that StructDL is superior to l0-norm or l1-norm regularized dictionary learning for classification. Extensive experiments have been performed on both synthetic simulations and real world applications, such as face recognition and object classification, to demonstrate the validity of the proposed DL framework.
Design and Dynamics of a Novel Solar Tracker With Parallel Mechanism

This paper proposes a two-axis-decoupled solar tracker based on parallel mechanism. Utilizing Grassmann line geometry, the type design of the two-axis solar tracker is investigated. Then, singularity is studied to obtain the workspace without singularities. By using the virtual work principle, the inverse dynamics is derived to find out the driving torque. Taking Beijing as a sample city where the solar tracker is placed, the motion trajectory of the tracker is planned to collect the maximum solar energy. The position of the mass center of the solar mirror on the platform is optimized to minimize the driving torque. The driving torque of the proposed tracker is compared with that of a conventional serial tracker, which shows that the proposed tracker can greatly reduce the driving torque and the reducers with large reduction ratio are not necessary. Thus, the complexity and power dissipation of the system can be reduced.
A Marx generator driven impulse radiating antenna

APELC has developed an Impulse Radiating Antenna (IRA) that consists of a TEM-horn-fed parabolic reflector that is directly driven by a 22-J, 400-kV Marx generator. The system is based on standard Marx generator designs offered by APELC. The Marx generator output couples directly to the TEM horn via a transition from a coaxial geometry that approximates a standard coaxial-to-parallel plate transition. Primary design considerations that facilitate achievement of high instantaneous radiated power include appropriate Marx generator rise time, transition design, and TEM horn focal point positioning. Data collected over the course of the system design is presented.
Blind Deblurring Using Internal Patch Recurrence

Recurrence of small image patches across different scales of a natural image has been previously used for solving ill-posed problems (e.g., superresolution from a single image). In this paper we show how this multi-scale property can also be used for “blind-deblurring”, namely, removal of an unknown blur from a blurry image. While patches repeat ‘as is’ across scales in a sharp natural image, this cross-scale recurrence significantly diminishes in blurry images. We exploit these deviations from ideal patch recurrence as a cue for recovering the underlying (unknown) blur kernel. More specifically, we look for the blur kernel k, such that if its effect is “undone” (if the blurry image is deconvolved with k), the patch similarity across scales of the image will be maximized. We report extensive experimental evaluations, which indicate that our approach compares favorably to state-of-the-art blind deblurring methods, and in particular, is more robust than them.
A Protocol for Interledger Payments

We present a protocol for payments across payment systems. It enables secure transfers between ledgers and allows anyone with accounts on two ledgers to create a connection between them. Ledger-provided escrow removes the need to trust these connectors. Connections can be composed to enable payments between any ledgers, creating a global graph of liquidity or Interledger. Unlike previous approaches, this protocol requires no global coordinating system or blockchain. Transfers are escrowed in series from the sender to the recipient and executed using one of two modes. In the Atomic mode, transfers are coordinated using an ad-hoc group of notaries selected by the participants. In the Universal mode, there is no external coordination. Instead, bounded execution windows, participant incentives and a “reverse” execution order enable secure payments between parties without shared trust in any system or institution.
Multiple systems in decision making.

Neuroeconomics seeks to gain a greater understanding of decision making by combining theoretical and methodological principles from the fields of psychology, economics, and neuroscience. Initial studies using this multidisciplinary approach have found evidence suggesting that the brain may be employing multiple levels of processing when making decisions, and this notion is consistent with dual-processing theories that have received extensive theoretical consideration in the field of cognitive psychology, with these theories arguing for the dissociation between automatic and controlled components of processing. While behavioral studies provide compelling support for the distinction between automatic and controlled processing in judgment and decision making, less is known if these components have a corresponding neural substrate, with some researchers arguing that there is no evidence suggesting a distinct neural basis. This chapter will discuss the behavioral evidence supporting the dissociation between automatic and controlled processing in decision making and review recent literature suggesting potential neural systems that may underlie these processes.
Domain Adaptation of Deformable Part-Based Models

The accuracy of object classifiers can significantly drop when the training data (source domain) and the application scenario (target domain) have inherent differences. Therefore, adapting the classifiers to the scenario in which they must operate is of paramount importance. We present novel domain adaptation (DA) methods for object detection. As proof of concept, we focus on adapting the state-of-the-art deformable part-based model (DPM) for pedestrian detection. We introduce an adaptive structural SVM (A-SSVM) that adapts a pre-learned classifier between different domains. By taking into account the inherent structure in feature space (e.g., the parts in a DPM), we propose a structure-aware A-SSVM (SA-SSVM). Neither A-SSVM nor SA-SSVM needs to revisit the source-domain training data to perform the adaptation. Rather, a low number of target-domain training examples (e.g., pedestrians) are used. To address the scenario where there are no target-domain annotated samples, we propose a self-adaptive DPM based on a self-paced learning (SPL) strategy and a Gaussian Process Regression (GPR). Two types of adaptation tasks are assessed: from both synthetic pedestrians and general persons (PASCAL VOC) to pedestrians imaged from an on-board camera. Results show that our proposals avoid accuracy drops as high as 15 points when comparing adapted and non-adapted detectors.
Looking Beyond the Image: Unsupervised Learning for Object Saliency and Detection

We propose a principled probabilistic formulation of object saliency as a sampling problem. This novel formulation allows us to learn, from a large corpus of unlabelled images, which patches of an image are of the greatest interest and most likely to correspond to an object. We then sample the object saliency map to propose object locations. We show that using only a single object location proposal per image, we are able to correctly select an object in over 42% of the images in the Pascal VOC 2007 dataset, substantially outperforming existing approaches. Furthermore, we show that our object proposal can be used as a simple unsupervised approach to the weakly supervised annotation problem. Our simple unsupervised approach to annotating objects of interest in images achieves a higher annotation accuracy than most weakly supervised approaches.
Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition

Attention-based learning for fine-grained image recognition remains a challenging task, where most of the existing methods treat each object part in isolation, while neglecting the correlations among them. In addition, the multi-stage or multi-scale mechanisms involved make the existing methods less efficient and hard to be trained end-to-end. In this paper, we propose a novel attention-based convolutional neural network (CNN) which regulates multiple object parts among different input images. Our method first learns multiple attention region features of each input image through the one-squeeze multi-excitation (OSME) module, and then apply the multi-attention multi-class constraint (MAMC) in a metric learning framework. For each anchor feature, the MAMC functions by pulling same-attention same-class features closer, while pushing different-attention or different-class features away. Our method can be easily trained end-to-end, and is highly efficient which requires only one training stage. Moreover, we introduce Dogs-in-the-Wild, a comprehensive dog species dataset that surpasses similar existing datasets by category coverage, data volume and annotation quality. Extensive experiments are conducted to show the substantial improvements of our method on four benchmark datasets.
Compressed knowledge transfer via factorization machine for heterogeneous collaborative recommendation

Collaborative recommendation has attracted various research works in recent years. However, an important problem setting, i.e., ‘‘a user examined several items but only rated a few’’, has not received much attention yet. We coin this problem heterogeneous collaborative recommendation (HCR) from the perspective of users’ heterogeneous feedbacks of implicit examinations and explicit ratings. In order to fully exploit such different types of feedbacks, we propose a novel and generic solution called compressed knowledge transfer via factorization machine (CKT-FM). Specifically, we assume that the compressed knowledge of user homophily and item correlation, i.e., user groups and item sets behind two types of feedbacks, are similar and then design a two-step transfer learning solution including compressed knowledge mining and integration. Our solution is able to transfer high quality knowledge via noise reduction, to model rich pairwise interactions among individual-level and cluster-level entities, and to adapt the potential inconsistent knowledge from implicit feedbacks to explicit feedbacks. Furthermore, the analysis on time complexity and space complexity shows that our solution is much more efficient than the state-of-the-art method for heterogeneous feedbacks. Extensive empirical studies on two large data sets show that our solution is significantly better than the state-of-the-art non-transfer learning method w.r.t. recommendation accuracy, and is much more efficient than that of leveraging the raw implicit examinations directly instead of compressed knowledge w.r.t. CPU time and memory usage. Hence, our CKT-FM strikes a good balance between effectiveness and efficiency of knowledge transfer in HCR.
Routing a Fleet of Vehicles for Dynamic Combined Pick-up and Deliveries Services

This work partially reports the results of a study aiming at the design and analysis of the performance of a multi-cab metropolitan transportation system. In our model we investigate a particular multi-vehicle many-to-many dynamic request dial-a-ride problem. We present a heuristic algorithm for this problem and some preliminary results. The algorithm is based on iteratively solving a singlevehicle subproblem at optimality: a pretty efficient dynamic programming routine has been devised for this purpose. This work has been carried out by researchers from both University of Rome “Tor Vergata” and Italian Energy Research Center ENEA as a line of a reasearch program, regarding urban mobility optimization, funded by ENEA and the Italian Ministry of Environment.
Random Walks for Text Semantic Similarity

Many tasks in NLP stand to benefit from robust measures of semantic similarity for units above the level of individual words. Rich semantic resources such as WordNet provide local semantic information at the lexical level. However, effectively combining this information to compute scores for phrases or sentences is an open problem. Our algorithm aggregates local relatedness information via a random walk over a graph constructed from an underlying lexical resource. The stationary distribution of the graph walk forms a “semantic signature” that can be compared to another such distribution to get a relatedness score for texts. On a paraphrase recognition task, the algorithm achieves an 18.5% relative reduction in error rate over a vector-space baseline. We also show that the graph walk similarity between texts has complementary value as a feature for recognizing textual entailment, improving on a competitive baseline system.
An LLC Resonant DC–DC Converter for Wide Output Voltage Range Battery Charging Applications

In this paper, resonant tank design procedure and practical design considerations are presented for a high performance LLC multiresonant dc-dc converter in a two-stage smart battery charger for neighborhood electric vehicle applications. The multiresonant converter has been analyzed and its performance characteristics are presented. It eliminates both low- and high-frequency current ripple on the battery, thus maximizing battery life without penalizing the volume of the charger. Simulation and experimental results are presented for a prototype unit converting 390 V from the input dc link to an output voltage range of 48-72 V dc at 650 W. The prototype achieves a peak efficiency of 96%.
Optimized contrast enhancement for real-time image and video dehazing

A fast and optimized dehazing algorithm for hazy images and videos is proposed in this work. Based on the observation that a hazy image exhibits low contrast in general, we restore the hazy image by enhancing its contrast. However, the overcompensation of the degraded contrast may truncate pixel values and cause information loss. Therefore, we formulate a cost function that consists of the contrast term and the information loss term. By minimizing the cost function, the proposed algorithm enhances the contrast and preserves the information optimally. Moreover, we extend the static image dehazing algorithm to real-time video dehazing. We reduce flickering artifacts in a dehazed video sequence by making transmission values temporally coherent. Experimental results show that the proposed algorithm effectively removes haze and is sufficiently fast for real-time dehazing applications.
On Analyzing Hashtags in Twitter

Hashtags, originally introduced in Twitter, are now becoming the most used way to tag short messages in social networks since this facilitates subsequent search, classification and clustering over those messages. However, extracting information from hashtags is difficult because their composition is not constrained by any (linguistic) rule and they usually appear in short and poorly written messages which are difficult to analyze with classic IR techniques. In this paper we address two challenging problems regarding the “meaning of hashtags”— namely, hashtag relatedness and hashtag classification — and we provide two main contributions. First we build a novel graph upon hashtags and (Wikipedia) entities drawn from the tweets by means of topic annotators (such as TagME); this graph will allow us to model in an efficacious way not only classic co-occurrences but also semantic relatedness among hashtags and entities, or between entities themselves. Based on this graph, we design algorithms that significantly improve state-of-the-art results upon known publicly available datasets. The second contribution is the construction and the public release to the research community of two new datasets: the former is a new dataset for hashtag relatedness, the latter is a dataset for hashtag classification that is up to two orders of magnitude larger than the existing ones. These datasets will be used to show the robustness and efficacy of our approaches, showing improvements in F1 up to two-digits in percentage (absolute).
Design of Stripline for EMC Testing

A comprehensive approach to the design of a stripline for EMC testing is given in this paper. The authors attention has been focused on the design items that are most crucial by the achievement of satisfactory value of the VSWR and the impedance matching at the feeding ports in the extended frequency range from 80 MHz to 1000 GHz. For this purpose, the Vivaldi-structure and other advanced structures were considered. The theoretical approach based on numerical simulations lead to conclusions which have been applied by the physical design and also evaluated by experimental results.
Evolving Deep Recurrent Neural Networks Using Ant Colony Optimization

This paper presents a novel strategy for using ant colony optimization (ACO) to evolve the structure of deep recurrent neural networks. While versions of ACO for continuous parameter optimization have been previously used to train the weights of neural networks, to the authors’ knowledge they have not been used to actually design neural networks. The strategy presented is used to evolve deep neural networks with up to 5 hidden and 5 recurrent layers for the challenging task of predicting general aviation flight data, and is shown to provide improvements of 63 % for airspeed, a 97 % for altitude and 120 % for pitch over previously best published results, while at the same time not requiring additional input neurons for residual values. The strategy presented also has many benefits for neuro evolution, including the fact that it is easily parallizable and scalable, and can operate using any method for training neural networks. Further, the networks it evolves can typically be trained in fewer iterations than fully connected networks.
Named Entity Recognition using FOX

Unstructured data still makes up an important portion of the Web. One key task towards transforming this unstructured data into structured data is named entity recognition. We demo FOX, the Federated knOwledge eXtraction framework, a highly accurate open-source framework that implements RESTful web services for named entity recognition. Our framework achieves a higher Fmeasure than state-of-the-art named entity recognition frameworks by combining the results of several approaches through ensemble learning. Moreover, it disambiguates and links named entities against DBpedia by relying on the AGDISTIS framework. As a result, FOX provides users with accurately disambiguated and linked named entities in several RDF serialization formats. We demonstrate the different interfaces implemented by FOX within use cases pertaining to extracting entities from news texts.