nils-herrmann/clean_scirepeval huggingface dataset:
Approximations with Minimum Packing Constraint Violation

We present e cient new randomized and deterministic methods for transforming optimal solutions for a type of relaxed integer linear program into provably good solutions for the corresponding NP-hard discrete optimization problem. Without any constraint violation, the -approximation problem for many problems of this type is itself NP-hard. Our methods provide polynomial-time -approximations while attempting to minimize the packing constraint violation. Our methods lead to the rst known approximation algorithms with provable performance guarantees for the s-median problem, the tree pruning problem, and the generalized assignment problem. These important problems have numerous applications to data compression, vector quantization, memory-based learning, computer graphics, image processing, clustering, regression, network location, scheduling, and communication. We provide evidence via reductions that our approximation algorithms are nearly optimal in terms of the packing constraint violation. We also discuss some recent applications of our techniques to scheduling problems. Support was provided in part by an National Science Foundation PresidentialYoung InvestigatorAward CCR{9047466 with matching funds from IBM, by NSF research grant CCR{9007851, by Army Research O ce grant DAAL03{91{G{0035, and by the O ce of Naval Research and the Defense Advanced Research Projects Agency under contract N00014{91{J{4052, ARPA order 8225. The authors can be reached by electronic mail at jhl@cs.brown.edu and jsv@cs.brown.edu, respectively.
Discourse Relation Sense Classification Using Cross-argument Semantic Similarity Based on Word Embeddings

This paper describes our system for the CoNLL 2016 Shared Task’s supplementary task on Discourse Relation Sense Classification. Our official submission employs a Logistic Regression classifier with several cross-argument similarity features based on word embeddings and performs with overall F-scores of 64.13 for the Dev set, 63.31 for the Test set and 54.69 for the Blind set, ranking first in the Overall ranking for the task. We compare the feature-based Logistic Regression classifier to different Convolutional Neural Network architectures. After the official submission we enriched our model for Non-Explicit relations by including similarities of explicit connectives with the relation arguments, and part of speech similarities based on modal verbs. This improved our Non-Explicit result by 1.46 points on the Dev set and by 0.36 points on the Blind set.
Computational sprinting on a hardware/software testbed

CMOS scaling trends have led to an inflection point where thermal constraints (especially in mobile devices that employ only passive cooling) preclude sustained operation of all transistors on a chip --- a phenomenon called "dark silicon." Recent research proposed computational sprinting --- exceeding sustainable thermal limits for short intervals --- to improve responsiveness in light of the bursty computation demands of many media-rich interactive mobile applications. Computational sprinting improves responsiveness by activating reserve cores (parallel sprinting) and/or boosting frequency/voltage (frequency sprinting) to power levels that far exceed the system's sustainable cooling capabilities, relying on thermal capacitance to buffer heat.
 Prior work analyzed the feasibility of sprinting through modeling and simulation. In this work, we investigate sprinting using a hardware/software testbed. First, we study unabridged sprints, wherein the computation completes before temperature becomes critical, demonstrating a 6.3x responsiveness gain, and a 6% energy efficiency improvement by racing to idle. We then analyze truncated sprints, wherein our software runtime system must intervene to prevent overheating by throttling parallelism and frequency before the computation is complete. To avoid oversubscription penalties (context switching inefficiencies after a truncated parallel sprint), we develop a sprint-aware task-based parallel runtime. We find that maximal-intensity sprinting is not always best, introduce the concept of sprint pacing, and evaluate an adaptive policy for selecting sprint intensity. We report initial results using a phase change heat sink to extend maximum sprint duration. Finally, we demonstrate that a sprint-and-rest operating regime can actually outperform thermally-limited sustained execution.
Exploiting synergies between semantic reasoning and personalization strategies in intelligent recommender systems: A case study

0164-1212/$ see front matter 2008 Elsevier Inc. A doi:10.1016/j.jss.2008.05.009 q Work funded by the Ministerio de Educación y C research project TSI2007-61599, by the Consellería Universitaria (Xunta de Galicia) incentives file 2007/00 de Promoción Xeral da Investigación de Consellería Comercio (Xunta de Galicia) PGIDIT05PXIC32204PN. * Corresponding author. E-mail address: yolanda@det.uvigo.es (Y. Blanco-F Current recommender systems attempt to identify appealing items for a user by applying syntactic matching techniques, which suffer from significant limitations that reduce the quality of the offered suggestions. To overcome this drawback, we have developed a domain-independent personalization strategy that borrows reasoning techniques from the Semantic Web, elaborating recommendations based on the semantic relationships inferred between the user’s preferences and the available items. Our reasoningbased approach improves the quality of the suggestions offered by the current personalization approaches, and greatly reduces their most severe limitations. To validate these claims, we have carried out a case study in the Digital TV field, in which our strategy selects TV programs interesting for the viewers from among the myriad of contents available in the digital streams. Our experimental evaluation compares the traditional approaches with our proposal in terms of both the number of TV programs suggested, and the users’ perception of the recommendations. Finally, we discuss concerns related to computational feasibility and scalability of our approach.
Scheduling techniques for hybrid circuit/packet networks

A range of new datacenter switch designs combine wireless or optical circuit technologies with electrical packet switching to deliver higher performance at lower cost than traditional packet-switched networks. These "hybrid" networks schedule large traffic demands via a high-rate circuits and remaining traffic with a lower-rate, traditional packet-switches. Achieving high utilization requires an efficient scheduling algorithm that can compute proper circuit configurations and balance traffic across the switches. Recent proposals, however, provide no such algorithm and rely on an omniscient oracle to compute optimal switch configurations.
 Finding the right balance of circuit and packet switch use is difficult: circuits must be reconfigured to serve different demands, incurring non-trivial switching delay, while the packet switch is bandwidth constrained. Adapting existing crossbar scheduling algorithms proves challenging with these constraints. In this paper, we formalize the hybrid switching problem, explore the design space of scheduling algorithms, and provide insight on using such algorithms in practice. We propose a heuristic-based algorithm, Solstice that provides a 2.9× increase in circuit utilization over traditional scheduling algorithms, while being within 14% of optimal, at scale.
Sentiment Classification of Spanish Reviews: An Approach based on Feature Selection and Machine Learning Methods

Sentiment analysis aims to extract users’ opinions from review documents. Nowadays, there are two main approaches for sentiment analysis: the semantic orientation and the machine learning. Sentiment analysis approaches based on Machine Learning (ML) methods work over a set of features extracted from the users’ opinions. However, the high dimensionality of the feature vector reduces the effectiveness of this approach. In this sense, we propose a sentiment classification method based on feature selection mechanisms and ML methods. The present method uses a hybrid feature extraction method based on POS pattern and dependency parsing. The features obtained are enriched semantically through commonsense knowledge bases. Then, a feature selection method is applied to eliminate the noisy and irrelevant features. Finally, a set of classifiers is trained in order to classify unknown data. To prove the effectiveness of our approach, we have conducted an evaluation in the movies and technological products domains. Also, our proposal was compared with well-known methods and algorithms used on the sentiment classification field. Our proposal obtained encouraging results based on the F-measure metric, ranging from 0.786 to 0.898 for the aforementioned domains.
Wearing Many (Social) Hats: How Different are Your Different Social Network Personae?

This paper investigates when users create profiles in different social networks, whether they are redundant expressions of the same persona, or they are adapted to each platform. Using the personal webpages of 116,998 users on About.me, we identify and extract matched user profiles on several major social networks including Facebook, Twitter, LinkedIn, and Instagram. We find evidence for distinct site-specific norms, such as differences in the language used in the text of the profile self-description, and the kind of picture used as profile image. By learning a model that robustly identifies the platform given a user’s profile image (0.657–0.829 AUC) or self-description (0.608–0.847 AUC), we confirm that users do adapt their behaviour to individual platforms in an identifiable and learnable manner. However, different genders and age groups adapt their behaviour differently from each other, and these differences are, in general, consistent across different platforms. We show that differences in social profile construction correspond to differences in how formal or informal
Kalman Filter Based Adaptive Reduction of Motion Artifact from Photoplethysmographic Signal

Artifact free photoplethysmographic (PPG) signals are necessary for non-invasive estimation of oxygen saturation (SpO2) in arterial blood. Movement of a patient corrupts the PPGs with motion artifacts, resulting in large errors in the computation of Sp02. This paper presents a study on using Kalman Filter in an innovative way by modeling both the Artillery Blood Pressure (ABP) and the unwanted signal, additive motion artifact, to reduce motion artifacts from corrupted PPG signals. Simulation results show acceptable performance regarding LMS and variable step LMS, thus establishing the efficacy of the proposed method. Kalman filter, Motion artifact, PPG, Photoplethysmography.
Energy Efficient Clustering Algorithms in Wireless Sensor Networks : A Survey

To maximize network lifetime in Wireless Sensor Networks (WSNs) the paths for data transfer are selected in such a way that the total energy consumed along the path is minimized. To support high scalability and better data aggregation, sensor nodes are often grouped into disjoint, non overlapping subsets called clusters. Clusters create hierarchical WSNs which incorporate efficient utilization of limited resources of sensor nodes and thus extends network lifetime. The objective of this paper is to present a state of the art survey on clustering algorithms reported in the literature of WSNs. Our paper presents a taxonomy of energy efficient clustering algorithms in WSNs. And also present timeline and description of LEACH and Its descendant in WSNs.
Esthetic rejuvenation of the temple.

Loss of volume in the temples is an early sign of aging that is often overlooked by both the physician and the patient. Augmentation of the temple using soft tissue fillers improves the contours of the upper face with the secondary effect of lengthening and lifting the lateral brow. After replacement of volume, treatment of the overlying skin with skin-tightening devices or laser resurfacing help to complete a comprehensive rejuvenation of the temple and upper one-third of the face.
Kissing nevus of the penis. Report of two cases and review of the
literature*


Kissing nevus is a curious type of nevus that was first described on the eyelids and rarely described on the penis. We report two cases of kissing nevus of the penis and review previously reported cases. The lesions of the kissing nevus of the penis showed characteristic mirror-image symmetry relative to the coronal sulcus. On histopathology, the lesion showed a compound nevus.
Current status of grafts and implants in rhinoplasty: part I. Autologous grafts.

After reading this article, the participant should be able to: 1. Understand the challenges in restoring volume and structural integrity in rhinoplasty. 2. Identify the appropriate uses of various autografts in aesthetic and reconstructive rhinoplasty (septal cartilage, auricular cartilage, costal cartilage, calvarial and nasal bone, and olecranon process of the ulna). 3. Identify the advantages and disadvantages of each of these autografts.



This review specifically addresses the use of autologous grafts in rhinoplasty. Autologous materials remain the preferred graft material for use in rhinoplasty because of their high biocompatibility and low risk of infection and extrusion. However, these advantages should be counterbalanced with the concerns of donor-site morbidity, graft availability, and graft resorption.
Aesthetic information collages: generating decorative displays that contain information

Normally, the primary purpose of an information display is to convey information. If information displays can be aesthetically interesting, that might be an added bonus. This paper considers an experiment in reversing this imperative. It describes the Kandinsky system which is designed to create displays which are first aesthetically interesting, and then as an added bonus, able to convey information. The Kandinsky system works on the basis of aesthetic properties specified by an artist (in a visual form). It then explores a space of collages composed from information bearing images, using an optimization technique to find compositions which best maintain the properties of the artist's aesthetic expression.
AC-BLSTM: Asymmetric Convolutional Bidirectional LSTM Networks for Text Classification

Recently deeplearning models have been shown to be capable of making remarkable performance in sentences and documents classification tasks. In this work, we propose a novel framework called ACBLSTM for modeling setences and documents, which combines the asymmetric convolution neural network (ACNN) with the Bidirectional Long ShortTerm Memory network (BLSTM). Experiment results demonstrate that our model achieves state-ofthe-art results on all six tasks, including sentiment analysis, question type classification, and subjectivity classification.
LIRIS-ACCEDE: A Video Database for Affective Content Analysis

Research in affective computing requires ground truth data for training and benchmarking computational models for machine-based emotion understanding. In this paper, we propose a large video database, namely LIRIS-ACCEDE, for affective content analysis and related applications, including video indexing, summarization or browsing. In contrast to existing datasets with very few video resources and limited accessibility due to copyright constraints, LIRIS-ACCEDE consists of 9,800 good quality video excerpts with a large content diversity. All excerpts are shared under creative commons licenses and can thus be freely distributed without copyright issues. Affective annotations were achieved using crowdsourcing through a pair-wise video comparison protocol, thereby ensuring that annotations are fully consistent, as testified by a high inter-annotator agreement, despite the large diversity of raters' cultural backgrounds. In addition, to enable fair comparison and landmark progresses of future affective computational models, we further provide four experimental protocols and a baseline for prediction of emotions using a large set of both visual and audio features. The dataset (the video clips, annotations, features and protocols) is publicly available at: http://liris-accede.ec-lyon.fr/.
Spectral fingerprints of large-scale neuronal interactions

Cognition results from interactions among functionally specialized but widely distributed brain regions; however, neuroscience has so far largely focused on characterizing the function of individual brain regions and neurons therein. Here we discuss recent studies that have instead investigated the interactions between brain regions during cognitive processes by assessing correlations between neuronal oscillations in different regions of the primate cerebral cortex. These studies have opened a new window onto the large-scale circuit mechanisms underlying sensorimotor decision-making and top-down attention. We propose that frequency-specific neuronal correlations in large-scale cortical networks may be 'fingerprints' of canonical neuronal computations underlying cognitive processes.
Multimodal presentation method for a dance training system

This paper presents a multimodal information presentation method for a basic dance training system. The system targets on beginners and enables them to learn basics of dances easily. One of the most effective ways of learning dances is to watch a video showing the performance of dance masters. However, some information cannot be conveyed well through video. One is the translational motion, especially that in the depth direction. We cannot tell exactly how far does the dancers move forward or backward. Another is the timing information. Although we can tell how to move our arms or legs from video, it is difficult to know when to start moving them. We solve the first issue by introducing an image display on a mobile robot. We can learn the amount of translation just by following the robot. We introduce active devices for the second issue. The active devices are composed of some vibro-motors and are developed to direct action-starting cues with vibration. Experimental results show the effectiveness of our multimodal information presentation method.
BODY SENSOR NETWORK – A WIRELESS SENSOR PLATFORM FOR PERVASIVE HEALTHCARE MONITORING

With recent advances in wireless sensor networks and embedded computing technologies, miniaturized pervasive health monitoring devices have become practically feasible. In addition to providing continuous monitoring and analysis of physiological parameters, the recently proposed Body Sensor Networks (BSN) incorporates context aware sensing for increased sensitivity and specificity. To facilitate research and development in BSN and multi-sensor data fusion, a BSN hardware development platform is presented. With its low power, flexible and compact design, the BSN nodes provide a versatile environment for wireless sensing research and development.
Towards Autonomic DDoS Mitigation using Software Defined Networking

Distributed Denial of Service attacks (DDoS) have remained as one of the most destructive attacks in the Internet for over two decades. Despite tremendous efforts on the design of DDoS defense strategies, few of them have been considered for widespread deployment due to strong design assumptions on the Internet infrastructure, prohibitive operational costs and complexity. Recently, the emergence of Software Defined Networking (SDN) has offered a solution to reduce network management complexity. It is also believed to facilitate security management thanks to its programmability. To explore the advantages of using SDN to mitigate DDoS attacks, we propose a distributed collaborative framework that allows the customers to request DDoS mitigation service from ISPs. Upon request, ISPs can change the label of the anomalous traffic and redirect them to security middleboxes, while attack detection and analysis modules are deployed at customer side, avoiding privacy leakage and other legal concerns. Our preliminary analysis demonstrates that SDN has promising potential to enable autonomic mitigation of DDoS attacks, as well as other large-scale attacks.
Innovative design for wheeled locomotion in rough terrain

In our paper we present an innovative locomotion concept for rough terrain based on six motorized wheels. Using rhombus configuration, the rover named Shrimp has a steering wheel in the front and the rear, and two wheels arranged on a bogie on each side. The front wheel has a spring suspension to guarantee optimal ground contact of all wheels at any time. The steering of the rover is realized by synchronizing the steering of the front and rear wheels and the speed difference of the bogie wheels. This allows for precision maneuvers and even turning on the spot with minimum slippage. The use of parallel articulations for the front wheel and the bogies enables to set a virtual center of rotation at the level of or below the wheel axis. This insures maximum stability and climbing abilities even for very low friction coefficients between the wheel and the ground. A well functioning prototype has been designed and manufactured. It shows excellent performance surpassing our expectations. The robot, measuring only about 60 cm in length and 20 cm in height, is able to passively overcome obstacles of up to two times its wheel diameter and can climb stairs with steps of over 20 cm.
Place Lab: Device Positioning Using Radio Beacons in the Wild

Location awareness is an important capability for mobile computing. Yet inexpensive, pervasive positioning—a requirement for wide-scale adoption of location-aware computing—has been elusive. We demonstrate a radio beacon-based approach to location, called Place Lab, that can overcome the lack of ubiquity and high-cost found in existing location sensing approaches. Using Place Lab, commodity laptops, PDAs and cell phones estimate their position by listening for the cell IDs of fixed radio beacons, such as wireless access points, and referencing the beacons’ positions in a cached database. We present experimental results showing that 802.11 and GSM beacons are sufficiently pervasive in the greater Seattle area to achieve 20-40 meter median accuracy with nearly 100% coverage measured by availability in people’s daily
Deep metric learning using Triplet network

Deep learning has proven itself as a successful set of models for learning useful semantic representations of data. These, however, are mostly implicitly learned as part of a classification task. In this paper we propose the triplet network model, which aims to learn useful representations by distance comparisons. A similar model was defined by Wang et al. (2014), tailor made for learning a ranking for image information retrieval. Here we demonstrate using various datasets that our model learns a better representation than that of its immediate competitor, the Siamese network. We also discuss future possible usage as a framework for unsupervised learning.