nils-herrmann/clean_scirepeval huggingface dataset:
Workload-aware database monitoring and consolidation

In most enterprises, databases are deployed on dedicated database servers. Often, these servers are underutilized much of the time. For example, in traces from almost 200 production servers from different organizations, we see an average CPU utilization of less than 4%. This unused capacity can be potentially harnessed to consolidate multiple databases on fewer machines, reducing hardware and operational costs. Virtual machine (VM) technology is one popular way to approach this problem. However, as we demonstrate in this paper, VMs fail to adequately support database consolidation, because databases place a unique and challenging set of demands on hardware resources, which are not well-suited to the assumptions made by VM-based consolidation.
 Instead, our system for database consolidation, named Kairos, uses novel techniques to measure the hardware requirements of database workloads, as well as models to predict the combined resource utilization of those workloads. We formalize the consolidation problem as a non-linear optimization program, aiming to minimize the number of servers and balance load, while achieving near-zero performance degradation. We compare Kairos against virtual machines, showing up to a factor of 12× higher throughput on a TPC-C-like benchmark. We also tested the effectiveness of our approach on real-world data collected from production servers at Wikia.com, Wikipedia, Second Life, and MIT CSAIL, showing absolute consolidation ratios ranging between 5.5:1 and 17:1.
Reducing Energy Consumption in LTE with Cell DTX

This paper discusses how energy consumption can be significantly reduced in mobile networks by introducing discontinuous transmission (DTX) on the base station side. By introducing DTX on the downlink, or cell DTX, we show that it is possible to achieve significant energy reductions in an LTE network. Cell DTX is most efficient when the traffic load is low in a cell but even when realistic traffic statistics are considered the gains are impressive. The technology potential for a metropolitan area is shown to be 90% reduced energy consumption compared to no use of cell DTX. The paper also discusses different drives for the increased focus on energy efficient network operation and also provides insights on the impact of cell DTX from a life cycle assessment perspective.
Variance component model to account for sample structure in genome-wide association studies

Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches, such as efficient mixed-model association (EMMA), can correct for a wide range of sample structures by explicitly accounting for pairwise relatedness between individuals, using high-density markers to model the phenotype distribution; but such approaches are computationally impractical. We report here a variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours. We apply this method to two human GWAS data sets, performing association analysis for ten quantitative traits from the Northern Finland Birth Cohort and seven common diseases from the Wellcome Trust Case Control Consortium. We find that EMMAX outperforms both principal component analysis and genomic control in correcting for sample structure.
Learning to Translate: A Query-Specific Combination Approach for Cross-Lingual Information Retrieval

When documents and queries are presented in different languages, the common approach is to translate the query into the document language. While there are a variety of query translation approaches, recent research suggests that combining multiple methods into a single ”structured query” is the most effective. In this paper, we introduce a novel approach for producing a unique combination recipe for each query, as it has also been shown that the optimal combination weights differ substantially across queries and other task specifics. Our query-specific combination method generates statistically significant improvements over other combination strategies presented in the literature, such as uniform and task-specific weighting. An in-depth empirical analysis presents insights about the effect of data size, domain differences, labeling and tuning on the end performance of our approach.
Evaluation of Commonsense Knowledge with Mechanical Turk

Efforts to automatically acquire world knowledge from text suffer from the lack of an easy means of evaluating the resulting knowledge. We describe initial experiments using Mechanical Turk to crowdsource evaluation to nonexperts for little cost, resulting in a collection of factoids with associated quality judgements. We describe the method of acquiring usable judgements from the public and the impact of such large-scale evaluation on the task of knowledge acquisition.
Self-Shielded High-Efficiency Yagi-Uda Antennas for 60 GHz Communications

A high-efficiency self-shielded microstrip-fed Yagi-Uda antenna has been developed for 60 GHz communications. The antenna is built on a Teflon substrate (εr = 2.2) with a thickness of 10 mils (0.254 mm). A 7-element design results in a measured S11 of <; -10 dB at 56.0 - 66.4 GHz with a gain >; 9.5 dBi at 58 - 63 GHz. The antenna shows excellent performance in free space and in the presence of metal-planes used for shielding purposes. A parametric study is done with metal plane heights from 2 mm to 11 mm, and the Yagi-Uda antenna results in a gain >; 12 dBi at 58 - 63 GHz for h = 5 - 8 mm. A 60 GHz four-element switched-beam Yagi-Uda array is also presented with top and bottom shielding planes, and allows for 180° angular coverage with <; 3 dB amplitude variations. This antenna is ideal for inclusion in complex platforms, such as laptops, for point-to-point communication systems, either as a single element or a switched-beam system.
Solving Cryptarithmetic Problems Using Parallel Genetic Algorithm

Cryptarithmetic is a class of constraint satisfaction problems which includes making mathematical relations between meaningful words using simple arithmetic operators like ‘plus’ in a way that the result is conceptually true, and assigning digits to the letters of these words and generating numbers in order to make correct arithmetic operations as well. A simple way to solve such problems is by depth first search (DFS) algorithm which has a big search space even for quite small problems. In this paper we proposed a solution to this problem with genetic algorithm and then optimized it by using parallelism. We also showed that the algorithm reaches a solution faster and in a smaller number of iterations than similar algorithms.
RAPID: Rating Pictorial Aesthetics using Deep Learning

Effective visual features are essential for computational aesthetic quality rating systems. Existing methods used machine learning and statistical modeling techniques on handcrafted features or generic image descriptors. A recently-published large-scale dataset, the AVA dataset, has further empowered machine learning based approaches. We present the RAPID (RAting PIctorial aesthetics using Deep learning) system, which adopts a novel deep neural network approach to enable automatic feature learning. The central idea is to incorporate heterogeneous inputs generated from the image, which include a global view and a local view, and to unify the feature learning and classifier training using a double-column deep convolutional neural network. In addition, we utilize the style attributes of images to help improve the aesthetic quality categorization accuracy. Experimental results show that our approach significantly outperforms the state of the art on the AVA dataset.
Piccolo: Building Fast, Distributed Programs with Partitioned Tables

Piccolo is a new data-centric programming model for writing parallel in-memory applications in data centers. Unlike existing data-flow models, Piccolo allows computation running on different machines to share distributed, mutable state via a key-value table interface. Piccolo enables efficient application implementations. In particular, applications can specify locality policies to exploit the locality of shared state access and Piccolo’s run-time automatically resolves write-write conflicts using userdefined accumulation functions. Using Piccolo, we have implemented applications for several problem domains, including the PageRank algorithm, k-means clustering and a distributed crawler. Experiments using 100 Amazon EC2 instances and a 12 machine cluster show Piccolo to be faster than existing data flow models for many problems, while providing similar fault-tolerance guarantees and a convenient programming interface.
Diagnosis and Management of Formerly Called "Dysfunctional Uterine Bleeding" According to PALM-COEIN FIGO Classification and the New Guidelines.

In 2011, the FIGO classification system (PALM-COEIN) was published to standardize terminology, diagnostic and investigations of causes of abnormal uterine bleeding (AUB). According to FIGO new classification, in the absence of structural etiology, the formerly called "dysfunctional uterine bleeding" should be avoided and clinicians should state if AUB are caused by coagulation disorders (AUB-C), ovulation disorder (AUB-O), or endometrial primary dysfunction (AUB-E). Since this publication, some societies have released or revised their guidelines for the diagnosis and the management of the formerly called "dysfunctional uterine bleeding" according new FIGO classification. In this review, we summarize the most relevant new guidelines for the diagnosis and the management of AUB-C, AUB-O, and AUB-E.
C-CP-ABE: Cooperative Ciphertext Policy Attribute-Based Encryption for the Internet of Things

Cipher text Policy Attribute-Based Encryption (CP-ABE) is an extremely powerful asymmetric encryption mechanism, but its complexity and its overhead cannot be neglected in an Internet of Things environment. Indeed, Internet of Things, by its heterogeneous nature, may contains highly resource-constrained devices that are not able to support the heavy overhead due to CP-ABE. Further, constrained devices, like sensors, often need to encrypt data as they are usually led to send sensitive data they collect to more powerful devices like storage servers. This paper proposes a novel approach for employing CP-ABE on highly resource-constrained sensor nodes in the IoT environments. The proposed approach exploits collaboration between heterogeneous nodes, to make feasible the implementation of CP-ABE in an IoT environment, by delegating costly operations to a set of assisting nodes. An analysis is conducted to verify that the proposed solution accomplishes safely and efficiently its objective.
Back-drivable and Inherently Safe Mechanism for Artificial Finger

This paper shows mechanisms for artificial finger based on a planetary gear system (PGS). Using the PGS as a transmitter provides an under-actuated system for driving three joints of a finger with back-drivability that is crucial characteristics for fingers as an end-effector when it interacts with external environment. This paper also shows the artificial finger employed with the originally developed mechanism called “double planetary gear system” (DPGS). The DPGS provides not only back-drivable and under-actuated flexion-extension of the three joints of a finger, which is identical to the former, but also adduction-abduction of the MP joint. Both of the above finger mechanisms are inherently safe due to being back-drivable with no electric device or sensor in the finger part. They are also rigorously solvable in kinematics and kinetics as shown in this paper.
Demo of FaceVR: real-time facial reenactment and eye gaze control in virtual reality

We introduce FaceVR, a novel method for gaze-aware facial reenactment in the Virtual Reality (VR) context. The key component of FaceVR is a robust algorithm to perform real-time facial motion capture of an actor who is wearing a head-mounted display (HMD), as well as a new data-driven approach for eye tracking from monocular videos. In addition to these face reconstruction components, FaceVR incorporates photo-realistic re-rendering in real time, thus allowing artificial modifications of face and eye appearances. For instance, we can alter facial expressions, change gaze directions, or remove the VR goggles in realistic re-renderings. In a live setup with a source and a target actor, we apply these newly-introduced algorithmic components. We assume that the source actor is wearing a VR device, and we capture his facial expressions and eye movement in real-time. For the target video, we mimic a similar tracking process; however, we use the source input to drive the animations of the target video, thus enabling gaze-aware facial reenactment. To render the modified target video on a stereo display, we augment our capture and reconstruction process with stereo data. In the end, FaceVR produces compelling results for a variety of applications, such as gaze-aware facial reenactment, reenactment in virtual reality, removal of VR goggles, and re-targeting of somebody's gaze direction in a video conferencing call.
TOWARDS A CLOUD-BASED DESIGN AND MANUFACTURING PARADIGM : LOOKING BACKWARD , LOOKING FORWARD

The rise of cloud computing is radically changing the way enterprises manage their information technology (IT) assets. Considering the benefits of cloud computing to the information technology sector, we present a review of current research initiatives and applications of the cloud computing paradigm related to product design and manufacturing. In particular, we focus on exploring the potential of utilizing cloud computing for selected aspects of collaborative design, distributed manufacturing, collective innovation, data mining, semantic web technology, and virtualization. In addition, we propose to expand the paradigm of cloud computing to the field of computer-aided design and manufacturing and propose a new concept of cloud-based design and manufacturing (CBDM). Specifically, we (1) propose a comprehensive definition of CBDM; (2) discuss its key characteristics; (3) relate current research in design and manufacture to CBDM; and (4) identify key research issues and future trends.
The Michelangelo phenomenon and personal well-being.

Previous research has demonstrated that when a close romantic partner views you and behaves toward you in a manner that is congruent with your ideal self, you experience movement toward your ideal self (termed the "Michelangelo phenomenon"; Drigotas, Rusbult, Wieselquist, & Whitton, 1999). The present research represents an attempt demonstrate the phenomenon's link to personal well-being. Results of a cross-sectional study of individuals in dating relationships, with a 2-month follow-up assessing breakup, replicated previous findings regarding relationship well-being and revealed strong links between the model and personal well-being, even when accounting for level of relationship satisfaction. Such results provide further evidence for the social construction of the self and personal well-being.
We need new tools to assess Orthorexia Nervosa. A commentary on “Prevalence of Orthorexia Nervosa among College Students Based on Bratman’s Test and Associated Tendencies”

The US physician Bratman’s remarkable observation that some individuals were becoming highly obsessive about healthy eating that drives them into pathology lead him to coin the term Orthorexia Nervosa (ON) (Bratman & Knight, 2000). Since this first report, however, research on ON has only been of mediocre quality. In fact, most studies have been conducted using flawed methodological approaches. As recently reported in an article published in the Journal Appetite, yet another study on ON investigating the “Prevalence of Orthorexia Nervosa among College Students Based on Bratman’s Test and Associated Tendencies“, based its findings on an assumingly flawed methodology (Bundros, Clifford, Silliman, & Morris, 2016). But first, let us start with an anecdote. Imagine being at a dinner partywith your friends. There is a large buffet lined upwith all sorts of different foods. A smorgasbord with several salads, cheeses and meats is deliciously arranged. A lot of the presented food is either marked “without XXX”, “free from YYY”, or is described as “vegan”, “paleo” or “gluten-free”. What people bring to dinner parties
Joint Syntactic and Semantic Parsing with Combinatory Categorial Grammar

We present an approach to training a joint syntactic and semantic parser that combines syntactic training information from CCGbank with semantic training information from a knowledge base via distant supervision. The trained parser produces a full syntactic parse of any sentence, while simultaneously producing logical forms for portions of the sentence that have a semantic representation within the parser’s predicate vocabulary. We demonstrate our approach by training a parser whose semantic representation contains 130 predicates from the NELL ontology. A semantic evaluation demonstrates that this parser produces logical forms better than both comparable prior work and a pipelined syntax-then-semantics approach. A syntactic evaluation on CCGbank demonstrates that the parser’s dependency Fscore is within 2.5% of state-of-the-art.
Design and kinematic analysis of a novel upper limb exoskeleton for rehabilitation of stroke patients

This paper details the design process and features of a novel upper limb rehabilitation exoskeleton named CLEVER (Compact, Low-weight, Ergonomic, Virtual/Augmented Reality Enhanced Rehabilitation) ARM. The research effort is focused on designing a lightweight and ergonomic upper-limb rehabilitation exoskeleton capable of producing diverse and perceptually rich training scenarios. To this end, the knowledge available in the literature of rehabilitation robotics is used along with formal conceptual design techniques. This paper briefly reviews the systematic approach used for design of the exoskeleton, and elaborates on the specific details of the proposed design concept and its advantages over other design possibilities. The kinematic structure of CLEVER ARM has eight degrees of freedom supporting the motion of shoulder girdle, glenohumeral joint, elbow and wrist. Six degrees of freedom of the exoskeleton are active, and the two degrees of freedom supporting the wrist motion are passive. Kinematics of the proposed design is studied analytically and experimentally with the aid of a 3D printed prototype. The paper is concluded by some remarks on the optimization of the design, motorization of device, and the fabrication challenges.
The bubble rebound obstacle avoidance algorithm for mobile robots

This paper proposes a simple, reactive algorithm for real time obstacle avoidance, aimed to be implemented on low cost, low power microcontrollers. According to this algorithm, the robot reacts to obstacles detected within an area called “sensitivity bubble”, whose shape and size are dynamically adjusted, depending on the kinematics of the robot. Upon detection of an obstacle, the robot “rebounds” in a direction having the lowest density of obstacles, and continues its motion in this direction until the goal becomes visible, or a new obstacle is encountered. Based on experimental results with simulators and real robots, the paper describes the performances and drawbacks of the algorithm, in comparison with other, simple obstacle avoidance algorithms.
Understanding user's query intent with wikipedia

Understanding the intent behind a user's query can help search engine to automatically route the query to some corresponding vertical search engines to obtain particularly relevant contents, thus, greatly improving user satisfaction. There are three major challenges to the query intent classification problem: (1) Intent representation; (2) Domain coverage and (3) Semantic interpretation. Current approaches to predict the user's intent mainly utilize machine learning techniques. However, it is difficult and often requires many human efforts to meet all these challenges by the statistical machine learning approaches. In this paper, we propose a general methodology to the problem of query intent classification. With very little human effort, our method can discover large quantities of intent concepts by leveraging Wikipedia, one of the best human knowledge base. The Wikipedia concepts are used as the intent representation space, thus, each intent domain is represented as a set of Wikipedia articles and categories. The intent of any input query is identified through mapping the query into the Wikipedia representation space. Compared with previous approaches, our proposed method can achieve much better coverage to classify queries in an intent domain even through the number of seed intent examples is very small. Moreover, the method is very general and can be easily applied to various intent domains. We demonstrate the effectiveness of this method in three different applications, i.e., travel, job, and person name. In each of the three cases, only a couple of seed intent queries are provided. We perform the quantitative evaluations in comparison with two baseline methods, and the experimental results shows that our method significantly outperforms other methods in each intent domain.
Analysis of magnetic circuit on ALA rotor synchronous reluctance motor

This article provides a novel analytical method of magnetic circuit on Axially-Laminated Anisotropic (ALA) rotor synchronous reluctance motor when the motor is magnetized on the d-axis. To simplify the calculation, the reluctance of stator magnet yoke and rotor magnetic laminations and leakage magnetic flux all are ignored. With regard to the uneven air-gap brought by the teeth and slots of the stator and rotor, the method resolves the problem with the equivalent air-gap length distribution function, and clarifies the magnetic circuit when the stator teeth are saturated or unsaturated. In order to conduct exact computation, the high order harmonics of the stator magnetic potential are also taken into account.
Combining OCR Outputs for Logical Document Structure Markup. Technical Background to the ACL 2012 Contributed Task

We describe how paperXML, a logical document structure markup for scholarly articles, is generated on the basis of OCR tool outputs. PaperXML has been initially developed for the ACL Anthology Searchbench. The main purpose was to robustly provide uniform access to sentences in ACL Anthology papers from the past 46 years, ranging from scanned, typewriter-written conference and workshop proceedings papers, up to recent high-quality typeset, born-digital journal articles, with varying layouts. PaperXML markup includes information on page and paragraph breaks, section headings, footnotes, tables, captions, boldface and italics character styles as well as bibliographic and publication metadata. The role of paperXML in the ACL Contributed Task Rediscovering 50 Years of Discoveries is to serve as fall-back source (1) for older, scanned papers (mostly published before the year 2000), for which born-digital PDF sources are not available, (2) for borndigital PDF papers on which the PDFExtract method failed, (3) for document parts where PDFExtract does not output useful markup such as currently for tables. We sketch transformation of paperXML into the ACL Contributed Task’s TEI P5 XML.
Document Classification by Inversion of Distributed Language Representations

The goal of this note is to point out that any distributed representation can be turned into a classifier through inversion via Bayes rule. The approach is simple and modular, in that it will work with any language representation whose training can be formulated as optimizing a probability model. In our application to 2 million sentences from Yelp reviews, we also find that it performs as well as or better than complex purpose-built algorithms.