nils-herrmann/clean_scirepeval huggingface dataset:
Using Deep Learning for Title-Based Semantic Subject Indexing to Reach Competitive Performance to Full-Text

For (semi-)automated subject indexing systems in digital libraries, it is often more practical to use metadata such as the title of a publication instead of the full-text or the abstract. Therefore, it is desirable to have good text mining and text classification algorithms that operate well already on the title of a publication. So far, the classification performance on titles is not competitive with the performance on the full-texts if the same number of training samples is used for training. However, it is much easier to obtain title data in large quantities and to use it for training than full-text data. In this paper, we investigate the question how models obtained from training on increasing amounts of title training data compare to models from training on a constant number of full-texts. We evaluate this question on a large-scale dataset from the medical domain (PubMed) and from economics (EconBiz). In these datasets, the titles and annotations of millions of publications are available, and they outnumber the available full-texts by a factor of 20 and 15, respectively. To exploit these large amounts of data to their full potential, we develop three strong deep learning classifiers and evaluate their performance on the two datasets. The results are promising. On the EconBiz dataset, all three classifiers outperform their full-text counterparts by a large margin. The best title-based classifier outperforms the best full-text method by 9.4%. On the PubMed dataset, the best title-based method almost reaches the performance of the best full-text classifier, with a difference of only 2.9%.
The Drosophila imd signaling pathway.

The fruit fly, Drosophila melanogaster, has helped us to understand how innate immunity is activated. In addition to the Toll receptor and the Toll signaling pathway, the Drosophila immune response is regulated by another evolutionarily conserved signaling cascade, the immune deficiency (Imd) pathway, which activates NF-κB. In fact, the Imd pathway controls the expression of most of the antimicrobial peptides in Drosophila; thus, it is indispensable for normal immunity in flies. In this article, we review the current literature on the Drosophila Imd pathway, with special emphasis on its role in the (patho)physiology of different organs. We discuss the systemic response, as well as local responses, in the epithelial and mucosal surfaces and the nervous system.
A CPW-fed band-notched slot antenna for UWB applications

An UWB elliptical slot antenna embedded with open-end slit on the tuning stub or parasitic strip on the aperture for achieving the band-notch characteristics has been proposed in this conference. Experimental results have also confirmed band-rejection capability for the proposed antenna at the desired band, as well as nearly omni-direction radiation features is still preserved. Finally, how to shrink the geometry dimensions of the UWB antenna will be investigated in the future.
Faster command selection on tablets with FastTap

Touch-based tablet UIs provide few shortcut mechanisms for rapid command selection; as a result, command selection on tablets often requires slow traversal of menus. We developed a new selection technique for multi-touch tablets, called FastTap, that uses thumb-and-finger touches to show and choose from a spatially-stable grid-based overlay interface. FastTap allows novices to view and inspect the full interface, but once item locations are known, FastTap allows people to select commands with a single quick thumb-and-finger tap. The interface helps users develop expertise, since the motor actions carried out as a novice rehearse the expert behavior. A controlled study showed that FastTap was significantly faster (by 33% per selection overall) than marking menus, both for novices and experts, and without reduction in accuracy or subjective preference. Our work introduces a new and efficient selection mechanism that supports rapid command execution on touch tablets, for both novices and experts.
Generative Adversarial Privacy

We present a data-driven framework called generative adversarial privacy (GAP). Inspired by recent advancements in generative adversarial networks (GANs), GAP allows the data holder to learn the privatization mechanism directly from the data. Under GAP, finding the optimal privacy mechanism is formulated as a constrained minimax game between a privatizer and an adversary. We show that for appropriately chosen adversarial loss functions, GAP provides privacy guarantees against strong information-theoretic adversaries. We also evaluate the performance of GAP on multi-dimensional Gaussian mixture models and the GENKI face database. KeywordsData Privacy, Differential Privacy, Adversarial Learning, Generative Adversarial Networks, Minimax Games,
Assessing Visual Quality of 3-D Polygonal Models

Recent advances in evaluating and measuring the perceived visual quality of three-dimensional (3-D) polygonal models are presented in this article, which analyzes the general process of objective quality assessment metrics and subjective user evaluation methods and presents a taxonomy of existing solutions. Simple geometric error computed directly on the 3-D models does not necessarily reflect the perceived visual quality; therefore, integrating perceptual issues for 3-D quality assessment is of great significance. This article discusses existing metrics, including perceptually based ones, computed either on 3-D data or on two-dimensional (2-D) projections, and evaluates their performance for their correlation with existing subjective studies.
Abstract Features in Feature Modeling

A software product line is a set of program variants, typically generated from a common code base. Feature models describe variability in product lines by documenting features and their valid combinations. In product-line engineering, we need to reason about variability and program variants for many different tasks. For example, given a feature model, we might want to determine the number of all valid feature combinations or compute specific feature combinations for testing. However, we found that contemporary reasoning approaches can only reason about feature combinations, not about program variants, because they do not take abstract features into account. Abstract features are features used to structure a feature model that, however, do not have any impact at implementation level. Using existing feature-model reasoning mechanisms for program variants leads to incorrect results. Hence, although abstract features represent domain decisions that do not affect the generation of a program variant. We raise awareness of the problem of abstract features for different kinds of analyses on feature models. We argue that, in order to reason about program variants, abstract features should be made explicit in feature models. We present a technique based on propositional formulas that enables to reason about program variants rather than feature combinations. In practice, our technique can save effort that is caused by considering the same program variant multiple times, for example, in product-line testing.
Keep-out distance of the IEEE 802.11af system from digital TV broadcast contour

Congestion in the Industrial, Scientific, and Medical (ISM) frequency band limits the expansion of the IEEE 802.11 Wireless Local Area Network (WLAN). Recently, due to the ‘digital switchover’ from analog to digital TV (DTV) broadcasting, a sizeable amount of bandwidth have been freed up in the conventional TV bands, resulting in the availability of TV white space (TVWS). The IEEE 802.11af is a standard for the WLAN technology that operates at the TVWS spectrum. TVWS operation must not cause harmful interference to the incumbent DTV service. This paper provides a method of computing the keep-out distance required between an IEEE 802.11af device and the DTV service contour, in order to keep the interference to a harmless level. The ITU-R P.1411-7 propagation model is used in the calculation. Four different DTV services are considered: Advanced Television Systems Committee (ATSC), Digital Video Broadcasting — Terrestrial (DVB-T), Integrated Services Digital Broadcasting — Terrestrial (ISDB-T), and Digital Terrestrial Multimedia Broadcasting (DTMB). The calculation results reveal that under many circumstances, allocating keep-out distance of 1 to 2.5 km is sufficient for the protection of DTV service.
Adaptive Stochastic Gradient Langevin Dynamics: Taming Convergence and Saddle Point Escape Time

In this paper, we propose a new adaptive stochastic gradient Langevin dynamics (ASGLD) algorithmic framework and its two specialized versions, namely adaptive stochastic gradient (ASG) and adaptive gradient Langevin dynamics(AGLD), for non-convex optimization problems. All proposed algorithms can escape from saddle points with at most $O(\log d)$ iterations, which is nearly dimension-free. Further, we show that ASGLD and ASG converge to a local minimum with at most $O(\log d/\epsilon^4)$ iterations. Also, ASGLD with full gradients or ASGLD with a slowly linearly increasing batch size converge to a local minimum with iterations bounded by $O(\log d/\epsilon^2)$, which outperforms existing first-order methods.
Sentence Alignment using Unfolding Recursive Autoencoders

In this paper, we propose a novel two step algorithm for sentence alignment in monolingual corpora using Unfolding Recursive Autoencoders. First, we use unfolding recursive auto-encoders (RAE) to learn feature vectors for phrases in syntactical tree of the sentence. To compare two sentences we use a similarity matrix which has dimensions proportional to the size of the two sentences. Since the similarity matrix generated to compare two sentences has varying dimension due to different sentence lengths, a dynamic pooling layer is used to map it to a matrix of fixed dimension. The resulting matrix is used to calculate the similarity scores between the two sentences. The second step of the algorithm captures the contexts in which the sentences occur in the document by using a dynamic programming algorithm for global alignment.
Collision Awareness Using Vibrotactile Arrays

What is often missing from many virtual worlds is a physical sense of the confinement and constraint of the virtual environment. To address this issue, we present a method for providing localized cutaneous vibratory feedback to the user's right arm. We created a sleeve of tactors linked to a real-time human model that activates when the corresponding body area collides with an object. The hypothesis is that vibrotactile feedback to body areas provides the wearer sufficient guidance to ascertain the existence and physical realism of access paths and body configurations. The results of human subject experiments clearly show that the use of full arm vibrotactile feedback improves performance over purely visual feedback in navigating the virtual environment. These results validate the empirical performance of this concept
Arabic sarcasm detection in Twitter

Sarcasm is a special form of irony or satirical wit in which people convey the opposite of what they mean. Sarcasm largely increases in social networks, especially in Twitter. Detecting sarcasm in tweets improves the automatic analysis tools that analyze the data to provide or enhance customer service and fabricate or enhance a product. Also, there are few studies that focus on detecting Arabic sarcasm in tweets. Consequently, we propose a classifier model that detects Arabic-sarcasm tweets by classifying them as sarcastic by setting some features that may declare a tweet as sarcastic using Weka. We evaluated our model through recall, precision, and f-score measurements that gave 0.659, 0.710, and 0.676 values, respectively, which these results are high especially when it comes to Arabic.
RFID-enabled Target Tracking and Following with a Mobile Robot Using Direction Finding Antennas

A stand-alone direction finding RFID reader is developed for mobile robot applications employing a dual-directional antenna. By adding search and localization capabilities to the current state of RFID technology, robots will be able to acquire and dock to a static target in a real environment without requiring a map or landmarks. Furthermore, we demonstrate RFID-enabled tracking and following of a target moving unpredictably with a mobile robot. The RFID reader keeps the robot aware of the direction of arrival (DOA) of the signal of interest toward which the dual-directional antenna faces the target transponder. The simulation results show that the proposed RFID system can track in real time the movement of the target transponder. To verify the effectiveness of the system in a real environment, we perform a variety of experiments in a hallway including target tracking and following with a commercial mobile robot.
Investigating the links between the subcomponents of executive function and academic achievement: a cross-cultural analysis of Chinese and American preschoolers.

Little is known about how components of executive function (EF) jointly and uniquely predict different aspects of academic achievement and how this may vary across cultural contexts. In the current study, 119 Chinese and 139 American preschoolers were tested on a battery of EF tasks (i.e., inhibition, working memory, and attentional control) as well as academic achievement tasks (i.e., reading and mathematics). Results demonstrate that although working memory performance in both cultures was comparable, Chinese children outperformed American children on inhibition and attentional control tasks. In addition, the relation between components of EF and achievement was similar in the two countries. Working memory uniquely predicted academic achievement, with some intriguing patterns in regard to tasks requiring complex processing. Inhibition uniquely predicted counting but did not uniquely predict calculation. Attentional control predicted most aspects of achievement uniformly and was the most robust predictor for reading in both countries. In sum, the data provide insight into both cultural variability and consistency in the development of EF during early childhood.
Boxy types: inference for higher-rank types and impredicativity

Languages with rich type systems are beginning to employ a blend of type inference and type checking, so that the type inference engine is guided by programmer-supplied type annotations. In this paper we show, for the first time, how to combine the virtues of two well-established ideas: unification-based inference, and bidi-rectional propagation of type annotations. The result is a type system that conservatively extends Hindley-Milner, and yet supports both higher-rank types and impredicativity.
Fundamental Concepts of Cyber Resilience: Introduction and Overview

Society is increasingly reliant upon complex and interconnected cyber systems to conduct daily life activities. From personal finance to managing defense capabilities to controlling a vast web of aircraft traffic, digitized information systems and software packages have become integrated at virtually all levels of individual and collective activity. While such integration has been met with immense increases in efficiency of service delivery, it has also been subject to a diverse body of threats from nefarious hackers, groups, and even state government bodies. Such cyber threats have shifted over time to affect various cyber functionalities, such as with Direct Denial of Service (DDoS), data theft, changes to data code, infection via computer virus, and many others.
Chrysaor: Fine-Grained, Fault-Tolerant Cloud-of-Clouds MapReduce

MapReduce is a framework for processing large data sets much used in the context of cloud computing. MapReduce implementations like Hadoop can tolerate crashes and file corruptions, but not arbitrary faults. Unfortunately, there is evidence that arbitrary faults do occur and can affect the correctness of MapReduce job executions. Furthermore, many outages of major cloud offerings have been reported, raising concerns about the dependence on a single cloud. In this paper we propose a novel execution system that allows to scale out MapReduce computations to a cloud-of-clouds and tolerate arbitrary faults, malicious faults, and cloud outages. Our system, Chrysaor, is based on a fine-grained replication scheme that tolerates faults at the task level. Our solution has three important properties: it tolerates the above-mentioned classes of faults at reasonable cost, it requires minimal modifications to the users' applications, and it does not involve changes to the Hadoop source code. We performed an extensive evaluation of our system in Amazon EC2, showing that our fine-grained solution is efficient in terms of computation by recovering only faulty tasks. This is achieved without incurring a significant penalty for the baseline case (i.e., without faults) in most workloads.
Application Of Neural Network To Technical Analysis Of Stock Market Prediction

This paper presents a neural network model for technical analysis of stock market, and its application to a buying and selling timing prediction system for stock index. When the numbers of learning samples are uneven among categories, the neural network with normal learning has the problem that it tries to improve only the prediction accuracy of most dominant category. In this paper, a learning method is proposed for improving prediction accuracy of other categories, controlling the numbers of learning samples by using information about the importance of each category. Experimental simulation using actual price data is carried out to demonstrate the usefulness of the method.
A Learning-Based Service for Cost and Performance Management of Cloud Databases

Data management applications deployed on IaaS cloud environments must simultaneously strive to minimize cost and provide good performance. Balancing these two goals requires complex decision-making across a number of axes: resource provisioning, query placement, and query scheduling. While previous works have addressed each axis in isolation for specific types of performance goals, this demonstration showcases WiSeDB, a cloud workload management advisor service that uses machine learning techniques to address all dimensions of the problem for customizable performance goals. In our demonstration, attendees will see WiSeDB in action for a variety of workloads and performance goals.
A Proof of Correctness for Egalitarian Paxos

This paper presents a proof of correctness for Egalitarian Paxos (EPaxos), a new distributed consensus algorithm based on Paxos. EPaxos achieves three goals: (1) availability without interruption as long as a simple majority of replicas are reachable—its availability is not interrupted when replicas crash or fail to respond; (2) uniform load balancing across all replicas—no replicas experience higher load because they have special roles; and (3) optimal commit latency in the wide-area when tolerating one and two failures, under realistic conditions. Egalitarian Paxos is to our knowledge the first distributed consensus protocol to achieve all of these goals efficiently: requiring only a simple majority of replicas to be non-faulty, using a number of messages linear in the number of replicas to choose a command, and committing commands after just one communication round (one round trip) in the common case or after at most two rounds in any case.  This work was supported by Intel Science & Technology Center for Cloud Computing, Google, and the National Science Foundation under award CCF-0964474. We would like to thank the members and companies of the PDL Consortium (including Actifio, American Power Conversion, EMC Corporation, Emulex, Facebook, Fusion-io, Google, Hewlett-Packard Labs, Hitachi, Huawei Technologies Co., Intel Corporation, Microsoft Research, NEC Laboratories, NetApp, Inc., Oracle Corporation, Panasas, Riverbed, Samsung Information Systems America, Seagate Technology, STEC, Inc., Symantec Corporation, VMware, Inc., and Western Digital) for their interest, insights, feedback, and support.
Multi-Objective Evolutionary Algorithm Based on Decomposition for Energy Efficient Coverage in Wireless Sensor Networks

Wireless sensor networks (WSNs) have become a hot area of research in recent years due to the realization of their ability in myriad applications including military surveillance, facility monitoring, target detection, and health care applications. However, many WSN design problems involve tradeoffs between multiple conflicting optimization objectives such as coverage preservation and energy conservation. Many of the existing sensor network design approaches, however, generally focus on a single optimization objective. For example, while both energy conservation in a cluster-based WSNs and coverage-maintenance protocols have been extensively studied in the past, these have not been integrated in a multi-objective optimization manner. This paper employs a recently developed multiobjective optimization algorithm, the so-called multi-objective evolutionary algorithm based on decomposition (MOEA/D) to solve simultaneously the coverage preservation and energy conservation design problems in cluster-based WSNs. The performance of the proposed approach, in terms of coverage and network lifetime is compared with a state-of-the-art evolutionary approach called NSGA II. Under the same environments, simulation results on different network topologies reveal that MOEA/D provides a feasible approach for extending the network lifetime while preserving more coverage area.
The health of homeless people in high-income countries: descriptive epidemiology, health consequences, and clinical and policy recommendations

In the European Union, more than 400,000 individuals are homeless on any one night and more than 600,000 are homeless in the USA. The causes of homelessness are an interaction between individual and structural factors. Individual factors include poverty, family problems, and mental health and substance misuse problems. The availability of low-cost housing is thought to be the most important structural determinant for homelessness. Homeless people have higher rates of premature mortality than the rest of the population, especially from suicide and unintentional injuries, and an increased prevalence of a range of infectious diseases, mental disorders, and substance misuse. High rates of non-communicable diseases have also been described with evidence of accelerated ageing. Although engagement with health services and adherence to treatments is often compromised, homeless people typically attend the emergency department more often than non-homeless people. We discuss several recommendations to improve the surveillance of morbidity and mortality in homeless people. Programmes focused on high-risk groups, such as individuals leaving prisons, psychiatric hospitals, and the child welfare system, and the introduction of national and state-wide plans that target homeless people are likely to improve outcomes.