nils-herrmann/clean_scirepeval huggingface dataset:
Mate retention tactics in Spain: personality, sex differences, and relationship status.

Mate retention is an important problem in romantic relationships because of mate poachers, infidelity, and the risk of outright defection. The current study (N=892) represents the first study of mate retention tactics conducted in Spain. We tested hypotheses about the effects of gender, relationship commitment status, and personality on mate retention tactics. Women and men differed in the use of resource display, appearance enhancement, intrasexual violence, and submission/self-abasement as mate retention tactics. Those in more committed relationships reported higher levels of resource display, appearance enhancement, love, and verbal signals of possession. Those in less committed relationships more often reported intentionally evoking jealousy in their partner as a mate retention tactic. Personality characteristics, particularly Neuroticism and Agreeableness, correlated in coherent ways with mate retention tactics, supporting two evolution-based hypotheses. Discussion focuses on the implications, future research directions, and interdisciplinary syntheses emerging between personality and social psychology and evolutionary psychology.
Deep learning in partially-labeled data streams

Of the considerable research on data streams, relatively little deals with classification where only some of the instances in the stream are labeled. Most state-of-the-art data-stream algorithms do not have an effective way of dealing with unlabeled instances from the same domain. In this paper we explore deep learning techniques that provide important advantages such as the ability to learn incrementally in constant memory, and from unlabeled examples. We develop two deep learning methods and explore empirically via a series of empirical evaluations the application to several data streams scenarios based on real data. We find that our methods can offer competitive accuracy as compared with existing popular data-stream learners.
Challenges of Sentiment Analysis for Dynamic Events

Efforts to assess people’s sentiments on Twitter have suggested that Twitter could be a valuable resource for studying political sentiment and that it reflects the offline political landscape. Many opinion mining systems and tools provide users with people’s attitudes toward products, people, or topics and their attributes/aspects. However, although it may appear simple, using sentiment analysis to predict election results is difficult, since it is empirically challenging to train a successful model to conduct sentiment analysis on tweet streams for a dynamic event such as an election. This article highlights some of the challenges related to sentiment analysis encountered during monitoring of the presidential election using Kno.e.sis’s Twitris system.
U-SCRUM: An Agile Methodology for Promoting Usability

SCRUM poses key challenges for usability (Baxter et al., 2008). First, product goals are set without an adequate study of the userpsilas needs and context. The user stories selected may not be good enough from the usability perspective. Second, user stories of usability import may not be prioritized high enough. Third, given the fact that a product owner thinks in terms of the minimal marketable set of features in a just-in-time process, it is difficult for the development team to get a holistic view of the desired product or features. This experience report proposes U-SCRUM as a variant of the SCRUM methodology. Unlike typical SCRUM, where at best a team member is responsible for usability, U-SCRUM is based on our experience with having two product owners, one focused on usability and the other on the more conventional functions. Our preliminary result is that U-SCRUM yields improved usability than SCRUM.
An Availability-Aware Virtual Machine Placement Approach for Dynamic Scaling of Cloud Applications

Cloud computing promises customers the on-demand ability to dynamically provision virtualization resources in face of workload variations. Most existing scaling approaches addressed this problem by allocating application to a certain amount of cloud resources. However, the problem of the availability of application influenced by VM-based physical locations during resource scaling process is a serious challenge due to dynamic complex workload and has not been widely discussed yet. In this paper, we present a novel availability-based computing model to describe availability attribute of one application in the hierarchical structured cloud. Moreover, we propose an availability-aware policy by performing both vertical and horizontal scaling to explore how and where to allocate computing resource. Simulation results indicate that our model captured the availability of cloud applications properly and proposed scaling approach achieves the objectives of meeting availability demands and minimizing the total communication cost.
Enhancing First Story Detection using Word Embeddings

In this paper we show how word embeddings can be used to increase the effectiveness of a state-of-the art Locality Sensitive Hashing (LSH) based first story detection (FSD) system over a standard tweet corpus. Vocabulary mismatch, in which related tweets use different words, is a serious hindrance to the effectiveness of a modern FSD system. In this case, a tweet could be flagged as a first story even if a related tweet, which uses different but synonymous words, was already returned as a first story. In this work, we propose a novel approach to mitigate this problem of lexical variation, based on tweet expansion. In particular, we propose to expand tweets with semantically related paraphrases identified via automatically mined word embeddings over a background tweet corpus. Through experimentation on a large data stream comprised of 50 million tweets, we show that FSD effectiveness can be improved by 9.5% over a state-of-the-art FSD system.
AUTOMATIC PCB DEFECT DETECTION USING IMAGE SUBTRACTION METHOD

A printed circuit board, or (PCB) is used to mechanically support and electrically connect electronic components using conductive pathways, track or signal traces etched from copper sheets laminated onto anon conductive substrate. The automatic inspection of PCBs serves a purpose which is traditional in computer technology. The purpose is to relieve human inspectors of the tedious and inefficient task of looking for those defects in PCBs which could lead to electric failure. In this project Machine Vision PCB Inspection System is applied at the first step of manufacturing, i.e., the making of bare PCB. We first compare a PCB standard image with a PCB image, using a simple subtraction algorithm that can highlight the main problem-regions. We have also seen the effect of noise in a PCB image that at what level this method is suitable to detect the faulty image. Our focus is to detect defects on printed circuit boards & to see the effect of noise. Typical defects that can be detected are over etchings (opens), under-etchings (shorts), holes etc. Index terms – Machine vision, PCB defects, Image Subtraction Algorithm, PCB Inspection
Crowdfunding the next hit : Microfunding online experience goods

The combination of limited individual information and costly information acquisition in markets for experience goods leads us to believe that significant peer effects drive demand in these markets. In this paper we model the effects of peers on the demand patterns of products in the market experience goods microfunding. By analyzing data from an online crowdfunding platform from 2006 to 2010 we are able to ascertain that peer effects, and not network externalities, influence consumption.
Photorealistic Style Transfer with Screened Poisson Equation

Recent work has shown impressive success in transferring painterly style to images. These approaches, however, fall short of photorealistic style transfer. Even when both the input and reference images are photographs, the output still exhibits distortions reminiscent of a painting. In this paper we propose an approach that takes as input a stylized image and makes it more photorealistic. It relies on the Screened Poisson Equation, maintaining the fidelity of the stylized image while constraining the gradients to those of the original input image. Our method is fast, simple, fully automatic and shows positive progress in making a stylized image photorealistic. Our results exhibit finer details and are less prone to artifacts than the state-of-the-art.
The action potential in mammalian central neurons

The action potential of the squid giant axon is formed by just two voltage-dependent conductances in the cell membrane, yet mammalian central neurons typically express more than a dozen different types of voltage-dependent ion channels. This rich repertoire of channels allows neurons to encode information by generating action potentials with a wide range of shapes, frequencies and patterns. Recent work offers an increasingly detailed understanding of how the expression of particular channel types underlies the remarkably diverse firing behaviour of various types of neurons.
X1000 real-time phoneme recognition VLSI using feed-forward deep neural networks

Deep neural networks show very good performance in phoneme and speech recognition applications when compared to previously used GMM (Gaussian Mixture Model)-based ones. However, efficient implementation of deep neural networks is difficult because the network size needs to be very large when high recognition accuracy is demanded. In this work, we develop a digital VLSI for phoneme recognition using deep neural networks and assess the design in terms of throughput, chip size, and power consumption. The developed VLSI employs a fixed-point optimization method that only uses +Δ, 0, and -Δ for representing each of the weight. The design employs 1,024 simple processing units in each layer, which however can be scaled easily according to the needed throughput, and the throughput of the architecture varies from 62.5 to 1,000 times of the real-time processing speed.
WiFi Direct and LTE D2D in action

With the evolution of high-performance multi-radio smartphones, Device-to-Device (D2D) communications became an attractive solution for enhancing the performance of cellular networks. Although D2D communications have been widely studied within past few years, the majority of the literature is confined to new theoretical proposals and did not consider implementation challenges. In fact, the implementation feasibility of D2D communications and its challenges are still a relevant research question. In this paper, we introduce a protocol that focuses on D2D communications using LTE and WiFi Direct technologies. We also show that currently available WiFi Direct features permits to deploy the D2D paradigm on top of the LTE cellular infrastructure, without requiring any fundamental change in LTE protocols.
The Performance Impact of Elliptic Curve Cryptography on DNSSEC Validation

The domain name system DNS is a core Internet infrastructure that translates names to machine-readable information, such as IP addresses. Security flaws in DNS led to a major overhaul, with the introduction of the DNS security DNSSEC extensions. DNSSEC adds integrity and authenticity to the DNS using digital signatures. DNSSEC, however, has its own concerns. It suffers from availability problems due to packet fragmentation and is a potent source of distributed denial-of-service attacks. In earlier work, we argued that many issues with DNSSEC stem from the choice of RSA as default signature algorithm. A switch to alternatives based on elliptic curve cryptography ECC can resolve these issues. Yet switching to ECC introduces a new problem: ECC signature validation is much slower than RSA validation. Thus, switching DNSSEC to ECC imposes a significant additional burden on DNS resolvers, pushing load toward the edges of the network. Therefore, in this paper, we study the question: will switching DNSSEC to ECC lead to problems for DNS resolvers, or can they handle the extra load? To answer this question, we developed a model that accurately predicts how many signature validations DNS resolvers have to perform. This allows us to calculate the additional CPU load ECC imposes on a resolver. Using real-world measurements from four DNS resolvers and with two open-source DNS implementations, we evaluate future scenarios where DNSSEC is universally deployed. Our results conclusively show that switching DNSSEC to ECC signature schemes does not impose an insurmountable load on DNS resolvers, even in worst case scenarios.
A novel 2-18GHz TEM double-ridged horn antenna for wideband applications

In this paper we will present the new design of TEM double-ridged horn antenna, resulting in a better VSWR and improved gain of antenna. A cavity back and a new technique for tapering the flared section of the TEM horn antenna are introduced to improve the return loss and matching of the impedance, respectively. By tapering the ridges of antenna both laterally and longitudinally it is possible to extend the operating frequency band while decreasing the size of antenna. The proposed antenna is simulated with two commercially available packages, namely Ansoft HFSS and CST microwave studio. Stimulation results for the VSWR, radiation patterns, and gain of the designed TEM horn antenna over the frequency band 2–18 GHz are presented.
Model-Based Anomaly Detection for Discrete Event Systems

Model-based anomaly detection in technical systems is an important application field of artificial intelligence. We consider discrete event systems, which is a system class to which a wide range of relevant technical systems belong and for which no comprehensive model-based anomaly detection approach exists so far. The original contributions of this paper are threefold: First, we identify the types of anomalies that occur in discrete event systems and we propose a tailored behavior model that captures all anomaly types, called probabilistic deterministic timed-transition automata (PDTTA). Second, we present a new algorithm to learn a PDTTA from sample observations of a system. Third, we describe an approach to detect anomalies based on a learned PDTTA. An empirical evaluation in a practical application, namely ATM fraud detection, shows promising results.
Efficient algorithms for Web services selection with end-to-end QoS constraints

Service-Oriented Architecture (SOA) provides a flexible framework for service composition. Using standard-based protocols (such as SOAP and WSDL), composite services can be constructed by integrating atomic services developed independently. Algorithms are needed to select service components with various QoS levels according to some application-dependent performance requirements. We design a broker-based architecture to facilitate the selection of QoS-based services. The objective of service selection is to maximize an application-specific utility function under the end-to-end QoS constraints. The problem is modeled in two ways: the combinatorial model and the graph model. The combinatorial model defines the problem as a multidimension multichoice 0-1 knapsack problem (MMKP). The graph model defines the problem as a multiconstraint optimal path (MCOP) problem. Efficient heuristic algorithms for service processes of different composition structures are presented in this article and their performances are studied by simulations. We also compare the pros and cons between the two models.
Treatment of Acrophobia in Virtual Reality : a Pilot Study

According to the DSM-IV (American Psychiatric Association, 1995), simple phobias consist of persistent fear of a circumscribed stimulus and consequent avoidance of that stimulus, where the person having this fear knows it is excessive or unreasonable. If the feared stimulus is heights, the person is said to suffer from acrophobia, or fear of heights. The most common and most successful treatment for acrophobia is graded exposure in-vivo. Here, the avoidance behavior is broken by exposing the patient to a hierarchy of feared stimuli, whereby the fear will first increase, after which habituation will occur and the fear will gradually diminish (Bouman, Scholing & Emmelkamp, 1992). In in-vivo treatment the patient is exposed to real stimuli. A promising alternative is graded exposure in Virtual Reality, where the patient can be treated in the safety and privacy of the therapist’s office and situations can be recreated which are hard to find or costly to reach. At this moment, research is being conducted at the Delft University of Technology and the University of Amsterdam aimed at developing a virtual reality system to be used by therapists for such VR Exposure Therapy (VRET). This article describes the results of a pilot study undertaken to explore the possibilities and characteristics of VRET and determine requirements for a system to support therapists
An Approach to Extract Special Skills to Improve the Performance of Resume Selection

In the Internet era, the enterprises and companies receive thousands of resumes from the job seekers. Currently available filtering techniques and search services help the recruiters to filter thousands of resumes to few hundred potential ones. Since these filtered resumes are similar to each other, it is difficult to identify the potential resumes by examining each resume. We are investigating the issues related to the development of approaches to improve the performance of resume selection process. We have extended the notion of special features and proposed an approach to identify resumes with special skill information. In the literature, the notion of special features have been applied to improve the process of product selection in E-commerce environment. However, extending the notion of special features for the development of approach to process resumes is a complex task as resumes contain unformatted text or semi-formatted text. In this paper, we have proposed an approach by considering only skills related information of the resumes. The experimental results on the real world data-set of resumes show that the proposed approach has the potential to improve the process of resume selection.
A comparison of path planning strategies for autonomous exploration and mapping of unknown environments

To date, a large number of algorithms to solve the problem of autonomous exploration and mapping has been presented. However, few efforts have been made to compare these techniques. In this paper, an extensive study of the most important methods for autonomous exploration and mapping of unknown environments is presented. Furthermore, a representative subset of these techniques has been chosen to be analysed. This subset contains methods that differ in the level of multi-robot coordination and in the grade of integration with the simultaneous localization and mapping (SLAM) algorithm. These exploration techniques were tested in simulation and compared using different criteria as exploration time or map quality. The results of this analysis are shown in this paper. The weaknesses and strengths of each strategy have been stated and the most appropriate algorithm for each application has been determined.
Applications and limitations of radiomics.

Radiomics is an emerging field in quantitative imaging that uses advanced imaging features to objectively and quantitatively describe tumour phenotypes. Radiomic features have recently drawn considerable interest due to its potential predictive power for treatment outcomes and cancer genetics, which may have important applications in personalized medicine. In this technical review, we describe applications and challenges of the radiomic field. We will review radiomic application areas and technical issues, as well as proper practices for the designs of radiomic studies.
Inter-Weighted Alignment Network for Sentence Pair Modeling

Sentence pair modeling is a crucial problem in the field of natural language processing. In this paper, we propose a model to measure the similarity of a sentence pair focusing on the interaction information. We utilize the word level similarity matrix to discover fine-grained alignment of two sentences. It should be emphasized that each word in a sentence has a different importance from the perspective of semantic composition, so we exploit two novel and efficient strategies to explicitly calculate a weight for each word. Although the proposed model only use a sequential LSTM for sentence modeling without any external resource such as syntactic parser tree and additional lexicon features, experimental results show that our model achieves state-of-the-art performance on three datasets of two tasks.
AppDialogue: Multi-App Dialogues for Intelligent Assistants

Ming Sun∗ Yun-Nung Chen∗ Zhenhao Hua† Yulian Tamres-Rudnicky‡ Arnab Dash? Alexander I. Rudnicky∗ ∗†?School of Computer Science, Carnegie Mellon University †Pinterest ?Ernst & Young ∗{mings,yvchen,air}@cs.cmu.edu †‡?{troyhua0426,yulie355,arnabdash92}@gmail.com Abstract Users will interact with an individual app on smart devices (e.g., phone, TV, car) to fulfill a specific goal (e.g. find a photographer), but users may also pursue more complex tasks that will span multiple domains and apps (e.g. plan a wedding ceremony). Planning and executing such multi-app tasks are typically managed by users, considering the required global context awareness. To investigate how users arrange domains/apps to fulfill complex tasks in their daily life, we conducted a user study on 14 participants to collect such data from their Android smart phones. This document 1) summarizes the techniques used in the data collection and 2) provides a brief statistical description of the data. This data guilds the future direction for researchers in the fields of conversational agent and personal assistant, etc. This data is available at http://AppDialogue.com.
A solution to the facility layout problem using simulated annealing

Ž . In this paper a solution in the continual plane to the Facility Layout Problem FLP is presented. It is based on Simulated Ž . Annealing SA , a relatively recent algorithm for solving hard combinatorial optimization problems, like FLP. This approach Ž . may be applied either in General Facility Layout Problems GFLP considering facilities areas, shapes and orientations or in Ž . Machine Layout problems MLP considering machine’s pick-up and drop-off points. It has been applied to real-life situations with useful results, indicating the effectiveness of this approach. q 1998 Elsevier Science B.V. All rights reserved.
Failure analysis of parameter-induced simulation crashes in climate models

Simulations using IPCC (Intergovernmental Panel on Climate Change)-class climate models are subject to fail or crash for a variety of reasons. Quantitative analysis of the failures can yield useful insights to better understand and improve the models. During the course of uncertainty quantification (UQ) ensemble simulations to assess the effects of ocean model parameter uncertainties on climate simulations, we experienced a series of simulation crashes within the Parallel Ocean Program (POP2) component of the Community Climate System Model (CCSM4). About 8.5 % of our CCSM4 simulations failed for numerical reasons at combinations of POP2 parameter values. We applied support vector machine (SVM) classification from machine learning to quantify and predict the probability of failure as a function of the values of 18 POP2 parameters. A committee of SVM classifiers readily predicted model failures in an independent validation ensemble, as assessed by the area under the receiver operating characteristic (ROC) curve metric (AUC > 0.96). The causes of the simulation failures were determined through a global sensitivity analysis. Combinations of 8 parameters related to ocean mixing and viscosity from three different POP2 parameterizations were the major sources of the failures. This information can be used to improve POP2 and CCSM4 by incorporating correlations across the relevant parameters. Our method can also be used to quantify, predict, and understand simulation crashes in other complex geoscientific models.