nils-herrmann/clean_scirepeval huggingface dataset:
A Selfie is Worth a Thousand Words: Mining Personal Patterns behind User Selfie-posting Behaviours

Selfies have become increasingly fashionable in the social media era. People are willing to share their selfies in various social media platforms such as Facebook, Instagram and Flicker. The popularity of selfie have caught researchers’ attention, especially psychologists. In computer vision and machine learning areas, little attention has been paid to this phenomenon as a valuable data source. In this paper, we focus on exploring the deeper personal patterns behind people’s different kinds of selfie-posting behaviours. We develop this work based on a dataset of WeChat, one of the most extensively used instant messaging platform in China. In particular, we first propose an unsupervised approach to classify the images posted by users. Based on the classification result, we construct three types of user-level features that reflect user preference, activity and posting habit. Based on these features, for a series of selfie related tasks, we build classifiers that can accurately predict two sets of users with opposite selfie-posting behaviours. We have found that people’s interest, activity and posting habit have a great influence on their selfie-posting behaviours. Taking selfie frequency as an example, the classification accuracy between selfie-posting addict and nonaddict can reach 89.36%. We also prove that using user’s image information to predict these behaviours achieve better performance than using text information. More importantly, for each set of users with a specific selfie-posting behaviour, we extract and visualize significant personal patterns about them. In addition, to concisely construct the relation between personal pattern and selfie-posting behaviour, we cluster users and extract their high-level attributes, revealing the correlation between these attributes and users’ selfie-posting behaviours. In the end, we demonstrate that users’ selfie-posting behaviour, as a good predictor, could predict their different preferences toward these high-level attributes accurately.
Mapping QTLs with epistatic effects and QTL×environment interactions by mixed linear model approaches

A new methodology based on mixed linear models was developed for mapping QTLs with digenic epistasis and QTL×environment (QE) interactions. Reliable estimates of QTL main effects (additive and epistasis effects) can be obtained by the maximum-likelihood estimation method, while QE interaction effects (additive×environment interaction and epistasis×environment interaction) can be predicted by the-best-linear-unbiased-prediction (BLUP) method. Likelihood ratio and t statistics were combined for testing hypotheses about QTL effects and QE interactions. Monte Carlo simulations were conducted for evaluating the unbiasedness, accuracy, and power for parameter estimation in QTL mapping. The results indicated that the mixed-model approaches could provide unbiased estimates for both positions and effects of QTLs, as well as unbiased predicted values for QE interactions. Additionally, the mixed-model approaches also showed high accuracy and power in mapping QTLs with epistatic effects and QE interactions. Based on the models and the methodology, a computer software program (QTLMapper version 1.0) was developed, which is suitable for interval mapping of QTLs with additive, additive×additive epistasis, and their environment interactions.
Mobile AD(D): Estimating Mobile App Session Times for Better Ads

While mobile advertisement is the dominant source of revenue for mobile apps, the usage patterns of mobile users, and thus their engagement and exposure times, may be in conflict with the effectiveness of current ads. Users engagement with apps can range from a few seconds to several minutes, depending on a number of factors such as users' locations, concurrent activities and goals. Despite the wide-range of engagement times, the current format of ad auctions dictates that ads are priced, sold and configured prior to actual viewing, that is regardless of the actual ad exposure time.
 We argue that the wealth of easy-to-gather contextual information on mobile devices is sufficient to allow advertisers to make better choices by effectively predicting exposure time. We analyze mobile device usage patters with a detailed two-week long user study of 37 users in the US and South Korea. After characterizing application session times, we use factor analysis to derive a simple predictive model and show that is able to offer improved accuracy compared to mean session time over 90% of the time. We make the case for including predicted ad exposure duration in the price of mobile advertisements and posit that such information could significantly impact the effectiveness of mobile ads by giving publishers the ability to tune campaigns for engagement length, and enable a more efficient market for ad impressions while lowering network utilization and device power consumption.
Audit Games with Multiple Defender Resources

Modern organizations (e.g., hospitals, social networks, government agencies) rely heavily on audit to detect and punish insiders who inappropriately access and disclose confidential information. Recent work on audit games models the strategic interaction between an auditor with a single audit resource and auditees as a Stackelberg game, augmenting associated well-studied security games with a configurable punishment parameter. We significantly generalize this audit game model to account for multiple audit resources where each resource is restricted to audit a subset of all potential violations, thus enabling application to practical auditing scenarios. We provide an FPTAS that computes an approximately optimal solution to the resulting non-convex optimization problem. The main technical novelty is in the design and correctness proof of an optimization transformation that enables the construction of this FPTAS. In addition, we experimentally demonstrate that this transformation significantly speeds up computation of solutions for a class of audit games and security games.
The QUIC Transport Protocol: Design and Internet-Scale Deployment

We present our experience with QUIC, an encrypted, multiplexed, and low-latency transport protocol designed from the ground up to improve transport performance for HTTPS traffic and to enable rapid deployment and continued evolution of transport mechanisms. QUIC has been globally deployed at Google on thousands of servers and is used to serve traffic to a range of clients including a widely-used web browser (Chrome) and a popular mobile video streaming app (YouTube). We estimate that 7% of Internet traffic is now QUIC. We describe our motivations for developing a new transport, the principles that guided our design, the Internet-scale process that we used to perform iterative experiments on QUIC, performance improvements seen by our various services, and our experience deploying QUIC globally. We also share lessons about transport design and the Internet ecosystem that we learned from our deployment.
Blogging at work and the corporate attention economy

The attention economy motivates participation in peer-produced sites on the Web like YouTube and Wikipedia. However, this economy appears to break down at work. We studied a large internal corporate blogging community using log files and interviews and found that employees expected to receive attention when they contributed to blogs, but these expectations often went unmet. Like in the external blogosphere, a few people received most of the attention, and many people received little or none. Employees expressed frustration if they invested time and received little or no perceived return on investment. While many corporations are looking to adopt Web-based communication tools like blogs, wikis, and forums, these efforts will fail unless employees are motivated to participate and contribute content. We identify where the attention economy breaks down in a corporate blog community and suggest mechanisms for improvement.
Enhanced Attacks on Defensively Distilled Deep Neural Networks

Deep neural networks (DNNs) have achieved tremendous success in many tasks of machine learning, such as the image classification. Unfortunately, researchers have shown that DNNs are easily attacked by adversarial examples, slightly perturbed images which can mislead DNNs to give incorrect classification results. Such attack has seriously hampered the deployment of DNN systems in areas where security or safety requirements are strict, such as autonomous cars, face recognition, malware detection. Defensive distillation is a mechanism aimed at training a robust DNN which significantly reduces the effectiveness of adversarial examples generation. However, the state-of-the-art attack can be successful on distilled networks with 100% probability. But it is a white-box attack which needs to know the inner information of DNN. Whereas, the black-box scenario is more general. In this paper, we first propose the -neighborhood attack, which can fool the defensively distilled networks with 100% success rate in the white-box setting, and it is fast to generate adversarial examples with good visual quality. On the basis of this attack, we further propose the regionbased attack against defensively distilled DNNs in the blackbox setting. And we also perform the bypass attack to indirectly break the distillation defense as a complementary method. The experimental results show that our black-box attacks have a considerable success rate on defensively distilled networks.
NVC-Hashmap: A Persistent and Concurrent Hashmap For Non-Volatile Memories

Non-volatile RAM (NVRAM) will fundamentally change in-memory databases as data structures do not have to be explicitly backed up to hard drives or SSDs, but can be inherently persistent in main memory. To guarantee consistency even in the case of power failures, programmers need to ensure that data is flushed from volatile CPU caches where it would be susceptible to power outages to NVRAM.
 In this paper, we present the NVC-Hashmap, a lock-free hashmap that is used for unordered dictionaries and delta indices in in-memory databases. The NVC-Hashmap is then evaluated in both stand-alone and integrated database benchmarks and compared to a B+-Tree based persistent data structure.
A Quick Artificial Bee Colony Algorithm for Image Thresholding

The computational complexity grows exponentially for multi-level thresholding (MT) with the increase of the number of thresholds. Taking Kapur’s entropy as the optimized objective function, the paper puts forward the modified quick artificial bee colony algorithm (MQABC), which employs a new distance strategy for neighborhood searches. The experimental results show that MQABC can search out the optimal thresholds efficiently, precisely, and speedily, and the thresholds are very close to the results examined by exhaustive searches. In comparison to the EMO (Electro-Magnetism optimization), which is based on Kapur’s entropy, the classical ABC algorithm, and MDGWO (modified discrete grey wolf optimizer) respectively, the experimental results demonstrate that MQABC has exciting advantages over the latter three in terms of the running time in image thesholding, while maintaining the efficient segmentation quality.
Impact of language on development of auditory-visual speech perception.

The McGurk effect paradigm was used to examine the developmental onset of inter-language differences between Japanese and English in auditory-visual speech perception. Participants were asked to identify syllables in audiovisual (with congruent or discrepant auditory and visual components), audio-only, and video-only presentations at various signal-to-noise levels. In Experiment 1 with two groups of adults, native speakers of Japanese and native speakers of English, the results on both percent visually influenced responses and reaction time supported previous reports of a weaker visual influence for Japanese participants. In Experiment 2, an additional three age groups (6, 8, and 11 years) in each language group were tested. The results showed that the degree of visual influence was low and equivalent for Japanese and English language 6-year-olds, and increased over age for English language participants, especially between 6 and 8 years, but remained the same for Japanese participants. This may be related to the fact that English language adults and older children processed visual speech information relatively faster than auditory information whereas no such inter-modal differences were found in the Japanese participants' reaction times.
A Wideband Bandpass Filter With Reconfigurable Bandwidth Based on Cross-Shaped Resonator

A wideband bandpass filter (BPF) with reconfigurable bandwidth (BW) is proposed based on a parallel-coupled line structure and a cross-shaped resonator with open stubs. The p-i-n diodes are used as the tuning elements, which can implement three reconfigurable BW states. The prototype of the designed filter reports an absolute BW tuning range of 1.22 GHz, while the fractional BW is varied from 34.8% to 56.5% when centered at 5.7 GHz. The simulation and measured results are in good agreement. Comparing with previous works, the proposed reconfigurable BPF features wider BW tuning range with maximum number of tuning states.
A Neural Framework for Generalized Topic Models

Topic models for text corpora comprise a popular family of methods that have inspired many extensions to encode properties such as sparsity, interactions with covariates, and the gradual evolution of topics. In this paper, we combine certain motivating ideas behind variations on topic models with modern techniques for variational inference to produce a flexible framework for topic modeling that allows for rapid exploration of different models. We first discuss how our framework relates to existing models, and then demonstrate that it achieves strong performance, with the introduction of sparsity controlling the trade off between perplexity and topic coherence. We have released our code and preprocessing scripts to support easy future comparisons and exploration.
Full-mouth minimally invasive adhesive rehabilitation to treat severe dental erosion: a case report.

Dental erosion is increasing, and only recently are clinicians starting to acknowledge the problem. A prospective clinical trial investigating which therapeutic approach must be undertaken to treat erosion and when is under way at the University of Geneva (Geneva Erosion Study). All patients affected by dental erosion who present with signs of dentin exposure are immediately treated using only adhesive techniques. In this article, the full-mouth adhesive rehabilitation of one of these patients affected by severe dental erosion (ACE class IV) is illustrated. By the end of the therapy, a very pleasing esthetic outcome had been achieved (esthetic success), all of the patient's teeth maintained their vitality, and the amount of tooth structure sacrificed to complete the adhesive full-mouth rehabilitation was negligible (biological success).
Mobile Mental Wellness Training for Stress Management: Feasibility and Design Implications Based on a One-Month Field Study

Prevention and management of work-related stress and related mental problems is a great challenge. Mobile applications are a promising way to integrate prevention strategies into the everyday lives of citizens.



The objectives of this study was to study the usage, acceptance, and usefulness of a mobile mental wellness training application among working-age individuals, and to derive preliminary design implications for mobile apps for stress management.



Oiva, a mobile app based on acceptance and commitment therapy (ACT), was designed to support active learning of skills related to mental wellness through brief ACT-based exercises in the daily life. A one-month field study with 15 working-age participants was organized to study the usage, acceptance, and usefulness of Oiva. The usage of Oiva was studied based on the usage log files of the application. Changes in wellness were measured by three validated questionnaires on stress, satisfaction with life (SWLS), and psychological flexibility (AAQ-II) at the beginning and at end of the study and by user experience questionnaires after one week's and one month's use. In-depth user experience interviews were conducted after one month's use to study the acceptance and user experiences of Oiva.



Oiva was used actively throughout the study. The average number of usage sessions was 16.8 (SD 2.4) and the total usage time per participant was 3 hours 12 minutes (SD 99 minutes). Significant pre-post improvements were obtained in stress ratings (mean 3.1 SD 0.2 vs mean 2.5 SD 0.1, P=.003) and satisfaction with life scores (mean 23.1 SD 1.3 vs mean 25.9 SD 0.8, P=.02), but not in psychological flexibility. Oiva was perceived easy to use, acceptable, and useful by the participants. A randomized controlled trial is ongoing to evaluate the effectiveness of Oiva on working-age individuals with stress problems.



A feasibility study of Oiva mobile mental wellness training app showed good acceptability, usefulness, and engagement among the working-age participants, and provided increased understanding on the essential features of mobile apps for stress management. Five design implications were derived based on the qualitative findings: (1) provide exercises for everyday life, (2) find proper place and time for challenging content, (3) focus on self-improvement and learning instead of external rewards, (4) guide gently but do not restrict choice, and (5) provide an easy and flexible tool for self-reflection.
Intrusion Detection System using Classification Technique

In today’s world people are extensively using internet and thus are also vulnerable to its flaws. Cyber security is the main area where these flaws are exploited. Intrusion is one way to exploit the internet for search of valuable information that may cause devastating damage, which can be personal or on a large scale. Thus Intrusion detection systems are placed for timely detection of such intrusion and alert the user about the same. Intrusion Detection using hybrid classification technique consist of a hybrid model i.e. misuse detection model (AdTree based) and Anomaly model (svm based).NSL-KDD intrusion detection dataset plays a vital role in calibrating intrusion detection system and is extensively used by the researchers working in the field of intrusion detection. This paper presents Association rule mining technique for IDS.
Fuzzy Logic in KNIME - Modules for Approximate Reasoning -

In this paper we describe the open source data analytics platform KNIME, focusing particularly on extensions and modules supporting fuzzy sets and fuzzy learning algorithms such as fuzzy clustering algorithms, rule induction methods, and interactive clustering tools. In addition we outline a number of experimental extensions, which are not yet part of the open source release and present two illustrative examples from real world applications to demonstrate the power of the KNIME extensions.
Values, Attitudes Toward Interpersonal Violence, and Interpersonal Violent Behavior

The relevance of human values for the study of the motivational sources of interpersonal violent behavior was investigated in various fields of the social sciences. However, several past studies mixed up values with other dimensions like attitudes, norms, or beliefs, and only a few systematically assessed the effect of values on violent behavior relying on a value theory. Furthermore, in other studies, violence was often analyzed as a composite index of different forms of delinquent behavior rather than as violence per se. In the current study we address these gaps in the literature by building upon Schwartz' theory of basic human values. We use it to explain attitudes toward interpersonal violence and interpersonal violent behavior. We analyze data of young people (n = 1,810) drawn from a German study in Duisburg, Germany, which assessed various types of self-reported violent behavior as well as values and attitudes toward violence. We test structural equation models in which we explain interpersonal violent behavior with basic human values, and where attitudes toward interpersonal violent behavior mediate this relation. Results show that self-transcendence and conservation values are associated negatively and power and stimulation values positively with interpersonal violent behavior. In addition, attitudes operate as a partial mediator for the former and as a full mediator for the latter in the relation between values and violent behavior. Despite a dominant association between attitudes and behavior, values themselves can significantly contribute to the explanation of violent behavior.
Prognostics-enhanced Automated Contingency Management for advanced autonomous systems

This paper introduces a novel prognostics-enhanced automated contingency management (or ACM+P) paradigm based on both current health state (diagnosis) and future health state estimates (prognosis) for advanced autonomous systems. Including prognostics in ACM system allows not only fault accommodation, but also fault mitigation via proper control actions based on short term prognosis, and moreover, the establishment of a long term operational plan that optimizes the utility of the entire system based on long term prognostics. Technical challenges are identified and addressed by a hierarchical ACM+P architecture that allows fault accommodation and mitigation at various levels in the system ranging from component level control reconfiguration, system level control reconfiguration, to high level mission re-planning and resource redistribution. The ACM+P paradigm was developed and evaluated in a high fidelity unmanned aerial vehicle (UAV) simulation environment with flight-proven baseline flight controller and simulated diagnostics and prognostics of flight control actuators. Simulation results are presented. The ACM+P concept, architecture and the generic methodologies presented in this paper are applicable to many advanced autonomous systems such as deep space probes, unmanned autonomous vehicles, and military and commercial aircrafts.
Online optimization for scheduling preemptable tasks on IaaS cloud systems

In Infrastructure-as-a-Service (IaaS) cloud computing, computational resources are provided to remote users in the form of leases. For a cloud user, he/she can request multiple cloud services simultaneously. In this case, parallel processing in the cloud system can improve the performance. When applying parallel processing in cloud computing, it is necessary to implement a mechanism to allocate resource and schedule the execution order of tasks. Furthermore, a resource optimization mechanism with preemptable task execution can increase the utilization of clouds. In this paper, we propose two online dynamic resource allocation algorithms for the IaaS cloud systemwith preemptable tasks. Our algorithms adjust the resource allocation dynamically based on the updated information of the actual task executions. And the experimental results show that our algorithms can significantly improve the performance in the situation where resource contention is fierce.