nils-herrmann/clean_scirepeval huggingface dataset:
Factors affecting O2O e-commerce take-away online review

The paper analyzed what affecting O2O e-commerce take-away industry online review in China in recent years, and how these factors work. Also, it obtained the ranking of these factors' influence. The study used Rost CM6.0 to draw some information from a third-party take-away platform, and established a model by using binary logistic regression. Lastly, SPSS was used to analyze data. The results are:1) affecting factors contain time, price, convenience, service and advertisement;2) time and price have negative effect on online review, while convenience, service and advertisement have positive effect on online review;3) the impact extent of convenience is high than that of other factors.
Hate Speech Detection: A Solved Problem? The Challenging Case of Long Tail on Twitter

In recent years, the increasing propagation of hate speech on social media and the urgent need for effective countermeasures have drawn significant investment from governments, companies, and researchers. A large number of methods have been developed for automated hate speech detection online. This aims to classify textual content into non-hate or hate speech, in which case the method may also identify the targeting characteristics (i.e., types of hate, such as race, and religion) in the hate speech. However, we notice significant difference between the performance of the two (i.e., non-hate v.s. hate). In this work, we argue for a focus on the latter problem for practical reasons. We show that it is a much more challenging task, as our analysis of the language in the typical datasets shows that hate speech lacks unique, discriminative features and therefore is found in the ‘long tail’ in a dataset that is difficult to discover. We then propose Deep Neural Network structures serving as feature extractors that are particularly effective for capturing the semantics of hate speech. Our methods are evaluated on the largest collection of hate speech datasets based on Twitter, and are shown to be able to outperform the best performing method by up to 5 percentage points in macro-average F1, or 8 percentage points in the more challenging case of identifying hateful content.
Select-additive learning: Improving generalization in multimodal sentiment analysis

Multimodal sentiment analysis is drawing an increasing amount of attention these days. It enables mining of opinions in video reviews which are now available aplenty on online platforms. However, multimodal sentiment analysis has only a few high-quality data sets annotated for training machine learning algorithms. These limited resources restrict the generalizability of models, where, for example, the unique characteristics of a few speakers (e.g., wearing glasses) may become a confounding factor for the sentiment classification task. In this paper, we propose a Select-Additive Learning (SAL) procedure that improves the generalizability of trained neural networks for multimodal sentiment analysis. In our experiments, we show that our SAL approach improves prediction accuracy significantly in all three modalities (verbal, acoustic, visual), as well as in their fusion. Our results show that SAL, even when trained on one dataset, achieves good generalization across two new test datasets.
A Novel Dual-Feed Slot-Coupling Feeding Technique for Circularly Polarized Patch Arrays

In this letter, a novel slot-coupling feeding technique has been adopted to realize a circularly polarized (CP) 2 × 2 microstrip array. Each array element is fed through two microstrip lines that are excited 90° out of phase (dual-feed technique) and coupled to a square patch by means of a square-ring slot realized in the feeding network ground plane. Design procedure, simulation results, and measurement data are presented for a 2 × 2 array working in the WiMax 3.3-3.8 GHz frequency band (14% percentage bandwidth). Due to both the symmetry properties of the novel slot-coupling feeding configuration and the implementation of a sequential rotation technique, excellent axial ratio (AR) performance is achieved in the WiMax band (AR < 1.35 dB at broadside) and for any direction in the antenna main beam (AR < 2.25 dB at 3.55 GHz). Actually, the 3-dB AR bandwidth is larger than the WiMax frequency band, as it goes up to about 30%.
CAPTCHA Breaking with Deep Learning

CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) are distortions of texts or images so that they are still recognizable to most humans but can become difficult for a computer to recognize. Additionally, many CAPTCHAs also inject noise or additional structures such as blobs or lines into the image to further make it difficult to recognize. They are primarily employed as security measures on websites to prevent bots from accessing or performing transactions on the site.
Advanced persistent threats: Behind the scenes

Advanced persistent threats (APTs) pose a significant risk to nearly every infrastructure. Due to the sophistication of these attacks, they are able to bypass existing security systems and largely infiltrate the target network. The prevention and detection of APT campaigns is also challenging, because of the fact that the attackers constantly change and evolve their advanced techniques and methods to stay undetected. In this paper we analyze 22 different APT reports and give an overview of the used techniques and methods. The analysis is focused on the three main phases of APT campaigns that allow to identify the relevant characteristics of such attacks. For each phase we describe the most commonly used techniques and methods. Through this analysis we could reveal different relevant characteristics of APT campaigns, for example that the usage of 0-day exploit is not common for APT attacks. Furthermore, the analysis shows that the dumping of credentials is a relevant step in the lateral movement phase for most APT campaigns. Based on the identified characteristics, we also propose concrete prevention and detection approaches that make it possible to identify crucial malicious activities that are performed during APT campaigns.
Friend Networking Sites and Their Relationship to Adolescents' Well-Being and Social Self-Esteem

The aim of this study was to investigate the consequences of friend networking sites (e.g., Friendster, MySpace) for adolescents' self-esteem and well-being. We conducted a survey among 881 adolescents (10-19-year-olds) who had an online profile on a Dutch friend networking site. Using structural equation modeling, we found that the frequency with which adolescents used the site had an indirect effect on their social self-esteem and well-being. The use of the friend networking site stimulated the number of relationships formed on the site, the frequency with which adolescents received feedback on their profiles, and the tone (i.e., positive vs. negative) of this feedback. Positive feedback on the profiles enhanced adolescents' social self-esteem and well-being, whereas negative feedback decreased their self-esteem and well-being.
Attentive Semantic Role Labeling with Boundary Indicator

The goal of semantic role labeling (SRL) is to discover the predicate-argument structure of a sentence, which plays a critical role in deep processing of natural language. This paper introduces simple yet effective auxiliary tags for dependency-based SRL to enhance a syntaxagnostic model with multi-hop self-attention. Our syntax-agnostic model achieves competitive performance with state-of-the-art models on the CoNLL-2009 benchmarks both for English and Chinese.
Feasibility of Primary Tumor Culture Models and Preclinical Prediction Assays for Head and Neck Cancer: A Narrative Review

Primary human tumor culture models allow for individualized drug sensitivity testing and are therefore a promising technique to achieve personalized treatment for cancer patients. This would especially be of interest for patients with advanced stage head and neck cancer. They are extensively treated with surgery, usually in combination with high-dose cisplatin chemoradiation. However, adding cisplatin to radiotherapy is associated with an increase in severe acute toxicity, while conferring only a minor overall survival benefit. Hence, there is a strong need for a preclinical model to identify patients that will respond to the intended treatment regimen and to test novel drugs. One of such models is the technique of culturing primary human tumor tissue. This review discusses the feasibility and success rate of existing primary head and neck tumor culturing techniques and their corresponding chemo- and radiosensitivity assays. A comprehensive literature search was performed and success factors for culturing in vitro are debated, together with the actual value of these models as preclinical prediction assay for individual patients. With this review, we aim to fill a gap in the understanding of primary culture models from head and neck tumors, with potential importance for other tumor types as well.
Virtual CPU validation

Testing the hypervisor is important for ensuring the correct operation and security of systems, but it is a hard and challenging task. We observe, however, that the challenge is similar in many respects to that of testing real CPUs. We thus propose to apply the testing environment of CPU vendors to hypervisors. We demonstrate the advantages of our proposal by adapting Intel's testing facility to the Linux KVM hypervisor. We uncover and fix 117 bugs, six of which are security vulnerabilities. We further find four flaws in Intel virtualization technology, causing a disparity between the observable behavior of code running on virtual and bare-metal servers.
A hybrid permanent magnet and wound field synchronous machine with displaced reluctance axis capable of symmetric four quadrant operation

This paper presents a novel hybrid permanent magnet and wound field synchronous machine geometry with a displaced reluctance axis. This concept is known for improving motor operation performance and efficiency at the cost of an inferior generator operation. To overcome this disadvantage, the proposed machine geometry is capable of inverting the magnetic asymmetry dynamically. Thereby, the positive effects of the magnetic asymmetry can be used in any operation point. This paper examines the theoretical background and shows the benefits of this geometry by means of simulation and measurement. The prototype achieves an increase in torque of 4 % and an increase in efficiency of 2 percentage points over a conventional electrically excited synchronous machine.
A comparative study of neural network models for lexical intent classification

Domain and intent classification are critical pre-processing steps for many speech understanding and dialog systems, as it allows for certain types of utterances to be routed to particular subsystems. In previous work, we explored many types of neural network (NN) architectures - some feedforward and some recurrent - for lexical intent classification and found that they improved upon more traditional statistical baselines. In this paper we carry out a more comprehensive comparison of NN models including the recently proposed gated recurrent unit network, for two domain/intent classification tasks. Furthermore, whereas the previous work was confined to relatively small and controlled datasets, we now include experiments based on a large set obtained from the Cortana personal assistant application. We compare feedforward, recurrent, and gated - such as LSTM and GRU - networks against each other. On both the ATIS intent task and the much larger Cortana domain classification tasks, gated networks outperform recurrent models, which in turn outperform feedforward networks. Also, we compared standard word vector models against a representation which encodes words as sets of character n-grams to mitigate the out-of-vocabulary problem. We find that in nearly all cases, the standard word vectors outperform character-based word representations. Best results are obtained by linearly combining scores from NN models with log likelihood ratios obtained from N-gram language models.
How Software Developers Mitigate Collaboration Friction with Chatbots

Margaret-Anne Storey University of Victoria Victoria, BC, Canada mstorey@uvic.ca  Modern software developers rely on an extensive set of social media tools and communication channels. The adoption of team communication platforms has led to the emergence of conversation-based tools and integrations, many of which are chatbots. Understanding how software developers manage their complex constellation of collaborators in conjunction with the practices and tools they use can bring valuable insights into socio-technical collaborative work in software development and other knowledge work domains.
Mining Exoticism from Visual Content with Fusion-based Deep Neural Networks

Exoticism is the charm of the unfamiliar, it often means unusual, mystery, and it can evoke the atmosphere of remote lands. Although it has received interest in different arts, like painting and music, no study has been conducted on understanding exoticism from a computational perspective. To the best of our knowledge, this work is the first to explore the problem of exoticism-aware image classification, aiming at automatically measuring the amount of exoticism in images and investigating the significant aspects of the task. The estimation of image exoticism could be applied in fields like advertising and travel suggestion, as well as to increase serendipity and diversity of recommendations and search results. We propose a Fusion-based Deep Neural Network (FDNN) for this task, which combines image representations learned by Deep Neural Networks with visual and semantic hand-crafted features. Comparisons with other Machine Learning models show that our proposed architecture is the best performing one, reaching accuracy over 83% and 91% on two different datasets. Moreover, experiments with classifiers exploiting both visual and semantic features allow to analyze what are the most important aspects for identifying exotic content. Ground truth has been gathered by retrieving exotic and not exotic images through a web search engine by posing queries with exotic and not exotic semantics, and then assessing the exoticism of the retrieved images via a crowdsourcing evaluation. The dataset is publicly released to promote advances in this novel field.
IP Geolocation through Reverse DNS

IP Geolocation databases are widely used in online services to map end user IP addresses to their geographical locations. However, they use proprietary geolocation methods and in some cases they have poor accuracy. We propose a systematic approach to use publicly accessible reverse DNS hostnames for geolocating IP addresses. Our method is designed to be combined with other geolocation data sources. We cast the task as a machine learning problem where for a given hostname, we generate and rank a list of potential location candidates. We evaluate our approach against three state of the art academic baselines and two state of the art commercial IP geolocation databases. We show that our work significantly outperforms the academic baselines, and is complementary and competitive with commercial databases. To aid reproducibility, we open source our entire approach.
Anaerobic threshold: the concept and methods of measurement.

The anaerobic threshold (AnT) is defined as the highest sustained intensity of exercise for which measurement of oxygen uptake can account for the entire energy requirement. At the AnT, the rate at which lactate appears in the blood will be equal to the rate of its disappearance. Although inadequate oxygen delivery may facilitate lactic acid production, there is no evidence that lactic acid production above the AnT results from inadequate oxygen delivery. There are many reasons for trying to quantify this intensity of exercise, including assessment of cardiovascular or pulmonary health, evaluation of training programs, and categorization of the intensity of exercise as mild, moderate, or intense. Several tests have been developed to determine the intensity of exercise associated with AnT: maximal lactate steady state, lactate minimum test, lactate threshold, OBLA, individual anaerobic threshold, and ventilatory threshold. Each approach permits an estimate of the intensity of exercise associated with AnT, but also has consistent and predictable error depending on protocol and the criteria used to identify the appropriate intensity of exercise. These tests are valuable, but when used to predict AnT, the term that describes the approach taken should be used to refer to the intensity that has been identified, rather than to refer to this intensity as the AnT.
"They Are Happier and Having Better Lives than I Am": The Impact of Using Facebook on Perceptions of Others' Lives

Facebook, as one of the most popular social networking sites among college students, provides a platform for people to manage others' impressions of them. People tend to present themselves in a favorable way on their Facebook profile. This research examines the impact of using Facebook on people's perceptions of others' lives. It is argued that those with deeper involvement with Facebook will have different perceptions of others than those less involved due to two reasons. First, Facebook users tend to base judgment on examples easily recalled (the availability heuristic). Second, Facebook users tend to attribute the positive content presented on Facebook to others' personality, rather than situational factors (correspondence bias), especially for those they do not know personally. Questionnaires, including items measuring years of using Facebook, time spent on Facebook each week, number of people listed as their Facebook "friends," and perceptions about others' lives, were completed by 425 undergraduate students taking classes across various academic disciplines at a state university in Utah. Surveys were collected during regular class period, except for two online classes where surveys were submitted online. The multivariate analysis indicated that those who have used Facebook longer agreed more that others were happier, and agreed less that life is fair, and those spending more time on Facebook each week agreed more that others were happier and had better lives. Furthermore, those that included more people whom they did not personally know as their Facebook "friends" agreed more that others had better lives.
Mobile interface design for low-literacy populations

Despite the proliferation of mobile health applications, few target low literacy users. This is a matter of concern because 43% of the United States population is functionally illiterate. To empower everyone to be a full participant in the evolving health system and prevent further disparities, we must understand the design needs of low literacy populations. In this paper, we present two complementary studies of four graphical user interface (GUI) widgets and three different cross-page navigation styles in mobile applications with a varying literacy, chronically-ill population. Participant's navigation and interaction styles were documented while they performed search tasks using high fidelity prototypes running on a mobile device. Results indicate that participants could use any non-text based GUI widgets. For navigation structures, users performed best when navigating a linear structure, but preferred the features of cross-linked navigation. Based on these findings, we provide some recommendations for designing accessible mobile applications for varying-literacy populations.
Internet of Things: Ubiquitous Home Control and Monitoring System using Android based Smart Phone

Th is paper presents a low cost and flexib le home control and monitoring system using an embedded micro -web server, with IP connectivity for accessing and controlling devices and appliances remotely using Android based Smart phone app. The proposed system does not require a dedicated server PC with respect to similar systems and offers a novel communicat ion protocol to monitor and control the home environment with more than just the switching functionality. To demonstrate the feasibility and effectiveness of this system, devices such as light switches, power p lug, temperature sensor and current sensor have been integrated with the proposed home control system.
Lip Reading in Profile

There has been a quantum leap in the performance of automated lip reading recently due to the application of neural network sequence models trained on a very large corpus of aligned text and face videos. However, this advance has only been demonstrated for frontal or near frontal faces, and so the question remains: can lips be read in profile to the same standard? The objective of this paper is to answer that question. We make three contributions: first, we obtain a new large aligned training corpus that contains profile faces, and select these using a face pose regressor network; second, we propose a curriculum learning procedure that is able to extend SyncNet  (a network to synchronize face movements and speech) progressively from frontal to profile faces; third, we demonstrate lip reading in profile for unseen videos. The trained model is evaluated on a held out test set, and is also shown to far surpass the state of the art on the OuluVS2 multi-view benchmark.
Energy production from biomass (Part 2): Conversion technologies.

The use of biomass to provide energy has been fundamental to the development of civilisation. In recent times pressures on the global environment have led to calls for an increased use of renewable energy sources, in lieu of fossil fuels. Biomass is one potential source of renewable energy and the conversion of plant material into a suitable form of energy, usually electricity or as a fuel for an internal combustion engine, can be achieved using a number of different routes, each with specific pros and cons. A brief review of the main conversion processes is presented, with specific regard to the production of a fuel suitable for spark ignition gas engines.
Reconfigurable THz metamaterial antenna based on graphene

The adoption of metamaterials in the development of terahertz (THz) antennas has led to tremendous progresses in the THz field. In this paper, a reconfigurable THz patch antenna based on graphene is presented, whose resonance frequency can be changed depending on the applied voltage. By using an array of split ring resonators (SRR) also made of graphene, both bandwidth and radiation properties are enhanced; it is found that both the resonance frequency and bandwidth change with the applied voltage.
Convolutional Interaction Network for Natural Language Inference

Attention-based neural models have achieved great success in natural language inference (NLI). In this paper, we propose the Convolutional Interaction Network (CIN), a general model to capture the interaction between two sentences, which can be an alternative to the attention mechanism for NLI. Specifically, CIN encodes one sentence with the filters dynamically generated based on another sentence. Since the filters may be designed to have various numbers and sizes, CIN can capture more complicated interaction patterns. Experiments on three very large datasets demonstrate CIN’s efficacy.