nils-herrmann/clean_scirepeval huggingface dataset:
Strong Baselines for Neural Semi-Supervised Learning under Domain Shift

Novel neural models have been proposed in recent years for learning under domain shift. Most models, however, only evaluate on a single task, on proprietary datasets, or compare to weak baselines, which makes comparison of models difficult. In this paper, we re-evaluate classic general-purpose bootstrapping approaches in the context of neural networks under domain shifts vs. recent neural approaches and propose a novel multi-task tri-training method that reduces the time and space complexity of classic tri-training. Extensive experiments on two benchmarks are negative: while our novel method establishes a new state-of-the-art for sentiment analysis, it does not fare consistently the best. More importantly, we arrive at the somewhat surprising conclusion that classic tri-training, with some additions, outperforms the state of the art. We conclude that classic approaches constitute an important and strong baseline.
Security Testing of Web Applications: A Search-Based Approach for Cross-Site Scripting Vulnerabilities

More and more web applications suffer the presence of cross-site scripting vulnerabilities that could be exploited by attackers to access sensitive information (such as credentials or credit card numbers). Hence proper tests are required to assess the security of web applications. In this paper, we resort to a search based approach for security testing web applications. We take advantage of static analysis to detect candidate cross-site scripting vulnerabilities. Input values that expose these vulnerabilities are searched by a genetic algorithm and, to help the genetic algorithm escape local optima, symbolic constraints are collected at run-time and passed to a solver. Search results represent test cases to be used by software developers to understand and fix security problems. We implemented this approach in a prototype and evaluated it on real world PHP code.
Modeling the impact of short- and long-term behavior on search personalization

User behavior provides many cues to improve the relevance of search results through personalization. One aspect of user behavior that provides especially strong signals for delivering better relevance is an individual's history of queries and clicked documents. Previous studies have explored how short-term behavior or long-term behavior can be predictive of relevance. Ours is the first study to assess how short-term (session) behavior and long-term (historic) behavior interact, and how each may be used in isolation or in combination to optimally contribute to gains in relevance through search personalization. Our key findings include: historic behavior provides substantial benefits at the start of a search session; short-term session behavior contributes the majority of gains in an extended search session; and the combination of session and historic behavior out-performs using either alone. We also characterize how the relative contribution of each model changes throughout the duration of a session. Our findings have implications for the design of search systems that leverage user behavior to personalize the search experience.
Control of Towing Kites for Seagoing Vessels

In this paper, we present the basic features of the flight control of the SkySails towing kite system. After introducing the coordinate definitions and the basic system dynamics, we introduce a novel model used for controller design and justify its main dynamics with results from system identification based on numerous sea trials. We then present the controller design, which we successfully use for operational flights for several years. Finally, we explain the generation of dynamical flight patterns.
Effects of Chalk dust on Peak Expiratory Flow Rate in School Teachers

Chalk and board is a traditional and still the most commonly used teaching aid in developing countries like India. Chalk is major source of fine particulate matter in classroom, which may produce pulmonary function impairments.  The objective of the study was to compare Peak Expiratory Flow Rate (PEFR) of school teachers exposed to chalk dust with matched controls.  A comparative study was conducted in total of 150 participants, 75 teachers (study group) exposed to chalk dust and 75 controls. PEFR was measured in all participants using Cipla’s peak flow meter with EU scale. PEFR in the study and control groups were compared for both males and females using unpaired t test.  PEFR was significantly lower in study group (teachers) as compared to controls in both males (t value -5.350, p<0.05) and females (t value -5.101, p<0.05).  Teachers using chalk and board are at an increased risk of developing occupationally related pulmonary function impairments and hence there is a need to shift from routine chalk and board to marker and whiteboard.
Wh-Elements in Situ : Syntax or LF ?

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Dense 3D face alignment from 2D video for real-time use

To enable real-time, person-independent 3D registration from 2D video, we developed a 3D cascade regression approach in which facial landmarks remain invariant across pose over a range of approximately 60 degrees. From a single 2D image of a person's face, a dense 3D shape is registered in real time for each frame. The algorithm utilizes a fast cascade regression framework trained on high-resolution 3D face-scans of posed and spontaneous emotion expression. The algorithm first estimates the location of a dense set of landmarks and their visibility, then reconstructs face shapes by fitting a part-based 3D model. Because no assumptions are required about illumination or surface properties, the method can be applied to a wide range of imaging conditions that include 2D video and uncalibrated multi-view video. The method has been validated in a battery of experiments that evaluate its precision of 3D reconstruction, extension to multi-view reconstruction, temporal integration for videos and 3D head-pose estimation. Experimental findings strongly support the validity of real-time, 3D registration and reconstruction from 2D video. The software is available online at http://zface.org.
Thread Lift: Classification, Technique, and How to Approach to the Patient

The thread lift technique has become popular because it is less invasive, requires a shorter operation, less downtime, and results in fewer postoperative complications. The advantage of the technique is that the thread can be inserted under the skin without the need for long incisions. Currently, there are a lot of thread lift techniques with respect to the specific types of thread used on specific areas, such as the mid-face, lower face, or neck area.  To review the thread lift technique for specific areas according to type of thread, patient selection, and how to match the most appropriate to the patient.  A literature review technique was conducted by searching PubMed and MEDLINE, then compiled and summarized.  We have divided our protocols into two sections: Protocols for short suture, and protocols for long suture techniques. We also created 3D pictures for each technique to enhance understanding and application in a clinical setting.  There are advantages and disadvantages to short suture and long suture techniques. The best outcome for each patient depends on appropriate patient selection and determining the most suitable technique for the defect and area of patient concern. Thread lift, thread lift method, thread lift technique, thread lift procedure, threading.
Certainty-Driven Consistency Loss for Semi-supervised Learning

The recently proposed semi-supervised learning methods exploit consistency loss between different predictions under random perturbations. Typically, a student model is trained to predict consistently with the targets generated by a noisy teacher. However, they ignore the fact that not all training data provide meaningful and reliable information in terms of consistency. For misclassified data, blindly minimizing the consistency loss around them can hinder learning. In this paper, we propose a novel certaintydriven consistency loss (CCL) to dynamically select data samples that have relatively low uncertainty. Specifically, we measure the variance or entropy of multiple predictions under random augmentations and dropout as an estimation of uncertainty. Then, we introduce two approaches, i.e. Filtering CCL and Temperature CCL to guide the student learn more meaningful and certain/reliable targets, and hence improve the quality of the gradients backpropagated to the student. Experiments demonstrate the advantages of the proposed method over the state-of-the-art semi-supervised deep learning methods on three benchmark datasets: SVHN, CIFAR10, and CIFAR100. Our method also shows robustness to noisy labels.
Integrating sustainability into innovation project portfolio management – A strategic perspective

Project portfolio management in relation to innovation has increasingly gained the attention of practitioners and academics during the last decade. While significant progress has been made in the pursuit of a process approach to achieve an effective project portfolio management, limited attention has been paid to the issue of how to integrate sustainability into innovation portfolio management decision making. The literature is lacking insights on how to manage the innovation project portfolio throughout the strategic analysis phase to the monitoring of the portfolio performance in relation to sustainability during the development phase of projects. This paper presents a 5step framework for integrating sustainability in the innovation project portfolio management process in the field of product development. The framework can be applied for the management of a portfolio of three project categories that involve breakthrough projects, platform projects and derivative projects. It is based on the assessment of various methods of project evaluation and selection, and a case analysis in the automotive industry. It enables the integration of the three dimensions of sustainability into the innovation project portfolio management process within firms. The three dimensions of sustainability involve ecological sustainability, social sustainability and economic sustainability. Another benefit is enhancing the ability of firms to achieve an effective balance of investment between the three dimensions of sustainability, taking the competitive approach of a firm toward the marketplace into account.
Pixel-wise Attentional Gating for Parsimonious Pixel Labeling

To achieve dynamic inference in pixel labeling tasks, we propose Pixel-wise Attentional Gating (PAG), which learns to selectively process a subset of spatial locations at each layer of a deep convolutional network. PAG is a generic, architecture-independent, problem-agnostic mechanism that can be readily “plugged in” to an existing model with fine-tuning. We utilize PAG in two ways: 1) learning spatially varying pooling fields that improve model performance without the extra computation cost associated with multi-scale pooling, and 2) learning a dynamic computation policy for each pixel to decrease total computation (FLOPs) while maintaining accuracy. We extensively evaluate PAG on a variety of per-pixel labeling tasks, including semantic segmentation, boundary detection, monocular depth and surface normal estimation. We demonstrate that PAG allows competitive or state-ofthe-art performance on these tasks. Our experiments show that PAG learns dynamic spatial allocation of computation over the input image which provides better performance trade-offs compared to related approaches (e.g., truncating deep models or dynamically skipping whole layers). Generally, we observe PAG can reduce computation by 10% without noticeable loss in accuracy and performance degrades gracefully when imposing stronger computational constraints.
Value-Difference Based Exploration: Adaptive Control between Epsilon-Greedy and Softmax

This paper proposes “Value-Difference Based Exploration combined with Softmax action selection” (VDBE-Softmax) as an adaptive exploration/exploitation policy for temporal-difference learning. The advantage of the proposed approach is that exploration actions are only selected in situations when the knowledge about the environment is uncertain, which is indicated by fluctuating values during learning. The method is evaluated in experiments having deterministic rewards and a mixture of both deterministic and stochastic rewards. The results show that a VDBE-Softmax policy can outperform ε-greedy, Softmax and VDBE policies in combination with onand off-policy learning algorithms such as Q-learning and Sarsa. Furthermore, it is also shown that VDBE-Softmax is more reliable in case of value-function oscillations.
Insulin-loaded PLGA microspheres for glucose-responsive release.

Porous poly(lactic-co-glycolic acid) (PLGA) microspheres were prepared, loaded with insulin, and then coated in poly(vinyl alcohol) (PVA) and a novel boronic acid-containing copolymer [poly(acrylamide phenyl boronic acid-co-N-vinylcaprolactam); p(AAPBA-co-NVCL)]. Multilayer microspheres were generated using a layer-by-layer approach depositing alternating coats of PVA and p(AAPBA-co-NVCL) on the PLGA surface, with the optimal system found to be that with eight alternating layers of each coating. The resultant material comprised spherical particles with a porous PLGA core and the pores covered in the coating layers. Insulin could successfully be loaded into the particles, with loading capacity and encapsulation efficiencies reaching 2.83 ± 0.15 and 82.6 ± 5.1% respectively, and was found to be present in the amorphous form. The insulin-loaded microspheres could regulate drug release in response to a changing concentration of glucose. In vitro and in vivo toxicology tests demonstrated that they are safe and have high biocompatibility. Using the multilayer microspheres to treat diabetic mice, we found they can effectively control blood sugar levels over at least 18 days, retaining their glucose-sensitive properties during this time. Therefore, the novel multilayer microspheres developed in this work have significant potential as smart drug-delivery systems for the treatment of diabetes.
Things rank and gross in nature: a review and synthesis of moral disgust.

Much like unpalatable foods, filthy restrooms, and bloody wounds, moral transgressions are often described as "disgusting." This linguistic similarity suggests that there is a link between moral disgust and more rudimentary forms of disgust associated with toxicity and disease. Critics have argued, however, that such references are purely metaphorical, or that moral disgust may be limited to transgressions that remind us of more basic disgust stimuli. Here we review the evidence that moral transgressions do genuinely evoke disgust, even when they do not reference physical disgust stimuli such as unusual sexual behaviors or the violation of purity norms. Moral transgressions presented verbally or visually and those presented as social transactions reliably elicit disgust, as assessed by implicit measures, explicit self-report, and facial behavior. Evoking physical disgust experimentally renders moral judgments more severe, and physical cleansing renders them more permissive or more stringent, depending on the object of the cleansing. Last, individual differences in the tendency to experience disgust toward physical stimuli are associated with variation in moral judgments and morally relevant sociopolitical attitudes. Taken together, these findings converge to support the conclusion that moral transgressions can in fact elicit disgust, suggesting that moral cognition may draw upon a primitive rejection response. We highlight a number of outstanding issues and conclude by describing 3 models of moral disgust, each of which aims to provide an account of the relationship between moral and physical disgust.
KI 2014: Advances in Artificial Intelligence

We propose a dynamic logic of lying, wherein a ‘lie that φ’ (where φ is a formula in the logic) is an action in the sense of dynamic modal logic, that is interpreted as a state transformer relative to φ. The states that are being transformed are pointed Kripke models encoding the uncertainty of agents about their beliefs. Lies can be about factual propositions but also about modal formulas, such as the beliefs of other agents or the belief consequences of the lies of other agents.
Privacy Risks in Recommender Systems Personalization and Privacy

R ecommender systems have become important tools in ecommerce. They combine one user’s ratings of products or services with ratings from other users to answer queries such as “Would I like X?” with predictions and suggestions. Users thus receive anonymous recommendations from people with similar tastes. While this process seems innocuous, it aggregates user preferences in ways analogous to statistical database queries, which can be exploited to identify information about a particular user. This is especially true for users with eclectic tastes who rate products across different types or domains in the systems. These straddlers highlight the conflict between personalization and privacy in recommender systems. While straddlers enable serendipitous recommendations, information about their existence could be used in conjunction with other data sources to uncover identities and reveal personal details. We use a graph-theoretic model to study the benefit from and risk to straddlers.
Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering

Accurately answering a question about a given image requires combining observations with general knowledge. While this is effortless for humans, reasoning with general knowledge remains an algorithmic challenge. To advance research in this direction a novel ‘fact-based’ visual question answering (FVQA) task has been introduced recently along with a large set of curated facts which link two entities, i.e., two possible answers, via a relation. Given a question-image pair, deep network techniques have been employed to successively reduce the large set of facts until one of the two entities of the final remaining fact is predicted as the answer. We observe that a successive process which considers one fact at a time to form a local decision is sub-optimal. Instead, we develop an entity graph and use a graph convolutional network to ‘reason’ about the correct answer by jointly considering all entities. We show on the challenging FVQA dataset that this leads to an improvement in accuracy of around 7% compared to the state of the art.
Deep Variational Reinforcement Learning for POMDPs

Many real-world sequential decision making problems are partially observable by nature, and the environment model is typically unknown. Consequently, there is great need for reinforcement learning methods that can tackle such problems given only a stream of rewards and incomplete and noisy observations. In this paper, we propose deep variational reinforcement learning (DVRL), which introduces an inductive bias that allows an agent to learn a generative model of the environment and perform inference in that model to effectively aggregate the available information. We develop an n-step approximation to the evidence lower bound (ELBO), allowing the model to be trained jointly with the policy. This ensures that the latent state representation is suitable for the control task. In experiments on Mountain Hike and flickering Atari we show that our method outperforms previous approaches relying on recurrent neural networks to encode the past.
TransA: An Adaptive Approach for Knowledge Graph Embedding

Knowledge representation is a major topic in AI, and many studies attempt to represent entities and relations of know ledge base in a continuous vector space. Among these attempts, translation-based methods build entity and relati on vectors by minimizing the translation loss from a head entity to a tail one. In spite of the success of these methods, translation-based methods also suffer from the oversimpli fied loss metric, and are not competitive enough to model various and complex entities/relations in knowledge bases. To address this issue, we propose TransA, an adaptive metric approach for embedding, utilizing the metric learning idea s to provide a more flexible embedding method. Experiments are conducted on the benchmark datasets and our proposed method makes significant and consistent improvements over the state-of-the-art baselines.
An Universal Image Attractiveness Ranking Framework

We propose a new framework to rank image attractiveness using a novel pairwise deep network trained with a large set of side-by-side multi-labeled image pairs from a web image index. The judges only provide relative ranking between two images without the need to directly assign an absolute score, or rate any predefined image attribute, thus making the rating more intuitive and accurate. We investigate a deep attractiveness rank net (DARN), a combination of deep convolutional neural network and rank net, to directly learn an attractiveness score mean and variance for each image and the underlying criteria the judges use to label each pair. The extension of this model (DARN-V2) is able to adapt to individual judge's personal preference. We also show the attractiveness of search results are significantly improved by using this attractiveness information in a real commercial search engine. We evaluate our model against other state-of-the-art models on our side-by-side web test data and another public aesthetic data set. With much less judgments (1M vs 50M), our model outperforms on side-by-side labeled data, and is comparable on data labeled by absolute score.
Factors analysis of IPv6 user acceptance against security aspects based on concept of technology acceptance model (TAM) and technology threat avoidance theory (TTAT)

The evolution of IPv6 technology had become a worldwide trend and showed a significant increase, particularly with the near-coming era named “Internet of Things” or so-called IOT. Concomitant with the transition process from version 4 to version 6, there are open security hole that considered to be vulnerable, mainly against cyber-attacks that poses a threat to companies implements IPv6 network topology. The purpose of this research is to create a model of acceptance of the factors that influenced the behavior of individuals in providing security within IPv6 network topology and analysis of factors that affects the acceptance of individuals in anticipating security with regards to IPv6 network topology. This study was conducted using both, quantitative method focuses on statistical processing on the result of questionnaire filled by respondents using Structural Equation Modeling (SEM), as well as qualitative method to conduct Focus Group Discussion (FGD) and interviews with experts from various background such as: practitioners, academician and government representatives. The results showed ease of use provides insignificant correlation to the referred behavior of avoiding threat on IPv6 environment.