nils-herrmann/clean_scirepeval huggingface dataset:
TI-CNN: Convolutional Neural Networks for Fake News Detection

With the development of social networks, fake news for various commercial and political purposes has been appearing in large numbers and gotten widespread in the online world. With deceptive words, people can get infected by the fake news very easily and will share them without any fact-checking. For instance, during the 2016 US president election, various kinds of fake news about the candidates widely spread through both official news media and the online social networks. These fake news is usually released to either smear the opponents or support the candidate on their side. The erroneous information in the fake news is usually written to motivate the voters’ irrational emotion and enthusiasm. Such kinds of fake news sometimes can bring about devastating effects, and an important goal in improving the credibility of online social networks is to identify the fake news timely. In this paper, we propose to study the “fake news detection” problem. Automatic fake news identification is extremely hard, since pure model based fact-checking for news is still an open problem, and few existing models can be applied to solve the problem. With a thorough investigation of a fake news data, lots of useful explicit features are identified from both the text words and images used in the fake news. Besides the explicit features, there also exist some hidden patterns in the words and images used in fake news, which can be captured with a set of latent features extracted via the multiple convolutional layers in our model. A model named as TI-CNN (Text and Image information based Convolutinal Neural Network) is proposed in this paper. By projecting the explicit and latent features into a unified feature space, TI-CNN is trained with both the text and image information simultaneously. Extensive experiments carried on the real-world fake news datasets have demonstrate the effectiveness of TI-CNN in solving the fake new detection problem.
Auto-Encoding Variational Neural Machine Translation

Translation data is often a byproduct of mixing different sources of data. This could be intentional such as by mixing data of different domains or including back-translated monolingual data, but often also is the result of how the bilingual dataset was constructed: a combination of different documents independently translated in different translation directions, by different translators, agencies, etc. Most neural machine translation models do not explicitly account for such variation in their probabilistic model. We attempt to model this by proposing a deep generative model that generates source and target sentences jointly from a shared sentence-level latent representation. The latent representation is designed to capture variations in the data distribution and allows the model to adjust its language and translation model accordingly. We show that such a model leads to superior performance over a strong conditional neural machine translation baseline in three settings: in-domain training where the training and test data are of the same domain, mixed-domain training where we train on a mix of domains and test on each domain separately, and in-domain training where we also include synthetic (noisy) back-translated data. We furthermore extend the model to be used in a semi-supervised setting in order to incorporate target monolingual data during training. Doing this we derive the commonly employed back-translation heuristic in the form of a variational approximation to the posterior over the missing source sentence. This allows for training the back-translation network jointly with the rest of the model on a shared objective designed for source-to-target translation with minimal need of pre-processing. We find that the performance of this approach is not on par with the back-translation heuristic, but does lead to improvement over a model trained on bilingual data alone.
SVR-based music mood classification and context-based music recommendation

With the advent of the ubiquitous era, context-based music recommendation has become one of rapidly emerging applications. Context-based music recommendation requires multidisciplinary efforts including low level feature extraction, music mood classification and human emotion prediction. Especially, in this paper, we focus on the implementation issues of context-based mood classification and music recommendation. For mood classification, we reformulate it into a regression problem based on support vector regression (SVR). Through the use of the SVR-based mood classifier, we achieved 87.8% accuracy. For music recommendation, we reason about the user's mood and situation using both collaborative filtering and ontology technology. We implement a prototype music recommendation system based on this scheme and report some of the results that we obtained.
Active Transfer Learning for Cross-System Recommendation

Recommender systems, especially the newly launched ones, have to deal with the data-sparsity issue, where little existing rating information is available. Recently, transfer learning has been proposed to address this problem by leveraging the knowledge from related recommender systems where rich collaborative data are available. However, most previous transfer learning models assume that entity-correspondences across different systems are given as input, which means that for any entity (e.g., a user or an item) in a target system, its corresponding entity in a source system is known. This assumption can hardly be satisfied in real-world scenarios where entity-correspondences across systems are usually unknown, and the cost of identifying them can be expensive. For example, it is extremely difficult to identify whether a user A from Facebook and a user B from Twitter are the same person. In this paper, we propose a framework to construct entity correspondence with limited budget by using active learning to facilitate knowledge transfer across recommender systems. Specifically, for the purpose of maximizing knowledge transfer, we first iteratively select entities in the target system based on our proposed criterion to query their correspondences in the source system. We then plug the actively constructed entity-correspondence mapping into a general transferred collaborative-filtering model to improve recommendation quality. We perform extensive experiments on real world datasets to verify the effectiveness of our proposed framework for this crosssystem recommendation problem.
The essential brand persona : storytelling and branding

Storytelling is a well known and ancient art form. Fascinating and compelling characters have animated literature around the world from the beginning of the written word. Today, scientific research has laid the foundations for a sound empirical understanding of storytelling as a clear aid to memory, as a means of making sense of the world, as a way to make and strengthen emotional connections, and as way of recognizing and identifying with brands of any type. Whether you are dealing with product brands or company brands, storytelling is essential to successful branding, since your brand is the sum of all your corporate behaviors and communications that inform your customers’ experiences with your product or company.
Precocious puberty in an infant with hepatoblastoma: a case report

The syndrome of isosexual precocious puberty associated with primary malignant hepatic tumors is rare. All previously reported cases in the literature are old and prognosis was grim.



We present the case of a 15-month-old Asian male baby who presented with precocious puberty associated with hepatoblastoma. Serum concentrations of alpha-fetoprotein and free testosterone were elevated, as was beta human chorionic gonadotropin hormone. He was treated with six courses of chemotherapy and underwent surgery. His surface markers as well as free testosterone level returned to normal during therapy. The child has now been off therapy for 18 months with no evidence of tumor recurrence at follow-up.



Virilizing hepatoblastoma is rare and reported with poor outcome, but the development of new chemotherapeutic agents and complete surgical resection are promising.
"Owl" and "Lizard": Patterns of Head Pose and Eye Pose in Driver Gaze Classification

Accurate, robust, inexpensive gaze tracking in the car can help keep a driver safe by facilitating the more effective study of how to improve (1) vehicle interfaces and (2) the design of future Advanced Driver Assistance Systems. In this paper, we estimate head pose and eye pose from monocular video using methods developed extensively in prior work and ask two new interesting questions. First, how much better can we classify driver gaze using head and eye pose versus just using head pose? Second, are there individual-specific gaze strategies that strongly correlate with how much gaze classification improves with the addition of eye pose information? We answer these questions by evaluating data drawn from an on-road study of 40 drivers. The main insight of the paper is conveyed through the analogy of an “owl” and “lizard” which describes the degree to which the eyes and the head move when shifting gaze. When the head moves a lot (“owl”), not much classification improvement is attained by estimating eye pose on top of head pose. On the other hand, when the head stays still and only the eyes move (“lizard”), classification accuracy increases significantly from adding in eye pose. We characterize how that accuracy varies between people, gaze strategies, and gaze regions.
Control PI difuso de ganancias programables para un sistema mecatrónico de posicionamiento angular-lineal

In this paper we shows the experimental results using a microcontroller and hardware integration with the EMC2 software, using the Fuzzy Gain Scheduling PI Controller in a mechatronic prototype. The structure of the fuzzy 157 Research in Computing Science 116 (2016) pp. 157–169; rec. 2016-03-23; acc. 2016-05-11 controller is composed by two-inputs and two-outputs, is a TITO system. The error control feedback and their derivative are the inputs, while the proportional and integral gains are the fuzzy controller outputs. Was defined five Gaussian membership functions for the fuzzy sets by each input, the product fuzzy logic operator (AND connective) and the centroid defuzzifier was used to infer the gains outputs. The structure of fuzzy rule base are type Sugeno, zero-order. The experimental result in closed-loop shows the viability end effectiveness of the position fuzzy controller strategy. To verify the robustness of this controller structure, two different experiments was making: undisturbed and disturbance both in closed-loop. This work presents comparative experimental results, using the Classical tune rule of Ziegler-Nichols and the Fuzzy Gain Scheduling PI Controller, for a mechatronic system widely used in various industries applications.
On Multi-Relational Link Prediction With Bilinear Models

We study bilinear embedding models for the task of multirelational link prediction and knowledge graph completion. Bilinear models belong to the most basic models for this task, they are comparably efficient to train and use, and they can provide good prediction performance. The main goal of this paper is to explore the expressiveness of and the connections between various bilinear models proposed in the literature. In particular, a substantial number of models can be represented as bilinear models with certain additional constraints enforced on the embeddings. We explore whether or not these constraints lead to universal models, which can in principle represent every set of relations, and whether or not there are subsumption relationships between various models. We report results of an independent experimental study that evaluates recent bilinear models in a common experimental setup. Finally, we provide evidence that relation-level ensembles of multiple bilinear models can achieve state-of-the art prediction performance.
Preserving Privacy by De-identifying Facial Images

In the context of sharing video surveillance data, a significant threat to privacy is face recognition software, which can automatically identify known people, such as from a database of drivers’ license photos, and thereby track people regardless of suspicion. This paper introduces an algorithm to protect the privacy of individuals in video surveillance data by de-identifying faces such that many facial characteristics remain but the face cannot be reliably recognized. A trivial solution to de-identifying faces involves blacking out each face. This thwarts any possible face recognition, but because all facial details are obscured, the result is of limited use. Many ad hoc attempts, such as covering eyes or randomly perturbing image pixels, fail to thwart face recognition because of the robustness of face recognition methods. This paper presents a new privacy-enabling algorithm, named k-Same, that scientifically limits the ability of face recognition software to reliably recognize faces while maintaining facial details in the images. The algorithm determines similarity between faces based on a distance metric and creates new faces by averaging image components, which may be the original image pixels ( k-Same-Pixel) or eigenvectors ( k-Same-Eigen). Results are presented on a standard collection of real face images with varying k. E. Newton, L. Sweeney, and B. Malin. Preserving Privacy by De-identifying Facial Images , Carnegie Mellon University, School of Computer Science, Technical Report, CMU-CS-03-119. P ittsburgh: March2003. 2 This research was supported in part by the Laboratory for International Data Privacy at Carnegie Mellon University and in part by the Defense Advanced Research Projects Agency and managed by the Naval Sea Systems Command under contract N00024-98-D-8124. E. Newton, L. Sweeney, and B. Malin. Preserving Privacy by De-identifying Facial Images , Carnegie Mellon University, School of Computer Science, Technical Report, CMU-CS-03-119. P ittsburgh: March2003. 3
NLMF: NonLinear Matrix Factorization Methods for Top-N Recommender Systems

Many existing state-of-the-art top-N recommendation methods model users and items in the same latent space and the recommendation scores are computed via the dot product between those vectors. These methods assume that the user preference is consistent across all the items that he/she has rated. This assumption is not necessarily true, since many users can have multiple personas/interests and their preferences can vary with each such interest. To address this, a recently proposed method modeled the users with multiple interests. In this paper, we build on this approach and model users using a much richer representation. We propose a method which models the user preference as a combination of having global preference and interest-specific preference. The proposed method uses a nonlinear model for predicting the recommendation score, which is used to perform top-N recommendation task. The recommendation score is computed as a sum of the scores from the components representing global preference and interest-specific preference. A comprehensive set of experiments on multiple datasets show that the proposed model outperforms other state-of-the-art methods for top-N recommendation task.
Human category learning.

Much recent evidence suggests some dramatic differences in the way people learn perceptual categories, depending on exactly how the categories were constructed. Four different kinds of category-learning tasks are currently popular-rule-based tasks, information-integration tasks, prototype distortion tasks, and the weather prediction task. The cognitive, neuropsychological, and neuroimaging results obtained using these four tasks are qualitatively different. Success in rule-based (explicit reasoning) tasks depends on frontal-striatal circuits and requires working memory and executive attention. Success in information-integration tasks requires a form of procedural learning and is sensitive to the nature and timing of feedback. Prototype distortion tasks induce perceptual (visual cortical) learning. A variety of different strategies can lead to success in the weather prediction task. Collectively, results from these four tasks provide strong evidence that human category learning is mediated by multiple, qualitatively distinct systems.
Exploring a national cybersecurity exercise for universities

In cybersecurity competitions, participants either create new or protect preconfigured information systems and then defend these systems against attack in a real-world setting. Institutions should consider important structural and resource-related issues before establishing such a competition. Critical infrastructures increasingly rely on information systems and on the Internet to provide connectivity between systems. Maintaining and protecting these systems requires an education in information warfare that doesn't merely theorize and describe such concepts. A hands-on, active learning experience lets students apply theoretical concepts in a physical environment. Craig Kaucher and John Saunders found that even for management-oriented graduate courses in information assurance, such an experience enhances the students' understanding of theoretical concepts. Cybersecurity exercises aim to provide this experience in a challenging and competitive environment. Many educational institutions use and implement these exercises as part of their computer science curriculum, and some are organizing competitions with commercial partners as capstone exercises, ad hoc hack-a-thons, and scenario-driven, multiday, defense-only competitions. Participants have exhibited much enthusiasm for these exercises, from the DEFCON capture-the-flag exercise to the US Military Academy's Cyber Defense Exercise (CDX). In February 2004, the US National Science Foundation sponsored the Cyber Security Exercise Workshop aimed at harnessing this enthusiasm and interest. The educators, students, and government and industry representatives attending the workshop discussed the feasibility and desirability of establishing regular cybersecurity exercises for postsecondary-level students. This article summarizes the workshop report.
Rozzle: De-cloaking Internet Malware

JavaScript-based malware attacks have increased in recent years and currently represent a signicant threat to the use of desktop computers, smartphones, and tablets. While static and runtime methods for malware detection have been proposed in the literature, both on the client side, for just-in-time in-browser detection, as well as offline, crawler-based malware discovery, these approaches encounter the same fundamental limitation. Web-based malware tends to be environment-specific, targeting a particular browser, often attacking specic versions of installed plugins. This targeting occurs because the malware exploits vulnerabilities in specific plugins and fails otherwise. As a result, a fundamental limitation for detecting a piece of malware is that malware is triggered infrequently, only showing itself when the right environment is present. We observe that, using fingerprinting techniques that capture and exploit unique properties of browser configurations, almost all existing malware can be made virtually impssible for malware scanners to detect. This paper proposes Rozzle, a JavaScript multi-execution virtual machine, as a way to explore multiple execution paths within a single execution so that environment-specific malware will reveal itself. Using large-scale experiments, we show that Rozzle increases the detection rate for offline runtime detection by almost seven times. In addition, Rozzle triples the effectiveness of online runtime detection. We show that Rozzle incurs virtually no runtime overhead and allows us to replace multiple VMs running different browser configurations with a single Rozzle-enabled browser, reducing the hardware requirements, network bandwidth, and power consumption.
Music recommendation by unified hypergraph: combining social media information and music content

Acoustic-based music recommender systems have received increasing interest in recent years. Due to the semantic gap between low level acoustic features and high level music concepts, many researchers have explored collaborative filtering techniques in music recommender systems. Traditional collaborative filtering music recommendation methods only focus on user rating information. However, there are various kinds of social media information, including different types of objects and relations among these objects, in music social communities such as Last.fm and Pandora. This information is valuable for music recommendation. However, there are two challenges to exploit this rich social media information: (a) There are many different types of objects and relations in music social communities, which makes it difficult to develop a unified framework taking into account all objects and relations. (b) In these communities, some relations are much more sophisticated than pairwise relation, and thus cannot be simply modeled by a graph. In this paper, we propose a novel music recommendation algorithm by using both multiple kinds of social media information and music acoustic-based content. Instead of graph, we use hypergraph to model the various objects and relations, and consider music recommendation as a ranking problem on this hypergraph. While an edge of an ordinary graph connects only two objects, a hyperedge represents a set of objects. In this way, hypergraph can be naturally used to model high-order relations. Experiments on a data set collected from the music social community Last.fm have demonstrated the effectiveness of our proposed algorithm.
Dishonest Behaviors in Online Rating Systems: Cyber Competition, Attack Models, and Attack Generator

Recently, online rating systems are gaining popularity. Dealing with unfair ratings in such systems has been recognized as an important but challenging problem. Many unfair rating detection approaches have been developed and evaluated against simple attack models. However, the lack of unfair rating data from real human users and realistic attack behavior models has become an obstacle toward developing reliable rating systems. To solve this problem, we design and launch a rating challenge to collect unfair rating data from real human users. In order to broaden the scope of the data collection, we also develop a comprehensive signal-based unfair rating detection system. Based on the analysis of real attack data, we discover important features in unfair ratings, build attack models, and develop an unfair rating generator. The models and generator developed in this paper can be directly used to test current rating aggregation systems, as well as to assist the design of future rating systems.
Forensic Odontology in the Era of Computer and Technology

We are living in the era of science and technology and it have infused with many aspects of our everyday life. With the advent of newer technologies the criminals have made full use of it which sometimes facade a challenging task to investigators such as forensic experts to catch the crime. This paper will discuss the need for computer forensics and application of technologies to be practiced in an effective and legal way, formalize basic technical issues, and point to references for further reading. It promotes the idea that the proficient practice of computer forensics and awareness of applicable laws is essential for today's networked organizations.