nils-herrmann/clean_scirepeval huggingface dataset:
Privacy-preserving Machine Learning as a Service

Machine learning algorithms based on deep Neural Networks (NN) have achieved remarkable results and are being extensively used in different domains. On the other hand, with increasing growth of cloud services, several Machine Learning as a Service (MLaaS) are offered where training and deploying machine learning models are performed on cloud providers’ infrastructure. However, machine learning algorithms require access to the raw data which is often privacy sensitive and can create potential security and privacy risks. To address this issue, we present CryptoDL, a framework that develops new techniques to provide solutions for applying deep neural network algorithms to encrypted data. In this paper, we provide the theoretical foundation for implementing deep neural network algorithms in encrypted domain and develop techniques to adopt neural networks within practical limitations of current homomorphic encryption schemes. We show that it is feasible and practical to train neural networks using encrypted data and to make encrypted predictions, and also return the predictions in an encrypted form. We demonstrate applicability of the proposed CryptoDL using a large number of datasets and evaluate its performance. The empirical results show that it provides accurate privacy-preserving training and classification.
COMPREHENSIVE STUDY AND ANALYSIS OF MALICIOUS WEBSITE DETECTION TECHNIQUES

Nowadays, World Wide Web (WWW) surfing is becoming a risky task with the Web becoming rich in all sorts of attack. Websites are the main source of many scams, phishing attacks, identity theft, SPAM commerce and malware. Nevertheless, browsers, blacklists, and popup blockers are not enough to protect users. According to this, fast and accurate systems still to be needed with the ability to detect new malicious content. By taking into consideration, researchers have developed various Malicious Website detection techniques in recent years. Analyzing those works available in the literature can provide good knowledge on this topic and also, it will lead to finding the recent problems in Malicious Website detection. Accordingly, I have planned to do a comprehensive study with the literature of Malicious Website detection techniques. To categorize the techniques, all articles that had the word “malicious detection” in its title or as its keyword published between January 2003 to august 2016, is first selected from the scientific journals: IEEE, Elsevier, Springer and international journals. After the collection of research articles, we discuss every research paper. In addition, this study gives an elaborate idea about malicious detection.
Natural Language Interfaces to Databases - An Introduction

A brief overview of the history of Nlidbs is rst given. Some advantages and disadvantages of Nlidbs are then discussed, comparing Nlidbs to formal query languages, form-based interfaces, and graphical interfaces. An introduction to some of the linguistic problems Nlidbs have to confront follows, for the beneet of readers less familiar with computational linguistics. The discussion then moves on to Nlidb architectures, porta-bility issues, restricted natural language input systems (including menu-based Nlidbs), and Nlidbs with reasoning capabilities. Some less explored areas of Nlidb research are then presented, namely database updates, meta-knowledge questions, temporal questions, and multi-modal Nlidbs. The paper ends with reeections on the current state of the art.
Lessons from Natural Language Inference in the Clinical Domain

State of the art models using deep neural networks have become very good in learning an accurate mapping from inputs to outputs. However, they still lack generalization capabilities in conditions that differ from the ones encountered during training. This is even more challenging in specialized, and knowledge intensive domains, where training data is limited. To address this gap, we introduce MedNLI1 – a dataset annotated by doctors, performing a natural language inference task (NLI), grounded in the medical history of patients. We present strategies to: 1) leverage transfer learning using datasets from the open domain, (e.g. SNLI) and 2) incorporate domain knowledge from external data and lexical sources (e.g. medical terminologies). Our results demonstrate performance gains using both strategies.
Psychological universals: what are they and how can we know?

Psychological universals, or core mental attributes shared by humans everywhere, are a foundational postulate of psychology, yet explicit analysis of how to identify such universals is lacking. This article offers a conceptual and methodological framework to guide the investigation of genuine universals through empirical analysis of psychological patterns across cultures. Issues of cross-cultural generalizability of psychological processes and 3 cross-cultural research strategies to probe universals are considered. Four distinct levels of hierarchically organized universals are possible: From strongest to weakest claims for universality, they are accessibility universals, functional universals, existential universals, and nonuniversals. Finally, universals are examined in relation to the questions of levels of analysis, evolutionary explanations of psychological processes, and management of cross-cultural relations.
Hamming Distance Metric Learning

Motivated by large-scale multimedia applications we propose to learn mappings from high-dimensional data to binary codes that preserve semantic similarity. Binary codes are well suited to large-scale applications as they are storage efficient and permit exact sub-linear kNN search. The framework is applicable to broad families of mappings, and uses a flexible form of triplet ranking loss. We overcome discontinuous optimization of the discrete mappings by minimizing a piecewise-smooth upper bound on empirical loss, inspired by latent structural SVMs. We develop a new loss-augmented inference algorithm that is quadratic in the code length. We show strong retrieval performance on CIFAR-10 and MNIST, with promising classification results using no more than kNN on the binary codes.
Towards Open World Recognition

With the of advent rich classification models and high computational power visual recognition systems have found many operational applications. Recognition in the real world poses multiple challenges that are not apparent in controlled lab environments. The datasets are dynamic and novel categories must be continuously detected and then added. At prediction time, a trained system has to deal with myriad unseen categories. Operational systems require minimal downtime, even to learn. To handle these operational issues, we present the problem of Open World Recognition and formally define it. We prove that thresholding sums of monotonically decreasing functions of distances in linearly transformed feature space can balance “open space risk” and empirical risk. Our theory extends existing algorithms for open world recognition. We present a protocol for evaluation of open world recognition systems. We present the Nearest Non-Outlier (NNO) algorithm that evolves model efficiently, adding object categories incrementally while detecting outliers and managing open space risk. We perform experiments on the ImageNet dataset with 1.2M+ images to validate the effectiveness of our method on large scale visual recognition tasks. NNO consistently yields superior results on open world recognition.
Maslow and the motivation hierarchy: measuring satisfaction of the needs.

For each of the 5 needs in Maslow's motivational hierarchy (physiological, safety-security, belongingness, esteem, and self-actualization), operational definitions were developed from Maslow's theory of motivation. New measures were created based on the operational definitions (1) to assess the satisfaction of each need, (2) to assess their expected correlations (a) with each of the other needs and (b) with four social and personality measures (i.e., family support, traditional values, anxiety/worry, and life satisfaction), and (3) to test the ability of the satisfaction level of each need to statistically predict the satisfaction level of the next higher-level need. Psychometric tests of the scales conducted on questionnaire results from 386 adult respondents from the general population lent strong support for the validity and reliability of all 5 needs measures. Significant positive correlations among the scales were also found; that is, the more each lower-level need was satisfied, the more the next higher-level need was satisfied. Additionally, as predicted, family support, traditional values, and life satisfaction had significant positive correlations with the satisfaction of all 5 needs, and the anxiety/worry facet of neuroticism had significant negative correlations with the satisfaction of all the needs. Multiple regression analyses revealed that the satisfaction of each higher-level need was statistically predicted by the satisfaction of the need immediately below it in the hierarchy, as expected from Maslow's theory.
The benefits of frequent positive affect: does happiness lead to success?

Numerous studies show that happy individuals are successful across multiple life domains, including marriage, friendship, income, work performance, and health. The authors suggest a conceptual model to account for these findings, arguing that the happiness-success link exists not only because success makes people happy, but also because positive affect engenders success. Three classes of evidence--crosssectional, longitudinal, and experimental--are documented to test their model. Relevant studies are described and their effect sizes combined meta-analytically. The results reveal that happiness is associated with and precedes numerous successful outcomes, as well as behaviors paralleling success. Furthermore, the evidence suggests that positive affect--the hallmark of well-being--may be the cause of many of the desirable characteristics, resources, and successes correlated with happiness. Limitations, empirical issues, and important future research questions are discussed.
A novel hybrid visual odometry using an RGB-D camera

RGB-D cameras provide both a color image and a depth image which contains the real depth information about per-pixel. The richness of their data and the development of low-cost sensors have combined to present an attractive opportunity for mobile robotics research. In this paper, we describe a novel hybrid visual odometry using an RGB-D camera. Different from the original method, it is a pure visual odometry method without any other information, such as inertial data. The important key is hybrid, which means that the odometry can be executed in two different processes depending on the conditions. It consists of two parts, including a feature-based visual odometry and a direct visual odometry. Details about the algorithm are discussed in the paper. Especially, the switch conditions are described in detail. Beside, we evaluate the continuity and robustness for the system on public dataset. The experiments demonstrate that our system has more stable continuity and better robustness.
Causes of status epilepticus.

Status epilepticus (SE) is the most extreme form of epilepsy. It describes a prolonged seizure that may occur in patients with previous epilepsy or in acute disorders of the central nervous system. It is one of the most common neurologic emergencies, with an incidence of up to 41 per 100,000 per year and an estimated mortality is 20%. The three major determinants of prognosis are the duration of SE, patient age, and the underlying cause. Common and easily recognized causes of SE include cerebrovascular disorders, brain trauma, infections, and low antiepileptic drug levels in patients with epilepsy. Less common causes present a clinical and diagnostic challenge, but are major determinants of prognosis. Among them, inflammatory causes and inborn errors of metabolism have gained wide interest; recent insights into these causes have contributed to a better understanding of the pathophysiology of SE and its appropriate treatment. This review focuses on the different etiologies of SE and emphasizes the importance of prompt recognition and treatment of the underlying causes.
A Deep Reinforcement Learning Chatbot (Short Version)

We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capable of conversing with humans on popular small talk topics through both speech and text. The system consists of an ensemble of natural language generation and retrieval models, including neural network and template-based models. By applying reinforcement learning to crowdsourced data and real-world user interactions, the system has been trained to select an appropriate response from the models in its ensemble. The system has been evaluated through A/B testing with realworld users, where it performed significantly better than other systems. The results highlight the potential of coupling ensemble systems with deep reinforcement learning as a fruitful path for developing real-world, open-domain conversational agents.
The role of HTML5 IndexedDB, the past, present and future

Over the past 20 years Web browsers have changed considerably from being a simple text display to now supporting complex multimedia applications. The client can now enjoy chatting, playing games and Internet banking. All these applications have something in common, they can be run on multiple platforms and in some cases they will run offline. With the introduction of HTML5 this evolution will continue, with browsers offering greater levels of functionality. This paper outlines the background study and the importance of new technologies, such as HTML5's new browser based storage called IndexedDB. We will show how the technology of storing data on the client side has changed over the time and how the technologies for storing data on the client will be used in future when considering known security issues. Further, we propose a solution to IndexedDB's known security issues in form of a security model, which will extend the current model.
A survey on automated dynamic malware-analysis techniques and tools

Anti-virus vendors are confronted with a multitude of potentially malicious samples today. Receiving thousands of new samples every day is not uncommon. The signatures that detect confirmed malicious threats are mainly still created manually, so it is important to discriminate between samples that pose a new unknown threat and those that are mere variants of known malware.
 This survey article provides an overview of techniques based on dynamic analysis that are used to analyze potentially malicious samples. It also covers analysis programs that leverage these It also covers analysis programs that employ these techniques to assist human analysts in assessing, in a timely and appropriate manner, whether a given sample deserves closer manual inspection due to its unknown malicious behavior.
Don't Throw Those Morphological Analyzers Away Just Yet: Neural Morphological Disambiguation for Arabic

This paper presents a model for Arabic morphological disambiguation based on Recurrent Neural Networks (RNN). We train Long Short-Term Memory (LSTM) cells in several configurations and embedding levels to model the various morphological features. Our experiments show that these models outperform state-of-theart systems without explicit use of feature engineering. However, adding learning features from a morphological analyzer to model the space of possible analyses provides additional improvement. We make use of the resulting morphological models for scoring and ranking the analyses of the morphological analyzer for morphological disambiguation. The results show significant gains in accuracy across several evaluation metrics. Our system results in 4.4% absolute increase over the state-of-the-art in full morphological analysis accuracy (30.6% relative error reduction), and 10.6% (31.5% relative error reduction) for out-of-vocabulary words.
Event-Based Control of the Inverted Pendulum : Swing up and Stabilization

Contrary to the classical (time-triggered) principle that calculates the control signal in a periodic fashion, an event-driven control is computed and updated only when a certain condition is satisfied. This notably enables to save computations in the control task while ensuring equivalent performance. In this paper, we develop and implement such strategies to control a nonlinear and unstable system, that is the inverted pendulum. We are first interested on the stabilization of the pendulum near its inverted position and propose an event-based control approach. This notably demonstrates the efficiency of the event-based scheme even in the case where the system has to be actively actuated to remain upright. We then study the swinging of the pendulum up to the desired position and propose a low-cost control law based on an energy function. The switch between both strategies is also analyzed. A real-time experimentation is realized and shows that a reduction of about 98% and 50% of samples less than the classical scheme is achieved for the swing up and stabilization parts respectively.
On the combination of domain-specific heuristics for author name disambiguation: the nearest cluster method

Author name disambiguation has been one of the hardest problems faced by digital libraries since their early days. Historically, supervised solutions have empirically outperformed those based on heuristics, but with the burden of having to rely on manually labeled training sets for the learning process. Moreover, most supervised solutions just apply some type of generic machine learning solution and do not exploit specific knowledge about the problem. In this article, we follow a similar reasoning, but in the opposite direction. Instead of extending an existing supervised solution, we propose a set of carefully designed heuristics and similarity functions, and apply supervision only to optimize such parameters for each particular dataset. As our experiments show, the result is a very effective, efficient and practical author name disambiguation method that can be used in many different scenarios. In fact, we show that our method can beat state-of-the-art supervised methods in terms of effectiveness in many situations while being orders of magnitude faster. It can also run without any training information, using only default parameters, and still be very competitive when compared to these supervised methods (beating several of them) and better than most existing unsupervised author name disambiguation solutions.
Applications of Artificial Intelligence in Machine Learning: Review and Prospect

Machine learning is one of the most exciting recent technologies in Artificial Intelligence. Learning algorithms in many applications that&apos;s we make use of daily. Every time a web search engine like Google or Bing is used to search the internet, one of the reasons that works so well is because a learning algorithm, one implemented by Google or Microsoft, has learned how to rank web pages. Every time Facebook is used and it recognizes friends&apos; photos, that&apos;s also machine learning. Spam filters in email saves the user from having to wade through tons of spam email, that&apos;s also a learning algorithm. In this paper, a brief review and future prospect of the vast applications of machine learning has been made.
Deep convolutional neural networks for multi-modality isointense infant brain image segmentation

The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development in health and disease. In the isointense stage (approximately 6-8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, making the tissue segmentation very challenging. Only a small number of existing methods have been designed for tissue segmentation in this isointense stage; however, they only used a single T1 or T2 images, or the combination of T1 and T2 images. In this paper, we propose to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images. CNNs are a type of deep models in which trainable filters and local neighborhood pooling operations are applied alternatingly on the raw input images, resulting in a hierarchy of increasingly complex features. Specifically, we used multi-modality information from T1, T2, and fractional anisotropy (FA) images as inputs and then generated the segmentation maps as outputs. The multiple intermediate layers applied convolution, pooling, normalization, and other operations to capture the highly nonlinear mappings between inputs and outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense stage brain images. Results showed that our proposed model significantly outperformed prior methods on infant brain tissue segmentation. In addition, our results indicated that integration of multi-modality images led to significant performance improvement.
Mind the tracker you wear: a security analysis of wearable health trackers

Wearable tracking devices have gained widespread usage and popularity because of the valuable services they offer, monitoring human's health parameters and, in general, assisting persons to take a better care of themselves. Nevertheless, the security risks associated with such devices can represent a concern among consumers, because of the sensitive information these devices deal with, like sleeping patterns, eating habits, heart rate and so on. In this paper, we analyse the key security and privacy features of two entry level health trackers from leading vendors (Jawbone and Fitbit), exploring possible attack vectors and vulnerabilities at several system levels. The results of the analysis show how these devices are vulnerable to several attacks (perpetrated with consumer-level devices equipped with just bluetooth and Wi-Fi) that can compromise users' data privacy and security, and eventually call the tracker vendors to raise the stakes against such attacks.
Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks

Convolutional neural networks (CNN) have recently shown outstanding image classification performance in the large- scale visual recognition challenge (ILSVRC2012). The success of CNNs is attributed to their ability to learn rich mid-level image representations as opposed to hand-designed low-level features used in other image classification methods. Learning CNNs, however, amounts to estimating millions of parameters and requires a very large number of annotated image samples. This property currently prevents application of CNNs to problems with limited training data. In this work we show how image representations learned with CNNs on large-scale annotated datasets can be efficiently transferred to other visual recognition tasks with limited amount of training data. We design a method to reuse layers trained on the ImageNet dataset to compute mid-level image representation for images in the PASCAL VOC dataset. We show that despite differences in image statistics and tasks in the two datasets, the transferred representation leads to significantly improved results for object and action classification, outperforming the current state of the art on Pascal VOC 2007 and 2012 datasets. We also show promising results for object and action localization.