nils-herrmann/clean_scirepeval huggingface dataset:
Behavioral Analysis of Insider Threat: A Survey and Bootstrapped Prediction in Imbalanced Data

The problem of insider threat is receiving increasing attention both within the computer science community as well as government and industry. This paper starts by presenting a broad, multidisciplinary survey of insider threat capturing contributions from computer scientists, psychologists, criminologists, and security practitioners. Subsequently, we present the behavioral analysis of insider threat (BAIT) framework, in which we conduct a detailed experiment involving 795 subjects on Amazon Mechanical Turk (AMT) in order to gauge the behaviors that real human subjects follow when attempting to exfiltrate data from within an organization. In the real world, the number of actual insiders found is very small, so supervised machine-learning methods encounter a challenge. Unlike past works, we develop bootstrapping algorithms that learn from highly imbalanced data, mostly unlabeled, and almost no history of user behavior from an insider threat perspective. We develop and evaluate seven algorithms using BAIT and show that they can produce a realistic (and acceptable) balance of precision and recall.
Artificial intelligence and the future of cybersecurity

A position paper toward an important and urgent discussion on how best use the potential of Artificial Intelligence in the context of cybersecurity. AI is often mentioned in papers on cybersecurity. But what is meant is using pre-existing AI techniques in cybersecurity. AI techniques are developed around applications. Cybersecurity has never been an area of concentration in AI. In this paper we argue that cybersecurity calls for new and specific AI techniques developed with that kind of application in mind. In practice, this paper is based on a broad overview of different approaches, which have the potential to be game changers in cybersecurity. This paper focuses on web application security and advocates the use of Knowledge Based Systems, probabilistic reasoning and Bayesian updating to control the probability of false positives and false negatives.
Broadband E-Band Power Amplifier MMIC Based on an AlGaN/GaN HEMT Technology with 30 dBm Output Power

This paper reports on the design of a power amplifier covering the entire E-band satellite communication bands (71-76 GHz & 81-86 GHz) and demonstrating a high saturated output power of more than 1 W across this frequency range of interest. The circuit was fabricated by using an advanced 100 nm GaN high-electron-mobility transistor technology with an AlN-interlayer epitaxy, demonstrating a transit frequency ft of more than 100 GHz and a power density as high as 1.9 W/mm at 94 GHz in continuous-wave load-pull operation. A state-space approach is applied for the device modeling, which enables a successful first-pass circuit design.
Uniform single-sided induction heating using multiphase, multi-resonant halbach windings

Traditional induction heating cooktops typically employ a circular planar coil in resonance excited by a switching inverter. This configuration results in stray magnetic fields below the coil and non-uniform heating profile on the top. This paper presents the concept of multiphase, multi-resonant induction heating using a Halbach winding arrangement to eliminate the shortcomings of the traditional cooktop. A multiphase winding structure enables a traveling magnetic field at the target, which results in uniform heating profile. Operating the phases at different resonances deliver power at different frequencies. The winding and drive structure developed in this paper can be applied to many other applications, including chargers and wireless power delivery systems.
Signal-dependent noise determinesmotorplanning

8 increase physical and chemical weathering, pulling more CO 2 out of the atmosphere and so cooling the global climate 8. The near-consensus of papers in a volume devoted to this topic 9 makes the uplift– weathering hypothesis a leading explanation for global cooling during the past 20 Myr. Still, ignorance of uplift histories across much of Tibet has made the hypothesis difficult to evaluate in full. In particular, with little direct evidence for uplift before 20 Myr ago, it is hard to claim that Tibetan uplift caused, or was even involved in, the global cooling that began 55 Myr ago and led to Antarctic glaciation by 36 Myr ago 10. If large-scale uplift did occur in northeastern Tibet as early as 37–33 Myr ago, chemical weathering of this high terrain could have contributed to global cooling then. Other evidence supports this idea. A striking increase in the global-ocean 87 Sr/ 86 Sr ratio began 40 to 35 Myr ago, and the extra 87 Sr probably came from the Tibet–Himalaya complex 11 , from both accelerated weathering and the exposure of rocks rich in 87 Sr. The early uplift inferred for northeast Tibet matches the initial upturn in this signal. Now the question is whether further exploration of Tibet will find evidence of even earlier uplift, especially during the cooling between 55 and 40 Myr ago 10. 725 100 YEARS AGO When the year's work is over and all sense of responsibility has left us, who has not occasionally set his fancy free to dream about the unknown, perhaps the unknowable? And what should more frequently cross our dreams than what is so persistently before us in our serious moments of consciousness — the universal law of gravitation. We can leave our spectroscopes and magnets at home, but we cannot fly from the mysterious force which causes the raindrops to fall from the clouds, and our children to tumble down the staircase. What is gravity? … Lord Kelvin is quoted as having pointed out that two sources or two sinks of incompressible liquid will attract each other with the orthodox distance law. Let us dream, then, of a world in which atoms are sources through which an invisible fluid is pouring into three-dimensioned space. … sinks would form another set of atoms, possibly equal to our own in all respects but one; they would mutually gravitate towards each other, but …
Exploring compact reinforcement-learning representations with linear regression

This paper presents a new algorithm for online linear regression whose efficiency guarantees satisfy the requirements of the KWIK (Knows What It Knows) framework. The algorithm improves on the complexity bounds of the current state-of-the-art procedure in this setting. We explore several applications of this algorithm for learning compact reinforcement-learning representations. We show that KWIK linear regression can be used to learn the reward function of a factored MDP and the probabilities of action outcomes in Stochastic STRIPS and Object Oriented MDPs, none of which have been proven to be efficiently learnable in the RL setting before. We also combine KWIK linear regression with other KWIK learners to learn larger portions of these models, including experiments on learning factored MDP transition and reward functions together.
On the Traveling Salesman Problem with Simple Temporal Constraints

Many real-world applications require the successful combination of spatial and temporal reasoning. In this paper, we study the general framework of the Traveling Salesman Problem with Simple Temporal Constraints. Representationally, this framework subsumes the Traveling Salesman Problem, Simple Temporal Problems, as well as many of the frameworks described in the literature. We analyze the theoretical properties of the combined problem providing strong inapproximability results for the general problem, and positive results for
TARP: ticket-based address resolution protocol

IP networks fundamentally rely on the address resolution protocol (ARP) for proper operation. Unfortunately, vulnerabilities in the ARP protocol enable a raft of IP-based impersonation, man-in-the-middle, or DoS attacks. Proposed countermeasures to these vulnerabilities have yet to simultaneously address backward compatibility and cost requirements. This paper introduces the ticket-based address resolution protocol (TARP). TARP implements security by distributing centrally issued secure MAC/IP address mapping attestations through existing ARP messages. We detail the TARP protocol and its implementation within the Linux operating system. Our experimental analysis shows that TARP improves the costs of implementing ARP security by as much as two orders of magnitude over existing protocols. We conclude by exploring a range of operational issues associated with deploying and administering ARP security
Deconfounded Lexicon Induction for Interpretable Social Science

NLP algorithms are increasingly used in computational social science to take linguistic observations and predict outcomes like human preferences or actions. Making these social models transparent and interpretable often requires identifying features in the input that predict outcomes while also controlling for potential confounds. We formalize this need as a new task: inducing a lexicon that is predictive of a set of target variables yet uncorrelated to a set of confounding variables. We introduce two deep learning algorithms for the task. The first uses a bifurcated architecture to separate the explanatory power of the text and confounds. The second uses an adversarial discriminator to force confound-invariant text encodings. Both elicit lexicons from learned weights and attentional scores. We use them to induce lexicons that are predictive of timely responses to consumer complaints (controlling for product), enrollment from course descriptions (controlling for subject), and sales from product descriptions (controlling for seller). In each domain our algorithms pick words that are associated with narrative persuasion; more predictive and less confound-related than those of standard feature weighting and lexicon induction techniques like regression and log odds.
Responses of Gut Microbiota and Glucose and Lipid Metabolism to Prebiotics in Genetic Obese and Diet-Induced Leptin-Resistant Mice

To investigate deep and comprehensive analysis of gut microbial communities and biological parameters after prebiotic administration in obese and diabetic mice.



Genetic (ob/ob) or diet-induced obese and diabetic mice were chronically fed with prebiotic-enriched diet or with a control diet. Extensive gut microbiota analyses, including quantitative PCR, pyrosequencing of the 16S rRNA, and phylogenetic microarrays, were performed in ob/ob mice. The impact of gut microbiota modulation on leptin sensitivity was investigated in diet-induced leptin-resistant mice. Metabolic parameters, gene expression, glucose homeostasis, and enteroendocrine-related L-cell function were documented in both models.



In ob/ob mice, prebiotic feeding decreased Firmicutes and increased Bacteroidetes phyla, but also changed 102 distinct taxa, 16 of which displayed a >10-fold change in abundance. In addition, prebiotics improved glucose tolerance, increased L-cell number and associated parameters (intestinal proglucagon mRNA expression and plasma glucagon-like peptide-1 levels), and reduced fat-mass development, oxidative stress, and low-grade inflammation. In high fat-fed mice, prebiotic treatment improved leptin sensitivity as well as metabolic parameters.



We conclude that specific gut microbiota modulation improves glucose homeostasis, leptin sensitivity, and target enteroendocrine cell activity in obese and diabetic mice. By profiling the gut microbiota, we identified a catalog of putative bacterial targets that may affect host metabolism in obesity and diabetes.
Multimodal Interfaces: A Survey of Principles, Models and Frameworks

The grand challenge of multimodal interface creation is to build reliable processing systems able to analyze and understand multiple communication means in real-time. This opens a number of associated issues covered by this chapter, such as heterogeneous data types fusion, architectures for real-time processing, dialog management, machine learning for multimodal interaction, modeling languages, frameworks, etc. This chapter does not intend to cover exhaustively all the issues related to multimodal interfaces creation and some hot topics, such as error handling, have been left aside. The chapter starts with the features and advantages associated with multimodal interaction, with a focus on particular findings and guidelines, as well as cognitive foundations underlying multimodal interaction. The chapter then focuses on the driving theoretical principles, time-sensitive software architectures and multimodal fusion and fission issues. Modeling of multimodal interaction as well as tools allowing rapid creation of multimodal interfaces are then presented. The article concludes with an outline of the current state of multimodal interaction research in Switzerland, and also summarizes the major future challenges in the field.
The Normalization Model of Attention

Attention has been found to have a wide variety of effects on the responses of neurons in visual cortex. We describe a model of attention that exhibits each of these different forms of attentional modulation, depending on the stimulus conditions and the spread (or selectivity) of the attention field in the model. The model helps reconcile proposals that have been taken to represent alternative theories of attention. We argue that the variety and complexity of the results reported in the literature emerge from the variety of empirical protocols that were used, such that the results observed in any one experiment depended on the stimulus conditions and the subject's attentional strategy, a notion that we define precisely in terms of the attention field in the model, but that has not typically been completely under experimental control.
Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering

The problem of Visual Question Answering (VQA) requires joint image and language understanding to answer a question about a given photograph. Recent approaches have applied deep image captioning methods based on recurrent LSTM networks to this problem, but have failed to model spatial inference. In this paper, we propose a memory network with spatial attention for the VQA task. Memory networks are recurrent neural networks with an explicit attention mechanism that selects certain parts of the information stored in memory. We store neuron activations from different spatial receptive fields in the memory, and use the question to choose relevant regions for computing the answer. We experiment with spatial attention architectures that use different question representations to choose regions, and also show that two attention steps (hops) obtain improved results compared to a single step. To understand the inference process learned by the network, we design synthetic questions that specifically require spatial inference and visualize the attention weights. We evaluate our model on two published visual question answering datasets, DAQUAR and VQA, and obtain promising results.
Robust Object Detection with Interleaved Categorization and Segmentation

This paper presents a novel method for detecting and localizing objects of a visual category in cluttered real-world scenes. Our approach considers object categorization and figure-ground segmentation as two interleaved processes that closely collaborate towards a common goal. As shown in our work, the tight coupling between those two processes allows them to benefit from each other and improve the combined performance. The core part of our approach is a highly flexible learned representation for object shape that can combine the information observed on different training examples in a probabilistic extension of the Generalized Hough Transform. The resulting approach can detect categorical objects in novel images and automatically infer a probabilistic segmentation from the recognition result. This segmentation is then in turn used to again improve recognition by allowing the system to focus its efforts on object pixels and to discard misleading influences from the background. Moreover, the information from where in the image a hypothesis draws its support is employed in an MDL based hypothesis verification stage to resolve ambiguities between overlapping hypotheses and factor out the effects of partial occlusion. An extensive evaluation on several large data sets shows that the proposed system is applicable to a range of different object categories, including both rigid and articulated objects. In addition, its flexible representation allows it to achieve competitive object detection performance already from training sets that are between one and two orders of magnitude smaller than those used in comparable systems.
COM: A method for mining and monitoring human activity patterns in home-based health monitoring systems

The increasing aging population in the coming decades will result in many complications for society and in particular for the healthcare system due to the shortage of healthcare professionals and healthcare facilities. To remedy this problem, researchers have pursued developing remote monitoring systems and assisted living technologies by utilizing recent advances in sensor and networking technology, as well as in the data mining and machine learning fields. In this article, we report on our fully automated approach for discovering and monitoring patterns of daily activities. Discovering and tracking patterns of daily activities can provide unprecedented opportunities for health monitoring and assisted living applications, especially for older adults and individuals with mental disabilities. Previous approaches usually rely on preselected activities or labeled data to track and monitor daily activities. In this article, we present a fully automated approach by discovering natural activity patterns and their variations in real-life data. We will show how our activity discovery component can be integrated with an activity recognition component to track and monitor various daily activity patterns. We also provide an activity visualization component to allow caregivers to visually observe and examine the activity patterns using a user-friendly interface. We validate our algorithms using real-life data obtained from two apartments during a three-month period.
Three computer-based models of storytelling: BRUTUS, MINSTREL and MEXICA

This paper attempts to establish criteria to analyse and evaluate computer models of creativity in writing. The paper provides a brief review of the antecedents of automatic story-generation and offers a proposal for the analysis and evaluation of computer models of creativity in writing. It reviews three major projects to develop computer-based storywriters published between 1993 and 2000 and analyses their approach, similarities, differences and contributions. It compares the three approaches and discusses implications for the modelling of creativity in writing and the design of future story generation systems.
Bootstrapping Biomedical Ontologies for Scientific Text using NELL

We describe an open information extraction system for biomedical text based on NELL (the NeverEnding Language Learner) , a system designed for extraction from Web text. NELL uses a coupled semi-supervised bootstrapping approach to learn new facts from text, given an initial ontology and a small number of “seeds” for each ontology category. In contrast to previous applications of NELL, in our task the initial ontology and seeds are automatically derived from existing resources. We show that NELL’s bootstrapping algorithm is susceptible to ambiguous seeds, which are frequent in the biomedical domain. Using NELL to extract facts from biomedical text quickly leads to semantic drift. To address this problem, we introduce a method for assessing seed quality, based on a larger corpus of data derived from the Web. In our method, seed quality is assessed at each iteration of the bootstrapping process. Experimental results show significant improvements over NELL’s original bootstrapping algorithm on two types of tasks: learning terms from biomedical categories, and named-entity recognition for biomedical entities using a learned lexicon.
Variational inference in nonconjugate models

Mean-field variational methods are widely used for approximate posterior inference in many probabilistic models. In a typical application, mean-field methods approximately compute the posterior with a coordinate-ascent optimization algorithm. When the model is conditionally conjugate, the coordinate updates are easily derived and in closed form. However, many models of interest—like the correlated topic model and Bayesian logistic regression—are nonconjugate. In these models, mean-field methods cannot be directly applied and practitioners have had to develop variational algorithms on a case-by-case basis. In this paper, we develop two generic methods for nonconjugate models, Laplace variational inference and delta method variational inference. Our methods have several advantages: they allow for easily derived variational algorithms with a wide class of nonconjugate models; they extend and unify some of the existing algorithms that have been derived for specific models; and they work well on real-world data sets. We studied our methods on the correlated topic model, Bayesian logistic regression, and hierarchical Bayesian logistic regression.
State Space Reduction For Hierarchical Reinforcement Learning

This paper provides new techniques for abstracting the state space of a Markov Decision Process (MDP). These techniques extend one of the recent minimization models, known as -reduction, to construct a partition space that has a smaller number of states than the original MDP. As a result, learning policies on the partition space should be faster than on the original state space. The technique presented here extends reduction to SMDPs by executing a policy instead of a single action, and grouping all states which have a small difference in transition probabilities and reward function under a given policy. When the reward structure is not known, a two-phase method for state aggregation is introduced and a theorem in this paper shows the solvability of tasks using the two-phase method partitions. These partitions can be further refined when the complete structure of reward is available. Simulations of different state spaces show that the policies in both MDP and this representation achieve similar results and the total learning time in partition space in presented approach is much smaller than the total amount of time spent on learning on the original state space.
Category-aware Next Point-of-Interest Recommendation via Listwise Bayesian Personalized Ranking

Next Point-of-Interest (POI) recommendation has become an important task for location-based social networks (LBSNs). However, previous efforts suffer from the high computational complexity, besides the transition pattern between POIs has not been well studied. In this paper, we proposed a twofold approach for next POI recommendation. First, the preferred next category is predicted by using a third-rank tensor optimized by a Listwise Bayesian Personalized Ranking (LBPR) approach. Specifically we introduce two functions, namely PlackettLuce model and cross entropy, to generate the likelihood of a ranking list for posterior computation. Then POI candidates filtered by the predicated category are ranked based on the spatial influence and category ranking influence. The experiments on two real-world datasets demonstrate the significant improvements of our methods over several state-ofthe-art methods.
Matching robot appearance and behavior to tasks to improve human-robot cooperation

A robot’s appearance and behavior provide cues to the robot’s abilities and propensities. We hypothesize that an appropriate match between a robot’s social cues and its task will improve people’s acceptance of and cooperation with the robot. In an experiment, people systematically preferred robots for jobs when the robot’s humanlikeness matched the sociability required in those jobs. In two other experiments, people complied more with a robot whose demeanor matched the seriousness of the task.