nils-herrmann/clean_scirepeval huggingface dataset:
Empirical research in on-line trust: a review and critical assessment

Lack of trust is one of the most frequently cited reasons for consumers not purchasing from Internet vendors. During the last four years a number of empirical studies have investigated the role of trust in the specific context of e-commerce, focusing on different aspects of this multi-dimensional construct. However, empirical research in this area is beset by conflicting conceptualizations of the trust construct, inadequate understanding of the relationships between trust, its antecedents and consequents, and the frequent use of trust scales that are neither theoretically derived nor rigorously validated. The major objective of this paper is to provide an integrative review of the empirical literature on trust in e-commerce in order to allow cumulative analysis of results. The interpretation and comparison of different empirical studies on on-line trust first requires conceptual clarification. A set of trust constructs is proposed that reflects both institutional phenomena (system trust) and personal and interpersonal forms of trust (dispositional trust, trusting beliefs, trusting intentions and trust-related behaviours), thus facilitating a multi-level and multi-dimensional analysis of research problems related to trust in e-commerce. r 2003
AST-Based Deep Learning for Detecting Malicious PowerShell

With the celebrated success of deep learning, some attempts to develop effective methods for detecting malicious PowerShell programs employ neural nets in a traditional natural language processing setup while others employ convolutional neural nets to detect obfuscated malicious commands at a character level. While these representations may express salient PowerShell properties, our hypothesis is that tools from static program analysis will be more effective. We propose a hybrid approach combining traditional program analysis (in the form of abstract syntax trees) and deep learning. This poster presents preliminary results of a fundamental step in our approach: learning embeddings for nodes of PowerShell ASTs. We classify malicious scripts by family type and explore embedded program vector representations.
Interpretable Convolutional Neural Networks

This paper proposes a method to modify a traditional convolutional neural network (CNN) into an interpretable CNN, in order to clarify knowledge representations in high conv-layers of the CNN. In an interpretable CNN, each filter in a high conv-layer represents a specific object part. Our interpretable CNNs use the same training data as ordinary CNNs without a need for any annotations of object parts or textures for supervision. The interpretable CNN automatically assigns each filter in a high conv-layer with an object part during the learning process. We can apply our method to different types of CNNs with various structures. The explicit knowledge representation in an interpretable CNN can help people understand the logic inside a CNN, i.e. what patterns are memorized by the CNN for prediction. Experiments have shown that filters in an interpretable CNN are more semantically meaningful than those in a traditional CNN. The code is available at https://github.com/zqs1022/interpretableCNN.
Lattice-Based Cryptography

Cryptographic applications of lattices include both cyptanalysis and the design of (provably secure) cryptographic functions. Cryptanalysis applications are usually based on lattice reduction techniques. The name “lattice based cryptography” typically refers to the second kind of applications: using lattices as a source of computational hardness in the construction of cryptographic functions which are at least as hard to break as solving some underlying lattice problem. The study of lattice based cryptography has been largely stimulated by Ajtai’s discovery in 1996  that certain variants of the knapsack problem are at least as hard to break on the average as the worst case instance of certain lattice problems, e.g., solving the Shortest Vector Problem (SVP) approximately within polynomial factors. Specifically, assuming that there is no efficient algorithm to approximate SVP within a factor g = nc in the worst case (where n is the dimension of the lattice, and c is an arbitrary constant independent of n), one can build knapsack-like cryptographic (one-way) functions that are almost certainly hard to break (when the key is chosen at random). In 1997 Ajtai and Dwork  also proposed a cryptosystem with similar average-case/worst-case connection properties: decrypting random challenges is at least as hard as solving the worst-case instance of a variant of the shortest vector problem, called the unique SVP (uSVP). Since finding approximate solutions to lattice problems gets easier and easier as the approximation factor grows, an important question in the area is to determine the smallest factor g such that one can build cryptographic functions that are as hard to break as finding g-approximate solutions to lattice problems. At the time of this writing, the strongest known results are Micciancio’s collision resistant hash functions  based on the inapproximability of the covering
Game-Theoretic Approach to Feedback-Driven Multi-stage Moving Target Defense

The static nature of computer networks allows malicious attackers to easily gather useful information about the network using network scanning and packet sniffing. The employment of secure perimeter firewalls and intrusion detection systems cannot fully protect the network from sophisticated attacks. As an alternative to the expensive and imperfect detection of attacks, it is possible to improve network security by manipulating the attack surface of the network in order to create a moving target defense. In this paper, we introduce a proactive defense scheme that dynamically alters the attack surface of the network to make it difficult for attackers to gather system information by increasing complexity and reducing its signatures. We use concepts from systems and control literature to design an optimal and efficient multi-stage defense mechanism based on a feedback information structure. The change of attack surface involves a reconfiguration cost and a utility gain resulting from risk reduction. We use informationand control-theoretic tools to provide closed-form optimal randomization strategies. The results are corroborated by a case study and several numerical examples.
Lifelong Generative Modeling

Lifelong learning is the problem of learning multiple consecutive tasks in an online manner and is essential towards the development of intelligent machines that can adapt to their surroundings. In this work we focus on learning a lifelong approach to generative modeling whereby we continuously incorporate newly observed distributions into our model representation. We utilize two models, aptly named the student and the teacher, in order to aggregate information about all past distributions without the preservation of any of the past data or previous models. The teacher is utilized as a form of compressed memory in order to allow for the student model to learn over the past as well as present data. We demonstrate why a naive approach to lifelong generative modeling fails and introduce a regularizer with which we demonstrate learning across a long range of distributions.
Distilling Task Knowledge from How-To Communities

Knowledge graphs have become a fundamental asset for search engines. A fair amount of user queries seek information on problem-solving tasks such as building a fence or repairing a bicycle. However, knowledge graphs completely lack this kind of how-to knowledge. This paper presents a method for automatically constructing a formal knowledge base on tasks and task-solving steps, by tapping the contents of online communities such as WikiHow. We employ Open-IE techniques to extract noisy candidates for tasks, steps and the required tools and other items. For cleaning and properly organizing this data, we devise embedding-based clustering techniques. The resulting knowledge base, HowToKB, includes a hierarchical taxonomy of disambiguated tasks, temporal orders of sub-tasks, and attributes for involved items. A comprehensive evaluation of HowToKB shows high accuracy. As an extrinsic use case, we evaluate automatically searching related YouTube videos for HowToKB tasks.
Preference elicitation as an optimization problem

The new user coldstart problem arises when a recommender system does not yet have any information about a user. A common solution to it is to generate a profile by asking the user to rate a number of items. Which items are selected determines the quality of the recommendations made, and thus has been studied extensively. We propose a new elicitation method to generate a static preference questionnaire (SPQ) that poses relative preference questions to the user. Using a latent factor model, we show that SPQ improves personalized recommendations by choosing a minimal and diverse set of questions. We are the first to rigorously prove which optimization task should be solved to select each question in static questionnaires. Our theoretical results are confirmed by extensive experimentation. We test the performance of SPQ on two real-world datasets, under two experimental conditions: simulated, when users behave according to a latent factor model (LFM), and real, in which only real user judgments are revealed as the system asks questions. We show that SPQ reduces the necessary length of a questionnaire by up to a factor of three compared to state-of-the-art preference elicitation methods. Moreover, solving the right optimization task, SPQ also performs better than baselines with dynamically generated questions.
Deception detection for news: Three types of fakes

A fake news detection system aims to assist users in detecting and filtering out varieties of potentially deceptive news. The prediction of the chances that a particular news item is intentionally deceptive is based on the analysis of previously seen truthful and deceptive news. A scarcity of deceptive news, available as corpora for predictive modeling, is a major stumbling block in this field of natural language processing (NLP) and deception detection. This paper discusses three types of fake news, each in contrast to genuine serious reporting, and weighs their pros and cons as a corpus for text analytics and predictive modeling. Filtering, vetting, and verifying online information continues to be essential in library and information science (LIS), as the lines between traditional news and online information are blurring.
Integrating Structure with Deep Reinforcement and Imitation Learning

Most deep reinforcement and imitation learning methods are data-driven and do not utilize the underlying problem structure. While these methods have achieved great success on many challenging tasks, several key problems such as generalization, data efficiency, compositionality etc. remain open. Utilizing problem structure in the form of architecture design, priors, structured losses, domain knowledge etc. may be a viable strategy to solve some of these problems. In this thesis, we present two approaches towards integrating problem structure with deep reinforcement and imitation learning methods. In the first part of the thesis, we consider reinforcement learning problems where parameters of the model vary with its phase while the agent attempts to learn through its interactions with the environment. We propose phase-parameterized policies and value function approximators which explicitly enforce a phase structure to the policy or value space to better model such environments. We apply our phase-parameterized reinforcement learning approach to both feed-forward and recurrent deep networks in the context of trajectory optimization and locomotion problems. Our experiments show that our proposed approach has superior modeling performance and leads to improved sample complexity when compared with traditional function approximators in cyclic and linear phase environments. In the second part of the thesis, we present a framework that incorporates structure in imitation learning by modelling the imitation of complex tasks or activities as a composition of easier subtasks. We propose a new algorithm based on the Generative Adversarial Imitation Learning (GAIL) framework which automatically learns sub-task policies from unsegmented demonstrations. Our approach leverages the idea of directed or causal information to segment demonstrations of complex tasks into simpler sub-tasks and learn sub-task policies that can then be composed together to perform complicated activities. We thus call our approach Directed-Information GAIL. We experiment with both discrete and continuous state-action environments and show that our proposed approach is able to find meaningful sub-tasks from unsegmented trajectories which are then be combined to perform more complicated tasks.
Logical Rule Induction and Theory Learning Using Neural Theorem Proving

A hallmark of human cognition is the ability to continually acquire and distill observations of the world into meaningful, predictive theories. In this paper we present a new mechanism for logical theory acquisition which takes a set of observed facts and learns to extract from them a set of logical rules and a small set of core facts which together entail the observations. Our approach is neuro-symbolic in the sense that the rule predicates and core facts are given dense vector representations. The rules are applied to the core facts using a soft unification procedure to infer additional facts. After k steps of forward inference, the consequences are compared to the initial observations and the rules and core facts are then encouraged towards representations that more faithfully generate the observations through inference. Our approach is based on a novel neural forward-chaining differentiable rule induction network. The rules are interpretable and learned compositionally from their predicates, which may be invented. We demonstrate the efficacy of our approach on a variety of ILP rule induction and domain theory learning datasets.
CONSUMER'S PERCEPTION AND PURCHASE INTENTIONS

In the last years, it has been seen a shifting on society's consumption patterns, from mass consumption to second-hand culture. Moreover, consumer's perception towards second-hand stores, has been changing throughout the history of second-hand markets, according to the society's values prevailing in each time. Thus, the purchase intentions regarding second-hand clothes are influence by motivational and moderating factors according to the consumer's perception. Therefore, it was employed the theory of Guiot and Roux (2010) on motivational factors towards second-hand shopping and previous researches on moderating factors towards second-hand shopping.  The purpose of this study is to explore consumer's perception and their purchase intentions towards second-hand clothing stores.  For this, a qualitative and abductive approach was employed, combined with an exploratory design. Semi-structured face-to-face interviews were conducted utilizing a convenience sampling approach.  The findings show that consumers perception and their purchase intentions are influenced by their age and the environment where they live. However, the environment affect people in different ways. From this study, it could be found that elderly consumers are influenced by values and beliefs towards second-hand clothes. Young people are very influenced by the concept of fashion when it comes to second-hand clothes. For adults, it could be observed that price and the sense of uniqueness driver their decisions towards second-hand clothes consumption. The main motivational factor towards second-hand shopping was price. On the other hand, risk of contamination was pointed as the main moderating factor towards second-hand purchase. The study also revealed two new motivational factors towards second-hand clothing shopping, such charity and curiosity. Managers of second-hand clothing stores can make use of these findings to guide their decisions, especially related to improvements that could be done in order to make consumers overcoming the moderating factors towards second-hand shopping. The findings of this study are especially useful for second-hand clothing stores in Borås, since it was suggested couple of improvements for those stores based on the participant's opinions.
Considerations for follow-the-leader motion of extensible tendon-driven continuum robots

Path following and follow-the-leader motion is particularly desirable for minimally-invasive surgery in confined spaces which can only be reached using tortuous paths, e.g. through natural orifices. While path following and followthe- leader motion can be achieved by hyper-redundant snake robots, their size is usually not applicable for medical applications. Continuum robots, such as tendon-driven or concentric tube mechanisms, fulfill the size requirements for minimally invasive surgery, but yet follow-the-leader motion is not inherently provided. In fact, parameters of the manipulator's section curvatures and translation have to be chosen wisely a priori. In this paper, we consider a tendon-driven continuum robot with extensible sections. After reformulating the forward kinematics model, we formulate prerequisites for follow-the-leader motion and present a general approach to determine a sequence of robot configurations to achieve follow-the-leader motion along a given 3D path. We evaluate our approach in a series of simulations with 3D paths composed of constant curvature arcs and general 3D paths described by B-spline curves. Our results show that mean path errors <;0.4mm and mean tip errors <;1.6mm can theoretically be achieved for constant curvature paths and <;2mm and <;3.1mm for general B-spline curves respectively.
Early antiviral response and virus-induced genes in fish.

In fish as in mammals, virus infections induce changes in the expression of many host genes. Studies conducted during the last fifteen years revealed a major contribution of the interferon system in fish antiviral response. This review describes the screening methods applied to compare the impact of virus infections on the transcriptome in different fish species. These approaches identified a "core" set of genes that are strongly induced in most viral infections. The "core" interferon-induced genes (ISGs) are generally conserved in vertebrates, some of them inhibiting a wide range of viruses in mammals. A selection of ISGs -PKR, vig-1/viperin, Mx, ISG15 and finTRIMs - is further analyzed here to illustrate the diversity and complexity of the mechanisms involved in establishing an antiviral state. Most of the ISG-based pathways remain to be directly determined in fish. Fish ISGs are often duplicated and the functional specialization of multigenic families will be of particular interest for future studies.
Recent Advances in Biomaterials for 3D Printing and Tissue Engineering

Three-dimensional printing has significant potential as a fabrication method in creating scaffolds for tissue engineering. The applications of 3D printing in the field of regenerative medicine and tissue engineering are limited by the variety of biomaterials that can be used in this technology. Many researchers have developed novel biomaterials and compositions to enable their use in 3D printing methods. The advantages of fabricating scaffolds using 3D printing are numerous, including the ability to create complex geometries, porosities, co-culture of multiple cells, and incorporate growth factors. In this review, recently-developed biomaterials for different tissues are discussed. Biomaterials used in 3D printing are categorized into ceramics, polymers, and composites. Due to the nature of 3D printing methods, most of the ceramics are combined with polymers to enhance their printability. Polymer-based biomaterials are 3D printed mostly using extrusion-based printing and have a broader range of applications in regenerative medicine. The goal of tissue engineering is to fabricate functional and viable organs and, to achieve this, multiple biomaterials and fabrication methods need to be researched.
Learning to Navigate in Cities Without a Map

Navigating through unstructured environments is a basic capability of intelligent creatures, and thus is of fundamental interest in the study and development of artificial intelligence. Long-range navigation is a complex cognitive task that relies on developing an internal representation of space, grounded by recognisable landmarks and robust visual processing, that can simultaneously support continuous self-localisation (“I am here”) and a representation of the goal (“I am going there”). Building upon recent research that applies deep reinforcement learning to maze navigation problems, we present an end-to-end deep reinforcement learning approach that can be applied on a city scale. Recognising that successful navigation relies on integration of general policies with locale-specific knowledge, we propose a dual pathway architecture that allows locale-specific features to be encapsulated, while still enabling transfer to multiple cities. A key contribution of this paper is an interactive navigation environment that uses Google Street View for its photographic content and worldwide coverage. Our baselines demonstrate that deep reinforcement learning agents can learn to navigate in multiple cities and to traverse to target destinations that may be kilometres away. The project webpage http://streetlearn.cc contains a video summarizing our research and showing the trained agent in diverse city environments and on the transfer task, the form to request the StreetLearn dataset and links to further resources. The StreetLearn environment code is available at https://github.com/deepmind/streetlearn.
Scalable Network Forensics

Scalable Network Forensics by Matthias Vallentin Doctor of Philosophy in Computer Science University of California, Berkeley Professor Vern Paxson, Chair Network forensics and incident response play a vital role in site operations, but for large networks can pose daunting difficulties to cope with the ever-growing volume of activity and resulting logs. On the one hand, logging sources can generate tens of thousands of events per second, which a system supporting comprehensive forensics must somehow continually ingest. On the other hand, operators greatly benefit from interactive exploration of disparate types of activity when analyzing an incident, which often leaves network operators scrambling to ferret out answers to key questions: How did the attackers get in? What did they do once inside? Where did they come from? What activity patterns serve as indicators reflecting their presence? How do we prevent this attack in the future? Operators can only answer such questions by drawing upon high-quality descriptions of past activity recorded over extended time. A typical analysis starts with a narrow piece of intelligence, such as a local system exhibiting questionable behavior, or a report from another site describing an attack they detected. The analyst then tries to locate the described behavior by examining past activity, often cross-correlating information of different types to build up additional context. Frequently, this process in turn produces new leads to explore iteratively (“peeling the onion”), continuing and expanding until ultimately the analyst converges on as complete of an understanding of the incident as they can extract from the available information. This process, however, remains manual and time-consuming, as no single storage system efficiently integrates the disparate sources of data that investigations often involve. While standard Security Information and Event Management (SIEM) solutions aggregate logs from different sources into a single database, their data models omit crucial semantics, and they struggle to scale to the data rates that large-scale environments require.