Reviewerly/exHarmony huggingface dataset:
Preventing Shoulder-Surfing Attacks using Digraph Substitution Rules and Pass-Image Output Feedback

In this paper, we focus on methods to prevent shoulder-surfing attacks. We initially adopted digraph substitution rules from PlayFair cipher as our proposed method. PlayFair cipher is a modern cryptography method, which exists at the intersection of the disciplines of mathematics and computer science. However, according to our preliminary study it was insufficient to prevent shoulder-surfing attacks. Thus, a new method had to be proposed. In this new proposed method, we improvised the digraph substitution rules and used these rules together with an output feedback method to determine a pass-image. Our proposed method was evaluated with a user study. The results showed our proposed method was robust against both direct observation and video-recorded shoulder-surfing attacks.
Handling Security, Usability, User Experience and Reliability in User-Centered Development Processes

Human-Computer Interaction (HCI) research has been focusing on properties such as usability, accessibility, inclusive design, user experience. As new security risks are emerging with the continuous increase of Internet interconnections and the development of the Internet of Things, more recently security, trust and resilience have also become important for the development of interactive systems. Since users have been identified as one of the major security weaknesses in today’s technologies, HCI becomes a fundamental pillar for designing more secure (but still usable) systems. However, interactive system properties might overlap and sometimes create conflicts in user-centered development processes. For example, security could reduce system usability by placing a burden on users when they have to deal with passwords. The HCI research has provided several tools and techniques that can support designers in making decisions, but there are no “cookbooks”. This workshop promotes sharing of experiences in managing and resolving conflicts of multiple interactive system properties within the context of a user-centered design process. We are concerned by theories, methods and approaches for dealing with interactive system properties, managing potential conflicts and trade-offs. This workshop is organized by the IFIP WG 13.2 on Methodology for User-Centered System Design and the IFIP WG 13.5 on Human Error, Resilience, Reliability and Safety in System Development.
Domain Adaptation of SRL Systems for Biological Processes

Domain adaptation remains one of the most challenging aspects in the wide-spread use of Semantic Role Labeling (SRL) systems.Current state-of-the-art methods are typically trained on large-scale datasets, but their performances do not directly transfer to lowresource domain-specific settings.In this paper, we propose two approaches for domain adaptation in biological domain that involve pre-training LSTM-CRF based on existing large-scale datasets and adapting it for a low-resource corpus of biological processes.Our first approach defines a mapping between the source labels and the target labels, and the other approach modifies the final CRF layer in sequence-labeling neural network architecture.We perform our experiments on Pro-cessBank (Berant et al., 2014) dataset which contains less than 200 paragraphs on biological processes.We improve over the previous state-of-the-art system on this dataset by 21 F1 points.We also show that, by incorporating event-event relationship in ProcessBank, we are able to achieve an additional 2.6 F1 gain, giving us possible insights into how to improve SRL systems for biological process using richer annotations.
KNG: The K-Norm Gradient Mechanism

This paper presents a new mechanism for producing sanitized statistical summaries that achieve \emph{differential privacy}, called the \emph{K-Norm Gradient} Mechanism, or KNG. This new approach maintains the strong flexibility of the exponential mechanism, while achieving the powerful utility performance of objective perturbation. KNG starts with an inherent objective function (often an empirical risk), and promotes summaries that are close to minimizing the objective by weighting according to how far the gradient of the objective function is from zero. Working with the gradient instead of the original objective function allows for additional flexibility as one can penalize using different norms. We show that, unlike the exponential mechanism, the noise added by KNG is asymptotically negligible compared to the statistical error for many problems. In addition to theoretical guarantees on privacy and utility, we confirm the utility of KNG empirically in the settings of linear and quantile regression through simulations.
Delimitation of Regions of Interest in Similarity Queries Visualization

In Content Based Image Retrieval (CBIR) systems, the visualization of queries allows to add the human visual perception in the analysis process and facilitate the discovery of knowledge. Content-based queries can be performed comparing features extracted from images, such as color, texture, and shape. In this paper we propose ways to delimit the region of interest to be visualized in the execution of queries by similarity in complex datasets. Limiting the amount of data to be visualized allows keeping the distribution of mapped data closer to the real distribution, besides allowing the application of more expensive computational methods for multidimensional projection. The proposed techniques were implemented in a prototype that allows visualizing only the region in which the query is being performed, mapping the data in three-dimensional spaces and allowing users to interact with them, being favored by human perception to improve the analysis and understanding of the data.
New Opportunities and Challenges of Geo-ICT Convergence Technology: GeoCPS and GeoAI

Cyber-physical systems (CPS) and artificial intelligence (AI), represented as innovative technologies of the 4th industrial revolution, are developing into Geo-ICT, which is ICT convergence technology based on geoscience knowledge. We proposed the contributions of Geo-ICT to both the mining industry and public sector from various perspectives of the 4th industrial revolution. It was confirmed that Geo-ICT could be an alternative to enhance public acceptance through the coevolution of geoscience and civil society. We compared smart factories and smart mining driven by CPS; through this comparison, we defined the concept of GeoCPS and suggested a direction for the development of smart mining technology. In addition, we reviewed conventional AI and cases where it could be applied to geoscience, and addressed the challenging issues of AI and data-driven informatization in geoscience. In particular, we presented a theory-guided data science model as a Geo-AI utilization model that can take into account the process and data characteristics of the geological resources domain and summarize the effects of synergy.
Argument Component Classification by Relation Identification by Neural Network and TextRank

In recent years, argumentation mining, which automatically extracts the structure of argumentation from unstructured documents such as essays and debates, is gaining attention.For argumentation mining applications, argumentcomponent classification is an important subtask.The existing methods can be classified into supervised methods and unsupervised methods.Many existing supervised methods use a classifier to identify the roles of argument components, such as claim or premise , but many of them use information of a single sentence without relying on the whole document.On the other hand, existing unsupervised document classification has the advantage of being able to use the whole document, but accuracy of these methods is not so high.In this paper, we propose a method for argument-component classification that combines relation identification by neural networks and TextRank to integrate relation informations (i.e. the strength of the relation).This method can use argumentation-specific knowledge by employing supervised learning on a corpus while maintaining the advantage of using the whole document.Experiments on two corpora, one consisting of student essays and the other of Wikipedia articles, show the effectiveness of this method.
The Internet of Things cybersecurity standardization

The Internet of Things (IoT) landscape is a quite fast growing marketplace with so many open questions coming on with.The main challenge to this area is still its security and so many current and incoming professionals need to tackle such a concern through.As it's well-known, the IoT solutions would rely of a wide variety of digital, embedded and mechatronics systems coping with the exposing to the web.So many experts would make an advice to pay attention to cybersecurity at the project's initiation, so there would be a plenty of hard work to research and development (R&D) teams to proceed with those requirements.If the role of R&D members is to deal with the cybersecurity demandswould that mean that the expert's community should ask those professionals about their opinion on any kind of technological standardization?The best known international standards would come from the Switzerland's standardization body being called the International Standard Organization (ISO).There would also be some concerns how trusted emerging technology got and so many organizations worldwide would try to put that question into the legal frameworks.Apparently, the international standardization of the IoT cyber defense is something that should get stressed out through a lot of official meetings and discussions.In this paper, we would attempt to provide a comprehensive insight how the international standardization works in the practice and why it is important to standardize the current tendencies.
A Real-Time Multi-Label Classification System for Short Videos

Efficient classification of short videos is of a great challenge in industry due to their large amounts and diverse semantics. In this paper, we present a real-time multi-label classification system to attain it. Specifically, a frame-level preprocessing strategy is first proposed to efficiently decode the videos for useful information. Then an image-based model is developed to achieve the final video-level classification. For the multi-label classification case, three modules are proposed to enhance the model's classification capability, relieve the label-imbalance issue and exploit the label correlation respectively. Our system achieves an accuracy of 86% on both private test sets of the challenge "Short Video Real-time Classification" in AI Challenger 2018, and on its open validation set it achieves an accuracy of 89.2% with a speed of 28ms per video on a workstation with an Intel Xeon CPU E5-2650 and a NVIDIA GPU TITAN Xp.
Electronic Library of the RSL: Development Stages and Features of Formation of Digital Collections

The article for the first time covers the history of the Electronic Library (EL) of the Russian State Library (RSL) from the beginning of its formation in the mid-1990s to the present. The author considers the stages of formation and development of the EL of the RSL, the changes that have occurred depending on various factors: changes in legislation in the field of intellectual activity, the emergence of new conceptual approaches and technological platforms. The article identifies two basic approaches to the creation of electronic library: formation of large electronic collections by type of publications or presented information and the creation of electronic version of the collections of documents stored in the RSL holdings. The author gives the key decisions made in relation to the EL: the resources that have passed into the public domain are freely available on the website of the RSL; all documents (not fragments) digitized on the basis of the RSL holdings shall be included in the EL of the RSL; the quality of electronic materials is strictly regulated, etc. The article presents the history of creation and features of large digital collections and separate collections, as well as participation in Russian and international projects and programs. The author considers the development of various electronic services, search capabilities, and features of access to different collections depending on the content. The article highlights the role of the “Electronic Library of Dissertations”, which became the basis for enriching the EL of the RSL with relevant scientific knowledge, for working out new technical and technological solutions, and for providing unique access opportunities within the current legislation. The author notes that the EL creators are focused on the organization of access to modern scientific and educational content within the framework of legislative initiatives, conceptual and special technical solutions, as well as on providing free access to materials of great cultural and historical value for Russia and the world community
A Bit More Than a Bit Is More Than a Bit Better

Abstract We study both the practical and theoretical efficiency of private information retrieval (PIR) protocols in a model wherein several untrusted servers work to obliviously service remote clients’ requests for data and yet no pair of servers colludes in a bid to violate said obliviousness. In exchange for such a strong security assumption, we obtain new PIR protocols exhibiting remarkable efficiency with respect to every cost metric—download, upload, computation, and round complexity—typically considered in the PIR literature. The new constructions extend a multiserver PIR protocol of Shah, Rashmi, and Ramchandran (ISIT 2014), which exhibits a remarkable property of its own: to fetch a b -bit record from a collection of r such records, the client need only download b + 1 bits total. We find that allowing “a bit more” download (and optionally introducing computational assumptions) yields a family of protocols offering very attractive trade-offs. In addition to Shah et al.’s protocol, this family includes as special cases (2-server instances of) the seminal protocol of Chor, Goldreich, Kushilevitz, and Sudan (FOCS 1995) and the recent DPF-based protocol of Boyle, Gilboa, and Ishai (CCS 2016). An implicit “folklore” axiom that dogmatically permeates the research literature on multiserver PIR posits that the latter protocols are the “most efficient” protocols possible in the perfectly and computationally private settings, respectively. Yet our findings soundly refute this supposed axiom: These special cases are (by far) the least performant representatives of our family, with essentially all other parameter settings yielding instances that are significantly faster.
Key recovery attack on Circulant UOV/Rainbow

UOV and Rainbow are multivariate signature schemes, which are known to be efficient and secure enough against known attacks under suitable parameter selections, and have been expected to be post-quantum cryptography. Recently, new variants of UOV and Rainbow, called Circulant UOV and Circulant Rainbow respectively, were proposed by Peng and Tang. In these variants, the signature generation is faster than the original schemes since circulant matrices appear in the process of signature generation. However, such circulant structures weaken the security. In this paper, we study the structures of these circulant variants and show that they are vulnerable against Kipnis-Shamir's attack.
PSynDB

Across many application domains, trusted parties who collect sensitive information need mechanisms to safely disseminate data. A favored approach is to generate synthetic data : a dataset similar to the original, hopefully retaining its statistical features, but one that does not reveal the private information of contributors to the data. We present PSynDB, a web-based synthetic table generator that is built on recent privacy technologies [10,11,15]. PSynDB satisfies the formal guarantee of differential privacy and generates synthetic tables with high accuracy for tasks that the user specifies as important. PSynDB allows users to browse expected error rates before running the mechanism, a useful feature for making important policy decisions, such as setting the privacy loss budget. When the user has finished configuration, the tool outputs a data synthesis program that can be ported to a trusted environment. There it can be safely executed on the private data to produce the private synthetic dataset for broad dissemination.
Cognitive Modeling and Cognitive Map Applying to the Knowledge Management in the Higher Education System

The article analyzes and summarizes the problems of higher education in the era of the knowledge economy. The authors use the methodology of cognitive modeling of weakly structured systems to build cognitive maps of the educational process. The author’s goal is to show the possibility of using cognitive modeling and cognitive maps in the university’s knowledge management system. To build cognitive maps, the authors identified target risk factors, basic risk factors and risk factors that affect the quality of the educational process in the higher education system. Cognitive maps are used as a tool to analyze the higher education quality and risk factors. The cognitive analysis of factors affecting the higher education quality and risks showed the need to change the role the universities play in the knowledge economy.
From type theory to setoids and back

Abstract A model of Martin-Löf extensional type theory with universes is formalized in Agda, an interactive proof system based on Martin-Löf intensional type theory. This may be understood, we claim, as a solution to the old problem of modeling the full extensional theory in the intensional theory. Types are interpreted as setoids, and the model is therefore a setoid model.We solve the problem of interpreting type universes by utilizing Aczel’s type of iterative sets and show how it can be made into a setoid of small setoids containing the necessary setoid constructions. In addition, we interpret the bracket types of Awodey and Bauer. Further quotient types should be interpretable.
Shortcut-Upsampling Block for 3D Face Reconstruction and Dense Alignment via Position Map

Joint 3D face reconstruction and dense alignment based on a single image has always been a challenge in computer vision. Recently, some work achieves these two goals simultaneously by adopting the position map to represent a 3D face. In this paper, we design a novel upsampling block, denoted as shortcut-upsampling block, to construct the position map. We first utilize the residual blocks to extract and downsample the feature maps from the 2D face image, and then utilize the proposed shortcut-upsampling blocks to convert the feature maps to the corresponding position map. Shortcut-upsampling block allows our model to be only 121M and achieve excellent performance on 3D face reconstruction and dense alignment. Compared with other deep learning methods based on 3D Morphable Model(3DMM), our model is the fastest when reconstructing a 3D face. In addition, through introducing a dynamic weight loss function, our model can converge the loss to a lower value and obtain a better performance on 3D face reconstruction.
Blackbox Attacks on Reinforcement Learning Agents Using Approximated Temporal Information

Recent research on reinforcement learning (RL) has suggested that trained agents are vulnerable to maliciously crafted adversarial samples. In this work, we show how such samples can be generalised from White-box and Grey-box attacks to a strong Black-box case, where the attacker has no knowledge of the agents, their training parameters and their training methods. We use sequence-to-sequence models to predict a single action or a sequence of future actions that a trained agent will make. First, we show our approximation model, based on time-series information from the agent, consistently predicts RL agents' future actions with high accuracy in a Black-box setup on a wide range of games and RL algorithms. Second, we find that although adversarial samples are transferable from the target model to our RL agents, they often outperform random Gaussian noise only marginally. This highlights a serious methodological deficiency in previous work on such agents; random jamming should have been taken as the baseline for evaluation. Third, we propose a novel use for adversarial samplesin Black-box attacks of RL agents: they can be used to trigger a trained agent to misbehave after a specific time delay. This appears to be a genuinely new type of attack. It potentially enables an attacker to use devices controlled by RL agents as time bombs.
Attributed Rhetorical Structure Grammar for Domain Text Summarization

This paper presents a new approach of automatic text summarization which combines domain oriented text analysis (DoTA) and rhetorical structure theory (RST) in a grammar form: the attributed rhetorical structure grammar (ARSG), where the non-terminal symbols are domain keywords, called domain relations, while the rhetorical relations serve as attributes. We developed machine learning algorithms for learning such a grammar from a corpus of sample domain texts, as well as parsing algorithms for the learned grammar, together with adjustable text summarization algorithms for generating domain specific summaries. Our practical experiments have shown that with support of domain knowledge the drawback of missing very large training data set can be effectively compensated. We have also shown that the knowledge based approach may be made more powerful by introducing grammar parsing and RST as inference engine. For checking the feasibility of model transfer, we introduced a technique for mapping a grammar from one domain to others with acceptable cost. We have also made a comprehensive comparison of our approach with some others.
Enhanced Face Detection Based on Haar-Like and MB-LBP Features

The effective real-time face detection framework proposed by Viola and Jones gained much popularity due its computational efficiency and its simplicity. A notable variant replaces the original Haar-like features with MB-LBP (Multi-Block Local Binary Pattern) which are defined by the local binary pattern operator, both detector types are integrated into the OpenCV library. However, each descriptor and its evaluation method has its own set of strengths and setbacks. In this paper, an enhanced two-layer face detector composed of both Haar-like and MB-LBP features is presented. Haar-like features are employed as a coarse filter but with a new evaluation involving dual threshold. The already established MB-LBPs are arranged as the fine filter of the detector. The Gentle AdaBoost learning algorithm is deployed for the training of the proposed detector to reach the classification and performance potential. Experiments show that in the early stages of classification, Haar features with dual threshold are more discriminative than MB-LBP and original Haar-like features with respect to number of features required and computation. Benchmarking the proposed detector demonstrate overall 12% higher detection rate at 17% false alarm over using MB-LBP features singly while performing with ×3 speedup.