Reviewerly/exHarmony huggingface dataset:
Schema mapping generation in the wild

Schema mappings enable declarative and executable specification of transformations between different schematic representations of application concepts. Most work on mapping generation has assumed that the source and target schemas are well defined, e.g., with declared keys and foreign keys, and that the mapping generation processes exist to support the data engineer in the labour-intensive process of producing a high-quality integration. However, organizations increasingly have access to numerous independently produced datasets, e.g., in a data lake, with a requirement to produce rapid, best-effort integrations, without extensive manual effort. As a result, there is a need to generate mappings in settings without declared relationships, and thus on the basis of inferred profiling data, and over large numbers of sources. Our contributions include a dynamic programming algorithm for exploring the space of potential mappings, and techniques for propagating profiling data through mappings, so that the fitness of candidate mappings can be estimated. The paper also describes how the resulting mappings can be used to populate single and multi-relation target schemas. Experimental results show the effectiveness and scalability of the approach in a variety of synthetic and real-world scenarios.
New Weak Admissibility Semantics for Abstract Argumentation

Baumann, Brewka and Ulbricht [, ] recently introduced weak admissibility as an alternative to Dung’s notion of admissibility [], and they used it to define weakly preferred, weakly complete and weakly grounded semantics of argumentation frameworks. In earlier work, we introduced two variants of their new semantics which we called qualified and semi-qualified semantics. We analysed all known variants of weak admissibility semantics with respect to some of the principles discussed in the literature on abstract argumentation, as well as some new principles we introduced to distinguish them all. Such a principle-based analysis can be used not only for selecting a semantics for an application, or for algorithmic design, but also for further research into weak admissibility semantics. In this paper, we introduce six new kinds of semantics based on weak admissibility, and we provide an initial principle-based analysis. The analysis illustrates various ways in which the new semantics improve on existing ones.
AMR-Net: Convolutional Neural Networks for Multi-resolution Steady Flow Prediction

We develop a convolutional neural network model to predict multi-resolution steady flow data. Based on the image-to-image translation model pix2pixHD, our model can predict high resolution flow fields from the set of patched signed distance functions. By patching the high resolution data, our model uses roughly the one third of memory used by pix2pixHD. The accuracy of our model is almost the same as the U-Net model using the unpatched high resolution data.
SoK: Opportunities for Software-Hardware-Security Codesign for Next Generation Secure Computing

Users are demanding increased data security. As a result, security is rapidly becoming a first-order design constraint in next generation computing systems. Researchers and practitioners are exploring various security technologies to meet user demand such as trusted execution environments (e.g., Intel SGX, ARM TrustZone), homomorphic encryption, and differential privacy. Each technique provides some degree of security, but differs with respect to threat coverage, performance overheads, as well as implementation and deployment challenges. In this paper, we present a systemization of knowledge (SoK) on these design considerations and trade-offs using several prominent security technologies. Our study exposes the need for software-hardware-security codesign to realize efficient and effective solutions of securing user data. In particular, we explore how design considerations across applications, hardware, and security mechanisms must be combined to overcome fundamental limitations in current technologies so that we can minimize performance overhead while achieving sufficient threat model coverage. Finally, we propose a set of guidelines to facilitate putting these secure computing technologies into practice.
Attacking ECDSA Leaking Discrete Bits with a More Efficient Lattice

A lattice attack on the Elliptic Curve Digital Signature Algorithm (ECDSA) implementation constructs a lattice related to the secret key by utilizing the information leaked and then recovers the secret key by finding a certain short lattice vector. When the information leaked is discrete bits, Fan et al. (CCS 2016) constructed an efficient lattice by translating the problem of recovering the secret key to the Extended Hidden Number Problem (EHNP). Following their works, we propose two new techniques to construct a more efficient lattice which has a lower dimension and a shorter target vector. Moreover, we further improve the success probability of the secret key recovery by adjusting the lattice. Therefore, it is much easier to recover the secret key. Specifically, injecting our techniques into the existing lattice attacks, we recover the secret key with fewer signatures or a higher success probability.
The Longitudinal IntermediaPlus (2014–2016): A Case Study in Structuring Unstructured Big Data

Abstract This article details the novel structure developed to handle, harmonize and document big data for reuse and long-term preservation. ‘ The Longitudinal IntermediaPlus (2014–2016 )’ big data dataset is uniquely rich: it covers an array of German online media extendable to cross-media channels and user information. The metadata file for this dataset, and its documentation, were recently deposited as its own MySQL database called charmstana_sample_14-16.sql ( https://data.gesis.org/sharing/#!Detail/10.7802/2030 ) ( cs 16) and is suitable for generating descriptive statistics. Analogous to the ‘Data View’ in spss , the charmstana_analysis ( ca ) contains the dataset’s numerical values. Both the cs 16 and ca MySQL files are needed to conduct analysis on the full database. The research challenge was to process large-scaled datasets into one longitudinal, big-data data source suitable for academic research, and according to fair principles. The authors review four methodological recommendations that can serve as a framework for solving big-data structuring challenges, using the harmonization software CharmStats.
Combining object-oriented paradigm and controlled natural language for requirements specification

Natural language is the dominant form of writing software requirements. Its essential ambiguity causes inconsistency of requirements, which leads to scope creep. On the other hand, formal requirements specification notations such as Z, Petri Nets, SysML, and others are difficult to understand by non-technical project stakeholders. They often become a barrier between developers and requirements providers. The article presents a controlled natural language that looks like English but is a strongly typed object-oriented language compiled to UML/XMI. Thus, it is easily understood, at the same time, by non-technical people, programmers, and computers. Moreover, it is formally verifiable and testable. It was designed, developed, and tested in three commercial software projects in order to validate the assumption that object-oriented design can be applied to requirements engineering at the level of specifications writing. The article outlines key features of the language and summarizes the experience obtained during its practical application.
Camera Pose Estimation Based on Feature Extraction and Description for Robotic Gastrointestinal Endoscopy

The application of robotics in gastrointestinal endoscopy has gained more and more attention over the past decade. The localization and navigation of the robotic gastrointestinal endoscopy is very important in robot-assisted gastrointestinal examination and surgery. The camera pose of the robotic gastrointestinal endoscopy can be estimated directly from the image sequence. However, due to the texture-less nature and strong specular reflections of the digestive tract surface, it is hard to detect enough keypoints to estimate the camera pose when using the traditional handcrafted method. In this paper, we propose an end-to-end CNN-based network to deal with this problem. Our network is trained in a self-supervised manner, and the network plays two roles, a dense feature descriptor and a feature detector simultaneously. The network takes the image sequence as input, and the featured keypoints and their corresponding descriptors as outputs. We demonstrate our algorithm on images captured in stomach phantom. The experimental results show that our method can effectively detect and describe the featured keypoints in challenging conditions.
Who are the arbitrageurs? Empirical evidence from Bitcoin traders in the Mt. Gox exchange platform

We mine the leaked history of trades on Mt. Gox, the dominant Bitcoin exchange from 2011 to early 2014, to detect the triangular arbitrage activity conducted within the platform. The availability of user identifiers per trade allows us to focus on the historical record of 440 investors, detected as arbitrageurs, and consequently to describe their trading behavior. We begin by showing that a considerable difference appears between arbitrageurs when indicators of their expertise are taken into account. In particular, we distinguish between those who conducted arbitrage in a single or in multiple markets: using this element as a proxy for trade ability, we find that arbitrage actions performed by expert users are on average non profitable when transaction costs are accounted for, while skilled investors conduct arbitrage at a positive and statistically significant premium. Next, we show that specific trading strategies, such as splitting orders or conducting arbitrage non aggressively, are further indicators of expertise that increase the profitability of arbitrage. Most importantly, we exploit within user (across hours and markets) variation and document that expert users make profits on arbitrage by reacting quickly to plausible exogenous variations on the official exchange rates. We present further evidence that such differences are chiefly due to a better ability of the latter in incorporating information, both on the transactions costs and on the exchange rates volatility, eventually resulting in a better timing choice at small time scale intervals. Our results support the hypothesis that arbitrageurs are few and sophisticated users.
MultiViewStereoNet: Fast Multi-View Stereo Depth Estimation using Incremental Viewpoint-Compensated Feature Extraction

We propose a novel learning-based method for multi-view stereo (MVS) depth estimation capable of recovering depth from images taken from known, but unconstrained, views. Existing MVS methods extract features from each image independently before projecting them onto a set of planes at candidate depths to compute matching costs. By projecting features after extraction, networks must learn rotation and scale invariant representations even though the relative poses of the cameras are known. In our approach, we compensate for viewpoint changes directly in the extraction layers, allowing the network to learn features that are projected by construction and reducing the need for rotation and scale invariance.Compensating for viewpoint changes naively, however, can be computationally expensive as the feature layers must either be applied multiple times (once per depth hypothesis), or replaced by 3D convolutions. We overcome this limitation in two ways. First, we only compute our matching cost volume at a coarse image scale before upsampling and refining the outputs. Second, we incrementally compute our projected features such that the bulk of the layers need only be executed a single time across all depth hypotheses. The combination of these two techniques allows our method to perform competitively with the state-of-the-art, while being significantly faster. We call our method MultiViewStereoNet and release our source code publicly for the benefit of the robotics community.
Semi-supervised Visual Feature Integration for Language Models through Sentence Visualization

Integrating visual features has been proved useful for natural language understanding tasks. Nevertheless, most existing multimodal language models highly rely on training on aligned image and text data. In this paper, we propose a novel semi-supervised visual integration framework for pre-trained language models. In the framework, the visual features are obtained through a sentence visualization and vision-language fusion mechanism. The uniqueness includes: 1) the integration is conducted via a semi-supervised framework and does not require aligned images for the processed sentences. 2) the framework works as an auxiliary component, and will not affect the language processing ability of the integrated language model. Experimental results on both natural language inference and reading comprehension tasks demonstrate that our framework improves the strong baseline language models. Considering that our framework only requires an image database, and does not require aligned images for the processed texts, it provides a feasible way for multimodal language learning.
Fingerspelling Recognition in the Wild with Fixed-Query based Visual Attention

We propose an end-to-end solution for recognizing fingerspelling using multi-scale attention with fixed-queries. Fingerspelling recognition in the wild gets challenging because of the multiple sub-problems involved - detecting the signing hand, tracking it across frames, and recognizing subtle variations in a hand gesture. While the current state-of-the-art handles these with external face/hand detectors, optical flow features, and iteratively refining the attention maps, our work proposes a deep learning model that takes in the RGB videos and recognizes fingerspelling with a single forward pass. Without any frame-level supervision, our proposed model learns to pay attention to informative regions in each frame, such as fingers, hand, and face, to recognize signs. Multi-scale features from these attended regions are then processed using a recurrent neural network to recognize the alphabet sequentially. We train our model using a curriculum learning strategy with simpler samples at the beginning, followed by challenging samples at a later stage. We have evaluated our approach on Chicago Fingerspelling Wild and WildPlus datasets and have achieved about 8% and 4% improvements, respectively, compared to the current state-of-the-art methods. Further analysis of our method shows that our attention mechanism is intuitive from a human perspective, and visualizing it offers useful insights into the working of the model.
The Adjacency Graphs of FSRs With Affine Characteristic Functions

We study the adjacency graphs of feedback shift registers (FSRs) with affine characteristic functions. We first show that, similar to the linear case, the output sequences of FSRs with affine characteristic functions also have the direct sum decompositions. The only difference from the linear case is that one component of the decomposition is a family of affine sequences, rather than linear sequences. Then, based on this fact, we give a relationship between the adjacency graphs of FSRs with affine characteristic functions and the generalized adjacency graphs of FSRs whose characteristic functions correspond to the components of the decomposition. This relationship establishes the algebraic structure of adjacency graphs and greatly simplifies the calculation of them. At last, we especially study the adjacency graph of the FSR whose characteristic function is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q_{k}(x)+1$ </tex-math></inline-formula> where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q_{k}(x)$ </tex-math></inline-formula> is the linear function corresponding to the polynomial <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(1+x)^{k}$ </tex-math></inline-formula> . We prove that for any two cycles of this FSR, the number of conjugate pairs shared by them is no more than 4 if <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> is a power of 2, and no more than 2 if <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> is not a power of 2.
Learning Multi-context Aware Location Representations from Large-scale Geotagged Images

With the ubiquity of sensor-equipped smartphones, it is common to have multimedia documents uploaded to the Internet that have GPS coordinates associated with them. Utilizing such geotags as an additional feature is intuitively appealing for improving the performance of location-aware applications. However, raw GPS coordinates are fine-grained location indicators without any semantic information. Existing methods on geotag semantic encoding mostly extract hand-crafted, application-specific location representations that heavily depend on large-scale supplementary data and thus cannot perform efficiently on mobile devices. In this paper, we present a machine learning based approach, termed GPS2Vec+, which learns rich location representations by capitalizing on the world-wide geotagged images. Once trained, the model has no dependence on the auxiliary data anymore so it encodes geotags highly efficiently by inference. We extract visual and semantic knowledge from image content and user-generated tags, and transfer the information into locations by using geotagged images as a bridge. To adapt to different application domains, we further present an attention-based fusion framework that estimates the importance of the learnt location representations under different contexts for effective feature fusion. Our location representations yield significant performance improvements over the state-of-the-art geotag encoding methods on image classification and venue annotation.
An Online Platform for Fully-Automated EO Processing Workflows for Developers and End-Users Alike

With the ongoing proliferation of satellite data, in particular open-access satellite imagery, from both optical and synthetic aperture radar (SAR) sensors, the number of downstream applications is rapidly growing. Developers of Earth Observation (EO)-based products and services, as well as expert and non-expert users of such tools, thus need access to a cloud computing infrastructure offering interoperable analysis functionality. Here, we present the versatility of such a cloud-based infrastructure called WASDI. WASDI, a web-advanced space development interface, is an online EO analytics platform where EO experts can develop and deploy applications (apps) and users can use them to processes satellite images on demand to generate value-added content.
Topic-to-Topic Modeling for COVID-19 Mortality

We examine a cohort of 4307 COVID-19 case fatalities from a de-identified national registry in the U.S. using Latent Dirichlet Allocation and group each patient by topic based on their pre-existing conditions in the years prior to infection and again during the last three weeks of life. We show that certain pre-existing condition topics have strong associations with certain COVID-19 mortality topics suggesting that the major clinical pathways leading to COVID-19 death may be through failures of already weakened organ systems. We then explore the demographics for these groups and generate several insights and hypotheses.
Answers set programs for non-transferable utility games: Expressiveness, complexity and applications

Coalitional games are mathematical models suited to study payoff distribution problems in cooperative scenarios. In abstract terms, a coalitional game can be specified by explicitly listing all possible—in fact, exponentially many—coalitions with their associated distributions. This naïve representation, however, quickly becomes infeasible over games involving many agents, thereby calling for suitable compact representations, that is, encoding mechanisms that (on some specific classes of games of interest) take an amount of space that grows polynomially with the number of agents. To date, a plethora of compact encodings have been already introduced and analyzed from the algorithm and computational viewpoints. Despite their specific technical differences, these encodings typically share the assumption that the utility associated with a coalition can be freely transferred among agents. Indeed, designing encoding mechanisms for the non-transferable utility (NTU) setting is a research issue that has been largely unexplored so far. The paper addresses this issue by proposing a compact encoding for representing and reasoning about the outcomes of NTU coalitional games founding on answer set programming. By exploiting the expressiveness of this well-known knowledge representation formalism, it is shown that the proposed representation can succinctly encode several games of interest within a wide range of application domains. Computational issues arising in the setting have been studied too, by addressing questions related to certain payoff distributions enjoying desirable stability properties. Eventually, a prototype system supporting the proposed framework has been implemented by leveraging a state-of-the-art answer set engine, and results of a thorough experimental activity conducted on top of it have been discussed.
Gesture-based Interaction Systems in Hospital Critical Environment

Gesture interfaces contribute to touchless interaction in systems inserted in critical hospital environments such as surgery rooms and intensive care units, among other areas where infection care is essential. However, defining a vocabulary of gestures that is adequate to this environment's physical characteristics, the sensors used, and the tasks performed by users while interacting is a challenge for developers. Methodologies and guidelines proposed in the literature to the gestures definition do not consider the characteristics of the critical environment, causing natural and innovative interaction to fail by not meeting the user's need. In this sense, our paper discusses the main challenges imposed by critical environments in gesture interaction development. As a result, we propose a set of recommendations that can contribute to developers of gesture interaction systems. Moreover, our research highlights and promotes a discussion about the need to develop specific methodologies to the critical environment context that supports the definition of gesture vocabulary.
Intelligent recommendation system based on knowledge graph for scientific research teams

With the continuous penetration of information technology into scientific research work, information resources with diverse structures have gathered into the scientific research team. Facing the needs of scientific research workers to manage and utilize scientific research resources, we provide a knowledge recommendation platform for university scientific research teams. In the face of multi-source heterogeneous resources in the scientific research field (such as literature data and work record data), we have established a resource knowledge base based on knowledge graph descriptions. In order to realize the effective recommendation of resources, we use the graph neural network model to design a personalized recommendation module while constantly updating our knowledge resources. After actual testing, our resource platform can better knowledge resources and push knowledge content.