Reviewerly/exHarmony huggingface dataset:
Deep Learning Based Registration Using Spatial Gradients and Noisy Segmentation Labels

Image registration is one of the most challenging problems in medical image analysis. In the recent years, deep learning based approaches became quite popular, providing fast and performing registration strategies. In this short paper, we summarise our work presented on Learn2Reg challenge 2020. The main contributions of our work rely on (i) a symmetric formulation, predicting the transformations from source to target and from target to source simultaneously, enforcing the trained representations to be similar and (ii) integration of variety of publicly available datasets used both for pretraining and for augmenting segmentation labels. Our method reports a mean dice of $0.64$ for task 3 and $0.85$ for task 4 on the test sets, taking third place on the challenge. Our code and models are publicly available at https://github.com/TheoEst/abdominal_registration and \https://github.com/TheoEst/hippocampus_registration.
Intelligent Approaches for the Automated Domain Ontology Extraction

The chapter presents the review of modern approaches for the domain terminology extraction, concept discovery, concept hierarchy derivation, and learning of non-taxonomic relations steps in the ontology learning task. The chapter presents the review of not only various approaches to solving these NLP tasks but also ready-made tools that implement these approaches.
Improved Attribute-Based Encryption Scheme in Fog Computing Environment for Healthcare Systems

In today's smart healthcare system, medical records of patients are exposed to a large number of users for various purposes, from monitoring the patients' health to data analysis. Preserving the privacy of a patient has become an important and challenging issue. outsourced Ciphertext-Policy Attribute-Based Encryption (CP-ABE) provides a solution for the data sharing and privacy preservation problem in the healthcare system in fog environment. However, the high computational cost in case of frequent attribute updates renders it infeasible for providing access control in healthcare systems. In this paper, we propose an efficient method to overcome the frequent attribute update problem of outsourced CP-ABE. In our proposed approach, we generate two keys for each user (a static key and a dynamic key) based on the constant and changing attributes of the users. Therefore, in case of an attribute change for a user, only the dynamic key is updated. Also, the key update is done at the fog nodes without compromising the security of the system. Thus, both the communication and the computational overhead associated with the key update in the outsourced CP-ABE scheme are reduced, making it an ideal solution for data access control in healthcare systems. The efficacy of our proposed approach is shown through theoretical analysis and experimentation.
PROBABILISTIC STABILITY, AGM REVISION OPERATORS AND MAXIMUM ENTROPY

Abstract Several authors have investigated the question of whether canonical logic-based accounts of belief revision, and especially the theory of AGM revision operators, are compatible with the dynamics of Bayesian conditioning. Here we show that Leitgeb’s stability rule for acceptance, which has been offered as a possible solution to the Lottery paradox, allows to bridge AGM revision and Bayesian update: using the stability rule, we prove that AGM revision operators emerge from Bayesian conditioning by an application of the principle of maximum entropy. In situations of information loss, or whenever the agent relies on a qualitative description of her information state—such as a plausibility ranking over hypotheses, or a belief set—the dynamics of AGM belief revision are compatible with Bayesian conditioning; indeed, through the maximum entropy principle, conditioning naturally generates AGM revision operators. This mitigates an impossibility theorem of Lin and Kelly for tracking Bayesian conditioning with AGM revision, and suggests an approach to the compatibility problem that highlights the information loss incurred by acceptance rules in passing from probabilistic to qualitative representations of belief.
What is the Meaning of Proofs?

The origins of proof-theoretic semantics lie in the question of what constitutes the meaning of the logical connectives and its response: the rules of inference that govern the use of the connective. However, what if we go a step further and ask about the meaning of a proof as a whole? In this paper we address this question and lay out a framework to distinguish sense and denotation of proofs. Two questions are central here. First of all, if we have two (syntactically) different derivations, does this always lead to a difference, firstly, in sense, and secondly, in denotation? The other question is about the relation between different kinds of proof systems (here: natural deduction vs. sequent calculi) with respect to this distinction. Do the different forms of representing a proof necessarily correspond to a difference in how the inferential steps are given? In our framework it will be possible to identify denotation as well as sense of proofs not only within one proof system but also between different kinds of proof systems. Thus, we give an account to distinguish a mere syntactic divergence from a divergence in meaning and a divergence in meaning from a divergence of proof objects analogous to Frege's distinction for singular terms and sentences.
Extracting N-ary Facts from Wikipedia Table Clusters

Tables in Wikipedia articles contain a wealth of knowledge that would be useful for many applications if it were structured in a more coherent, queryable form. An important problem is that many of such tables contain the same type of knowledge, but have different layouts and/or schemata. Moreover, some tables refer to entities that we can link to Knowledge Bases (KBs), while others do not. Finally, some tables express entity-attribute relations, while others contain more complex n-ary relations. We propose a novel knowledge extraction technique that tackles these problems. Our method first transforms and clusters similar tables into fewer unified ones to overcome the problem of table diversity. Then, the unified tables are linked to the KB so that knowledge about popular entities propagates to the unpopular ones. Finally, our method applies a technique that relies on functional dependencies to judiciously interpret the table and extract n-ary relations. Our experiments over 1.5M Wikipedia tables show that our clustering can group many semantically similar tables. This leads to the extraction of many novel n-ary relations.
Comparison of Agile Maturity Models: Reflecting the Real Needs

Agile software development is considered as a game changer by some [1]. There are others who approach the domain more skeptical [2]. Clearly, there is a gap in terms of how Agile is perceived. This gap could be reduced, if proper measures would have been timely employed. Agile maturity assessment models have been helpful in providing means and guidance for reducing this gap. Yet, a detailed look into existing models, comparative studies and literature reviews in the domain suggests that the agile maturity assessment models themselves are far from being mature. Further, the gap between what the proposed models offer and what industry really needs is frequently discussed by those comparative studies. In this study, we reflect on the existing agile maturity assessment models, compare them against a comprehensive set of criteria derived from the literature and from our background in assessing the capabilities of software development organizations. We conclude that none of the analyzed agile maturity assessment models are sound enough to be used in a practical context. Nevertheless, some of the models have interesting elements that can be reused for the development of a new agile assessment model.
Effect of Reduced Dimensionality on Deep learning for Human Activity Recognition

Inertial sensor is used to measure angular velocity, acceleration etc when attached to a human body part while performing daily or sports activities. Deep learning classifier models such as Long Short-term Memory (LSTM) networks are capable of extracting high dimensional features from signal sequences to represent human activities. However, a trade-off between the computational complexity and the performance of activity recognition system should be maintained if to be used in a low resource device. In this paper, the original signal is segmented into windows and a set of statistical feature measures is extracted from each window. A stacked LSTM network is trained with the sequences of concatenated feature measures instead of the original signal to represent activities. Using feature selection methods: correlation metric as heat map and feature importance, only significant features are selected to train the stacked LSTM network along with conventional classifiers such as support vector machine, random forest and k-nearest neighbor. To evaluate the system performance, a new dataset collection, named Human Physical Activities (HPA) dataset, for sports activities using four inertial sensors is used along with an existing inertial sensor based dataset, HuGaBD. The new dataset consists of nine static and dynamic movements in outdoor setting. The result shows that for all classifiers and both datasets, correlation metric heatmap extracted features result to better performance, except precision values. LSTM, even with limited number of features outperformed, conventional classifiers with 91.1% and 93.4% accuracy for HuGaBD and HPA dataset respectively. Experimental analysis shows that entropy and median feature measure are highly contributing in classification accuracy and theses are essential features for human activity recognition.
GAEAT

Knowledge graph embedding (KGE) encodes components of a KG including entities and relations into continuous low vector space. Most existing methods focus on treating entities and relations in triples independently and thus failing to capture the complex and hidden information that is inherently implicit inside the local neighborhood surrounding a triple. In this paper, we present a new approach for knowledge graph completion called GAEAT (Graph Auto-encoder Attention Network Embedding), which can encapsulate both entity and relation features. Specifically, we construct a triple-level auto-encoder by extending graph attention mechanisms to obtain latent representations of entities and relations simultaneously. To justify our proposed model, we evaluate GAEAT on two real-world datasets. The experimental results demonstrate that GAEAT can outperform state-of-the-art KGE models in knowledge graph completion task, which validates the effectiveness of GAEAT. The source code of this paper can be obtained from https://github.com/TomersHan/GAEAT.
Employing word mover's distance for cross‐lingual plagiarized text detection

Abstract While the blossom of globalization has achieved many successes, cross‐lingual plagiarism, however, has become popular. Detecting plagiarism across languages requires the capability of comparing semantical similarities between texts of different languages. Previous works rely on massive bilingual resources such as comparable corpus, parallel corpus, and even commercial machine translation as references. However, towards domain‐specific applications, collecting such resources is labor‐intensive and impractical. Also, the absence of interpretability of existing methods lead to the difficulty of investigating retrieval results by humans. Hence, it is imperative to have a resource‐light and interpretable method for cross‐lingual plagiarism detection. In this study, we propose a new detection method, called CL‐WMD, which is built upon word embedding techniques. CL‐WMD requires only a small set of translation pairs to constitute a bilingual reference and calculates semantical distances between texts by word mover's distance, which can provide explicable word alignment information between two compared text spans. Our experiments are conducted under a bilingual scientific publication corpus composed of two typologically diverse languages: English and Chinese. The results demonstrate that CL‐WMD has higher accuracy than most existing methods and achieves better or comparable performance when compared to the translation‐based method in paragraph‐level and sentence‐level plagiarism detection tasks.
(<i>τ</i>, <i>m</i>)‐<scp>slicedBucket</scp> privacy model for sequential anonymization for improving privacy and utility

Abstract In a real‐world scenario for privacy‐preserving data publishing, the original data are anonymized and released periodically. Each release may vary in number of records due to insert, update, and delete operations. An intruder can combine, that is, correlate different releases to compromise the privacy of the individual records. Most of the literature, such as τ ‐safety, τ ‐safe ( l, k )‐diversity, have an inconsistency in record signatures and adds counterfeit tuples with high generalization that causes privacy breach and information loss. In this paper, we propose an improved privacy model ( τ , m )‐slicedBucket, having a novel idea of “Cache” table to address these limitations. We indicate that a collusion attack can be performed for breaching the privacy of τ ‐safe ( l, k )‐diversity privacy model, and demonstrate it through formal modeling. The objective of the proposed ( τ , m )‐slicedBucket privacy model is to set a tradeoff between strong privacy and enhanced utility. Furthermore, we formally model and analyze the proposed model to show that the collusion attack is no longer applicable. Extensive experiments reveal that the proposed approach outperforms the existing models.
Analysis of Compatibility in Open Source Android Mobile Apps

Non-functional requirements (NFRs) form an intrinsic part of any software system. Compatibility between versions or different platforms of a software product is a form of NFRs. In this paper, we have studied Compatibility in open-source mobile apps. We are interested in understanding the different aspects of mobile incompatibility, their frequency of occurrence from a user perspective, and how much effort developers have spent on it. We have conducted a study on 40 randomly selected open-source mobile apps from the Google Play Store and have analyzed 258,056 commits (extracted from their version control system) to identify incompatibility issues in apps. We have also studied 205,847 reviews to identify and categorize compatibility requirements from user reviews. Both app commits and app reviews were processed by a pipeline of Natural Language Processing steps. We evaluated the efficiency of four Machine Learning classifiers to analyze compatibility. This was done by classifying commit messages and analyzing user reviews. We observed that the Logistic Regression classifier produced the best overall results. For the same data set, we classified compatibility types. In that case, the Support Vector Machine classifier performed marginally better over the other classifiers. Addressing the relative effort spent on compatibility, we found that 3.16% of the developer's effort is dedicated to compatibility issues. At the same time, we observed that 4.30% of user reviews report compatibility issues in apps. In conclusion, we see more demand for future research on (i)the gap between the time spent by the developers and the frequency of occurrence of compatibility issues, and (ii) the degree of responsiveness on actual user concerns.
A Capsule Network-based Model for Learning Node Embeddings

In this paper, we focus on learning low-dimensional embeddings for nodes in graph-structured data. To achieve this, we propose Caps2NE -- a new unsupervised embedding model leveraging a network of two capsule layers. Caps2NE induces a routing process to aggregate feature vectors of context neighbors of a given target node at the first capsule layer, then feed these features into the second capsule layer to infer a plausible embedding for the target node. Experimental results show that our proposed Caps2NE obtains state-of-the-art performances on benchmark datasets for the node classification task. Our code is available at: https://github.com/daiquocnguyen/Caps2NE.
Trustworthiness Perceptions in Code Review

Background: Automated program repair and other bug-fixing approaches are gaining attention in the software engineering community. Automation shows promise in reducing bug fixing costs. However, many developers express reluctance about accepting machine-generated patches into their codebases.
Quarry Areas Segmentation on Satellite Images by Convolutional Neural Networks

This article presents results of two deep learning algorithms for sand quarries detection on high-resolution aerial photos. Planet database of high-resolution aerial images was collected. Input images contain blue, green, red and near-infrared channels. Before the training process there was implemented the equalization of brightness histogram which allows to cope with the problem of obscuration of satellite images. To implement numerical experiments there were extracted smaller patches. Training and test sets were enlarged by three types of data augmentation. Convolutional neural networks were pretrained on the SpaceNet dataset and tuned on the Planet database. Deep learning algorithms were launched on NVIDIA DGX-1 supercomputer. Special metrics, such as F1, precision, recall and Dice coefficient allowed to compare the quality of developed models.
Research on Video Noise-reduction Based on Low Rank Representation

Data in the real world are usually high-dimensional and noisy, but in many cases, especially in computer vision research, we want to learn low-dimensional structures for problem processing. In order to solve the problems of duplicate data and noise in high-dimensional data, a video noise-reduction algorithm based on low-rank representation is proposed. By reducing the dimension of video data by principal component analysis (PCA), noise-reduction problem of video data is transformed into a low-rank matrix approximation problem. As a result, a low-rank matrix decomposition method is obtained. The resulting optimization problem is effectively solved by the augmented Lagrange multiplier (ALM). Experiments verify the effectiveness of the video noise-reduction method in the case of serious data destruction, and show that it is effective and robust compared with the existing methods.
Evaluation of Information Elements in a Cyber Incident Report

An analyst's main task within cyber security is to monitor, analyze, and decide on appropriate remedies for detected attacks. Analysts should ideally produce incident reports for record keeping and traceability, but also for enabling information sharing.This paper investigates which information elements that need to be included in an incident report to support incident management in a time-critical and high mental workload situation. Background information on existing reporting templates were drawn from the literature. Five different templates, a data exchange format, as well as two cyber situational awareness frameworks were analyzed. Then a novel reporting template from the military domain was scrutinized during a live cyber defense exercise.The results show that the information elements in the various existing templates differ, probably due to the different areas of use. The proposed military template was found to be useful to a high degree, even for incident reporting in the civilian sector. The new template can be improved by introducing additional fields, such as, e.g. descriptions of victims, attackers and assessments of attackers' motives.
How to leverage data security in a post-Covid world

Cyber security is often pushed down the list of priorities, and this was abundantly clear as businesses had to rush to get employees up and running for business continuity in recent months. Many already had some level of protection in place, but with a lasting switch to remote working and changing work habits and practices, there must be a refreshed emphasis on data security to protect employees, customers and businesses. Cyber security is often pushed down the list of priorities and this became abundantly clear as businesses had to rush to get employees up and running when working from home in recent months. With a lasting switch to remote working and changing work habits and practices, there must be a refreshed emphasis on data security. The new normal of hybrid workforces must have the correct layers of security that protect all types of employees. It's about increasing awareness and improving email culture, argues Andrea Babbs of Vipre Security.
Accelerating Large-Scale Prioritized Graph Computations by Hotness Balanced Partition

Prioritized computation is shown promising performance for a large class of graph algorithms. It prioritizes the execution of some vertices that play important roles in determining convergence. For large-scale distributed graph processing, graph partitioning is an important preprocessing step that aims to balance workload and to reduce communication costs between workers. However, existing graph partitioning methods are designed for round-robin synchronous distributed frameworks. They balance workload without distinction of vertex importance and fail to consider the characteristics of priority-based scheduling, which may limit the benefit of prioritized graph computation. In this article, to accelerate prioritized iterative graph computations, we propose Hotness Balanced Partition (HBP). In prioritized graph computation, high priority vertices are likely to be executed more frequently and are likely to pass more messages, which result in hot vertices. Based on this observation, we partition graph by distributing vertices with distinction according to their hotness rather than blindly distributing vertices with equal weights, aiming to evenly distribute the hot vertices among workers. We further provide two HBP algorithms: a streaming-based algorithm for efficient one-pass processing and a distributed algorithm for distributed processing. Our results show that our proposed partitioning methods outperform the state-of-the-art partitioning methods, Fennel, HotGraph, and SNE.
Against Rigidity for General Terms

The difficulties with extending Kripke’s notion of rigidity from singular terms to general terms are well-known. The most serious are overgeneralization and the related problem of trivialization. Proposed solutions fall into two camps. One proposes restricting rigidity to terms that apply essentially. The most detailed version of this is rigid application as elaborated and defended by Michael Devitt. The other solution is to happily accept that all common nouns and unstructured general terms as well as some structured ones are rigid, thus embracing the overgeneralization charge. This approach has been defended by Genoveva Martí and José Martínez-Fernández, Joseph LaPorte, Nathan Salmon, Arthur Sullivan, and Michael Johnson, among others. Neither of the proposed solutions is satisfactory. The one view offers no systematic way of classifying general terms as rigid, the other includes too many terms as rigid. Overgeneralization remains a significant problem. The notion of rigidity cannot be satisfactorily extended to general terms, and there is no reason why we should regret this.