Reviewerly/exHarmony huggingface dataset:
Ontology learning methods from text - an extensive knowledge-based approach

Ontologies are a key element of the Semantic Web. They aim to capture basic knowledge by providing appropriate terms and formal relationships between them, so that they can be used in a machine-processable manner. Accordingly they enable automatic aggregation and practical use as well as unexpected reuse of distributed data sources. Ontologies may come from many different sources, pursuing different goals and quality criteria. However, performed manually ontology construction is a very complex and tedious task, thus many methods proposed offer automatic or semi-automatic way for ontology construction. Many of the methods have their own, specific features. Therefore, this paper proposes an extensive knowledge-based approach covering the domain of ontology learning methods from text. This work aims to collect the knowledge of available approaches for ontology learning and the prominent differences between them, drawing on best practices in ontology engineering. The proposed approach refers to methods and aims to enrich knowledge in the field of ontology learning (OL). In this paper, the author’s ontology contains a set of various types of methods with main techniques used, and the necessary features in the miscellaneous approaches. The proposed an extensive knowledge-based approach uses a reasoning mechanism based on competency questions for individual approaches to determine their ontology learning method profiles. The validation stage has also been carried out. At the same time, it is an extension of the previous study in the form of a repository of knowledge about OL tools. In addition, the combination of both ontologies: tools and methods aim to provide a more efficient OL solution from text.
ACM-NDC study 2019---2020

research-article Share on ACM-NDC study 2019---2020: eighth annual study of non-doctoral-granting departments in computing Authors: Stuart Zweben The Ohio State University The Ohio State UniversityView Profile , Jodi Tims Northeastern University Northeastern UniversityView Profile , Yan Timanovsky Association for Computing Machinery Association for Computing MachineryView Profile Authors Info & Claims ACM InroadsVolume 11Issue 3September 2020 pp 26–37https://doi.org/10.1145/3410475Published:18 August 2020Publication History 7citation495DownloadsMetricsTotal Citations7Total Downloads495Last 12 Months32Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Abstractions for computing all robotic sensors that suffice to solve a planning problem

Whether a robot can perform some specific task depends on several aspects, including the robot's sensors and the plans it possesses. We are interested in search algorithms that treat plans and sensor designs jointly, yielding solutions-i.e., plan and sensor characterization pairs-if and only if they exist. Such algorithms can help roboticists explore the space of sensors to aid in making design trade-offs. Generalizing prior work where sensors are modeled abstractly as sensor maps on p-graphs, the present paper increases the potential sensors which can be sought significantly. But doing so enlarges a problem currently on the outer limits of being considered tractable. Toward taming this complexity, two contributions are made: (1) we show how to represent the search space for this more general problem and describe data structures that enable whole sets of sensors to be summarized via a single special representative; (2) we give a means by which other structure (either task domain knowledge, sensor technology or fabrication constraints) can be incorporated to reduce the sets to be enumerated. These lead to algorithms that we have implemented and which suffice to solve particular problem instances, albeit only of small scale. Nevertheless, the algorithm aids in helping understand what attributes sensors must possess and what information they must provide in order to ensure a robot can achieve its goals despite non-determinism.
A Framework for Prediction of Cramps during Activities of Daily Living in Elderly

A framework to predict cramps during Activities of Daily Living (ADL) to contribute towards assisted living and healthy aging of the constantly increasing elderly population, in the future of Internet of Things (IoT)-based interconnected living environments, for instance Smart Homes and Smart Cities is proposed in this paper. The framework leverages the immense potential at the intersection of IoT, Big Data, Human Computer Interaction, Artificial Intelligence and their interrelated disciplines. Leg cramps are known to be associated with all age groups but studies [1], [2] have shown that cramps are more frequent in older adults and have a multitude of risks and associated impacts, both temporary and permanent, which affect their quality of life and their abilities to perform ADL – which are essential for one’s sustenance. The challenge in this field is to predict cramps to minimize their effects on the performance of older adults during ADL, with an aim to improve their quality of life in the future of living environments. Addressing this challenge through development of a framework for Ambient Assisted Living (AAL) of elderly in Smart Homes serves as the main motivation for this work. To evaluate the efficacy of this proposed approach it has been tested on a dataset of ADL and the performance characteristics are discussed. A case study has also been performed where performance characteristics of various learning models are compared to deduce the best learning model for development of this framework. The results presented uphold the relevance and immense potential of this work for contributing towards AAL of the elderly population in Smart Homes through accurate prediction of cramps during ADL.
Adaptive super-resolution for person re-identification with low-resolution images

Person re-identification is challenging with low-resolution query and high-resolution gallery images. To address the resolution mismatch, many methods perform super-resolution (SR) on low-resolution queries with specifying a single scale factor. However, using a single SR module, whichever scale factor is specified, always brings both advantages and drawbacks in recovering and identifying identity information. A larger scale factor recovers more details but produces excessive artifacts, while a smaller one is on the contrary. To exploit their complementary property for more robust recovery and identification, we propose the Adaptive Person Super-Resolution (APSR) model. APSR jointly trains and fuses multiple SR modules based on their generated visual contents, to fully compensate and learn the complementary identity features in an end-to-end manner. To improve the robustness to artifacts during fusion, our model further learns informative features by online dividing and integrating the generated body regions. Extensive experiments verify the effectiveness of our method with state-of-the-art performances.
Preserving Patients’ Privacy in Medical IoT Using Blockchain

Medical IoT is a collection of devices and applications that are connected to healthcare systems via the Internet. Wearable devices and body sensors are used to track individuals’ medical conditions. The collected data is processed, analyzed, and stored in the cloud platforms to provide healthcare services. The data does not only include personal information like users’ identity and location but also consists of sensitive information such as mental status, drug addiction, sexual orientation, and genetics. Therefore, preserving an individual’s privacy remains a huge challenge for IoT service providers. The existing techniques significantly reduce the originality of data which affects the application’s efficiency. Therefore, in this paper, we propose the idea of using blockchains and smart contract to preserve privacy while obtaining data usability.
RelationNet2: Deep Comparison Network for Few-Shot Learning

Few-shot deep learning is a topical challenge area for scaling visual recognition to open ended growth of unseen new classes with limited labeled examples. A promising approach is based on metric learning, which trains a deep embedding to support image similarity matching. Our insight is that effective general purpose matching requires non-linear comparison of features at multiple abstraction levels. We thus propose a new deep comparison network comprised of embedding and relation modules that learn multiple non-linear distance metrics based on different levels of features simultaneously. Furthermore, to reduce over-fitting and enable the use of deeper embeddings, we represent images as distributions rather than vectors via learning parameterized Gaussian noise regularization. The resulting network achieves excellent performance on both miniImageNet and tieredImageNet.
UIOT-FMT: A Universal Format for Collection and Aggregation of Data from Smart Devices

Information Technology (IT) has become an essential part of our lives and due to the emergence of the Internet-of-Things (IoT), technology has encompassed a majority of things that humans rely on in their daily lives. Furthermore, as IT becomes more relevant in daily lives, the need for IT to serve public emergency services has become more important. However, due to the infancy status of IoT, there is a need for a data consortium that would prove to be best used in servicing policing in a technological driven society. This paper will discuss the plausibility of creating a universal format for use in carrying out public services, such as emergency response by the police and regular law maintenance. In this research we will discuss what the police requires in their line-of-duty and how smart devices can be used to satisfy those needs. A data formatting framework is developed and demonstrated, with the goal of showing what can be done to unifying data from smart city sensors.
A Soft Alignment Model for Bug Deduplication

Bug tracking systems (BTS) are widely used in software projects. An important task in such systems consists of identifying duplicate bug reports, i.e., distinct reports related to the same software issue. For several reasons, reporting bugs that have already been reported is quite frequent, making their manual triage impractical in large BTSs. In this paper, we present a novel deep learning network based on soft-attention alignment to improve duplicate bug report detection. For a given pair of possibly duplicate reports, the attention mechanism computes interdependent representations for each report, which is more powerful than previous approaches. We evaluate our model on four well-known datasets derived from BTSs of four popular open-source projects. Our evaluation is based on a ranking-based metric, which is more realistic than decision-making metrics used in many previous works. Achieved results demonstrate that our model outperforms state-of-the-art systems and strong baselines in different scenarios. Finally, an ablation study is performed to confirm that the proposed architecture improves the duplicate bug reports detection.
Secure Single-Server Nearly-Identical Image Deduplication

Cloud computing is often utilized for file storage. Clients of cloud storage services want to ensure the privacy of their data, and both clients and servers want to use as little storage as possible. Cross-user deduplication is one method to reduce the amount of storage a server uses. Deduplication and privacy are naturally conflicting goals, especially for nearly-identical ("fuzzy") deduplication, as some information about the data must be used to perform deduplication. Prior solutions thus utilize multiple servers, or only function for exact deduplication. In this paper, we present a single-server protocol for cross-user nearly-identical deduplication based on secure LSH (SLSH). We formally define our ideal security, and rigorously prove our protocol secure against fully malicious, colluding adversaries with a proof by simulation. We show experimentally that the individual parts of the protocol are computationally feasible, and further discuss practical issues of security and efficiency.
Affinity for technology interaction and fields of study

Affinity for Technology Interaction (ATI) describes whether a person approaches or avoids interaction with technology [1], making it an important variable in human-centered design.
Basics of blockchain

A blockchain is a distributed network, which can be used as a digital ledger as well as a mechanism that enables the secure transfer of assets without a central authority. Like the Internet is a technology that facilitates the digital flow of information, blockchain is a technology that facilitates the digital exchange of units of value. Anything from currencies to land titles to votes can be tokenized, stored, and exchanged on a blockchain network. In addition to the secure transfer of value, blockchain technology provides a permanent record of transactions and a single version of the truth called a network state, which is fully transparent in real-time for the benefit of all participants. In this chapter, we briefly describe the overview of blockchain and their characteristics that defines a blockchain is a prominent technology.
A Second-Order Subregion Pooling Network for Breast Lesion Segmentation in Ultrasound

Breast lesion segmentation in ultrasound images is a fundamental task for clinical diagnosis of the disease. Unfortunately, existing methods mainly rely on the entire image to learn the global context information, which neglects the spatial relation and results in ambiguity in the segmentation results. In this paper, we propose a novel second-order subregion pooling network (\(S^2P\)-Net) for boosting the breast lesion segmentation in ultrasound images. In our \(S^2P\)-Net, an attention-weighted subregion pooling (ASP) module is introduced in each encoder block of segmentation network to refine features by aggregating global features from the whole image and local information of subregions. Moreover, in each subregion, a guided multi-dimension second-order pooling (GMP) block is designed to leverage additional guidance information and multiple feature dimensions to learn powerful second-order covariance representations. Experimental results on two datasets demonstrate that our proposed \(S^2P\)-Net outperforms state-of-the-art methods.
Chance Constrained Simultaneous Path Planning and Task Assignment for Multiple Robots with Stochastic Path Costs

We present a novel algorithm for simultaneous task assignment and path planning on a graph (or roadmap) with stochastic edge costs. In this problem, the initially unassigned robots and tasks are located at known positions in a roadmap. We want to assign a unique task to each robot and compute a path for the robot to go to its assigned task location. Given the mean and variance of travel cost of each edge, our goal is to develop algorithms that, with high probability, the total path cost of the robot team is below a minimum value in any realization of the stochastic travel costs. We formulate the problem as a chance-constrained simultaneous task assignment and path planning problem (CC-STAP). We prove that the optimal solution of CC-STAP can be obtained by solving a sequence of deterministic simultaneous task assignment and path planning problems in which the travel cost is a linear combination of mean and variance of the edge cost. We show that the deterministic problem can be solved in two steps. In the first step, robots compute the shortest paths to the task locations and in the second step, the robots solve a linear assignment problem with the costs obtained in the first step. We also propose a distributed algorithm that solves CC-STAP near-optimally. We present simulation results on randomly generated networks and data to demonstrate that our algorithm is scalable with the number of robots (or tasks) and the size of the network.
End-to-end Learning for Inter-Vehicle Distance and Relative Velocity Estimation in ADAS with a Monocular Camera

Inter-vehicle distance and relative velocity estimations are two basic functions for any ADAS (Advanced driver-assistance systems). In this paper, we propose a monocular camera based inter-vehicle distance and relative velocity estimation method based on end-to-end training of a deep neural network. The key novelty of our method is the integration of multiple visual clues provided by any two time-consecutive monocular frames, which include deep feature clue, scene geometry clue, as well as temporal optical flow clue. We also propose a vehicle-centric sampling mechanism to alleviate the effect of perspective distortion in the motion field (i.e. optical flow). We implement the method by a light-weight deep neural network. Extensive experiments are conducted which confirm the superior performance of our method over other state-of-the-art methods, in terms of estimation accuracy, computational speed, and memory footprint.
Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices

Universal lesion detection from computed tomography (CT) slices is important for comprehensive disease screening. Since each lesion can locate in multiple adjacent slices, 3D context modeling is of great significance for developing automated lesion detection algorithms. In this work, we propose a Modified Pseudo-3D Feature Pyramid Network (MP3D FPN) that leverages depthwise separable convolutional filters and a group transform module (GTM) to efficiently extract 3D context enhanced 2D features for universal lesion detection in CT slices. To facilitate faster convergence, a novel 3D network pre-training method is derived using solely large-scale 2D object detection dataset in the natural image domain. We demonstrate that with the novel pre-training method, the proposed MP3D FPN achieves state-of-the-art detection performance on the DeepLesion dataset (3.48% absolute improvement in the sensitivity of FPs@0.5), significantly surpassing the baseline method by up to 6.06% (in MAP@0.5) which adopts 2D convolution for 3D context modeling. Moreover, the proposed 3D pre-trained weights can potentially be used to boost the performance of other 3D medical image analysis tasks.
Individual differences in expecting coherence relations: Exploring the variability in sensitivity to contextual signals in discourse

The current study investigated how a contextual list signal influences comprehenders’ inference generation of upcoming discourse relations and whether individual differences in working memory capacity and linguistic experience influence the generation of these inferences. Participants were asked to complete two-sentence stories, the first sentence of which contained an expression of quantity (a few, multiple). Several individual-difference measures were calculated to explore whether individual characteristics can explain the sensitivity to the contextual list signal. The results revealed that participants were sensitive to a contextual list signal (i.e., they provided list continuations), and this sensitivity was modulated by the participants’ linguistic experience, as measured by an author recognition test. The results showed no evidence that working memory affected participants’ responses. These results extend prior research by showing that contextual signals influence participants’ coherence-relation-inference generation. Further, the results of the current study emphasize the importance of individual reader characteristics when it comes to coherence-relation inferences.
Learnable MIMO Detection Networks Based on Inexact ADMM

In this article, we present a new iterative MIMO detection algorithm based on inexact alternating direction method of multipliers. Each iteration is considered as a neural network layer with learnable parameters, which are optimized by the stochastic gradient descent algorithm with a training data set of the received vectors and the ground truth transmitted signals. Numerical results show that the proposed algorithm outperforms the existing learnable detection network and it achieves near-optimal performance close to the sphere decoder in the case of a large number of receive antennas.
Efficiency in Real-Time Webcam Gaze Tracking

Efficiency and ease of use are essential for practical applications of camera based eye/gaze-tracking. Gaze tracking involves estimating where a person is looking on a screen based on face images from a computer-facing camera. In this paper we investigate two complementary forms of efficiency in gaze tracking: 1. The computational efficiency of the system which is dominated by the inference speed of a CNN predicting gaze-vectors; 2. The usability efficiency which is determined by the tediousness of the mandatory calibration of the gaze-vector to a computer screen. To do so, we evaluate the computational speed/accuracy trade-off for the CNN and the calibration effort/accuracy trade-off for screen calibration. For the CNN, we evaluate the full face, two-eyes, and single eye input. For screen calibration, we measure the number of calibration points needed and evaluate three types of calibration: 1. pure geometry, 2. pure machine learning, and 3. hybrid geometric regression. Results suggest that a single eye input and geometric regression calibration achieve the best trade-off.
Cryptanalysis of Full LowMC and LowMC-M with Algebraic Techniques

In this paper, we revisit the difference enumeration technique for LowMC and develop new algebraic techniques to achieve efficient key-recovery attacks. In the original difference enumeration attack framework, an inevitable step is to precompute and store a set of intermediate state differences for efficient checking via the binary search. Our first observation is that Bar-On et al.'s general algebraic technique developed for SPNs with partial nonlinear layers can be utilized to fulfill the same task, which can make the memory complexity negligible as there is no need to store a huge set of state differences any more. Benefiting from this technique, we could significantly improve the attacks on LowMC when the block size is much larger than the key size and even break LowMC with such a kind of parameter. On the other hand, with our new key-recovery technique, we could significantly improve the time to retrieve the full key if given only a single pair of input and output messages together with the difference trail that they take, which was stated as an interesting question by Rechberger et al. at ToSC 2018. Combining both techniques, with only 2 chosen plaintexts, we could break 4 rounds of LowMC adopting a full S-Box layer with block size of 129, 192 and 255 bits, respectively, which are the 3 recommended parameters for Picnic3, an alternative third-round candidate in NIST's Post-Quantum Cryptography competition. We have to emphasize that our attacks do not indicate that Picnic3 is broken as the Picnic use-case is very different and an attacker cannot even freely choose 2 plaintexts to encrypt for a concrete LowMC instance. However, such parameters are deemed as secure in the latest LowMC. Moreover, much more rounds of seven instances of the backdoor cipher LowMC-M as proposed by Peyrin and Wang in CRYPTO 2020 can be broken without finding the backdoor by making full use of the allowed $$2^{64}$$ data. The above mentioned attacks are all achieved with negligible memory.