Reviewerly/exHarmony huggingface dataset:
Convex Risk Bounded Continuous-Time Trajectory Planning in Uncertain Nonconvex Environments

In this paper, we address the trajectory planning problem in uncertain nonconvex static and dynamic environments that contain obstacles with probabilistic location, size, and geometry.To address this problem, we provide a risk bounded trajectory planning method that looks for continuous-time trajectories with guaranteed bounded risk over the planning time horizon.Risk is defined as the probability of collision with uncertain obstacles.Existing approaches to address risk bounded trajectory planning problems either are limited to Gaussian uncertainties and convex obstacles or rely on sampling-based methods that need uncertainty samples and time discretization.To address the risk bounded trajectory planning problem, we leverage the notion of risk contours to transform the risk bounded planning problem into a deterministic optimization problem.Risk contours are the set of all points in the uncertain environment with guaranteed bounded risk.The obtained deterministic optimization is, in general, nonlinear and nonconvex time-varying optimization.We provide convex methods based on sum-of-squares optimization to efficiently solve the obtained nonconvex time-varying optimization problem and obtain the continuous-time risk bounded trajectories without time discretization.The provided approach deals with arbitrary probabilistic uncertainties, nonconvex and nonlinear, static and dynamic obstacles, and is suitable for online trajectory planning problems.
Generalized multi-view learning based on generalized eigenvalues proximal support vector machines

Multi-view learning based on generalized eigenvalue proximal support vector machines has brought enormous success by mining the consistency information of two views. Nevertheless, it only aims to handle two-view cases and cannot handle generalized multi-view learning cases (above two views). It also omits the complementarity information among views. In this paper, two generalized multi-view extensions of generalized eigenvalue proximal support vector machines are presented which take advantage of the multi-view co-regularization term to mine the consistency information and the weighted value to mine complementarity information. Experimental results performed on synthetic and real world datasets demonstrate that they can provide higher performance than the relevant two-view classification algorithms. • We propose two new general multi-view learning models. • They fully utilize the consensus and complementarity principle among views. • They are extended to nonlinear cases by the kernel trick. • An iterative algorithm is developed to obtain the solution. • Experimental results validate the effectiveness of the proposed methods.
Combining Blockchain and IoT for Decentralized Healthcare Data Management

The emergence of the Internet of Things (IoT) has resulted in a significant increase in research on e-health. As the amount of patient data grows, it has become increasingly challenging to protect patients' privacy. Patient data is commonly stored in the cloud, making it difficult for users to control and protect their information. Moreover, the recent rise in security and surveillance breaches in the healthcare industry has highlighted the need for a better approach to data storage and protection. Traditional models that rely on third-party control over patients' healthcare data are no longer reliable, as they have proven vulnerable to security breaches. To address these issues, blockchain technology has emerged as a promising solution. Blockchain-based protocols have the potential to provide a secure and efficient system for e-health applications that does not require trust in third-party intermediaries. The proposed protocol outlined in this paper uses a blockchain-based approach to manage patient data securely and efficiently. Unlike Bitcoin, which is primarily used for financial transactions, the protocol described here is designed specifically for e-health applications. It employs a consensus mechanism that is more suitable for resource constrained IoT devices, thereby reducing network costs and increasing efficiency. The proposed protocol also provides a privacy-preserving access control mechanism that enables patients to have more control over their healthcare data. By leveraging blockchain technology, the protocol ensures that only authorized individuals can access the patient's data, which helps prevent data breaches and other security issues. Finally, the security and privacy of the proposed protocol are analysed to ensure that it meets the necessary standards for data protection. The protocol's effectiveness and efficiency are tested under different scenarios to ensure that it can perform reliably and consistently. Finally, the protocol proposed in this paper shows that how blockchain can be used to provide a secure and efficient system that empowers patients to take control of their healthcare data.
Attention-Mask Dense Merger (Attendense) Deep HDR for Ghost Removal

High Dynamic Range (HDR) reconstruction is the process of producing an HDR image from a set of Standard Dynamic Range (SDR) images with different exposure times. This is a particularly challenging problem when relative camera or object motion exists between the available SDR images. Recently, deep learning methods, specifically those based on convolutional neural networks (CNNs) have been developed for HDR and shown to achieve unprecedented quality gains. Invariably an image alignment phase precedes the CNN mapping and merging. In practice, this alignment step greatly increases the computational burden of deep HDR methods often rendering them unsuitable for real-time composition. We propose a new deep HDR technique that does not need any explicit alignment of SDR images. Instead, a novel attention mask is developed that enables the network to focus on parts of the scene with considerable motion. Further, a dense merger is proposed that leads to an economical network. Evaluation over benchmark databases reveals that the proposed AttenDense network achieves high quality HDR results with significantly reduced computation time than state of the art. Further, the incorporation of domain knowledge (development of a custom attention mask) allows a more graceful decay in performance in the face of limited training.
Zoomless Maps: External Labeling Methods for the Interactive Exploration of Dense Point Sets at a Fixed Map Scale

Visualizing spatial data on small-screen devices such as smartphones and smartwatches poses new challenges in computational cartography. The current interfaces for map exploration require their users to zoom in and out frequently. Indeed, zooming and panning are tools suitable for choosing the map extent corresponding to an area of interest. They are not as suitable, however, for resolving the graphical clutter caused by a high feature density since zooming in to a large map scale leads to a loss of context. Therefore, in this paper, we present new external labeling methods that allow a user to navigate through dense sets of points of interest while keeping the current map extent fixed. We provide a unified model, in which labels are placed at the boundary of the map and visually associated with the corresponding features via connecting lines, which are called leaders. Since the screen space is limited, labeling all features at the same time is impractical. Therefore, at any time, we label a subset of the features. We offer interaction techniques to change the current selection of features systematically and, thus, give the user access to all features. We distinguish three methods, which allow the user either to slide the labels along the bottom side of the map or to browse the labels based on pages or stacks. We present a generic algorithmic framework that provides us with the possibility of expressing the different variants of interaction techniques as optimization problems in a unified way. We propose both exact algorithms and fast and simple heuristics that solve the optimization problems taking into account different criteria such as the ranking of the labels, the total leader length as well as the distance between leaders. In experiments on real-world data we evaluate these algorithms and discuss the three variants with respect to their strengths and weaknesses proving the flexibility of the presented algorithmic framework.
A Machine Learning Tool Using Digital Microscopy (Morphogo) for the Identification of Abnormal Lymphocytes in the Bone Marrow

Morphological analysis of the bone marrow is an essential step in the diagnosis of hematological disease. The conventional analysis of bone marrow smears is performed under a manual microscope, which is labor-intensive and subject to interobserver variability. The morphological differential diagnosis of abnormal lymphocytes from normal lymphocytes is still challenging. The digital pathology methods integrated with advances in machine learning enable new diagnostic features/algorithms from digital bone marrow cell images in order to optimize classification, thus providing a robust and faster screening diagnostic tool. We have developed a machine learning system, Morphogo, based on algorithms to discriminate abnormal lymphocytes from normal lymphocytes using digital imaging analysis. We retrospectively reviewed 347 cases of bone marrow digital images. Among them, 53 cases had a clinical history and the diagnosis of marrow involvement with lymphoma was confirmed either by morphology or flow cytometry. We split the 53 cases into two groups for training and testing with 43 and 10 cases, respectively. The selected 15,353 cell images were reviewed by pathologists, based on morphological visual appearance, from 43 patients whose diagnosis was confirmed by complementary tests. To expand the range and the precision of recognizing the lymphoid cells in the marrow by automated digital microscopy systems, we developed an algorithm that incorporated color and texture in addition to geometrical cytological features of the variable lymphocyte images which were applied as the training data set. The selected images from the 10 patients were analyzed by the trained artificial intelligence-based recognition system and compared with the final diagnosis rendered by pathologists. The positive predictive value for the identification of the categories of reactive/normal lymphocytes and abnormal lymphoid cells was 99.04%. It seems likely that further training and improvement of the algorithms will facilitate further subclassification of specific lineage subset pathology, e.g., diffuse large B-cell lymphoma from chronic lymphocytic leukemia/small lymphocytic lymphoma, follicular lymphoma, mantle cell lymphoma or even hairy cell leukemia in cases of abnormal malignant lymphocyte classes in the future. This research demonstrated the feasibility of digital pathology and emerging machine learning approaches to automatically diagnose lymphoma cells in the bone marrow based on cytological-histological analyses.
Scalable Event-Triggered Data Fusion for Autonomous Cooperative Swarm Localization

We present a Kalman filter-based method for event-triggered decentralized data fusion in cooperative localization problems that can be scaled to large numbers of networked autonomous robots operating in a swarm. This scaling is accomplished without significantly increasing the number of states each robot must estimate locally or the amount of data transferred through the network, thanks to implicit measurement fusion updates afforded by event-triggered communication. To keep state estimates suitably synchronized in this event-triggered approach, we exploit a novel method for performing reduced order partial covariance intersection between local estimates, such that only the overlapping set of swarm states between any two communicating agents&#x0027; estimates need to be fused. This reduces the number of swarm states each agent must track to only the subset of swarm states that have a significant effect on that agent&#x0027;s &#x2018;ownship&#x2019; state estimates. This leads to significantly simpler onboard computation for each agent and reduces the amount of data that must be transferred through the network when synchronization and measurement messages are considered together. Cooperative localization simulations for a 30 robot swarm show that the method is able to achieve good localization performance, even for greatly reduced measurement sharing and greatly reduced overall data transfer compared to full state decentralized fusion.
Automatic composition of guitar tabs by transformers and groove modeling

Deep learning algorithms are increasingly developed for learning to compose music in the form of MIDI files. However, whether such algorithms work well for composing guitar tabs, which are quite different from MIDIs, remain relatively unexplored. To address this, we build a model for composing fingerstyle guitar tabs with Transformer-XL, a neural sequence model architecture. With this model, we investigate the following research questions. First, whether the neural net generates note sequences with meaningful note-string combinations, which is important for the guitar but not other instruments such as the piano. Second, whether it generates compositions with coherent rhythmic groove, crucial for fingerstyle guitar music. And, finally, how pleasant the composed music is in comparison to real, human-made compositions. Our work provides preliminary empirical evidence of the promise of deep learning for tab composition, and suggests areas for future study.
What Are Critical Success Factors of DevOps Projects? A Systematic Literature Review

DevOps is a software development and operation practice, and a recent addition to a large family of different kinds of software process models. The model was born as it was observed that information systems operations and information system developments are closely integrated activities for the success of any organization. Thus, DevOps methods are an addition for the companies to improve overall performance in their software development process and operations. DevOps fills the gap between the development and operations teams and maintains the collaboration among information technology professionals for delivering the software applications. Due to its recent emergence, there are relatively little research done, at least when compared to the other software process models, on DevOps and its successful usage. Previously, some empirical research studies have been conducted on the success factors of DevOps, but a synthesis of these findings is needed. This paper aims to find out various critical success factors of DevOps projects that have been discussed in prior research by following a systematic literature review. Based on our extensive keyword search and after applying inclusion/exclusion criteria, we have included 30 research articles in this paper. The identified critical success factors were categorized into technical, organizational and social and cultural dimensions. Finally, this study offers a comprehensive framework depicting how the critical success factors impact or drive DevOps success.
Interactive Tables for Social Experiences at Home

Compared to work environments, the digitization of homes is progressing only slowly. While existing Smart Home applications focus on automatization of, for example, light or heat, there is little discussion about strengthening interpersonal relationships as a potential benefit of smart systems. Since the central dining- or kitchen table is the traditional “social interface” of a home, it seems worthwhile to research domestic interactive tables’ potentials of digital support. This paper presents a review of domestic interactive table concepts as well as a survey about pleasurable moments at (non-digital) domestic tables. Based on this, we develop four design principles, Sociality, Flexibility, Distinctiveness, and Homeyness, to offer orientation for the design of future domestic interactive tables.
Cloud Resource Scaling for Time-Bounded and Unbounded Big Data Streaming Applications

Recent advancements in technology have led to a deluge of big data streams that require real-time analysis with strict latency constraints. A major challenge, however, is determining the amount of resources required by applications processing these streams given their high volume, velocity and variety. The majority of research efforts on resource scaling in the cloud are investigated from the cloud provider's perspective with little consideration for multiple resource bottlenecks. We aim at analyzing the resource scaling problem from an application provider's point of view such that efficient scaling decisions can be made. This paper provides two contributions to the study of resource scaling for big data streaming applications in the cloud. First, we present a Layered Multi-dimensional Hidden Markov Model (LMD-HMM) for managing time-bounded streaming applications. Second, to cater to unbounded streaming applications, we propose a framework based on a Layered Multi-dimensional Hidden Semi-Markov Model (LMD-HSMM). The parameters in our models are evaluated using modified Forward and Backward algorithms. Our detailed experimental evaluation results show that LMD-HMM is very effective with respect to cloud resource prediction for bounded streaming applications running for shorter periods while the LMD-HSMM accurately predicts the resource usage for streaming applications running for longer periods.
On the Plausibility of Fully Homomorphic Encryption for RAMs

We initiate the study of fully homomorphic encryption for RAMs (RAM-FHE). This is a public-key encryption scheme where, given an encryption of a large database D, anybody can efficiently compute an encryption of P(D) for an arbitrary RAM program P. The running time over the encrypted data should be as close as possible to the worst case running time of P, which may be sub-linear in the data size. A central difficulty in constructing a RAM-FHE scheme is hiding the sequence of memory addresses accessed by P. This is particularly problematic because an adversary may homomorphically evaluate many programs over the same ciphertext, therefore effectively “rewinding” any mechanism for making memory accesses oblivious. We identify a necessary prerequisite towards constructing RAM-FHE that we call rewindable oblivious RAM (rewindable ORAM), which provides security even in this strong adversarial setting. We show how to construct rewindable ORAM using symmetric-key doubly efficient PIR (SK-DEPIR) (Canetti-Holmgren-Richelson, Boyle-Ishai-Pass-Wootters: TCC ’17). We then show how to use rewindable ORAM, along with virtual black-box (VBB) obfuscation for specific circuits, to construct RAM-FHE. The latter primitive can be heuristically instantiated using existing indistinguishability obfuscation candidates. Overall, we obtain a RAM-FHE scheme where the multiplicative overhead in running time is polylogarithmic in the database size N. Our basic scheme is single-hop, but we also extend it to obtain multi-hop RAM-FHE with overhead $$N^\epsilon $$ for arbitrarily small $$\epsilon >0$$ . We view our work as the first evidence that RAM-FHE is likely to exist.
Guided Architecture Trade Space Exploration: Fusing Model Based Engineering &amp; Design by Shopping

Advances in model-based system engineering have greatly increased the predictive power of models and the analyses that can be run on them. At the same time, designs have become more modular and component-based. It can be difficult to manually explore all possible system designs due to the sheer number of possible architectures and configurations; design space exploration has arisen as a solution to this challenge. In this work, we present the Guided Architecture Trade Space Explorer (GATSE), software which connects an existing model based engineering language (AADL) and tool (OSATE) to an existing design space exploration tool (ATSV). GATSE, AADL, and OSATE are all designed to be easily extended by users, which enables relatively straightforward domain-customizations. ATSV, combined with these customizations, lets system designers shop for candidate architectures and interactively explore the architectural trade space according to any quantifiable quality attribute or system characteristic. We evaluate GATSE according to an established framework for variable system architectures, and demonstrate its use on an avionics subsystem.
Designing and Evaluating Head-based Pointing on Smartphones for People with Motor Impairments

Head-based pointing is an alternative input method for people with motor impairments to access computing devices. This paper proposes a calibration-free head-tracking input mechanism for mobile devices that makes use of the front-facing camera that is standard on most devices. To evaluate our design, we performed two Fitts’ Law studies. First, a comparison study of our method with an existing head-based pointing solution, Eva Facial Mouse, with subjects without motor impairments. Second, we conducted what we believe is the first Fitts’ Law study using a mobile head tracker with subjects with motor impairments. We extend prior studies with a greater range of index of difficulties (IDs) [1.62, 5.20] bits and achieved promising throughput (average 0.61 bps with motor impairments and 0.90 bps without). We found that users’ throughput was 0.95 bps on average in our most difficult task (IDs: 5.20 bits), which involved selecting a target half the size of the Android recommendation for a touch target after moving nearly the full height of the screen. This suggests the system is capable of fine precision tasks. We summarize our observations and the lessons from our user studies into a set of design guidelines for head-based pointing systems.
FactRank: Developing automated claim detection for Dutch-language fact-checkers

Fact-checking has always been a central task of journalism, but given the ever-growing amount and speed of news offline and online, as well as the growing amounts of misinformation and disinformation, it is becoming increasingly important to support human fact-checkers with (semi-)automated methods to make their work more efficient. Within fact-checking, the detection of check-worthy claims is a crucial initial step, since it limits the number of claims that require or deserve to be checked for their truthfulness. In this paper, we present FactRank, a novel claim detection tool for journalists specifically created for the Dutch language. To the best of our knowledge, this is the first and still the only such tool for Dutch. FactRank thus complements existing online claim detection tools for English and (a small number of) other languages. FactRank performs similarly to claim detection in ClaimBuster, the state-of-the-art fact-checking tool for English. Our comparisons with a human baseline also indicate that given how much even expert human fact-checkers disagree, there may be a natural “upper bound” on the accuracy of check-worthiness detection by machine-learning methods. The specific quality of FactRank derives from the interdisciplinary and iterative process in which it was created, which includes not only a high-performance deep-learning neural network architecture, but also a principled approach to defining and operationalising the concept of check-worthiness via a detailed codebook. This codebook was created jointly by expert fact-checkers from the two countries that have Dutch as an official language (Belgium/Flanders and the Netherlands). We expect FactRank to be very useful exactly because of the way we defined check-worthiness, and because of how we have made this explicit and traceable.