Reviewerly/exHarmony huggingface dataset:
Blockchain for Financial Technology: Challenges and Opportunities for India

The chapter explores the challenges and opportunities of Blockchain-based Financial Technology applications from the Indian perspective. Blockchain has a recent hype worldwide and India responds to it making itself open to both the challenges and opportunities. India has niche societal characteristics, which makes its Blockchain confrontation unique than the other countries exposed to this technology. This chapter contributes to the understanding of these niche characteristics to identify the unique challenges and opportunities of implementing the Blockchain technology from the Indian perspective.
Toy user interface design—Tools for Child–Computer Interaction

A Toy User Interface (ToyUI) is a setup combination of one or more toy components with other hardware or software components. As part of new technologies that permeate the Child–Computer Interaction (CCI) domain, a ToyUI can combine physical toy components with social robots, smartphones, tablets, game consoles, among other computing artifacts. This paper compiles a collection of design tools that we develop to support interdisciplinary creators in prototyping innovative ToyUI setups. The design tools aim to help the CCI community understand the benefits of hardware and software integration while delivering solutions that can meet privacy by design principles. We apply the Design Science framework to assess the problem context and propose and evaluate the design tools following a Human-Centered Design (HCD) perspective. The tools cover steps from inspiration to ideation and implementation, including user research, brainstorming, data collection planning, and low to high-fidelity prototyping tools. We detail the current version of the design tools and six years of results through qualitative evaluation in an educational setting (with 255 creators in seven different institutions). We also propose novel versions of the tools to support remote teamwork and education, and we highlight training challenges during the social distancing context.
Topology-Preserving 3D Image Segmentation Based on Hyperelastic Regularization

Image segmentation is to extract meaningful objects from a given image. For degraded images due to occlusions, obscurities or noises, the accuracy of the segmentation result can be severely affected. To alleviate this problem, prior information about the target object is usually introduced. In Chan et al. (J Math Imaging Vis 60(3):401–421, 2018), a topology-preserving registration-based segmentation model was proposed, which is restricted to segment 2D images only. In this paper, we propose a novel 3D topology-preserving registration-based segmentation model with the hyperelastic regularization, which can handle both 2D and 3D images. The existence of the solution of the proposed model is established. We also propose a converging iterative scheme to solve the proposed model. Numerical experiments have been carried out on the synthetic and real images, which demonstrate the effectiveness of our proposed model.
Exploring Automated GDPR-Compliance in Requirements Engineering: A Systematic Mapping Study

The General Data Protection Regulation (GDPR), adopted in 2018, profoundly impacts information processing organizations as they must comply with this regulation. In this research, we consider GDPR-compliance as a high-level goal in software development that should be addressed at the outset of software development, meaning during requirements engineering (RE). In this work, we hypothesize that natural language processing (NLP) can offer a viable means to automate this process. We conducted a systematic mapping study to explore the existing literature on the intersection of GDPR, NLP, and RE. As a result, we identified 448 relevant studies, of which the majority (420) were related to NLP and RE. Research on the intersection of GDPR and NLP yielded nine studies, while 20 studies were related to GDPR and RE. Even though only one study was identified on the convergence of GDPR, NLP, and RE, the mapping results indicate opportunities for bridging the gap between these fields. In particular, we identified possibilities for introducing NLP techniques to automate manual RE tasks in the crossing of GDPR and RE, in addition to possibilities of using NLP-based machine learning techniques to achieve GDPR-compliance in RE.
Subquadratic SNARGs in the Random Oracle Model

In a seminal work, Micali (FOCS 1994) gave the first succinct non-interactive argument (SNARG) in the random oracle model (ROM). The construction combines a PCP and a cryptographic commitment, and has several attractive features: it is plausibly post-quantum; it can be heuristically instantiated via lightweight cryptography; and it has a transparent (public-coin) parameter setup. However, it also has a significant drawback: a large argument size. In this work, we provide a new construction that achieves a smaller argument size. This is the first progress on the Micali construction since it was introduced over 25 years ago. A SNARG in the ROM is $$(t,\mathsf {\epsilon })$$ -secure if every $$t$$ -query malicious prover can convince the verifier of a false statement with probability at most $$\mathsf {\epsilon }$$ . For $$(t,\mathsf {\epsilon })$$ -security, the argument size of all known SNARGs in the ROM (including Micali's) is $$\tilde{O}((\log (t/\mathsf {\epsilon }))^2)$$ bits, even if one were to rely on conjectured probabilistic proofs well beyond current techniques. In practice, these costs lead to SNARGs that are much larger than constructions based on other (pre-quantum and costly) tools. This has led many to believe that SNARGs in the ROM are inherently quadratic. We show that this is not the case. We present a SNARG in the ROM with a sub-quadratic argument size: $$\tilde{O}(\log (t/\mathsf {\epsilon }) \cdot \log t)$$ . Our construction relies on a strong soundness notion for PCPs and a weak binding notion for commitments. We hope that our work paves the way for understanding if a linear argument size, that is $$O(\log (t/\mathsf {\epsilon }))$$ , is achievable in the ROM.
Utility-Preserving Privacy Protection of Textual Documents via Word Embeddings

A great variety of mechanisms have been proposed to protect structured databases with numerical and categorical attributes; however, little attention has been devoted to unstructured textual data. Textual data protection requires first detecting sensitive pieces of text and then masking those pieces via suppression or generalization. Current solutions rely on classifiers that can recognize a fixed set of (allegedly sensitive) named entities. Yet, such approaches fall short of providing adequate protection because in reality references to sensitive information are not limited to a predefined set of entity types, and not all the appearances of certain entity type result in disclosure. In this work we propose a more general and flexible based on the notion of word embedding. By means of word embeddings we build vectors that numerically capture the semantic relationships of the textual terms. Then we evaluate the disclosure caused by the terms on the entity to be protected according to the similarity between their vector representations. Our method also preserves the semantics (and, therefore, the utility) of the document by replacing risky terms with privacy-preserving generalizations. Empirical results show that our approach offers much more robust protection and greater utility preservation than methods based on named entity recognition.
The nature of build changes

Abstract Build systems are an essential part of modern software projects. As software projects change continuously, it is crucial to understand how the build system changes because neglecting its maintenance can, at best, lead to expensive build breakage, or at worst, introduce user-reported defects due to incorrectly compiled, linked, packaged, or deployed official releases. Recent studies have investigated the (co-)evolution of build configurations and reasons for build breakage; however, the prior analysis focused on a coarse-grained outcome ( i.e. , either build changing or not). In this paper, we present BuildDiff , an approach to extract detailed build changes from Maven build files and classify them into 143 change types. In a manual evaluation of 400 build-changing commits, we show that BuildDiff can extract and classify build changes with average precision, recall, and f1-scores of 0.97, 0.98, and 0.97, respectively. We then present two studies using the build changes extracted from 144 open source Java projects to study the frequency and time of build changes. The results show that the top-10 most frequent change types account for 51% of the build changes. Among them, changes to version numbers and changes to dependencies of the projects occur most frequently. We also observe frequently co-occurring changes, such as changes to the source code management definitions, and corresponding changes to the dependency management system and the dependency declaration. Furthermore, our results show that build changes frequently occur around release days. In particular, critical changes, such as updates to plugin configuration parts and dependency insertions, are performed before a release day. The contributions of this paper lay in the foundation for future research, such as for analyzing the (co-)evolution of build files with other artifacts, improving effort estimation approaches by incorporating necessary modifications to the build system specification, or automatic repair approaches for configuration code. Furthermore, our detailed change information enables improvements of refactoring approaches for build configurations and improvements of prediction models to identify error-prone build files.
Fast contrast and naturalness preserving image recolouring for dichromats

People with colour vision deficiency (CVD) may have difficulty recognising or discriminating colours. To enhance their visual details, some methods have been developed to enhance colour contrast. Recently, researchers realized the importance of preserving naturalness in the processed images to avoid decreasing the quality of vision (QOV) and state-of-the-art image recolouring methods succeeded in realising both contrast enhancement and naturalness preservation by utilising optimisation model. However, the high computational cost of these methods prevents their wide application in daily lives. Moreover, these methods cannot automatically resolve the trade-off between contrast enhancement and naturalness preservation. This paper proposes Optimized Dichromacy Projection (ODP) as a novel high-speed image recolouring technique for dichromacy compensation. ODP achieves both contrast enhancement and naturalness preservation by computing an optimized projection from the colour gamut of normal vision to the colour gamut of dichromacy and can automatically adapt the weight of contrast enhancement and naturalness preservation to the property of individual images. Qualitative, quantitative, and subjective evaluation experiments show that the proposed method can generate images of qualities competitive to the state-of-the-art methods while drastically improving computation time.
Design and Development of a Smart Wheelchair for the Disabled People

About 15% of the world population is currently suffering from one or some form of disability. Among the various cases of disability, incompetence of mobility is the most common drawback, which significantly affects the ordinary life of the patient. The traditional approach is to bind the patient to a conventional wheelchair. However, it fails to provide the user with a taste of freedom as the user will require continuous assistance from others. Although the user can manually propel the system, it will take quite some physical effort. A smart wheelchair with different driving modes can significantly reduce effort. In this research, we designed a smart-wheelchair contains two types of drive system like the thumb and gesture control system. The design of the User Interface (UI) is such that, the user to easily navigate across the menu and choose the preferred control system. A heart rate sensor is incorporated to compute the medical conditions of the patient. In case of any emergency, the build-in response system will be activated and provide a notification to the concerned person.
Benchmarking Framework for Multitemporal SAR Despeckling

In this article, we propose a novel benchmarking framework for a quantitative assessment of the performance of despeckling algorithms for multitemporal synthetic aperture radar (SAR) imagery. A number of canonical scenes and data sets are analyzed so as to investigate both speckle reduction and feature preservation capabilities of the filters. Despeckling performance is evaluated by proper quality measures that are defined according to the scene. Due to the lack of real-world speckle-free SAR images, the proposed benchmarking tool relies on an accurate and well-assessed SAR simulator which allows for generating realistic SAR images accounting for electromagnetic (EM) and geometrical parameters of the sensed surface. Accuracy and convergence properties of filters are first measured on scenes with stationary reflectivity. Then, for a more realistic performance prediction in practical situations, the effects of temporal changes of the scene reflectivity on the despeckled images are measured on time series with time-varying reflectivity. In the latter case, performance parameters are intended to measure the capability of the filter to preserve both the perturbation and its impact on the other bands. The whole benchmarking framework is applied to a representative set of state-of-the-art multitemporal filters. Interestingly, their performance as evaluated by means of our framework is well in agreement with (qualitative) visual inspections by SAR specialists. Proposed quality metrics are measured under the hypothesis of uncorrelated bands, which defines the best case for most multitemporal filters. A numerical sensitivity analysis of the performance of filters against correlation coefficient is carried out to investigate the temporal correlation effects on the despeckled time series.
An improved dynamic window approach for local trajectory planning in the environment with dense objects

Abstract The Dynamic Window Approach (DWA) has been one of the most popular solutions in local trajectory planning due to the advantages of movement fluency. However, the traditional DWA faces the challenge of low-efficiency in the case of local trajectory planning since the robot cannot perceive the density of the obstacle. In this paper, we propose an improved DWA to solve this problem. First, we use multi-sensor technology and new evaluation algorithms to enable the capability of density perception for the disorderly environment. Second, the evaluation factor related to the density change is introduced into the path evaluation function, which will facilitate the robot to perceive the distribution of dense objects in advance. Computer simulation and field experiments show that the efficiency of the improved DWA is increased by 25%. The improved DWA not only considers the original factors such as heading angle, but also introduces a density factor to evaluate the next path, so as to avoid entering dense areas in advance. It can be seen that the improved DWA has relatively stable speed and acceleration, and can avoid dense areas in advance, which can be widely used in robots running in dense environments.
There is no evidence that meaning maps capture semantic information relevant to gaze guidance: Reply to Henderson, Hayes, Peacock, and Rehrig (2021)

The concerns raised by Henderson, Hayes, Peacock, and Rehrig (2021) are based on misconceptions of our work. We show that Meaning Maps (MMs) do not predict gaze guidance better than a state-of-the-art saliency model that is based on semantically-neutral, high-level features. We argue that there is therefore no evidence to date that MMs index anything beyond these features. Furthermore, we show that although alterations in meaning cause changes in gaze guidance, MMs fail to capture these alterations. We agree that semantic information is important in the guidance of eye-movements, but the contribution of MMs for understanding its role remains elusive.
Development of a Novel Omnidirectional Treadmill-Based Locomotion Interface Device with Running Capability

To achieve an immersive virtual reality (VR) environment, omnidirectional treadmills (ODTs) allow users to perform locomotion in any direction. However, existing ODTs are heavy and complex, and operate at low speeds. This limits fast user motion and prevents natural interactions in real applications such as military training programs and interactive games. In this paper, we introduce a novel locomotion interface device with running capability, which uses an omnidirectional treadmill with a new power transmission mechanism and a locomotion controller that enables the user to make fast movements. As a result of the improved power transmission performance due to the simple and relatively lightweight structure, the proposed two-dimensional treadmill can generate a maximum speed of 3 m/s, with an acceleration of 3 m/s2. Moreover, through a pilot test with the proposed locomotion interface device, we verified that the fast directional changes during walking and running with the designed speed adaptation controller do not exceed the acceleration performance of the proposed system. Due to its wide range of movement speeds and acceleration capabilities, and lack of any motion constraints, the proposed locomotion interface device with a novel ODT can be used as a representative platform in various VR environments to enhance the immersive experience.
Tackling Contradiction Detection in German Using Machine Translation and End-to-End Recurrent Neural Networks

Natural Language Inference, and specifically Contradiction Detection, is still an unexplored topic with respect to German text. In this paper, we apply Recurrent Neural Network (RNN) methods to learn contradiction-specific sentence embeddings. Our data set for evaluation is a machine-translated version of the Stanford Natural Language Inference (SNLI) corpus. The results are compared to a baseline using unsupervised vectorization techniques, namely tf-idf and Flair, as well as state-of-the art transformer-based (MBERT) methods. We find that the end-to-end models outperform the models trained on unsupervised embeddings, which makes them the better choice in an empirical use case. The RNN methods also perform superior to MBERT on the translated data set.
Embedding ranking-oriented recommender system graphs

Graph-based recommender systems (GRSs) analyze the structural information available in the graphical representation of data to make better recommendations, especially when direct user-item relation data is sparse. Ranking-oriented GRSs mostly use graphical representation of preference (or rank) data for measuring node similarities, from which they can infer recommendations using neighborhood-based methods. In this paper, we propose PGRec, a novel model-based ranking-oriented recommendation framework. Unlike many other graph-based methods, PGRec extracts vector representations for users and preferences from a novel graph structure called PrefGraph, which models entity relations, feedbacks, and content. A general graph-embedding process is improved and applied to extract vector representations for entities. The resulting embeddings are then used for predicting the target user's unknown pairwise preferences by a neural network based on which a recommendation list is generated for the target user. We have evaluated the proposed method's performance against the state of the art model-based and neighborhood-based recommendation algorithms. Our experiments show that PGRec outperforms the baseline algorithms in terms of the NDCG metric in several datasets.
Neural OCR Post-Hoc Correction of Historical Corpora

Abstract Optical character recognition (OCR) is crucial for a deeper access to historical collections. OCR needs to account for orthographic variations, typefaces, or language evolution (i.e., new letters, word spellings), as the main source of character, word, or word segmentation transcription errors. For digital corpora of historical prints, the errors are further exacerbated due to low scan quality and lack of language standardization. For the task of OCR post-hoc correction, we propose a neural approach based on a combination of recurrent (RNN) and deep convolutional network (ConvNet) to correct OCR transcription errors. At character level we flexibly capture errors, and decode the corrected output based on a novel attention mechanism. Accounting for the input and output similarity, we propose a new loss function that rewards the model’s correcting behavior. Evaluation on a historical book corpus in German language shows that our models are robust in capturing diverse OCR transcription errors and reduce the word error rate of 32.3% by more than 89%.
Automatic segmentation of left and right ventricles in cardiac MRI using 3D-ASM and deep learning

Segmentation of the left and right ventricles in cardiac MRI (Magnetic Resonance Imaging) is a prerequisite step for evaluating global and regional cardiac function. This work presents a novel and robust schema for MRI segmentation by combining the advantages of deep learning localization and 3D-ASM (3D Active Shape Model) restriction without any user interaction. Three fundamental techniques are exploited: (1) manual 2D contours are used to build distance maps to get 3D ground truth shape, (2) derived right ventricle points are employed to rotate the coarse initial shape for a refined bi-ventricle initial estimation, (3) segmentation results from deep learning are utilised to build distance maps for the 3D-ASM matching process to help image intensity modelling. The datasets used for experimenting the cine MRI data are 1000 cases from UK Biobank, 500 subjects are selected to train CNN (Convolution Neural Network) parameters, and the remaining 500 cases are adopted for validation. Specifically, cases are used to rebuild point distribution and image intensity models, and also utilized to train CNN. In addition, the left 500 cases are used to perform the validation experiments. For the segmentation of the RV (Right Ventricle) endocardial contour, LV (Left Ventricle) endo- and epicardial contours, overlap, Jaccard similarity index, Point-to-surface errors and cardiac functional parameters are calculated. Experimental results show that the proposed method has advantages over the previous approaches.