Reviewerly/exHarmony huggingface dataset:
A New Method for Detecting Altered Text in Document Images

As more and more office documents are captured, stored, and shared in digital format, and as image editing software are becoming increasingly more powerful, there is a growing concern about document authenticity. To prevent illicit activities, this paper presents a new method for detecting altered text in document images. The proposed method explores the relationship between positive and negative coefficients of DCT to extract the effect of distortions caused by tampering by fusing reconstructed images of respective positive and negative coefficients, which results in Positive-Negative DCT coefficients Fusion (PNDF). To take advantage of spatial information, we propose to fuse R, G, and B color channels of input images, which results in RGBF (RGB Fusion). Next, the same fusion operation is used for fusing PNDF and RGBF, which results in a fused image for the original input one. We compute a histogram to extract features from the fused image, which results in a feature vector. The feature vector is then fed to a deep neural network for classifying altered text images. The proposed method is tested on our own dataset and the standard datasets from the ICPR 2018 Fraud Contest, Altered Handwriting (AH), and faked IMEI number images. The results show that the proposed method is effective and the proposed method outperforms the existing methods irrespective of image type.
Resolution-Optimal Motion Planning for Steerable Needles

Medical steerable needles can follow 3D curvilinear trajectories inside body tissue, enabling them to move around critical anatomical structures and precisely reach clinically significant targets in a minimally invasive way. Automating needle steering, with motion planning as a key component, has the potential to maximize the accuracy, precision, speed, and safety of steerable needle procedures. In this paper, we introduce the first resolution-optimal motion planner for steerable needles that offers excellent practical performance in terms of runtime while simultaneously providing strong theoretical guarantees on completeness and the global optimality of the motion plan in finite time. Compared to state-of-the-art steerable needle motion planners, simulation experiments on realistic scenarios of lung biopsy demonstrate that our proposed planner is faster in generating higher-quality plans while incorporating clinically relevant cost functions. This indicates that the theoretical guarantees of the proposed planner have a practical impact on the motion plan quality, which is valuable for computing motion plans that minimize patient trauma.
Robustness Assessment for Adversarial Machine Learning: Problems, Solutions and a Survey of Current Neural Networks and Defenses

In adversarial machine learning, there are a huge number of attacks ofvarious types which evaluates robustness for new models and defences a dauntingtask. To make matters worse, there is an inherent bias in attacks and defences.Here, we organize the problems faced (model dependence, insufficientevaluation, false adversarial samples and perturbation dependent results) andpropose a model agnostic dual ($L_0$ and $L_\infty$) quality assessment methodtogether with the concept of robustness levels to tackle them. We validate thedual quality assessment on state-of-the-art models (WideResNet, ResNet,AllConv, DenseNet, NIN, LeNet and CapsNet) as well as the current hardestdefences proposed at ICLR 2018 and the widely known adversarial training,showing that current models and defences are vulnerable in all levels ofrobustness. The robustness assessment show that depending on the metric used(i.e., $L_0$ or $L_\infty$) the robustness may change significantly andtherefore duality should be taken into account for a correct assessment.Moreover, a mathematical derivation, as well as a counterexample, suggest that$L_1$ and $L_2$ metrics alone are not enough to avoid false adversarialsamples. Interestingly, a by-product of the assessment proposed is a novel$L_\infty$ black-box method which requires even less perturbation than theOne-Pixel Attack (only 12\% of One-Pixel Attack's amount of perturbation) toachieve similar results. Thus, this paper elucidates the problems of robustnessevaluation, proposes a dual quality assessment to tackle them as well as surveythe robustness of current models and defences. Code available athttp://bit.ly/DualQualityAssessment.
Living and Learning with Technology: Language Learning with Mobile Devices

Mobile-assisted language learning has expanded beyond relatively humble beginnings of replicating computer-based activities assigned in formal language learning contexts, and it now often consists of a complex mix of formal and informal learning activities that take place at different times and places throughout learners’ daily lives. Decisions about what tools to use are often driven by the costs, choice of hardware, and the knowledge of what tools actually exist, and how these tools are then used will depend on learners’ skills, motivation, as well as their short- and long-term goals. Language learning no longer needs to be an activity that is separated from learners’ daily lifestyles, but can be an integral part of their lives in the long term. The complexities involved in understanding each individual learner’s own ecological context and how these ecologies shift over time will be discussed with an eye on optimising learning opportunities for learners that will keep them engaged in learning throughout their lives.
Digital Signatures with Memory-Tight Security in the Multi-challenge Setting

The standard security notion for digital signatures is “single-challenge” (SC) EUF-CMA security, where the adversary outputs a single message-signature pair and “wins” if it is a forgery. Auerbach et al. (CRYPTO 2017) introduced memory-tightness of reductions and argued that the right security goal in this setting is actually a stronger “multi-challenge” (MC) definition, where an adversary may output many message-signature pairs and “wins” if at least one is a forgery. Currently, no construction from simple standard assumptions is known to achieve full tightness with respect to time, success probability, and memory simultaneously. Previous works showed that memory-tight signatures cannot be achieved via certain natural classes of reductions (Auerbach et al., CRYPTO 2017; Wang et al., EUROCRYPT 2018). These impossibility results may give the impression that the construction of memory-tight signatures is difficult or even impossible.We show that this impression is false, by giving the first constructions of signature schemes with full tightness in all dimensions in the MC setting. To circumvent the known impossibility results, we first introduce the notion of canonical reductions in the SC setting. We prove a general theorem establishing that every signature scheme with a canonical reduction is already memory-tightly secure in the MC setting, provided that it is strongly unforgeable, the adversary receives only one signature per message, and assuming the existence of a tightly-secure pseudorandom function. We then achieve memory-tight many-signatures-per-message security in the MC setting by a simple additional generic transformation. This yields the first memory-tightly, strongly EUF-CMA-secure signature schemes in the MC setting. Finally, we show that standard security proofs often already can be viewed as canonical reductions. Concretely, we show this for signatures from lossy identification schemes (Abdalla et al., EUROCRYPT 2012), two variants of RSA Full-Domain Hash (Bellare and Rogaway, EUROCRYPT 1996), and two variants of BLS signatures (Boneh et al., ASIACRYPT 2001).
Mining Clinical Process from Hospital Information System: A Granular Computing Approach

Data mining methods in medicine is a very important tool for developing automated decision support systems. However, since information granularity of disease codes used in hospital information system is coarser than that of real clinical definitions of diseases and their treatment, automated data curation is needed to extract knowledge useful for clinical decision making. This paper proposes automated construction of clinical process plan from nursing order histories and discharge summaries stored in hospital information system with curation of disease codes as follows. First, the system applies EM clustering to estimate subgrouping of a given disease code from clinical cases. Second, it decomposes the original datasets into datasets of subgroups by using granular homogenization. Thirdly, clinical pathway generation method is applied to the datasets. Fourthly, classification models of subgroups are constructed by using the analysis of discharge summaries to capture the meaning of each subgroup. Finally, the clinical pathway of a given disease code is output as the combination of the classifiers of subgroups and the the pathways of the corresponding subgroups. The proposed method was evaluated on the datasets extracted hospital information system in Shimane University Hosptial. The obtained results show that more plausible clinical pathways were obtained, compared with previously introduced methods.
The impact of the COVID-19 pandemic on cybercrime

The COVID-19 pandemic is accompanied by a cyber pandemic, involving changes in the modi operandi of perpetrators of various crimes, and an infodemic, associated with the spread of disinformation. The article analyses the impact of the COVID-19 pandemic on cybercrime and presents the latest research on the number of cybercrime cases in Poland and their growth dynamics. It determines the factors that contribute to the commission of a crime and prevent easy identification of criminals. It also suggests the legal and organisational changes that could reduce the number and effects of the most frequently recorded cyberattacks at a time of COVID-19. Particular attention is paid to legal problems of the growing phenomenon of identity theft, and the need to ensure better protection of users from phishing, including through education and proactive security measures consisting in blocking Internet domains used for fraudulent attempts to obtain data and financial resources. © 2021 The Author(s).
Differential Privacy in Constant Function Market Makers

Constant function market makers (CFMMs) are the most popular mechanism for facilitating decentralized trading. While these mechanisms have facilitated hundreds of billions of dollars of trades, they provide users with little to no privacy. Recent work illustrates that privacy cannot be achieved in CFMMs without forcing worse pricing and/or latency on end users. This paper quantifies the trade-off between pricing and privacy in CFMMs. We analyze a simple privacy-enhancing mechanism called Uniform Random Execution and prove that it provides $$(\epsilon , \delta )$$ -differential privacy. The privacy parameter $$\epsilon $$ depends on the curvature of the CFMM trading function and the number of trades executed. This mechanism can be implemented in any blockchain system that allows smart contracts to access a verifiable random function. Our results provide an optimistic outlook on providing partial privacy in CFMMs.
Fast and Efficient Terrain-Aware Motion Planning for Exploration Rovers

This paper presents a fast, energy-efficient, and low computational cost traversal solution on sloped terrain. The use of grid-based search algorithms requires high computational power and takes a long time because almost every point on the map is visited. An approach that does not depend on the global map but can also navigate towards the target can be presented as a new solution. A cost map for motion planning using depth field and color image data is formed in real-time. The proposed motion planning algorithm, named SAFARI, utilizes four cost layers to efficiently evaluate its surroundings. To reduce the computational overhead, only select features are evaluated and the rover's motion planning cycle speed is increased. SAFARI has been tested against path planning alternatives and has also been proven to work with simulations and field tests. This concept is expected to be used in space applications and cave exploration tasks.
Feature selection for predicting live migration characteristics of virtual machines

Virtual machine migration is widely used in cloud data centers to scale and maintain the stability of cloud services. However, the performance metrics of virtual machine (VM) applications during migration that are set in the Service Level Agreements may deteriorate. Before starting a migration, it is necessary to evaluate the migration characteristics that affect the quality of service. These characteristics are the total migration time and virtual machine downtime, which are random variables that depend on a variety of factors. The prediction is based on the VM monitoring data. In this paper, we select the most suitable factors for forecasting five types of migrations: precopy migration, postcopy migration, and modification of precopy migration such as CPU throttling, data compression, and delta compression of modified memory pages. To do this, we analyzed a dataset that includes data on five types of migrations, approximately 8000 records of each type. Using correlation analysis, the factors that mostly affect the total migration time and the VM downtime are chosen. These characteristics are predicted using machine learning methods such as linear regression and the support vector machine. It is shown that the number of factors can be reduced almost twice with the same quality of the forecast. In general, linear regression provides relatively high accuracy in predicting the total migration time and the duration of virtual machine downtime. At the same time, the observed nonlinearity in the correlations shows that it is advisable to use the support vector machine to improve the quality of the forecast.
A Comprehensive Evaluation of Metadata-Based Features to Classify Research Paper’s Topics

The existing plethora of document classification techniques exploits different data sources either from the content or metadata of research articles. Various journal publishers like Springer, Elsevier, IEEE, etc., do not provide open access to the content of research articles, whereas metadata is freely available there. Metadata like title, keyword, and abstract can serve as a better alternative to the content in various scenarios. In the current literature, researchers have assessed the role of some of the metadata individually. We believe that the collective contribution of metadata parameters can play a significant role in classifying research papers. This paper presents a comprehensive evaluation of the role of metadata, individually as well as in combinations to achieve the objective of research paper classification. Moreover, we have classified the research articles into ACM hierarchy root categories (e.g. general literature, hardware, software, etc.). In this comprehensive evaluation, we have assessed all the possible combinations of metadata features against different classifiers such as Random Forest, K Nearest Neighbor, and Decision Tree. The results of this research reveal that the title keywords combination outperforms other combinations with an F-measure score of 0.88.
Why Do Organizations Adopt Agile Scaling Frameworks?

Background: The benefits of agile methods in small, co-located projects have inspired their adoption in large firms and projects. Scaling frameworks, such as Large-Scale Scrum (LeSS) and the Scaled Agile Framework (SAFe), have been proposed by practitioners to scale agile to larger contexts, and become rather widely adopted in the industry. Despite the popularity of the frameworks, the knowledge on the reasons, expected benefits, and satisfaction of organizations adopting them is still limited.
Digital signature scheme on the 2 x 2 matrix algebra

The article considers the structure of the 2x2 matrix algebra set over a ground finite field GF(p). It is shown that this algebra contains three types of commutative subalgebras of order p2, which differ in the value of the order of their multiplicative group. Formulas describing the number of subalgebras of every type are derived. A new post-quantum digital signature scheme is introduced based on a novel form of the hidden discrete logarithm problem. The scheme is characterized in using scalar multiplication as an additional operation masking the hidden cyclic group in which the basic exponentiation operation is performed when generating the public key. The advantage of the developed signature scheme is the comparatively high performance of the signature generation and verification algorithms as well as the possibility to implement a blind signature protocol on its base.
Discovering Curriculum Components for a New Product Development Course

Presently, new product development is deemed the core activity of an organization's competitive strategy, but there is a surprising dearth of research on what curriculum components should be embedded in the new product development course. This study aims to introduce a methodology that can carry out curriculum design with limited prior knowledge. First, latent semantic analysis is applied to extract the main research themes from the subject matter literature, which are considered as potential curriculum components. Next, the revised Bloom's taxonomy is applied to develop a questionnaire for establishing the learning objectives. Finally, the study uses the modified Delphi method to verify the eligibility of these curriculum components, concluding that the most effective approach for imparting knowledge and skills regarding new product development is to adopt a capstone course and equip students with creativity and advanced technology and design for sustainability through a project-based learning approach.
Geometrical models for simulating the properties of underwater bodies using digital image processing

In spite of its transparency, the determination process for the properties of submersible object into water is a difficult task. One of these difficulties arises from the differences in refractive indices between two different mediums such as air and water which changes the visual appearance of the immersed object. Such difficulties will provide unprecise measurements and hence the need for analyzing and having a mathematical model for such situation is a critical need. This paper introduced seven mathematical models to simulate the properties of two bodies (floating and submersible objects) under the effect of light attenuation and refraction phenomena caused by differences in refractive indices between air and water. Results show an accuracy in describing object’s properties in underwater environment for both floating and submersible used bodies. The proposed models gave a good indicator in detecting underwater object’s properties with lowest values for Mean Square Error (MSE) which had been used here. The lowest value for MSE had been detected upon scaling factor followed by Magnification and object’s length estimator at final.
Construction and Evaluation of Robust Interpretation Models for Breast Cancer Metastasis Prediction

Interpretability of machine learning (ML) models represents the extent to which a model’s decision-making process can be understood by model developers and/or end users. Transcriptomics-based cancer prognosis models, for example, while achieving good accuracy, are usually hard to interpret, due to the high-dimensional feature space and the complexity of models. As interpretability is critical for the transparency and fairness of ML models, several algorithms have been proposed to improve the interpretability of arbitrary classifiers. However, evaluation of these algorithms often requires substantial domain knowledge. Here, we propose a breast cancer metastasis prediction model using a very small number of biologically interpretable features, and a simple yet novel model interpretation approach that can provide personalized interpretations. In addition, we contributed, to the best of our knowledge, the first method to quantitatively compare different interpretation algorithms. Experimental results show that our model not only achieved competitive prediction accuracy, but also higher inter-classifier interpretation consistency than state-of-the-art interpretation methods. Importantly, our interpretation results can improve the generalizability of the prediction models. Overall, this work provides several novel ideas to construct and evaluate interpretable ML models that can be valuable to both the cancer machine learning community and related application domains.
EVRNet: Efficient Video Restoration on Edge Devices

In video transmission applications, video signals are transmitted over lossy channels, resulting in low-quality received signals. To re- store videos on recipient edge devices in real-time, we introduce an efficient video restoration network, EVRNet. EVRNet efficiently allocates parameters inside the network using alignment, differential, and fusion modules. With extensive experiments on different video restoration tasks (deblocking, denoising, and super-resolution), we demonstrate that EVRNet delivers competitive performance to existing methods with significantly fewer parameters and MACs. For example, EVRNet has 260× fewer parameters and 958× fewer MACs than enhanced deformable convolution-based video restoration net- work (EDVR) for 4× video super-resolution while its SSIM score is 0.018 less than EDVR. We also evaluated the performance of EVR-Net under multiple distortions on unseen dataset to demonstrate its ability in modeling variable-length sequences under both camera and object motion.
Dynamic Logic of Legal Competences

We propose a new formalization of legal competences, and in particular for the Hohfeldian categories of power and immunity, through a deontic reinterpretation of dynamic epistemic logic. We argue that this logic explicitly captures the norm-changing character of legal competences while providing a sophisticated reduction of the latter to static normative positions. The logic is completely axiomatizable, and we apply it to a concrete case in German contract law to illustrate that it can capture the distinction between legal ability and legal permissibility.
iRegNet: Non-Rigid Registration of MRI to Interventional US for Brain-Shift Compensation Using Convolutional Neural Networks

Accurate and safe neurosurgical intervention can be affected by intra-operative tissue deformation, known as brain-shift. In this study, we propose an automatic, fast, and accurate deformable method, called iRegNet, for registering pre-operative magnetic resonance images to intra-operative ultrasound volumes to compensate for brain-shift. iRegNet is a robust end-to-end deep learning approach for the non-linear registration of MRI-iUS images in the context of image-guided neurosurgery. Pre-operative MRI (as moving image) and iUS (as fixed image) are first appended to our convolutional neural network, after which a non-rigid transformation field is estimated. The MRI image is then transformed using the output displacement field to the iUS coordinate system. Extensive experiments have been conducted on two multi-location databases, which are the BITE and the RESECT. Quantitatively, iRegNet reduced the mean landmark errors from pre-registration value of (4.18 ± 1.84 and 5.35 ± 4.19 mm) to the lowest value of (1.47 ± 0.61 and 0.84 ± 0.16 mm) for the BITE and RESECT datasets, respectively. Additional qualitative validation of this study was conducted by two expert neurosurgeons through overlaying MRI-iUS pairs before and after the deformable registration. Experimental findings show that our proposed iRegNet is fast and achieves state-of-the-art accuracies outperforming state-of-the-art approaches. Furthermore, the proposed iRegNet can deliver competitive results, even in the case of non-trained images as proof of its generality and can therefore be valuable in intra-operative neurosurgical guidance.