Reviewerly/exHarmony huggingface dataset:
Oppidum - A Serious-AR-Game About Celtic Life and History

Augmented Reality (AR) and (Serious) Games have been used to support cultural heritage experiences for a widespread audience. Combining both methods, we present a multiplayer Serious-AR-Game for Celtic Life and History. As a game the player experiences fun and competition which leads to intrinsic motivation. In contrast to existing games with a Celtic theme, our approach has been focused on historical accuracy to ensure a learning-centered foundation. The goal is to build a Celtic village with a functioning economic cycle. Especially by interacting in AR, the game becomes more perceptible, intriguing and vivid. Based on tangibles (markers tracked by a camera), playful, interactable replications of historically accurate Celtic buildings can be explored. The player can see their interior and also get information about the Celts by interacting with various objects or tools of their daily life. The collected knowledge is necessary to succeed in the ‘quiz-war’ against an opponent. This project was evaluated (N = 20) based on a questionnaire (GEQ) [10] to measure the game experience as well as the transferred knowledge and to get directed feedback for further development.
Enhancing Transformation-Based Defenses Against Adversarial Attacks with a Distribution Classifier

Adversarial attacks on convolutional neural networks (CNN) have gained significant attention and there have been active research efforts on defense mechanisms. Stochastic input transformation methods have been proposed, where the idea is to recover the image from adversarial attack by random transformation, and to take the majority vote as consensus among the random samples. However, the transformation improves the accuracy on adversarial images at the expense of the accuracy on clean images. While it is intuitive that the accuracy on clean images would deteriorate, the exact mechanism in which how this occurs is unclear. In this paper, we study the distribution of softmax induced by stochastic transformations. We observe that with random transformations on the clean images, although the mass of the softmax distribution could shift to the wrong class, the resulting distribution of softmax could be used to correct the prediction. Furthermore, on the adversarial counterparts, with the image transformation, the resulting shapes of the distribution of softmax are similar to the distributions from the clean images. With these observations, we propose a method to improve existing transformation-based defenses. We train a separate lightweight distribution classifier to recognize distinct features in the distributions of softmax outputs of transformed images. Our empirical studies show that our distribution classifier, by training on distributions obtained from clean images only, outperforms majority voting for both clean and adversarial images. Our method is generic and can be integrated with existing transformation-based defenses.
Homomorphic Encryption: The ‘Holy Grail’ for Big Data Analytics and Legal Compliance in the Pharmaceutical and Healthcare Sector?

The pharmaceutical and healthcare sector is a prime target for cybercriminals around the world. These cyber-attacks represent significant challenges in the context of data protection and data security. The General Data Protection Regulation (GDPR) imposes strict rules regarding the processing and analysis of personal data. In conventional approaches, data analysts request data from various sources. Then, they anonymise or pseudonymise the data using various tools and techniques. These methods often use powerful algorithms to ensure a high level of security. However, these methods tend to either reduce the quality of data for further analysis or they expose the data while decrypting it for analysis. Homomorphic Encryption (HE) has recently been touted as the ‘Holy Grail’ of cryptography since it allows the analysis of big data sets without ever needing to decrypt and thus compromising the confidentiality of the data. This provides a whole new layer of protection and at the same time allows the processing of data for secondary use and scientific research. While HE is not a new technology, it is still in the early stages of development. In this piece, we will introduce a new automated tool for searching and analysing encrypted data using HE techniques, which is being developed within the scope of the EnergyShield project.
Fast Encoding and Decoding Algorithms for Arbitrary $(n,k)$ Reed-Solomon Codes Over $\mathbb{F}_{2^m}$

Recently, a new polynomial basis over finite fields was proposed such that the computational complexity of the fast Fourier transform (FFT) is O(n log n). Based on FFTs, the encoding and decoding algorithms for Reed-Solomon (RS) codes were proposed, which are shown to have the lowest computational complexity in the literature. However, these algorithms require that the code length and the number of parity symbols must be power of two. In this letter, we present the encoding and decoding algorithms for arbitrary RS codes based on FFTs. Furthermore, these new algorithms also reach the best known complexity bound.
Lifting with Simple Gadgets and Applications to Circuit and Proof Complexity

We significantly strengthen and generalize the theorem lifting Nullstellensatz degree to monotone span program size by Pitassi and Robere (2018) so that it works for any gadget with high enough rank, in particular, for useful gadgets such as equality and greater-than. We apply our generalized theorem to solve three open problems: ; We present the first result that demonstrates a separation in proof power for cutting planes with unbounded versus polynomially bounded coefficients. Specifically, we exhibit CNF formulas that can be refuted in quadratic length and constant line space in cutting planes with unbounded coefficients, but for which there are no refutations in subexponential length and subpolynomial line space if coefficients are restricted to be of polynomial magnitude. : We give the first explicit separation between monotone Boolean formulas and monotone real formulas. Specifically, we give an explicit family of functions that can be computed with monotone real formulas of nearly linear size but require monotone Boolean formulas of exponential size. Previously only a non-explicit separation was known. : We give the strongest separation to-date between monotone Boolean formulas and monotone Boolean circuits. Namely, we show that the classical GEN problem, which has polynomial-size monotone Boolean circuits, requires monotone Boolean formulas of size 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ω(n/polylog(n))</sup> . An important technical ingredient, which may be of independent interest, is that we show that the Nullstellensatz degree of refuting the pebbling formula over a DAG G over any field coincides exactly with the reversible pebbling price of G. In particular, this implies that the standard decision tree complexity and the parity decision tree complexity of the corresponding falsified clause search problem are equal. This is an extended abstract. The full version of the paper is available at https://arxiv.org/abs/2001.02144.
Increasing American Political Tolerance: A Framework Excluding Hate Speech

According to prior research, political tolerance has either stagnated since the 1970s (if to be tolerant one must be tolerant of every group in all circumstances) or steadily increased (if tolerance is measured using an index, averaging across groups). Using General Social Survey cross-sectional and panel data on civil liberties, this article proposes a new framework: separating out the groups that use hate speech from those that may be only controversial. The United States is unique among Western liberal democracies in not having a prohibition against hate speech. By applying a dichotomous hate speech framework to measuring political tolerance, this article finds that the proportion of Americans who are always tolerant has increased by 8 percentage points from 1996 to 2018. Meanwhile, tolerance of groups that use hate speech has remained flat and even decreased among groups that historically were more tolerant of such groups, including the college educated.
Efficient VM Placement Policy for Data Centre in Cloud Environment

Cloud computing now becomes the accomplished infrastructure model for delivering the cloud service to its user over the Internet. Virtualization is one of the key concepts, which makes the dynamic sharing of physical resources of cloud more effective. In virtualization, virtual machine is placed over the physical machine in data centre and VM placement is one of the challenging issues in cloud computing, so cloud service provider needs good VM placement policy to increase the utilization of physical resources as well as reduce the load-balancing problem. In this paper, we proposed a new VM placement policy called Efficient of service Best Fit Decreasing (EBFD). From the simulation experiments result, we see that the proposed policy performs much better performance compared with first-fit and best-fit policy.
Action graphs for proactive robot assistance in smart environments

Smart environments can already observe the actions of a human through pervasive sensors. Based on these observations, our work aims to predict the actions a human is likely to perform next. Predictions can enable a robot to proactively assist humans by autonomously executing an action on their beha lf. In this paper, Action Graphs are introduced to model the order constraints between actions. Action Graphs are derived from a problem defined in Planning Domain Definition Language (PDDL). When an action is observed, the node values are updated and next actions predicted. Subsequently, a robot executes one of the predicted actions if it does not impact the flow of the human by obstructing or delaying them. Our Action Graph approach is applied to a kitchen domain.
Predicting Optimal Hypertension Treatment Pathways Using Recurrent Neural Networks

In ambulatory care settings, physicians largely rely on clinical guidelines and guideline-based clinical decision support (CDS) systems to make decisions on hypertension treatment. However, current clinical evidence, which is the knowledge base of clinical guidelines, is insufficient to support definitive optimal treatment. The goal of this study is to test the feasibility of using deep learning predictive models to identify optimal hypertension treatment pathways for individual patients, based on empirical data available from an electronic health record database. This study used data on 245,499 unique patients who were initially diagnosed with essential hypertension and received anti-hypertensive treatment from January 1, 2001 to December 31, 2010 in ambulatory care settings. We used recurrent neural networks (RNN), including long short-term memory (LSTM) and bi-directional LSTM, to create risk-adapted models to predict the probability of reaching the BP control targets associated with different BP treatment regimens. The ratios for the training set, the validation set, and the test set were 6:2:2. The samples for each set were independently randomly drawn from individual years with corresponding proportions. The LSTM models achieved high accuracy when predicting individual probability of reaching BP goals on different treatments: for systolic BP (<140 mmHg), diastolic BP (<90 mmHg), and both systolic BP and diastolic BP (<140/90 mmHg), F1-scores were 0.928, 0.960, and 0.913, respectively. The results demonstrated the potential of using predictive models to select optimal hypertension treatment pathways. Along with clinical guidelines and guideline-based CDS systems, the LSTM models could be used as a powerful decision-support tool to form risk-adapted, personalized strategies for hypertension treatment plans, especially for difficult-to-treat patients.
The SPDE Approach to Matérn Fields: Graph Representations

This paper investigates Gaussian Markov random field approximations to nonstationary Gaussian fields using graph representations of stochastic partial differential equations. We establish approximation error guarantees building on the theory of spectral convergence of graph Laplacians. The proposed graph representations provide a generalization of the Matérn model to unstructured point clouds, and facilitate inference and sampling using linear algebra methods for sparse matrices. In addition, they bridge and unify several models in Bayesian inverse problems, spatial statistics and graph-based machine learning. We demonstrate through examples in these three disciplines that the unity revealed by graph representations facilitates the exchange of ideas across them.
Uncertainty: two probabilities for the three states of neutrosophy

Uncertainty is inherent to the real world: everything is only probable, precision like in measurements is finite, noise is everywhere... Also, science is based on a modeling of reality that can only be approximate. Therefore we postulate that uncertainty should be considered in our models, and for making this more easy we propose a simple operational conceptualization of uncertainty. Starting from the simple model of associating a probability p to a statement supposed to be true our proposed modeling bridges the gap towards the most complex representation proposed by neutrosophy as a triplet of probabilities. The neutrosophic representation consists in using a triplet of probabilities (t,i,f) instead of just a single probability. In this triplet, t represents the probability of the statement to be true, and f it's the probability to be false. The specific point of neutrosophy it that the probability i represents the probability of the statement to be uncertain, imprecise, or neutral among other significations according to the application. Our proposed representation uses only 2 probabilities instead of 3, and it can be easily translated into the neutrosophic representation. By being simpler we renounce to some power of representing the uncertain but we encourage the modeling of uncertainty (instead of ignoring it) by making this simpler. Briefly said, the prepare the path towards using neutrosophy. Our proposed representation of uncertainty consist, for a statement, not only to add its probability to be true p, but also a second probability pp to model the confidence we have in the first probability p. This second parameter pp represents the plausibility of p, therefore the opposite of its uncertainty. This is the confidence given to the value of p, in short pp is the probability of p (hence the name pp), This is simple to understand, and that allows calculations of combined events using classical probability such as based on the concepts of mean and variance. The stringent advantage of our modeling by the couple (p,pp) is that experts can be easily interrogated to provide their expertise by asking them simply the chance they give to an event a occur (this is p) and the confidence they have in that prediction (which is pp). We give also a formula to transform from our model to the neutrosophic representation. Finally, a short discussion on the entropy as a measure of uncertainty is done.
Stacked squeeze-and-excitation recurrent residual network for visual-semantic matching

In recent years, visual-textual matching has been widely studied in the intersection of computer vision and natural language processing communities. A feasible scheme for learning discriminative representations is leveraging hierarchical features to align both modalities at multiple semantic levels. However, most existing approaches rely on pre-trained object detectors or semantic parsers to generate multi-level representations, whose performance is overly dependent on the extra supervision and thereby leads to its vulnerability. In this paper, we introduce a Stacked Squeeze-and-Excitation Recurrent Residual Network (SER2-Net) for visual-textual matching. Firstly, an efficient multi-level representation module is presented to produce a series of semantically discriminative features without the aid of extra supervision, which is built by stacking the squeeze-and-excitation recurrent residual (SER2) learning components. Specifically, SER2 incorporates the residual learning and inverse recurrent connection into the squeeze-and-excitation learning block, which allows for utilizing complementary current information and residual information to improve the modality-specific representation ability. Besides, to capture the implicit correlations contained among multi-level features, we propose a novel objective namely Cross-modal Semantic Discrepancy (CMSD) loss, which is characterized by exploiting the interdependency among different semantic levels to narrow the cross-modal distribution discrepancy. Extensive experiments on two benchmark datasets validate the superiority of our model, which compares favorably with the state-of-the-art approaches.
Feature importance of the aortic anatomy on endovascular aneurysm repair (EVAR) using Boruta and Bayesian MCMC

A retrospective study of the abdominal aortic aneurysm (AAA) with EVAR treated patients. The third-party collected the data from twelve vascular centres in Indonesia during 2012-2017. Patient demographics and computed tomography data were evaluated with Osirix MD Software. During five years, we had 148 EVAR cases done using Endurant stent graft (Medtronic). In this paper, we perform Bayesian modelling and selection of feature selection by Boruta. Before performing the models, we will determine the selection of dependent variables start with the Age, Class, and Sex. It will get what is important to be dependent and independent. The difference between Bayesian and the classical method is the introduction of prior information in the form of probability distributions. In addition, to determine the parameters using the Bayesian method obtained from the probability statement. Parameter estimation in Bayesian is no longer a point estimate but, on the contrary, is a statistical distribution. In other words, Bayesian states that a parameter is a variable that has a distribution. Bayesian has become a popular method in modern statistical analysis. Bayesian is applied to a broad spectrum in the scientific and research fields. Bayesian data analysis involves learning from data that uses probability models for many observations and some information to be studied. In other words, analysing statistical models are by combining prior knowledge about the model or parameters of the model. In a nutshell, the simulation results obtained modelling with Bayesian-ZIP-MCMC R2 87.52 and Bayesian-Boruta R2 88.28%.
Automatic classification and recognition of complex documents based on Faster RCNN

OCR(Optical Character Recognition) has been widely used in digital document processing, but the current OCR technology only works well in simple document processing. In contrast, complex documents contain a lot of non-text information (icons, forms, signatures, seals, noise, etc.). Most OCR systems often misinterpret these non-textual information as text when dealing with complex documents, resulting in the wrong target recognition. Automatic document segmentation and recognition technology not only improves the processing accuracy of OCR, but also enhances the processing of documents. Therefore, this project will study how to use Faster RCNN (fast regional convolution) technology to automatically classify and identify text, icon, table, noise and other objects in complex documents. Faster RCNN is one of the mainstream frameworks in the field of target detection. Although it has been applied to text area recognition, it has not been reported in the division and recognition of complex documents. This project specifically studies the complex document image preprocessing technology, RPN (Region Proposal Network) technology and target recognition technology involved in the Faster RCNN technology. It enables fast and accurate separation of seal area, text area and page number area from complex documents. It provides accurate target area for the next step of OCR recognition and document enhancement, and realizes accurate recognition and enhancement.
Handling Rare Entities for Neural Sequence Labeling

One great challenge in neural sequence labeling is the data sparsity problem for rare entity words and phrases.Most of test set entities appear only few times and are even unseen in training corpus, yielding large number of out-of-vocabulary (OOV) and low-frequency (LF) entities during evaluation.In this work, we propose approaches to address this problem.For OOV entities, we introduce local context reconstruction to implicitly incorporate contextual information into their representations.For LF entities, we present delexicalized entity identification to explicitly extract their frequency-agnostic and entity-typespecific representations.Extensive experiments on multiple benchmark datasets show that our model has significantly outperformed all previous methods and achieved new startof-the-art results.Notably, our methods surpass the model fine-tuned on pre-trained language models without external resource.
On the generalization benefit of noise in stochastic gradient descent

It has long been argued that minibatch stochastic gradient descent can generalize better than large batch gradient descent in deep neural networks. However recent papers have questioned this claim, arguing that this effect is simply a consequence of suboptimal hyperparameter tuning or insufficient compute budgets when the batch size is large. In this paper, we perform carefully designed experiments and rigorous hyperparameter sweeps on a range of popular models, which verify that small or moderately large batch sizes can substantially outperform very large batches on the test set. This occurs even when both models are trained for the same number of iterations and large batches achieve smaller training losses. Our results confirm that the noise in stochastic gradients can enhance generalization. We study how the optimal learning rate schedule changes as the epoch budget grows, and we provide a theoretical account of our observations based on the stochastic differential equation perspective of SGD dynamics.
Let's Agree to Agree: Neural Networks Share Classification Order on Real Datasets

We report a series of robust empirical observations, demonstrating that deep Neural Networks learn the examples in both the training and test sets in a similar order. This phenomenon is observed in all the commonly used benchmarks we evaluated, including many image classification benchmarks, and one text classification benchmark. While this phenomenon is strongest for models of the same architecture, it also crosses architectural boundaries -- models of different architectures start by learning the same examples, after which the more powerful model may continue to learn additional examples. We further show that this pattern of results reflects the interplay between the way neural networks learn benchmark datasets. Thus, when fixing the architecture, we show synthetic datasets where this pattern ceases to exist. When fixing the dataset, we show that other learning paradigms may learn the data in a different order. We hypothesize that our results reflect how neural networks discover structure in natural datasets.