Miracle-dz/UniFAR-FTUs huggingface dataset:
Three-Dimensional CFD Simulation and Experimental Assessment of the Performance of a H-Shape Vertical-Axis Wind Turbine at Design and Off-Design Conditions

The paper presents the results of a computational study on the aerodynamics and the performance of a small-scale Vertical-Axis Wind Turbine (VAWT) for distributed micro-generation. The complexity of VAWT aerodynamics, which are inherently unsteady and three-dimensional, makes high-fidelity flow models extremely demanding in terms of computational cost, limiting the analysis to mainly 2D or 2.5D Computational Fluid-Dynamics (CFD) approaches. This paper discusses how a proper setting of the computational model opens the way for carrying out fully 3D unsteady CFD simulations of a VAWT. Key aspects of the flow model and of the numerical solution are discussed, in view of limiting the computational cost while maintaining the reliability of the predictions. A set of operating conditions is considered, in terms of tip-speed-ratio (TSR), covering both peak efficiency condition as well as off-design operation. The fidelity of the numerical predictions is assessed via a systematic comparison with the experimental benchmark data available for this turbine, consisting of both performance and wake measurements carried out in the large-scale wind tunnel of the Politecnico di Milano. The analysis of the flow field on the equatorial plane allows highlighting its time-dependent evolution, with the aim of identifying both the periodic flow structures and the onset of dynamic stall. The full three-dimensional character of the computations allows investigating the aerodynamics of the struts and the evolution of the trailing vorticity at the tip of the blades, eventually resulting in periodic large-scale vortices.
A Bi-Attention Adversarial Network for Prostate Cancer Segmentation

Prostate cancer is one of the most prevalent cancers among men. Early detection of this cancer could effectively increase the survival rate of the patient. In this paper, we propose a Bi-attention adversarial network for the prostate cancer segmentation automatically. The proposed architecture consists of the generator network and discriminator network. The generator network aims to generate the predicted mask of the input image, while the discriminator network aims to further improve the generator performance with adversarial learning by discriminating the generator predicted mask and the true label mask. For better improving the segmentation performance, we combine two attention mechanisms with the generator network to learn more global and local features. Extensive experiments on the T2-weighted (T2W) images have demonstrated our model could achieve state-of-the-art segmentation performance compared with other methods.
AutoGAMESS: A Python package for automation of GAMESS(US) Raman calculations

The ab initio quantum chemistry software GAMESS(US) (M. S. Gordon & Schmidt, 2005; M. W. Schmidt et al., 1993) is capable of calculating a variety of molecular properties. One of the many popular uses of GAMESS(US) is the prediction of properties of volatile and unstable species that have not been experimentally characterized or quantified before by chemists, physicists, astro-chemists and astro-physicists (Bennett, Ennis, & Kaiser, 2014; Burda, Pavelka, & Šimánek, 2004; Hickman, Miles, Hayden, & Talbi, 2005; Pacifici, Verdicchio, Lago, Lombardi, & Costantini, 2013). Applications of, and research done using, GAMESS(US) is not limited to uncharacterized species; it’s also widely used in material characterization or material property prediction research. Research utilizing these types of ab initio calculations typically require calculations with multiple steps required to achieve each final result. For instance, a Raman activity prediction first requires a geometry optimization and Hessian calculation be performed on the molecule, making automation extremely beneficial. This leads to complicated and tedious workflows slowing a user’s research.
Weakly Supervised Multilingual Causality Extraction from Wikipedia

We present a method for extracting causality knowledge from Wikipedia, such as Protectionism -> Trade war, where the cause and effect entities correspond to Wikipedia articles. Such causality knowledge is easy to verify by reading corresponding Wikipedia articles, to translate to multiple languages through Wikidata, and to connect to knowledge bases derived from Wikipedia. Our method exploits Wikipedia article sections that describe causality and the redundancy stemming from the multilinguality of Wikipedia. Experiments showed that our method achieved precision and recall above 98% and 64%, respectively. In particular, it could extract causalities whose cause and effect were written distantly in a Wikipedia article. We have released the code and data for further research.
SEMI-AUTOMATIC MULTI-CAMERA VIDEO ANNOTATION TOOL FOR OBJECT TRACKING AND OPTICAL FLOW BENCHMARK CREATION

This paper addresses a problem of video annotation for object tracking evaluation. Contemporary object tracking methods relies heavily on optical flow calculation techniques. State of the art optical flow calculation methods are not enough to provide robust and accurate tracking under object occlusion and shading conditions. Thus, comprehensive visual object tracking datasets are required for deep analysis, benchmarking and improvement of object tracking methods. In most cases publicly available real video datasets does not include ground truth for optical flow evaluation, so new datasets might be in demand. Public video annotation tools for object tracking dataset labeling were reviewed. Lack of open source video annotation tools with functions of automatic optical flow markers and object bounding box visual tracking was noticed. In this work we developed a new open video annotation tool that is suitable for object bounding box labeling as well as optical flow annotation in multicamera video recordings. This tool makes it possible to specify the feature point and/or moving object position on the first video frame and track its position automatically in the subsequent video frames. This tool supports manual adjustment of the tracked position if needed. Multi-layer modular architecture of the annotation tool simplifies its testing, maintenance and further extension by the community.
A Fraud-Detection Fuzzy Logic Based System for the Sudanese Financial Sector

– Financial fraud considered as a global issue that faces the financial sector and economy; as a result, many financial institutions loose hundreds of millions of dollars annually due to fraud. In Sudan, there are difficulties of getting real data from banks and the unavailability of systems which explain the reasons of suspicious transaction. Hence, there is a need for transparent techniques which can automatically detect fraud with high accuracy and identify its causes and common patterns. Some of the Artificial Intelligence (AI) techniques provide good predictive models, nevertheless they are considered as black-box models which are not easy to understand and analyze. In this paper, we developed a novel intelligent type-2 Fuzzy Logic Systems (FLSs) which can detect fraud in debit cards using real world dataset extracted from financial institutions in Sudan. FLSs provide white-box transparent models which employ linguistic labels and IF-Then rules which could be easily analyzed, interpreted and augmented by the fraud experts. The proposed type-2 FLS system learnt its fuzzy sets parameters from data using Fuzzy C-means (FCM) clustering as well as learning the FLS rules from data. The proposed system has the potential to result in highly accurate automatic fraud-detection for the Sudanese financial institutions and banking sectors.
Deep neural networks can predict human behavior in arcade games

In a standard experimental paradigm typically used in cognitive neuroscience, time is discretized into distinct events and the presented stimulus material is sampled from a low number of categories. While this approach allows conducting highly controlled experiments, its ecological validity is limited, as in real life the human brain has to operate on a continuous time scale and also has to process complex stimuli that typically cannot be assigned to a low-dimensional stimulus space. The encoding model approach has been introduced to address these issues by using highdimensional sets of stimulus features as a means to analyze neuroimaging data from complex and timecontinuous tasks. Recently, activations from deep neural networks (DNNs) were proposed to serve as features in the encoding model approach. However, it has been argued that such DNN-based features might be uninformative for human neuroimaging data, as the behavior of a trained DNN does not necessarily have to resemble human behavior on a given task. Here, we present preliminary evidence (N = 1) that DNN activations from the top network layer can predict human behavior with high fidelity in three different Atari 2600 arcade games based on a linear model. These findings clear the way for extending this type of analysis to neuroimaging data, testing whether DNN activations extracted from hidden layers explain variance in the fMRI signal of task-related brain regions.
Three State PCN Marking

This document proposes metering and marking mechanisms for PCN- enabled nodes to label packets with pre-congestion information. The marker marks all PCN packets with an admission-stop (AS) codepoint if the PCN traffic rate on a link exceeds its admissible rate (AR) and when it exceeds its supportable rate (SR), it marks some of those packets exceeding SR with an excess-traffic (ET) codepoint. The flows with ET-marked packets will be terminated until the aggregate PCN traffic on the path decreases below its SR. This document proposes metering and marking mechanisms for these objectives.
Radio Network Coding Requires Logarithmic Overhead

We consider the celebrated radio network model for abstracting communication in wireless networks. In this model, in any round, each node in the network may broadcast a message to all its neighbors. However, a node is able to hear a message broadcast by a neighbor only if no collision occurred, meaning that it was the only neighbor broadcasting. While the (noiseless) radio network model received a lot of attention over the last few decades, the effect of noise on radio networks is still not well understood. In this paper, we take a step forward and show that making radio network protocols resilient to noise may require a substantial performance overhead. Specifically, we construct a multi-hop network and a communication protocol over this network that works in T rounds when there is no noise. We prove that any scheme that simulates our protocol and is resilient to stochastic noise, requires at least cT log(n) rounds, for some constant c. This stands in contrast to our previous result (STOC, 2018), showing that protocols over the single-hop (clique) network can be made noise resilient with only a constant overhead. Our result also settles a recent conjecture by Censor-Hillel, Haeupler, Hershkowitz, Zuzic (2018). We complement the above result by giving a scheme to simulate any protocol with a fixed order of transmissions with only an O(log (n)) overhead.
Ubiquitous Tracking for Distributed Mixed Reality Environments

The fields of Wearable Computing, Augmented Reality and Ubiquitous Computing are in principle highly convergent, as they all focus on environments where users perceive, and intuitively interact with, their surroundings. However, the reality as observed in research labs and limited commercial deployments has been that bugetary and technical constraints have actually kept these fields separate and distinct. One manifestation of this separation is in the choice of sensors used to build systems in each domain. A truly cross-disciplinary project has to incorporate sensors of much greater heterogeneity than has occurred heretofore. The way in which sensors are deployed results in spatial seams that can act as obstacles to the provision of services across different areas. This thesis examines the different architectural approach to handling events from different tracking systems. A new architecture is proposed that maintains a consistent spatial model of objects and their inter-relationships, and configures the flow of tracking data to satisfy the needs of heterogeneous clients and the constraints imposed by the environment, or environments in the distributed case.
Analyzing disentanglement of visual objects in semi-supervised neural networks

A fundamental goal of visual systems is to condense images into compact representations of the relevant information they contain. Ideally, these representations would consist of the independent “generative factors” that fully determine, on a semantic level, the visual input. Such a “disentangled” representation could consist of the identity of a background scene, and the identity, position, pose, and size of an object. Recent research in deep neural networks (DNNs) has focused on achieving disentangled representations, through unsupervised learning, of single objects or faces in isolation. We trained and analyzed a popular DNN model of disentanglement, the β-variational autoencoder (β-VAE) on a new dataset, containing a “foreground” white circle and “background” isotropic Gaussian. We show that the neural network autoencoder architecture we use can achieve a perfectly disentangled latent representation with supervised learning, but only achieves partial disentanglement when using the unsupervised β-VAE loss function. On our dataset, higher β values result in higher reconstruction loss and greater entanglement. We propose that further inductive bias is needed to achieve better disentanglement, such as a representation which factorizes static properties and their dynamics.
An Ontological Graph Identification Method for Improving Localization of IP Prefix Hijacking in Network Systems

IP prefix hijacking continues to be a pervasive cyber security threat to the core internet routing infrastructure. The data security of multiple cloud-based services is also susceptible to these threats, due to the high dependency on traditional routing protocols. Although a number of hijacking detection techniques have been recently proposed, no existing system has effectively addressed the problem of detecting malicious transit Autonomous System (AS) services in any detected hijacking occurrences. The ability to locate and isolate malicious services is critical for conducting a necessary mitigation strategy at an early stage, to minimise the impact of the attack, to restore cloud services quickly. In this paper, we propose an effective real-time processing method, so-called Ontological Graph Identification (OGI), for detecting IP prefix hijacking of nodes and suspicious transit nodes caused by the hijacked nodes through ASs. The proposed method is evaluated using the two public datasets of RIPE RIS and RouteView. Experimental results revealed improved performance for the detection of malicious transit nodes compared with peer techniques. It is, therefore, shown that the proposed method has utility in automating the process of investigating nodes with suspicious activities in real network systems.
Tree-structured Decoding for Solving Math Word Problems

Automatically solving math word problems is an interesting research topic that needs to bridge natural language descriptions and formal math equations. Previous studies introduced end-to-end neural network methods, but these approaches did not efficiently consider an important characteristic of the equation, i.e., an abstract syntax tree. To address this problem, we propose a tree-structured decoding method that generates the abstract syntax tree of the equation in a top-down manner. In addition, our approach can automatically stop during decoding without a redundant stop token. The experimental results show that our method achieves single model state-of-the-art performance on Math23K, which is the largest dataset on this task.
Asynchronous Deep Interaction Network for Natural Language Inference

Natural language inference aims to predict whether a premise sentence can infer another hypothesis sentence. Existing methods typically have framed the reasoning problem as a semantic matching task. The both sentences are encoded and interacted symmetrically and in parallel. However, in the process of reasoning, the role of the two sentences is obviously different, and the sentence pairs for NLI are asymmetrical corpora. In this paper, we propose an asynchronous deep interaction network (ADIN) to complete the task. ADIN is a neural network structure stacked with multiple inference sub-layers, and each sub-layer consists of two local inference modules in an asymmetrical manner. Different from previous methods, this model deconstructs the reasoning process and implements the asynchronous and multi-step reasoning. Experiment results show that ADIN achieves competitive performance and outperforms strong baselines on three popular benchmarks: SNLI, MultiNLI, and SciTail.
Visual asymmetry in artificial neural networks

Visual search is among the most developed topics of research in human perception but in the domain of machine learning/computer vision, research on visual search in neural networks is relatively new. Most tasks here typically focus on identification or recognition of photo-realistic objects in real world images. In contrast, a typical experiment aimed at understanding human visual search asks the human participant to search for a target among a set of distractors where both the target and the distractors are simplified stimuli like X’s and O’s, basic shapes and the like. The long term goal of this project is to use machine learning to explain asymmetries in human search performance. An asymmetry in search occurs when the performance (measured in terms of latency or accuracy) is different when a target and distractor set are inverted for example, searching for a Q among many O’s is easier than searching for an O among Q’s. Doing this is important because accounts of known asymmetries are largely ad-hoc, specific to individual cases, and lack computational specificity. To do this, we would first have to evaluate the performance of an artificial neural network on different forms of tasks involving visual search and then juxtapose it with the performance of humans on the same task. The research presented in this thesis began with a purpose
Implicit Scene Segmentation in Deeper Convolutional Neural Networks

Feedforward deep convolutional neural networks (DCNNs) are matching and even surpassing human performance on object recognition. This performance suggests that activation of a loose collection of image features could support the recognition of natural object categories, without dedicated systems to solve specific visual subtasks. Recent findings in humans however, suggest that while feedforward activity may suffice for sparse scenes with isolated objects, additional visual operations ('routines') that aid the recognition process (e.g. segmentation or grouping) are needed for more complex scenes. Linking human visual processing to performance of DCNNs with increasing depth, we here explored if, how, and when object information is differentiated from the backgrounds they appear on. To this end, we controlled the information in both objects and backgrounds, as well as the relationship between them by adding noise, manipulating background congruence and systematically occluding parts of the image. Results indicated less distinction between objectand background features for more shallow networks. For those networks, we observed a benefit of training on segmented objects (as compared to unsegmented objects). Overall, deeper networks trained on natural (unsegmented) scenes seem to perform implicit 'segmentation' of the objects from their background, possibly by improved selection of relevant features.
Encoding Efficiency and Computational Cost Assessment of State-Of-The-Art Point Cloud Codecs

Point clouds have recently emerged as a suitable solution to generate and display 3D digital models due to their capacity of representing high resolution images and videos through multiple viewpoints. However, as they are usually made up of thousands up to billions of points, advanced techniques of data compression are essential to store and transmit this type of data. This paper compares the two state-of-the-art solutions for point cloud compression, the Point Cloud Codec (PCC) and the Test Model Category 2 (TMC2), in terms of compression efficiency and encoding time. Experimental results show that the compression efficiency for geometry information is highly dependent upon the available bitrate for both TMC2 and PCC. However, for texture compression TMC2 almost always achieves the best results. The experiments have also shown that TMC2 presents a computational cost from 22.2 to 26 times larger the observed in PCC.
OCT Volumetric Data Restoration with Latent Distribution of Refractive Index

This work proposes a novel restoration model for optical coherence tomography (OCT) data. The authors have been developing a multi-frequency swept (MS) en-face OCT device that can help understand the mechanism of the sensory epithelium in the cochlear. Although the device has merit in acquiring moving tissues, the broadened light gives a weak response; thus, some signal restorations are demanded. This work proposes the introduction of a formulation for OCT data restoration as a convex optimization problem by assuming a latent refractive index distribution. An algorithm to solve the problem with the primal-dual splitting (PDS) framework is then derived. The PDS has an advantage of requiring no inverse matrix operation and being able to handle high-dimensional data. The significance of the proposed model is verified by simulations on artificial data, followed by an experiment with the actual observation of 256 256 2000 voxels.
Library services to an aging population: A nation-wide study in the United States

This study presents a large-scale study of public library services to older adults in the United States. A random sampling method was used to identify public libraries (n=226) for the study. Results suggest that libraries serve their aging communities in multiple ways. Some libraries provide a plethora of specialized programs focused on the specific needs of older adults. Others extend core library services to ensure they are accessible to older adults. Others invest in infrastructure and staff development to prepare for an aging society. Some do not provide any specialized programs or services for older adults. There is great unevenness in terms of library services for older adults across the nation. The discussion suggests additional work needed to better understand this unevenness, and to address it.