Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart Contracts and Their Identity Crisis

Many expect Smart Contracts (SC’s) to disrupt the way contracts are done implying that SC have the potential to affect all commercial relationships. SC’s are automatization tools; therefore, proponents claim that SC’s can reduce transaction costs through disintermediation and risk reduction. This is an over-simplification of the role of relationships, contract law, and risk. We believe there is a gap in the understanding of the capabilities of SC’s. With that in mind we seek to define an amorphous term and clarify the capabilities of SC’s, intending to facilitate future SC research. We’ve examined the legal, technical, and IS views from an academic and practitioner’s perspective. We conclude that SC’s have taken many forms, becoming a suitcase word for any sort of code stored on a blockchain, including the embodiment of contractual terms; and that the immutable nature of SC’s is a barrier to their adoption in uncertain and multi-contextual environments.
Multi-Kernel Supervised Hashing with Graph Regularization for Cross-Modal Retrieval

Hashing based approximate nearest neighbor search has received considerable attention due to the demand of fast query for big multimedia data. Cross-modal hashing focuses on retrieval tasks across different modalities, which is more useful in practical applications. In this paper, we propose a novel two-stage cross-modal hashing method, referred to as Multi-Kernel Supervised Hashing with Graph Regularization (MKSRH). To better capture the essential attribute of original data, MKSRH first maps original data to a kernel space constructed by a linear combination of multiple kernel functions. Then the preliminary hash functions are learned using the Adaboost framework in the kernel space. To produce more accurate hash codes, the obtained hash function are then refined using a graph regularization based strategy. Experimental results on two canonical datasets show that MKSRH significantly outperforms than some typical cross-modal hashing methods, demonstrating the effectiveness and the superiority of the proposed approach.
Learning Graph Distances with Message Passing Neural Networks

Graph representations have been widely used in pattern recognition thanks to their powerful representation formalism and rich theoretical background. A number of error-tolerant graph matching algorithms such as graph edit distance have been proposed for computing a distance between two labelled graphs. However, they typically suffer from a high computational complexity, which makes it difficult to apply these matching algorithms in a real scenario. In this paper, we propose an efficient graph distance based on the emerging field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure and learns a metric with a siamese network approach. The performance of the proposed graph distance is validated in two application cases, graph classification and graph retrieval of handwritten words, and shows a promising performance when compared with (approximate) graph edit distance benchmarks.
Beyond Two-stream: Skeleton-based Three-stream Networks for Action Recognition in Videos

Two-stream architecture, trained on stacked optical flows and image frames, has demonstrated excellent performance for human action recognition in videos and served as the basis of many advanced architectures. To improve performance, we propose a novel architecture of three-stream networks based on skeletons, extending two-stream architecture while retaining the input as video only. Recently, it has become possible to successfully detect skeletons from videos, which contain high-level joint-motion information and are complementary to low-level optical flows and image frames. However, skeleton data lack object information (e.g. a book) involved in the action, which is essential to distinguish such actions as reading, writing, and typing. Therefore, we fuse the complementary information on skeleton data, optical flows, and image frames in our three-stream networks. Furthermore, we crop the human and object region using skeleton data for sparse spatial sampling. In addition, we calculate adaptive weights when fusing the three streams to further improve performance. The experimental results demonstrate that the proposed three-stream networks outperform the state-of-the-art techniques on the NTU RGB+G dataset, significantly improving the accuracy from 80.8% to 86.4%.
Multiple-Mode Interference and Phase Standard Deviation of VLF Wave Propagation in an Anisotropic Earth–Ionosphere Waveguide

In this paper, the multiple-mode interference (MMI) and phase standard deviation of very low frequency (VLF) wave propagation in an anisotropic earth–ionosphere waveguide are treated analytically. The phase stability of a VLF wave is dependent on the characteristics of each propagable mode, such as the excitation factor, the attenuation rate, the phase velocity, as well as their derivatives with respect to the effective ionosphere height. Computations show that the phase stability in the multiple-mode zone is much poorer than that in the single-mode zone, and the phase standard deviations evaluated considering the ionospheric anisotropy are more consistent with the measured data. It is also found that there exist pronounced directional differences in the interference distributions, and the effect of MMI is weakened as the geomagnetic inclination angle increases. By comparison of the phase standard deviation and the field strength, we find that the maxima of the phase standard deviation coincide with the minima of the field strength. Moreover, the MMI zone is much broader in nighttime than in daytime, and in an anisotropic waveguide, the interference will present evident aggregation at certain heights.
Data-Driven State-Increment Statistical Model and Its Application in Autonomous Driving

The aim of trajectory planning is to generate a feasible, collision-free trajectory to guide an autonomous vehicle from the initial state to the goal state safely. However, it is difficult to guarantee that the trajectory is feasible for the vehicle and the real path of the vehicle is collision-free when the vehicle follows the trajectory. In this paper, a state-increment statistical model (SISM) is proposed to describe the kinodynamic constraints of a vehicle by modeling the controller, the actuator, and the vehicle model jointly. The SISM consists of Gaussian distributions of lateral error increments in all state subspaces which are composed of the curvature radius, the velocity, and the lateral error. It is a data-driven modeling approach that can improve the SISM via increasing the number of samples of the increment-state, which is composed of the state and its corresponding increment of the lateral error. According to the SISM, the experience cost functions are designed to evaluate the trajectories for searching the best one with the lowest cost, and the real path can be predicted directly according to the planned trajectory and the vehicle state. The predicted path can be utilized effectually to evaluate the safety of the vehicle motion.
Riemannian Metric Learning based on Curvature Flow

In machine learning, a high dimensional dataset such as the digital image of human face is often considered as a point set distributed on a (differentiable) manifold. In many cases the intrinsic dimension of this manifold is highly lower than the representation dimension. In order to ease data processing, manifold-based dimension reduction algorithms were put forward since this century. The real purpose of manifold learning (MAL) is to learn a suitable metric in the low dimensional space. One main limitation of the existing MAL algorithms is that they all do not consider the intrinsic curvature of the embedded manifold, which means that the intrinsic geodesic distance cannot be uncovered by these algorithms. The intrinsic geodesic distance on the manifold is measured by Riemannian metric which is highly affected by Riemannian curvature. With this idea in mind, our work proposes to formulate a new algorithm by adding the curvature information to metric learning. We study a new curvature flow from input dataset. By employing this curvature flow, we obtain a Mahalanobis metric which can better uncover the intrinsic structure of the embedded manifold. To show the effectiveness of our proposed method, we compare our algorithm with several traditional MAL algorithms.
A hard x-ray photoemission study of transparent conducting fluorine-doped tin dioxide

Fluorine-doped tin oxide (FTO) is a commercially successful transparent conducting oxide with very good electrical (resistivities < 1 $\times$103 $\Omega{\cdot}$ cm) and optical properties (transmittance >85%). These properties coupled with cheap and large-scale deposition on float-glass lines means FTO has found commercial use in, for example, low emissivity windows and solar cells. However, despite its widespread application, a detailed understanding is lacking of the doping and defects in FTO. Recent work [1] has suggested that the fluorine interstitial plays a major role in limiting the conductivity of FTO. Here we present synchrotron radiation high energy x-ray photoemission spectroscopy (XPS) of the fluorine 1s core level of FTO films without in situ surface preparation. This probes deeper than standard XPS and shows that the fluorine interstitial is present not just at the surface of the films and is not an artefact of argon ion sputtering for surface preparation.
Physics Inspired Methods for Crowd Video Surveillance and Analysis: A Survey

Crowd analysis is very important for human behavior analysis, safety science, computational simulation, and computer vision applications. One of the most popular applications is video surveillance that plays an important role in crowd behavior analysis including real-time crowd behavior detection and information retrieval. In the field of video surveillance, many kinds of methods have been proposed for analyzing crowds, such as machine learning, signal processing, and physical model-based methods. As a kind of collective movements, crowd behavior contains many physical attributes, such as velocity, direction of motion, interaction force, and energy. Therefore, a lot of methods and models derived from physical ideas have been applied in many frameworks of crowd behavior analysis. This survey reviews the development of physical methods of crowd analysis in detail. The physics-inspired methods in crowd video analysis are summarized into three categories including fluid dynamics, interaction force, and complex crowd motion systems. Furthermore, the existing public databases for crowd video analysis are collated in this paper. Finally, the future research directions of the open issues of crowd video surveillance are also discussed.
A Novel Framework for Online Supervised Learning with Feature Selection

Current online learning methods suffer issues such as lower convergence rates and limited capability to recover the support of the true features compared to their offline counterparts. In this paper, we present a novel framework for online learning based on running averages and introduce a series of online versions of some popular existing offline methods such as Elastic Net, Minimax Concave Penalty and Feature Selection with Annealing. The framework can handle an arbitrarily large number of observations with the restriction that the data dimension is not too large, e.g. p<50,000. We prove the equivalence between our online methods and their offline counterparts and give theoretical true feature recovery and convergence guarantees for some of them. In contrast to the existing online methods, the proposed methods can extract models with any desired sparsity level at any time. Numerical experiments indicate that our new methods enjoy high accuracy of true feature recovery and a fast convergence rate, compared with standard online and offline algorithms. We also show how the running averages framework can be used for model adaptation in the presence of model drift. Finally, we present some applications to large datasets where again the proposed framework shows competitive results compared to popular online and offline algorithms.
Prediction of Cardiovascular Disease Using Machine Learning Algorithms

Healthcare is an inevitable task to be done in human life. Cardiovascular disease is a broad category for a range of diseases that are affecting heart and blood vessels. The early methods of forecasting the cardiovascular diseases helped in making decisions about the changes to have occurred in high-risk patients which resulted in the reduction of their risks. The health care industry contains lots of medical data, therefore machine learning algorithms are required to make decisions effectively in the prediction of heart diseases. Recent research has delved into uniting these techniques to provide hybrid machine learning algorithms. In the proposed research, data pre-processing uses techniques like the removal of noisy data, removal of missing data, filling default values if applicable and classification of attributes for prediction and decision making at different levels. The performance of the diagnosis model is obtained by using methods like classification, accuracy, sensitivity and specificity analysis. This project proposes a prediction model to predict whether a people have a heart disease or not and to provide an awareness or diagnosis on that. This is done by comparing the accuracies of applying rules to the individual results of Support Vector Machine, Gradient Boosting, Random forest, Naive Bayes classifier and logistic regression on the dataset taken in a region to present an accurate model of predicting cardiovascular disease.
Development of Data Acquisition System using non-invasive Hardware and 3D software for Electric all terrain Vehicle

As today's modern vehicles are equipped with tons of electronics, data acquisition becomes easy as the necessary data regarding the vehicle is already available in the ECUs. The objective of this paper is to build a data acquisition system for a vehicle. The system is a part of Electric All-terrain Car developed to participate in SAE India BAJA 2018. The DAQ is designed using non invasive hardware and 3D graphic user interface in Unity 3D game engine. The proposed DAQ system aims at acquiring, interpreting, visualizing and analyzing the Suspension Travel, Inclination, Current Consumption and SoC, GPS location of an electric all-terrain vehicle. A low cost Ultrasonic Reflector sensor is used to measure the suspension travel.
Fast Skin Lesion Segmentation via Fully Convolutional Network with Residual Architecture and CRF

Melanoma is known to be the most fatal form of skin cancers. In order to achieve automated diagnosis of such disease, a system is needed to accurately locate suspicious skin lesions using images captured by standard digital cameras. Recently, there exists a trend for the use of Fully Convolutional Net-work(FCN) to perform image segmentation task. In this paper, we propose a FCN-based processing pipeline that incorporates a deep neural net and a graphical model, to attain a segmentation mask of lesion region from normal skin. Our method extends the residual network by adding a transposed convolution layer to yield a FCN architecture. We demonstrate that the noisy outcome from FCN can be refined by a fully connected Conditional Random Field(CRF). Our model enjoys three major advantages over existing algorithms: simpler process pipeline, state-of-art accuracy in terms of segmentation sensitivity(95.6%) and fast inference time.
Integrated Shield Edge Diffraction Model for Narrow Obstructing Objects

A new simple and accurate model defined as shield edge diffraction is derived and validated suitable for frequencies above 10 GHz diffracting around obstructions that are narrow compared to the Fresnel zone width. The model includes new simple Fresnel diffraction parameters similar to those used with traditional knife edge diffraction, which can in the same way be integrated into deterministic and empirical path loss models. The capability of the model extends beyond current single and double knife edge models whereby it includes the effects of the antennas’ far-field distances as well as their gain and phase patterns, which subsequently have a severe effect on the diffraction loss in short-range links. The models are validated using both the anechoic chamber as well as real environment-based measurements at 10–12 and 26 GHz.
Zone2Vec: Distributed Representation Learning of Urban Zones

A metropolis consists of zones segmented by major roads. People travel between zones to conduct social activities. To analyze the characteristics of the entire city, we can explore regions' features and find region-wise latent relationships. In this paper, we propose a semantic associated zone embedding (SAZE) method using distributed representation learning. SAZE can generate zone embeddings which extract more comprehensive characteristics of each zone and fit for many urban computing tasks, rather than the task-oriented methods. To feed our SAZE, we not only consider the connections between zones via trajectory, but also embed its intrinsic properties. Furthermore, we apply SAZE to two tasks, zone classification and zone clustering visualization, respectively. For each task, we compare SAZE with other state-of-the-art baseline methods and the results have demonstrated the advantage of our model over the several methods.
Stochastic Training of Residual Networks: a Differential Equation Viewpoint

During the last few years, significant attention has been paid to the stochastic training of artificial neural networks, which is known as an effective regularization approach that helps improve the generalization capability of trained models. In this work, the method of modified equations is applied to show that the residual network and its variants with noise injection can be regarded as weak approximations of stochastic differential equations. Such observations enable us to bridge the stochastic training processes with the optimal control of backward Kolmogorov's equations. This not only offers a novel perspective on the effects of regularization from the loss landscape viewpoint but also sheds light on the design of more reliable and efficient stochastic training strategies. As an example, we propose a new way to utilize Bernoulli dropout within the plain residual network architecture and conduct experiments on a real-world image classification task to substantiate our theoretical findings.
Factors Inhibiting the Adoption of Cloud Community Systems in Dynamic B2B Networks: The Case of Air Cargo

While factors inhibiting information system (IS) adoption in the individual and organizational context are thoroughly investigated, research on the inter-organizational perspective remains widely disregarded. Recent studies reveal that cloud community systems (CCS) provide numerous benefits for organizations. However, in dynamic B2B networks high numbers of CCS fail and thus additional inhibitors of CCS adoption can be expected. We selected the air cargo hub in Frankfurt, Germany as a suitable research case because of existing dynamics and an underutilized CCS. By using a grounded theory approach and the technological, organizational and environmental (TOE) framework, we explored 21 factors inhibiting CCS adoption in dynamic B2B networks and categorized these into the TOE framework. Eight factors confirm existing literature. Caused by the specificities of dynamic B2B networks, we propose a re-categorization of six factors and a necessary extension of the TOE framework by the inter-organizational context (I) for the remaining seven new factors.
Automated Approach for DDOS Attacks Detection Based on Naive Bayes Multinomial Classifier

Now days use of internet for the getting and sharing of knowledge is very common. The end users those are accessing internet or system are vulnerable to malicious user attacks which results into legitimate user prevented from accessing the websites. Recently there are several of methods presented for application layer DDoS attacks by considering the different properties of attacks. However most of methods are suffered from the poor accuracy performance of DDoS attack detection at application layer. Hence DDoS attacks has been low volume & act own as a legitimate transaction on layer seven means application layer hence such attacks are not detected easily by IDS (Intrusion Detection Systems) or firewall systems. We believe that, the accuracy and efficiency of attacks detection is based on correctness of capture data traffic. In state-of-art methods, there is no provision to remove the noisy data from the capture logs and hence leads to incorrect detection results. In this paper we presented the real time computer networks data capturing for normal as well as attack infected traffics, then design the preprocessing algorithm to remove irrelevant data to optimize the attack detection performance which helps to naïve bayes algorithm to classification. In this architecture, we use LOIC doss attack generator tool to create attack at packet capturing time of communication network. The experimental results show that proposed data pre-processing method with naïve bayes multinomial is easy and efficient as compared to state-of-art solutions. To classify normal packets and DDoS attack we use naïve bayes multinomial classifier
Stroke Patients’ Acceptance of a Smart Garment for Supporting Upper Extremity Rehabilitation

The objective is to evaluate to which extent that Zishi a garment equipped with sensors that can support posture monitoring can be used in upper extremity rehabilitation training of stroke patients. Seventeen stroke survivors (mean age: 55 years old, SD =13.5) were recruited in three hospitals in Shanghai. Patients performed 4 tasks (analytical shoulder flexion, functional shoulder flexion placing a cooking pot, analytical flexion in the scapular plane, and functional flexion in the scapular plane placing a bottle of water) with guided feedback on a tablet that was provided through inertial sensors embedded in the Zishi system at the scapula and the thoracic spine region. After performing the training tasks, patients completed four questionnaires for assessing their motivation, their acceptance of the system, its credibility, and usability. The study participants were highly motivated to train with Zishi and the system was rated high usability, while the subjects had moderate confidence with technology supported training in comparison with the training with therapists. The patients respond positively to using Zishi to support rehabilitation training in a clinical setting. Further developments need to address more on engaging and adaptive feedback. This paper paves the way for larger scale effectiveness studies.
Multi-scale Recurrent Encoder-Decoder Network for Dense Temporal Classification

The temporal events in video sequences often have long-term dependencies which are difficult to be handled by a convolutional neural network (CNN). Especially, the dense pixel-wise prediction of video frames is a difficult problem for the CNN because huge memories and a large number of parameters are needed to learn the temporal correlation. To overcome these difficulties, we propose a recurrent encoder-decoder network which compresses the spatiotemporal features at the encoder and restores them to the original sized results at the decoder. We adopt a convolutional long short-term memory (LSTM) into the encoder-decoder architecture, which successfully learns the spatiotemporal relation with relatively a small number of parameters. The proposed network is applied to one of the dense pixel-prediction problems, specifically, the background subtraction in video sequences. The proposed network is trained with limited duration of video frames, and yet it shows good generalization performance for different videos and time duration. Also, by additional video specific learning, it shows the best performance on a benchmark dataset (CDnet 2014).