Miracle-dz/UniFAR-FTUs huggingface dataset:
Unsupervised machine learning for network-centric anomaly detection in IoT

Industry 4.0 holds the promise of greater automation and productivity but also introduces new security risks to critical industrial control systems from unsecured devices and machines. Networks need to play a larger role in stopping attacks before they disrupt essential infrastructure as host-centric IT security solutions, such as anti-virus and software patching, have been ineffective in preventing IoT devices from getting compromised. We propose a network-centric, behavior-learning based, anomaly detection approach for securing such vulnerable environments. We demonstrate that the predictability of TCP traffic from IoT devices can be exploited to detect different types of DDoS attacks in real-time, using unsupervised machine learning (ML). From a small set of features, our ML classifier can separate normal and anomalous traffic. Our approach can be incorporated in a larger system for identifying compromised end-points despite IP spoofing, thus allowing the use of SDN-based mechanisms for blocking attack traffic close to the source. Compared to supervised ML methods, our unsupervised ML approaches are easier to instrument and are more effective in detecting new and unseen attacks.
Suitability of Blockchains to Enable and Support Networking Functions: State of Art

The underlying network infrastructure faces challenges from addressing maintenance, security, performance, and scalability to make the network more reliable and stable. Software-defined networking, blockchain, and network function virtualization were proposed and realized to address such issues in both academic and industry wise. This paper analyzes and summarizes works from implementing different categories of blockchains as an element or enabler of network functions to resolve the limitation. Blockchain as a network function has been proposed to give support to the underlying network infrastructure to provide services that have less lag, are more cost-effective, have better performance, guarantee security between participating parties, and protect the privacy of the users. This paper provides a review of recent work that makes use of blockchain to address such networking related challenges and the possible setbacks in the proposal.
VideoPipe: Building Video Stream Processing Pipelines at the Edge

Real-time video processing in the home, with the benefits of low latency and strong privacy guarantees, enables virtual reality (VR) applications, augmented reality (AR) applications and other next-gen interactive applications. However, processing video feeds with computationally expensive machine learning algorithms may be impractical on a single device due to resource limitations. Fortunately, there are ubiquitous underutilized heterogeneous edge devices in the home. In this paper, we propose VideoPipe, a system that bridges the gap and runs flexible video processing pipelines on multiple devices. Towards this end, with inspirations from Function-as-a-Service (FaaS) architecture, we have unified the runtime environments of the edge devices. We do this by introducing modules, which are the basic units of a video processing pipeline and can be executed on any device. With the uniform design of input and output interfaces, we can easily connect any of the edge devices to form a video processing pipeline. Moreover, as some devices support containers, we further design and implement stateless services for more computationally expensive tasks such as object detection, pose detection and image classification. As they are stateless, they can be shared across pipelines and can be scaled easily if necessary. To evaluate the performance of our system, we design and implement a fitness application on three devices connected through Wi-Fi. We also implement a gesture-based Internet of Things (IoT) control application. Experimental results show the the promises of VideoPipe for efficient video analytics on the edge.
UNDERSTANDING AFFECTIVE DYNAMICS OF LEARNING TOWARD A UBIQUITOUS LEARNING SYSTEM

Understanding student learning behaviors is of prime importance for educational research. Many complex factors influence learning processes, but one collective impact of all these factors is how they affect learning and the degree of motivation. In this study, we discuss the current state of human affect detection in education, our proposed affect change model and its implications. This study adopts dataset from ASSISTments online learning platform, which consists of student interaction data, and ground truth labels for affect states coded by Baker Rodrigo Ocumpaugh Monitoring Protocol (BROMP) certified coders to develop and validate the affect change model. We show that the proposed affect change model in combination with the adoption of machine learning algorithms will support the development of a ubiquitous learning system that tracks the student learning process within the context of contributing factors and provide interventions when needed.
A Pipeline Computing Method of SpTV for Three-Order Tensors on CPU and GPU

Tensors have drawn a growing attention in many applications, such as physics, engineering science, social networks, recommended systems. Tensor decomposition is the key to explore the inherent intrinsic data relationship of tensor. There are many sparse tensor and vector multiplications (SpTV) in tensor decomposition. We analyze a variety of storage formats of sparse tensors and develop a piecewise compression strategy to improve the storage efficiency of large sparse tensors. This compression strategy can avoid storing a large number of empty slices and empty fibers in sparse tensors, and thus the storage space is significantly reduced. A parallel algorithm for the SpTV based on the high-order compressed format based on slices is designed to greatly improve its computing performance on graphics processing unit. Each tensor is cut into multiple slices to form a series of sparse matrix and vector multiplications, which form the pipelined parallelism. The transmission time of the slices can be hidden through pipelined parallel to further optimize the performance of the SpTV.
The growth of brightest cluster galaxies and intracluster light over the past 10 billion years

We constrain the evolution of the brightest cluster galaxy plus intracluster light (BCG + ICL) using an ensemble of 42 galaxy groups and clusters that span redshifts of z = 0.05−1.75 and masses of M500,c= 2 × 1013−1015 M⊙. Specifically, we measure the relationship between the BCG + ICL stellar mass M⋆ and M500,c at projected radii 10 < r < 100 kpc for three different epochs. At intermediate redshift ($\bar{z}=0.40$), where we have the best data, we find M⋆ ∝ M500,c0.48 ± 0.06. Fixing the exponent of this power law for all redshifts, we constrain the normalization of this relation to be 2.08 ± 0.21 times higher at $\bar{z}=0.40$ than at high redshift ($\bar{z}=1.55$). We find no change in the relation from intermediate to low redshift ($\bar{z}=0.10$). In other words, for fixed M500,c, M⋆ at 10 < r < 100 kpc increases from $\bar{z}=1.55$ to $\bar{z}=0.40$ and not significantly thereafter. Theoretical models predict that the physical mass growth of the cluster from z = 1.5 to z = 0 within r500,c is 1.4×, excluding evolution due to definition of r500,c. We find that M⋆ within the central 100 kpc increases by ∼3.8× over the same period. Thus, the growth of M⋆ in this central region is more than a factor of 2 greater than the physical mass growth of the cluster as a whole. Furthermore, the concentration of the BCG + ICL stellar mass, defined by the ratio of stellar mass within 10 kpc to the total stellar mass within 100 kpc, decreases with increasing M500,c at all z. We interpret this result as evidence for inside–out growth of the BCG + ICL over the past 10 Gyr, with stellar mass assembly occurring at larger radii at later times.
Three-dimensional cell culture model for hepatocytes opens a new avenue of real world research on liver

3-demensional (3D) culture model is a valuable in vitro tool to study liver biology, metabolism, organogenesis, tissue morphology, drug discovery and cell-based assays. Compelling evidence suggests that cells cultured in 3D model exhibit superior liver-specific functions over the conventional 2-dimentional (2D) culture in evaluating hepatobiliary drug disposition and drug-induced hepatotoxicity due to the in vivo-like physiological condition recapitulated by 3D model technologies. We will review the attributes of 3D culture model in acquiring relevant liver phenotypes and functionalities, discuss the critical niche factors found to modulate hepatocytes and highlight recent advancements on 3D cell culture technologies to achieve next-level in vitro tool for preclinical study.
Defending with Shared Resources on a Network

In this paper we consider a defending problem on a network. In the model, the defender holds a total defending resource of R, which can be distributed to the nodes of the network. The defending resource allocated to a node can be shared by its neighbors. There is a weight associated with every edge that represents the efficiency defending resources are shared between neighboring nodes. We consider the setting when each attack can affect not only the target node, but its neighbors as well. Assuming that nodes in the network have different treasures to defend and different defending requirements, the defender aims at allocating the defending resource to the nodes to minimize the loss due to attack. We give polynomial time exact algorithms for two important special cases of the network defending problem. For the case when an attack can only affect the target node, we present an LP-based exact algorithm. For the case when defending resources cannot be shared, we present a max-flow-based exact algorithm. We show that the general problem is NP-hard, and we give a 2-approximation algorithm based on LP-rounding. Moreover, by giving a matching lower bound of 2 on the integrality gap on the LP relaxation, we show that our rounding is tight.
Analysis of Generalized Expectation-Maximization for Gaussian Mixture Models: A Control Systems Perspective.

The Expectation-Maximization (EM) algorithm is one of the most popular methods used to solve the problem of parametric distribution-based clustering in unsupervised learning. In this paper, we propose to analyze a subclass of generalized EM (GEM) algorithms in the context of Gaussian mixture models, where the maximization step in the EM is replaced by an increasing step. We show that this subclass of GEM algorithms can be understood as a linear time-invariant (LTI) system with a feedback nonlinearity. Therefore, we explore some of its convergence properties by leveraging tools from robust control theory. Lastly, we explain how the proposed GEM can be designed, and present a pedagogical example to understand the advantages of the proposed approach.
Adaptive Routing Between Capsules

Capsule network is the most recent exciting advancement in the deep learning field and represents positional information by stacking features into vectors. The dynamic routing algorithm is used in the capsule network, however, there are some disadvantages such as the inability to stack multiple layers and a large amount of computation. In this paper, we propose an adaptive routing algorithm that can solve the problems mentioned above. First, the low-layer capsules adaptively adjust their direction and length in the routing algorithm and removing the influence of the coupling coefficient on the gradient propagation, so that the network can work when stacked in multiple layers. Then, the iterative process of routing is simplified to reduce the amount of computation and we introduce the gradient coefficient $\lambda$. Further, we tested the performance of our proposed adaptive routing algorithm on CIFAR10, Fashion-MNIST, SVHN and MNIST, while achieving better results than the dynamic routing algorithm.
Python vs. R: A Text Mining Approach for analyzing the Research Trends in Scopus Database

In the contemporary world, with the incubation of advanced technologies and tremendous outbursts of research works, analyzing big data to incorporate research strategies becomes more helpful using the tools and techniques presented in the current research scenario. This paper indeed tries to tackle the most prominent challenges relating to big data analysis by utilizing a text mining approach to analyze research data published in the field of production management as a case to begin with. The study has been conducted by considering research data of International Journal of Production Research (IJPR) indexed in Scopus between 1961-2017 by dividing the analysis incurred into 3 fragments being 1961-1990, 1991-2010 and finally 2011-2017 as a case to highlight the focus of journal. This has indeed provided multi-faceted benefits such as increasing the effectiveness of the procured data with well-established comparisons between R and Python Programming along with providing detailed research trends on the research work incubated. The results of the study highlighted some most prominent topics in the existing IJPR literature such as system's optimization, supplier selection, process design, etc. providing well-established details relating to ongoing research works. The study also compared both languages suiting to a particular field of study for better comprehension and vastness of the research topics. The current research work is one of the part of a copyright work with registration number SW-10310/2018 titled Program for Analyzing Key Trends in Research Data-set. It has been designed in Python for carrying out detailed content analysis based on the available research database in bib format as in the current context it has been applied for IJPR journal and can be replicated on articles of any domain found using keyword search.
Low‐rank tensor completion with sparse regularization in a transformed domain

Tensor completion is a challenging problem with various applications, especially in recovering incomplete visual data. Considering a color image or gray video as a three‐dimensional tensor, many related models based on the low‐rank prior of the tensor have been proposed. However, the low‐rank prior may not be enough to recover the original tensor from the observed incomplete tensor. In this paper, we propose a tensor completion method to recover color images and gray videos by exploiting both the low‐rank and sparse prior of the observed tensor. Specifically, the tensor completion task can be formulated as a low‐rank minimization problem with a sparse regularizer. The low‐rank property is depicted by the tensor truncated nuclear norm based on tensor singular value decomposition which is a better approximation of tensor tubal rank than tensor nuclear norm. While the sparse regularizer is imposed by a ℓ1‐norm in a discrete cosine transformation domain, which can better employ the local sparse property of the incomplete data. To solve the optimization problem, we employ an alternating direction method of multipliers in which we only need to solve several subproblems which have closed‐form solutions. Substantial experiments on real‐world images and videos show that the proposed method has better performances than the existing state‐of‐the‐art methods.
The AI Liability Puzzle and a Fund-Based Work-Around

Certainty around the regulatory environment is crucial to facilitate responsible AI innovation and its social acceptance. However, the existing legal liability system is inapt to assign responsibility where a potentially harmful conduct and/or the harm itself are unforeseeable, yet some instantiations of AI and/or the harms they may trigger are not foreseeable in the legal sense. The unpredictability of how courts would handle such cases makes the risks involved in the investment and use of AI incalculable, creating an environment that is not conducive to innovation and may deprive society of some benefits AI could provide. To tackle this problem, we propose to draw insights from financial regulatory best-practices and establish a system of AI guarantee schemes. We envisage the system to form part of the broader market-structuring regulatory framework, with the primary function to provide a readily available, clear, and transparent funding mechanism to compensate claims that are either extremely hard or impossible to realize via conventional litigation. We propose at least partial industry-funding, with funding arrangements depending on whether it would pursue other potential policy goals.
Dual affine moment invariants

Affine transformation is one of the most common transformations in nature, which is an important issue in the field of computer vision and shape analysis. And affine transformations often occur in both shape and color space simultaneously, which can be termed as Dual-Affine Transformation (DAT). In general, we should derive invariants of different data formats separately, such as 2D color images, 3D color objects, or even higher-dimensional data. To the best of our knowledge, there is no general framework to derive invariants for all of these data formats. In this paper, we propose a general framework to derive moment invariants under DAT for objects in M-dimensional space with N channels, which can be called dual-affine moment invariants (DAMI). Following this framework, we present the generating formula of DAMI under DAT for 3D color objects. Then, we instantiated a complete set of DAMI for 3D color objects with orders and degrees no greater than 4. Finally, we analyze the characteristic of these DAMI and conduct classification experiments to evaluate the stability and discriminability of them. The results prove that DAMI is robust for DAT. Our derivation framework can be applied to data in any dimension with any number of channels.
Multiple Face Analyses through Adversarial Learning

This inherent relations among multiple face analysis tasks, such as landmark detection, head pose estimation, gender recognition and face attribute estimation are crucial to boost the performance of each task, but have not been thoroughly explored since typically these multiple face analysis tasks are handled as separate tasks. In this paper, we propose a novel deep multi-task adversarial learning method to localize facial landmark, estimate head pose and recognize gender jointly or estimate multiple face attributes simultaneously through exploring their dependencies from both image representation-level and label-level. Specifically, the proposed method consists of a deep recognition network R and a discriminator D. The deep recognition network is used to learn the shared middle-level image representation and conducts multiple face analysis tasks simultaneously. Through multi-task learning mechanism, the recognition network explores the dependencies among multiple face analysis tasks, such as facial landmark localization, head pose estimation, gender recognition and face attribute estimation from image representation-level. The discriminator is introduced to enforce the distribution of the multiple face analysis tasks to converge to that inherent in the ground-truth labels. During training, the recognizer tries to confuse the discriminator, while the discriminator competes with the recognizer through distinguishing the predicted label combination from the ground-truth one. Though adversarial learning, we explore the dependencies among multiple face analysis tasks from label-level. Experimental results on four benchmark databases, i.e., the AFLW database, the Multi-PIE database, the CelebA database and the LFWA database, demonstrate the effectiveness of the proposed method for multiple face analyses.
A Multicriteria Macroeconomic Model with Intertemporal Equity and Spatial Spillovers

We analyze a macroeconomic model with intergenerational equity considerations and spatial spillovers, which gives rise to a multicriteria optimization problem. Intergenerational equity requires to add in the definition of social welfare a long run sustainability criterion to the traditional discounted utilitarian criterion. The spatial structure allows for the possibility of heterogeneiity and spatial diffusion implies that all locations within the spatial domain are interconnected via spatial spillovers. We rely on different techniques (scalarization, $\epsilon$-constraint method and goal programming) to analyze such a spatial multicriteria problem, relying on numerical approaches to illustrate the nature of the trade-off between the discounted utilitarian and the sustainability criteria.
Comparison of Deep learning models on time series forecasting : a case study of Dissolved Oxygen Prediction

Deep learning has achieved impressive prediction performance in the field of sequence learning recently. Dissolved oxygen prediction, as a kind of time-series forecasting, is suitable for this technique. Although many researchers have developed hybrid models or variant models based on deep learning techniques, there is no comprehensive and sound comparison among the deep learning models in this field currently. Plus, most previous studies focused on one-step forecasting by using a small data set. As the convenient access to high-frequency data, this paper compares multi-step deep learning forecasting by using walk-forward validation. Specifically, we test Convolutional Neural Network (CNN), Temporal Convolutional Network (TCN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Recurrent Neural Network (BiRNN) based on the real-time data recorded automatically at a fixed observation point in the Yangtze River from 2012 to 2016. By comparing the average accumulated statistical metrics of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination in each time step, We find for multi-step time series forecasting, the average performance of each time step does not decrease linearly. GRU outperforms other models with significant advantages.
Simultaneous Mapping and Target Driven Navigation

This work presents a modular architecture for simultaneous mapping and target driven navigation in indoors environments. The semantic and appearance stored in 2.5D map is distilled from RGB images, semantic segmentation and outputs of object detectors by convolutional neural networks. Given this representation, the mapping module learns to localize the agent and register consecutive observations in the map. The navigation task is then formulated as a problem of learning a policy for reaching semantic targets using current observations and the up-to-date map. We demonstrate that the use of semantic information improves localization accuracy and the ability of storing spatial semantic map aids the target driven navigation policy. The two modules are evaluated separately and jointly on Active Vision Dataset and Matterport3D environments, demonstrating improved performance on both localization and navigation tasks.
SimVAE: Simulator-Assisted Training forInterpretable Generative Models

This paper presents a simulator-assisted training method (SimVAE) for variational autoencoders (VAE) that leads to a disentangled and interpretable latent space. Training SimVAE is a two-step process in which first a deep generator network(decoder) is trained to approximate the simulator. During this step, the simulator acts as the data source or as a teacher network. Then an inference network (encoder)is trained to invert the decoder. As such, upon complete training, the encoder represents an approximately inverted simulator. By decoupling the training of the encoder and decoder we bypass some of the difficulties that arise in training generative models such as VAEs and generative adversarial networks (GANs). We show applications of our approach in a variety of domains such as circuit design, graphics de-rendering and other natural science problems that involve inference via simulation.