Reviewerly/exHarmony huggingface dataset:
Study of Adversarial Machine Learning with Infrared Examples for Surveillance Applications

Adversarial examples are theorized to exist for every type of neural network application. Adversarial examples have been proven to exist in neural networks for visual-spectrum applications and that they are highly transferable between such neural network applications. In this paper, we study the existence of adversarial examples for Infrared neural networks that are applicable to military and surveillance applications. This paper specifically studies the effectiveness of adversarial attacks against neural networks trained on simulated Infrared imagery and the effectiveness of adversarial training. Our research demonstrates the effectiveness of adversarial attacks on neural networks trained on Infrared imagery, something that hasn’t been shown in prior works. Our research shows that an increase in accuracy was shown in both adversarial and unperturbed Infrared images after adversarial training. Adversarial training optimized for the L∞ norm leads to an increase in performance against both adversarial and non-adversarial targets.
The enabling impact of digital technologies on distributed energy resources integration

The future of energy systems is currently driven by three key trends: electrification, decarbonization, and digitalization. A growing share of renewable generation is without doubt part of the solution to accommodate the increasing electricity demand with a low-carbon emission constraint. The question addressed in this review paper is to what extent digitalization will enable the decentralization of renewable energy generation close to the point of consumption. Digital technologies, namely, Big Data, Artificial Intelligence, IoT, and Distributed Ledgers, will have a positive impact on renewable Distributed Energy Resources adoption by contributing to achieve a better balance between supply and demand at the edge of the grid and by improving the hosting capacity of distribution networks. However, decentralized energy production is still limited by structural factors (e.g., low power density of renewable resources, economies of scale in renewable generation and storage), regulatory factors (e.g., access to the grid, pricing policy), and digital technology constraints (e.g., limited number of transaction per second). As a result, the likely scenario of development is a reasonable mix between decentralized and (also digital enabled) centralized renewable-based energy generation interconnected together with an engaged customer and highly developed and smarter distribution and transmission networks.
PCBChain: Lightweight Reconfigurable Blockchain Primitives for Secure IoT Applications

In the era of ubiquitous intelligence, the Internet of Things (IoT) holds the promise as a breakthrough technology to enable diverse applications that benefit societal problems. Yet interconnecting myriad heterogeneous IoT devices across various application domains remain a security challenge. Decentralized technology has recently emerged as a powerful primitive in building distributed applications to facilitate secure transactions between mutually distrustful parties in a trustworthy manner. Unfortunately, these decentralized protocols demand computing resources and power far beyond the reach of resource-constrained IoT devices, preventing the full adoption of distributed consensus platform in the IoT setting. In this article, we address the key bottleneck to enable blockchain in resource-constrained IoT devices. We propose a lightweight implementation of proof-of-work (PoW) mining with reconfigurable hardware primitives. By replacing the hash and cryptographic functions in classic blockchain protocol with secure and efficient hardware implementations, our proposed solution can significantly reduce hardware resources and power overheads of PoW mining, while improving the transaction speed of large-scale IoT systems. Finally, we demonstrate the algorithm by proposing an antispoofing solution for GPS navigation among lightweight IoT devices. As a replacement for position computation, a mining process generates the expected coordinates with the correct initial value and function configuration.
An Optimal Transport Formulation of the Ensemble Kalman Filter

Controlled interacting particle systems such as the ensemble Kalman filter (EnKF) and the feedback particle filter (FPF) are numerical algorithms to approximate the solution of the nonlinear filtering problem in continuous time. The distinguishing feature of these algorithms is that the Bayesian update step is implemented using a feedback control law. It has been noted in the literature that the control law is not unique. This is the main problem addressed in this article. To obtain a unique control law, the filtering problem is formulated here as an optimal transportation problem. An explicit formula for the (mean-field type) optimal control law is derived in the linear Gaussian setting. Comparisons are made with the control laws for different types of EnKF algorithms described in the literature. Via empirical approximation of the mean-field control law, a finite-N controlled interacting particle algorithm is obtained. For this algorithm, the equations for empirical mean and covariance are derived and shown to be identical to the Kalman filter. This allows strong conclusions on convergence and error properties based on the classical filter stability theory for the Kalman filter. It is shown that, under certain technical conditions, the mean squared error converges to zero even with a finite number of particles. A detailed propagation of chaos analysis is carried out for the finite-N algorithm. The analysis is used to prove weak convergence of the empirical distribution as N→ ∞. For a certain simplified filtering problem, analytical comparison of the mse with the importance sampling-based algorithms is described. The analysis helps explain the favorable scaling properties of the control-based algorithms reported in several numerical studies in recent literature.
The MALICIOUS Framework: Embedding Backdoors into Tweakable Block Ciphers

Inserting backdoors in encryption algorithms has long seemed like a very interesting, yet difficult problem. Most attempts have been unsuccessful for symmetric-key primitives so far and it remains an open problem how to build such ciphers. In this work, we propose the MALICIOUS framework, a new method to build tweakable block ciphers that have backdoors hidden which allows to retrieve the secret key. Our backdoor is differential in nature: a specific related-tweak differential path with high probability is hidden during the design phase of the cipher. We explain how any entity knowing the backdoor can practically recover the secret key of a user and we also argue why even knowing the presence of the backdoor and the workings of the cipher will not permit to retrieve the backdoor for an external user. We analyze the security of our construction in the classical black-box model and we show that retrieving the backdoor (the hidden high-probability differential path) is very difficult. We instantiate our framework by proposing the LowMC-M construction, a new family of tweakable block ciphers based on instances of the LowMC cipher, which allow such backdoor embedding. Generating LowMC-M instances is trivial and the LowMC-M family has basically the same efficiency as the LowMC instances it is based on.
Distributed clustering in peer to peer networks using multi-objective whale optimization

Multi-objective clustering algorithms have superiority over its single objective counterparts as they include additional knowledge on properties of data in the form of objectives to discover the underlying clusters present in various datasets. This paper proposes a distributed clustering algorithm using multi-objective whale optimization (DMOWOA) for peer to peer network. The algorithm minimizes two objective functions to perform clustering, namely : Total Euclidean Deviation and Total Symmetrical Deviation. Both objective values are shared using diffusion method of cooperation to obtain correct partitioning at each peer. A single solution from the non-dominated solutions is selected as final solution based on its minimum distance to origin in the normalized objective space. The proposed algorithm’s performance is evaluated on four synthetic and five real-life wireless sensor network datasets (Canada weather station dataset, Delhi air pollution content dataset, Intel laboratory dataset, Washington cook agronomy farm dataset and Thames river water quality dataset). The comparison is carried out with multi-objective distributed particle swarm optimization (DMOPSO), distributed K-Means (DK-Means) and other seven recently developed nature inspired multi-objective clustering techniques. The proposed algorithm in most of the cases outperforms the existing techniques in terms of statistical measures Minkowski Score, Dunn index and Silhouette index. The average rank of the proposed algorithm is also better in Kruskal–Wallis statistical test.
Design of a Sparsity-Aware Reconfigurable Deep Learning Accelerator Supporting Various Types of Operations

The superiority of various Deep Neural Networks (DNN) models, such as Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), and Recurrent Neural Networks (RNN), has been proven in various real-world applications and has received much attention. However, different DNN models include various types of operations. For example, CNN models are usually composed of convolutional layers (Conv) and fully-connected layers (FC). Furthermore, some light CNN models such as MobileNet adopt depthwise separable convolution with depthwise convolution (DWC) and pointwise convolution (PWC) to compress the models. In addition to regular convolution, de-convolution (De-Conv) is also widely used in many GAN models. Moreover, many RNN models also employ long-short-term memory (LSTM) to control update of internal states and data. Such a high diversity of various DNN operations poses great design challenges in implementing reconfigurable Deep Learning (DL) accelerators, which can support various types of DNN operations. Most recent DL accelerators focus only on some DNN operations, which lacks computing flexibility. In this paper, by exploiting the sparsity in current DNN models, we design sparsity-aware DL hardware accelerators that can support efficient computation of various DNN operations, including Conv, DeConv, DWC, PWC, FC, and LSTM. Through reconfiguring dataflow and parallelizing different operations, the proposed designs not only improve system performance but also increase hardware utilization with a significant reduction of power consumption in memory accesses and arithmetic computations.
Building an Authentic and Ethical Keyword Search by applying Decentralised (Blockchain) Verification

As a very appealing computing standard, cloud computing makes it feasible for resource constrained clients to experience rate effective and flexible resources at ease. In the prevailing systems, the conventional cloud storage scheme is bound to operate its primitive functions in centralized mode. This reason possess problems like data availability, information protection and excessive operational charge. With the improvement of block chain generation, the decentralized scheme has entered the general public view. Compared to contemporary and cutting edge cloud storage, they are predicted to be more scalable, secured and comfortable. But, the use of end-to-end encryption in a decentralized scheme, limits the search functionality as the existing searchable encryption methods are designed only for the centralized device. Moreover, the existing decentralized cloud storage doesn't restrict any data user to get access to all of the files for any keyword search made purposefully through them. Thus, in this manner, user integrity of files and keyword searches are compromised throughout the network. Therefore, as a manner to solve these complications, we systematically join techniques of encryption and hashing to furnish reliable keyword search over encrypted files in decentralized storage. Consequently using the block chain system, the architecture remains incorruptible and protects all the confidential files and keywords in a fully controlled file access environment.
Weakly supervised image classification and pointwise localization with graph convolutional networks

In computer vision, the research community has been looking to how to benefit from weakly supervised learning that utilizes easily obtained image-level labels to train neural network models. The existing deep convolutional neural networks for weakly supervised learning, however, generally do not fully exploit the label dependencies in an image. To make full use of this information, in this paper, we propose a new framework for weakly supervised learning of deep convolutional neural networks, introducing graph convolutional networks to capture the semantic label co-occurrence in an image. Moreover, we propose a novel initialization method for label embedding in graph convolutional networks, which enables a smoother optimization for interrelationships learning. Extensive experiments and comparisons on four public benchmark datasets (PASCAL VOC 2007, PASCAL VOC 2012, Microsoft COCO, and NUS-WIDE) show the superior performance of our approach in both image classification and weakly supervised pointwise object localization. These results lead us to conclude that the label dependencies in the input image can provide valuable evidence for learning strongly localized features.
Encryption algorithm in cloud computing

The exponential growth of cloud computing, an efficient data security system becomes essential for cloud computing-based environments. Encryption algorithms perform a critical role not only in information security systems, but also plays a significant role for several cloud computing-based systems. In this research article, we have proposed a new encryption-based algorithm for cloud computing analyzed. The proposed algorithm has been compared with various other traditionally used popular encryption algorithms primarily including DES, AES, Blowfish as well as proposed algorithm. Various parameters like time for encryption for the various sizes of the blocks, the avalanche effect on the plain text, have been used to analyse the performance of the proposed algorithm. The analysis of experimental results implemented on MATLAB highlights that proposed algorithms performs 64 percent better with considered parameters and with the key has 57 percent better results as compared with the other traditional existing algorithms.
Replica symmetry breaking in neural networks: a few steps toward rigorous results

In this paper we adapt the broken replica interpolation technique (developed by Francesco Guerra to deal with the Sherrington-Kirkpatrick model, namely a pairwise mean-field spin-glass whose couplings are i.i.d. standard Gaussian variables) in order to work also with the Hopfield model (i.e. a pairwise mean-field neural-network whose couplings are drawn according to Hebb's learning rule): this is accomplished by grafting Guerra's telescopic averages on the transport equation technique, recently developed by some of the authors. As an overture, we apply the technique to solve the Sherrington-Kirkpatrick model with i.i.d. Gaussian couplings centered at J0 and with finite variance J; the mean J0 provides a ferromagnetic contribution to be detected in a noisy environment tuned by J, hence making this model a natural test-case to be investigated before addressing the Hopfield model. For both the models, an explicit expression of their quenched free energy in terms of their natural order parameters is obtained at the Kth step (K arbitrary, but finite) of replica-symmetry-breaking. In particular, for the Hopfield model, by assuming that the overlaps respect Parisi's decomposition (following the ziqqurat ansatz) and that the Mattis magnetization is self-averaging, we recover previous results obtained via replica-trick by Amit, Crisanti and Gutfreund (1RSB) and by Steffan and Kühn (2RSB).
A Federated Learning Approach to Routing in Challenged SDN-Enabled Edge Networks

The edge computing paradigm allows computationally intensive tasks to be offloaded from small devices to nearby (more) powerful servers, via an edge network. The intersection between such edge computing paradigm and Machine Learning (ML), in general, and deep learning in particular, has brought to light several advantages for network operators: from automating management tasks, to gain additional insights on their networks. Most of the existing approaches that use ML to drive routing and traffic control decisions are valuable but rarely focus on challenged networks, that are characterized by continually varying network conditions and the high volume of traffic generated by edge devices. In particular, recently proposed distributed ML-based architectures require either a long synchronization phase or a training phase that is unsustainable for challenged networks. In this paper, we fill this knowledge gap with Blaster, a federated architecture for routing packets within a distributed edge network, to improve the application's performance and allow scalability of data-intensive applications. We also propose a novel path selection model that uses Long Short Term Memory (LSTM) to predict the optimal route. Finally, we present some initial results obtained by testing our approach via simulations and with a prototype deployed over the GENI testbed. By leveraging a Federated Learning (FL) model, our approach shows that we can optimize the communication between SDN controllers, preserving bandwidth for the data traffic.
Unsupervised Feature Learning Architecture with Multi-clustering Integration RBM

In this paper, we present a novel unsupervised feature learning architecture, which consists of a multi-clustering integration module and a variant of RBM termed multi-clustering integration RBM (MIRBM). In the multi-clustering integration module, we apply three clusterers (K-means, affinity propagation and spectral clustering algorithms) to obtain three different clustering partitions (CPs) without any background knowledge or label. Then, an unanimous voting strategy is used to generate a local clustering partition (LCP). The novel MIRBM model is a core feature encoding part of the proposed unsupervised feature learning architecture. The novelty of it is that the LCP as an unsupervised guidance is integrated into one step contrastive divergence ( <inline-formula><tex-math notation="LaTeX">${\mathtt{{CD}}}_{1}$</tex-math></inline-formula> ) learning to guide the distribution of the hidden layer features. For the instance in the same LCP cluster, the hidden and reconstructed hidden layer features of the MIRBM model in the proposed architecture tend to constrict together in the training process. Meanwhile, each LCP center tends to disperse from each other as much as possible in the hidden and reconstructed hidden layer during training. The experiments demonstrate that the proposed unsupervised feature learning architecture has more powerful feature representation and generalization capability than the state-of-the-art models for clustering tasks in the Microsoft Research Asia Multimedia (MSRA-MM)2.0 dataset.
High Performance Offline Handwritten Chinese Text Recognition with a New Data Preprocessing and Augmentation Pipeline

Offline handwritten text recognition (HCTR) has been a long-standing research topic. To build robust and high-performance offline HCTR systems, it is natural to develop data preprocessing and augmentation techniques, which, however, have not been fully explored. In this paper, we propose a data preprocessing and augmentation pipeline and a CNN-ResLSTM model for high-performance offline HCTR. The data preprocessing and augmentation pipeline consists of three steps: training text sample generation, text sample preprocessing and text sample synthesis. The CNN-ResLSTM model is derived by introducing residual connections into the RNN part of the CRNN architecture. Experiments show that on the proposed CNN-ResLSTM, the data preprocessing and augmentation pipeline can effectively and robustly improve the system performance: On two standard benchmarks, namely the CASIA-HWDB and the ICDAR-2013 handwriting competition dataset, the proposed approach achieves state-of-the-art results with correct rates of 97.28% and 96.99%, respectively. Furthermore, to make our model more practical, we employ model acceleration and compression techniques to build a fast and compact model without sacrificing the accuracy.
Data Fusion Algorithm for Heterogeneous Wireless Sensor Networks Based on Extreme Learning Machine Optimized by Particle Swarm Optimization

Data fusion can reduce the data communication time between sensor nodes, reduce energy consumption, and prolong the lifetime of the network, making it an important research focus in the field of heterogeneous wireless sensor networks (HWSNs). Normal sensor nodes are susceptible to external environmental interferences, which affect the measurement results. In addition, raw data contain redundant information. The transmission of redundant information consumes excess energy, thereby reducing the lifetime of the network. We propose a data fusion method based on an extreme learning machine optimized by particle swarm optimization for HWSNs. The spatiotemporal correlation between the data of the HWSNs is determined, and the extreme learning machine method is used to process the data collected by the sensor nodes in the hierarchical routing structure of the HWSN. The particle swarm optimization algorithm is used to optimize the input weight matrix and the hidden layer bias of the extreme learning machine. An output weight matrix is created to reduce the number of hidden layer nodes and improve the generalization ability of the model. The data fusion model fuses the original data collected by the sensor nodes. The simulation results show that the proposed algorithm reduces network energy consumption and improves the lifetime of the network, the efficiency of data fusion, and the reliability of data transmission compared with other data fusion methods.
TagDC: A tag recommendation method for software information sites with a combination of deep learning and collaborative filtering

Software information sites (e.g., StackOverflow, Freecode, etc.) are increasingly essential for software developers to share knowledge, communicate new techniques, and collaborate. With the rapid growth of software objects, tags are widely applied to aid developers' various operations on software information sites. Since tags are freely and optionally selected by developers, the differences in background, expression habits, and understanding of software objects among developers may cause inconsistent or inappropriate tags. To alleviate the problems of tag synonyms and tag explosion, we propose TagDC, i.e., a composite Tag recommendation method with Deep learning and Collaborative filtering. TagDC consists of two complementary modules: the word learning enhanced CNN capsule module (TagDC-DL) and the collaborative filtering module (TagDC-CF). It can improve the understanding of software objects from different perspectives. Given a new software object, TagDC can calculate a list of the combined confidence probabilities of tags and then recommend TOP-K tags by ranking the probabilities in the list. We evaluated our TagDC on nine datasets with different scales. The experimental results show that TagDC achieves a better effectiveness against two state-of-the-art baseline methods (i.e., TagCNN and FastTagRec) with a substantial improvement.