Reviewerly/exHarmony huggingface dataset:
Broker Bridging Mechanism for Providing Anonymity in MQTT

With the growth of the number of smart devices the range of fields where they are used is growing too, and it is essential to protect the communication between them. In addition to data integrity and confidentiality, for which standard mechanisms exists, a security service that may also be required is anonymity, allowing entities to communicate with each other in such a way that no third party knows that they are the participants of a certain message exchange. In this paper we propose a mechanism for creating anonymous communications using MQTT protocol. The design of our solution is based on dynamic broker bridging mechanism and allows clients to subscribe and to publish to a topic remaining incognito.
Human Motion Recognition Based on Improved 3-Dimensional Convolutional Neural Network

In recent years, deep Convolutional neural networks(CNNs) have made fantastic progress in static image recognition, but the ability to model motion information on behavioral video is weak. Therefore, our paper put forward a new time transition layer that models variable temporal convolution kernel depths. We embed this new Hybrid Model in our proposed 3D CNN. We extend the DenseNet architecture with 3D filters and pooling kernels. It will take time as training a 3D convolutional neural network requires a large number of tagged data sets to start training from the input. Therefore, the focus of this paper is on simple and effective technique of passing 2D convolutional neural network pre-trained data to a randomly initialized 3D convolutional neural network for stable weight initialization, where we can still achieve our experimental results by appropriately reducing the number of 3D convolutional neural network training samples. Experiments show that the network can make a more accurate classification of behavioral video, identify it in the UCF-101 database, and compare it with other classical algorithms that have appeared in recent years. The results reflect the superiority of the algorithm.
A Contingent Exploration on Big Data Tools

In past few years, size of the data is growing exponentially by extreme fast rates, for instance, the size of data growth was ten times faster in the growth due to various means such as the data from mobile devices, remote sensing, sensing aerial devices, recording frequency of radio waves. Until most recently, most of the data was never analysed and most of the time it was discarded. The data stored requires lots of storage space whereas later due to lack of storage space the data is either ignored or deleted due to lack of storage space to process the data. Sometimes, we are even capable of storing the data but until that data is not processed, it is raw useless data to us because that will not be able to fetch with new insights. In the analysis, we face two types of challenges, first is the lack of storage space and second a suitable software to process this data. In this paper, we have discussed about the evolution of 4 V’s of big data, levels of big data tools, various data tools along with a comparative analysis of those tools on the basis of distinguished features like mode of software, data processing, language support, data flow security, latency and fault tolerance is also generalized for better understanding.
A Novel Chaos-Based Physical Layer Security Transmission Scheme for Internet of Things

The Internet of Things (IoT) has attracted much attention in many fields, such as healthcare, transportation, finance and other critical infrastructures. But how to ensure the security of whole IoT system and the privacy of users has always been a challenge. Physical layer as the cornerstone of IoT, appears to be a promising direction for enhancing the security of IoT. As a consequence, we propose a novel chaotic physical layer security transmission scheme in the OFDM-based IoT transmission system. By encrypting the Discrete Fourier Transform (DFT) matrix, the proposed scheme not only provides the confidentiality of physical layer information transmission, but also effectively addresses the issues on the extreme high PAPR of the OFDM symbols. Moreover, it does not require any additional sideband information and has low computational complexity in theory. Compared with other existing schemes, the proposed scheme achieves the higher confidentiality of information transmission with the capability to resist chosen-plaintext attacks, statistical-attacks, and brute-force attacks simultaneously.
Implementation of optimization algorithms on Wisconsin Breast cancer dataset using deep neural network

In women, the 2nd most common cancer is breast cancer annually about 16 lakh women are identified. Detection and treatment in the early stage improve survival rates. Dataset of Wisconsin Breast cancer will provide the features of the digitized image. In this paper, a model is developed and different optimization algorithms were implemented to access the correctness of classifying data with respect to accuracy which is feasible for computer-aided diagnosis. Machine learning can assist and alert expert radiologist more effectively than current screening techniques. In this paper RMS propagation (Root Mean Square Propagation) and SGD (stochastic gradient descent) optimization algorithms were implemented on a deep neural network with sigmoid neurons and accuracy is compared.
iSeqL

Exploratory analysis of unstructured text is a difficult task, particularly when defining and extracting domain-specific concepts. We present iSeqL, an interactive tool for the rapid construction of customized text mining models through sequence labeling. With iSeqL, analysts engage in an active learning loop, labeling text instances and iteratively assessing trained models by viewing model predictions in the context of both individual text instances and task-specific visualizations of the full dataset. To build suitable models with limited training data, iSeqL leverages transfer learning and pre-trained contextual word embeddings within a recurrent neural architecture. Through case studies and an online experiment, we demonstrate the use of iSeqL to quickly bootstrap models sufficiently accurate to perform in-depth exploratory analysis. With less than an hour of annotation effort, iSeqL users are able to generate stable outputs over custom extracted entities, including context-sensitive discovery of phrases that were never manually labeled.
Automorphism groups and new constructions of maximum additive rank metric codes with restrictions

Let d,n∈Z+ such that 1≤d≤n. A d-code C⊂Fqn×n is a subset of order n square matrices with the property that for all pairs of distinct elements in C, the rank of their difference is greater than or equal to d. A d-code with as many as possible elements is called a maximum d-code. The integer d is also called the minimum distance of the code. When d<n, a classical example of such an object is the so-called generalized Gabidulin code (Gabidulin and Kshevetskiy (2005)). In Delsarte and Goethals (1975), Schmidt (2015) and K.U. Schmidt (2018), several classes of maximum d-codes made up respectively of symmetric, alternating and Hermitian matrices were exhibited. In this article we focus on such examples. Precisely, we determine their automorphism groups and solve the equivalence issue for them. Finally, we exhibit a maximum symmetric 2-code which is not equivalent to the one with same parameters constructed in Schmidt (2015).
Addressing Irregularity in Sparse Neural Networks through a Cooperative Software/Hardware Approach

Neural networks have become the dominant algorithms rapidly as they achieve state-of-the-art performance in a broad range of applications such as image recognition, speech recognition, and natural language processing. However, neural networks keep moving toward deeper and larger architectures, posing a great challenge to hardware systems due to the huge amount of data and computations. Although sparsity has emerged as an effective solution for reducing the intensity of computation and memory accesses directly, irregularity caused by sparsity (including sparse synapses and neurons) prevents accelerators from completely leveraging the benefits, i.e., it also introduces costly indexing module in accelerators. In this article, we propose a cooperative software/hardware approach to address the irregularity of sparse neural networks efficiently. Initially, we observe the local convergence, namely larger weights tend to gather into small clusters during training. Based on that key observation, we propose a software-based coarse-grained pruning technique to reduce the irregularity of sparse synapses drastically. The coarse-grained pruning technique, together with local quantization, significantly reduces the size of indexes and improves the network compression ratio. We further design a multi-core hardware accelerator, Cambricon-SE, to address the remaining irregularity of sparse synapses and neurons efficiently. The novel accelerator have three key features: 1) selector modulesto filter unnecessary synapses and neurons, 2) compress/decompress modules for exploiting the sparsity in data transmission (which is rarely studied in previous work), and 3) a multi-core architecture with elevated throughput to meet the real-time processing requirement. Compared against a state-of-the-art sparse neural network accelerator, our accelerator is 1.20x and 2.72x better in terms of performance and energy efficiency, respectively. Moreover, for real-time video analysis tasks, Cambricon-SE can process 1080p video at the speed of 76.59 fps.
Feature-Flow Interpretation of Deep Convolutional Neural Networks

Despite the great success of deep convolutional neural networks (DCNNs) in computer vision tasks, their black-box aspect remains a critical concern. The interpretability of DCNN models has been attracting increasing attention. In this work, we propose a novel model, Feature-fLOW INterpretation (FLOWIN) model, to interpret a DCNN by its feature-flow. The FLOWIN can express deep-layer features as a sparse representation of shallow-layer features. Based on that, it distills the optimal feature-flow for the prediction of a given instance, starting from deep layers to shallow layers. Therefore, the FLOWIN can provide an instance-specific interpretation, which presents its feature-flow units and their interpretable meanings for its network decision. The FLOWIN can also give the quantitative interpretation in which the contribution of each flow unit in different layers is used to interpret the net decision. From the class-level view, we can further understand networks by studying feature-flows within and between classes. The FLOWIN not only provides the visualization of the feature-flow but also studies feature-flow quantitatively by investigating its density and similarity metrics. In our experiments, the FLOWIN is evaluated on different datasets and networks by quantitative and qualitative ways to show its interpretability.
Security Issues in Cloud Computing

Cloud Computing is the resource provided over the Internet for the use of data storage by the user. It is an environment used to enable a cost-effective storage of a large amount of shared pool of resources over the Internet. It has a rapid expansion over the few years for large companies and private sectors. Cloud computing with its numerous benefits also faces data privacy and security issues that causes threats in the clouds. This paper, hence, lists the various security issues in the clouds based on their confidentiality, integrity and availability and the corresponding solutions to those threats and issues in the cloud environment. It also lists a comparative study of all the solutions to the security issues so that the best-fit solutions can be identified based on the CIA triad. This paper analyses the solutions of security issues faced by the cloud users and providers.
Real-Time Multi-Person Pose Tracking using Data Assimilation

We propose a framework for the integration of data assimilation and machine learning methods in human pose estimation, with the aim of enabling any pose estimation method to be run in real-time, whilst also increasing consistency and accuracy. Data assimilation and machine learning are complementary methods: the former allows us to make use of information about the underlying dynamics of a system but lacks the flexibility of a data-based model, which we can instead obtain with the latter. Our framework presents a real-time tracking module for any single or multi-person pose estimation system. Specifically, tracking is performed by a number of Kalman filters initiated for each new person appearing in a motion sequence. This permits tracking of multiple skeletons and reduces the frequency that computationally expensive pose estimation has to be run, enabling online pose tracking. The module tracks for N frames while the pose estimates are calculated for frame N + 1 . This also results in increased consistency of person identification and reduced inaccuracies due to missing joint locations and inversion of left-and right-side joints.
Estimating Viewed Image Categories from fMRI Activity via Multi-view Bayesian Generative Model

Researches for estimating what people view from their brain activity have attracted wide attention. Many existing methods focus on only relationship between brain activity and visual features extracted from viewed images. In this paper, we propose a multi-view Bayesian generative model (MVBGM), which adopts a new view, i.e., category features obtained from viewed images. MVBGM based on automatic feature selection under the Bayesian approach can also avoid overfitting caused by high dimensional features. Experimental results show that MVBGM can estimate viewed image categories from brain activity more accurately than existing methods.
Probabilistic Approach for System Identification using Machine Learning

This paper gives a clear understanding of how a system can be modeled in a non-parametric and probabilistic way using machine learning based on historical data with taking into account uncertain noise added to the output of the system. We explained key concepts step by step and end up with a unique algorithm. Moreover, we demonstrated an illustrative example of the proposed algorithm in case of nonlinear dynamic system.
Detecting the Starting Frame of Actions in Video

In this work, we address the problem of precisely localizing key frames of an action, for example, the precise time that a pitcher releases a baseball, or the precise time that a crowd begins to applaud. Key frame localization is a largely overlooked and important action-recognition problem, for example in the field of neuroscience, in which we would like to understand the neural activity that produces the start of a bout of an action. To address this problem, we introduce a novel structured loss function that properly weights the types of errors that matter in such applications: it more heavily penalizes extra and missed action start detections over small misalignments. Our structured loss is based on the best matching between predicted and labeled action starts. We train recurrent neural networks (RNNs) to minimize differentiable approximations of this loss. To evaluate these methods, we introduce the Mouse Reach Dataset, a large, annotated video dataset of mice performing a sequence of actions. The dataset was collected and labeled by experts for the purpose of neuroscience research. On this dataset, we demonstrate that our method outperforms related approaches and baseline methods using an unstructured loss.
An improved architecture for urban building extraction based on depthwise separable convolution

Accurate extraction of urban buildings is a key problem in urban remote sensing image processing. It can be applied to many kinds of urban problems, such as data statistics of urban management and smart cities. In recent years, the deep learning model based on convolutional neural network is widely used in the field of target recognition and semantic segmentation. In this paper, based on U-Net for urban building extraction from remote sensing image, we propose a neural network architecture for urban building extraction from remote sensing image. We use depth separable convolution to improve it and adjust the process of network super parametric optimization according to the characteristics of building. We call this new architecture XU-Net. We evaluate the performance of XU-Net through experiments with INRIA aerial image data set. The result shows that XU-Net is not only feasible but also efficient. Moreover, XU-Net reduces number of parameters 89%, from 18.8M to 2.13M, compared to classical architecture U-Net, at the same time, it guarantees the accuracy can reach 97.5%.
Satellite and Underwater Sonar Image Matching Using Deep Learning

Aerial images provide rich information about the Earth surface, while underwater perception is severely limited due to the physical characteristic of water. This work proposes a new problem domain where a matching process fuses aerial and underwater images. The proposal is designed to aid underwater navigation in partially structured environments such as marinas and harbors. A pipeline combining image processing techniques and Convolutional Neural Networks is presented. The method is validated in a real dataset with quantitative and qualitative results.
Atomic nonclassicality in the Jaynes-Cummings model

The Jaynes-Cummings model, a fundamental and ideal paradigm for the study of atom-field interaction with remarkable beauty and complexity, plays an instrumental role in cavity quantum electrodynamics and related experiments. The dynamics of both the atom and the field in this ubiquitous model, which exhibit a number of intriguing nonclassical effects, have been widely studied and engineered from various perspectives. In particular, the collapses and revivals for several physical quantities have been revealed, with important implications for quantum foundations and technologies. In this work, we study the atomic nonclassicality in the Jaynes-Cummings model in terms of an information-theoretic quantifier, reveal its basic features, and compare them with various well known observations of atomic dynamics. The sensitivity of the dynamics on both the initial atomic states and field states indicates rich patterns of atom-field interaction, and may be exploited to estimate, prepare, and engineer atomic states via fields, or vice versa.
On the role of the local oscillator intensity in optical homodyne-like tomography

In a recent research (Olivares et al., 2019 [18]) we have demonstrated that a homodyne-like scheme, exploiting a "low-intensity" local oscillator, can be used to perform optical state tomography of both quantum and classical states of light. The reconstruction method directly uses the homodyne-like probability distribution retrieved from the detector. Here, we further investigate the role played by the local oscillator in this respect. In particular, we study to some extent how its intensity affects the quantum-state reconstruction procedure by focusing on the case of the Fock states |1〉 and |2〉, whose homodyne-like probability distributions are sensibly affected by the actual value of the LO intensity. The analysis is performed on Monte Carlo simulated experiments taking also into account the quantum detection efficiency.
A Driving Decision Strategy(DDS) Based on Machine learning for an autonomous vehicle

A current autonomous vehicle determines its driving strategy by considering only external factors (Pedestrians, road conditions, etc.) without considering the interior condition of the vehicle. To solve the problem, this paper proposes "A Driving Decision Strategy(DDS) Based on Machine learning for an autonomous vehicle" which determines the optimal strategy of an autonomous vehicle by analyzing not only the external factors, but also the internal factors of the vehicle (consumable conditions, RPM levels etc.). The DDS learns a genetic algorithm using sensor data from vehicles stored in the cloud and determines the optimal driving strategy of an autonomous vehicle. This paper compared the DDS with MLP and RF neural network models to validate the DDS. In the experiment, the DDS had a loss rate approximately 5% lower than existing vehicle gateways and the DDS determined RPM, speed, steering angle and lane changes 40% faster than the MLP and 22% faster than the RF.
A mobile application to monitor mode, content and duration of health navigation services for people living with HIV in Guatemala

Health navigation is increasingly being used to support people living with HIV, but timely monitoring of navigation is challenging due to the burden of reporting a high volume of diverse activities. We designed a mobile application (app) for navigators to report their interactions with men who have sex with men living with HIV (n = 374), including: (1) mode of support; (2) content; and (3) duration. We assessed sociodemographic and behavioral characteristics of the study sample and calculated monitoring system indicators. We also conducted qualitative interviews with navigators (n = 7) and used thematic analysis to assess app acceptability and usability. From January 2017 to June 2018, 95.3% of participants interacted with their navigator at least one time and 4281 reports were recorded by nine navigators. The median number of interactions per participant was 10 (range: 1–46). The majority of interactions (71.6%) occurred remotely. Most frequently covered topics included: appointment reminders (36.9%), employment (19.9%), and family (15.5%). Navigators indicated that the system was easy to use, but some did not use it in real time as intended. Timely access to navigator data enabled feedback and continuous training. These data can also facilitate analysis of intensity and content of interactions to improve tailoring and sustainability.
A novel zero watermark optimization algorithm based on Gabor transform and discrete cosine transform

Summary Zero watermark has no intervention on the carrier image, which is invisible in nature, and completely solves the problem of mutual constraint between robustness and invisibility in the traditional digital watermark technology. In order to improve the robustness of the zero watermark, a zero watermark algorithm based on Gabor transform and discrete cosine transform (DCT) is proposed in this paper. The algorithm performs Gabor transformation on the carrier image to obtain the directional characteristics of the image. Then it performs DCT and blocks, and performs singular value decomposition (SVD) on each subblock. The feature matrix is constructed using the largest eigenvalue of the subblock, and then XORed with the encrypted watermark to form a zero watermark. The algorithm utilizes the advantages of Gabor transform in the adjustment of scale and direction and selects the optimized parameters to extract image features. At the same time, the information concentration and decorrelation of DCT and SVD are utilized to improve the robustness and antiattack capability of the algorithm. The experimental results show that the proposed algorithm is robust to filtering, JPEG compression, noise, shearing, rotation, and other attacks, especially effective in antishear attack and rotation attack.