Reviewerly/exHarmony huggingface dataset:
Blockchain and Machine Learning Inspired Secure Smart Home Communication Network

With the increasing growth rate of smart home devices and their interconnectivity via the Internet of Things (IoT), security threats to the communication network have become a concern. This paper proposes a learning engine for a smart home communication network that utilizes blockchain-based secure communication and a cloud-based data evaluation layer to segregate and rank data on the basis of three broad categories of Transactions (T), namely Smart T, Mod T, and Avoid T. The learning engine utilizes a neural network for the training and classification of the categories that helps the blockchain layer with improvisation in the decision-making process. The contributions of this paper include the application of a secure blockchain layer for user authentication and the generation of a ledger for the communication network; the utilization of the cloud-based data evaluation layer; the enhancement of an SI-based algorithm for training; and the utilization of a neural engine for the precise training and classification of categories. The proposed algorithm outperformed the Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system, the data fusion technique, and artificial intelligence Internet of Things technology in providing electronic information engineering and analyzing optimization schemes in terms of the computation complexity, false authentication rate, and qualitative parameters with a lower average computation complexity; in addition, it ensures a secure, efficient smart home communication network to enhance the lifestyle of human beings.
Single-shot Feature Selection for Multi-task Recommendations

Multi-task Recommender Systems (MTRSs) has become increasingly prevalent in a variety of real-world applications due to their exceptional training efficiency and recommendation quality. However, conventional MTRSs often input all relevant feature fields without distinguishing their contributions to different tasks, which can lead to confusion and a decline in performance. Existing feature selection methods may neglect task relations or require significant computation during model training in multi-task setting. To this end, this paper proposes a novel Single-shot Feature Selection framework for MTRSs, referred to as MultiSFS, which is capable of selecting feature fields for each task while considering task relations in a single-shot manner. Specifically, MultiSFS first efficiently obtains task-specific feature importance through a single forward-backward pass. Then, a data-task bipartite graph is constructed to learn field-level task relations. Subsequently, MultiSFS merges the feature importance according to task relations and selects feature fields for different tasks. To demonstrate the effectiveness and properties of MultiSFS, we integrate it with representative MTRS models and evaluate on three real-world datasets. The implementation code is available online to ease reproducibility.
Causal Discovery from Temporal Data

Temporal data representing chronological observations of complex systems can be ubiquitously collected in smart industry, medicine, finance and etc. In the last decade, many tasks have been studied for mining temporal data and offered significant value for various applications. Among these tasks, causal discovery aims to understand the underlying generation mechanism of temporal data and has attracted much research attention. According to whether the data is calibrated, existing causal discovery approaches can be divided into two subtasks, i.e., multivariate time-series causal discovery, and event sequence causal discovery. Previous tutorials or surveys have primarily focused on causal discovery from time-series data and disregarded the second ones. In this tutorial, we elucidate the correlation between the two subtasks and provide a comprehensive review of the existing solutions. Moreover, we offer some potential applications and summarize new perspectives for discovering causal relations from temporal data. We hope the audiences can obtain a systematic overview of this topic and inspire some new ideas for their own research.
The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models

In this paper, we consider the problem of sparsifying BERT models, which are a key building block for natural language processing, in order to reduce their storage and computational cost.We introduce the Optimal BERT Surgeon (oBERT), an efficient and accurate pruning method based on approximate second-order information, which we show to yield state-ofthe-art results for compression in both stages of language tasks: pre-training and fine-tuning.Specifically, oBERT extends existing work on second-order pruning by allowing for pruning blocks of weights, and is the first such method that is applicable at BERT scale.Second, we investigate compounding compression approaches to obtain highly compressed but accurate models for deployment on edge devices.These models significantly push boundaries of the current state-of-the-art sparse BERT models with respect to all metrics: model size, inference speed and task accuracy.For example, relative to the dense BERT BASE , we obtain 10x model size compression with < 1% accuracy drop, 10x CPU-inference speedup with < 2% accuracy drop, and 29x CPU-inference speedup with < 7.5% accuracy drop.
UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction

Relational triple extraction is challenging for its difficulty in capturing rich correlations between entities and relations.Existing works suffer from 1) heterogeneous representations of entities and relations, and 2) heterogeneous modeling of entity-entity interactions and entity-relation interactions.Therefore, the rich correlations are not fully exploited by existing works.In this paper, we propose UniRel to address these challenges.Specifically, we unify the representations of entities and relations by jointly encoding them within a concatenated natural language sequence, and unify the modeling of interactions with a proposed Interaction Map, which is built upon the off-the-shelf self-attention mechanism within any Transformer block.With comprehensive experiments on two popular relational triple extraction datasets, we demonstrate that UniRel is more effective and computationally efficient.The source code is available at https://github.com/wtangdev/UniRel.
Retaining Learned Behavior During Real-Time Neuroevolution

Creating software-controlled agents in videogames who can learn and adapt to player behavior is a difficult task. Using the real-time NeuroEvolution of Augmenting Topologies (rtNEAT) method for evolving increasingly complex artificial neural networks in real-time has been shown to be an effective way of achieving behaviors beyond simple scripted character behavior. In NERO, a videogame built to showcase the features of rtNEAT, agents are trained in various tasks, including shooting enemies, avoiding enemies, and navigating around obstacles. Training the neural networks to perform a series of distinct tasks can be problematic: the longer they train in a new task, the more likely it is that they will forget their skills. This paper investigates a technique for increasing the probability that a population will remember old skills as they learn new ones. By setting aside the most fit individuals at a time when a skill has been learned and then occasionally introducing their offspring into the population, the skill is retained. How large to make this milestone pool of individuals and how often to insert the offspring of the milestone pool into the general population is the primary focus of this paper.
Load Balancing in Cloud Computing

Distributed computing is the best innovation today for every one of those individuals who needs to go with least speculation on foundation and needs to redistribute the weight of taking care of specialized issues to outsider by paying the charges for the administrations used. Today there is gigantic measure of interest from the customers to utilize cloud innovation as it gives various highlights and remove the heap of looking after foundation. This has made a tremendous measure of burden on servers . So it is must to deal with issues identified with load adjusting. This is essentially to see that the heap on a specific server is held most extreme to its edge level. So it can deal with the undertaking and furthermore can finish it in a quicker way. It limits the cost and time associated with the major computational models and improves appropriate usage of assets and framework execution. Numerous calculations are prescribed by different specialists from everywhere throughout the world to take care of the issue of burden adjusting. In this paper, we present another calculation named as combo calculation to address the issue of burden adjusting in a cloud situation. Catchphrases - Cloud registering improvement Load Balancing Network
Optimization of Distributions Differences for Classification

In this paper, we introduce a new classification algorithm called the optimization of distribution differences (ODD). The algorithm aims to find a transformation from the feature space to a new space where the instances in the same class are as close as possible to one another, whereas the gravity centers of these classes are as far as possible from one another. This aim is formulated as a multiobjective optimization problem that is solved by a hybrid of an evolutionary strategy and the quasi-Newton method. The choice of the transformation function is flexible and could be any continuous space function. We experiment with a linear and a nonlinear transformation in this paper. We show that the algorithm can outperform eight other classification methods, namely naive Bayes, support vector machines, linear discriminant analysis, multilayer perceptrons, decision trees, and k -nearest neighbors, and two recently proposed classification methods, in 12 standard classification data sets. Our results show that the method is less sensitive to the imbalanced number of instances compared with these methods. We also show that ODD maintains its performance better than other classification methods in these data sets and hence offers a better generalization ability.
Characterizing Computational Adaptive Expertise

Our research is exploring the role that computational and analytical abilities play in innovation, in the context of engineering design education.We are applying the learning framework of adaptive expertise to focus our work and guide the research.The model of adaptive expertise has been presented as a way of thinking about how to prepare learners to flexibly respond to new learning situations, which is precisely what students are expected to do in the context of developing design solutions.We focus on "computational adaptive expertise," which we abbreviate CADEX, since a major portion of an engineering curriculum focuses on developing analytical and computational knowledge.Yet, students often struggle with applying or transferring computational knowledge in the context of design.The current paper presents an overview of adaptive expertise and relates this concept specifically to engineering design education.In addition, the paper presents an overview of the research plan we are presently using to study CADEX in the context of a senior level biomedical engineering design course.
A Novel Rotation Forest Modality Based on Hybrid NNs: RF (ScPSO-NN)

Neural Network (NN), hybrid NN methods and Rotation Forest (RF) ensemble classifier are preferred in pattern analysis owing to their ability for finding efficient solutions on different problems. NN architecture usually includes backpropagation type algorithms in which error is exposed to fluctuations. Hybrid NN methods are generally designed to improve the classification performance of NN. Scout Particle Swarm Optimization (ScPSO) is one of these optimization algorithms including the effective parts of Particle Swarm Optimization (PSO) and Artificial Bee Colony Optimization (ABC). Moreover, RF algorithm usually indicates the same performance as in hybrid NN methods, although it is comprised of Decision Tree (DT) classifiers. At this point, our paper investigates whether RF using the hybrid NNs can outperform other ensemble classifiers in binary-medical pattern classification, or not. With this intention, PSO, ABC and ScPSO are placed in NN algorithms instead of back propagation, and hybrid methods (PSO-NN, ABC-NN and ScPSO-NN) are realized. As a result, RF (PSO-NN), RF (ABC-NN) and RF (ScPSO-NN) architectures are obtained. Classification Accuracy (CA), Area Under Curve (AUC), Sensitivity, Specificity, F-measure, Gmean and Precision metrics are used for a statistical performance comparison, and a test based on 2-fold cross validation method was realized on five medical datasets.
The optimization of parallel convolutional RBM based on Spark

With the extensive attention and research of the scholars in deep learning, the convolutional restricted Boltzmann machine (CRBM) model based on restricted Boltzmann machine (RBM) is widely used in image recognition, speech recognition, etc. However, time consuming training still seems to be an unneglectable issue. To solve this problem, this paper mainly uses optimized parallel CRBM based on Spark, and proposes a parallel comparison divergence algorithm based on Spark and uses it to train the CRBM model to improve the training speed. The experiments show that the method is faster than traditional sequential algorithm. We train the CRBM with the method and apply it to breast X-ray image classification. The experiments show that it can improve the precision and the speed of training compared with traditional algorithm.
Spatial Signal Detection Using Continuous Shrinkage Priors

Motivated by the problem of detecting changes in two-dimensional X-ray diffraction data, we propose a Bayesian spatial model for sparse signal detection in image data. Our model places considerable mass near zero and has heavy tails to reflect the prior belief that the image signal is zero for most pixels and large for an important subset. We show that the spatial prior places mass on nearby locations simultaneously being zero, and also allows for nearby locations to simultaneously be large signals. The form of the prior also facilitates efficient computing for large images. We conduct a simulation study to evaluate the properties of the proposed prior and show that it outperforms other spatial models. We apply our method in the analysis of X-ray diffraction data from a two-dimensional area detector to detect changes in the pattern when the material is exposed to an electric field.
Aircraft Landing Using Dynamic Two-Dimensional Image-Based Guidance Control

This paper proposes a two-dimensional (2-D) image-based controller to automatically steer a fixed-wing unmanned aerial vehicle (UAV) during the first three stages of landing: alignment, glide-slope, and flare. Observable image features of the runway and its textured ground are exploited to derive a feedback controller for the automatic maneuver. The proposed controller ensures the horizontal position alignment and a smooth touchdown of the aircraft without estimating the height above the runway. In addition, the 2-D image-based control structure adopted also enforces wind disturbance rejection, without the need for an explicit wind estimator. Simulation results are presented to illustrate the performance of the controller.
Secured Transmission of Medical Images in Radiology Using AES Technique

Medical imaging technology results in more than thousands of images per patient and transmitting them securely over an insecure network is a challenging task still. Maintaining the data integrity against intruder is important. This paper discusses a hybrid method which combines image processing and cryptography to make sure that all images are securely transmitted. In the proposed method, a Digitally Reconstructed Radiograph (DRR) is generated from all the images of a 3D volume of the patient, and then divided the DRR image into four equal quadrants. The zigzag pattern was applied to all these sixteen quadrants. Each quadrant was separately encrypted in block cipher mode using AES algorithm. At the receiver side, the DRR was regenerated from all the transmitted images and was compared with the deciphered blocks using histogram comparison of each block. The method was applied to CT images of 40 patients of brain tumor, CT colonography, and nasopharynx dataset. The image injection techniques were applied and tested the result with the addition of image, deletion of an image and through modification of Hounsfield values.
Tax Services and Tax Service Providers’ Changing Role in the IoT and AmI Environment

New technology trends including Ambient IntelligenceAmbient intelligence (AmIAmI ), Machine Learning (ML), Internet of Things (IoTIoT ), Cloud and Edge computing all have an important role to play in further developing the tax administration processes. The main challenge is to effectively reform the Revenue Administration (RA) services in today’s electronic age. Business activities have become more global and digitalized; and revenue administrations’ traditional services are also developing fast through the use of smart devices and smart software applications in the IoT-distributed computing environment. In this digital world, tax service providers also have a big responsibility, in fact, a requirement, to use and adopt revenue administrations’ smart e-services built with some intelligence to improve automation. Understanding the need of the hour, tax service providersTax service providers are attempting to develop IoT environments with AmI for fulfilling citizens’ tax obligations. It is understandable and should be acceptable that in doing so and through increased smart automation, their workload is going to decrease drastically. In this chapter, it is aimed to examine RA tax services provision in the IoT environment in some leading countries and discuss, in some detail, how the role of tax service providers is changing in this improving smart taxation environment.
STCN: Stochastic Temporal Convolutional Networks.

Convolutional architectures have recently been shown to be competitive on many sequence modelling tasks when compared to the de-facto standard of recurrent neural networks (RNNs), while providing computational and modeling advantages due to inherent parallelism. However, currently there remains a performance gap to more expressive stochastic RNN variants, especially those with several layers of dependent random variables. In this work, we propose stochastic temporal convolutional networks (STCNs), a novel architecture that combines the computational advantages of temporal convolutional networks (TCN) with the representational power and robustness of stochastic latent spaces. In particular, we propose a hierarchy of stochastic latent variables that captures temporal dependencies at different time-scales. The architecture is modular and flexible due to the decoupling of the deterministic and stochastic layers. We show that the proposed architecture achieves state of the art log-likelihoods across several tasks. Finally, the model is capable of predicting high-quality synthetic samples over a long-range temporal horizon in modeling of handwritten text.
Majority Voting Algorithm for Diagnosing of Imbalanced Malaria Disease

Vector borne diseases like malaria fever is one of the most elevating issues in medical domain. Accurate identification of a patient from the given set of samples and classification becomes one of the challenging task when dealing with imbalanced datasets. Many conventional machine learning and data mining algorithms are shows poor performance to classify skewed distributed data because they are trained very well with the majority class samples only. Proposing an ensemble method called majority voting defined with a set of machine learning algorithms namely decision tree—C4.5, Naive Bayesian and K-Nearest Neighbor (KNN) classifiers. Classification of samples can be done based on the majority voting of classifiers. Experiment results stating that voting ensemble method shows classification accuracy of 95.2% on imbalanced malaria disease data whereas dealing with balanced malaria disease data voting ensembler shows 92.1% of accuracy. Consequently voting shows 100% classification report on precision, Recall and F1-Score on imbalanced malaria disease data sets whereas on balanced malaria disease data voting shows 96% of Precision, Recall and F1-Score metrics.
Efficient Segmentation of Medical Images Using Dilated Residual Networks

Medical image segmentation is an essential part in many medical applications such as automatic measurement of tumour size, volume of organs and content-based image retrieval, etc. Various fully convolutional architectures have been proposed in the literature to tackle this problem. Lack of generalization is one of the main challenge in the field of medical imaging and all existing fully convolutional architectures involve huge number of parameters which make them prone to overfit the data. In this study, we proposed an efficient convolutional architecture called Dilated Residual Network (DRN) for medical image segmentation. By the design of DRN architecture, we have reduced number of parameters involved drastically, making the architecture less prone to overfitting hence by improving the generalization ability. We demonstrate that DRN outperforms the previous state of the art architecture called U-Net in medical image segmentation on various datasets in terms of training time, Dice score and Jaccard score. The source code (based on Keras with Tensorflow as the background) of the DRN and sample train and test image results are available at https://github.com/LokeshBonta/Dilated-Residual-Networks .
Best Practices to Train Deep Models on Imbalanced Datasets—A Case Study on Animal Detection in Aerial Imagery

We introduce recommendations to train a Convolutional Neural Network for grid-based detection on a dataset that has a substantial class imbalance. These include curriculum learning, hard negative mining, a special border class, and more. We evaluate the recommendations on the problem of animal detection in aerial images, where we obtain an increase in precision from 9% to 40% at high recalls, compared to state-of-the-art. Data related to this paper are available at: http://doi.org/10.5281/zenodo.609023 .
A Data Driven Approach to Wind Plant Control using Moth-Flame Optimization (MFO) Algorithm

One of the main issues of the wind plant power generation nowadays is that the current stand alone controller of each turbine in the wind plant is not able to cope with chaotic nature of wake aerodynamic effect. Therefore, it is necessary to re-tune the controller of each turbine in the wind plant such that the total power generation is improved. This article presents an investigation of a data driven approach using moth-flame optimization algorithm (MFO) to the problem of improving wind plants power generation. The MFO based technique is applied to search the turbine’s optimum controller such that the aggregation power generation of a wind plant is maximized. The MFO is a population based optimization method that mimics the behavior of moths that navigate on specific angle with respect to the moon location. Here, it is expected that the MFO can solve the control accuracy problem in the existing algorithms for maximizing wind plant. A row of wind turbines plant with wake aerodynamic effect among turbines is adopted to demonstrate the effectiveness of the MFO based technique. The model of the wind plant is derived based on the real Horns Rev wind plant in Denmark. The performance of the proposed MFO algorithm is analyzed in terms of the statistical analysis of the total power generation. Numerical results show that the MFO based approach generates better total wind power generation than spiral dynamic algorithm (SDA) based approach and safe experimentation dynamics (SED) based approach.