Reviewerly/exHarmony huggingface dataset:
PLEDGE: An IoT-oriented Proof-of-Honesty based Blockchain Consensus Protocol

The existing lottery-based consensus algorithms, such as Proof-of-Work, and Proof-of-Stake, are mostly used for blockchain-based financial technology applications. Similarly, the Byzantine Fault Tolerance algorithms do provide consensus finality, yet they are either communications intensive, vulnerable to Denial-of-Service attacks, poorly scalable, or have a low faulty node tolerance level. Moreover, these algorithms are not designed for the Internet of Things systems that require near-real-time transaction confirmation, maximum fault tolerance, and appropriate transaction validation rules. Hence, we propose "Pledge," a unique Proof-of-Honesty based consensus protocol to reduce the possibility of malicious behavior during blockchain consensus. Pledge also introduces the Internet of Things centric transaction validation rules. Initial experimentation shows that Pledge is economical and secure with low communications complexity and low latency in transaction confirmation.
MLR-SNet: Transferable LR Schedules for Heterogeneous Tasks

The learning rate (LR) is one of the most important hyperparameters in stochastic gradient descent (SGD) algorithm for training deep neural networks (DNN). However, current hand-designed LR schedules need to manually pre-specify a fixed form, which limits their ability to adapt to practical non-convex optimization problems due to the significant diversification of training dynamics. Meanwhile, it always needs to search proper LR schedules from scratch for new tasks, which, however, are often largely different with task variations, like data modalities, network architectures, or training data capacities. To address this learning-rate-schedule setting issue, we propose to parameterize LR schedules with an explicit mapping formulation, called MLR-SNet. The learnable parameterized structure brings more flexibility for MLR-SNet to learn a proper LR schedule to comply with the training dynamics of DNN. Image and text classification benchmark experiments substantiate the capability of our method for achieving proper LR schedules. Moreover, the explicit parameterized structure makes the meta-learned LR schedules capable of being transferable and plug-and-play, which can be easily generalized to new heterogeneous tasks. We transfer our meta-learned MLR-SNet to query tasks like different training epochs, network architectures, data modalities, dataset sizes from the training ones, and achieve comparable or even better performance compared with hand-designed LR schedules specifically designed for the query tasks. The robustness of MLR-SNet is also substantiated when the training data are biased with corrupted noise. We further prove the convergence of the SGD algorithm equipped with LR schedule produced by our MLR-SNet, with the convergence rate comparable to the best-known ones of the algorithm for solving the problem. The source code of our method is released at https://github.com/xjtushujun/MLR-SNet.
Energy Aware Metaheuristic Approaches to Virtual Machine Migration in Cloud Computing

Abstract In mandate to drop the power utilization, virtual machine migration is utilized for cloud computing. At present many algorithms are there, that diminishing the energy consumption with the assistance of virtual machine migration and power proficient planning of VMs. The target to migrate the virtual machine from one under-burden server to other over-burden server is to have ideal outcomes. Over-burden or Under-burden host detection strategy utilized as a part of proposed work is Inter Quartile Range. After detection of an Over-burden or Under-burden host, the following stage is to pick the specific virtual machines to relocate from one under-burden host to the over-burden host. VM choice arrangements Minimum Migration Time (MMT) along with various Meta heuristic optimizations are executed. For assessing proposed algorithm, CloudSim is used to assess and look at the execution of proposed algorithm. To analyze the effectiveness of planned system seven parameters the power utilization, standard deviation of host, standard deviation of virtual machine placement, standard deviation of virtual machine migration, SLA performance degradation due to migration, and total SLA violation are considered.
Self-Sparse Generative Adversarial Networks

Generative Adversarial Networks (GANs) are an unsupervised generative model that learns data distribution through adversarial training. However, recent experiments indicated that GANs are difficult to train due to the requirement of optimization in the high dimensional parameter space and the zero gradient problem. In this work, we propose a Self Sparse Generative Adversarial Network (Self-Sparse GAN) that reduces the parameter space and alleviates the zero gradient problem. In the Self-Sparse GAN, we design a Self-Adaptive Sparse Transform Module (SASTM) comprising the sparsity decomposition and feature-map recombination, which can be applied on multi-channel feature maps to obtain sparse feature maps. The key idea of Self-Sparse GAN is to add the SASTM following every deconvolution layer in the generator, which can adaptively reduce the parameter space by utilizing the sparsity in multi-channel feature maps. We theoretically prove that the SASTM can not only reduce the search space of the convolution kernel weight of the generator but also alleviate the zero gradient problem by maintaining meaningful features in the Batch Normalization layer and driving the weight of deconvolution layers away from being negative. The experimental results show that our method achieves the best FID scores for image generation compared with WGAN-GP on MNIST, Fashion-MNIST, CIFAR-10, STL-10, mini-ImageNet, CELEBA-HQ, and LSUN bedrooms, and the relative decrease of FID is 4.76% ~ 21.84%.
Recognition of Arabic Handwritten Diacritics using the new database DBAHD

Abstract Recognition of handwritten Arabic characters is gaining momentum and research in this area has increased considerably in recent years. However, research remains modest compared to that performed in other scripts. This is mainly due to the morphology of Arabic writing, in particular its richness in diacritical marks. This signs are generally recognized by adopting structural or morphological measures. However, the difficulty and variability of handwriting can sometimes be misleading, thus influencing the results obtained. This article presents a new database for Arabic handwritten diacritics (DBAHD). It is designed to serve the Arabic handwriting recognition systems based on segmentation and machine learning.
Modelling multi-level prosody and spectral features using deep neural network for an automatic tonal and non-tonal pre-classification-based Indian language identification system

In this paper an attempt has been made to prepare an automatic tonal and non-tonal pre-classification-based Indian language identification (LID) system using multi-level prosody and spectral features. Languages are first categorized into tonal and non-tonal groups, and then, from among the languages of the respective groups, individual languages are identified. The system uses syllable, word (tri-syllable) and phrase level (multi-word) prosody (collectively called multi-level prosody) along with spectral features, namely Mel-frequency cepstral coefficients (MFCCs), Mean Hilbert envelope coefficients (MHEC), and shifted delta cepstral coefficients of MFCCs and MHECs for the pre-classification task. Multi-level analysis of spectral features has also been proposed and the complementarity of the syllable, word and phrase level (spectral + prosody) has been examined for pre-classification-based LID task. Four different models, particularly, Gaussian Mixture Model (GMM)-Universal Background Model (UBM), Artificial Neural Network (ANN), i-vector based support vector machine (SVM) and Deep Neural Network (DNN) have been developed to identify the languages. Experiments have been carried out on National Institute of Technology Silchar language database (NITS-LD) and OGI Multi-language Telephone Speech corpus (OGI-MLTS). The experiments confirm that both prosody and (spectral + prosody) obtained from syllable-, word- and phrase-level carry complementary information for pre-classification-based LID task. At the pre-classification stage, DNN models based on multi-level (prosody + MFCC) features, coupled with score combination technique results in the lowest EER value of 9.6% for NITS-LD. For OGI-MLTS database, the lowest EER value of 10.2% is observed for multi-level (prosody + MHEC). The pre-classification module helps to improve the performance of baseline single-stage LID system by 3.2% and 4.2% for NITS-LD and OGI-MLTS database respectively.
Detection of Anomaly in Videos Using Convolutional Autoencoder and Generative Adversarial Network model

Anomaly in the crowds can create a threat to public security. Manual detection of abnormal crowd behavior in CCTV footage by human operators is a very difficult and time-consuming task. There is a need for automated detection of abnormal events in a video. The proposed automated method is based on the Convolutional Autoencoder and Generative Adversarial Network (GAN) model which are used to extract the features and motion patterns of the videos. The selected features are then classified by using different classifiers such as Support Vector Machine (SVM), Naïve Bayes, Decision Tree, and Linear Regression, for differentiation of various types of abnormalities in videos. The proposed method is validated on the UCF crime video dataset. Accuracy, sensitivity, specificity and loss function are the performance parameters used for the evaluation of the proposed methodology. The visual and parametric results of the proposed method showed promising results in the form of accuracy as compared with the existing techniques.
Short Text Classification Model Based on Multi-Attention

Short text classification plays an important role in NLP and its applications span a wide range of activities such as sentiment analysis, spam detection. Recently, attention mechanism is widely used in text classification task. Inspired by this, a text classification model based on multi-attention network(MAN) is proposed in this study, which perform well in extracting information related to text category. In our model, we combine the textual information based on multi-attention mechanism, which enables model to focus on global information of the sentence. We tested effectiveness of our model using several standard text classification datasets. Experiment told that our model achieved state-of-the-art results on all datasets.
Untargeted Poisoning Attack Detection in Federated Learning via Behavior AttestationAl

Federated Learning (FL) is a paradigm in Machine Learning (ML) that addresses data privacy, security, access rights and access to heterogeneous information issues by training a global model using distributed nodes. Despite its advantages, there is an increased potential for cyberattacks on FL-based ML techniques that can undermine the benefits. Model-poisoning attacks on FL target the availability of the model. The adversarial objective is to disrupt the training. We propose attestedFL, a defense mechanism that monitors the training of individual nodes through state persistence in order to detect a malicious <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">worker</i> in small to medium federation size. A fine-grained assessment of the history of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">worker</i> permits the evaluation of its behavior in time and results in innovative detection strategies. We present three lines of defense that aim at assessing if the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">worker</i> is reliable by observing if the node is truly training, while advancing towards a goal. Our defense exposes an attacker’s malicious behavior and removes unreliable nodes from the aggregation process so that the FL process converge faster. attestedFL increased the accuracy of the model in different FL settings, under different attacking patterns, and scenarios e.g., attacks performed at different stages of the convergence, colluding attackers, and continuous attacks.
Essential Approaches to Dual-Track Agile: Results from a Grey Literature Review

Context: Nowadays, companies are challenged by increasing market dynamics, rapid changes and disruptive participants entering the market. To survive in such an environment, companies must be able to quickly discover product ideas that meet the needs of both customers and the company and deliver these products to customers. Dual-track agile is a new type of agile development that combines product discovery and delivery activities in parallel, iterative, and cyclical ways. At present, many companies have difficulties in finding and establishing suitable approaches for implementing dual-track agile in their business context. Objective: In order to gain a better understanding of how product discovery and product delivery can interact with each other and how this interaction can be implemented in practice, this paper aims to identify suitable approaches to dual-track agile. Method: We conducted a grey literature review (GLR) according to the guidelines to Garousi et al. Results: Several approaches that support the integration of product discovery with product delivery were identified. This paper presents a selection of these approaches, i.e., the Discovery-Delivery Cycle model, Now-Next-Later Product Roadmaps, Lean Sprints, Product Kata, and Dual-Track Scrum. The approaches differ in their granularity but are similar in their underlying rationales. All approaches aim to ensure that only validated ideas turn into products and thus promise to lead to products that are better received by their users.
Pairwise attention network for cross-domain image recognition

In recent years, the domain adaption has received wide attention from machine learning communities because of differences in data distribution or the lack of training data in a practical machine learning task. In this work, we propose a P airwise A ttention N etwork ( PAN for short) for addressing cross-domain image recognition task. In this model, different local features and the global-feature are concatenated to obtain different attention estimators, and then they are combined to get the attention map. In this way, we can focus on the important parts of an image, and ignore the irrelative regions. Moreover, attention consistency is also embedded in PAN to make sure consistent interest regions in the same class. Besides, to improve the feature discrimination, an embedding discriminative subspace is learned where it maps positive sample pairs aligned in a hypersphere and negative sample pairs separated. Extensive experimental results on the MNIST-USPS, office, and Visda-2017 datasets demonstrate that PAN can outperform state-of-the-art methods in terms of average accuracy.
Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge

While a large number of causal inference models for estimating individualized treatment effects (ITE) have been developed, selecting the best one poses a unique challenge, since the counterfactuals are never observed. The problem is challenged further in the unsupervised domain adaptation (UDA) setting where we have access to labeled samples in the source domain but desire selecting an ITE model that achieves good performance on a target domain where only unlabeled samples are available. Existing selection techniques for UDA are designed for predictive models and are sub-optimal for causal inference because they (1) do not account for the missing counterfactuals and (2) only examine the discriminative density ratios between the input covariates in the source and target domain and do not factor in the model’s predictions in the target domain. We leverage the invariance of causal structures across domains to introduce a novel model selection metric specifically designed for ITE models under UDA. We propose selecting models whose predictions of the effects of interventions satisfy invariant causal structures in the target domain. Experimentally, our method selects ITE models that are more robust to covariate shifts on a variety of datasets, including estimating the effect of ventilation in COVID-19 patients.
Logic Learning in Adaline Neural Network

In this paper, Adaline Neural Network (ADNN) has been explored to simulate the actual signal processing between input and output. One of the drawback of the conventional ADNN is the use of the non-systematic rule that defines the learning of the network. This research incorporates logic programming that consists of various prominent logical representation. These logical rules will be a symbolic rule that defines the learning mechanism of ADNN. All the mentioned logical rule are tested with different learning rate that leads to minimization of the Mean Square Error (MSE). This paper uncovered the best logical rule that could be governed in ADNN with the lowest MSE value. The thorough comparison of the performance of the ADNN was discussed based on the performance MSE. The outcome obtained from this paper will be beneficial in various field of knowledge that requires immense data processing effort such as in engineering, healthcare, marketing, and business.
Smart Storytelling: Video and Text Risk Communication to Increase MFA Acceptability

Exposure of passwords for authentication and access management is a ubiquitous and constant threat. Yet, reliable solutions, including multi-factor authentication (MFA), face issues with wide-spread adoption. Prior research shows that making MFA mandatory helps with tool adoption but is detrimental to users' mental models and leads to security-avoidance behavior. To explore feasible solutions, we implemented text-and video-based risk communication strategies to evaluate if either mode of risk communication was useful. We sought to explore users' technical biases to further examine the mental models that are associated with safer security habits. Our study of N = 620 participants found that users are aware of frequent security attacks, including phishing. We found that text- and video-based communication is often useful when information is aligned with individual actions and their consequences, which can range from benign to catastrophic. Shorter mental-model-aligned video snippets piqued user interest in MFA. On the other hand, detailed risk communication videos or textual descriptions improved users' understanding of MFA and the potential risks of non-usage. Our study indicates that, beyond usability and comprehensive education, risk communication offers the potential to increase MFA adoption.
Data Augmentation to Improve the Performance of a Convolutional Neural Network on Image Classification

Deep learning has become a fundamental tool to extract meaningful information from big data. However, it needs a huge amount of high-quality data to build an accurate classifier. In many situations, the size of the training dataset is not sufficiently large to effectively train a model. This paper presents a Convolutional Neural Network trained on a very small dataset, discussing the impact of data augmentation, feature extraction and fine-tuning on the accuracy of the model. The results show that having a small dataset, those approaches are very effective when dealing with image data.
Capsule Network with Shortcut Routing

Capsules are fundamental informative units that are introduced into capsule networks to manipulate the hierarchical presentation of patterns. The part-hole relationship of an entity is learned through capsule layers, using a routing-by-agreement mechanism that is approximated by a voting procedure. Nevertheless, existing routing methods are computationally inefficient. We address this issue by proposing a novel routing mechanism, namely "shortcut routing", that directly learns to activate global capsules from local capsules. In our method, the number of operations in the routing procedure is reduced by omitting the capsules in intermediate layers, resulting in lighter routing. To further address the computational problem, we investigate an attention-based approach, and propose fuzzy coefficients, which have been found to be efficient than mixture coefficients from EM routing. Our method achieves on-par classification results on the Mnist (99.52%), smallnorb (93.91%), and affNist (89.02%) datasets. Compared to EM routing, our fuzzy-based and attention-based routing methods attain reductions of 1.42 and 2.5 in terms of the number of calculations.
Resource Allocation for Wireless Federated Edge Learning based on Data Importance

The implementation of artificial intelligence (AI) in wireless networks is becoming more and more popular because of the growing number of mobile devices and the availability of huge amount of data. Directly transmitting data for centralized learning will cause long communication latency and may incur severe privacy issue as well. To address these issues, we consider the importance-aware federated edge learning (FEEL) system in this paper. Based on the relation between loss decay and gradient norm, a learning efficiency maximization problem is formulated by jointly considering the communication resource allocation and data selection. The closed-form results for optimal communication resource allocation and data selection are both developed, where some insights are also highlighted. Finally, the test results show that the proposed algorithm can effectively reduce the training latency and improve the learning accuracy as compared with some benchmark algorithms.
Linguistic and Psychological Features of the Reddit News Post

The phenomenon of social networking platform has been considered in the article. The Reddit online social chatting community has been selected as a basis for the study. The features of linguistic, psychological, textual semantic analysis have been applied to ten discussion comment charts concerning the latest random news - the death of Indian actor, Irfan Khan. The comments in Reddit social network have been allocated and linguistically marked with implicit or explicit comments. Linguistic Inquiry and Word Count (LIWC) program has been used. It proved the textual analysis of the Reddit comment section statistically.
Piecewise Linear Path Following for a Unicycle Using Transverse Feedback Linearization

Motion planning and path following together constitute a critical part of the decision-making hierarchy in autonomous ground vehicles. One of the simplest instances of this architecture is when the path planner generates waypoints that define a sequence of collision-free line segments from a start location to goal destination and when the vehicle’s kinematic model is taken to be Dubin’s vehicle. The low-level feedback controller can then be designed by treating the path following problem as a set stabilization problem; one such approach is called transverse feedback linearization (TFL). However, for a Dubin’s vehicle with only one input, the direction of traversal along the path is completely determined by the vehicle’s initial condition. In this article, we provide easily checkable sufficient conditions that only need to be checked once at run time and a simple systematic design procedure, which guarantees that the robot traverses the path while coming within a user-specified distance of the waypoints; the procedure is also empirically verified.