Reviewerly/exHarmony huggingface dataset:
A Weighted Aggregating SGD for Scalable Parallelization in Deep Learning

We investigate the stochastic optimization problem and develop a scalable parallel computing algorithm for deep learning tasks. The key of our study involves a reformation of the objective function for the stochastic optimization in neural network models. We propose a novel update rule, named weighted aggregating stochastic gradient decent, after theoretically analyzing the characteristics of the newly formalized objective function. The new rule introduces a weighted aggregation scheme based on the performance of local workers and does not require a center variable. It assesses the relative importance of local workers and accepts them according to their contributions. Our new rule also allows the implementation of both synchronous and asynchronous parallelization and can result in varying convergence rates. For method evaluation, we benchmark our schemes against the mainstream algorithms, including the elastic averaging SGD in training deep neural networks for classification tasks. We conduct extensive experiments on several classic datasets, and the results confirm the strength of our scheme in accelerating the training of deep architecture and scalable parallelization.
Fuzzy Cloud Access Security Broker for Requirements Negotiation and Prioritization

A Cloud Access Security Broker (CASB) may be a safekeeping necessity point or cloud-based program that's set between cloud benefit clients and cloud applications of cloud computing which is utilized to run the dimensionality, heterogeneity, and equivocalness associated with cloud administrations. They permit the organization to amplify the reach of their security approaches past their claim framework to third-party computer program and capacity. In our work, we proposed a strategy which is designed on fuzzy CASB for requirements negotiation and prioritization. In the end, the usage of CASB is proved with the help of a case study.
Sampling-based Motion Planning for Aerial Pick-and-Place

This paper presents a motion planning approach for an aerial pick-and-place task where an aerial manipulator is supposed to pick up or place an object at locations specified as way-points. In particular, we focus on situations where such way-point constraints are imposed on certain partial state variables, rather than on full state variables. Our proposed framework, based on rapidly exploring random trees star (RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> ) in a bidirectional manner, enables an aerial manipulator to find an optimal trajectory that satisfies way-point constraints with only partial specifications. Here, we suggest an extra merging process to integrate the trees, each originated from the start and goal point. In the merging process, we search various candidate points satisfying a given condition that partially constrains state variables, and select a way-point with full specifications optimal in the perspective of the entire trajectory. Simulation and experiment results are included to validate the proposed framework.
Reliable clustering of Bernoulli mixture models

A Bernoulli Mixture Model (BMM) is a finite mixture of random binary vectors with independent dimensions.The problem of clustering BMM data arises in a variety of real-world applications, ranging from population genetics to activity analysis in social networks.In this paper, we analyze the clusterability of BMMs from a theoretical perspective, when the number of clusters is unknown.In particular, we stipulate a set of conditions on the sample complexity and dimension of the model in order to guarantee the Probably Approximately Correct (PAC)-clusterability of a dataset.To the best of our knowledge, these findings are the first non-asymptotic bounds on the sample complexity of learning or clustering BMMs.
Syn2Real: Forgery Classification via Unsupervised Domain Adaptation

In recent years, image manipulation is becoming increasingly more accessible, yielding more natural-looking images, owing to the modern tools in image processing and computer vision techniques. The task of the identification of forged images has become very challenging. Amongst different types of forgeries, the cases of Copy-Move forgery are increasing manifold, due to the difficulties involved to detect this tampering. To tackle such problems, publicly available datasets are insufficient. Addressing this issue, we employed unsupervised domain adaptation to learn the discriminative features from a large dataset and classify the forged images in new domains by feature space mapping. We synthesized a forgery dataset using image inpainting and copy-move forgery algorithm. However, models trained on these synthetic datasets have a significant drop in performance when tested on more realistic data. We improvised the F1 score on CASIA and CoMoFoD dataset to 80.3% and 78.8%, respectively outperforming state-of-the-art copy-move classification algorithms. Our approach can be helpful in those cases where the classification of data is unavailable.
Nearest Neighbor Classifiers Versus Random Forests and Support Vector Machines

Recent experimental studies have shown that the support vector machine and the random forest are "stand out" models that outperform most other supervised learning methods in benchmarked evaluations on real-world data sets. Studies have also established that both these models are closely related to nearest neighbor classifiers. Due to this connection, one would expect a similar performance from a nearest neighbor classifier; however, the latter lags greatly behind the support vector machine and the random forest in most evaluations. On the contrary, the nearest neighbor classifier has great theoretical potential because of its ability to model complex decision boundaries with low bias. Furthermore, it theoretically guarantees an error of no more than twice the Bayes error rate on an infinite data set. In this paper, we examine this obvious disconnect between the great theoretical promise and empirical reality of the nearest neighbor classifier. An additional point is that the random forest and support vector machine can be considered eager forms of the (lazy) nearest neighbor classifier. Inspired by the connections between nearest neighbor classifiers, support vector machines and random forest models, we propose a nearest neighbor classifier that is competitive to these models.
Circulant Binary Convolutional Networks for Object Recognition

The rapidly decreasing computation and memory cost has recently driven the success of many applications in the field of deep learning. Practical applications of deep learning in resource-limited hardware, such as embedded devices and smart phones, however, remain challenging. For binary convolutional networks, the reason lies in the degraded representation caused by binarizing full-precision filters. To address this problem, we propose new circulant filters (CiFs) and a circulant binary convolution (CBConv) to enhance the capacity of binarized convolutional features via our circulant back propagation (CBP). The CiFs can be easily incorporated into existing deep convolutional neural networks (DCNNs), which leads to new Circulant Binary Convolutional Networks (CBCNs). Extensive experiments confirm that the performance gap between the 1-bit and full-precision DCNNs is minimized by increasing the filter diversity, which further increases the representational ability in our networks. Our experiments on ImageNet show that CBCNs achieve 61.4% top-1 accuracy with ResNet18. Compared to the state-of-the-art such as XNOR, CBCNs can achieve up to 10% higher top-1 accuracy with more powerful representational ability. Also, CBCNs approximately achieve a storage reduction about 32 times. In particular, our method shows strong generalization on the object recognition task, i.e., face recognition, facial expression recognition and person re-identification.
Analysing Social Media Forums to Discover Potential Causes of Phasic Shifts in Cryptocurrency Price Series

The recent extreme volatility in cryptocurrency prices occurred in the setting of popular social media forums devoted to the discussion of cryptocurrencies. We develop a framework that discovers potential causes of phasic shifts in the price movement captured by social media discussions. This draws on principles developed in healthcare epidemiology where, similarly, only observational data are available. Such causes may have a major, one-off effect or recurring effects on the trend in the price series. We find a one-off effect of regulatory bans on bitcoin, the repeated effects of rival innovations on ether and the influence of technical traders, captured through discussion of market price, on both cryptocurrencies. The results for Bitcoin differ from Ethereum, which is consistent with the observed differences in the timing of the highest price and the price phases. This framework could be applied to a wide range of cryptocurrency price series where there exists a relevant social media text source. Identified causes with a recurring effect may have value in predictive modelling, whilst one-off causes may provide insight into unpredictable black swan events that can have a major impact on a system.
Multi Robot Path Planning Parameter Analysis Based on Particle Swarm Optimization (PSO) in an Intricate Unknown Environments

Through Particle Swarm Optimization (PSO) path planning in an intricate environment turns out to be a novel approach for robot's multi path planning. Automation and detection capabilities of robots are the major challenges, to overcome these problems optimized path needs to be established. Robot path planning is one of the main problem that deals with the computation of collision free path for the given robot (agent) with the map, which helps it to operate. When the environment is known and the target location is estimated then only the path establishment is possible. The work we have presented on our paper totally focusses on the path planning problem. We have taken only one case into consideration, according to it the robot (agent) tracks the coordinated targets and reach towards the unknown environment through obstacle avoidance technique when the location of the target is unknown. Important parameters that we have taken to asses these algorithms are: (a) Number of visited node we consider as (Move). (b) Area explored considered as (Coverage). (c) Distance travelled considered as (Energy) and time elapsed as (Time).
Breaking the Code on Broken Tablets: The Learning Challenge for Annotated Cuneiform Script in Normalized 2D and 3D Datasets

The number of known cuneiform tablets is assumed to be in the hundreds of thousands. The Hilprecht Archive Online contains 1977 high-resolution 3D scans of tablets. The online cuneiform database CDLI catalogs metadata for more than 100.000 tablets. While both are accessible publicly, large-scale machine learning and pattern recognition on cuneiform tablets remain elusive. The data is only accessible by searching web pages, the tablet identifiers between collections are inconsistent, and the 3D data is unprepared and challenging for automated processing. We pave the way for large-scale analyses of cuneiform tablets by assembling a cross-referenced benchmark dataset of processed cuneiform tablets: (i) frontally aligned 3D tablets with pre-computed high-dimensional surface features, (ii) six-views raster images for off-the-shelf image processing, and (iii) metadata, transcriptions, and transliterations, for a subset of 707 tablets, for learning alignment between 3D data, image and linguistic expression. This is the first dataset of its kind and of its size in cuneiform research. This benchmark dataset is prepared for ease-of-use and immediate availability for computational researches, lowering the barrier to experiment and apply standard methods of analysis, at https://doi.org/10.11588/data/IE8CCN.
Memorability of Enhanced Informational Graphics

Design enhancements are often added to charts, signage, and infographics to help garner attention or communicate a message. Though it is argued that they may also detract user’s focus on the underlying information, prior studies have contributed evidence that visual design enhancements and even simple decorations can improve memory of visual displays. However, there is limited empirical knowledge about how the type of aesthetic enhancements infuences memory, and what informational and data elements are remembered from a visualization. We conducted a user study testing chart types (line, pie, and bar), the presence of color, and whether added enhancements were contextually related to the data topic presented in each chart. We found that enhancements relevant to the data helped in the recall of title and thematic elements, but enhancements did not signifcantly affect recall of specifc data values. This suggests that using relevant enhancements can have a positive effect on memorability of some chart content, but only if the design styles are chosen well to match the information topic. Recall of chart topics for unrelated embellishments was worse than plain, un-enhanced charts, which suggests that visual enhancement can distract or interfere with memorability if the viewer does not understand a meaningful connection between informational topic and design modifcations.
On Critical Sampling of Time-Vertex Graph Signals

Joint time-vertex graph signals are pervasive in real-world. This paper focuses on the fundamental problem of sampling and reconstruction of joint time-vertex graph signals. We prove the existence and the necessary condition of a critical sampling set using minimum number of samples in time and graph domain respectively. The theory proposed in this paper suggests to assign heterogeneous sampling pattern for each node in a network under the constraint of minimum resources. An efficient algorithm is also provided to construct a critical sampling set.
Sanskrit Stopword Analysis Through Morphological Analyzer and Its Gujarati Equivalent for MT System

The identification and removal of a stopword is a common preprocessing task in many natural language processing implementations. The morphologically parsed information of stopword is also relevant in analysis of various NLP tasks. The list of most common seventy-five Sanskrit stopwords are evaluated using rule-based morphological analyzer. Most stopwords were classified as indeclinables and pronouns. The Gujarati equivalent of stopwords is retrieved using bilingual dictionary so as to cache the data for faster retrieval during MT process.
Intelligent Driver Emotion Monitoring Based on Lightweight Multilayer Random Forests

In this paper, we propose a lightweight multi-layer random forest (LMRF) model. The LMRF model is a non-neural network-style deep model composed of arbitrary forests rather than layers. DNN is a powerful algorithm for facial recognition (FER), but there are too many parameters, careful parameter tuning, large amounts of training data, black box models, and pretrained architecture required for a current DNN. To overcome the burden of real-time processing DNN, we use the proposed LMRF with two tree structures per layer and a small number of trees for high-speed FER. We conducted experiments using an actual driving database captured using a near-infrared (NIR) camera to monitor the driver's emotions. The proposed LMRF provides similar FER accuracy to DNN with a small number of hyperparameters, and the faster processing time using the CPU.
Making a Batik Dataset for Text to Image Synthesis Using Generative Adversarial Networks

Batik is a cultural heritage as well as the identity of the Indonesian nation that needs to be preserved. The use of deep learning allows the process of making batik patterns done by computer through the mechanism of text-to-image synthesis without humans needing to make it directly. The main contribution of this research is to produce a synthetic batik pattern that is similar to the original without removing the characteristics possessed by each batik pattern. This process of text synthesis to images uses the Generative Adversarial Networks (GAN) by first creating a system that can learn from a datasets. A varied and structured dataset can make it easier for the system to learn faster. In this study, a batik dataset was created for the synthesis of text into images.
Additive Noise Level Estimation Based on Singular Value Decomposition (SVD) in Natural Digital Images

True Noise level estimation is a seminal research of interest in the topic of digital image processing especially in blind noise removal methods. In this study, an estimation of additive white Gaussian noise (AWGN) in digital natural images is introduced. The adaptive noise level estimation is designed mainly based on singular value decomposition (SVD) of the natural images. The proposed technique contains two pivotal stages. Firstly, typical noise level estimate is utilized in order to manipulate the algorithm factors to be used in the second stage of the proposed technique. Secondly, the adjusted parameters are used in SVD in order to speed up the estimation processes and increase the accuracy rate of the noise level estimation. The experimental results depict that the proposed algorithm performs professionally over a several ranges of visual details which is presented in PSNR and MSE in AWGN removal methods. In addition, in terms of time complexity, the proposed algorithm in second stage shows significant performance in terms of computational load and achieves high running speed.
AMENDER: An Attentive and Aggregate Multi-layered Network for Dataset Recommendation

In this paper, we study the problem of recommending the appropriate datasets for authors, which is implemented to infer the proximity between authors and datasets by leveraging the information from a three-layered network, composed by authors, papers and datasets. To link author-dataset semantically by taking advantage of the rich content information of papers in the intermediate layer, we design an attentive and aggregate multi-layer network learning model. The aggregation is for integrating the intra-layer information of paper content and citations, while the attention is used for coordinating authors at the top-layer and datasets at the bottom-layer in the semantic space learned from papers in the intermediate layer. The experimental study demonstrates the superiority of our method compared with the solutions that extend existing models to our problem.
Auto-Scaling in the Cloud Environment

Every software application has its own minimum set of requirements like CPU, storage, memory, networking, and power. These have to be integrated into a specific configuration to allow the smooth functioning of the software application. When data traffic becomes higher than expected, higher resources are required. There may not be enough time to provision new resources manually; in such cases, an auto-scaling system is required for managing these situations. Cloud computing means using data, programs, and other resources pooled in the data center and accessed through the internet instead of the user's computer. In the chapter, the authors discussed some aspects related to cloud computing like cloud workload, load balancing, load balancing algorithms, scaling techniques, and auto-scaling to fulfill cloud workload balancing requirements.
Exact entanglement dynamics mediated by leaky optical cavities

We present a quantum-state-diffusion equation to characterize the dynamics of a generic atomic system due to the coupling to a leaky cavity mode. As quantum resources, the population, the coherence and even the entanglement of the system would gradually leak out of the cavity. The effect from the leakage of the cavity-mode to the uncontrollable degrees of freedom, e.g. environment, is however not always negative to particular applications of these quantum resources. We investigate the competition between these two mechanisms in the framework of the non-Markovian open-quantum-system dynamics, where the leaky mode serves as a general two-layer bosonic environment. Numerical calculations based on our non-perturbative approach reveal a generation of quantum entanglement in the qubit system due to the coupling to a resonant leaky cavity.
Industrial Automation of Process for Transformer Monitoring System Using IoT Analytics

Multiple devices interconnected with each other via the Internet are the key concept behind IoT. It allows autonomous devices with the possibility to use the Internet for communication and exchange of data. This paper focuses on monitoring the transformer in real-time fault detection and records distinct operating parameters of the transformer like voltage imbalance, load current, transformer oil levels, temperature, vibration. Based on these parameters, the transformers fail state (i.e. a state where transformer malfunctions or completely stops working) and health (i.e. voltage, current, oil levels, temperature and vibration) are predicted by making use of an artificial neural network (ANN) algorithm. Use of this technology can minimize working efforts, thereby improving accuracy, stability, efficiency. Thus, remote monitoring and machine control are achieved, as well as ANNs help to determine the performance and yield appropriate measures accordingly. In this case, sensors are used to sense the important parameters of equipment such as current, voltage oil level in any operating transformer. By analyzing relevant data using ANNs, this system will be beneficial in many industries. Likewise, this system is generalized to be used in a wide array of industrial automated machines.
Cloud-Based Autonomic Computing Framework for Securing SCADA Systems

This chapter proposes an autonomic computing security framework for protecting cloud-based supervisory control and data acquisition (SCADA) systems against cyber threats. Autonomic computing paradigm is based on intelligent computing that can autonomously take actions under given conditions. These technologies have been successfully applied to many problem domains requiring autonomous operations. One such area of national interest is SCADA systems that monitor critical infrastructures such as transportation networks, large manufacturing, business and health facilities, power generation, and distribution networks. The SCADA systems have evolved from isolated systems into a complex, highly connected systems requiring constant availability. The migration of such systems from in-house to cloud infrastructures has gradually gained prominence. The deployments over cloud infrastructures have brought new cyber security threats, challenges, and mitigation opportunities. SCADA deployment to cloud makes it imperative to adopt newer architectures and measures that can proactively and autonomously react to an impending threat.