Reviewerly/exHarmony huggingface dataset:
TDDM4IoTS: A Test-Driven Development Methodology for Internet of Things (IoT)-Based Systems

This paper presents a development methodology for Internet of Things (IoT)-based Systems (IoTS) that gathers ideas from several of the most outstanding software development paradigms nowadays, such as Model-Driven Engineering (MDE) and Test-Driven Development (TDD), in addition to incorporating the principles that govern agile software development methodologies, such as SCRUM and XP. The methodology presented here, called Test-Driven Development Methodology for IoTS (TDDM4IoTS), has been proposed after an exhaustive review of different software development methodologies, leading us to conclude that none of them are specially oriented towards the development of IoTS. The methodology mainly consists of eleven phases, whose order of application can be established by the team that will develop the project in question. In this paper, we suggest an order to follow, as well as existing software tools that could be used as support for obtaining the corresponding deliverables at each phase.
On Ledger Inconsistency Time in Bitcoin's Blockchain Delivery Network

In this work we analyze the blockchain forking events, blockchain partitioning, and duration of inconsistent state of the ledger in a Bitcoin delivery network. Using a comprehensive probabilistic model, we obtain the probability distribution of two- and three-way forks, the forked partition sizes, and the duration of ledger inconsistency until the resolution. We show that the three-way forking probability is substantially lower than that of a two-way forking and that the partition sizes in the case of two-way forking tend to equalize when the number of nodes increases. Finally, we show that the duration of ledger inconsistency state exhibits long tail probability distribution which means that successive forking events can force the ledger to remain inconsistent for long time.
Multimodal Fusion of Speech and Gesture Recognition based on Deep Learning

Abstract This paper proposes a multimodal fusion architecture based on deep learning. The architecture consists of two forms: speech command and hand gesture. First, the speech and gesture commands input by users are recognized by CNN for speech command recognition and LSTM for hand gesture recognition respectively. Secondly, the obtained results are searched by keywords and compared by similarity degree to obtain recognition results. Finally, the two results are fused to output the final instructions. Experiments show that the proposed multi-mode fusion model is superior to the single-mode fusion model.
A New Improved Learning Algorithm for Convolutional Neural Networks

The back-propagation (BP) algorithm is usually used to train convolutional neural networks (CNNs) and has made greater progress in image classification. It updates weights with the gradient descent, and the farther the sample is from the target, the greater the contribution of it to the weight change. However, the influence of samples classified correctly but that are close to the classification boundary is diminished. This paper defines the classification confidence as the degree to which a sample belongs to its correct category, and divides samples of each category into dangerous and safe according to a dynamic classification confidence threshold. Then a new learning algorithm is presented to penalize the loss function with danger samples but not all samples to enable CNN to pay more attention to danger samples and to learn effective information more accurately. The experiment results, carried out on the MNIST dataset and three sub-datasets of CIFAR-10, showed that for the MNIST dataset, the accuracy of Non-improve CNN reached 99.246%, while that of PCNN reached 99.3%; for three sub-datasets of CIFAR-10, the accuracies of Non-improve CNN are 96.15%, 88.93%, and 94.92%, respectively, while those of PCNN are 96.44%, 89.37%, and 95.22%, respectively.
Leveraging Artificial Intelligence for Effective Recruitment and Selection Processes

In recent years, artificial intelligence (AI) has revolutionized all the management functions across the globe. AI has touched all aspects of management through automation and robotization by substituting human beings at the workplaces with greater efficiency and lower costs. Most of the repetitive and standardized jobs and some of the highly specialized jobs are now being performed by machines powered by AI. Recruitment and selection function, which is quite critical for the success of organizations, has also been significantly disrupted by AI. Now, a good number of small, medium, and large companies are using AI for staffing. Some of the finest AI-driven tools for managing the entire hiring process are available and being adopted rapidly. This research paper is a humble attempt at examining how AI-powered hiring is changing the traditional recruitment and selection processes. Besides, concerns of various stakeholders have also been discussed in the paper.
Graphs cannot be indexed in polynomial time for sub-quadratic time string matching, unless SETH fails

The string matching problem on a node-labeled graph G=(V,E) asks whether a given pattern string P equals the concatenation of node labels of some path in G. This is a basic primitive in various problems in bioinformatics, graph databases, or networks, recently proven to have a O(|E||P|)-time lower bound, under the Orthogonal Vectors Hypothesis (OVH) (Equi et al. (2019) [11]). We consider its indexed version, where the graph is indexed to support string queries. We show that, under OVH, no polynomial-time index of the graph performed in time O(|E|α) can support querying P in time O(|P|+|E|δ|P|β), with either δ<1 or β<1. We present our techniques as a general framework, introducing the notion of linear independent-components (lic) reduction, from which we derive our result. This allow us to also translate the quadratic conditional lower bound of Backurs and Indyk (2015) [47] for the problem of matching a query string inside a text, under edit distance, into an analogous tight quadratic lower bound for its indexed version. This improves the recent result of Cohen-Addad, Feuilloley and Starikovskaya (2019) [48], with a slightly different boundary condition. We also apply our technique to obtain the first quadratic indexing lower bounds for Fréchet distance and rooted unlabeled subtree-isomorphism queries.
Big Data Analyses of ZeroNet Sites for Exploring the New Generation DarkWeb

ZeroNet is a new generation typical dark web, which uses the Bitcoin encryption algorithm and BitTorrent technology to build a distributed and censored-resistant communication network. Based on our cumulative studies on the onion router, we present a big data analyses framework for automated multi-categorization of ZeroNet websites to facilitate analyst situational awareness of new content that emerges from this dynamic landscape. Over the last two years, our team has developed a distributed crawling infrastructure called ZeroCrawler that automatically crawls and updates ZeroNet websites in realtime. It stores data into a research repository designed to help better understand ZeroNet's hidden service ecosystem. The analysis component of our framework is called Automated Multi-Categorization Labeling (AMCL), which introduces a three-stage thematic labeling strategy: (1) it learns descriptive and discriminative keywords for different categories, and (2) get a probability distribution of the keywords for different categories, and then (3) uses these terms to map ZeroNet website content to several labels. We also present empirical results of AMCL and our ongoing experimentation with it, as we have gained experience applying it to the entirety of our ZeroNet repository, now over 3000 indexed websites. The experimental results show that AMCL can discover categories on previously unlabeled websites, and we discuss applications of AMCL in supporting various analyses and investigations of the ZeroNet websites.
Audio-Visual Fusion for Aggression Detection Using Deep Neural Networks

Multimodal fusion has become a hot topic in computer vision. For surveillance applications detecting unwanted behavior such as aggression is a complex task. The definition of aggression is ambiguous. Hence, it is difficult to describe this high level concept with simple rules or trivial algorithms. Here, aggression can be defined as an action of disturbing others. In this paper, we study aggression in the context of surveillance and its detection from acoustic and visual cues using deep neural networks. We suggest an approach that uses the merging of audio and video modalities with extra information called meta-features. We use an intermediate level which combines the predictions from each modality, and meta-features that have an influence on the fusion process. We show that detecting aggression can be more accurate with the appropriate features and simple deep neural network models. Our experiments prove that our proposed fusion method outperforms the standard fusion strategies such as feature and decision level fusion, and it acquires the best performance.
Association Rule Mining Method Based on the Similarity Metric of Tuple-Relation in Indoor Environment

Association rules can detect the association pattern between POIs (point of interest) and serve the application of indoor location. In this paper, a new index, tuple-relation, is defined, which reflects the association strength between POI sets in indoor environment. This index considers the potential association information such as spatial and semantic information between indoor POI sets. On this basis, a new R-FP-growth (tuple-relation frequent pattern growth) algorithm for mining association rules in indoor environment is proposed, which makes comprehensive use of the co-occurrence probability, conditional probability, and multiple potential association information among POI sets, to form a new support-confidence-relation constraint framework and to improve the quality and application value of mining results. Experiments are performed, using real Wi-Fi positioning trajectory data from a shopping mall. Experimental results show that the tuple-relation calculation method based on cosine similarity has the best effect, with an accuracy of 87%, and 19% higher than that of the traditional FP-growth algorithm.
Light Structure from Pin Motion: Geometric Point Light Source Calibration

Abstract We present a method for geometric point light source calibration. Unlike prior works that use Lambertian spheres, mirror spheres, or mirror planes, we use a calibration target consisting of a plane and small shadow casters at unknown positions above the plane. We show that shadow observations from a moving calibration target under a fixed light follow the principles of pinhole camera geometry and epipolar geometry, allowing joint recovery of the light position and 3D shadow caster positions, equivalent to how conventional structure from motion jointly recovers camera parameters and 3D feature positions from observed 2D features. Moreover, we devised a unified light model that works with nearby point lights as well as distant light in one common framework. Our evaluation shows that our method yields light estimates that are stable and more accurate than existing techniques while having a much simpler setup and requiring less manual labor.
Automated Prediction of Phishing Websites Using Deep Convolutional Neural Network

Phishing is one of the ruinous issues encountered by the World Wide Web (WWW) and steers to the financial catastrophes for individuals and businesses. It has been perpetually a perplexing issue to identify phishing attacks with high exactness. The tremendous outcomes in the area of classification have been succeeded by the state-of-the-art invention of the deep convolutional neural networks (DCNNs). This paper is concerned with an accurate identifying approach for web phishing attacks based on deep convolutional neural networks. Our developed model has the ability to classify the attacked phishing websites from legitimate sites. However, due to the limitation of samples in the dataset, other machine learning algorithms (SVM, AdaBoost, Decision Tree, KNN) cannot perform proficiently for analyzing the data. In this respect, our proposed Deep Convolution Neural Network (DCNN) model has an automated approach to predict the phishing sites within the earlier stage. The empirical results show that the overall accuracy of 99% is achieved by the recommended methodology.
A modified surrogate-assisted multi-swarm artificial bee colony for complex numerical optimization problems

Artificial bee colony (ABC) algorithm has been widely used in solving complex optimization due to its few control parameters and outstanding global search capability. However, ABC suffers from slow convergence rate, which limits its real-world applications. To overcome such disadvantage, this paper proposes a surrogate-assisted multi-swarm artificial bee colony (SAMSABC). The multiple swarm strategy is employed to further keep the diversity. To enhance the local exploitation capability, the orthogonal method is utilized to provide a guide vector. Moreover, to avoid wasting the computation resources, the fitness estimation strategy for artificial bee colony algorithm, as a surrogate-assistance technology, is designed. Finally, the experimental results of 20 benchmark functions verify its outstanding performance on solving complex numerical optimization problems.
Sparse metric-based mesh saliency

In this paper, we propose an accurate and robust approach to salient region detection for 3D polygonal surface meshes. The salient regions of a mesh are those that geometrically stand out from their contexts and therefore are semantically important for geometry processing and shape analysis. However, a suitable definition of region contexts for saliency detection remains elusive in the field, and the previous methods fail to produce saliency maps that agree well with human annotations. We address these issues by computing saliency in a global manner and enforcing sparsity for more accurate saliency detection. Specifically, we represent the geometry of a mesh using a metric that globally encodes the shape distances between every pair of local regions. We then propose a sparsity-enforcing rarity optimization problem, solving which allows us to obtain a compact set of salient regions globally distinct from each other. We build a perceptually motivated 3D eye fixation dataset and use a large-scale Schelling saliency dataset for extensive benchmarking of saliency detection methods. The results show that our computed saliency maps are closer to the ground-truth. To showcase the usefulness of our saliency maps for geometry processing, we apply them to feature point localization and achieve higher accuracy compared to established feature detectors.
Decision for Vertical Handover based on Naive Bayes Algorithm

The main aim of this research paper is to design and implement decision for Vertical Handover using Naïve Bayes Algorithm. The accuracy of the designed algorithm is validated using analytical and statistical frameworks. The main parameters for Vertical Handover considered in the algorithm are Received Signal Strength, Quality of Service Bandwidth and Network Coverage. Quality of Service is derived on the basis of network attributes like packet loss, latency and jitter.
Artificial neural network for predicting earthquake casualties and damages in Indonesia

Abstract The paper is intended to develop a model to predict the number of damaged buildings and casualties due to earthquake using ANN (Artificial Neural Network). This model is expected to be able to generate the type and amount of relief supplies required by those affected during the emergency phase. This research develops ANN using supervised learning paradigm, and backpropagation learning algorithm. The applied ANN network architecture is a multiple-layer system, with 1 (one) neuron used in both input and output layer, and 95 (ninety-five) neurons used in the hidden layer yielding 0.99971 as the greatest value of the correlation coefficient. The output variable in this study is the earthquake impact consisting of six variables. While the input variables (predictors) in this study consisting of eight variables. The model in this study utilizes 123 seismic datasets, divided into 100 data (80%) for the training process and 23 data (20%) for the testing process. This research adds to the existing research and demonstrates the application of ANN in predicting the numbers of damaged buildings and casualties. The model is useful in supporting and strengthening preparedness and emergency relief activities due to earthquake disaster.
Active Learning Yields Better Training Data for Scientific Named Entity Recognition

Despite significant progress in natural language processing, machine learning models require substantial expertannotated training data to perform well in tasks such as named entity recognition (NER) and entity relations extraction. Furthermore, NER is often more complicated when working with scientific text. For example, in polymer science, chemical structure may be encoded using nonstandard naming conventions, the same concept can be expressed using many different terms (synonymy), and authors may refer to polymers with ad-hoc labels. These challenges, which are not unique to polymer science, make it difficult to generate training data, as specialized skills are needed to label text correctly. We have previously designed polyNER, a semi-automated system for efficient identification of scientific entities in text. PolyNER applies word embedding models to generate entity-rich corpora for productive expert labeling, and then uses the resulting labeled data to bootstrap a context-based classifier. PolyNER facilitates a labeling process that is otherwise tedious and expensive. Here, we use active learning to efficiently obtain more annotations from experts and improve performance. Our approach requires just five hours of expert time to achieve discrimination capacity comparable to that of a state-of-the-art chemical NER toolkit.
A Decentralized Solution for Web Hosting

This paper proposes a decentralized solution for web hosting based on interplanetary file system (IPFS) and Ethereum blockchain. Particularly, we use Ethereum smart contracts to manage the IPFS network and the web hosting service. IPFS platform is used to store data and to host websites. All storage miner nodes on the IPFS network offer the pinning service to ensure that source codes of the websites and users' data are retained long-term. Moreover, these nodes also enable the interplanetary name space (IPNS) service for creating and updating mutable links to IPFS contents. TXT record is also used in the domain name system (DNS) to map domain names to IPNS addresses for hosted websites. For privacy-preserving data storage, websites need to be deployed an encryption algorithm. The proposed model that combines between the IPFS and blockchain networks to form a platform providing the decentralized web hosting service. Experiment illustrates building and hosting a web application on the IPFS network. Experimental results show that, compared to the traditional web hosting model, the hosted web application on the proposed platform ensures the confidentiality, integrity, and availability.
Performance Analysis of PID controller for Path Planning of a Quadcopter

Unmanned Aerial Vehicles (UAV) have become more and more popular in military, commercial and several other applications. Due to its structural simplicity and complexity on controls, quadcopters have become the topic of research in recent years. Focus of this work is to understand the basics of the quadcopter dynamics and to develop an autonomous navigation and trajectory planning for quadcopters. The dynamic model of the quadcopter used for the study is based on Newton-Euler equations. A PID based control on the Quadcopter model available in MATLAB & Simulink is tested and verified by simulating the model for different types of trajectories. The controller parameters are tuned to achieve minimal tracking error without compromising on the controller response time.
Ensembling Convolutional Neural Networks for Perceptual Image Quality Assessment

Perceptual Image quality assessment is a challenging problem especially in the absence of reference information. No-reference quality assessment is required for a number of applications such as quality assessment of image acquisition, enhancement and communication scenarios. Conventionally the problem is addressed by extracting natural scene statistics but recent development of deep learning has paved the way of deep learning based methods. Convolutional Neural Networks (CNN) has shown surprising performance for the task of visual classification but they have some inherent limitations such as high computational requirements, limitations of scalability and model variance. Ensemble learning methods are used to improve the generalization performance of machine learning methods but their application to CNN is limited due to their already high computational requirements. We have proposed an approach to train a single CNN model with a learning rate scheduler and save its training states at regular intervals. These saved model states are treated as base models and some of them are selected to construct ensemble with weighted averaging. The proposed methods has provided promising results and indicate its utility for training of advanced architectures for ensemble learning.
Encoding, Exchange and Manipulation of Captured Immersive VR Sessions for Learning Environments: the PRISMIN Framework

Capturing immersive VR sessions performed by remote learners using head-mounted displays (HMDs) may provide valuable insights on their interaction patterns, virtual scene saliency and spatial analysis. Large collected records can be exploited as transferable data for learning assessment, detect unexpected interactions or fine-tune immersive VR environments. Within the online learning segment, the exchange of such records among different peers over the network presents several challenges related to data transport and/or its decoding routines. In the presented work, we investigate applications of an image-based encoding model and its implemented architecture to capture users’ interactions performed during VR sessions. We present the PRISMIN framework and how the underneath image-based encoding can be exploited to exchange and manipulate captured VR sessions, comparing it to existing approaches. Qualitative and quantitative results are presented in order to assess the encoding model and the developed open-source framework.
Human Pose Estimation and Activity Classification Using Machine Learning Approach

Human pose estimationArunnehru, J. is mainly used for the purpose of training the robots to incorporate in a way whichNandhana Davi, A. K. the actions are performed in reality. The human pose estimation is the highly exploring topic in the field of computer vision research. The objective of the dynamic pose estimation is to estimate the human pose in all the available datasets. It begins withSharan, R. Raghul mapping the skeletal coordinates and also visualizing the coordinates. The features within the coordinates are being obtained with theNambiar, Poornima G. help of proportionate distance features and the divergence between the coordinates. The feature extraction has taken place separately for RGB and depth dataset. The computed data is being present in the same format as that of the vector space that is being allocated for the feature extractor. The classifiers such as support vector machines (SVMs), k-nearest neighbor (KNN), and the decision tree are being used. The major use of this learning process is to help the robot to train it similar to that of the human functionalities. This functionality can be used in any learning and training procedures to build the human-robotic system. The proper understanding of how every action takes place is learned in this process and thereby training any system with similar measures is simplified with this procedure.