Reviewerly/exHarmony huggingface dataset:
Proposal for a New Security Association (SA) Negotiation Process in IPSec

IPSec is a highly-used security protocol for securing transactions on the Internet both for version 4 and version 6. To address this issue, IPSec uses several protocols and mechanisms as part of its operation. The SA negotiation process is part of it, enabling the definition of security settings. This paper offers a new architecture that guarantees SA generation with higher efficiency for multicast communications. This article addresses all the concerns while reducing latency. The SA negotiation process consists of four phases with the use of Diffie Hellman (DH) in Phase 2 to exchange a secret. DH is not immune to a certain number of attacks and the risk it incurs, impacts on the IPSec via the SA negotiation. Hence, it is important to assess the latency the process execution incurs. The analysis of this process shows some shortcomings related to the DH mechanism and the overhead. We have proposed to put S-WANE instead of DH; this replacement operation eliminates phase 3 as well.
Mining Survey Data

Surveys are commonly used as an important data collection tool for empirical research in many applications such as social sciences, marketing and pedagogy. Survey data is becoming one of the major data sources in the era of big data. Conventional statistic tools are utilized to perform survey data analysis. Methods in data mining can extend the capabilities of statistics to explore and discover possible nuggets in massive data. While data mining on general databases has been intensive studied, very few has been done on survey data. Considering the specialities of survey data, this paper describes strategies in mining survey data using computational methods. A novel method for data preparation and dependent pattern mining is presented. Experiments on a real survey dataset were conducted to evaluate the strategies. Results on finding meaningful patterns are reported and discussed.
Efficient Deep Gaussian Process Models for Variable-Sized Inputs

Deep Gaussian processes (DGP) have appealing Bayesian properties, can handle variable-sized data, and learn deep features. Their limitation is that they do not scale well with the size of the data. Existing approaches address this using a deep random feature (DRF) expansion model, which makes inference tractable by approximating DGPs. However, DRF is not suitable for variable-sized input data such as trees, graphs, and sequences. We introduce the GP-DRF, a novel Bayesian model with an input layer of GPs, followed by DRF layers. The key advantage is that the combination of GP and DRF leads to a tractable model that can both handle a variable-sized input as well as learn deep long-range dependency structures of the data. We provide a novel efficient method to simultaneously infer the posterior of GP's latent vectors and infer the posterior of DRF's internal weights and random frequencies. Our experiments show that GP-DRF outperforms the standard GP model and DRF model across many datasets. Furthermore, they demonstrate that GP-DRF enables improved uncertainty quantification compared to GP and DRF alone, with respect to a Bhattacharyya distance assessment.
Empowering Software Engineering with Artificial Intelligence

A huge amount of data is constantly generated from the development, maintenance and operation of software products. Buried under this Big Data is insight and patterns that are valuable to the management and development of software projects. The rise of Artificial Intelligence (AI) empowers us to develop next-generation analytics methods to transform software engineering in both quality and productivity. This paper outlines a vision where cutting-edge AI machine learning techniques can be leveraged to develop new data-driven, automated methods for software effort estimation, code patch formulation and risk prediction, all of which are in the context of modern software development settings.
A Low-Memory Learning Formulation for a Kernel-and-Range Network

Recently, a learning method based on the kernel and the range space projections has been introduced. This method has been applied to learn the multilayer network analytically with interpretable relationships among the weight matrices. However, the learning method carries a high-memory demand during training. In this study, a low-memory formulation is proposed to address this issue of high-memory demand. The developed method is inspired by a recursive implementation of the Moore-Penrose inverse and is shown to be mathematically equivalent to the original batch learning. Next, we further improved our proposed low-memory formulation to annul the potential divergence caused by rounding errors. The regression and classification behaviors of the proposed learning method are demonstrated using both synthetic and benchmark datasets. Our experiments confirm that the proposed formulation consumes significantly lower memory.
The basics of artificial intelligence at school informatics course: background and directions of development

The article describes the history of the inclusion of issues related to the study of artificial intelligence in school informatics textbooks. A modern concept is proposed for considering this theme at school lessons, based on the practical implementation of tasks using the capabilities of the Python language.
MIDS: End-to-End Personalized Response Generation in Untrimmed Multi-Role Dialogue

Multi-role dialogue is a challenging issue in nature language process (NLP), which needs not only to understand the sentences, but also to simulate the interaction among roles. However, existing methods treat all roles’ speeches as one sequence and assume that only two speakers take turn to talk, which blurs the characteristics of roles and rarely happen in daily life. To address these issues, we propose a Multi-role Interposition Dialogue System (MIDS) which generates reasonable responses based on dialogue context and next speaker prediction. MIDS employs multiple role-defined encoders to understand each speaker, and an independent sequence model to predict the next speaker. The independent sequence model also works as a scheduler to integrate encoders with weights. Then, an attention-enhanced decoder generates responses based on dialogue context, speaker prediction and integrated encoders. Moreover, with the help of the unique speaker prediction, MIDS is able to generate diverse responses and join conversation actively when appropriate. Experimental results demonstrate that MIDS significantly improves the accuracy of speaker prediction and reduces the perplexity of generation over baselines. Furthermore, MIDS is able to interact with users without cue during real-life online conversations. This work marks a first step towards simulating multi-role dialogue generation.
A Super-Resolution Generative Adversarial Network with Simplified Gradient Penalty and Relativistic Discriminator

Generative Adversarial Network (GAN) has been employed for single image super-resolution (SISR). However, unregularized GAN is difficult for training. This is due gradient descent based GAN optimization is not easy to convergence, thus limiting its performance for image super-resolution. In this paper, a relativistic super-resolution GAN with a simplified gradient penalty (RSRGAN-GP) is proposed for single image super-resolution. In the proposed method, a compact residual network optimized by removing Batch-Normalization layers is employed as the generator to estimate photo-realistic images of 4× upscaling. Further, we introduce a residual network, which also has no Batch-Normalization layers as the conditional discriminator and adopt a simplified gradient regularization to penalize it for stabilizing the super-resolution GAN training, thus guaranteeing high-quality image reconstruction. Additionally, the super-resolution GAN is enhanced with a relativistic discriminator, which produces sharp and rich-detail images at no extra computational cost. The results on benchmark datasets show that our proposed method can effectively improve the visual quality of super-resolved images and achieves competitive performance compared with related works.
A Statistical approach to evaluate the efficiency and effectiveness of the Machine Learning algorithms analyzing Sentiments

In the process of analyzing sentiments for a given dataset various machine learning techniques are used. The models using these learning algorithms help in determining the sentiments across the textual documents. There is a need to evaluate the effectiveness of the models in terms of analyzing and predicting sentiments. This paper provides a statistical approach to measure the effectiveness of the models and also evaluates their effectiveness with respect to the data representations. Here an experimental research is carried out with an inductive mode to measure and evaluate the models. The models are built using Decision Tree, Naive Bayes and Support Vector Machines. Data has been represented using features of Term Frequency and Inverse Document Frequency and Bag-of-words. Statistical tools used for measuring the models are Chi-square test and Analysis of Variance.
Assessment of Academic Information System Quality from Two Perspectives: Product Quality and Quality in Use

Academic Information System (AIS) is an information system which is currently very much needed for an institution, especially for institutions in education. An Academic Information System (AIS) is one of the software products. Software definitely requires an assessment to measure its quality so that it can be developed better. Therefore, this study aims to measure the quality of an Academic Information System (AIS) in a university institution, namely University of Trunojoyo Madura. The assessment was carried out by testing methods on 2 models, namely usability based on ISO/IEC 25023: 2016 and the usability based on Nielsen's model. This study contributes to the combination of analysis of the results of each assessment model so that there is a continuity between the results of the assessment according to the ISO/IEC 25023: 2016 standard with the assessment according to the results of the questionnaire on the Nielsen model. In this study it was concluded that AIS from University of Trunojoyo Madura according to the assessment of usability based on ISO/IEC 25023: 2016 and usability based on Nielsen's model had good results even though there were several things that had to be added.
Ensembling 3D CNN Framework for Video Recognition

Video-based behavior recognition is a challenging research topic. The three dimensional convolution neural network (3D CNN) is effectively adopted to capture features from videos directly. 3D CNN is extended by two-dimensional convolution neural network, in which a time dimension is added. 3D CNN is better than two-dimensional convolution network in expressing effective motion information, and it has certain advantages. In order to make better use of the valuable features extracted from the original video information, only stacked RGB frame data sets can be used as the input of network. Ensembling 3D CNN framework for video recognition is proposed in the paper. Firstly, the pre-training model of Sports-1M is initialized firstly, and a 3D convolution neural network based on multi-level feature fusion is constructed. . The final high-dimensional feature combination is obtained by fusing multiple convolution features. Then 3D convolutional neural network based on ensemble learning is proposed to increase motion information, enrich motion features and enhance the robustness of single feature representation. Three incomplete training data sets are obtained by Bagging algorithm. To get different networks, three data sets are employed to train three 3D convolution neural networks respectively, and the output of the three networks is integrated. The output features of the three networks are input into the SVM classifier through the Stacking algorithm and the final results are obtained. The integration effects of different ensemble methods are compared. The experimental results show that the method of this work can improve recognition accuracy on UCF-101 data set effectively.
Reward Potentials for Planning with Learned Neural Network Transition Models

Optimal planning with respect to learned neural network (NN) models in continuous action and state spaces using mixed-integer linear programming (MILP) is a challenging task for branch-and-bound solvers due to the poor linear relaxation of the underlying MILP model. For a given set of features, potential heuristics provide an efficient framework for computing bounds on cost (reward) functions. In this paper, we model the problem of finding optimal potential bounds for learned NN models as a bilevel program, and solve it using a novel finite-time constraint generation algorithm. We then strengthen the linear relaxation of the underlying MILP model by introducing constraints to bound the reward function based on the precomputed reward potentials. Experimentally, we show that our algorithm efficiently computes reward potentials for learned NN models, and that the overhead of computing reward potentials is justified by the overall strengthening of the underlying MILP model for the task of planning over long horizons.
Statistical Metric-Theoretic Approach to Activity Recognition Based on Accelerometer Data

Providing accurate information on people’s actions, activities, and behaviors is one of the key tasks in ubiquitous computing and it has a wide range of applications including healthcare, well being, smart homes, gaming, sports, etc. In the domain of Human Activity Recognition, the primary goal is to determine the action a user is performing based on data collected through some sensor modality. Common modalities adopted to this end include visual and Inertial Measurement Units (IMUs), with the latter taking precedence in recent times due to their unobtrusiveness, low cost and mobility. In this work we consider the accelerometer signals streamed through a wearable IMU unit and use this data to recognize the user’s activity. We develop a novel approach based on representing the coming signal as a probability distribution function and then use some distance metric to infer the dissimilarity between probability distributions corresponding to different accelerometer signals in order to infer the correct activity. Experiments are performed on 14 activities of daily living with results showing promising potential for this technique.
On an effective multigrid solver for solving a class of variational problems with application to image segmentation

In this paper we reformulate a class of non-linear variational models for global and selective image segmentation and obtain convergent multigrid solutions. In contrast, non-linear multigrid schemes do not converge for these problems with strong non-linearity and non-smoothness (jumps). Our new approach is to reformulate the non-linear models, using splitting techniques, to generate linear models in a higher dimension which are easier to solve and amenable to the linear multigrid framework. Although splitting techniques are well studied in isolation, direct application of a splitting idea is not sufficient and it is the combination of two splitting approaches and linear multigrid theory approaches which results in a highly effective multigrid algorithm. Numerical results demonstrate the fast convergence of the new multigrid methods.
Face Synthesis and Recognition Using Disentangled Representation-Learning Wasserstein GAN

We propose the Disentangled Representation-learning Wasserstein GAN (DR-WGAN) trained on augmented data for face recognition and face synthesis across pose. We improve the state-of-the-art DR-GAN with the Wasserstein loss considered in the discriminator so that the generative and adversarial framework can be better trained. The improved training leads to better face disentanglement and synthesis. We also highlight the influences of imbalanced training data on the disentangled facial representation learning, and point out the difficulty of generating faces of extreme poses. We explore the recently proposed nonlinear 3D Morphable Model (3DMM) to augment the training data, and verify the contributions made by the learning on augmented data. Additionally, we also compare different data normalization schemes and reveal the benefit of using the group normalization. The proposed framework is verified through the experiments on benchmark databases, and compared with contemporary approaches for performance evaluation.
Efficient Astronomical Data Condensation Using Approximate Nearest Neighbors

Abstract Extracting useful information from astronomical observations represents one of the most challenging tasks of data exploration. This is largely due to the volume of the data acquired using advanced observational tools. While other challenges typical for the class of big data problems (like data variety) are also present, the size of datasets represents the most significant obstacle in visualization and subsequent analysis. This paper studies an efficient data condensation algorithm aimed at providing its compact representation. It is based on fast nearest neighbor calculation using tree structures and parallel processing. In addition to that, the possibility of using approximate identification of neighbors, to even further improve the algorithm time performance, is also evaluated. The properties of the proposed approach, both in terms of performance and condensation quality, are experimentally assessed on astronomical datasets related to the GAIA mission. It is concluded that the introduced technique might serve as a scalable method of alleviating the problem of the dataset size.
Cake Cam

Tourists often turn to strangers when they need a photographer while traveling; however, they do so at a cost. Strangers are not typically trained photographers, nor are they telepathically intuiting what composition the tourist wants. Existing smartphone camera interfaces do not communicate the desired framing to the stranger, and prior work in mobile photography guidance does not manage the 3D movement required when composing the tourist's ideal photo. We offer a new kind of mobile interaction for communicating the intended photo to a stranger without instructions. In our methodology, the tourist first composes a photo with the desired framing. The app, Cake Cam, then stores the camera position and orientation. Finally, 3D augmented reality markers guide the stranger to retake the photo with the tourist now standing in the frame. Our study resulted in more accurate camera placements and required fewer additional instructions than the traditional tourist photography method (n=40).
An Efficient Hash-Tree-Based Algorithm in Mining Sequential Patterns with Topology Constraint

Warnings happen a lot in real transmission networks. These warnings can affect people's lives. It is significant to analyze the alarm association rules in the network. Many algorithms can help solve this problem but not considering the actual physical significance. Therefore, in this study, we mine the association rules in warning weblogs based on a sequential mining algorithm (GSP) with topology structure. We define a topology constraint from network physical connection data. Under the topology constraint, network nodes have topology relation if they are directly connected or have a common adjacency node. In addition, due to the large amount of data, we implement the hash-tree search method to improve the mining efficiency. The theoretical solution is feasible and the simulation results verify our method. In simulation, the topology constraint improves the accuracy for 86%-96% and decreases the run time greatly at the same time. The hash-tree based mining results show that hash tree efficiency improvements are in 3-30% while the number of patterns remains unchanged. In conclusion, using our method can mine association rules efficiently and accurately in warning weblogs.
Resolution in Quantum Imaging with Undetected Photons

In 2004, a novel quantum imaging scheme was in-troduced [1]. In this scheme, the photons interacting with an object are not detected to construct the im-age. The scheme is based on the effect of “induced coherence without induced emission" [2,3].
SLA-Aware and Energy-Efficient VM Consolidation in Cloud Data Centers Using Host State Binary Decision Tree Prediction Model

For cloud data center, Virtual Machine (VM) consolidation is an effective way to save energy and improve efficiency. However, inappropriate consolidation of VMs, especially aggressive consolidation, can lead to performance problems, and even more serious Service Level Agreement (SLA) violations. Therefore, it is very important to solve the tradeoff between reduction in energy use and reduction of SLA violation level. In this paper, we propose two Host State Detection algorithms and an improved VM placement algorithm based on our proposed Host State Binary Decision Tree Prediction model for SLA-aware and energy-efficient consolidation of VMs in cloud data centers. We propose two formulas of conditions for host state estimate, and our model uses them to build a Binary Decision Tree manually for host state detection. We extend Cloudsim simulator to evaluate our algorithms by using PlanetLab workload and random workload. The experimental results show that our proposed model can significantly reduce SLA violation rates while keeping energy cost efficient, it can reduce the metric of SLAV by at most 98.12% and the metric of Energy by at most 33.96% for real world workload.
Amplifier-based MOS analog neural network implementation and weights optimization

Neural networks are achieving state-of-the-art performance in many applications, from speech recognition to computer vision. A neuron in a multi-layer network needs to multiply each input by its weight, sum the results and perform an activation function. This paper presents a variation of the implementation of an amplifier-based MOS analog neuron capable of performing these tasks and also the optimization of the synaptic weights using in-loop circuit simulations. MOS transistors operating in the triode region are used as variable resistors to convert the input and weight voltage to a proportional input current. To test the analog neuron in full networks, an automatic generator is developed to produce a netlist based on the number of neurons on each layer, inputs and weights. Simulation results using a CMOS 180 nm technology demonstrate the neuron proper transfer function and its functionality while trained in test datasets.
Text-Line Extraction from Historical Kannada Document

In this work, we propose identification of text line from a historical Kannada document. The proposed method consists of three stages: initially, preprocess the image by using Sauvola’s method and then apply the connected component and projection profile method to detect the text position of the text line. Finally, each text line is segmented based on projection points. The propose method is evaluated on Kannada historical document. Experimentation is carried out on the seventeen Kannada historical documents, in which the total number of lines together in all documents is 217 lines. We have tried few trail-and-error methods to identify the lines in the historical document image. Using the first method, we have detected 140 lines, but multiple lines were seen between each text line; the accuracy using this method was 64.51%. In the second method, we could detect 107 and the accuracy achieved was 49.30%. By using the third method, we could clearly detect 178, with reduced number of lines in between the text lines, and the accuracy in this case is 82.02%. Hence, we can conclude that using the third method most of the lines were precisely detected and obtained encouraging the result.