Reviewerly/exHarmony huggingface dataset:
Context-Aware Service Modeling and Conflicts Discovery Based on Petri Net

In smart environments, context-aware service sense various kinds of context information and reacts automatically to adapt to changing circumstances. These adaptions may lead to context changes which trigger other services elsewhere in the environment and thus produce cascading reactions. Conflict adaptions or unexpected side effects may exist in this process. This article focuses on service conflicts related to frustrations in service execution and violations of the service's objective. A petri net-based approach is proposed to model and simulate context changes as well as services execution, and a conflicts discovery mechanism was built to detect potential conflicts. The case study of smart home scenarios shows how the method helps locate conflicts caused by different services, validating the effectiveness of the proposed approach.
Energy-Efficient Analytics for Geographically Distributed Big Data

Big data analytics on geographically distributed datasets (across data centers or clusters) has been attracting increased interest in both academia and industry, posing significant complications for system and algorithm design. In this paper, we systematically investigate the geodistributed big data analytics framework by analyzing the fine-grained paradigm and key design principles. We present a dynamic global manager selection algorithm to minimize energy consumption cost by fully exploiting the system diversities in geography and variation over time. The algorithm makes real-time decisions based on measurable system parameters through stochastic optimization methods, while achieving performance balance between energy cost and latency. Extensive trace-driven simulations verify the effectiveness and efficiency of the proposed algorithm. We also highlight several potential research directions that remain open and require future elaborations in analyzing geodistributed big data.
Covering arrays for some equivalence classes of words

Abstract Covering arrays for words of length over a ‐letter alphabet are arrays with entries from the alphabet so that for each choice of columns, each of the ‐letter words appears at least once among the rows of the selected columns. We study two schemes in which all words are not considered to be different. In the first case known as partitioning hash families, words are equivalent if they induce the same partition of a element set. In the second case, words of the same weight are equivalent. In both cases, we produce logarithmic upper bounds on the minimum size of a covering array. Definitive results for , as well as general results, are provided.
Second Order Differential Evolution for Constrained Optimization

In this paper, second order differential evolution (SODE) algorithm is considered to solve the constrained optimization problems. After offspring are generated by the second order differential evolution, the ε constrained method is chosen for selection in this paper. In order to show that second order differential vector is better than differential vector in solving constrained optimization problems, differential evolution (DE) with the ε constrained method is used for performance comparison. The experiments on 12 test functions from IEEE CEC 2006 demonstrate that second order differential evolution shows better or at least competitive performance against DE when dealing with constrained optimization problems.
Gated Transfer Network for Transfer Learning

Deep neural networks have led to a series of breakthroughs in computer vision given sufficient annotated training datasets. For novel tasks with limited labeled data, the prevalent approach is to transfer the knowledge learned in the pre-trained models to the new tasks by fine-tuning. Classic model fine-tuning utilizes the fact that well trained neural networks appear to learn cross domain features. These features are treated equally during transfer learning. In this paper, we explore the impact of feature selection in model fine-tuning by introducing a transfer module, which assigns weights to features extracted from pretrained models. The proposed transfer module proves the importance of feature selection for transferring models from source to target domains. It is shown to significantly improve upon fine-tuning results with only marginal extra computational cost. We also incorporate an auxiliary classifier as an extra regularizer to avoid over-fitting. Finally, we build a Gated Transfer Network (GTN) based on our transfer module and achieve state-of-the-art results on six different tasks.
Automatic Diet Generation by Artificial Bee Colony Algorithm

The overweight in the population has become a problem due to the deficiency on the nutritional contributions, increasing the number of people with diseases. The origin of this problem lies in the way people eat, with a poor nutritional quality and in excessive quantities. To solve this, it is necessary that people consider balance diets with the nutritional expectation and the necessary food to improve people’s health and reduce the rates of overweight and obesity. The diet design can be stated as an optimization problem and solved using different algorithms. In this paper, an Artificial Bee Colony (ABC) algorithm has been proposed to automatically design diets considering the physical characteristics of the subjects to find the best diet that satisfies their nutritional requirements using the USDA National Nutrient Database. Particularly, this research is focused on relatively healthy people between 18 and 55 years old to help them to avoid nutritional related diseases. The proposed methodology is compared against particle swarm optimization using the Harris-Benedict equation in order to verify if is capable to achieve the calorie goal.
Lazy Learning Based Segregation of Top-3 South Indian Languages with LSF-A Feature

Identification of language from voice signals is known as automatic language identification. It is very important for speech recognition in multi lingual countries like India where people use more than a single language while talking. Language identification for South Indian languages is difficult for a person without prior knowledge. In this paper, the top 3 most spoken languages of South India namely Telugu, Tamil and Kannada has been distinguished with the help of line spectral frequency based features namely LSF-A (Line spectral frequency-Approximation). Experiments have been performed on multiple datasets having as many as 21700 clips and a highest accuracy of 99.70% has been obtained with a lazy learning-based classifier.
Virtual Workshops on the Road: Co-designing with Drivers, Within Context in Real-Time

An investigation was performed of the characteristics of real-time, virtually present, contextual inquiry between automobile drivers and automotive designers. 28 participants were remotely interviewed while they were in one of two contexts: either sitting in a bare isolated meeting room or when operating a driving simulator. Open questions of the type involved in concept design were used as the basis of the real time interview. The interview transcripts were analysed by means of discourse analysis, thematic analysis and evaluations of degree of creativity. The results revealed that the real time automotive interview context provided richer information in terms of the quantity of the words expressed, the variety of the words expressed, and in terms of the judged degree of creativity of the statements.
Random Walk with Clustering for Image Segmentation

We propose a method that uses k-mean clustering and Random walk algorithm for image segmentation. The use of the random walk algorithm is widespread as it segments thin and elongated parts and can produce a complete division of the image. However if there is any minute discontinuity the unwanted part may get segmented. To avoid this we first partition the image into group of clusters then apply random walk algorithm. Comparing the results of segmented image with clustering and without clustering it is shown that the proposed algorithm is most effective.
Towards a Constraint Solver for Proving Confluence with Invariant and Equivalence of Realistic CHR Programs

Confluence of a nondeterministic program ensures a functional input-output relation, freeing the programmer from considering the actual scheduling strategy, and allowing optimized and perhaps parallel implementations. The more general property of confluence modulo equivalence ensures that equivalent inputs are related to equivalent outputs, that need not be identical. Confluence under invariants is also considered. Constraint Handling Rules (CHR) is an important example of a rewrite based logic programming language, and we aim at a mechanizable method for proving confluence modulo equivalence of terminating CHR programs. While earlier approaches to confluence for CHR programs concern an idealized logic subset, we refer to a semantics compatible with standard Prolog-based implementations. We specify a meta-level constraint language in which invariants and equivalences can be expressed and manipulated as is needed for confluence proofs, thus extending our previous theoretical results towards a practical implementation.
An Unsupervised Learning Classifier with Competitive Error Performance

An unsupervised learning classification model is described. It achieves classification error probability competitive with that of popular supervised learning classifiers such as SVM or kNN. The model is based on the incremental execution of small step shift and rotation operations upon selected discriminative hyperplanes at the arrival of input samples. When applied, in conjunction with a selected feature extractor, to a subset of the ImageNet dataset benchmark, it yields 6.2% Top 3 probability of error; this exceeds by merely about 2% the result achieved by (supervised) k-Nearest Neighbor, both using same feature extractor. This result may also be contrasted with popular unsupervised learning schemes such as k-Means which is shown to be practically useless on same dataset.
Gini Index With Local Mean Based For Determining K Value In K-Nearest Neighbor Classification

Abstract A process that explains and differentiates the data class is called Classification. The nearest neighbor is calculated based on the distance of each data, especially to determine the k value in the data. To fix K-Nearest Neighbor, it is necessary to test data class and train with Local Mean Based K-Nearest Neighbor using the closest distance measurement of Manhattan to each local mean of each data class. Gini Index is used in the process of calculating each weight in the data attribute. In this research, Gini Index, K-Fold Cross Validation and Local Mean are needed in the K-Nearest Neighbor classification. In Iris data the lowest k value is k=1, k=48, k=49, and k=50 accuracy is 94.67%, while the highest k value is k=12 and k=13 accuracy is 97.33%. So the result of the highest k value becomes the best k value in this study. Likewise with the Ionosphere data the lowest k value of k=50 accuracy is 86.92%, while the highest k value is k=2 with accuracy 92.89%, the Ionosphere data is the best k=2 and two Voice Gender and Lower Back data.
The Role of Unconscious Bias in Software Project Failures

Failures of large-scale software projects continue at an alarming rate; moreover processes and frameworks known to address the (well-documented) problematic issues remain under-utilized. In this paper we posit that unconscious bias, rather than incompetence or unprofessionalism, explains these trends. We present a quantitative model of personalities that makes simple, testable predictions about biases. We analyze the main biases of, in particular, software managers in this paper - and how their biases contribute to software failures. We demonstrate biases in the process of looking for software solutions through text-analysis. We analyze a popular development framework, SAFe, for how well it addresses these biases and offer modifications to the framework that should better avoid the effects from these biases.
Superresolution of Single Gaofen-4 Visible-Light and Near-Infrared (VNIR) Image Based on Texture Image Extraction

Superresolution (SR) is an important compensatory mean for geostationary orbit (GEO) satellites because of their low spatial resolution caused by orbital height. The multiple-image SR (MISR) methods generally have better reconstruction effects than single-image SR (SISR) methods. However, MISR methods require multiple images of the same place, which are susceptible to geometric distortion and radiation differences. Gaofen-4 (GF-4) is a GEO satellite equipped with a visible-light and near-infrared (VNIR) sensor and provides images with five bands. Based on the similarity of spatial information between different bands, this paper proposes an SISR method suitable for GF-4 VNIR images. The radiation differences are eliminated by extracting texture images. The high-resolution (HR) texture image is reconstructed by projections onto convex sets. The final SR result is fused by HR texture image and upsampled VNIR image. Tested by real GF-4 data, the reconstructed results of this method have better sharpness and more abundant information than comparable methods.
Spherically symmetric random permutations

We consider random permutations which are spherically symmetric with respect to a metric on the symmetric group S n and are consistent as n varies. The extreme infinitely spherically symmetric permutation‐valued processes are identified for the Hamming, Kendall‐tau and Cayley metrics. The proofs in all three cases are based on a unified approach through stochastic monotonicity.
A Novel PSOEDE Algorithm for Vehicle Scheduling Problem in Public Transportation

One of the problems in public transportation is the vehicle scheduling problem (VSP), which can reduce the bus company cost and meet the demand of passengers’ minimum waiting time. This paper proposes an ensemble differential algorithm based on particle swarm optimization (abbreviated as PSOEDE) to solve the VSP. In PSOEDE algorithm, the mutation process is designed by dividing the original process into two parts: the first part combines the PSO operator with the improved mutation strategy to enhance the global search ability, while the second part is to randomly select two mutation strategies (i.e. random learning and optimal learning) to improve the diversity of population. In addition, the random selection methods of the parameters and crossover strategies are proposed and applied in the total PSOEDE algorithm. The effectiveness and superiority of the proposed PSOEDE algorithm in dealing with the VSP are verified using the simulation experiments and six comparison algorithms.
Deep Feature Fusion Multiple Instance Learning for WaDang Recognition

WaDang recognition can provide valuable information for digital protection of cultural relics. However, WaDang recognition is challenging due to a great number of variations, such as different characters, different background pattern, and different scale and rotation. In this paper, we try to solve these problems by using hand-crafted and deep features and integrate them into multiple instance learning, in which the features of instances are divided into two parts and their similarity is computed by multiple kernel learning. The experiments on our collected WaDang images, MUSK, and COREL datasets show that the proposed algorithm is effective and its performance is compared with other state-of-art algorithms.
Succinct data structure for dynamic trees with faster queries

Navarro and Sadakane [19] gave a dynamic succinct data structure for storing an ordinal tree. The structure supports tree queries in either O ( log ⁡ n / log ⁡ log ⁡ n ) or O ( log ⁡ n ) time, and insertion or deletion of a single node in O ( log ⁡ n ) time. In this paper we improve the result of Navarro and Sadakane by reducing the time complexities of some queries (e.g. degree and level_ancestor) from O ( log ⁡ n ) to O ( log ⁡ n / log ⁡ log ⁡ n ) .
SMCA-CNN: Learning a Semantic Mask and Cross-Scale Adaptive Feature for Robust Crowd Counting

Density-based crowd counting methods with deep convolutional neural network (CNN) have achieved the state of the art on the challenging datasets. Experimental results showed that the performance of these methods suffers from two problems: 1) Background interference problem: there are some estimated spurious density values in the background regions which degrade the counting accuracy. 2) Cross-scale problem: the scale of human heads varies greatly in crowd images which lead to poorer quality of the density maps. In this study, we aim to address the two problems for enhancing the counting accuracy. To address the former, a light semantic mask module (SMM) is proposed to learn the semantic masks of crowd images where the ground-truth semantic masks generated from the ground-truth density map are taken as the supervision information. To tackle the latter, we propose a span architecture (SA) to effectively capture the large-scale-variation information in the crowd images by building the cross-scale features from the pyramidal structure of a deep CNN. To adaptively leverage the salient cross-scale features, a Cross-scale Adaptive Module (CAM) is delicately designed. In the end, integrating all elements above, an end-to-end trainable and single-column crowd counting model called the SMCA-CNN is developed and trained with a joint loss function consisting of the cross-entropy loss and Euclidean loss. Extensive experiments on five challenging datasets demonstrate the effectiveness of our SMCA-CNN. Compared with the previous state-of-the-art methods, our model achieves 17.1% lower MAE on dataset UCF_CC_50 and 23.6% lower MAE on the newly published dataset UCF-QNRF.
Hybrid algorithm for resource provisioning with low cost and time using improved cost-based algorithm in hybrid cloud computing

Cloud computing is used for processing resources that are conveyed as an administration over a network and a prototype to enable beneficial on-interest network access to a general loch of configurable reckoning resources which are rapidly provisioned and discharged. While adopting cloud computing, major challenges like resource provisioning, resource allocation and security are arising. Only prevailing resource provisioning algorithm are depending upon single tier application utilizing meta-heuristic methodology. Here, we presented a multi-tier application for provisioning dynamic resources utilizing meta-heuristic methodology like Ant Colony Optimization algorithm (ACO), Simulated Annealing (SA) algorithm and hybrid algorithm which fuses ACO and SA and also an improved cost based scheduling is used to schedule jobs within the cloud with reduced cost. Implementation outcomes displays the efficiency of provisioning resources using ACO-SA algorithm in multitier application of hybrid cloud is greater than other resource provisioning algorithms in cloud computing.
Fast Multi Semantic Pyramids via Cross Fusing Inherent Features for Different-Scale Detection

Currently, there are many works exploring how to more fully and efficiently use multi-scale feature maps of deep convolutional neural networks to improve the performance of object detection. But most of these works are devoted to predicting, respectively, on multi-scale feature maps or blending multi-scale feature maps for enriching representation. In this paper, we present a new method of cross fusing feature, named multi-semantic pyramids (MSP), for detecting different-scale objects. Various scale objects are predicted, respectively, by corresponding semantic pyramids (SP), each SP can produce rich semantic features for predicting via reusing inherent multi-scale feature maps from the network backbone. Through promoting the reuse of inherent feature layers, our MSP can improve detection performance with marginal extra cost. In addition, since the reuse connection of the MSP facilitates the conduction of the gradient, the convergence of the network is greatly improved. The experimental results on the PASCAL VOC and COCO datasets illustrate that our MSP can achieve more competitive detection accuracy.
Analysis on Combination of Watermarking Algorithm: Modified Least Significant Bit Algorithm with Least Significant Bit+1

Abstract To protect someone’s copyright on digital media such as digital image, watermarking technique can be used, which is a technique to insert copyright marks into digital media. The algorithms that commonly used in the watermark insertion is the Least Significant Bit (LSB). But there is deficiency in LSB algorithm. LSB algorithm requires one byte image pixel to insert one bit watermark. The more byte pixels are inserted, the higher the difference between watermarked image with the original image. Embedded watermark can also easily be known because it is definitely placed on the LSB bits of the pixel image. From these problems, in this research we analyze the combination of two watermarking algorithms: Modified Least Significant Bit (MLSB) with Least Significant Bit (LSB+1). MLSB have the advantage in compressing the watermark to be inserted from eight bit/character into five bit/character. LSB+1 algorithm will cover the deficiency of MLSB algorithm, which the location of the watermark bit is easy to be discovered. Added in this research, the watermark bit had inserted in random image pixel. The Combined algorithm tested by using five image that have different sizes and dimensions, which each of the image inserted by text watermark that have different length. The purpose is to find the value of these three comparison parameters: Mean Squared Error value (MSE), the size of the result image and processing time. It concluded that for the three parameters, the combined algorithm of MLSB-LSB+1 have done better than the LSB+1algorithm but slightly worse than the MLSB algorithm. This happens due to MLSBLSB+1 besides inheriting the advantages of each algorithm, also inherited deficiency of the LSB+1 algorithm which is because of the insertion is in the one bit before the least significant bit of the image pixel, the value of the pixel is greater for two point(in decimal).
Fostering the Culture of Cyber Security

As a side effect of globalisation and massive cyber services the number of crimes both perpetrated at local and global level is growing up. Governments and Law Enforcement Agencies are aware of this and look for potential countermeasures not only following traditional solutions. Technological countermeasures are not enough there is a need to foster the Culture of Cyber Security. The present paper introduces an innovative Cyber Range to be integrated in the foreseen European Cyber Range Network. The proposed solution provides tools to test the resilience of networks and systems by exposing them to realistic nation-state cyber threats in a secure facility with the latest tools, techniques and malware, this facilitate the testing of critical technologies with enhanced agility, flexibility and scalability, it helps to strengthen the stability, security and performance of cyberinfrastructures and IT systems used by government and private organisations. The document describes a platform devoted to easily create virtual environments devoted to cyberwarfare training and cybertechnology development. The solution is in line with typical simulator's features will be fed by real case study and will create a knowledge base of cyber treats and related extended effects and mitigation/counteractions. A specific features is the identification of the zero-day vulnerabilities in order to reduce or eliminate the Window of Vulnerability (WoV) and identify main attack vectors.Main outcomes are: improved situational awareness of cyber warfare scenarios, rapid identification of zero-day vulnerabilities, environment for the development of countermeasures, training environment for practitioners.