Reviewerly/exHarmony huggingface dataset:
Adaptive Centroid Placement Based SNIC for Superpixel Segmentation

The proposed image segmentation algorithm identifies information-rich versus low information regions based on surface entropy. Thereafter on the information-rich regions, the mean shift algorithm is applied to generate possible centroid initialization points. This enables the Simple Non-Iterative Clustering (SNIC) algorithm when initialized through the proposed mechanism to provide more concentrated segmentation in those information-rich regions and sparse segmentation in the low information regions.
Towards realistic mimicking of grey wolves hunting process for bounded single objective optimization

Canis lupus (grey) wolves hunt for their prey in a pack. There exist evolutionary algorithms based on the hunting pattern of Canis lupus wolves known as Grey wolf optimizer (GWO). It is a powerful optimizer and had produced competitive results for many difficult problems. There already exist several variants of GWO because of its simplicity and exploitative qualities. GWO has been proved to be a very good exploiter. However, the modeling of grey wolves concentrates more on exploitation rather than exploration and thus results in the problem of local stagnation. To enhance the exploration capabilities, dynamic behavior is added to prey. It was assumed that the prey was static in the original GWO wherein the prey always tries to run away from the predator. The proposed algorithm enhances diversity by varying the position of prey with the help of Levy distribution rather than considering it to be static. This paper aims at introducing a novel, realistic version of GWO wherein the new population is generated with the help of Levy flight distribution of prey as well as with the different class of wolves by a suitable modification in the hierarchy of wolves. Also, the three primary strides of chasing, looking, circling, and assaulting of prey, are executed. However, the new population which is created has a better exploration because of levy flight distribution of prey position. The algorithm is tested on a well-known suite of 23 benchmark problems. The outcomes show that the proposed algorithm, GWOLF can give either better or competitive results as contrasted with the original GWO and PSO metaheuristics.
Search Economics for Single-Objective Real-Parameter optimization

An effective search algorithm for solving the continuous global optimization problem based on a new metaheuristic algorithm named search economics is presented in this paper. The proposed algorithm works by first dividing the solution space into a set of subspaces and then assigning searchers to these subspaces based on the so-called “expected value” of each subspace. The expected value contains not only objective values of searched solutions but also objective values of new probe solutions and computation costs invested in each subspace. This makes it possible for the proposed algorithm to dynamically adjust the computing resources invested in each subspace during the convergence process. To evaluate the performance of the proposed algorithm, we compare it with four state-of-the-art search algorithms; namely, jSO, EBOwithCMAR, ELSHADE-SPACMA, and L-SHADE-RSP, for solving 30 CEC 2017 benchmark functions. The experimental results show that the proposed algorithm can find better results than all the other search algorithms compared in this study, especially for complex, high-dimensional functions, hybrid functions, and composition functions.
Improving Software Maintainability Predictions using Data Oversampling and Hybridized Techniques

Software systems being developed in today’s era are inherently large and complex. Maintaining these software is a big challenge before the software industry. Since software maintenance demands a high cost, this activity becomes even more challenging. In order to reduce the maintenance cost, it becomes crucial to know the maintainability of software modules/ classes in the initial stages of software development. Many efforts have been made to identify the maintainability of modules in the initial development stages in which prediction models have a lot of roles. Prediction models are trained from past historical data and should consist of an adequate number of instances of low maintainability and high maintainability class/modules. But this is not usually the case, and because of this, we are not able to train the prediction models properly. This situation shows data imbalance. In this direction, in this paper, we will handle the imbalanced data problem so that prediction models can be properly trained. We apply four oversampling techniques in this paper and train the prediction models for maintainability by hybridized techniques. The results of the paper advocate the effectiveness of examined oversampling techniques along with the hybridized classification techniques to develop competent maintainability prediction models.
Defending Against Adversarial Iris Examples UsingWavelet Decomposition

Deep neural networks have presented impressive performance in biometric applications. However, their performance is highly at risk when facing carefully crafted input samples known as adversarial examples. In this paper, we present three defense strategies to detect adversarial iris examples. These defense strategies are based on wavelet domain denoising of the input examples by investigating each wavelet sub-band and removing the sub-bands that are most affected by the adversary. The first proposed defense strategy reconstructs multiple denoised versions of the input example through manipulating the mid- and high-frequency components of the wavelet domain representation of the input example and makes a decision upon the classification result of the majority of the denoised examples. The second and third proposed defense strategies aim to denoise each wavelet domain sub-band and determine the sub-bands that are most likely affected by the adversary using the reconstruction error computed for each sub-band. We test the performance of the proposed defense strategies against several attack scenarios and compare the results with five state of the art defense strategies.
Historical-Cultural Sustainability Model for Archaeological Sites in Mexico Using Virtual Technologies

The use of virtual and immersion technologies has expanded considerably due to their impact on user experience, economy, knowledge, and sustainable conservation of cultural heritage according to studies conducted in various parts of the world in different disciplines (architecture, economy, entertainment, health, tourism, etc.), including on tourism in Mexico. These technologies are used in some archaeological sites, but development and implementation are scarce due to the lack of economic strategies, infrastructure, and human capital, which are preventing the sustainable exploitation of those sites, although some of these sites have met the basic requirements for providing a better experience to visitors. However, these sites should be studied to propose integral solutions not only to improve the tourist experience, but also to assist in their protection, conservation, and sustainable development. Here, we used knowledge from the soft systems methodology and the hologram generation system to generate proposals to solve the problem described. The result is a sustainable historical-cultural model based on the systemic approach, whose objective is to positively impact the visitor experience while maintaining harmony with the environment.
Hybrid Dictionary Learning and Matching for Video-based Face Verification

We propose a hybrid dictionary learning and matching approach using deep features for unconstrained videobased face verification. Popular off-the-shelf image-based deep neural networks often fail to effectively exploit multiple frames for video-based verification. Unlike recurrent neural network-based approaches which require an external large-scale annotated data for training, the proposed unsupervised approach can effectively model both structural and temporal information of face features in target videos using structural and dynamical dictionaries, respectively. We propose an iterative optimization procedure to learn the dynamical dictionaries from videos. Using the learned dictionaries, we model video-to-video similarity as subspace-to-subspace similarity which is not only more robust but also utilizes the information in multiple frames better than the widely used reconstruction error-based measures, where the subspaces are spanned by the learned dictionaries. Experiments on challenging video-based face verification datasets, including Multiple Biometric Grand Challenge (MBGC), Face and Ocular Challenge Series (FOCS) and IARPA JANUS Benchmark A (IJB-A) datasets, demonstrate that the proposed method can effectively learn robust and discriminative representations for videos and improve the face verification performance.
Reducing inequality through technology diffusion: the case of 3D printing in public libraries

ABSTRACT 3D printers are hailed as the next revolutionary technology, but will they be responsible innovations and help decrease poverty and inequality? This paper determines the availability and accessibility of 3D printing technology in low-income communities through public libraries and gives insights on how libraries use 3D printers. By examining the 2013 Digital Inclusion Survey and conducting interviews, we find that libraries are quickly acquiring 3D printers; however, the technology is not being fully adopted by the patrons due to the lack of training, software and practical applications of the technology. Also, we found out that the cost to use a 3D printer in public libraries is relatively low, and does not prevent patrons from accessing the technology. Overall, we believe that libraries will only play a small role in providing availability and accessibility to 3D printing technology for marginalized communities.
Dynamic Replication Based on a Data Classification Model in Cloud Computing

Cloud Computing provides on demand resources for customers and enterprises to outsource their online activities efficiently and less expensively. However, the cloud environment is heterogeneous and very dynamic, storage node failures and increasing demands on data can lead to data unavailability situations leading to a decrease in quality of service. Cloud service providers face the challenge of ensuring maximum data availability and reliability. Replication of data to different nodes in the cloud has become the most common solution for achieving good performance in terms of load balancing, response time and availability. In this article, we propose a new dynamic replication strategy based on a data classification model that would adapt the replication process according to user behavior towards data. This strategy dynamically and adaptively creates the replicas necessary in order to obtain the desired performance such as, reduced response time and improved system availability while ensuring the quality of service. The solution also attempts to meet customer requirements by respecting the SLA contract. The CloudSim simulator was used to evaluate the proposed strategy and compare it to other strategies. The results obtained showed an improvement in the criteria studied in a satisfactory manner.
Differential Evolution variants combined in a Hybrid Dynamic Island Model

Several Variants of the Differential Evolution (DE) algorithm have been among the winners of competitions on bound constrained optimization problems in distinct editions of the Congress on Evolutionary Computation (CEC). This paper compares some of those variants presented in the last five years. Those algorithms are also combined in two adaptive dynamic Island Models (IM), which adjust the topology according to the algorithms applied in their islands. The Island Models are able to adaptively direct more solutions to the islands with more qualified algorithms to solve the problem. The results allow to verify whether the combination of the recently proposed DE variants in an adaptive dynamic structure is better than using each one individually.
Efficient Hardware Design Architectures for BCH Product Codes in the Frequency Domain

We propose efficient encoding and decoding hardware architectures for (n, k), t-error correcting Bose-Chaudhuri-Hocquenghem (BCH) product codes in the frequency domain. Using the properties of conjugate classes over a finite field, we reduce the algorithmic complexity of the encoder, leading to a significant reduction in the hardware complexity. A low latency (2t+2) decoder for the above encoder is also designed. For a particular case of n=15 and t=2, the architectures were implemented on a Kintex 7 KC-705 FPGA kit, giving high throughputs of 22.5 Gbps and 5.6 Gbps at 100 MHz for the encoder and decoder respectively.
Search Result Diversification with Guarantee of Topic Proportionality

Search result diversification based on topic proportionality considers a document as a bag of weighted topics and aims to reorder or down-sample a ranked list in a way that maintains topic proportionality. The goal is to show the topic distribution from an ambiguous query at all points in the revised list, hoping to satisfy all users in expectation. One effective approach, PM-2, greedily selects the best topic that maintains proportionality at each ranking position and then selects the document that best represents that topic. From a theoretical perspective, this approach does not provide any guarantee that topic proportionality holds in the small ranked list. Moreover, this approach does not take query-document relevance into account. We propose a Linear Programming (LP) formulation, LP-QL, that maintains topic proportionality and simultaneously maximizes relevance. We show that this approach satisfies topic proportionality constraints in expectation. Empirically, it achieves a 5.5% performance gain (significant) in terms of alpha-NDCG compared to PM-2 when we use LDA as the topic modelling approach. Furthermore, we propose LP-PM-2 that integrates the solution of LP-QL with PM-2. LP-PM-2 achieves 3.2% performance gain (significant) over PM-2 in terms of alpha-NDCG with term based topic modeling approach. All of our experiments are based on a popular web document collection, ClueWeb09 Category B, and the queries are taken from TREC Web Track's diversity task.
The Impact of Control-Display Gain in Kinesthetic Search

Abstract Kinesthetic interaction typically employs force-feedback devices for providing the kinesthetic input and feedback. However, the length of the mechanical arm limits the space that users can interact with. To overcome this challenge, a large control-display (CD) gain (&gt;1) is often used to transfer a small movement of the arm to a large movement of the onscreen interaction point. Although a large gain is commonly used, its effects on task performance (e.g., task completion time and accuracy) and user experience in kinesthetic interaction remain unclear. In this study, we compared a large CD gain with the unit CD gain as the baseline in a task involving kinesthetic search. Our results showed that the large gain reduced task completion time at the cost of task accuracy. Two gains did not differ in their effects on perceived hand fatigue, naturalness, and pleasantness, but the large gain negatively influenced user confidence of successful task completion.
Exploring Human–AI Control Over Dynamic Transitions Between Individual and Collaborative Learning

Dynamically transitioning between individual and collaborative learning activities during a class session (i.e., in an un-planned way, as-the-need-arises) has advantages for students, but existing orchestration tools are not designed to support such transitions. This work reports findings from a technology probe study that explored alternative designs for classroom co-orchestration support for dynamically transitioning between individual and collaborative learning, focused on how control over the transitions should be divided or shared among teachers, students, and orchestration system. This study involved 1) a pilot in an authentic classroom scenario with AI support for individual and collaborative learning; and 2) design workshops and interviews with students and teachers. Findings from the study suggest the need for hybrid control between students, teachers, and AI systems over transitions as well as for adaptivity and/or adaptability for different classrooms, teachers, and students' prior knowledge. This study is the first to explore human–AI control over dynamic transitions between individual and collaborative learning in actual classrooms.
Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming Approach

Efficient identification of people and objects, segmentation of regions of interest and extraction of relevant data in images, texts, audios and videos are evolving considerably in these past years, which deep learning methods, combined with recent improvements in computational resources, contributed greatly for this achievement. Although its outstanding potential, development of efficient architectures and modules requires expert knowledge and amount of resource time available. In this paper, we propose an evolutionary-based neural architecture search approach for efficient discovery of convolutional models in a dynamic search space, within only 24 GPU hours. With its efficient search environment and phenotype representation, Gene Expression Programming is adapted for network's cell generation. Despite having limited GPU resource time and broad search space, our proposal achieved similar state-of-the-art to manually-designed convolutional networks and also NAS-generated ones, even beating similar constrained evolutionary-based NAS works. The best cells in different runs achieved stable results, with a mean error of 2.82 % in CIFAR-10 dataset (which the best model achieved an error of 2.67 %) and 18.83 % for CIFAR-100 (best model with 18.16 %). For ImageNet in the mobile setting, our best model achieved top-l and top-5 errors of 29.51 % and 10.37 %, respectively. Although evolutionary-based NAS works were reported to require a considerable amount of GPU time for architecture search, our approach obtained promising results in little time, encouraging further experiments in evolutionary-based NAS, for search and network representation improvements.
Clustering based Adaptive Differential Evolution for Numerical Optimization

In this paper, a fuzzy C-means clustering approach is suggested for segregating the initial population of Differential Evolution (DE) on the basis of the membership function. The proposed algorithm called FCADE further incorporates adaptive crossover and mutation strategies into the segregated population. Three variants of FCADE are proposed and are applied to selected CEC2005 benchmark problems. The results, when compared with some of the well-known adaptive algorithms indicates the competence of the strategies proposed in the present study in enhancing the performance of the DE algorithm.
On-chip Memory Optimized CNN Accelerator with Efficient Partial-sum Accumulation

In convolutional neural networks (CNNs), data movement inside convolution layers between memory and PEs is most energy dominant. This paper proposes a convolution processing dataflow that reduces both the number of memory accesses and the on-chip buffer capacity for convolution operations. Based on the dataflow, we design an on-chip buffer-minimized CNN accelerator. Compared with the state-of-the-art CNN accelerator, the proposed CNN accelerator utilizes 2.30 times less on-chip buffer and 2.18 times energy efficiency to achieve the same data throughput under Alexnet. The proposed architecture is able to achieve higher data throughput with the almost constant on-chip buffer capacity.
Active learning of causal structures with deep reinforcement learning

We study the problem of experiment design to learn causal structures from interventional data. We consider an active learning setting in which the experimenter decides to intervene on one of the variables in the system in each step and uses the results of the intervention to recover further causal relationships among the variables. The goal is to fully identify the causal structures with minimum number of interventions. We present the first deep reinforcement learning based solution for the problem of experiment design. In the proposed method, we embed input graphs to vectors using a graph neural network and feed them to another neural network which outputs a variable for performing intervention in each step. Both networks are trained jointly via a Q-iteration algorithm. Experimental results show that the proposed method achieves competitive performance in recovering causal structures with respect to previous works, while significantly reducing execution time in dense graphs.
Incorporating Strategy Adoption into Genetic Algorithm Enabled Multi-Agent Systems

Genetic Algorithm (GA) is a widely adopted optimization technique under evolutionary optimization. Inspired by the evolutionary operators of selection, crossover and mutation, Genetic Algorithms have been used to successfully solve myriad optimization problems in a wide range of domains, including in optimizing multi-agent systems. On the other hand, Evolutionary Game Theory (EGT) is used to model social-economic systems by mimicking social evolution by adopting neighborhood strategies in a stochastic manner. In this work, an extended GA is proposed for multi-agent systems, which incorporates the strategy adoption in EGT into GA enabled multi-agent systems. The proposed extended GA algorithm is applied to an example multi-robot navigation application. The proposed algorithm gives promising results in terms of the convergence time, compared to the GA based approach. Possible applications of the proposed algorithm are also discussed, while indicating potential future research directions.
Evolving Cryptographic Boolean Functions with Minimal Multiplicative Complexity

The multiplicative complexity (MC) is a cryptographic criterion that describes the vulnerability of a Boolean function to certain algebraic attacks, and in many important cryptographic applications also determines the computational cost. In this paper, we use Cartesian genetic programming to find various types of cryptographic Boolean functions, improve their implementation to achieve the minimal MC, and examine how difficult these optimized functions are to find in comparison to functions than only need to satisfy some base cryptographic criteria. To provide a comparison with other state-of-the-art optimization approaches, we also use our method to improve the implementation of several generic benchmark circuits. Our results provide new upper limits on MC of certain functions, show that our approach is competitive, and also that finding functions with an implementation that has better MC is not mutually exclusive with improving other performance criteria.
PEACEPACT: Prioritizing Examples to Accelerate Perturbation-Based Adversary Generation for DNN Classification Testing

Deep neural networks (DNNs) have been widely used in classification tasks. Studies have shown that DNNs may be fooled by artificial examples known as adversaries. A common technique for testing the robustness of a classification is to apply perturbations (such as random noise) to existing examples and try many of them iteratively, but it is very tedious and time-consuming. In this paper, we propose a technique to select adversaries more effectively. We study the vulnerability of examples by exploiting their class distinguishability. In this way, we can evaluate the probability of generating adversaries from each example, and prioritize all the examples accordingly. We have conducted an empirical study using a classic DNN model on four common datasets. The results reveal that the vulnerability of examples has a strong relationship with distinguishability. The effectiveness of our technique is demonstrated through 98.90 to 99.68% improvements in the F-measure.