Reviewerly/exHarmony huggingface dataset:
Population Based Equilibrium in Hybrid SA/PSO for Combinatorial Optimization

This article introduces a hybrid algorithm combining simulated annealing (SA) and particle swarm optimization (PSO) to improve the convergence time of a series of combinatorial optimization problems. The implementation carried out a dynamic determination of the equilibrium loops in SA through a simple, yet effective determination based on the recent performance of the swarm members. In particular, the authors demonstrated that strong improvements in convergence time followed from a marginal decrease in global search efficiency compared to that of SA alone, for several benchmark instances of the traveling salesperson problem (TSP). Following testing on 4 additional city list TSP problems, a 30% decrease in convergence time was achieved. All in all, the hybrid implementation minimized the reliance on parameter tuning of SA, leading to significant improvements to convergence time compared to those obtained with SA alone for the 15 benchmark problems tested.
Topology hiding routing based on learning with errors

Abstract The protocol of onion routing constitutes the underpinning of Onion Routing network for anonymous communication. However, since the main idea behind such protocol is to hierarchically peel the IP head and decode each crypted routing address, the topology with regards to path length or long‐term direction cannot be preserved. Owing to topology exposure, lots of attacks such as denial of service and differential flow analysis can be effectively conducted to violate the security of it. Moreover, considering that the path should be constructed by a sender in advance, onion routing is infeasible to be transplanted to ad hoc networks, let alone other defects such as heavy burden about computational complexity and package length. In this paper, a topology‐hiding routing protocol is proposed to address most of the aforementioned problems recurring to homomorphic learning with errors. Security analysis illustrated that nothing but only the IPs of adjacent hops will be revealed to any router. Also the computational complexity on sender side as well as the package length outperforms those of traditional onion routing algorithm in view of comparative simulation.
Learning-based power prediction for geo-distributed Data Centers: weather parameter analysis

Abstract Nowadays, the fast rate of technological advances, such as cloud computing, has led to the rapid growth of the Data Center (DC) market as well as their power consumption. Therefore, DC power management has become increasingly important. While power forecasting can greatly help DC power management and reduce energy consumption and cost. Power forecasting predicts the potential energy failures or sudden fluctuations in power intake from utility grid. However, it is hard especially when variable renewable energies (RE) as well as free cooling such as air economizers are also used. Geo-distributed DCs face an even harder issue: since in addition to local conditions, the overall status of the entire system of collaborating DCs should also be considered. The conventional approach to forecast power consumption in such complicated cases is to construct a closed form formula for power. This is a tedious task that not only needs expert knowledge of how each single cooling or RE system works, but also needs to be done individually for each DC and repeated all over again for each new DC or change of equipment. One alternative is to use machine learning so as to learn over time how the system consumes power in varying conditions of weather, workload, and internal structure in multiple geo-distributed locations. However, due to the wide range of effective features as well as trade-off between the accuracy and processing overhead, one important issue is to obtain an optimal set of more influential features. In this study, we analyze the correlation among geo-distributed DC power patterns with their weather parameters (based on different DC situations and infrastructure) and extract a set of influential features. Afterward, we apply the obtained features to provide a power consumption forecasting model that predict the power pattern of each collaborating DC in a cloud. Our experimental results show that the proposed prediction model for geo-distributed DCs reaches the accuracy of 87.2%.
Mining shape of expertise: A novel approach based on convolutional neural network

Expert finding addresses the task of retrieving and ranking talented people on the subject of user query. It is a practical issue in the Community Question Answering networks. Recruiters looking for knowledgeable people for their job positions are the most important clients of expert finding systems. In addition to employee expertise, the cost of hiring new staff is another significant concern for organizations. An efficient solution to cope with this concern is to hire T-shaped experts that are cost-effective. In this study, we have proposed a new deep model for T-shaped experts finding based on Convolutional Neural Networks. The proposed model tries to match queries and users by extracting local and position-invariant features from their corresponding documents. In other words, it detects users’ shape of expertise by learning patterns from documents of users and queries simultaneously. The proposed model contains two parallel CNN’s that extract latent vectors of users and queries based on their corresponding documents and join them together in the last layer to match queries with users. Experiments on a large subset of Stack Overflow documents indicate the effectiveness of the proposed method against baselines in terms of NDCG, MRR, and ERR evaluation metrics.
FLOWGAN:Unbalanced Network Encrypted Traffic Identification Method Based on GAN

It is crucial to accurately identify the type of traffic and application so that it can enable various policy-driven network management and security monitoring. However, with the increasing adoption of Internet applications use encryption protocols to transmit data, traffic classification is becoming more difficult. Although existing machine learning methods and novel deep learning methods have many advantages, which can solve the drawbacks of port and payload based methods, but there are still some shortcomings, one of which is the imbalanced property of network traffic data. In this paper, we proposed a GAN based method called FlowGAN to tackle with the problem of class imbalance for traffic classification. As an instance of Generative Adversarial Network (GAN), FlowGAN leverages the superiority of GAN's data augmentation to produce synthetic traffic data for classes with few samples. Furthermore, we trained a classical deep learning model, Multilayer perceptron (MLP) based network traffic classifier to evaluate the performance of FlowGAN. Based on the public dataset 'ISCX', our experimental results show that our proposed FlowGAN can outperform an unbalanced dataset and balancing dataset by the oversampling method in terms of data augmentation. Based on the public dataset ISCX, our experimental results show that the recognition performance of FlowGAN on small samples, compared with the unbalanced dataset, Precision, Recall, and F1-score increased by 13.2%, 17.0%, and 15.6% on average, compared with the balanced dataset Precision, Recall, F1-score increased by 2.15%, 2.06%, 2.12% on average.
A Specially Optimized One-Stage Network for Object Detection in Remote Sensing Images

With great significance in military and civilian applications, detecting indistinguishable small objects in wide-scale remote sensing images is still a challenging topic. In this letter, we propose a specially optimized one-stage network (SOON) focusing on extracting spatial information of high-resolution images by understanding and analyzing the combination of feature and semantic information of small objects. The SOON model consists of feature enhancement, multiscale detection, and feature fusion. The first part is implemented by constructing a receptive field enhancement (RFE) module and incorporating it into the network's specific parts where the information of small objects mainly exists. The second part is achieved by four detectors with different sensitivities, which access to the fused and enhanced features to enable the network to make full use of features in different scales. The third part consolidates the high-level and low-level features by adopting upsampling, concatenation, and convolution operations to build a feature pyramid structure, which explicitly yields strong feature representation and semantic information. In addition, we introduce the soft-nonmaximum suppression to preserve accurate bounding boxes in the postprocessing stage for densely arranged objects. Note that the split and merge strategy and the multiscale training strategy are employed. Extensive experiments and thorough analysis are performed on the NorthWestern Polytechnical University Very-High-Resolution (NWPU VHR)-10-v2 data set and the airplane, car and ship (ACS) data set as compared with several state-of-the-art methods. The satisfactory performance in experiments verifies the effectiveness of the design and optimization.
A Dynamic Gesture Recognition Interface for Smart Home Control based on Croatian Sign Language

Deaf and hard-of-hearing people are facing many challenges in everyday life. Their communication is based on the use of a sign language, and the ability of the cultural/social environment to fully understand such a language defines whether or not it will be accessible for them. Technology is a key factor that has the potential to provide solutions to achieve a higher accessibility and therefore improve the quality of life of deaf and hard-of-hearing people. In this paper, we introduce a smart home automatization system specifically designed to provide real-time sign language recognition. The contribution of this paper implies several elements. Novel hierarchical architecture is presented, including resource-and-time-aware modules—a wake-up module and high-performance sign recognition module based on the Conv3D network. To achieve high-performance classification, multi-modal fusion of RGB and depth modality was used with the temporal alignment. Then, a small Croatian sign language database containing 25 different language signs for the use in smart home environment was created in collaboration with the deaf community. The system was deployed on a Nvidia Jetson TX2 embedded system with StereoLabs ZED M stereo camera for online testing. Obtained results demonstrate that the proposed practical solution is a viable approach for real-time smart home control.
SWOT

Nowadays, software organizations are deploying agile methods in the geographically distributed environment to get several benefits i.e. significant return on investment it produces, fast project delivery, and managing the dynamic behavior of the project development. The execution of agile practices in Global Software Development (GSD) is more complex than collocated environment (single site development). The objectives of this study is to explore the factors which positively and negatively impact on scaling program of agile development in the GSD context and propose a framework based on the factors for successful scaling of agile programs in GSD context. This study is carried out in two stages. In the first stage, a Systematic Literature Review (SLR) protocol was adopted to investigate the factors. In the second stage, the SWOT analytical framework was followed to classify the factors into four categories: strengths, weaknesses, opportunities, and threats. In the SWOT framework, the factors in strengths and weaknesses categories represent the internal aspects of the organizations while the opportunities and threats categories indicate the external aspects of organization. The findings of this study reported 24 factors. The identified 13 factors positively and 11 factors negatively impact on scaling program of agile development respectively. Moreover, a framework based on SWOT analysis is proposed that provided the classification of factors into four categories. Based on the research outcomes, we can conclude that the identified factors and their classification into SWOT categories provide a robust framework that will assist the agile practitioners for successfully scale agile development in the GSD organizations.
Lighter but Efficient Bit-Depth Expansion Network

With the development of display technology, bit-depth expansion (BDE) has emerged as a basic process to display low-bit-depth image and video resources on high-bit-depth monitors. Most current BDE methods are based on traditional algorithms, and the few existing methods based on deep neural networks still suffer from loss of pixel-level details or from high computational cost. This paper proposes a lightweight but efficient BDE network that can effectively improve the capacity of shallow network by introducing a residual-block-in-residual-block structure. Furthermore, the proposed network adopts residual network architecture and dilated convolution to balance the preservation of pixel-level information and the expansion of the receptive field. Hence, the proposed method can also totally remove significant artifacts from very low-bit-depth images. Experimental results demonstrate that the proposed method can achieve performance comparable to or even better than that of some state-of-the-art methods while having much lighter architecture and fewer parameters.
LessLex: Linking Multilingual Embeddings to SenSe Representations of LEXical Items

We present LESSLEX, a novel multilingual lexical resource. Different from the vast majority of existing approaches, we ground our embeddings on a sense inventory made available from the BabelNet semantic network. In this setting, multilingual access is governed by the mapping of terms onto their underlying sense descriptions, such that all vectors co-exist in the same semantic space. As a result, for each term we have thus the “blended” terminological vector along with those describing all senses associated to that term. LESSLEX has been tested on three tasks relevant to lexical semantics: conceptual similarity, contextual similarity, and semantic text similarity. We experimented over the principal data sets for such tasks in their multilingual and crosslingual variants, improving on or closely approaching state-of-the-art results. We conclude by arguing that LESSLEX vectors may be relevant for practical applications and for research on conceptual and lexical access and competence.
GAN-based person search via deep complementary classifier with center-constrained Triplet loss

This paper addresses the person search task, which is a computer vision technology that finds the location of a pedestrian and retrieves it in a video taken by a single camera or multiple cameras. This task is much more challenging than the conventional settings for person re-identification or pedestrian detection since the search is susceptible to factors such as different resolutions, similar pedestrians, lighting, viewing angles and occlusion. Moreover, the person search task is a typical big data-small sample problem because each pedestrian only has a few images. It is difficult for the model to learn the discriminant features of pedestrians with a small quantity of pedestrian data. This paper proposes a framework for person search that uses the original training set without collecting extra data by implementing a generative adversarial network (GAN) to generate unlabeled samples. We propose a deep complementary classifier for pedestrian detection to leverage complementary object regions for pedestrian/non-pedestrian classification. In the re-identification section, we propose a center-constrained triplet loss that avoids the complicated triplet selection of the triplet loss and simultaneously pushes away all the distances of rather similar negative centers and the positive center. Experiments show that the GAN-generated data can effectively help to improve the discriminating ability of the CNN model. On the two large-scale datasets, CUHK-SYSU and PRW, we achieve a performance improvement over the baseline CNN. We additionally apply the proposed center-constrained triplet loss and complementary classifiers in the training model, and we achieve mAP improvements over the original method of +1.9% on CUHK-SYSU and +2.5% on PRW.
Mixed Reality Media-Enabled Public Participation in Urban Planning

Public participation in urban planning processes is affected by what is known as the “paradox of participation”: in early planning phases, when there is still sufficient room for decision-making, only a few citizens participate, while in late phases, when decisions can usually only be revised at great expense, a high level of public participation can be observed. The resulting delayed and more costly planning processes could be partially prevented by shifting public participation activities from late to early phases of planning processes. The reasons for the low level of participation of citizens in early planning phases are seen as the lack of clarity and the absence of concern due to a high level of abstraction. In this article, we examine the approach of using Mixed Reality (MR) media to remedy the paradox of participation in planning processes. Methodically, the context of MR media in urban planning is developed initially. Then the results of a literature study on MR media in urban planning are presented. Finally, the advantages of the use of MR media in urban planning contexts are summarized. In summary, MR media appear to be a promising approach for resolving the paradox of participation. However, future work should systematically structure MR media in urban planning according to characteristics supported, such as application contexts, planning objectives and modes of collaboration.
Face Segmentation: A Journey From Classical to Deep Learning Paradigm, Approaches, Trends, and Directions

Face segmentation represents an active area of research within the bio-metric community in particular and the computer vision community in general. Over the last two decades, methods for face segmentation have received increasing attention due to their diverse applications in several human-face image analysis tasks. Although many algorithms have been developed to address the problem, face segmentation is still a challenge not being completely solved, particularly for images taken in wild, unconstrained conditions. In this paper, we present a comprehensive review of face segmentation, focusing on methods for both the constrained and unconstrained environmental conditions. The article illustrates the advantages and disadvantages of previously proposed methods in state-of-the-art (SOA). The approaches presented comprise the seminal works on face segmentation and culminating in SOA approaches of the deep learning architecture. An extensive comparison of the previous approaches is intuitively presented, with a discussion of the potential directions for future research on the topic. We believe this comprehensive review and recap will contribute to a number of application domains, and will augment the knowledge of the research community.
Data migration using datalog program synthesis

This paper presents a new technique for migrating data between different schemas. Our method expresses the schema mapping as a Datalog program and automatically synthesizes a Datalog program from simple input-output examples to perform data migration. This approach can transform data between different types of schemas (e.g., relational-tograph, document-to-relational) and performs synthesis efficiently by leveraging the semantics of Datalog. We implement the proposed technique as a tool called Dynamite and show its effectiveness by evaluating Dynamite on 28 realistic data migration scenarios.
ShinyLearner: A containerized benchmarking tool for machine-learning classification of tabular data

Classification algorithms assign observations to groups based on patterns in data. The machine-learning community have developed myriad classification algorithms, which are used in diverse life science research domains. Algorithm choice can affect classification accuracy dramatically, so it is crucial that researchers optimize the choice of which algorithm(s) to apply in a given research domain on the basis of empirical evidence. In benchmark studies, multiple algorithms are applied to multiple datasets, and the researcher examines overall trends. In addition, the researcher may evaluate multiple hyperparameter combinations for each algorithm and use feature selection to reduce data dimensionality. Although software implementations of classification algorithms are widely available, robust benchmark comparisons are difficult to perform when researchers wish to compare algorithms that span multiple software packages. Programming interfaces, data formats, and evaluation procedures differ across software packages; and dependency conflicts may arise during installation.To address these challenges, we created ShinyLearner, an open-source project for integrating machine-learning packages into software containers. ShinyLearner provides a uniform interface for performing classification, irrespective of the library that implements each algorithm, thus facilitating benchmark comparisons. In addition, ShinyLearner enables researchers to optimize hyperparameters and select features via nested cross-validation; it tracks all nested operations and generates output files that make these steps transparent. ShinyLearner includes a Web interface to help users more easily construct the commands necessary to perform benchmark comparisons. ShinyLearner is freely available at https://github.com/srp33/ShinyLearner.This software is a resource to researchers who wish to benchmark multiple classification or feature-selection algorithms on a given dataset. We hope it will serve as example of combining the benefits of software containerization with a user-friendly approach.
MSG-CapsGAN: Multi-Scale Gradient Capsule GAN for Face Super Resolution

One of the most useful sub-fields of Super-Resolution (SR) is face SR. Given a Low-Resolution (LR) image of a face, the High-Resolution (HR) counterpart is demanded. However, performing SR task on extremely low resolution images is very challenging due to the image distortion in the HR results. Many deep learning-based SR approaches have intended to solve this issue by using attribute domain information. However, they require more complex data and even additional networks. To simplify this process and yet preserve the precision, a novel Multi-Scale Gradient GAN with Capsule Network as its discriminator is proposed in this paper. MSG-CapsGAN surpassed the state-of-the-art face SR networks in terms of PSNR. This network is a step towards a precise pose invariant SR system.
Robot vs. Voice Assistant

Entertainment applications for robots are often based on voice interaction. In these scenarios, the robot is primarily used as a voice assistant with a physical body. We conducted a study to investigate whether the entertainment value of a robot during a game can be attributed to the experience of voice interaction alone, or whether it is related to the physical presence of a robot and its expressivity through gestures and movement. The study examined the user experience for three different set-ups of a quiz game (with voice assistant, robot without animation, and animated robot). The results indicate that the perceived hedonic quality increases with physical presence and movement of the robot. Pragmatic quality was not rated differently for the three game versions. Additional qualitative interviews suggest that it might be desirable to design not overly expressive, but meaningful robot behavior for entertainment applications, in order to promote both, hedonic and pragmatic experience.