Reviewerly/exHarmony huggingface dataset:
Improved Cosine Similarity-based Artificial Bee Colony Optimization scheme for reactive and dynamic service composition

Reliable and dynamic composition of web services is considered as essential for ensuring continuous services to the users since they are responsible for integrating varsity of applications in spite of their independency. The significant development in web services domain in the last two decades enable the option of devising novel service composition and service selection schemes for optimal performance and success rate of dynamic web service composition. Majority of the research works proposed for dynamic composition of web services was confirmed to be formulated using the characteristics of Quality of Service (QoS) or Transactional features of workflow. Improved Cosine Similarity-based Artificial Bee Colony Optimization Scheme for Web Service Composition (ICS-ABCO-WSC) is proposed for integrating the characteristics of QoS and transactional features for determining optimal candidate service solution from the workflow modeled graph generated during reactive service composition. ICS-ABCO-WSC is proved to enhance the rate of exploitation and exploration by incorporating opposition learning in employee bee phase and, combinatorial search strategical equations and enhance rate factor in the employee and onlooker bee phase respectively. The success rate and optimality index derived using experimental investigation of ICS-ABCO scheme is proved to be 26% and 32% excellent to compared baseline graph-modeled web service composition techniques. This improvement is realized in ICS-ABCO-WSC due to its potential of enhancing the precision and acceleration rate of converging solution achieved in Artificial Bee Colony Optimization technique.
Quantum invariants and the graph isomorphism problem

Three graph invariants are introduced which may be measured from a quantum graph state and form examples of a framework under which other graph invariants can be constructed. Each invariant is based on distinguishing a different number of qubits. This is done by applying different measurements to the qubits to be distinguished. The performance of these invariants is evaluated and compared to classical invariants. We verify that the invariants can distinguish all nonisomorphic graphs with nine or fewer nodes. The invariants have also been applied to ``classically hard'' strongly regular graphs, successfully distinguishing all strongly regular graphs of up to 29 nodes, and preliminarily to weighted graphs. We have found that, although it is possible to prepare states with a polynomial number of operations, the average number of preparations required to distinguish nonisomorphic graph states scales exponentially with the number of nodes. We have so far been unable to find operators which reliably compare graphs and reduce the required number of preparations to feasible levels.
Topic-Based Exploration and Embedded Visualizations for Research Idea Generation

This work analyzes sensemaking frameworks and experiments with an iteratively designed visual analysis tool to identify design implications for facilitating research idea generation using visualizations. Our tool, ThoughtFlow, structures and visualizes literature collections using topic models to bridge the information gap between core activities during research ideation. To help users stay focused on a topic while discovering relevant documents, we designed and analyzed usage patterns for two types of embedded visualization that help determine document relevance while minimizing distraction. We analyzed how research ideation outcomes and processes differ when using ThoughtFlow and conventional search engines by augmenting insight-based evaluation with concept-map analysis. Our results suggest that operations afforded by topic models match well with later ideation stages when coherent topics have emerged, but not with early stages when users are still relying heavily on individual keywords to gather background knowledge. We also present qualitative evidence that citation sparklines encourage more exploration of recommended references, and that a preference for paper thumbnails may depend on the consistency between the evidence and the current mental frame.
A Comparison of Methods for Mitigating Within-Task Luminance Change for Eyewear-Based Cognitive Load Measurement

Eye activity-based within-task cognitive load measurement (CLM) is currently not feasible in everyday situations. One important issue to be addressed to move such CLM beyond controlled laboratory environments is determining practical methods for mitigating the pupillary light reflex (PLR) effect in CLM. In this paper, four approaches to dealing with the PLR effect within a modified verbal digit span task are investigated: ignore the PLR, exclude PLR data, compensate for PLR and use PLR features for measurement. During experimental work, cognitive load and the PLR were induced with a modified verbal digit span task and changes in brightness of a large monitor, respectively. The “exclude PLR,” “compensate for PLR,” and “use PLR features” methods were found to improve classification performance by up to 18.5% relative to the “ignore PLR” method, which yielded the worst classification accuracy of 58% using an average pupil diameter feature. Features derived from the transient properties of the PLR response associated with cognitive load were found to yield the superior classification accuracy of 70%, which is an improvement compared with previously published approaches which treated the PLR responses as interference. The findings from this paper suggest that the PLR cannot be easily ignored or normalized, and clearly demonstrate the importance of PLR-aware feature extraction for the design of future eyewear-based always-on CLM in conditions that are more realistic than a darkened, controlled laboratory.
A Decision-Making Support for Business Process Outsourcing to a Multi-Cloud Environment

The business process outsourcing (BPO) remains among the most adopted strategies by organizations to cope with the harsh competition of today's market. This practice has been intensified with the advent of the Cloud computing that has attracted companies by its new way of consuming and paying for resources. Nevertheless, the exposure of business processes (BPs) to Cloud-related threats and non-compliance risks as well as the diversity of Cloud offers in terms of price and quality are ones of the biggest obstacles in BPO to the Cloud. The organizations should therefore examine different factors to determine which BP parts to outsource and to assign them the most suitable Cloud offers. This article proposes a decision-making support called DMS4BPO that helps organizations in outsourcing their BPs to the Cloud by considering security, compliance, cost and performance criteria. DMS4BPO includes on the one hand an AHP-based method for the Cloud offer selection, and on the other hand, a Business Intelligence-based method for the exploitation of the execution history.
AndroKit: A toolkit for forensics analysis of web browsers on android platform

Due to the pervasive nature of smart phones and devices, users are becoming more and more dependent on such devices for accessing online information. Pervasive use of smart devices has significantly enlarged the attack surface and resulted in a proportional complication of cyber threat intelligence gathering. For such devices, web browsers have become a primary means for accessing information provided on Internet as well as file systems and therefore, web browser forensics is an important component of cyber threat intelligence. The basics of web browser forensics revolve around the artifacts such as web sites visited, malicious URLs, time stamps, counts of access, search histories, cookies, downloaded activities etc. However, leveraging and locating this information can be challenging without the needed prerequisite information. This paper presents how to perform forensics analysis of data structures used by popular web browsers such as Chrome, Opera, Mozilla Firefox, and Dolphin on Android and how a forensic investigator can acquire forensic artifacts from web browsers. To strengthen digital investigation, a toolkit named as AndroKit is proposed for Android web browsers forensics. The paper demonstrates that the AndroKit can successfully acquire and analyze forensic evidence such as Web History, Downloads, Cookies, Bookmarks, Chrome stored user credentials, decode base64 encoded images, Tabs information etc. Finally, a comparative analysis of AndroKit with standard forensic tool-kits such as Oxygen forensics, Andriller, MOBILedit and Belkasoft evidence center has been presented.
Content driven clustering algorithm combining density and distance functions

Density and distance based clustering are two distinct approaches to the same problem. In this contribution, a novel algorithm is presented in order to exploit the benefits of both approaches. This is achieved, not by combining those approaches into a single notion, but by utilizing the advantages of each one, depending on what each step of the algorithm aims to achieve. To be precise, the Window Density Function is utilized to provide regions of high density and hence a region of clusters or a part of a cluster. Affinity Propagation is, consequently, utilized to provide a group of clusters within such a region. Finally, these regions are merged to form actual clusters. The proposed methodology is tested on a variety of synthetic and real-life datasets. The algorithm presented in this contribution outperforms other well-known algorithms, with which it is compared to, in the majority of the datasets used.
Testing and Analysis of Chen Chaotic Mapping for Speech Cryptography

Abstract The attempts to improve the security of the speech signals are increasing with new encryption schemes. Many recent researches of speech encryption algorithms have been increasingly based on chaotic systems, but the drawbacks of weak security and small key space in one-dimensional chaotic cryptosystems are conspicuous. In this article, a new speech encryption scheme is used. It employs one of the three-dimensional chaotic systems to encrypt a source signal by permutations, thereby confusing the relationship between the cipher signal and the plain signal. A higher-order polynomial equation and rotating matrix are used to strengthen the proposed method. It significantly increases the resistance to attacks. The proposed system has the advantages of smaller iteration times and bigger key space. The system has entertained high-security analysis such as the signal-to-noise ratio, peak signal-to-noise ratio, auto correlation, key sensitivity analysis, number of sample change rate, unified average changing intensity, and histogram analysis. The analysis results illustrate that the proposed system is highly efficient and robust to threats.
Computational sustainability and the PHESS platform: Using affective computing as social indicators

The use of ubiquitous devices on intelligent environment enables opportunities to solve complex problems and react to changes quicker. Namely the use of computational resources to assist the management of environment through predicament of parameters based on sustainable indicators applied to social indicators and intelligent environments. This paper considers a computational sustainability platform which manages contexts supported by principles of computational sustainability and the assurance of sustainable scenarios. An application case study based on the definition of social indicators based on mood analysis demonstrates the application of the platform and some of its innovative functionalities. It uses different types of indicators from classical sustainability dimensions in order to demonstrate the platform. Context gathering and predicative services are used based on these indicators obtained from the environment over public services, sensors networks and ubiquitous devices which are used to create indicators based on the fusion of data.
Putting it all together: Geophysical data integration

The integration of information from multiple geophysical and other prospection surveys of archaeological sites and regions leads to a richer and more complete understanding of subsurface content, structure, and physical relationships. Such fusions of information occur within a single geophysical data set or between two or more geophysical and other prospection sources in one, two, or three dimensions. An absolute requirement is the accurate coregistration of all information to the same coordinate space. Data integrations occur at two levels. At the feature level, discrete objects that denote archaeological features are defined, usually subjectively, through the manual digitization of features interpreted in the data, although there is growing interest in automated feature identification and extraction. At the pixel level, distributional issues of skewness and outliers, high levels of noise that obfuscate targets of interest, and a lack of correlation between largely independent dimensions must be confronted. Nevertheless, successful fusions occur using computer graphic methods, simple arithmetic combinations, and advanced multivariate methods, including principal components analysis and supervised and unsupervised classifications. Four case studies are presented that illustrate some of these approaches and offer advancement into new domains.
A Context-Aware Capsule Network for Multi-label Classification

Recently proposed Capsule Network is a brain inspired architecture that brings a new paradigm to deep learning by modelling input domain variations through vector based representations. Despite being a seminal contribution, CapsNet does not explicitly model structured relationships between the detected entities and among the capsule features for related inputs. Motivated by the working of cortical network in HVS, we seek to resolve CapsNet limitations by proposing several intuitive modifications to the CapsNet architecture. We introduce, (1) a novel routing weight initialization technique, (2) an improved CapsNet design that exploits semantic relationships between the primary capsule activations using a densely connected Conditional Random Field and (3) a Cholesky transformation based correlation module to learn a general priority scheme. Our proposed design allows CapsNet to scale better to more complex problems, such as the multi-label classification task, where semantically related categories co-exist with various interdependencies. We present theoretical bases for our extensions and demonstrate significant improvements on ADE20K scene dataset.
A novel method for graph matching based on belief propagation

Graph matching is a fundamental NP-problem in computer vision and pattern recognition. In this paper, we propose a robust approximate graph matching method. The match between two graphs is formulated as an optimization problem and a novel energy function that performs random sample consensus (RANSAC) checking on the max-pooled supports is proposed. Then a belief propagation(BP) algorithm, which can assemble the spatial supports of the local neighbors in the context of the given points, is used to minimize the energy function. To achieve the one-to-(at most)-one matching constraint, we present a method for removing bad matches based on the topological structure of the graphs. Experimental results demonstrate that the proposed method outperforms other state-of-the-art graph matching methods in matching accuracy.
Secure Estimation for Attitude and Heading Reference Systems Under Sparse Attacks

This paper focuses on the problem of secure attitude estimation for autonomous vehicles. Based on the established AHRS measuring model and the attack model, we have decomposed the optimal Kalman estimate into a linear combination of local state estimates. We then propose a convex optimization-based approach, instead of the weighted sum approach, to combine the local estimate into a more secure estimate. It is shown that the proposed secure estimator coincides with the Kalman estimator with certain probability when there is no attack, and can be stable when p elements of the model state are compromised. Simulations have been conducted to validate the proposed secure filter under single and multiple measurement attacks.
Amortized efficiency of generation, ranking and unranking left-child sequences in lexicographic order

A new type of sequences called left-child sequences (LC-sequences for short) was recently introduced by Wu et al. (2014) for representing binary trees. They pointed out that such sequences have a natural interpretation from the view point of data structure and gave a characterization of them. Based on this characterization, there is an easily implementing algorithm that uses generate-and-test approach to filter all LC-sequences of binary trees with n internal nodes in lexicographic order, while in general it is not efficient at all. In this paper, we first design two novel rotations that allow us to drastically alter the shape of binary trees (and thus their corresponding LC-sequences). As an application, these operations can be employed to generate all LC-sequences in lexicographic order. Accordingly, we present a more efficient algorithm associated with the new types of rotations for generating all LC-sequences and show that it takes only constant amortized running cost. Moreover, we extend our study to the ranking and unranking problems. By integrating a measure called "left distances" introduced by Mäkinen (1987) to represent binary trees, we develop efficient ranking and unranking algorithms for LC-sequences in lexicographic order. With the help of aggregate analysis, we show that both ranking and unranking algorithms can be run in amortized cost of O(n) time and space.
Visibility Constrained Generative Model for Depth-Based 3D Facial Pose Tracking

In this paper, we propose a generative framework that unifies depth-based 3D facial pose tracking and face model adaptation on-the-fly, in the unconstrained scenarios with heavy occlusions and arbitrary facial expression variations. Specifically, we introduce a statistical 3D morphable model that flexibly describes the distribution of points on the surface of the face model, with an efficient switchable online adaptation that gradually captures the identity of the tracked subject and rapidly constructs a suitable face model when the subject changes. Moreover, unlike prior art that employed ICP-based facial pose estimation, to improve robustness to occlusions, we propose a ray visibility constraint that regularizes the pose based on the face model's visibility with respect to the input point cloud. Ablation studies and experimental results on Biwi and ICT-3DHP datasets demonstrate that the proposed framework is effective and outperforms completing state-of-the-art depth-based methods.
“Stream loss”: ConvNet learning for face verification using unlabeled videos in the wild

Face recognition tasks have seen a significantly improved performance due to ConvNets. However, less attention has been given to face verification from videos. This paper makes two contributions along these lines. First, we propose a method, called stream loss, for learning ConvNets using unlabeled videos in the wild. Second, we present an approach for generating a face verification dataset from videos in which the labeled streams can be created automatically without human annotation intervention. Using this approach, we have assembled a widely scalable dataset, FaceSequence, which includes 1.5M streams capturing ∼ 500K individuals. Using this dataset, we trained our network to minimize the stream loss. The network achieves accuracy comparable to the state-of-the-art on the LFW and YTF datasets with much smaller model complexity. We also fine-tuned the network using the IJB-A dataset. The validation results show competitive accuracy compared with the best previous video face verification results.
Discrete-Continuous Transformation Matching for Dense Semantic Correspondence

Techniques for dense semantic correspondence have provided limited ability to deal with the geometric variations that commonly exist between semantically similar images. While variations due to scale and rotation have been examined, there is a lack of practical solutions for more complex deformations such as affine transformations because of the tremendous size of the associated solution space. To address this problem, we present a discrete-continuous transformation matching (DCTM) framework where dense affine transformation fields are inferred through a discrete label optimization in which the labels are iteratively updated via continuous regularization. In this way, our approach draws solutions from the continuous space of affine transformations in a manner that can be computed efficiently through constant-time edge-aware filtering and a proposed affine-varying CNN-based descriptor. Furthermore, leveraging correspondence consistency and confidence-guided filtering in each iteration facilitates the convergence of our method. Experimental results show that this model outperforms the state-of-the-art methods for dense semantic correspondence on various benchmarks and applications.
Network security situation prediction in the cloud environment based on grey neural network1

Existing network security prediction methods for the cloud environment are limited in terms of both accuracy and real-time performance. In this paper, we address these issues with a proposal for a method based on grey neural network to predict networ
IntersectionExplorer, a multi-perspective approach for exploring recommendations

In this paper, we advent a novel approach to foster exploration of recommendations: IntersectionExplorer, a scalable visualization that interleaves the output of several recommender engines with human-generated data, such as user bookmarks and tags, as a basis to increase exploration and thereby enhance the potential to find relevant items. We evaluated the viability of IntersectionExplorer in the context of conference paper recommendation, through three user studies performed in different settings to understand the usefulness of the tool for diverse audiences and scenarios. We analyzed several dimensions of user experience and other, more objective, measures of performance. Results indicate that users found IntersectionExplorer to be a relatively fast and effortless tool to navigate through conference papers. Objective measures of performance linked to interaction showed that users were not only interested in exploring combinations of machine-produced recommendations with bookmarks of users and tags, but also that this “augmentation” actually resulted in increased likelihood of finding relevant papers in explorations. Overall, the findings suggest the viability of IntersectionExplorer as an effective tool, and indicate that its multi-perspective approach to exploring recommendations has great promise as a way of addressing the complex human-recommender system interaction problem.