Reviewerly/exHarmony huggingface dataset:
Hierarchical attribute based encryption with continuous leakage-resilience

Attribute based encryption (ABE) is widely applied in cloud computing settings due to its fine-grained access control. Most ABE schemes do not consider the side channel attacks which may leak the secret information of cryptosystems. Leakage-resilient cryptography aims to model security for various side channel attacks. In this paper, we first give the formal definition and security model of hierarchical attribute based encryption (HABE) with continuous leakage-resilience. Furthermore, we present a ciphertext-policy HABE scheme with continuous leakage-resilience. The proposed scheme is resilient to master key leakage and secret key leakage. We prove the security of our scheme under composite order bilinear group assumptions by using dual system encryption techniques. The performance of leakage-resilience is analyzed theoretically. If the depth of our proposed scheme is 1, the relative leakage ratio is almost up to 1/3. In addition, we give the performance comparison through experiments.
An efficient symbiotic organisms search algorithm with chaotic optimization strategy for multi-objective task scheduling problems in cloud computing environment

In Cloud Computing model, users are charged according to the usage of resources and desired Quality of Service (QoS). Multi-objective task scheduling problem based on desired QoS is an NP-Complete problem. Due to the NP-Complete nature of task scheduling problems and huge search space presented by large scale problem instances, many of the existing solution algorithms cannot effectively obtain global optimum solutions. In this paper, a chaotic symbiotic organisms search (CMSOS) algorithm is proposed to solve multi-objective large scale task scheduling optimization problem on IaaS cloud computing environment. Chaotic optimization strategy is employed to generate initial ecosystem (population), and random sequence based components of the phases of SOS are replaced with chaotic sequence to ensure diversity among organisms for global convergence. In addition, chaotic local search strategy is applied to Pareto Fronts generated by SOS algorithms to avoid entrapment in local optima. The performance of the proposed CMSOS algorithm is evaluated on CloudSim simulator toolkit, using both standard workload traces and synthesized workloads for larger problem instances of up to 5000. Moreover, the performance of the proposed CMSOS algorithm was found to be competitive with the existing with the existing multi-objective task scheduling optimization algorithms. The CMSOS algorithm obtained significant improved optimal trade-offs between execution time (makespan) and financial cost (cost) with no computational overhead. Therefore, the proposed algorithms have potentials to improve the performance of QoS delivery.
A Framework of Adaptive Multiscale Wavelet Decomposition for Signals on Undirected Graphs

The state-of-the-art graph wavelet decomposition was constructed by maximum spanning tree (MST)-based downsampling and two-channel graph wavelet filter banks. In this work, we first show that: 1) the existing MST-based downsampling could become unbalanced, i.e., the sampling rate is far from 1/2, which eventually leads to low representation efficiency of the wavelet decomposition; and 2) not only low-pass components, but also some high-pass ones can be decomposed to potentially achieve better decomposition performance. Based on these observations, we propose a new framework of adaptive multiscale graph wavelet decomposition for signals defined on undirected graphs. Specifically, our framework consists of two phases. Phase 1, called pre-processing, addresses the downsampling unbalance issues. We design maximal decomposition level estimation, unbalance detection, and unbalance reduction algorithms such that the downsampling rates of all levels are close to 1/2. Phase 2 concerns about adaptively finding low- or high-pass components that are worthy to be decomposed to improve the compactness of the decomposition. We suggest a graph signal Shannon-entropy-based adaptive decomposition algorithm. With applications on synthetic and real-world graph signals, we demonstrate that our framework provides better performance in terms of downsampling balance and signal compression, compared with other graph wavelet decomposition methods.
A comparative study of Spanish adult students’ attitudes to ICT in classroom, blended and distance language learning modes

Fully online and hybrid or blended courses that integrate Information and Communication Technologies (ICT) and complement traditional classroom practice are on the rise nowadays in the field of language teaching. The aim of these courses is to offer flexible learning formats for learners of all ages in the way of tailor-made instruction which can adapt to their needs and expectations. They seem to be suited to diverse educational and social needs and provide, as well as demand, active engagement. There is a large body of research regarding computer supported education, though few studies have compared adult students' attitudes towards ICT in different kinds of courses. Our goal in this study is to analyse and compare adult students’ attitudes towards ICT in three different formal learning settings: classroom face-to-face (N = 184), blended (N = 243) and distance (N = 200) language learning modes. We measure and compare the students' self-perceived confidence in digital competence, the ICT impact on their learning and their perception of the learning potential of ICT. Data were collected through a stratified random sample taking the students' learning mode into account and were subjected to descriptive and inferential analyses via SPSS 23.0 software. Results confirm a relationship between students' age, occupation and technology use in general, and between technology use and increased self-perceived confidence in digital competence in the distance language learning mode in particular. On the whole, students acknowledge the beneficial effect or impact of ICT on their learning and expect their learning to be of high value in the future.
Mapping hydrothermal alteration zones and lineaments associated with orogenic gold mineralization using ASTER data: A case study from the Sanandaj-Sirjan Zone, Iran

The Sanandaj-Sirjan Zone (SSZ) is considered as an important region for gold exploration in the western sector of Iran. Its mountainous topography and unpaved routes make its study challenging for researchers and raise the costs for mining companies strating new exploration plans. Gold mineralization mainly occurs as irregular to lenticular sulfide-bearing quartz veins along shear zones in deformed mafic to intermediate metavolcanic and metasedimentary rocks. In this investigation, ASTER data are used for mapping hydrothermal alteration minerals and to better discriminate geological structural features associated with orogenic gold occurrences in the area. Image transformation techniques such as specialized band ratioing and Principal Component Analysis are used to delineate lithological units and alteration minerals. Supervised classification techniques, namely Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) are applied to detect subtle differences between indicator alteration minerals associated with ground-truth gold locations in the area. The directional filtering technique is applied to help in tracing along the strike the different linear structures. Results demonstrate that the integration of image transformation techniques and supervised classification of ASTER data with fieldwork and geochemical exploration studies has a great efficiency in targeting new prospects of gold mineralization in the SSZ. The approach used in this research provides a fast, cost-efficient means to start a comprehensive geological and geochemical exploration programs in the study area and elsewhere in similar regions.
An evolving recurrent interval type-2 intuitionistic fuzzy neural network for online learning and time series prediction

The prediction of time series has both the theoretical value and practical significance in reality. However, since the high nonlinear and noises in the time series, it is still an open problem to tackle with the uncertainties and fuzziness in the forecasting process. In this article, an evolving recurrent interval type-2 intuitionistic fuzzy neural network (eRIT2IFNN) is proposed for time series prediction and regression problems. The eRIT2IFNN employs interval type-2 intuitionistic fuzzy sets to enhance the modeling of uncertainties by intuitionistic evaluation and noise tolerance of the system. In the eRIT2IFNN, the antecedent part of each fuzzy rule is defined using intuitionistic interval type-2 fuzzy sets, and the consequent realizes the Takagi–Sugeno–Kang type fuzzy inference mechanism. In order to utilize the prior knowledge including intuitionistic information, a local internal feedback is established by feeding the rule firing strength of each rule to itself eRIT2IFNN is fully adaptive to the evolving of sequence data by online learning of structure and parameters. A modified density-based clustering is implemented for the structure learning, where both densities and membership degrees are involved to determine the fuzzy rules. Performance of eRIT2IFNN is evaluated using a set of benchmark problems and compared with existing fuzzy inference systems. Moreover, the eRIT2IFNN is tested for identification of dynamics under both noise-free and noisy environments. Finally, a group of practical financial price-tracking problems including high-frequency data of financial future, commodity future and precious metal are used for the evaluation of the proposed inference system.
Scale Pyramid Network for Crowd Counting

Crowd counting is a concerned yet challenging task in computer vision. The difficulty is particularly pronounced by scale variations in crowd images. Most state-of-art approaches tackle the multi-scale problem by adopting multi-column CNN architectures where different columns are designed with different filter sizes to adapt to variable pedestrian/object sizes. However, the structure is bloated and inefficient, and it is infeasible to adopt multiple deep columns due to the huge resource cost. We instead propose a Scale Pyramid Network (SPN) which adopts a shared single deep column structure and extracts multi-scale information in high layers by Scale Pyramid Module. In Scale Pyramid Module, we specifically employ different rates of dilated convolutions in parallel instead of traditional convolutions with different sizes. Compared to other methods of coping with scale issues, our single column structure with Scale Pyramid Module can get more accurate estimation with simpler structure and less complexity of training. And our Scale Pyramid Module can be easily applied to a deep network. Experimental results on four datasets show that our method achieves state-of-the-art performance. On ShanghaiTech Part_A dataset which is challenging for its highly congested scenes and scale variation, we achieve 9.5% lower MAE and 13.5% lower MSE than the previous state-of-the-art method. We also extend our model on TRANCOS vehicle counting dataset and significantly achieve 5.9% lower GAME(0), 10% lower GAME(1), 24.5% lower GAME(2), 38.7% lower GAME(3) than the previous state-of-the-art method. The experimental results prove the robustness of our model for crowd counting, especially with scale variations.
SoK: The Challenges, Pitfalls, and Perils of Using Hardware Performance Counters for Security

Hardware Performance Counters (HPCs) have been available in processors for more than a decade. These counters can be used to monitor and measure events that occur at the CPU level. Modern processors provide hundreds of hardware events that can be monitored, and with each new processor architecture more are added. Yet, there has been little in the way of systematic studies on how performance counters can best be utilized to accurately monitor events in real-world settings. Especially when it comes to the use of HPCs for security applications, measurement imprecisions or incorrect assumptions regarding the measured values can undermine the offered protection. To shed light on this issue, we embarked on a year-long effort to (i) study the best practices for obtaining accurate measurement of events using performance counters, (ii) understand the challenges and pitfalls of using HPCs in various settings, and (iii) explore ways to obtain consistent and accurate measurements across different settings and architectures. Additionally, we then empirically evaluated the way HPCs have been used throughout a wide variety of papers. Not wanting to stop there, we explored whether these widely used techniques are in fact obtaining performance counter data correctly. As part of that assessment, we (iv) extended the seminal work of Weaver and McKee from almost 10 years ago on non-determinism in HPCs, and applied our findings to 56 papers across various application domains. In that follow-up study, we found the acceptance of HPCs in security applications is in stark contrast to other application areas - especially in the last five years. Given that, we studied an additional representative set of 41 works from the security literature that rely on HPCs, to better elucidate how the intricacies we discovered can impact the soundness and correctness of their approaches and conclusions. Toward that goal, we (i) empirically evaluated how failure to accommodate for various subtleties in the use of HPCs can undermine the effectiveness of security applications, specifically in the case of exploit prevention and malware detection. Lastly, we showed how (ii) an adversary can manipulate HPCs to bypass certain security defenses.
The Potential of Blockchain Technology for Health Information Exchange: Experimental Study From Patients’ Perspectives

Nowadays, a number of mechanisms and tools are being used by health care organizations and physicians to electronically exchange the personal health information of patients. The main objectives of different methods of health information exchange (HIE) are to reduce health care costs, minimize medical errors, and improve the coordination of interorganizational information exchange across health care entities. The main challenges associated with the common HIE systems are privacy concerns, security risks, low visibility of system transparency, and lack of patient control. Blockchain technology is likely to disrupt the current information exchange models utilized in the health care industry.Little is known about patients' perceptions and attitudes toward the implementation of blockchain-enabled HIE networks, and it is still not clear if patients (as one of the main HIE stakeholders) are likely to opt in to the applications of this technology in HIE initiatives. Thus, this study aimed at exploring the core value of blockchain technology in the health care industry from health care consumers' views.To recognize the potential applications of blockchain technology in health care practices, we designed 16 information exchange scenarios for controlled Web-based experiments. Overall, 2013 respondents participated in 16 Web-based experiments. Each experiment described an information exchange condition characterized by 4 exchange mechanisms (ie, direct, lookup, patient-centered, and blockchain), 2 types of health information (ie, sensitive vs nonsensitive), and 2 types of privacy policy (weak vs strong).The findings show that there are significant differences in patients' perceptions of various exchange mechanisms with regard to patient privacy concern, trust in competency and integrity, opt-in intention, and willingness to share information. Interestingly, participants hold a favorable attitude toward the implementation of blockchain-based exchange mechanisms for privacy protection, coordination, and information exchange purposes. This study proposed the potentials and limitations of a blockchain-based attempt in the HIE context.The results of this research should be of interest to both academics and practitioners. The findings propose potential limitations of a blockchain-based HIE that should be addressed by health care organizations to exchange personal health information in a secure and private manner. This study can contribute to the research in the blockchain area and enrich the literature on the use of blockchain in HIE efforts. Practitioners can also identify how to leverage the benefit of blockchain to promote HIE initiatives nationwide.
R²GAN: Cross-Modal Recipe Retrieval With Generative Adversarial Network

Representing procedure text such as recipe for crossmodal retrieval is inherently a difficult problem, not mentioning to generate image from recipe for visualization. This paper studies a new version of GAN, named Recipe Retrieval Generative Adversarial Network (R2GAN), to explore the feasibility of generating image from procedure text for retrieval problem. The motivation of using GAN is twofold: learning compatible cross-modal features in an adversarial way, and explanation of search results by showing the images generated from recipes. The novelty of R2GAN comes from architecture design, specifically a GAN with one generator and dual discriminators is used, which makes the generation of image from recipe a feasible idea. Furthermore, empowered by the generated images, a two-level ranking loss in both embedding and image spaces are considered. These add-ons not only result in excellent retrieval performance, but also generate close-to-realistic food images useful for explaining ranking of recipes. On recipe1M dataset, R2GAN demonstrates high scalability to data size, outperforms all the existing approaches, and generates images intuitive for human to interpret the search results.
Robust Histopathology Image Analysis: To Label or to Synthesize?

Detection, segmentation and classification of nuclei are fundamental analysis operations in digital pathology. Existing state-of-the-art approaches demand extensive amount of supervised training data from pathologists and may still perform poorly in images from unseen tissue types. We propose an unsupervised approach for histopathology image segmentation that synthesizes heterogeneous sets of training image patches, of every tissue type. Although our synthetic patches are not always of high quality, we harness the motley crew of generated samples through a generally applicable importance sampling method. This proposed approach, for the first time, re-weighs the training loss over synthetic data so that the ideal (unbiased) generalization loss over the true data distribution is minimized. This enables us to use a random polygon generator to synthesize approximate cellular structures (i.e., nuclear masks) for which no real examples are given in many tissue types, and hence, GAN-based methods are not suited. In addition, we propose a hybrid synthesis pipeline that utilizes textures in real histopathology patches and GAN models, to tackle heterogeneity in tissue textures. Compared with existing state-of-the-art supervised models, our approach generalizes significantly better on cancer types without training data. Even in cancer types with training data, our approach achieves the same performance without supervision cost. We release code and segmentation results on over 5000 Whole Slide Images (WSI) in The Cancer Genome Atlas (TCGA) repository, a dataset that would be orders of magnitude larger than what is available today.
MOELS: Multiobjective Evolutionary List Scheduling for Cloud Workflows

Cloud computing has nowadays become a dominant technology to reduce the computation cost by elastically providing resources to users on a pay-per-use basis. More and more scientific and business applications represented by workflows have been moved or are in active transition to cloud platforms. Therefore, efficient cloud workflow scheduling methods are in high demand. This paper investigates how to simultaneously optimize makespan and economical cost for workflow scheduling in clouds and proposes a multiobjective evolutionary list scheduling (MOELS) algorithm to address it. It embeds the classic list scheduling into a powerful multiobjective evolutionary algorithm (MOEA): a genome is represented by a scheduling sequence and a preference weight and is interpreted to a scheduling solution via a specifically designed list scheduling heuristic, and the genomes in the population are evolved through tailored genetic operators. The simulation experiments with the real-world data show that MOELS outperforms some state-of-the-art methods as it can always achieve a higher hypervolume (HV) value. Note to Practitioners-This paper describes a novel method called MOELS for minimizing both costs and makespan when deploying a workflow into a cloud datacenter. MOELS seamlessly combines a list scheduling heuristic and an evolutionary algorithm to have complementary advantages. It is compared with two state-of-the-art algorithms MOHEFT (multiobjective heterogeneous earliest finish time) and EMS-C (evolutionary multiobjective scheduling for cloud) in the simulation experiments. The results show that the average hypervolume value from MOELS is 3.42% higher than that of MOHEFT, and 2.27% higher than that of EMS-C. The runtime that MOELS requires rises moderately as a workflow size increases.
DevOps in practice: A multiple case study of five companies

Context: DevOps is considered important in the ability to frequently and reliably update a system in operational state. DevOps presumes cross-functional collaboration and automation between software development and operations. DevOps adoption and implementation in companies is non-trivial due to required changes in technical, organisational and cultural aspects. Objectives: This exploratory study presents detailed descriptions of how DevOps is implemented in practice. The context of our empirical investigation is web application and service development in small and medium sized companies. Method: A multiple-case study was conducted in five different development contexts with successful DevOps implementations since its benefits, such as quick releases and minimum deployment errors, were achieved. Data was mainly collected through interviews with 26 practitioners and observations made at the companies. Data was analysed by first coding each case individually using a set of predefined themes and thereafter perform a cross-case synthesis. Results: Our analysis yielded some of the following results: (i) software development team attaining ownership and responsibility to deploy software changes in production is crucial in DevOps. (ii) toolchain usage and support in deployment pipeline activities accelerates the delivery of software changes, bug fixes and handling of production incidents. (ii) the delivery speed to production is affected by context factors, such as manual approvals by the product owner (iii) steep learning curve for new skills is experienced by both software developers and operations staff, who also have to cope with working under pressure. Conclusion: Our findings contributes to the overall understanding of DevOps concept, practices and its perceived impacts, particularly in small and medium sized companies. We discuss two practical implications of the results.