Reviewerly/exHarmony huggingface dataset:
SSIR: Secure similarity image retrieval in IoT

With the development of the Internet of Things (IoT) technology, we are entering a new era of computing technology. This emerging technology allows connectivity for everyone and everything. Thus, IoT encapsulates some intelligence in Internet-connected objects to communicate, exchange information, sharing multimedia data, take a decision, invoke various actions and provide amazing services through smart devices. A huge amount of sensitive information and multimedia data such as images and videos are sharing which generates traffic in the network. Moreover, multimedia data contains high dimension property, which affects image processing including storage and retrieval in IoT environment. Additionally, limited storage capabilities and computing power are drawbacks of smart devices which makes the situation more complicated. To overcome this problem, users prefer to store their multimedia data on a cloud server to make local storage and computation flexible in smart devices, but still security and privacy concerns. To get rid of this challenges, the emergence of Intel Software Guard Extension (SGX) provide a new solution. In this paper, a novel secure similarity image retrieval (SSIR) scheme based on the primitive of content-based image retrieval (CBIR) is proposed to protect the images search. In addition, the proposed SSIR scheme enables the resource-constrained clients to move the process of images as well as the outsource searching operations to the cloud server which reduce the cost. Furthermore, the security analysis shows our scheme is secure in the proposed model. Experiment results illustrate that our scheme is well fitting for images retrieval in practical applications.
Speech acts in professional maritime discourse: A pragmatic risk analysis of bridge team communication directives and commissives in full-mission simulation

The paper studies verbal maritime communication by categorising spontaneous professional discourse observed in co-operative full-mission simulation exercises into the illocutionary points of commissives and directives according to Searle's original classification. The research adopts a Corpus Pragmatics approach by combining vertical Corpus Linguistics methods with horizontal Pragmatics analyses. Between-group analyses of speech acts by native and non-native speakers of English are carried out and possible risks of miscommunication classified and compared. On the basis of the circular Osgood-Schramm communication model the sender–receiver interaction is investigated for either speaker group. Findings include both quantitative and qualitative between-group differences in locutionary, illocutionary and perlocutionary speech acts. These differences are evaluated as causal factors in effective communicative acts and as contributory factors for miscommunication in the maritime domain.
Robust obstacle detection for advanced driver assistance systems using distortions of inverse perspective mapping of a monocular camera

This paper presents a robust method for generic obstacle detection and collision warning in Advanced Driver Assistance Systems (ADAS). The highlight of our method is the ability to detect all obstacles without prior knowledge and detect partially occluded obstacles including the obstacles that have not completely appeared in the frame (truncated obstacles). Our results show an improvement of 90% more true positives per frame compared to one of the state-of-the-art methods. Our proposed method is robust to variations in illumination and to a wide variety of vehicles and obstacles that are encountered while driving. Distortions are introduced when Inverse Perspective Mapping (IPM) projects the camera image onto the road surface plane. In this paper, we first show that the angular distortion in the IPM domain belonging to obstacle edges varies as a function of their corresponding 2D location in the camera plane. We use this information to perform proposal generation. We propose a novel proposal assessment method based on fusing statistical properties from both the IPM image and the camera image to perform robust outlier elimination and false positive reduction. We also present an annotated obstacle detection dataset derived from various source videos that can serve as a benchmark for the evaluation of future obstacle detection algorithms. The source videos containing diverse illumination and traffic conditions are derived from multiple publicly available datasets.
Interactive Data Exploration with Smart Drill-Down

We present <i>smart drill-down</i> , an operator for interactively exploring a relational table to discover and summarize “interesting” groups of tuples. Each group of tuples is described by a <i>rule</i> . For instance, the rule <inline-formula><tex-math notation="LaTeX">$(a, b, \star, 1000)$</tex-math></inline-formula> tells us that there are 1,000 tuples with value <inline-formula><tex-math notation="LaTeX">$a$</tex-math></inline-formula> in the first column and <inline-formula><tex-math notation="LaTeX">$b$</tex-math></inline-formula> in the second column (and any value in the third column). Smart drill-down presents an analyst with a list of rules that together describe interesting aspects of the table. The analyst can tailor the definition of interesting, and can interactively apply smart drill-down on an existing rule to explore that part of the table. We demonstrate that the underlying optimization problems are <small>NP-Hard</small> , and describe an algorithm for finding the approximately optimal list of rules to display when the user uses a smart drill-down, and a dynamic sampling scheme for efficiently interacting with large tables. Finally, we perform experiments on real datasets on our experimental prototype to demonstrate the usefulness of smart drill-down and study the performance of our algorithms.
Event-Based Dynamic Graph Visualisation

Dynamic graph drawing algorithms take as input a series of timeslices that standard, force-directed algorithms can exploit to compute a layout. However, often dynamic graphs are expressed as a series of events where the nodes and edges have real coordinates along the time dimension that are not confined to discrete timeslices. Current techniques for dynamic graph drawing impose a set of timeslices on this event-based data in order to draw the dynamic graph, but it is unclear how many timeslices should be selected: too many timeslices slows the computation of the layout, while too few timeslices obscures important temporal features, such as causality. To address these limitations, we introduce a novel model for drawing event-based dynamic graphs and the first dynamic graph drawing algorithm, DynNoSlice, that is capable of drawing dynamic graphs in this model. DynNoSlice is an offline, force-directed algorithm that draws event-based, dynamic graphs in the space-time cube (2D+time). We also present a method to extract representative small multiples from the space-time cube. To demonstrate the advantages of our approach, DynNoSlice is compared with state-of-the-art timeslicing methods using a metrics-based experiment. Finally, we present case studies of event-based dynamic data visualised with the new model and algorithm.
Towards agent-based petri net decision making modelling for cloud service composition: A literature survey

In recent years, various complex and similar-function cloud services with different quality of service features are offered in services pool. Such diversity of services complicates the efficient selection of atomic services and the automatic provision of composited ones. We aim to shed light on the significance of agent-based decision-making methods for boosting cloud services' composition and modelling such complex systems using petri net (PN) techniques. In this paper, we report a survey of relevant and related literature focusing on agent-based petri net decision-making modelling for cloud service composition (CSC). We analyse the surveyed works and explain their strengths and weaknesses. We found how these technologies and techniques can be well-suited for CSC requirements in overcoming the most important documented challenges. Aside from agent technologies for boosting CSC, multi-criteria decision-making (MCDM) methods are suitable for ensuring service selection for composition operatively because they match end-user requirements with the trade-off criteria that cloud services are characterised with and are well-suited for the agent-based architecture. Furthermore, PNs are appropriate for supporting the modelling of such systems as groups of independent and loosely coupled modules. We suggest using a couple of agent-based problem-solving techniques to efficiently composite singular services automatically. This utilisation must be supported with an integrated and hybrid MCDM methodology to facilitate the ranking and selection of compromise solutions. The proposed solutions will be impressive if the whole process is systematically and mathematically formulated by PNs. This survey can be used by researchers aiming to join this line of research for further achievement.
Fraud Detection Model Based on Multi-Verse Features Extraction Approach for Smart City Applications

The modern adoption of e-Commerce has accelerated the need for effective customer protection, as part of the roadmap to expanding e-Commerce in smart cities. Before fully adopting e-Commerce for smart city applications, there are a few challenges that need to be addressed; mainly security and fraud risks. Here, credit cards are the most popular target, as a fraudster does not need much to carry out a 'card not present' transaction. Many techniques are used in identifying sources of cyber fraud, yet their detection accuracy degrades substantially when they are applied to datasets with an extremely large number of features and training samples. The objective of this research is to propose a new model to accurately identify sources of fraud in e-Commerce based on features extraction. This research is needed for early detection of fraud in order to avoid further occurrences of these crimes. In attaining the objectives of this research, an intensive data analysis will be performed to extract fraudulent detection patterns, which assists in constructing an effective classifier to identify sources of fraud. To construct the classifier, a Support Vector Machine (SVM) will be used as the base of our proposed model, along with a new algorithm called the multi-verse optimizer for fraudulent features extraction. The proposed approach, the credit card fraud detection model, is based on multi-verse features extraction (CCFD-MVFEX). The significance of this research comes from its ability to optimize the accuracy of detecting sources of fraud in e-Commerce, as well as effectively training the fraud detection model on different sets of fraudulent features. This will leverage the confidence of e-Commerce users. The results illustrate that the produced features extraction model can identify fraudulent sources with high accuracy rates compared with other representative detection methods, using four online data sources.
Microchemical characterization of placer gold grains from the Meyos-Essabikoula area, Ntem complex, southern Cameroon

Gold occurs as a native metal, usually containing silver, and in some cases mercury, copper, and palladium. It may also occur as inclusions within sulfur-rich minerals, such as pyrite and arsenopyrite. The style and variety of gold mineralization is influenced by the geological setting, chemistry of the ore fluids, and the nature of their interactions with rocks. Gold grains liberated from bedrock into surficial sediments during weathering and erosion are chemically stable and may be characterized according to their mineralogy: i.e the alloy composition and suite of mineral inclusions revealed within polished sections, characteristics faithful to gold from the hypogene source. This approach has been applied to placer gold grains from the Meyos-Essabikoula area, Cameroon, where the source of gold is not yet confirmed due to poor outcrop exposure. A total of 221 alluvial gold grains from 10 sites, tributaries of Sing and Bivele River over the Ntem Complex have been studied using Electron Probe Micro-Analysis (EMPA) to determine the concentration of minor alloying metals, (notably Au, Ag, Cu, and Hg) and Scanning Electron Microscopy (SEM) in order to evaluate the assemblage of mineral inclusions within the gold. Most of the grains are sub-rounded with pitted surfaces and inclusions of pyrrhotite, acanthite, and chalcopyrite were observed. The grains are AuAg alloys ranging from 54.4 to 99.8 wt% Au, 0.1–48.4 wt% Ag, 0.1–0.8 wt% Hg and 0–0.3 wt% Cu. The presence of Fe oxide (magnetite) inclusions containing Cr and V (to around 5 wt %) has not been reported elsewhere and suggests a strong interaction between hot reducing ore fluids and local mafic lithologies.
Temporal Treemaps: Static Visualization of Evolving Trees

We consider temporally evolving trees with changing topology and data: tree nodes may persist for a time range, merge or split, and the associated data may change. Essentially, one can think of this as a time series of trees with a node correspondence per hierarchy level between consecutive time steps. Existing visualization approaches for such data include animated 2D treemaps, where the dynamically changing layout makes it difficult to observe the data in its entirety. We present a method to visualize this dynamic data in a static, nested, and space-filling visualization. This is based on two major contributions: First, the layout constitutes a graph drawing problem. We approach it for the entire time span at once using a combination of a heuristic and simulated annealing. Second, we propose a rendering that emphasizes the hierarchy through an adaption of the classic cushion treemaps. We showcase the wide range of applicability using data from feature tracking in time-dependent scalar fields, evolution of file system hierarchies, and world population.
The Whole Is More Than Its Parts? From Explicit to Implicit Pose Normalization

Fine-grained classification describes the automated recognition of visually similar object categories like birds species. Previous works were usually based on explicit pose normalization, i.e., the detection and description of object parts. However, recent models based on a final global average or bilinear pooling have achieved a comparable accuracy without this concept. In this paper, we analyze the advantages of these approaches over generic CNNs and explicit pose normalization approaches. We also show how they can achieve an implicit normalization of the object pose. A novel visualization technique called activation flow is introduced to investigate limitations in pose handling in traditional CNNs like AlexNet and VGG. Afterward, we present and compare the explicit pose normalization approach neural activation constellations and a generalized framework for the final global average and bilinear pooling called α-pooling. We observe that the latter often achieves a higher accuracy improving common CNN models by up to 22.9 percent, but lacks the interpretability of the explicit approaches. We present a visualization approach for understanding and analyzing predictions of the model to address this issue. Furthermore, we show that our approaches for fine-grained recognition are beneficial for other fields like action recognition.
A stereovision measurement for large deformation of light structures

The vibration characterized by large deformation of light structures is important in aerospace engineering, while it is very difficult to be obtained. The traditional contact method is not available because of unwanted mass-loading. In this paper, a non-contact stereovision method is proposed for measuring large deformation vibration of light structures. The vibration responses are measured by the modified 3D point stereovision method. But the stereovision measurement is difficult to gain excitation information, which results in the modal identification method based on transfer function is not applicable. Thus the modal identification without excitation information is required for the development of the vibration stereovision measurement. The time domain modal identification method based on mathematical model is adopted. As comparison, the contact sensor method is also carried out and the transfer function modal identification method is employed for this method. The cantilever-beam experimental results show that the modal parameters using this proposed method agree with the theoretical analysis results much better than contact sensor method. In addition, the 3D point stereovision measurement can provide more information about mode shapes than the traditional sensor method for large deformation of light structures.
Learning-Based Hand Motion Capture and Understanding in Assembly Process

Manual assembly is still an essential part in modern manufacturing. Understanding the actual state of the assembly process can not only improve quality control of products, but also collect comprehensive data for production planning and proficiency assessments. Addressing the rising complexity led by the uncertainty in manual assembly, this paper presents an efficient approach to automatically capture and analyze hand operations in the assembly process. In this paper, a detection-based tracking method is introduced to capture trajectories of hand movement from the camera installed in each workstation. Then, the actions in hand trajectories are identified with a novel temporal action localization model. The experimental results have proved that our method reached the application level with high accuracy and a low computational cost. The proposed system is lightweight enough to be quickly set up on an embedded computing device for real-time online inference and on a cloud server for offline analysis as well.
A TEM study of particles carried by ascending gas flows from the Bairendaba lead-zinc deposit, Inner Mongolia, China

In order to find deep-seated deposit, there is a need to develop non-traditional surface geochemistry. Geogas is of particular interest. Particles transported by geogas from the deeply concealed deposits potentially hold great information. This study investigates particles carried by ascending gas flows from the Bairendaba lead-zinc deposit. This deposit is covered by a thick exotic substance, which hinders probing the ore body directly. The samples were collected using a static sampling method and were analysed by transmission electron microscopy (TEM) to observe particle features (such as shape, component, size, structure, and form of polymerization); these particles contain information on buried deposits. Numerous Pb-, Zn-, and Cu-bearing particles, as well as particles containing other metal compounds, occur as individuals or aggregates in the ascending gas flows. Most particles have a large oxygen (O) content; this is likely caused by the course of gas flows ascending from the deep ore body to the ground surface. Sulphur (S) is commonly associated with Pb, Cu, As, Fe, and Sb and occurs as sulphate in this deposit. Most metallic elements occur as sulphate, oxide, and carbonate in the detected particles. In contrast to the samples collected in the peripheral area, samples collected from the concealed deposit include numerous particles containing metallogenic elements, although particles containing major elements can be found in both areas. Moreover, the trend of each Pb- or Zn-bearing particle along a unique sampling line is directly related to the distribution of the corresponding ore bodies; this indicates that the particles were generated at a certain depth, carried by the ascending gas flow and directly provide important information regarding the concealed ore bodies. The anomalous area can be used as a basis for further prospect analysis and improve the prospecting success rate.
Personalized information recommendation model based on context contribution and item correlation

The traditional information recommender system gives little consideration to the influence of contexts on users and correlations between items, thus affects the quality of personalized information recommendation service. In order to address the issue, the main contribution of this paper is to propose the measurement for context contribution and item correlation, and designs a novel personalized information recommendation model. First, the paper proposes an improved association rule algorithm based on FP-Tree to improve efficiency of user’s behavior pattern mining in big-data environment. Second, the paper puts forward a user interest extraction algorithm based on improved FP-Tree and context contribution to measure and model user preferences in personalized information recommendation service. Third, the paper presents an improved collaborative filtering algorithm based on item correlation. In order to deal with data sparseness, an “item-item” matrix is constructed by using frequent itemsets in association rules. Then, the paper uses context contribution to replace item score, which can improve the accuracy of the similarity measurement between items. Experiments show that the model is more effective and accurate than other existing methods.
ACAS: An anomaly-based cause aware auto-scaling framework for clouds

Cloud computing as a model to deliver distributed resource and services on the pay-as-you-go policy has become increasingly popular for all organizations including industry. However, the inherent dynamicity in this environment makes it prone to various types of performance problems which introduce many challenges in the area of distributed resource management. Advances in the big data learning approaches can bring the opportunity for a data aware dynamic management of resources in the cloud. The collected data from the performance indicators of the system can be a valuable source of information to identify unusual behaviors in the resource consumptions or application performance. Different types of problems can cause the performance degradations at VM or system level. System administrators are overwhelmed with the huge amount of data to be analyzed to find the problems and overall health of the system. In this paper, we argue that a better selection of dynamic resource scaling policies can be employed for better performance by predicting the anomalies in the system and narrowing down the possible cause of the anomaly to one of the attributes of the system. Therefore, we propose a 2-level cause aware auto-scaling framework which leverages two types of resource management solutions, horizontal and vertical, as the corrective actions when the performance is degraded. We show the effectiveness of vertical scaling strategy as a quick solution for cases that a VM is exposed to some type of the local anomaly, while the horizontal scaling solutions can be used for system wide anomaly to add new VMs in the system. Moreover, our data analysis module can predict anomalies to give sufficient time to the scaling system to make an effective scaling decision. The proposed unsupervised anomaly detection module leverages a new updating strategy for renewing the models which considers the changes in the state of the system to reduce the overhead of recurrent model trainings. We have performed a comparison of the proposed framework with an approach which is used by several popular cloud providers to show the advantage of mixing the multi-level auto-scaling with the knowledge of anomaly detection analysis in resolving performance problems in the cloud.