Reviewerly/exHarmony huggingface dataset:
MULTITOUR: A multiple itinerary tourists recommendation engine

The importance of tourism in today’s world is immense as it is a big source of revenue and employment generation for a country. Tourists face a variety of challenges during planning of their itinerary as well as in the selection of appropriate tour packages which consist of multiple itineraries in terms of their interests and different constraints. In order to overcome these challenges, in this work we propose an algorithm called MULTITOUR, for recommending multiple itineraries based on the tourist’s interest, popularity of itineraries and the cost associated with these itineraries which is derived from real-life travel sequences of tourist using the geo-tagged photos. The MULTITOUR algorithm can be further extended when a tourist wishes to visit unfamiliar places. Using the Flickr dataset, we have derived the similar user characteristics for recommending the multiple itineraries. The experimental results indicate that the MULTITOUR algorithm out-performs in terms of tour Precision, Recall, F1-Score, accuracy, tour popularity, interest of the tourist and the number of itineraries recommended as compared to the baseline algorithms.
Lightweight Privacy-Preserving Raw Data Publishing Scheme

Data publishing or data sharing is an important part of analyzing network environments and improving the Quality of Service (QoS) in the Internet of Things (IoT). In order to stimulate data providers (i.e., IoT end-users) to contribute their data, privacy requirement is necessary when data is collected and published. In traditional privacy preservation techniques, such as k-anonymity, data aggregation and differential privacy, data is modified, aggregated, or added noise, the utility of the published data are reduced. Privacy-preserving raw data publishing is a more valuable solution, and <inline-formula><tex-math notation="LaTeX">$n$</tex-math><alternatives><mml:math><mml:mi>n</mml:mi></mml:math><inline-graphic xlink:href="liu-ieq1-2974183.gif"/></alternatives></inline-formula>-source anonymity based raw data collection is most promising by delinking raw data and their sources. In this article, a lightweight raw data collection scheme for publishing is proposed, in which the rawness and the unlinkability of published data are all really guaranteed with Shamir&#x2019;s secret sharing, and shuffling algorithm. Moreover, it is lightweight and practical for the IoT environment by the performance evaluation.
Human behavior as the source of data in the education system

Information technologies are fundamentally changing modern society. Almost any human activity becomes the source of data for possible analysis and processing. The authors have analyzed more than hundred examples of data mining in modern society presented in open sources focusing on the field of education. In this study, the interdisciplinary approach is used. It makes possible to consider human activity as the basis for data analysis as the complex social phenomenon from the perspective of the sociology of communication and global Internet usage. At the same time, the authors note that the current development of technologies allows analyzing behavior of people not only in the Internet (online), but also in everyday life (offline) with the help of individual devices (cameras in smartphones and computers, fitness trackers, etc.) and with the use of facial recognition for various social situations. This study focuses on learning process and human behavior usage as the source of data in this area because information and communication technologies have changed the format of modern education. Therefore a significant part of education content has moved to the online environment. Integration of various data concerning e-learning, human movement, Biological Feedback can establish complex digital education model with prognostic and recommendatory functions that take into account behavior, individual characteristics, knowledge and skills in dynamics. This model supports a lifelong trajectory of personal development not limited by initial and final indicators and framework of educational institution. Nowadays technologies that allow tracking human behavior are causing discussions related to ethics and the issue of human freedom, though they provide deeper analysis of people's activities. Certainly these opportunities can be the benefit for society and miscellaneous social environment.
Self-blast state detection of glass insulators based on stochastic configuration networks and a feedback transfer learning mechanism

The self-blast state of a glass insulator directly affects the safety and reliability of transmission lines. To address the insufficient generalization ability of existing detection methods for insulator self-blast states and the drawbacks of deep neural network structures, the theories of transfer learning and closed-loop control are drawn upon to provide an intelligent detection method for the self-blast states of glass insulators. The method proposed in this paper is based on stochastic configuration networks and a feedback transfer learning mechanism. First, to reduce the redundancy of convolutional kernels in the channel extent, the interleaved group convolution strategy is employed to reconstruct the convolutional layers of the Inception network. Second, in view of the different feature applicabilities of different glass insulator images and based on the adaptive convolution module groups, the data structure of the dynamic feature space of insulator images is built with a certain mapping relationship from global to local. Then, the discriminative measure index is used to evaluate the discriminative information of the feature space to enhance the interpretability of the compact feature spance. Third, the fully connected feature vector of the compact feature space is sent to stochastic configuration networks (SCNs), which have universal approximation property to establish the classification criteria of the self-blast states of insulator images with generalization ability. Finally, an imitation of human thinking patterns is employed that exhibits repeated deliberation and comparison. Consequently, based on generalized error and entropy theories, the evaluation index of the objective function is established to evaluate the uncertain detection results of the self-blast states of glass insulator images in real time. Then, the dynamic transfer learning mechanism is constructed based on the constraint of the measurement index of uncertain detection results to realize self-optimizing regulation of the feature space that exhibits multihierarchy and discrimination and reconstructed classification criteria. The experimental results show that compared with other algorithms, the proposed method enhances the generalization ability and detection accuracy of the model.
TAGSwipe: Touch Assisted Gaze Swipe for Text Entry

The conventional dwell-based methods for text entry by gaze are typically slow and uncomfortable. A swipe-based method that maps gaze path into words offers an alternative. However, it requires the user to explicitly indicate the beginning and ending of a word, which is typically achieved by tedious gaze-only selection. This paper introduces TAGSwipe, a bi-modal method that combines the simplicity of touch with the speed of gaze for swiping through a word. The result is an efficient and comfortable dwell-free text entry method. In the lab study TAGSwipe achieved an average text entry rate of 15.46 wpm and significantly outperformed conventional swipe-based and dwell-based methods in efficacy and user satisfaction.
Designing a Streaming Algorithm for Outlier Detection in Data Mining—An Incrementa Approach

To design an algorithm for detecting outliers over streaming data has become an important task in many common applications, arising in areas such as fraud detections, network analysis, environment monitoring and so forth. Due to the fact that real-time data may arrive in the form of streams rather than batches, properties such as concept drift, temporal context, transiency, and uncertainty need to be considered. In addition, data processing needs to be incremental with limited memory resource, and scalable. These facts create big challenges for existing outlier detection algorithms in terms of their accuracies when they are implemented in an incremental fashion, especially in the streaming environment. To address these problems, we first propose C_KDE_WR, which uses sliding window and kernel function to process the streaming data online, and reports its results demonstrating high throughput on handling real-time streaming data, implemented in a CUDA framework on Graphics Processing Unit (GPU). We also present another algorithm, C_LOF, based on a very popular and effective outlier detection algorithm called Local Outlier Factor (LOF) which unfortunately works only on batched data. Using a novel incremental approach that compensates the drawback of high complexity in LOF, we show how to implement it in a streaming context and to obtain results in a timely manner. Like C_KDE_WR, C_LOF also employs sliding-window and statistical-summary to help making decision based on the data in the current window. It also addresses all those challenges of streaming data as addressed in C_KDE_WR. In addition, we report the comparative evaluation on the accuracy of C_KDE_WR with the state-of-the-art SOD_GPU using Precision, Recall and F-score metrics. Furthermore, a t-test is also performed to demonstrate the significance of the improvement. We further report the testing results of C_LOF on different parameter settings and drew ROC and PR curve with their area under the curve (AUC) and Average Precision (AP) values calculated respectively. Experimental results show that C_LOF can overcome the masquerading problem, which often exists in outlier detection on streaming data. We provide complexity analysis and report experiment results on the accuracy of both C_KDE_WR and C_LOF algorithms in order to evaluate their effectiveness as well as their efficiencies.
Real-Time Driving Scene Semantic Segmentation

Real-time understanding of surrounding environment is an essential yet challenging task for autonomous driving system. The system must not only deliver accurate result but also low latency performance. In this paper, we focus on the task of fast-and-accurate semantic segmentation. An efficient and powerful deep neural network termed as Driving Segmentation Network (DSNet) and a novel loss function Object Weighted Focal Loss are proposed. In designing DSNet, our goal is to achieve the best capacity with constrained model complexity. We design efficient and powerful unit inspired by ShuffleNet V2 and also integrate many successful techniques to achieve excellent balance between accuracy and speed. DSNet has 0.9 million of parameters, achieves 71.8% mean Intersection-over-Union (IoU) on Cityscapes validation set, 69.3% on test set, and runs 100+ frames per second (FPS) at resolution 640 × 360 on NVIDIA 1080Ti. In order to improve performance on minor and hard objects which are crucial in driving scene, Object Weighted Focal Loss (OWFL) is proposed to deal with the serious class imbalance issue in pixel-wise segmentation task. It could effectively improve the overall mean IoU of minor and hard objects by increasing loss contribution from them. Experiments show that DSNet performs 2.7% points higher on minor and hard objects compared with fast-and-accurate model ERFNet under similar accuracy. These traits imply that DSNet has great potential for practical autonomous driving application.
MEGA

Complex relationships among entities can be modeled very effectively using hypergraphs. Hypergraphs model real-world data by allowing a hyperedge to include two or more entities. Clustering of hypergraphs enables us to group the similar entities together. While most existing algorithms solely consider the connection structure of a hypergraph to solve the clustering problem, we can boost the clustering performance by considering various features associated with the entities as well as auxiliary relationships among the entities. Also, we can further improve the clustering performance if some of the labels are known and we incorporate them into a clustering model. In this paper, we propose a semi-supervised clustering framework for hypergraphs that is able to easily incorporate not only multiple relationships among the entities but also multiple attributes and content of the entities from diverse sources. Furthermore, by showing the close relationship between the hypergraph normalized cut and the weighted kernel K-Means, we also develop an efficient multilevel hypergraph clustering method which provides a good initialization with our semi-supervised multi-view clustering algorithm. Experimental results show that our algorithm is effective in detecting the ground-truth clusters and significantly outperforms other state-of-the-art methods.
Depthwise Nonlocal Module for Fast Salient Object Detection Using a Single Thread

Recently, deep convolutional neural networks have achieved significant success in salient object detection. However, existing state-of-the-art methods require high-end GPUs to achieve real-time performance, which makes it hard to adapt to low cost or portable devices. Although generic network architectures have been proposed to speed up inference on mobile devices, they are tailored to the task of image classification or semantic segmentation, and struggle to capture intrachannel and interchannel correlations that are essential for contrast modeling in salient object detection. Motivated by the above observations, we design a new deep-learning algorithm for fast salient object detection. The proposed algorithm for the first time achieves competitive accuracy and high inference efficiency simultaneously with a single CPU thread. Specifically, we propose a novel depthwise nonlocal module (DNL), which implicitly models contrast via harvesting intrachannel and interchannel correlations in a self-attention manner. In addition, we introduce a depthwise nonlocal network architecture that incorporates both DNLs module and inverted residual blocks. The experimental results show that our proposed network attains very competitive accuracy on a wide range of salient object detection datasets while achieving state-of-the-art efficiency among all existing deep-learning-based algorithms.
A survey on graph-based methods for similarity searches in metric spaces

Technology development has accelerated the volume growth of complex data, such as images, videos, time series, and georeferenced data. Similarity search is a widely used approach to retrieve complex data, which aims at retrieving similar data according to intrinsic characteristics of the data. Therefore, to facilitate the retrieval of complex data using similarity searches, one needs to organize large collections of data in a way that similar data can be retrieved efficiently. Many access methods were proposed in the literature to speed up similarity data retrieval from large databases. Recently, graph-based methods have emerged as a very efficient alternative for similarity retrieval, with reports indicating those methods outperformed other non-graph-based methods in several scenarios. However, to the best of our knowledge, there is no previous work with experimental analysis on a comprehensive number of graph-based methods using the same search algorithm and execution environment. Our main contribution is a survey on graph-based methods used for similarity searches. We present a review on graph-based methods (types of graphs and search algorithms) as well as a detailed discussion on the applicability of search algorithms (with exact or approximate answers) in each graph type. Our main focus is on static methods in metric spaces. This survey also includes an experimental evaluation of representative graphs implemented in a common platform. We evaluate the relative performance behavior of these graphs concerning the main construction and query parameters for a variety of real-world datasets. We also show results using synthetic datasets evaluating the performance of different graph types according to different dataset features. Our experimental results reinforce the tradeoff between graph construction cost and search performance according to the construction and search parameters.
Volumetric Estimation of the Damaged Area in the Human Brain from 2D MR Image

In this article, we present a volumetric estimate of the mutilated part in the human brain from a typical 2D MR image that is directly rendered from the scanner, which is first of its kind in the field of medical imaging. The proposed concept necessitates segmentation of the MR image for the identification of dimensions in the damaged region. Once the dimensions are identified from the resultant segmented image, the volume of the damaged region is evaluated through the Gauss Derivation theorem. The pixel to the distance scaling, which is fed as an input for the Gauss Derivation is from an earlier medical diagnosis from another researcher. The proposed algorithm was experimented with real-time images and the obtained results were examined against the real-time scenarios and were observed to exhibit better accuracy.
Dynamic virtual machine consolidation using a multi-agent system to optimise energy efficiency in cloud computing

Cloud computing has seen rapid growth in the last decade, as a result of this there is an increasing concern about the energy requirement of cloud data centres. One approach to tackle this issue is dynamic virtual machine consolidation (DVMC) where virtual machines (VMs) are packed into fewer hosts, while hosts which have no VMs are placed into a hibernate mode. DVMC is traditionally solved with strategies that build on centralised topologies. One disadvantage of these solutions is that they don′t scale well in very large data centres as the manager node becomes a bottleneck and a single point of failure. The nature of the problem lends itself to a decentralised solution which is preferred over a centralised one when a problem is: inherently modular, large in scale, in a very dynamic environment, and when communication channels can experience message delays and loss. We propose Gossip Contracts (GC), a new multi-agent framework for crafting decentralised co-operation strategies. GC is inspired by the Gossip and Contract Net protocols. Using GC we developed a GC-based DVMC strategy and compared it to two popular strategies: Sercon, a centralised strategy, and ecoCloud, a distributed strategy. One of the comparison experiments uses Google cluster-usage traces; a dataset of real usage data from a Google data centre. The GC-based strategy performs best with respect to SLA Violations, and is similar to or outperforms other strategies with respect to power consumption. By its decentralised nature, GC does not have a single point of failure.
Pedagogical aspects to support students’ evolving digital competence at school

(2020). Pedagogical aspects to support students' evolving digital competence at school. European Early Childhood Education Research Journal: Vol. 28, Digital Childhoods, Multimodality and STEM, pp. 182-196.
Toward a stage theory of the development of employees’ information security behavior

Existing behavioral information security research proposes continuum or non-stage models that focus on finding static determinants for information security behavior (ISB) that remains unchanged. Such models cannot explain a case where the reasons for ISB change. However, the underlying reasons and motives for users’ ISB are not static but may change over time. To understand the change in reasoning between different antecedents, we examine stage theorizing in other fields and develop the requirements for an emergent theory of the development of employees’ ISB: (1) the content of stages based on the stage elements and their stage-specific attributes; (2) the stage-independent element explaining the instability of ISB; and (3) the temporal order of stages based on developmental progression. To illustrate the stage theory requirements in an information security context, we suggest four stages: intuitive thinking, declarative thinking, agency-related thinking, and routine-related thinking. We propose that learning is a key driver of change between the stages. According to our theorizing, employees start with intuitive beliefs and later develop routine-related thinking. Furthermore, using interview data collected from employees in a multinational company, we illustrate the differences in the stages. For future information security research, we conceptualize ISB change in terms of stages and contribute a theoretical framework that can be empirically validated. In relation to practice, understanding the differences between the stages offers a foundation for identifying the stage-specific challenges that lead to non-compliance and the corresponding information security training aimed at tackling these challenges. Given that users’ ISB follows stages, although not in a specific order, identifying such stages can improve the effectiveness of information security training interventions within organizations.
Teaching Pervasive Computing: A Report and a Look Ahead From a Dagstuhl Seminar

Reports on the events and findings from the Dagstuhl Seminar entitled “Ubiquitous Computing Education: Why, What, and How” to explore these questions in more detail.2 The workshop gathered 26 faculty members and one undergraduate student3 to discuss the current state of ubiquitous computing education and to provide ideas for how to improve on our current practices. In this column, we discuss, and expand upon, the work of the seminar, including a detailed overview of the challenges of teaching pervasive computing, proposing a curriculum for students with different backgrounds, and exploring innovative active learning methods for pervasive computing.
Lightweight 3D Human Pose Estimation Network Training Using Teacher-Student Learning

We present MoVNect, a lightweight deep neural network to capture 3D human pose using a single RGB camera. To improve the overall performance of the model, we apply the teacher-student learning method based knowledge distillation to 3D human pose estimation. Real-time post-processing makes the CNN output yield temporally stable 3D skeletal information, which can be used in applications directly. We implement a 3D avatar application running on mobile in real-time to demonstrate that our network achieves both high accuracy and fast inference time. Extensive evaluations show the advantages of our lightweight model with the proposed training method over previous 3D pose estimation methods on the Human3.6M dataset and mobile devices.
Formal Analysis of Smart Contract Based on Colored Petri Nets

Smart contracts increasingly cause attention for its ability to widen blockchain's application scope. However, the security of contracts is vital to its wide deployment. In this article, we propose a multilevel smart contract modeling solution to analyze the security of contract. We improve the program logic rules for bytecode and apply the Hoare condition to create a Colored Petri Net (CPN) model. The model detection method provided by the CPN tools can show the full-state space and the wrong execution path, which help us analyze the security of the contract from several perspectives. The example shows that the counter-example path given by the contract model is accord with our expected results based on code analysis, proving the correctness of the solution. In addition, we design a highly automated modeling method, introducing custom call libraries and a path derivation algorithm based on backtracking, which improves the efficiency and pertinence of the dynamic simulation of CPN models.