Reviewerly/exHarmony huggingface dataset:
The Psychology of Internet Fraud Victimisation: a Systematic Review

Existing theories of fraud provide some insight into how criminals target and exploit people in the online environment; whilst reference to psychological explanations is common, the actual use of established behavioural theories and/or methods in these studies is often limited. In particular, there is less understanding of why certain people/demographics are likely to respond to fraudulent communications. This systematic review will provide a timely synthesis of the leading psychologically based literature to establish the key theories and empirical research that promise to impact on anti-fraud policies and campaigns. Relevant databases and websites were searched using terms related to psychology and fraud victimisation. A total of 44 papers were extracted and 34 included in the final analysis. The studies range in their scope and methods; overall, three main factors were identified: message (n = 6), experiential (n = 7), and dispositional (n = 21), although there was some overlap between these (for example, mapping message factors onto the dispositional traits of the victim). Despite a growing body of research, the total number of studies able to identify specific psychological processes associated with increased susceptibility to online fraud victimisation was limited. Messages are targeted to appeal to specific psychological vulnerabilities, the most successful linking message with human factors, for example, time-limited communications designed to enact peripheral rather than central information processing. Suggestions for future research and practical interventions are discussed.
DeepCO3: Deep Instance Co-Segmentation by Co-Peak Search and Co-Saliency Detection

In this paper, we address a new task called instance co-segmentation. Given a set of images jointly covering object instances of a specific category, instance co-segmentation aims to identify all of these instances and segment each of them, i.e. generating one mask for each instance. This task is important since instance-level segmentation is preferable for humans and many vision applications. It is also challenging because no pixel-wise annotated training data are available and the number of instances in each image is unknown. We solve this task by dividing it into two sub-tasks, co-peak search and instance mask segmentation. In the former sub-task, we develop a CNN-based network to detect the co-peaks as well as co-saliency maps for a pair of images. A co-peak has two endpoints, one in each image, that are local maxima in the response maps and similar to each other. Thereby, the two endpoints are potentially covered by a pair of instances of the same category. In the latter subtask, we design a ranking function that takes the detected co-peaks and co-saliency maps as inputs and can select the object proposals to produce the final results. Our method for instance co-segmentation and its variant for object colocalization are evaluated on four datasets, and achieve favorable performance against the state-of-the-art methods. The source codes and the collected datasets are available at https://github.com/KuangJuiHsu/DeepCO3/
Sparse attention based separable dilated convolutional neural network for targeted sentiment analysis

Long short-term memory networks (LSTM) and classical convolutional neural networks (CNN) are two critical methods for the task of targeted sentiment analysis, but LSTM are difficult to parallelize and time-inefficient, and classical CNN can only capture local semantic features. To this end, this paper first proposes a sparse attention based separable dilated convolutional neural network (SA-SDCCN), which consists of multichannel embedding layer, separable dilated convolution module, sparse attention layer, and output layer. Specifically, our work is mainly concentrated on the first three parts. In multichannel embedding layer, semantic and sentiment embeddings are incorporated into an embedding tensor, which builds richer representations over the input sequence. In separable dilated convolution module, long-range contextual semantic information is explored and multi-scale contextual semantic dependencies are aggregated simultaneously through diverse dilation rates. Moreover, the separable structure further reduces the model parameters. In sparse attention layer, sentiment-oriented components are noticed according to the features of specific target entity. Finally, some experiments on three benchmark datasets demonstrate that SA-SDCCN achieves comparable or even better performance than state-of-the-art methods in terms of higher parallelism and lower computational cost.
Guided Stereo Matching

Stereo is a prominent technique to infer dense depth maps from images, and deep learning further pushed forward the state-of-the-art, making end-to-end architectures unrivaled when enough data is available for training. However, deep networks suffer from significant drops in accuracy when dealing with new environments. Therefore, in this paper, we introduce Guided Stereo Matching, a novel paradigm leveraging a small amount of sparse, yet reliable depth measurements retrieved from an external source enabling to ameliorate this weakness. The additional sparse cues required by our method can be obtained with any strategy (e.g., a LiDAR) and used to enhance features linked to corresponding disparity hypotheses. Our formulation is general and fully differentiable, thus enabling to exploit the additional sparse inputs in pre-trained deep stereo networks as well as for training a new instance from scratch. Extensive experiments on three standard datasets and two state-of-the-art deep architectures show that even with a small set of sparse input cues, i) the proposed paradigm enables significant improvements to pre-trained networks. Moreover, ii) training from scratch notably increases accuracy and robustness to domain shifts. Finally, iii) it is suited and effective even with traditional stereo algorithms such as SGM.
ChanEstNet: A Deep Learning Based Channel Estimation for High-Speed Scenarios

Aiming at the problem that the downlink channel estimation performance is limited due to the fast time-varying and non-stationary characteristics in the high-speed mobile scenarios, we propose a channel estimation network based on deep learning, called ChanEstNet. ChanEstNet uses the convolutional neural network (CNN) to extract channel response feature vectors and recurrent neural network (RNN) for channel estimation. We use a large amount of high-speed channel data to conduct offline training for the learning network, fully exploit the channel information in the training sample, make it learn the characteristics of fast time-varying and non-stationary channels, and better track the features of channels changing in high-speed environments. The simulation results show that in the high-speed mobile scenarios, compared with the traditional methods, the proposed channel estimation method has low computational complexity and significant performance improvement.
Efficient Quantum Algorithms for GHZ and <i>W</i> States, and Implementation on the IBM Quantum Computer

We propose efficient algorithms with logarithmic step complexities for the generation of entangled $GHZ_N$ and $W_N$ states useful for quantum networks, and we demonstrate an implementation on the IBM quantum computer up to $N=16$. Improved quality is then investigated using full quantum tomography for low-$N$ GHZ and W states. This is completed by parity oscillations and histogram distance for large $N$ GHZ and W states respectively. We are capable to robustly build states with about twice the number of quantum bits which were previously achieved. Finally we attempt quantum error correction on GHZ using recent schemes proposed in the literature, but with the present amount of decoherence they prove detrimental.
Monotonic classification: An overview on algorithms, performance measures and data sets

Currently, knowledge discovery in databases is an essential first step when identifying valid, novel and useful patterns for decision making. There are many real-world scenarios, such as bankruptcy prediction, option pricing or medical diagnosis, where the classification models to be learned need to fulfill restrictions of monotonicity (i.e. the target class label should not decrease when input attributes values increase). For instance, it is rational to assume that a higher debt ratio of a company should never result in a lower level of bankruptcy risk. Consequently, there is a growing interest from the data mining research community concerning monotonic predictive models. This paper aims to present an overview of the literature in the field, analyzing existing techniques and proposing a taxonomy of the algorithms based on the type of model generated. For each method, we review the quality metrics considered in the evaluation and the different data sets and monotonic problems used in the analysis. In this way, this paper serves as an overview of monotonic classification research in specialized literature and can be used as a functional guide for the field.
Semi-supervised User Profiling with Heterogeneous Graph Attention Networks

Aiming to represent user characteristics and personal interests, the task of user profiling is playing an increasingly important role for many real-world applications, e.g., e-commerce and social networks platforms. By exploiting the data like texts and user behaviors, most existing solutions address user profiling as a classification task, where each user is formulated as an individual data instance. Nevertheless, a user's profile is not only reflected from her/his affiliated data, but also can be inferred from other users, e.g., the users that have similar co-purchase behaviors in e-commerce, the friends in social networks, etc. In this paper, we approach user profiling in a semi-supervised manner, developing a generic solution based on heterogeneous graph learning. On the graph, nodes represent the entities of interest (e.g., users, items, attributes of items, etc.), and edges represent the interactions between entities. Our heterogeneous graph attention networks (HGAT) method learns the representation for each entity by accounting for the graph structure, and exploits the attention mechanism to discriminate the importance of each neighbor entity. Through such a learning scheme, HGAT can leverage both unsupervised information and limited labels of users to build the predictor. Extensive experiments on a real-world e-commerce dataset verify the effectiveness and rationality of our HGAT for user profiling.
Social media opinion summarization using emotion cognition and convolutional neural networks

Quickly and accurately summarizing representative opinions is a key step for assessing microblog sentiments. The Ortony-Clore-Collins (OCC) model of emotion can offer a rule-based emotion export mechanism. In this paper, we propose an OCC model and a Convolutional Neural Network (CNN) based opinion summarization method for Chinese microblogging systems. We test the proposed method using real world microblog data. We then compare the accuracy of manual sentiment annotation to the accuracy using our OCC-based sentiment classification rule library. Experimental results from analyzing three real-world microblog datasets demonstrate the efficacy of our proposed method. Our study highlights the potential of combining emotion cognition with deep learning in sentiment analysis of social media data.
A high-speed interval type 2 fuzzy system approach for dynamic parameter adaptation in metaheuristics

Fuzzy dynamic adaptation of parameters in meta-heuristic algorithms has recently been shown to provide an improvement in efficiency with respect to meta-heuristic algorithms with static parameters. However, executing a fuzzy inference in each iteration represents an increase in the computational cost, and this is even more critical in the case of using Type-2 Fuzzy Logic systems. On the other hand, fuzzy dynamic adaptation with Type-2 Fuzzy Logic has shown better performance when compared with respect to Type-1 Fuzzy Logic in diverse areas of application; therefore, the goal of this paper is aimed at reducing the computational cost of Type-2 Fuzzy Logic processing for dynamic adaptation of parameters in meta-heuristic algorithms. To reduce the computational cost of processing the Interval Type-2 Fuzzy system for dynamic adaptation of metaheuristic parameters, the use of an approximation to the Continuous Karnik–Mendel method (CEKM) is proposed. The proposed approach provides an analytical approximation to the CEKM method, in this way reducing the computational cost of evaluating the Interval Type-2 Fuzzy System. The performance of the proposed approach was tested with five benchmark functions and with one benchmark control problem. The proposed approach was tested with two different meta-heuristic algorithms, the Differential Evolution algorithm (DE) and the Harmony Search algorithm (HS), in both cases achieving a reduction in the computational cost, while maintaining the performance with respect to the Type-2 Dynamic adaptation of parameters with the conventional type reduction methods.
Cartesian genetic programming: its status and future

Cartesian genetic programming, a well-established method of genetic programming, is approximately 20 years old. It represents solutions to computational problems as graphs. Its genetic encoding includes explicitly redundant genes which are well-known to assist in effective evolutionary search. In this article, we review and compare many of the important aspects of the method and findings discussed since its inception. In the process, we make many suggestions for further work which could improve the efficiency of the CGP for solving computational problems.
RYF-Net: Deep Fusion Network for Single Image Haze Removal

Haze removal from a single image is a challenging task. Estimation of accurate scene transmission map (TrMap) is the key to reconstruct the haze-free scene. In this paper, we propose a convolutional neural network based architecture to estimate the TrMap of the hazy scene. The proposed network takes the hazy image as an input and extracts the haze relevant features using proposed RNet and YNet through RGB and YCbCr color spaces respectively and generates two TrMaps. Further, we propose a novel TrMap fusion network (FNet) to integrate two TrMaPs and estimate robust TrMap for the hazy scene. To analyze the robustness of FNet, we tested it on combinations of TrMaps obtained from existing state-of-the-art methods. Performance evaluation of the proposed approach has been carried out using the structural similarity index, mean square error and peak signal to noise ratio. We conduct experiments on five datasets namely: D-HAZY, Imagenet, Indoor SOTS, HazeRD and set of real-world hazy images. Performance analysis shows that the proposed approach outperforms the existing state-of-the-art methods for single image dehazing. Further, we extended our work to address high-level vision task such as object detection in hazy scenes. It is observed that there is a significant improvement in accurate object detection in hazy scenes using proposed approach.
Environmental education and disaster mitigation through language learning

Abstract This study aims to determine the knowledge of the environment, attitudes towards the environment, and skills in designing learning that contains the environment of prospective language teachers. In addition to the environment, this research also links environmental learning with disaster mitigation. This study uses a survey method. The instrument used was a questionnaire in the form of a question sheet consisting of twelve questions relating to the attitudes and knowledge of prospective teacher students regarding environmentally charged learning and disaster mitigation. The results of the study indicate that students still need to improve knowledge about the environment and disaster mitigation to be able to design language learning with environmental education and disaster mitigation. When becoming a teacher, scholar have been able to integrate environmental education and disaster mitigation to provide students with an understanding of it and foster an attitude of environmental awareness and care for the environment.
Exploring implicit spaces for constrained sampling-based planning

We present a review and reformulation of manifold constrained sampling-based motion planning within a unifying framework, IMACS (implicit manifold configuration space). IMACS enables a broad class of motion planners to plan in the presence of manifold constraints, decoupling the choice of motion planning algorithm and method for constraint adherence into orthogonal choices. We show that implicit configuration spaces defined by constraints can be presented to sampling-based planners by addressing two key fundamental primitives, sampling and local planning, and that IMACS preserves theoretical properties of probabilistic completeness and asymptotic optimality through these primitives. Within IMACS, we implement projection- and continuation-based methods for constraint adherence, and demonstrate the framework on a range of planners with both methods in simulated and realistic scenarios. Our results show that the choice of method for constraint adherence depends on many factors and that novel combinations of planners and methods of constraint adherence can be more effective than previous approaches. Our implementation of IMACS is open source within the Open Motion Planning Library and is easily extended for novel planners and constraint spaces.
A Real-Time Patient Monitoring Framework for Fall Detection

Fall detection is a major problem in the healthcare department. Elderly people are more prone to fall than others. There are more than 50% of injury-related hospitalizations in people aged over 65. Commercial fall detection devices are expensive and charge a monthly fee for their services. A more affordable and adaptable system is necessary for retirement homes and clinics to build a smart city powered by IoT and artificial intelligence. An effective fall detection system would detect a fall and send an alarm to the appropriate authorities. We propose a framework that uses edge computing where instead of sending data to the cloud, wearable devices send data to a nearby edge device like a laptop or mobile device for real-time analysis. We use cheap wearable sensor devices from MbientLab, an open source streaming engine called Apache Flink for streaming data analytics, and a long short-term memory (LSTM) network model for fall classification. The model is trained using a published dataset called “MobiAct.” Using the trained model, we analyse optimal sampling rates, sensor placement, and multistream data correction. Our edge computing framework can perform real-time streaming data analytics to detect falls with an accuracy of 95.8%.
A feature transfer enabled multi-task deep learning model on medical imaging

Object detection, segmentation, and classification are three common tasks in medical image analysis. Multi-task deep learning (MTL) tackles these three tasks jointly, which provides two advantages—saving computational cost and improving robustness against overfitting. Existing multi-task deep models start with learning each task as an individual objective in parallel and then integrate the tasks at the end of the architecture with one cost function. Such architecture fails to take advantage of the combined power of the features from each individual task at an early stage of the training. In this research, we propose a new architecture, FT-MTL-Net, an MTL model enabled by feature transfer. Traditional transfer learning deals with the same or similar task (e.g., classification) from different data sources (a.k.a. domain). The underlying assumption is that the knowledge gained from various source domains may help the learning task on the target domain. Our proposed FT-MTL-Net utilizes the different tasks from the same domain. Considering that features from the tasks are different views of the domain, the combined feature maps can be well exploited using knowledge from multiple views to enhance the generalizability. To evaluate the validity of the proposed approach, FT-MTL-Net is compared with models from literature including eight classification models, four detection models, and three segmentation models using a publicly available Full Filed Digital Mammogram dataset for breast cancer diagnosis. Experimental results show that the proposed FT-MTL-Net outperforms the competing models in classification and detection and has comparable results in segmentation.
Visual question answering model based on visual relationship detection

visual question answering (VQA) is a learning task involving two major fields of computer vision and natural language processing. The development of deep learning technology has contributed to the advancement of this research area. Although the research on the question answering model has made great progress, the low accuracy of the VQA model is mainly because the current question answering model structure is relatively simple, the attention mechanism of model is deviated from human attention and lacks a higher level of logical reasoning ability. In response to the above problems, we propose a VQA model based on multi-objective visual relationship detection. Firstly, the appearance feature is used to replace the image features from the original object, and the appearance model is extended by the principle of word vector similarity. The appearance features and relationship predicates are then fed into the word vector space and represented by a fixed length vector. Finally, through the concatenation of elements between the image feature and the question vector are fed into the classifier to generate an output answer. Our method is benchmarked on the DQAUAR data set, and evaluated by the Acc [email protected] and [email protected]
A convolutional neural-based learning classifier system for detecting database intrusion via insider attack

Role-based access control (RBAC) in databases provides a valuable level of abstraction to promote security administration at the business enterprise level. With the capacity for adaptation and learning, machine learning algorithms are suitable for modeling normal data access patterns based on large amounts of data and presenting robust statistical models that are not sensitive to user changes. We propose a convolutional neural-based learning classifier system (CN-LCS) that models the role of queries by combining conventional learning classifier system (LCS) with convolutional neural network (CNN) for a database intrusion detection system based on the RBAC mechanism. The combination of modified Pittsburgh-style LCSs for the optimization of feature selection rules and one-dimensional CNNs for modeling and classification in place of traditional rule generation outperforms other machine learning classifiers on a synthetic query dataset. In order to quantitatively compare the inclusion of rule generation and modeling processes in the CN-LCS, we have conducted 10-fold cross-validation tests and analysis through a paired sampled t-test.