Reviewerly/exHarmony huggingface dataset:
Security Enhanced Sentence Similarity Computing Model Based on Convolutional Neural Network

Deep learning model shows great advantages in various fields. However, researchers pay attention to how to improve the accuracy of the model, while ignoring the security considerations. The problem of controlling the judgment result of deep learning model by attack examples and then affecting the system decision-making is gradually exposed. In order to improve the security of sentence similarity analysis model, we propose a convolution neural network model based on attention mechanism. First of all, the mutual information between sentences is correlated by attention weighting. Then, it is input into improved convolutional neural network. In addition, we add attack examples to the input, which is generated by the firefly algorithm. In the attack example, we replace the words in the sentence to some extent, which results in the adversarial data with great semantic change but slight sentence structure change. To a certain extent, the addition of attack example increases the ability of model to identify adversarial data and improves the robustness of the model. Experimental results show that the accuracy, recall rate and F1 value of the model are due to other baseline models.
Self-secured devices: High performance and secure I/O access in TrustZone-based systems

Arm TrustZone is a hardware technology that adds significant value to the ongoing security picture. TrustZone-based systems typically consolidate multiple environments into the same platform, requiring resources to be shared among them. Currently, hardware devices on TrustZone-enabled system-on-chip (SoC) solutions can only be configured as secure or non-secure, which means the dual-world concept of TrustZone is not spread to the inner logic of the devices. The traditional passthrough model dictates that both worlds cannot use the same device concurrently. Furthermore, existing shared device access methods have been proven to cause a negative impact on the overall system in terms of security and performance. This work introduces the concept of self-secured devices, a novel approach for shared device access in TrustZone-based architectures. This concept extends the TrustZone dual-world model to the device itself, providing a secure and non-secure logical interface in a single device instance. The solution was deployed and evaluated on the LTZVisor, an open-source and lightweight TrustZone-assisted hypervisor. The obtained results are encouraging, demonstrating that our solution requires only a few additional hardware resources when compared with the native device implementation, while providing a secure solution for device sharing.
Person Foreground Segmentation by Learning Multi-Domain Networks

Separating the dominant person from the complex background is significant to the human-related research and photo-editing based applications. Existing segmentation algorithms are either too general to separate the person region accurately, or not capable of achieving real-time speed. In this paper, we introduce the multi-domain learning framework into a novel baseline model to construct the Multi-domain TriSeNet Networks for the real-time single person image segmentation. We first divide training data into different subdomains based on the characteristics of single person images, then apply a multi-branch Feature Fusion Module (FFM) to decouple the networks into the domain-independent and the domain-specific layers. To further enhance the accuracy, a self-supervised learning strategy is proposed to dig out domain relations during training. It helps transfer domain-specific knowledge by improving predictive consistency among different FFM branches. Moreover, we create a large-scale single person image segmentation dataset named MSSP20k, which consists of 22,100 pixel-level annotated images in the real world. The MSSP20k dataset is more complex and challenging than existing public ones in terms of scalability and variety. Experiments show that our Multi-domain TriSeNet outperforms state-of-the-art approaches on both public and the newly built datasets with real-time speed.
Enhancing Object Detection Performance Through Sensor Pose Definition with Bayesian Optimization

Robots equipped with vision systems at the end-effector provide a powerful combination in industrial contexts. While much attention is dedicated to machine vision algorithms, the optimization of the vision system pose is not properly addressed (to increase object detection performance). A complete pipeline for such optimization is proposed. To this aim, Bayesian Optimization is employed. A Franka EMIKA Panda robot has been used as a robotic platform, equipped at its end-effector with an Intel© RealSense D400. Achieved results show the high-fidelity reconstruction of the real working environment for the offline optimization (i.e., performed simulations), together with capabilities of the proposed Bayesian Optimization-based approach to defining the sensor pose in a limited number of experimental trials (50 maximum iterations has been considered).
Performance Evaluation of Source Camera Attribution by Using Likelihood Ratio Methods

Performance evaluation of source camera attribution methods typically stop at the level of analysis of hard to interpret similarity scores. Standard analytic tools include Detection Error Trade-off or Receiver Operating Characteristic curves, or other scalar performance metrics, such as Equal Error Rate or error rates at a specific decision threshold. However, the main drawback of similarity scores is their lack of probabilistic interpretation and thereby their lack of usability in forensic investigation, when assisting the trier of fact to make more sound and more informed decisions. The main objective of this work is to demonstrate a transition from the similarity scores to likelihood ratios in the scope of digital evidence evaluation, which not only have probabilistic meaning, but can be immediately incorporated into the forensic casework and combined with the rest of the case-related forensic. Likelihood ratios are calculated from the Photo Response Non-Uniformity source attribution similarity scores. The experiments conducted aim to compare different strategies applied to both digital images and videos, by considering their respective peculiarities. The results are presented in a format compatible with the guideline for validation of forensic likelihood ratio methods.
Determinants of community participation in a watershed development program in Southern Ethiopia

Community participation is critically important for the effectiveness of a development project. Different factors influence community participation, and these factors are not the same across all communities. This study aimed to identify the socioeconomic and watershed impact-related factors influencing community participation in a watershed development program in southern Ethiopia. Data were collected from 1,064 respondents (87.7% males) and in nine districts in southern Ethiopia. Descriptive statistics and logistic regression explored which variables predict higher rates of community participation, and whether determining variables changed from before to after the program was implemented. The results show that the variables predicting community participation in the watershed development program were significantly different before and after the program’s implementation. The findings highlight the importance of demonstrating the positive impacts of community development interventions as early in the program as possible to ensure more and continued participation in relevant development projects.
Applying Stepped Task in Remote Unmoderated Test: A Case Report

Internet firms have shared a common sense that agile user research is significant for agile developments. The unmoderated test, as one of the most commonly used qualitative approaches, has been widely adopted. Such an approach requires participants’ trained skill of think aloud, and the remote unmoderated test platforms compensated for such a lack with their huge, trained user database. However, there is an inevasible bias that participants go for bonuses, thereby muck the tests or please the researcher. Would it reduce bias by innovatively applying stepped tasks in the remote unmoderated test? The research carries out pilot tests for validating the feasibility and identify the correct approach of applying stepped task in the situation of the remote unmoderated test. The user test of an online shopping platform is elaborated as a case study for validation of such an innovative approach. The research concludes that stepped tasks can be applied in remote unmoderated tests. However, the form of stepped task applied in remote unmoderated tests differs from the traditional moderated test which requires face-to-face communication between researcher and participants. To apply stepped task in the situations as remote usability test and unmoderated test, researchers need to 1) design tasks within a natural user journey and add connecting tasks which do not represent test objectives as to hide the test motivation from the participants; 2) observe user’s natural behaviors at the beginning, and investigate user’s motivations latter; 3) define stepped tasks’ level of detail through piolet tests.
Cybersecurity Vulnerabilities in FinTech

This chapter instigates cybersecurity vulnerabilities in FinTech. It discusses several general and specific cybersecurity vulnerabilities exploited in the past. Specific cybersecurity vulnerabilities consist of human, technology, and transaction vulnerabilities. The overall purpose of identifying different types of vulnerabilities is to understand the user behavior in using technology to perform digital transactions. Vulnerabilities exist in every component of the FinTech ecosystem, especially the financial institutions and startups because they use innovative technology and transform traditional financial institutions into contemporary FinTech institutions. Technology developers are also related to cybersecurity vulnerabilities as they need to be aware of potential threats that can exercise vulnerabilities and flaws in the technology that they are developing.
The Impact of the Collective Members’ Independence on the Prediction Accuracy

Decision making is one of the most frequently performed processes nowadays.Our future or other people's future may depend on the decision we make, therefore we try to take it as good as possible.The rapid development of technology facilitates it for us, as we are able to store, process and analyse more and more data.However, the power of human knowledge extended by experience and intuition still overperforms computer systems in solving such problems.The power of the combined knowledge of a group of people is known as a collective intelligence.In this paper, we present an analysis of one of the properties describing the consensus, id est the influence between its members.We base our work on graph theory and a multi-agent system approach and provide an experimental evaluation and a statistical analysis of obtained results.As a result, we observed four relationships between the considered measures describing the collective and the accuracy of collective prediction.
IXVC: An interactive pipeline for explaining visual clusters in dimensionality reduction visualizations with decision trees

High-dimensional data with many features are usually challenging to represent with standard visualization techniques. Usually, one has to resort to dimensionality reduction techniques such as PCA, MDS or t-SNE to represent such data. Such dimensionality reduction techniques make it possible to highlight the high-dimensional structures of data. In many of such visualizations, comparable instances appear to form visual clusters. However, no feedback is directly given by these techniques to the user about the features that make the instances cluster together in the visualization. As such, the interpretation of which features define a given visual cluster is a complicated task. In this paper, we propose a novel interactive approach (called Interactive eXplanation of Visual Clusters — IXVC) to explain dimensionality reduction visualizations by mapping their clusters to explanations provided by decision trees. The decision trees use features in high-dimensional data to explain two-dimensional clusters, filling the gap between the dimensionality reduction visualization and the original data.
Analysing Hot Facebook Users Posts’ Sentiment Using Deep Learning

The explosion of social networks has created many new careers and new entertainment. Money now can be earned by sitting in one place and streaming games; reviewing food, movies, music; or just simply showing a pretty face. Social networks make all of those things, and more, possible. As long as a person is famous on social networks, they can earn money by doing almost everything. So how to be famous, or in social network’s language, what gets someone “followers” on social media? Is being positive, telling funny stories, showing sunshine and flower pictures enough? Or they can be a pessimistic person, ranting about everything and people still worship them like a god? This research collects and analyses hot Facebook users posts’ sentiment to see if what someone posts on Facebook could make them famous and also determines the accuracy of using deep learning in analysing Vietnamese social media contents sentiment. There have been several studies for social media content sentiment analysing, but none with Vietnamese social media contents of famous Vietnamese people. In this study, a data set of Vietnamese hot Facebook users’ posts is labelled and organized to be shared with the language research community generally, and the Vietnamese language research specifically.
Scalable Bayesian Transport Maps for High-Dimensional Non-Gaussian Spatial Fields

A multivariate distribution can be described by a triangular transport map from the target distribution to a simple reference distribution. We propose Bayesian nonparametric inference on the transport map by modeling its components using Gaussian processes. This enables regularization and uncertainty quantification of the map estimation, while resulting in a closed-form and invertible posterior map. We then focus on inferring the distribution of a nonstationary spatial field from a small number of replicates. We develop specific transport-map priors that are highly flexible and are motivated by the behavior of a large class of stochastic processes. Our approach is scalable to high-dimensional distributions due to data-dependent sparsity and parallel computations. We also discuss extensions, including Dirichlet process mixtures for flexible marginals. We present numerical results to demonstrate the accuracy, scalability, and usefulness of our methods, including statistical emulation of non-Gaussian climate-model output. Supplementary materials for this article are available online.
A Contract Theory Based Incentive Mechanism for Federated Learning

Federated learning (FL) serves as a data privacy-preserved machine learning paradigm, and realizes the collaborative model trained by distributed clients. To accomplish an FL task, the task publisher needs to pay financial incentives to the FL server offloads the task to the contributing FL clients. However, it is challenging to design proper incentives for FL clients because the task is privately trained by the clients. This paper proposes a contract theory-based FL task training model toward minimizing the incentive budget subject to clients being individually rational and incentive compatible in each FL training round. We design a two-dimensional contract model by formally defining two private types of clients, namely data quality and computation effort. To effectively aggregate the trained models, a contract-based aggregator is proposed. We analyze the feasible and optimal contract solutions for the proposed contract model. The experimental results demonstrate that the proposed framework and contract model can effectively improve the generation accuracy of FL tasks. Moreover, the generalization accuracy of the FL tasks can be improved by the proposed incentive mechanism, the contract-based aggregation is applied.
Creativity in Generative Musical Networks: Evidence From Two Case Studies

Deep learning, one of the fastest-growing branches of artificial intelligence, has become one of the most relevant research and development areas of the last years, especially since 2012, when a neural network surpassed the most advanced image classification techniques of the time. This spectacular development has not been alien to the world of the arts, as recent advances in generative networks have made possible the artificial creation of high-quality content such as images, movies or music. We believe that these novel generative models propose a great challenge to our current understanding of computational creativity. If a robot can now create music that an expert cannot distinguish from music composed by a human, or create novel musical entities that were not known at training time, or exhibit conceptual leaps, does it mean that the machine is then creative? We believe that the emergence of these generative models clearly signals that much more research needs to be done in this area. We would like to contribute to this debate with two case studies of our own: TimbreNet, a variational auto-encoder network trained to generate audio-based musical chords, and StyleGAN Pianorolls, a generative adversarial network capable of creating short musical excerpts, despite the fact that it was trained with images and not musical data. We discuss and assess these generative models in terms of their creativity and we show that they are in practice capable of learning musical concepts that are not obvious based on the training data, and we hypothesize that these deep models, based on our current understanding of creativity in robots and machines, can be considered, in fact, creative.
Genetic Algorithm Assisted HIDMS-PSO: A New Hybrid Algorithm for Global Optimisation

In this paper, a new hybrid algorithm, GA-HIDMS-PSO, is introduced by hybridising the state-of-the-art particle swarm optimisation (PSO) variant, the heterogeneous improved dynamic multi-swarm PSO (HIDMS-PSO) with a genetic algorithm (GA).The new hybrid model exploits the heterogeneous features of HIDMS-PSO and the evolutionary characteristics of the GA.In the GA-HIDMS-PSO architecture, HIDMS-PSO acts as the primary search engine, and the GA is employed as the secondary method to assist and slow down the loss of diversity for selected proportions of homogeneous and heterogeneous subpopulations of the HIDMS-PSO algorithm.Both methods run consecutively.As the primary search method, HIDMS-PSO runs for longer periods compared with the GA.The HIDMS-PSO provides the initial solutions for the GA from both homogeneous and heterogeneous subpopulations and final solutions returned from the GA replace prior solutions in the HIDMS-PSO which resumes the search process with potentially more diverse particles to guide the swarm.The GA-HIDMS-PSO algorithm's performance was tested on the 30 and 50 dimensional CEC'05 and CEC'17 test suites.The results were compared with 24 algorithms, with 12 state-of-the-art PSO variants and 12 other metaheuristics.GA-HIDMS-PSO outperformed all 24 comparison algorithms on both test suites for both 30 and 50 dimensions.
Don’t forget to assess: How teachers check for new and deeper learning when integrating virtual reality in the classroom

Teachers consider learning goals as they implement new technologies like Virtual Reality in the classroom, but may overlook or struggle with verifying whether new learning actually occurred. We interviewed six US high school teachers and surveyed 277 of their students to understand the pedagogical implications and challenges of coordinating assessments with the integration of VR in their classrooms. Some teachers devised new assessment strategies to capture the deep, constructivist, learning they saw occurring in their students as a result of VR. Students’ responses suggest they needed more explicit goals for the VR class activities, but students provided insights about the benefits they perceived for their retention of relevant information. Future work should continue to explore the alignment of learning objectives, technology, and assessment.Supplemental data for this article is available online at https://doi.org/10.1080/15391523.2021.1950083 .
Benchmarking Serverless Workloads on Kubernetes

As a disruptive paradigm in the cloud landscape, Serverless Computing is attracting attention because of its unique value propositions to reduce operating costs and outsource infrastructure management. Nevertheless, enterprise Functionas-a-Service (FaaS) platforms may pose significant risks such as vendor lock-in, lack of security control due to multi-tenancy, complicated pricing models, and legal and regulatory compliance— particularly in mobile computing scenarios. This work proposes a production-grade fault-tolerant serverless architecture based on a highly-available Kubernetes topology using an open-source framework, deployed on OpenStack instances, and benchmarked with a realistic scaled-down Azure workload traces dataset. By measuring success rate, throughput, latency, and auto scalability, we have managed to assess not only resilience but also sustained performance under a logistic model for three distinct representative workloads. Our test executions show, with 95%–confidence, that between 70 and 90 concurrent users can access the system while experiencing acceptable performance. Beyond the breaking point identified (i.e. 91 transactions per second), the Kubernetes cluster has to be scaled-up or scaled out to meet the QoS and availability requirements.
Discovering Fuzzy Frequent Spatial Patterns in Large Quantitative Spatiotemporal databases

Finding fuzzy frequent patterns in a quantitative database is a challenging problem of significant importance in many real-world applications. Past studies focused on mining these patterns in quantitative transactional databases by disregarding the spatiotemporal characteristics of an item in the database. This paper proposes a generic model of fuzzy frequent spatial pattern (FFSP) that may exist in a quantitative spatiotemporal database. Discovering FFSPs in a database is nontrivial and challenging due to its huge search space and high computational cost. A novel pruning technique, called neighborhood pruning, has been introduced to effectively reduce the search space and the computational cost of finding the desired itemsets. This technique facilitates the mining of FFSPs in large real-world databases practicable. We also present an efficient algorithm, called Fuzzy Frequent Spatial Pattern-Miner (FFSP-Miner), to find all desired patterns in the database. Experimental results demonstrate that FFSP-Miner is both memory and runtime efficient. Finally, we discuss the usefulness of our model with a case study on air pollution analytics.