Reviewerly/exHarmony huggingface dataset:
Runtime Analysis of Restricted Tournament Selection for Bimodal Optimisation

Niching methods have been developed to maintain the population diversity, to investigate many peaks in parallel, and to reduce the effect of genetic drift. We present the first rigorous runtime analyses of restricted tournament selection (RTS), embedded in a (μ+1) EA, and analyse its effectiveness at finding both optima of the bimodal function TwoMax. In RTS, an offspring competes against the closest individual, with respect to some distance measure, amongst w (window size) population members (chosen uniformly at random with replacement), to encourage competition within the same niche. We prove that RTS finds both optima on TwoMax efficiently if the window size w is large enough. However, if w is too small, RTS fails to find both optima even in exponential time, with high probability. We further consider a variant of RTS selecting individuals for the tournament without replacement. It yields a more diverse tournament and is more effective at preventing one niche from taking over the other. However, this comes at the expense of a slower progress towards optima when a niche collapses to a single individual. Our theoretical results are accompanied by experimental studies that shed light on parameters not covered by the theoretical results and support a conjectured lower runtime bound.
360-Degree VR Video Watermarking Based on Spherical Wavelet Transform

Similar to conventional video, the increasingly popular 360 <?TeX $^{\circ }$?> virtual reality (VR) video requires copyright protection mechanisms. The classic approach for copyright protection is the introduction of a digital watermark into the video sequence. Due to the nature of spherical panorama, traditional watermarking schemes that are dedicated to planar media cannot work efficiently for 360 <?TeX $^{\circ }$?> VR video. In this article, we propose a spherical wavelet watermarking scheme to accommodate 360 <?TeX $^{\circ }$?> VR video. With our scheme, the watermark is first embedded into the spherical wavelet transform domain of the 360 <?TeX $^{\circ }$?> VR video. The spherical geometry of the 360 <?TeX $^{\circ }$?> VR video is used as the host space for the watermark so that the proposed watermarking scheme is compatible with the multiple projection formats of 360 <?TeX $^{\circ }$?> VR video. Second, the just noticeable difference model, suitable for head-mounted displays (HMDs), is used to control the imperceptibility of the watermark on the viewport. Third, besides detecting the watermark from the spherical projection, the proposed watermarking scheme also supports detecting watermarks robustly from the viewport projection. The watermark in the spherical domain can protect not only the 360 <?TeX $^{\circ }$?> VR video but also its corresponding viewports. The experimental results show that the embedded watermarks are reliably extracted both from the spherical and the viewport projections of the 360 <?TeX $^{\circ }$?> VR video, and the robustness of the proposed scheme to various copyright attacks is significantly better than that of the competing planar-domain approaches when detecting the watermark from viewport projection.
Daily life Log Recognition based on Automatic Features for Health care Physical Exercise via IMU Sensors

Wearable inertial based sensors are strong enablers for the acquisition of human daily life-log data. Eventually, many motion devices have often degraded the performance of wearable sensors due to inner/outer environmental effects. In addition, key decisions are made based on human life-log recognition results and precise recognition of human life-logs with lower limits of uncertainty is significantly important. For this purpose, many motion devices have been used in last decade, in order to recognize daily life activities. In this paper, we proposed an efficient model for better recognition results for healthcare patient's daily life-log patterns. We designed a 1D Haar based extraction algorithm and different statistical features to extract valuable features. For activity classification, we used Quadratic Discrimination Analysis (QDA) optimized by Artificial Neural Network (ANN) on two benchmarks PAMAP2 dataset and our self-annotated IM-SB database. The outcome of our system illustrates that our proposed model competes with other advanced methods in term of exactness and effectiveness.
Rabbit Holes and Taste Distortion: Distribution-Aware Recommendation with Evolving Interests

To mitigate the rabbit hole effect in recommendations, conventional distribution-aware recommendation systems aim to ensure that a user's prior interest areas are reflected in the recommendations that the system makes. For example, a user who historically prefers comedies to dramas by 2:1 should see a similar ratio in recommended movies. Such approaches have proven to be an important building block for recommendation tasks. However, existing distribution-aware approaches enforce that the target taste distribution should exactly match a user's prior interests (typically revealed through training data), based on the assumption that users' taste distribution is fundamentally static. This assumption can lead to large estimation errors. We empirically identify this taste distortion problem through a data-driven study over multiple datasets. We show how taste preferences dynamically shift and how the design of a calibration mechanism should be designed with these shifts in mind. We further demonstrate how to incorporate these shifts into a taste enhanced calibrated recommender system, which results in simultaneously mitigated both the rabbit hole effect and taste distortion problem.
Linked Data Processing for Human-in-the-Loop in Cyber–Physical Systems

There are several kinds of smart devices, such as smartphones, sensors, and smart wearable devices, included in the Human-in-the-Loop (HITL) system, but different devices have their own data processing and programming paradigm. Programmers usually need to design the same data processing logic for different devices by using a different programming model. How to mapping the same code to different devices without any change is an emerging topic in the HITL system. Furthermore, the intelligent data processing for the smart CPS sector is experiencing significant growth in data volume, driven by a large number of smart devices that are anticipated in the near further. All these smart devices are expected to improve the overall HITL system performance marvelously. A large number of devices can also outstandingly increase the data volume, which needs to be processed in real time. How to process large-scale data on a smart device in real time is another challenge. Focused on these challenges, this article proposed a computing device-aware HITL CPS data processing framework, named Barge, aiming to map the regular code to the different hardware without any change. In Barge, a semantic model, an architecture-driven programming model, and a graph partition scheme are included. The semantic model is used to express the user-defined graph algorithms by using the domain-specific language. The architecture-driven programming model will execute the graph algorithms on a different device in parallel. Furthermore, the graph partition scheme will partition the large-scale graphs into suitable partitions by aware of the topology to make the partitioned data suitable for kinds of smart devices. We believe that our work would open a wide range of opportunities to improve the performance of large-scale graph processing for HITL systems.
Dynamic Resource Allocation for Hierarchical Federated Learning

One of the enabling technologies of Edge Intelligence is the privacy preserving machine learning paradigm called Federated Learning (FL). However, communication inefficiency remains a key bottleneck in FL. To reduce node failures and device dropouts, the Hierarchical Federated Learning (HFL) framework has been proposed whereby cluster heads are designated to support the data owners through intermediate model aggregation. This decentralized learning approach reduces the reliance on a central controller, e.g., the model owner. However, the issues of resource allocation and incentive design are not well-studied in the HFL framework. In this paper, we consider a two-level resource allocation and incentive mechanism design problem. In the lower level, the cluster heads offer rewards in exchange of the data owners' participation, and the data owners are free to choose among any clusters to join. Specifically, we apply the evolutionary game theory to model the dynamics of the cluster selection process. In the upper level, given that each cluster head can choose to serve a model owner, the model owners have to compete for the services of the cluster head. As such, we propose a deep learning based auction mechanism to derive the valuation of each cluster head's services. The performance evaluation shows the uniqueness and stability of our proposed evolutionary game, as well as the revenue maximizing property of the deep learning based auction.
From Multi-Modal Property Dataset to Robot-Centric Conceptual Knowledge About Household Objects

Conceptual knowledge about objects is essential for humans, as well as for animals, to interact with their environment. On this basis, the objects can be understood as tools, a selection process can be implemented and their usage can be planned in order to achieve a specific goal. The conceptual knowledge, in this case, is primarily concerned about the physical properties and functional properties observed in the objects. Similarly tool-use applications in robotics require such conceptual knowledge about objects for substitute selection among other purposes. State-of-the-art methods employ a top-down approach where hand-crafted symbolic knowledge, which is defined from a human perspective, is grounded into sensory data afterwards. However, due to different sensing and acting capabilities of robots, a robot's conceptual understanding of objects (e.g., light/heavy) will vary and therefore should be generated from the robot's perspective entirely, which entails robot-centric conceptual knowledge about objects. A similar bottom-up argument has been put forth in cognitive science that humans and animals alike develop conceptual understanding of objects based on their own perceptual experiences with objects. With this goal in mind, we propose an extensible property estimation framework which consists of estimations methods to obtain the quantitative measurements of physical properties (rigidity, weight, etc.) and functional properties (containment, support, etc.) from household objects. This property estimation forms the basis for our second contribution: Generation of robot-centric conceptual knowledge. Our approach employs unsupervised clustering methods to transform numerical property data into symbols, and Bivariate Joint Frequency Distributions and Sample Proportion to generate conceptual knowledge about objects using the robot-centric symbols. A preliminary implementation of the proposed framework is employed to acquire a dataset comprising six physical and four functional properties of 110 household objects. This Robot-Centric dataSet (RoCS) is used to evaluate the framework regarding the property estimation methods and the semantics of the considered properties within the dataset. Furthermore, the dataset includes the derived robot-centric conceptual knowledge using the proposed framework. The application of the conceptual knowledge about objects is then evaluated by examining its usefulness in a tool substitution scenario.
Convolutional Neural Network in the Classification of Architectural Styles of Buildings

The article focuses on the implementation of the project for the classification of architectural styles of buildings. We use modern deep learning methods using convolutional neural networks for realization of this project. Image classification using CNN has many implementations. However, the recognition of architectural styles is not fully researched. This article presents a neural network capable of determining from a photograph of a building its belonging to a certain style of architecture. We train a deep CNN classifier model to classify buildings belonging to 15 different architectural styles. We use a data set that we have collected from open sources of the global Internet to do this.
Joint algorithm for blind estimation of FH signal based on STFT and PWVD

UAVs often use frequency hopping signals as their communication links, Obtaining the parameters of the drone signal can interfere with its normal operation. This paper proposes a joint algorithm for blind estimation of frequency hopping signals based on STFT and PWVD. First of all, perform STFT transformation and PWVD transformation on the intercepted UAV frequency hopping signal,;Secondly, perform the Hadamard product operation on the transformation result, get a stable joint time-frequency analysis graph; At last, the jump speed is obtained by the maximum time-frequency value at each moment, The reciprocal of the jump speed is the period value. The simulation experiment proved that, The joint algorithm still has high accuracy for frequency hopping signal parameter estimation under the condition of low signal-to-noise ratio; The Monte Carlo algorithm also verifies the effectiveness of this algorithm.
Phishing With A Darknet: Imitation of Onion Services

In this work we analyse the use of malicious mimicry and cloning of darknet marketplaces and other `onion services' as means for phishing, akin to traditional `typosquatting' on the web. This phenomenon occurs due to the complex trust relationships in Tor's onion services, and particularly the complex webs of trust enabled by darknet markets and similar services. To do so, we built a modular scraper tool to identify networks of maliciously cloned darknet marketplaces; in addition to other characteristics of onion services, in aggregate. The networks of phishing sites identified by this scraper were then subject to clustering and analysis to identify the method of phishing and the networks of ownership across these sites. We present a novel discovery mechanism for sites, means for clustering and analysis of onion service phishing and clone sites, and an analysis of their spectrum of sophistication.
Background and foreground disentangled generative adversarial network for scene image synthesis

Despite recent generative models have made remarkable progress on adversarial image synthesis, it is still a pivotal and frontier problem to generate high-fidelity images containing diverse entities and complex scene layouts from structured descriptions. To this end, we present a Background and Foreground Disentangled Generative Adversarial Network (BFD-GAN) to synthesize high-quality images from scene graphs. First, our method uses the graph convolutional network to infer a semantic background from the input scene graph. Then, the foreground parsing module that encourages unsupervised generation, is proposed to calculate semantically related foregrounds with fine-grained geometric properties. Furthermore, we also employ the foreground-background integrating module for the final image generation, during which the foreground-relation aware attention is introduced to refine and fuse the inferred foregrounds into the background. Evaluated on the COCO-Stuff and Visual Genome datasets, we benchmark our model against existing methods and show that our BFD-GAN is more capable of generating complex backgrounds and corresponding sharp foregrounds with given scene structures.
Ghost Thread

Cache-based side channel attacks pose a serious threat to computer security. Numerous cache attacks have been demonstrated, highlighting the need for effective and efficient defense mechanisms to shield systems from this threat. In this paper, we propose a novel application-level protection mechanism, called Ghost Thread. Ghost Thread is a flexible library that allows a user to protect cache accesses to a requested sensitive region to mitigate cache-based side channel attacks. This is accomplished by injecting random cache accesses to the sensitive cache region by separate threads. Compared with prior work that injects noise in a modified OS and hardware, our novel approach is applicable to commodity OS and hardware. Compared with other user-space mitigation mechanisms, our novel approach does not require any special hardware support, and it only requires slight code changes in the protected application making it readily deployable. Evaluation results on an Apache server show that Ghost Thread provides both strong protection and negligible overhead on real-world applications where only a fragment requires protection. In the worst-case scenario where the entire application requires protection, Ghost Thread still incurs negligible overhead when a system is under utilized, and moderate overhead when a system is fully utilized.
Back-end semantics for multimodal dialog on XR devices

Extended Reality (XR) devices have great potential to become the next wave in mobile interaction. They provide powerful, easy-to-use Augmented Reality (AR) and/or Mixed Reality (MR) in conjunction with multimodal interaction facilities using gaze, gesture, and speech. However, current implementations typically lack a coherent semantic representation for the virtual elements, backend-communication, and dialog capabilities. Existing devices are often restricted to mere command and control interactions. To improve these shortcomings and realize enhanced system capabilities and comprehensive interactivity, we have developed a flexible modular approach that integrates powerful back-end platforms using standard API interfaces. As a concrete example, we present our distributed implementation of a multimodal dialog system on the Microsoft Hololens®. It uses the SiAM-dp multimodal dialog platform as a back-end service and an Open Semantic Framework (OSF) back-end server to extract the semantic models for creating the dialog domain model.
A Parsimonious and Practical Approach to Detecting Offensive Speech

With the proliferation of hateful and offensive speech on social media platforms such as Twitter, machine learning approaches to detect such toxic content have gained prominence. Despite these advances, real-time detection of such speech, while it is being shared on these platforms, remains a challenge for two reasons. First, these approaches train complex models on a plethora of features, which calls into question their computational efficiency for real-time deployment. Moreover, they require sizeable, manually annotated data sets from the same context, and annotating large data sets is extremely time-consuming, error-prone and cumbersome. This paper proposes a parsimonious and practical approach for the detection of offensive speech that alleviates these challenges. The approach is parsimonious because through a comprehensive evaluation of commonly used machine learning models (Logistic Regression, Random Forest, Neural Networks) on two public domain data sets it demonstrates that a simple Logistic Regression model trained on unigrams with frequency counts can detect hate speech with high accuracy of over 90%. It is practical because it demonstrates how an existing labeled training data set can be used to train models that can detect offensive content from a completely unknown data set with moderate accuracy. Based on these findings, the paper offers guidance on the characteristics that may be desirable in benchmark training data sets for offensive speech detection.
Dr.Emotion: Disentangled Representation Learning for Emotion Analysis on Social Media to Improve Community Resilience in the COVID-19 Era and Beyond

During the pandemic caused by coronavirus disease (COVID-19), social media has played an important role by enabling people to discuss their experiences and feelings of this global crisis. To help combat the prolonged pandemic that has exposed vulnerabilities impacting community resilience, in this paper, based on our established large-scale COVID-19 related social media data, we propose and develop an integrated framework (named Dr.Emotion) to learn disentangled representations of social media posts (i.e., tweets) for emotion analysis and thus to gain deep insights into public perceptions towards COVID-19. In Dr.Emotion, for given social media posts, we first post-train a transformer-based model to obtain the initial post embeddings. Since users may implicitly express their emotions in social media posts which could be highly entangled with other descriptive information in the post content, to address this challenge for emotion analysis, we propose an adversarial disentangler by integrating emotion-independent (i.e., sentiment-neutral) priors of the posts generated by another post-trained transformer-based model to separate and disentangle the implicitly encoded emotions from the content in latent space for emotion classification at the first attempt. Extensive experimental studies are conducted to fully evaluate Dr.Emotion and promising results demonstrate its performance in emotion analysis by comparison with the state-of-the-art baseline methods. By exploiting our developed Dr.Emotion, we further perform emotion analysis over a large number of social media posts and provide in-depth investigation from both temporal and geographical perspectives, based on which additional work can be conducted to extract and transform the constructive ideas, experiences and support into actionable information to improve community resilience in responses to a variety of crises created by COVID-19 and well beyond.
Investigating Challenges and Opportunities of the Touchless Hand Interaction and Machine Learning Agents to Support Kinesthetic Learning in Augmented Reality

Augmented Reality (AR), with its potential to bridge the virtual and real environments, creates new possibilities to develop more engaging and productive learning experiences. Evidence is beginning to emerge that this sophisticated technology offers new ways to improve the learning process for better interaction and engagement with students. Recently, AR has garnered much attention as an interactive technology that facilitates direct interaction with virtual objects in the real world. These virtual objects can be surrogates for real world teaching resources, allowing for virtual labs. Thus AR could allow learning experiences that would not be possible in impoverished educational systems around the world. Interestingly though, concepts such as virtual hand interaction and techniques such as machine learning are still not widely investigated in the domain of AR learning. The need for touchless interaction technologies has exceptionally increased in this post-COVID world. There are also existing pedagogical approaches that have not been explored in great detail in this new medium, such as Kinesthetic learning or ”Learning by Doing”. Using the touchless interaction hand interaction technology and machine learning agents, this research aims to address this gap by exploring these underutilised technologies to demonstrate the efficiency of AR learning. It will explore the different hand tracking APIs to integrate the virtual hand interaction, testing the devices’ compatibility with these APIs and integrating machine learning agents using reinforcement learning to develop an AR learning framework that can provide more productive and interactive learning experiences.