Reviewerly/exHarmony huggingface dataset:
Promoting Online Safety

Social media offers great power and potential to all kinds of users, and it is not free from threats and risks that come along with the adoption of new tools and innovations. There is cyber stalking, sexting, bullying happening substantially. Anonymity of the virtual world has contributed to online harassment and lack of awareness. This research assesses the awareness and perception of female college students of Indian universities. The opinion of senior government officials in regulating social media to improve cyber resilience is sought. Using judgement sampling technique, 463 responses were collected through questionnaire method. The majority of respondents perceive social media as a useful place for infotainment. There is awareness however that respondents don't want to limit themselves and they are open to posting pictures, tweeting, commenting on unknown posts. Among many online platforms, incidence of cyber harassment is high on social media platforms. The main contribution of this study is to emphasize the need to treat cyber behaviour as a foundational course in today's parallel world.
Eye and hand movements when playing a dynamic computer game (Pong)

Most eye movement studies investigate the behavior of a head-fixed observer looking at multiple repetitions of fixed trials on a computer monitor. Here we used a more naturalistic, complementary approach to investigate the interplay between eye and hand movements during natural sequential behavior in a dynamic scene. Participants played a modified version of the video game ‘Pong’ on an iPad by controlling a paddle via the touchscreen. Their task was to keep a linearly moving dot (ball) in the game by hitting it with the paddle to prevent it from passing. Gaze positions were recorded with a mobile Tobii Glasses 3 eye tracker and were mapped onto the tablet via computer vision algorithms. We manipulated the ball speed (slow vs. fast) and the shape of the paddles (bar vs. rhombus) in a blocked, counterbalanced order. Results of the overall playing performance (number of hits) revealed an impact of both, ball speed and shape of the paddle. For the faster speed and the rhombus shape participants’ performance decreased. Gaze data showed that participants tracked the ball with combinations of pursuit and saccades which adapted to the specific conditions and play time. Participants made more frequent saccades when interacting with faster moving targets. With increasing time playing, participants used more pursuit. Overall, the results indicate that participants adapt their eye movement strategies to the dynamic environment. There is a larger focus on tracking the ball under more difficult conditions. The increased proportions of pursuit over the course of the experiment presumably support a higher performance in the game.
The Future of Web 3

This article examines the ways in which Web 3.0 technologies can be advanced through research and innovation across disciplines. To completely benefit from the semantic web, efforts must be made to establish semantic standards and compensate data providers. To ensure ethical AI in the Web 3.0 era, researchers in the field must fixate on creating systems that are both reliable and readily explicable. Enhanced scalability, usability, and governance models are among the objectives of developing decentralized technologies like blockchain. When decentralized and AI-enabled systems become the norm, security, privacy, and accountability research will become increasingly crucial. Multi Stakeholder engagement will be required to resolve governance concerns in order to maximize the benefits of Web 3.0 while minimizing the risks.
Multi-Fisher and Triple-Domain Feature Enhancement-Based Short Utterance Speaker Verification for IoT Smart Service

Speech authentication in IoT smart services typically involves short utterances. However, due to the short duration of these utterances (e.g. less than 3 seconds) and the limited enrolment and/or test data available, it is difficult to learn enough information to accurately distinguish the person. As a result, speaker recognition from short utterances is very challenging. In this paper, in the acoustic end, we propose a Multi-Fisher feature enhancement method to enrich short utterance effective information. The method aggregates the effective feature parameters by assigning the corresponding Multi-Fisher ratio weights to these parameters. In the architecture end, we propose a triple-domain feature joint learning method to enhance discriminative information from multiple dimensions. This approach provides different dimensions of information through the different physical meanings of speech in the time domain, the frequency domain and the spectral domain. Extensive experiments were conducted on VoxCeleb, SITW and NIST SRE short-duration text-independent speaker verification tasksC containing speech samples of varying lengths and scenarios. The results show that our proposed method outperforms existing acoustic feature extraction approaches and state-of-the-art deep learning architectures by at least 6% and 12%, respectively. The ablation experiments further illustrate that our proposed approaches can achieve substantial improvement over previous methods.
Fault Diagnosis of High-Voltage Circuit Breakers via Hybrid Classifier by DS Evidence Fusion Algorithm

Accurate and timely fault diagnosis is of significance for the stability of high-voltage circuit breaker (HVCB), which plays an important role in ensuring the safety of the power system. Current fault diagnosis techniques generally depend on an individual classifier. In this study, combining support vector machine (SVM) with extreme learning machine (ELM) by Dempster-Shafer (DS) evidence fusion algorithm, we propose DS_SE, a hybrid classifier for clearance joint fault diagnosis of HVCB. At first, through variational mode decomposition (VMD), the energy distribution is extracted as the feature value of vibration signals. Then DS evidence fusion algorithm is proposed for fusing analysis of evidence from different sub-classifiers and sensors. Extensive evaluation of DS_SE based on a real Zn12 HVCB indicates that it can achieve better performance by fusing conflicting evidence.
Signal and System Minimum Mismatch Modeling (3M) with Order Selection Analysis

&lt;p&gt;In the data-driven modeling of linear, time-invariant structures, the choice of the number of parameters (order selection) is critical. It is known that the modeling error, resulting from the maximum likelihood estimate, cannot be directly used for order estimation as this error is a monotonically decreasing function of model order. The most well-known order selection methods, including Akaike information criterion (AIC), Bayesian information criterion (BIC), and minimum description length (MDL), propose an additive penalty term to the modeling error. A more recently proposed method, reconstruction error minimization (REM) concentrates on a different error to provide optimal order selection using a statistical learning approach. A closed-form expression of REM has been provided for the order selection in linear models, including finite impulse responses and has shown superiority over other order selection methods. The existing method of REM calculation uses a Gaussian approximation of the Chi- squared distribution. This work first provides the exact modeling of REM using the Chi-squared distribution. Next, the use of REM is extended for all-pole modeling and pole-zero modeling. For autoregressive (AR) order selection, a method denoted by minimum mismatch modeling (3M) is introduced. It has also been shown that REM is a special case of 3M. Simulation results show the advantages of the proposed method over the existing order selection methods by avoiding overparametrization or underpamaterization in favour of mean squared error (MSE) minimization. In addition, a practical application of eye blink artifact removal from electroencephalogram (EEG) data shows that the proposed method efficiently models the true background EEG while eliminating artifacts efficiently. Furthermore, the application of 3M for EEG sleep-stage classification enabled the classification process to be automated. It is worth mentioning that the use of 3M shows that different sleep-stages have different orders, which can be further used as a feature for classification. &lt;/p&gt;
Unravelling the State of the Art of Blockchain Development for Improved Infrastructure Delivery in the Built Environment: A Bibliometric Review

Information management is critical to the smooth operation of infrastructure delivery. It is the process of gathering, storing, distributing, archiving, and deleting/destroying data. Information and data management are critical to decision-making toward achieving improved, resilient, and responsive infrastructure delivery. While innovative tools abound for managing information in the built sector, barriers such as technical challenges, expertise needed, and cost of procurement inhibit the adoption of these technologies. In contrast, blockchain offers opportunities for accessibility, lighter and faster transaction speed, and secured data processing. However, research in this area is limited and slowly emerging. Therefore, this study adopts a bibliometric review to avail current progress, critical discussions, and vital strategies to advance the adoption of Blockchain development in the construction industry. Blockchain has emerged as important to built environment innovation discourses with energy management, payment processing, documentation, and asset lifecycle management opportunities. This study is therefore important in improving extant studies and availing future directions. This paper uses bibliometric analysis to examine blockchain as a tool for infrastructure delivery in the built environment. Empirical data from Scopus was analysed to establish the publication and citation pattern, specifically publications published between 2011 and 2021. This also identifies research trends, common gaps, and best practices among researchers who use Vosviewer. The most influential authors, institutions, journals, countries, and cooperation networks are then identified. This study presents the relevance of blockchain in the built environment and its movement, adoption, and adaptation, which further enables the spark of innovative ideas to realize and comprehend the current and future technological transformation in infrastructure delivery.
AMLNet: Attention Multibranch Loss CNN Models for Fine-Grained Vehicle Recognition

For fine-grained vehicle recognition tasks, small interclass variation and large intraclass variation are key factors that can degrade recognition performance. Therefore, mining fine-grained discriminative features is an important way to improve performance. Although previous studies have achieved some successes, the standard model still has some drawbacks. First, global features are ignored when extracting discriminative features. Second, the potential interactions between different regions are not sufficiently considered. To address this challenge, we propose an attention multibranch loss convolutional neural network (AMLNet) in a weakly supervised manner to learn features from different regions and explore the interactions between them. Specifically, a pretrained model is used to initially extract rich vehicle feature information, and attention is enhanced using a multifeature fusion global cross-channel loss network (MGCL) and cross-channel pooling enhanced attention feature network (CCPA) to mine discriminative features while enhancing global feature representation. We conduct comparative experiments on two public datasets, and experimental results show that the proposed method achieves better recognition performance than other advanced algorithms. Thus, the proposed method is more effective when applied to fine-grained vehicle recognition tasks.
Life-size telepresence using holographic display

Despite massive advancements in the telecommunications industry over the last few decades, telepresence video-based teleconferencing is still far from giving a physical co-presence experience. Applications like Skype, Zoom and WebEx are constrained as it is only able to display the head and shoulders of the users on one monitor. It offers the user flat two-dimensional (2D) upper body imageries, a fixed viewpoint, inconsistent gaze direction, and restriction. Therefore, the problem arises when video-based teleconferencing is unable to support effective communication in telepresence. Thus, this research aims to propose life-size telepresence by using a projector-based holographic display. To actualize the aim, there are three phases have been proposed in this research. The first phase is to enable the tracking technique using the Kinect sensor. The second phase is to enable networking using Agora.io. The last phase is to set up the holographic display using a projector before the evaluation phase. Based on the evaluation results, the life-size telepresence using holographic display application has succeeded in achieving the project aim and shown the life-size display increases the co-presence experience of the users and increases the efficient communication between the users.
An effective framework for Scrum projects cost stimation through agile model technique

Software process models provide a wider culture of software development for a variety of IT sectors by establishing standardization from the outset. The significance of business units adopting Agile Development as a workable development technique has undergone a substantial paradigm change. ASD encompasses a variety of agile techniques, with Scrum receiving the greatest ratings for acceptance and popularity. The current environment requires agile-based software development. As user requirements and specifications change constantly, there is a rising necessity for large software system upgrades. Agile projects can be unpredictable, making it challenging to precisely estimate project size, effort, cost, duration, and timeline. This topic raises awareness of the issue of inflated estimations of effort among prospective researchers worldwide. The misalignment of size metrics and cost drivers, ignorance of testing work, team members&apos; failure to comprehend user narrative size and complexity, and other aspects that are either under or not included in the estimates. Therefore, it is essential to precisely track effort and expense for various software projects based on various development models that contain brand-new and innovative software development phases. A software project will always be successful if it is finished on schedule and within the allocated budget if the total effort and cost estimate is accurate,mistake.This project intends to advance the estimation frontier of research by bridging the estimated andreal effort gap using soft computing Techniques.
Heterogeneous Decentralized Machine Unlearning with Seed Model Distillation

As some recent information security legislation endowed users with unconditional rights to be forgotten by any trained machine learning model, personalized IoT service providers have to put unlearning functionality into their consideration. The most straightforward method to unlearn users' contribution is to retrain the model from the initial state, which is not realistic in high throughput applications with frequent unlearning requests. Though some machine unlearning frameworks have been proposed to speed up the retraining process, they fail to match decentralized learning scenarios. In this paper, we design a decentralized unlearning framework called HDUS, which uses distilled seed models to construct erasable ensembles for all clients. Moreover, the framework is compatible with heterogeneous on-device models, representing stronger scalability in real-world applications. Extensive experiments on three real-world datasets show that our HDUS achieves state-of-the-art performance.
Towards a Holodeck-style Simulation Game

We introduce Infinitia, a simulation game system that uses generative image and language models at play time to reshape all aspects of the setting and NPCs based on a short description from the player, in a way similar to how settings are created on the fictional Holodeck. Building off the ideas of the Generative Agents paper, our system introduces gameplay elements, such as infinite generated fantasy worlds, controllability of NPC behavior, humorous dialogue, cost & time efficiency, collaboration between players and elements of non-determinism among in-game events. Infinitia is implemented in the Unity engine with a server-client architecture, facilitating the addition of exciting features by community developers in the future. Furthermore, it uses a multiplayer framework to allow humans to be present and interact in the simulation. The simulation will be available in open-alpha shortly at https://infinitia.ai/ and we are looking forward to building upon it with the community.
Towards Ubiquitous Intelligent Hand Interaction

The development of ubiquitous computing and sensing devices has brought about novel interaction scenarios such as mixed reality and IoT (e.g., smart home), which pose new demands for the next generation of natural user interfaces (NUI). Human hand, benefit for the large degree-of-freedom, serves as a medium through which people interact with the external world in their daily lives, thus also being regarded as the main entry of NUI. Unfortunately, current hand tracking system is largely confined on first perspective vision-based solutions, which suffer from optical artifacts and are not practical in ubiquitous environments. In my thesis, I rethink this problem by analyzing the underlying logic in terms of sensor, behavior, and semantics, constituting a research framework for achieving ubiquitous intelligent hand interaction. Then I summarize my previous research topics and illustrated the future research directions based on my research framework.
RL-LABEL: A Deep Reinforcement Learning Approach Intended for AR Label Placement in Dynamic Scenarios

Labels are widely used in augmented reality (AR) to display digital information. Ensuring the readability of AR labels requires placing them occlusion-free while keeping visual linkings legible, especially when multiple labels exist in the scene. Although existing optimization-based methods, such as force-based methods, are effective in managing AR labels in static scenarios, they often struggle in dynamic scenarios with constantly moving objects. This is due to their focus on generating layouts optimal for the current moment, neglecting future moments and leading to sub-optimal or unstable layouts over time. In this work, we present RL-LABEL, a deep reinforcement learning-based method for managing the placement of AR labels in scenarios involving moving objects. RL-LABEL considers the current and predicted future states of objects and labels, such as positions and velocities, as well as the user's viewpoint, to make informed decisions about label placement. It balances the trade-offs between immediate and long-term objectives. Our experiments on two real-world datasets show that RL-LABEL effectively learns the decision-making process for long-term optimization, outperforming two baselines (i.e., no view management and a force-based method) by minimizing label occlusions, line intersections, and label movement distance. Additionally, a user study involving 18 participants indicates that RL-LABEL excels over the baselines in aiding users to identify, compare, and summarize data on AR labels within dynamic scenes.
A Preliminary Study on a Conceptual Game Feature Generation and Recommendation System

This paper introduces a system used to generate game feature suggestions based on a text prompt. Trained on the game descriptions of almost 60k games, it uses the word embeddings of a small GLoVe model to extract features and entities found in thematically similar games which are then passed through a generator model to generate new features for a user's prompt. We perform a short user study comparing the features generated from a fine-tuned GPT-2 model, a model using the ConceptNet, and human-authored game features. Although human suggestions won the overall majority of votes, the GPT-2 model outperformed the human suggestions in certain games. This system is part of a larger game design assistant tool that is able to collaborate with users at a conceptual level.
AdvisingNets: Learning to Distinguish Correct and Wrong Classifications via Nearest-Neighbor Explanations

Besides providing insights into how an image classifier makes its predictions, nearest-neighbor examples also help humans make more accurate decisions. Yet, leveraging this type of explanation to improve both human-AI team accuracy and classifier's accuracy remains an open question. In this paper, we aim to increase both types of accuracy by (1) comparing the input image with post-hoc, nearest-neighbor explanations using a novel network (AdvisingNet), and (2) employing a new reranking algorithm. Over different baseline models, our method consistently improves the image classification accuracy on CUB-200 and Cars-196 datasets. Interestingly, we also reach the state-of-the-art human-AI team accuracy on CUB-200 where both humans and an AdvisingNet make decisions on complementary subsets of images.
Go Beyond Imagination: Maximizing Episodic Reachability with World Models

Efficient exploration is a challenging topic in reinforcement learning, especially for sparse reward tasks. To deal with the reward sparsity, people commonly apply intrinsic rewards to motivate agents to explore the state space efficiently. In this paper, we introduce a new intrinsic reward design called GoBI - Go Beyond Imagination, which combines the traditional lifelong novelty motivation with an episodic intrinsic reward that is designed to maximize the stepwise reachability expansion. More specifically, we apply learned world models to generate predicted future states with random actions. States with more unique predictions that are not in episodic memory are assigned high intrinsic rewards. Our method greatly outperforms previous state-of-the-art methods on 12 of the most challenging Minigrid navigation tasks and improves the sample efficiency on locomotion tasks from DeepMind Control Suite.
Linear Oscillation: A Novel Activation Function for Vision Transformer

Activation functions are the linchpins of deep learning, profoundly influencing both the representational capacity and training dynamics of neural networks. They shape not only the nature of representations but also optimize convergence rates and enhance generalization potential. Appreciating this critical role, we present the Linear Oscillation (LoC) activation function, defined as $f(x) = x \times \sin(\alpha x + \beta)$. Distinct from conventional activation functions which primarily introduce non-linearity, LoC seamlessly blends linear trajectories with oscillatory deviations. The nomenclature "Linear Oscillation" is a nod to its unique attribute of infusing linear activations with harmonious oscillations, capturing the essence of the "Importance of Confusion". This concept of "controlled confusion" within network activations is posited to foster more robust learning, particularly in contexts that necessitate discerning subtle patterns. Our empirical studies reveal that, when integrated into diverse neural architectures, the LoC activation function consistently outperforms established counterparts like ReLU and Sigmoid. The stellar performance exhibited by the avant-garde Vision Transformer model using LoC further validates its efficacy. This study illuminates the remarkable benefits of the LoC over other prominent activation functions. It champions the notion that intermittently introducing deliberate complexity or "confusion" during training can spur more profound and nuanced learning. This accentuates the pivotal role of judiciously selected activation functions in shaping the future of neural network training.