Reviewerly/exHarmony huggingface dataset:
A novel cell partition method by introducing Silhouette Coefficient for fast approximate nearest neighbor search

In this paper, a novel cell partition method is proposed to accelerate the approximate nearest neighbor (ANN) search. The traditional cell-level elimination of database vectors, such as the inverted file system (IVF), has limitations on accelerating the search process. Therefore, the Silhouette Coefficient is introduced to realize more accurate and effective vector-level pruning. The value of the Silhouette Coefficient is used as a novel criterion for dividing the Voronoi cell into the center region and the border region. In addition, a dynamic threshold selection model is introduced to determine the sampling rate for each cell adaptively. The proposed method can greatly improve the search speed by reducing the vector-level distance calculation. Besides, the value of the Silhouette Coefficient is saved by offline generating a rank in each Voronoi cell, which does not bring any extra memory cost. Through experiments, it can be seen that for two widely-used datasets of SIFT1M and GIST1M, with no more than 1% accuracy reduction, the search speed can be significantly increased by about 44% compared with the standard inverted file system. And compared with state-of-the-art ANN search methods that aim at acceleration, the proposed method can also achieve higher search accuracy with almost the same computational consumption.
Gaze-based Mode-Switching to Enhance Interaction with Menus on Tablets

In design work, a common task is the interaction with menus to change the drawing mode. Done frequently, this can become a tedious and fatiguing task, especially for tablets where users physically employ a stylus or finger touch. As our eyes are naturally involved in visual search and acquisition of desired menu items, we propose gaze to shortcut the physical movement. We investigate gaze-based mode-switching for menus in tablets by a novel mode-switching methodology, assessing a gaze-only (dwell-time) and multimodal (gaze and tap) technique, compared to hand-based interaction. The results suggest that users can efficiently alternate between manual and eye input when interacting with the menu; both gaze-based techniques have lower physical demand and individual speed-error trade-offs. This led to a novel technique that substantially reduces time by unifying mode-selection and mode-application. Our work points to new roles for our eyes to efficiently short-cut menu actions during the workflow.
An Asymmetric Optical Cryptosystem Using Physically Unclonable Functions in the Fresnel Domain

In this paper, we propose a new asymmetric cryptosystem for phase image encryption, using the physically unclonable functions (PUFs) as security keys. For encryption, the original amplitude image is first converted into a phase image and modulated with a PUF to obtain a complex image. This complex image is then illuminated with a plane wave, and the complex wavefront at a distance d is recorded. The real part of the complex wavefront is further processed to obtain the encrypted image and the imaginary part is kept as the private key. The polar decomposition approach is utilized to generate two more private security keys and to enable the multi-user capability in the cryptosystem. Numerical simulations confirm the feasibility of the proposed method.
Step by Step: A Gradual Approach for Dense Video Captioning

Dense video captioning aims to localize and describe events for storytelling in untrimmed videos.It is a conceptually very challenging task that requires concise, relevant, and coherent captioning based on high-quality event localization.Unlike simple temporal action localization tasks without overlapping events, dense video captioning requires detecting multiple/overlapping regions in order to branch out the video story.Most existing methods generate numerous candidate event proposals and then eliminate duplicate ones using a event proposal selection algorithm (e.g., non-maximum suppression) or generate event proposals directly through box prediction and binary classification mechanisms, similar to object detection tasks.Despite these efforts, the aforementioned approaches tend to fail to localize overlapping events into different stories, hindering high-quality captioning.In this paper, we propose SBS, a dense video captioning framework with a gradual approach that addresses the challenge of localizing overlapping events and eventually constructs high-quality captioning.SBS accurately estimates the number of explicit events for each video snippet and then detects the boundaries context/activities, which are the details for generating the event proposals.Based on both the number of events and boundaries, SBS generates the event proposals.SBS encodes the context of the event sequence and finally generates sentences describing the event proposals.Our framework is fairly effective in localizing multiple/overlapping events, thus experimental results show the state-of-the-art performance compared to the existing methods.
European Union’s Regulating of Social Media: A Discourse Analysis of the Digital Services Act

Traditional conceptions of democratic publics are changing due to the rise of social media as a global communication tool. While social media brings people together globally and creates new spaces for creativity and resistance, it is also a space of harassment, discrimination, and violence. As recent debates about hate speech and the distribution of “fake news” have shown, the political responsibilities and consequences of regulating online content remain unclear. More recently, the EU is increasingly paying attention to platform providers. How is the EU legitimizing its new approach to social media platform regulation and how will this legislation shape transnational publics? This article contributes to ongoing debates on platform regulation by governments and other political authorities (especially the EU as a transnational legislator) and discussions about the shape of online publics. By applying a discourse analytical perspective, key legitimation narratives can be explored. I argue that the EU claims political authority over corporate interests by introducing new legislation to regulate social media platforms with the Digital Services Act. On the one hand, the EU imagines an idealized democratic online public without harmful and illegal content. On the other hand, the new legislation serves the EU’s agenda on digital sovereignty, taking back control from big and US-based enterprises. There is a strong consensus about four legitimation narratives: (a) “What is illegal offline has to be illegal online”; (b) the EU is “taking back control”; (c) the EU is “protecting small businesses, consumers, and our citizens against big tech”; (d) the EU is developing “a golden standard and rulebook beyond the EU.” Held together by the idea of democratic procedures, authority, and sovereignty, these narratives are demanding more action from social media providers to act on harmful and illegal content.
Recognition of human action for scene understanding using world cup optimization and transfer learning approach

Understanding human activities is one of the vital steps in visual scene recognition. Human daily activities include diverse scenes with multiple objects having complex interrelationships with each other. Representation of human activities finds application in areas such as surveillance, health care systems, entertainment, automated patient monitoring systems, and so on. Our work focuses on classifying scenes into different classes of human activities like waving hands, gardening, walking, running, etc. The dataset classes were pre-processed using the fuzzy color stacking technique. We adopted the transfer learning concept of pretrained deep CNN models. Our proposed methodology employs pretrained AlexNet, SqueezeNet, ResNet, and DenseNet for feature extraction. The adaptive World Cup Optimization (WCO) algorithm is used halfway to select the superior dominant features. Then, these dominant features are classified by the fully connected classifier layer of DenseNet 201. Evaluation of the performance matrices showed an accuracy of 94.7% with DenseNet as the feature extractor and WCO for feature selection compared to other models. Also, our proposed methodology proved to be superior to its counterpart without feature selection. Thus, we could improve the quality of the classification model by providing double filtering using the WCO feature selection process.
Two-Tier Feature Extraction with Metaheuristics-Based Automated Forensic Speaker Verification Model

While speaker verification represents a critically important application of speaker recognition, it is also the most challenging and least well-understood application. Robust feature extraction plays an integral role in enhancing the efficiency of forensic speaker verification. Although the speech signal is a continuous one-dimensional time series, most recent models depend on recurrent neural network (RNN) or convolutional neural network (CNN) models, which are not able to exhaustively represent human speech, thus opening themselves up to speech forgery. As a result, to accurately simulate human speech and to further ensure speaker authenticity, we must establish a reliable technique. This research article presents a Two-Tier Feature Extraction with Metaheuristics-Based Automated Forensic Speaker Verification (TTFEM-AFSV) model, which aims to overcome the limitations of the previous models. The TTFEM-AFSV model focuses on verifying speakers in forensic applications by exploiting the average median filtering (AMF) technique to discard the noise in speech signals. Subsequently, the MFCC and spectrograms are considered as the inputs to the deep convolutional neural network-based Inception v3 model, and the Ant Lion Optimizer (ALO) algorithm is utilized to fine-tune the hyperparameters related to the Inception v3 model. Finally, a long short-term memory with a recurrent neural network (LSTM-RNN) mechanism is employed as a classifier for automated speaker recognition. The performance validation of the TTFEM-AFSV model was tested in a series of experiments. Comparative study revealed the significantly improved performance of the TTFEM-AFSV model over recent approaches.
Enhanced quantum teleportation using multi-qubit logical states

In this paper, an innovative approach is suggested for quantum teleportation of multi-qubit physical state into an error correctable multi-qubit logical state. For this purpose, an efficient quantum error correction scheme is applied to detect and correct one bit flip and/or phase flip error in the teleported logical state. In addition, the proposed mechanism is substantiated by teleporting an eight-qubit physical state via four-qubit cluster state which involves three-qubit logical states as a quantum channel. The proposed scheme may be physically realized in the future fault tolerant quantum technologies.
Deep Learning-Based Context-Aware Recommender System Considering Change in Preference

In order to predict and recommend what users want, users’ information is required, and more information is required to improve the performance of the recommender system. As IoT devices and smartphones have made it possible to know the user’s context, context-aware recommender systems have emerged to predict preferences by considering the user’s context. A context-aware recommender system uses contextual information such as time, weather, and location to predict preferences. However, a user’s preferences are not always the same in a given context. They may follow trends or make different choices due to changes in their personal environment. Therefore, in this paper, we propose a context-aware recommender system that considers the change in users’ preferences over time. The proposed method is a context-aware recommender system that uses Matrix Factorization with a preference transition matrix to capture and reflect the changes in users’ preferences. To evaluate the performance of the proposed method, we compared the performance with the traditional recommender system, context-aware recommender system, and dynamic recommender system, and confirmed that the performance of the proposed method is better than the existing methods.
Fast, Lightweight, and Efficient Cybersecurity Optimization for Tactical–Operational Management

The increase in frequency and complexity of cyberattacks has heightened concerns regarding cybersecurity and created an urgent need for organizations to take action. To effectively address this challenge, a comprehensive and integrated approach is required involving a cross-functional cybersecurity workforce that spans tactical and operational levels. In this context there can be various combinations of cybersecurity actions that affect different functional domains and that allow for meeting the established requirements. In these cases, agreement will be needed, but finding high-quality combinations requires analysis from all perspectives on a case-by-case basis. With a large number of cybersecurity factors to consider, the size of the search space of potential combinations becomes unmanageable without automation. To solve this issue, we propose Fast, Lightweight, and Efficient Cybersecurity Optimization (FLECO), an adaptive, constrained, and multi-objective genetic algorithm that reduces the time required to identify sets of high-quality cybersecurity actions. FLECO enables productive discussions on viable solutions by the cross-functional cybersecurity workforce within an organization, fostering managing meetings where decisions are taken and boosting the overall cybersecurity management process. Our proposal is novel in its application of evolutionary computing to solve a managerial issue in cybersecurity and enhance the tactical–operational cybersecurity management process.
VSFCM: A Novel Viewpoint-Driven Subspace Fuzzy C-Means Algorithm

Nowadays, most fuzzy clustering algorithms are sensitive to the initialization results of clustering algorithms and have a weak ability to handle high-dimensional data. To solve these problems, we developed the viewpoint-driven subspace fuzzy c-means (VSFCM) algorithm. Firstly, we propose a new cut-off distance. Based on this, we establish the cut-off distance-induced clustering initialization (CDCI) method and use it as a new strategy for cluster center initialization and viewpoint selection. Secondly, by taking the viewpoint obtained by CDCI as the entry point of knowledge, a new fuzzy clustering strategy driven by knowledge and data is formed. Based upon these, we put forward the VSFCM algorithm combined with viewpoints, separation terms, and subspace fuzzy feature weights. Moreover, compared with the symmetric weights obtained by other subspace clustering algorithms, the weights of the VSFCM algorithm exhibit significant asymmetry. That is, they assign greater weights to features that contribute more, which is validated on the artificial dataset DATA2 in the experimental section. The experimental results compared with multiple advanced clustering algorithms on the three types of datasets validate that the proposed VSFCM algorithm has the best performance in five indicators. It is demonstrated that the initialization method CDCI is more effective, the feature weight allocation of VSFCM is more consistent with the asymmetry of experimental data, and it can achieve better convergence speed while displaying better clustering efficiency.
Review of Anomaly Detection Algorithms for Data Streams

With the rapid development of emerging technologies such as self-media, the Internet of Things, and cloud computing, massive data applications are crossing the threshold of the era of real-time analysis and value realization, which makes data streams ubiquitous in all kinds of industries. Therefore, detecting anomalies in such data streams could be very important and full of challenges. For example, in industries such as electricity and finance, data stream anomalies often contain information that can help avoiding risks and support decision making. However, most traditional anomaly detection algorithms rely on acquiring global information about the data, which is hard to apply to stream data scenarios. Currently, the reviews of the algorithm in the field of anomaly detection, both domestically and internationally, tend to focus on the exposition of anomaly detection algorithms in static data environments, while lacking in the induction and analysis of anomaly detection algorithms in the context of streaming data. As a result, unlike the existing literature reviews, this review provides the current mainstream anomaly detection algorithms in data streaming scenarios and categorizes them into three types on the basis of their fundamental principles: (1) based on offline learning; (2) based on semi-online learning; (3) based on online learning. This review discusses the current state of research on data stream anomaly detection and studies the key issues in various algorithms for detecting anomalies in data streams on the basis of concise summarization. Moreover, the review conducts a detailed comparison of the pros and cons of the algorithms. Finally, the future challenges in the field are analyzed, and future research directions are proposed.
Towards Secure Classical-Quantum Systems

Quantum computing has emerged as a promising paradigm, offering significant advancements in solving complex problems that are intractable for classical computers. These systems often involve integrated classical-quantum architectures, where classical components control and communicate with quantum devices. While this integration unlocks the potential of quantum computing, it also introduces new security vulnerabilities and challenges that must be addressed to ensure secure and reliable classical-quantum computing. This paper provides a comprehensive overview of the security concerns related to classical-quantum systems and discusses potential countermeasures. Specifically, we first investigate secure communication with a quantum device through side-channel analysis of post-quantum encryption algorithms. Next, we analyze security vulnerabilities in quantum devices. Finally, we explore mitigation strategies as well as the role of quantum compilation for securing quantum devices. By examining and addressing these critical security concerns, we aim to contribute to the development of a secure and robust foundation for the future of quantum computing. This work will be a stepping stone in secure and trustworthy deployment of integrated classicalquantum systems across various application domains.
Formulating Cybersecurity Requirements for Autonomous Ships Using the SQUARE Methodology

Artificial intelligence (AI) technology is crucial for developing autonomous ships in the maritime industry. Autonomous ships, based on the collected information, recognize the environment without any human intervention and operate themselves using their own judgment. However, ship-to-land connectivity increased, owing to the real-time monitoring and remote control (for unexpected circumstances) from land; this poses a potential cyberthreat to various data collected inside and outside the ships and to the applied AI technology. For the safety of autonomous ships, cybersecurity around AI technology needs to be considered, in addition to the cybersecurity of the ship systems. By identifying various vulnerabilities and via research cases of the ship systems and AI technologies, this study presents possible cyberattack scenarios on the AI technologies applied to autonomous ships. Based on these attack scenarios, cyberthreats and cybersecurity requirements are formulated for autonomous ships by employing the security quality requirements engineering (SQUARE) methodology.
An innovative approach based on optimization for the determination of initial conditions of continuous-time chaotic system as a random number generator

Security has been one of the important problems in the processing, storage and transmission of information. The transfer of information to virtual environments with technological developments has made it necessary to examine the concept of security with different dimensions. Data security can be achieved to a great extent by using randomly generated unpredictable bit sequences in encryption. In this study, approximately 1,000,000 random bits were generated for use in computer science. The Mode2(x) method and the unpredictable nature of chaotic systems are used for random number generation. With this hybrid structure used in the study, it is aimed to gain a different perspective to future studies. However, it is known that the sensitivity of chaotic systems to initial conditions is very high. The most important contribution of the study to the literature is that the initial conditions were determined using optimization algorithms. In this study, Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) were used to calculate the initial conditions. To the authors' knowledge, this is the first study to use optimization to determine initial conditions for continuous-time chaotic systems. NIST SP 800-22 Statistical Test Suite was used to analyze the reliability of the generated random bits. It was seen that the proposed method passed all statistical tests successfully (100 %). Histogram and floating frequency analysis were used for the analysis of random numbers obtained from the generated bits. Histogram plots showed uniform distribution. It is seen that there is a homogeneous distribution in the sliding frequency analysis.
Pre-clustering active learning method for automatic classification of building structures in urban areas

Identifying the structures of buildings in urban areas is a prerequisite for robust urban planning and regeneration. Owing to the diverse structural designs of urban buildings, automated approaches are required to classify building structures. Supervised machine learning is usually employed to classify various building characteristics. However, this approach requires significant labeling effort. Therefore, this paper proposes a new pre-clustering active learning method for building structure classification. The proposed method captures the statistical characteristics of samples and enhances the recognition of the most valuable training samples, thereby substantially reducing the labeling workload and improving the efficiency and effectiveness of classification. This method was tested via the classification of 3718 buildings in Beijing, China, into five common structures. The results showed that the proposed method could reduce labeling effort by 60% while achieving a promising 90% F1 score for overall classification performance, thus indicating its effectiveness.
‘Embodiment is the future’: What is embodiment and is it the future paradigm for learning and teaching in higher education?

In this opinion piece I present a brief introduction to embodiment and offer practical examples of embodied pedagogy. My overarching argument is that embodiment theory and practice merit a paradigmatic status in higher education pedagogies and a sub-theme is that using embodiment as a foundation for pedagogic praxis is usefully done in collaboration with the wealth of embodiment professionals working in different embodiment modalities across the globe. This piece includes some of the observations and practices I have encountered from such professionals.