Reviewerly/exHarmony huggingface dataset:
Aptenodytes Forsteri optimization algorithm based on adaptive perturbation of oscillation and mutation operation for image multi-threshold segmentation

Multi-threshold segmentation is widely used in image segmentation, but its computational complexity increases with the number of thresholds, which can compromise segmentation precision. Additionally, existing heuristic methods for multi-threshold segmentation still suffer from slow convergence and premature convergence. To address these limitations, this study proposes a novel Aptenodytes Forsteri optimization algorithm with an adaptive perturbation of oscillation and mutation operation (AFOA-APM) to determine the optimal image threshold. AFOA-APM improves upon existing methods with several key innovations: firstly, an adaptive perturbation of the oscillation strategy (AP) improves exploitation ability; secondly, improved methods for move strategies 2 and 3 reduce time complexity and the probability of stagnation; thirdly, a random mutation strategy keeps diversity and preserves inheritance; and finally, an adaptive selection strategy balances search capabilities. AFOA-APM is validated using CEC 2017 benchmark functions and image segmentation problems, and results demonstrate its superior performance in terms of quality, consistency, and accuracy. These findings indicate that AFOA-APM has a high potential to address image multi-threshold segmentation problems effectively.
AR Cinema: Visual Storytelling and Embodied Experiences with Augmented Reality Filters and Backgrounds

Abstract Through the simultaneous presence of graphically composed and materially existing elements, augmented reality (AR) offers ephemeral digital content that is the result of the momentary and, thus, unrepeatable alignment of a physical body and world and an AR system. Capturing the performative and embodied angles of screen-based AR through a combined film-analytical and cognitive lens, this paper focuses on how interfaces, content, and AR-manipulated bodies serve as apparatus for cinematic composition as well as storytelling and user engagement. Observing interactions with AR filters and backgrounds, we reflect on how users’ bodies and expressions that are mirrored on screen are translated into an immersive digital storyworld that exists in the temporal and spatial context of the AR experience and the related technology. AR filters and backgrounds’ affective quality, thus, lies in bodily control and in the creative act of choosing and moderating body characteristics, postures, and positions in real time in relation to the surrounding digitally manipulated or recorded environment. By moderating the representations of bodies and spaces as well as their interplay, AR users actively shape the visual composition of the on-screen space and, thereby, the visual narrative.
Suspicious Minds: the Problem of Trust and Conversational Agents

Abstract In recent years, the field of natural language processing has seen substantial developments, resulting in powerful voice-based interactive services. The quality of the voice and interactivity are sometimes so good that the artificial can no longer be differentiated from real persons. Thus, discerning whether an interactional partner is a human or an artificial agent is no longer merely a theoretical question but a practical problem society faces. Consequently, the ‘Turing test’ has moved from the laboratory into the wild. The passage from the theoretical to the practical domain also accentuates understanding as a topic of continued inquiry. When interactions are successful but the artificial agent has not been identified as such, can it also be said that the interlocutors have understood each other? In what ways does understanding figure in real-world human–computer interactions? Based on empirical observations, this study shows how we need two parallel conceptions of understanding to address these questions. By departing from ethnomethodology and conversation analysis, we illustrate how parties in a conversation regularly deploy two forms of analysis (categorial and sequential) to understand their interactional partners. The interplay between these forms of analysis shapes the developing sense of interactional exchanges and is crucial for established relations. Furthermore, outside of experimental settings, any problems in identifying and categorizing an interactional partner raise concerns regarding trust and suspicion. When suspicion is roused, shared understanding is disrupted. Therefore, this study concludes that the proliferation of conversational systems, fueled by artificial intelligence, may have unintended consequences, including impacts on human–human interactions.
From Surplus and Scarcity toward Abundance: Understanding the Use of ICT in Food Resource Sharing Practices

Food practices have become an important context for questions around sustainability. Within HCI, sustainable HCI and human-food-interaction have developed as a response. We argue, nevertheless, that food practices as a social activity remain relatively under-examined, and further that sustainable food practices hinge on communal activity. We present the results of action-oriented research with a grassroots movement committed to sustainable food practices at a local, communal level, thereby demonstrating the role of ICT in making food resource sharing a viable practice. We suggest that the current focus on food sharing might usefully be supplemented by attention to food resource sharing, an approach that aligns with a paradigm shift from surplus to abundance. We argue for a design that aims to encourage food resource sharing at a local level but that also has wider ramifications. These “glocal” endeavors recognize the complexity of prosumption practices and foster aspirations for “deep change” in food systems.
Persistent Human–Machine Interfaces for Robotic Arm Control Via Gaze and Eye Direction Tracking

Recent advances in sensors and electronics have enabled electrooculogram (EOG) detection systems for capturing eye movements. However, EOG signals are susceptible to the sensor's skin‐contact quality, limiting the precise detection of eye angles and gaze. Herein, a two‐camera eye‐tracking system and a data classification method for persistent human–machine interfaces (HMIs) are introduced. Machine‐learning technology is used for a continuous real‐time classification of gaze and eye directions, to precisely control a robotic arm. In addition, a deep‐learning algorithm for classifying eye directions is developed and the pupil center‐corneal reflection method of an eye tracker for gaze tracking is utilized. A supervisory control and data acquisition architecture that can be universally applied to any screen‐based HMI task are used by the system. It is shown in the study that the classification algorithm using deep learning enables exceptional accuracy (99.99%) with the number of actions per command (≥64), the highest performance compared to other HMI systems. Demonstrating real‐time control of a robotic arm captures the unique advantages of the precise eye‐tracking system for playing chess and manipulating dice. Overall, this paper shows the HMI system's potential for remote control of surgery robots, warehouse systems, and construction tools.
RecON: Online learning for sensorless freehand 3D ultrasound reconstruction

Sensorless freehand 3D ultrasound (US) reconstruction based on deep networks shows promising advantages, such as large field of view, relatively high resolution, low cost, and ease of use. However, existing methods mainly consider vanilla scan strategies with limited inter-frame variations. These methods thus are degraded on complex but routine scan sequences in clinics. In this context, we propose a novel online learning framework for freehand 3D US reconstruction under complex scan strategies with diverse scanning velocities and poses. First, we devise a motion-weighted training loss in training phase to regularize the scan variation frame-by-frame and better mitigate the negative effects of uneven inter-frame velocity. Second, we effectively drive online learning with local-to-global pseudo supervisions. It mines both the frame-level contextual consistency and the path-level similarity constraint to improve the inter-frame transformation estimation. We explore a global adversarial shape before transferring the latent anatomical prior as supervision. Third, we build a feasible differentiable reconstruction approximation to enable the end-to-end optimization of our online learning. Experimental results illustrate that our freehand 3D US reconstruction framework outperformed current methods on two large, simulated datasets and one real dataset. In addition, we applied the proposed framework to clinical scan videos to further validate its effectiveness and generalizability.
Comparison of hierarchical clustering and neural network clustering: an analysis on precision dominance

A comparison of neural network clustering (NNC) and hierarchical clustering (HC) is conducted to assess computing dominance of two machine learning (ML) methods for classifying a populous data of large number of variables into clusters. An accurate clustering disposition is imperative to investigate assembly-influence of predictors on a system over a course of time. Moreover, categorically designated representation of variables can assist in scaling down a wide data without loss of essential system knowledge. For NNC, a self-organizing map (SOM)-training was used on a local aqua system to learn distribution and topology of variables in an input space. Ternary features of SOM; sample hits, neighbouring weight distances and weight planes were investigated to institute an optical inference of system's structural attributes. For HC, constitutional partitioning of the data was executed through a coupled dissimilarity-linkage matrix operation. The validation of this approach was established through a higher value of cophenetic coefficient. Additionally, an HC-feature of stem-division was used to determine cluster boundaries. SOM visuals reported two locations' samples for remarkable concentration analogy and presence of 4 extremely out of range concentration parameter from among 16 samples. NNC analysis also demonstrated that singular conduct of 18 independent components over a period of time can be comparably inquired through aggregate influence of 6 clusters containing these components. However, a precise number of 7 clusters was retrieved through HC analysis for segmentation of the system. Composing elements of each cluster were also distinctly provided. It is concluded that simultaneous categorization of system's predictors (water components) and inputs (locations) through NNC and HC is valid to the precision probability of 0.8, as compared to data segmentation conducted with either of the methods exclusively. It is also established that cluster genesis through combined HC's linkage and dissimilarity algorithms and NNC is more reliable than individual optical assessment of NNC, where varying a map size in SOM will alter the association of inputs' weights to neurons, providing a new consolidation of clusters.
A lightweight few-shot marine object detection network for unmanned surface vehicles

Unmanned surface vehicles (USVs) are playing an important role in marine research, exploration and development. However, the perception capability of USVs is limited due to some objective factors. Firstly, the perception modules usually need to be deployed on processing devices with limited computing power, considering the power consumption and thermal dissipation. Secondly, collecting abundant training datasets of marine objects is a rather difficult mission due to complex environmental factors, such as illumination, weather and sea conditions. Thirdly, the perception modules cannot adapt to detection task of new objects fast enough, especially in few-shot scenarios they cannot perform well. To solve the above problems, we improved the ShuffleNet and designed Context Attention Enhancement FPN (CAE-FPN) to get an efficient lightweight network which is called ISDet. Then, a Progressive Gradient and Dynamic Learning Rate for MAML (PD-MAML) is proposed to solve the instability problem in meta training process with few-shot scenario. And a feature reweighting module is proposed to adapt our designed ISDet to new few-shot category. Experiments show that compared with other lightweight state-of-the-art networks, the proposed ISDet achieves better mean average precision with less model size, and it adapts to a new few-shot category fast while maintains the detection precision of existing categories.
MIMO Terahertz Quantum Key Distribution Under Restricted Eavesdropping

Quantum key distribution (QKD) can provide unconditional security to next generation communication networks guaranteed by the laws of quantum physics.This paper studies the secret key rate (SKR) of a continuous variable quantum key distribution (CV-QKD) system using multiple-input multipleoutput (MIMO) transmission and operating at Terahertz (THz) frequencies.Distinct from previous works, we consider a practical "restricted" eavesdropping scenario in which Eve can collect only a fraction of photons lost in the environment.We propose a system model for the MIMO THz CV-QKD system that accounts for restricted eavesdropping via a lossy wireless channel between Alice and Eve.We derive for this system new SKR expressions for both coherent-state-based and squeezed-state-based CV-QKD protocols.Our results show that previous analysis assuming unrestricted eavesdropping leads to overly pessimistic SKRs, and that in practice, the achievable SKR can be significantly increased under restricted eavesdropping.The increase in the SKR is quantified by the simplified SKR expansions derived in this paper.Our results also reveal that squeezing is beneficial for improving the SKR only for unrestricted eavesdropping.However, in a practical setting with restricted eavesdropping, increased squeezing leads to a reduction in the SKR.
Improved Finite-Key Security Analysis of Measurement-Device-Independent Quantum Key Distribution Against a Trojan-Horse Attack

Measurement-device-independent quantum key distribution (MDI QKD) is a promising method for remote key sharing that can eliminate all detector side-channel attacks. However, current security proofs often overlook the potential information leakages from the legitimate users' devices. In a quantum version of Trojan-horse attack (THA), a malicious eavesdropper can inject bright light into the sources of a MDI QKD system and then analyze the back-reflected light to obtain their setting choices, thereby compromising the final security. Here, we derive the finite-key security bounds of decoy-state MDI QKD in the presence of THA, which significantly outperform previous analyses in terms of the secret-key rate and transmission distance. Specifically, we analyze a symmetric three-intensity decoy-state MDI QKD protocol and an efficient four-intensity decoy-state MDI QKD protocol. Our results represent a fundamental step in guaranteeing the implementation security of quantum communication systems.
CF-YOLOX: An Autonomous Driving Detection Model for Multi-Scale Object Detection

In self-driving cars, object detection algorithms are becoming increasingly important, and the accurate and fast recognition of objects is critical to realize autonomous driving. The existing detection algorithms are not ideal for the detection of small objects. This paper proposes a YOLOX-based network model for multi-scale object detection tasks in complex scenes. This method adds a CBAM-G module to the backbone of the original network, which performs grouping operations on CBAM. It changes the height and width of the convolution kernel of the spatial attention module to 7 × 1 to improve the ability of the model to extract prominent features. We proposed an object-contextual feature fusion module, which can provide more semantic information and improve the perception of multi-scale objects. Finally, we considered the problem of fewer samples and less loss of small objects and introduced a scaling factor that could increase the loss of small objects to improve the detection ability of small objects. We validated the effectiveness of the proposed method on the KITTI dataset, and the mAP value was 2.46% higher than the original model. Experimental comparisons showed that our model achieved superior detection performance compared to other models.
A lightweight deep learning model based recommender system by sentiment analysis

Recommender systems based on sentiment analysis become challenging due to the presence of enormous data available over the internet. With the lack of proper data cleaning and analysis methods, existing machine learning (ML) techniques fail to generate accurate recommendations. To overcome this issue, this paper proposes a Light Deep Learning (LightDL)-based recommender system that uses Twitter-based reviews. First, the data is collected from Twitter and cleaned by subsequent data cleaning processes. Then, this pre-processed data is fed into the LightDL model, which learns the important features like hashtags, unigrams, multigrams, etc. from each piece of data. Here, we have learned about four groups of features, including semantic features, syntactic features, symbolic features, and tweet-based features. Finally, the data is classified into positive, negative, and neutral categories according to the learned features. On the basis of classified sentiment, the review is generated to the users. Finally, the model is evaluated in terms of accuracy, precision, recall, f-measure, and error rate through extensive experiments in Matlab. The proposed LightDL model outperforms in all performance measures; specifically, it achieves 95% accuracy for the Twitter dataset.
CF3DNet: A learning-based approach for single-shot 3D reconstruction from circular fringes

3D reconstruction by fringe projection is an ill-posed problem that is of significance in many practical applications. Conventional algorithms employ multiple steps like fringe analysis, phase unwrapping, and calibration to reconstruct the 3D profile. Recent deep learning (DL) methods circumvent this multi-step approach by directly mapping the deformations in linear fringes caused by objects to the absolute phase shifts. However, this mapping is not one-to-one for the discontinuous objects due to 2π periodicity of linear fringes, and hence it is impossible to reconstruct using a single deformed fringe. In this work, we propose Circular Fringe-to-3D reconstruction Network (CF3DNet), a learning-based approach for 3D reconstruction from circular fringes, that can establish a one-to-one mapping between the phase deformations and the absolute phase shifts. This is viable because the radially symmetric circular fringes can distinctly record the lateral shifts caused by objects, and the learning-based approach can uniquely map them to absolute phase shifts. Thus, the proposed CF3DNet can reconstruct discontinuous object profiles, which is infeasible with linear fringe-based methods. Further, to evade the tedious process of real data collection and to bring generalization, the model is trained with synthetically generated smooth, discontinuous and Computer-Aided Design (CAD) object profiles. The results demonstrate that the proposed method led to tenfold improved performance over linear fringe-based methods in 3D reconstruction of various synthetic objects, and accurately estimate the real object profile compared to the other state-of-art methods. The extensive analysis presented in this work shows that the proposed method can work with carrier frequencies as low as 10 cycles/row to reconstruct dynamic ranges up to 100 radians under high noise, paving a way for the development of a cost-effective fringe projection system.
Multimedia Data Processing Technology and Application Based on Deep Learning

With deep learning being widely used in various research fields, it is introduced into the research and analysis of multimedia data processing technology and application. First, the flow of multimedia data processing, the development of multimedia data, and the realization of multimedia data processing technology are explained and analyzed. Then, the related network results of deep learning (convolution network structure and countermeasure neural network structure) are put forward, and the image comparison of the activation function and the loss function of deep learning is analyzed, which provides functional algorithm support for the experimental analysis of deep learning in multimedia data processing technology. Finally, through the analysis of experimental data, it is concluded that deep learning has stronger advantages in the application research of multimedia data processing technology compared with other learning methods. In the multimedia data processing, the multimedia data processing technology is obviously superior to the data mining technology and data compression technology. Finally, under the support of deep learning data, we conclude that multimedia data processing technology is widely used and quoted in various fields. Therefore, with the development of multimedia, the amount of multimedia data is increasing; so, we should vigorously develop multimedia data processing technology in an all-round way.
Homomorphic Encryption on GPU

Homomorphic encryption (HE) is a cryptosystem that allows the secure processing of encrypted data. One of the most popular HE schemes is the Brakerski-Fan-Vercauteren (BFV), which supports somewhat (SWHE) and fully homomorphic encryption (FHE). Since overly involved arithmetic operations of HE schemes are amenable to concurrent computation, GPU devices can be instrumental in facilitating the practical use of HE in real world applications thanks to their superior parallel processing capacity. This paper presents an optimized and highly parallelized GPU library to accelerate the BFV scheme. This library includes state-of-the-art implementations of Number Theoretic Transform (NTT) and inverse NTT that minimize the GPU kernel function calls. It makes efficient use of the GPU memory hierarchy and computes 128 NTT operations for ring dimension of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">14</sup> only in 176.1 μs on RTX 3060Ti GPU. To the best of our knowledge, this is the fastest implementation in the literature. The library also improves the performance of the homomorphic operations of the BFV scheme. Although the library can be independently used, it is also fully integrated with the Microsoft SEAL library, which is a well-known HE library that also implements the BFV scheme. For one ciphertext multiplication, for the ring dimension 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">14</sup> and the modulus bit size of 438, our GPU implementation offers 63.4 times speedup over the SEAL library running on a high-end CPU. The library compares favorably with other state-of-the-art GPU implementations of NTT and BFV operations. Finally, we implement a privacy-preserving application that classifies encrypted genome data for tumor types and achieves speedups of 42.98 and 5.7 over CPU implementations using single and 16 threads, respectively. Our results indicate that GPU implementations can facilitate the deployment of homomorphic cryptographic libraries in real-world privacy-preserving applications.