Reviewerly/exHarmony huggingface dataset:
Build Belonging and Trust Proactively: A Humanized Intelligent Streamer Assistant with Personality, Emotion and Memory

Live streaming has become a prevalent form of online entertainment and commerce, where real-time interactions occur between streamers and their audiences. Currently, streamer assistants have some shortcomings in terms of personality and emotional expression. These shortcomings undermine the live streaming effect and audience experience, thereby damaging the streamer’s popularity and income. In this paper, we present the Intelligent Streamer Assistant with Personality, Emotion, and Memory (ISAPEM) framework, which aims to utilize playful animal avatars to establish a sense of belonging and trust proactively with the audience. Firstly, we determine the assistant’s personality. Subsequently, the assistant determines its emotions according to its personality and the danmaku (bullet chats/comments) context analysis, ranging from trust and joy to sadness. Next, the assistant displays matched expressions and actions, and then generates consistent dialogue using large language models (LLMs). For example, when faced with a challenging question in the danmaku, the assistant might appear perplexed, then reach for the corresponding danmaku, catch it, and swallow it. Finally, the assistant stores and analyzes danmaku interaction data to remember and understand the audience’s needs and preferences. Preliminary experimental findings indicate that the ISAPEM framework can create a warmer experience for the audiences and enhance their willingness to interact, which has the potential to foster a sense of belonging and trust among the audiences. This study proposes a novel design framework for streamer assistants that integrates cutting-edge anthropomorphic design cues (ADCs) with a danmaku-based physical interaction mode, expanding the application and interaction modes of novel ADCs and LLMs.
Cluster Head Selection and Secured Routing Using Glowworm Swarm Algorithm and Hybrid Security Algorithm for Over IoT-WSNs

WSNs (Wireless Sensor Networks) have received a lot of attention in a number of industries due to its self-configurability, simplicity in maintenance, and scaling capacities. WSN is configured with extra nodes in order to transport data around the network. The usual characteristics of sensor networks are low bandwidth, low energy, constrained power supply, and little memory space, which makes security particularly difficult in these networks. CH (Cluster heads) in a cluster experience heavier traffic loads than other cluster nodes. This results in the hotspot problems. Therefore, in a cluster-oriented routing scheme, picking the right CH is crucial. The two-level security is recommended in this research to safeguard data transmission via the WSN. A novel CH selection methodology is presented in order to increase the network's lifespan and energy effectiveness. This work also combines the fruit fly algorithm (FGF) with the GSA (firefly swarm algorithm) based on fitness to choose the optimal CH. To defend the network from assaults, it employs a hybrid security method that combines the Diffie-Hellman key exchange mechanism and the ECC (elliptic curve cryptography) algorithm. The suggested GSA-HSA protocol consistently produces good performance results in terms of MSR (Message Success Rate), E2E (End-to-End) Latency, NT (Network Throughput), and network throughput. average AEE (energy efficiency). The suggested approach outperforms all prior research by giving the wireless sensor network combination security, optimised coverage, and energy savings.
QRAuth: A Secure and Accessible Web Authentication Alternative to FIDO2

A recently popular alternative being proposed to password-based web authentication is FIDO2 standard. Although phishing-resistant password-less authentication with FIDO2 is a step in the right direction, it falls short in numerous usability and accessibility aspects. FIDO2 protocols requiring specific hardware and software devices that must be "FIDO certified" could prevent it from reaching a wide audience. Furthermore, end-users’ perception, acceptance, and usability concerns can potentially hinder widespread adoption. As an answer to these shortcomings, we present a QR-Code-based authentication protocol that offers the same security guarantees of public key cryptography coupled with more accessible infrastructure that can be easily integrated into existing systems.
Noise Non-Differentiable in Deep Learning End-to-End Image Watermarking Models

In recent years, deep learning-based watermarking algorithms have been widely studied due to the powerful feature extraction capability of deep learning. The deep learning watermarking framework consists of three parts: encoder, noise layer, and decoder. In order to better coordinate the simultaneous update of encoder-decoder parameters, end-to-end training is often used, and this training method requires that the noise layer must be composed of differentiable attacks, otherwise the update of model parameters cannot be achieved through the back propagation effect. In this paper, we analyze the causes of noise non-differentiable and summarize the existing literature methods to solve the non-differentiable problem, and analyze the differences between different methods by comparing the experimental findings. An iterative training approach is proposed to improve the robustness effect of the model in the face of real attacks by collaborative codec training and learning of real noise distribution, with an average bit prediction accuracy (BPA) of 97.56%.
Fine-Grained Visual Categorization with Window Restrain for Intelligent Products

Fine grained visual classification (FGVC) refers to the classification of subcategories in a dataset that belong to the same basic category. The difficulty lies in the less inter class sample differences and the more intra class sample differences, which are more challenging compared to traditional classification tasks. Recently, the use of Vision Transformer (ViT) methods has achieved significant results in FGVC scenes. However, mainstream research methods are based on the enhancement idea of local features, ignoring the interference of irrelevant background features on local discriminative regions. Therefore, in order to address existing deficiencies of Vision transformers being susceptible to complex background interference and insufficient ability to extract local features in fine-grained classification scenes (FGVC), this paper proposes a window restrain module (WRES) and combines it with the backbone network of standard Vision Transformer, for the task of fine grained classification of intelligent products, effectively enhancing the Vision Transformer model's ability to extract features from local discriminative regions and achieving end-to-end training. The results of experiments show that the accuracy of the Vision Transformer network incorporating WRES modules in CUB-200-2011 reaches 92.7%, which is 2.4% higher than the standard ViT method. It also outperforms the current fine-grained visual classification methods based on CNN and Vision Transformer on other FGVC benchmark datasets and intelligent products FGVC tasks.
CellSecure: Securing Image Data in Industrial Internet-of-Things via Cellular Automata and Chaos-Based Encryption

In the era of Industrial IoT (IIoT) and Industry 4.0, ensuring secure data transmission has become a critical concern. Among other data types, images are widely transmitted and utilized across various IIoT applications, ranging from sensor-generated visual data and real-time remote monitoring to quality control in production lines. The encryption of these images is essential for maintaining operational integrity, data confidentiality, and seamless integration with analytics platforms. This paper addresses these critical concerns by proposing a robust image encryption algorithm tailored for IIoT and Cyber-Physical Systems (CPS). The algorithm combines Rule-30 cellular automata with chaotic scrambling and substitution. The Rule 30 cellular automata serves as an efficient mechanism for generating pseudo-random sequences that enable fast encryption and decryption cycles suitable for realtime sensor data in industrial settings. Most importantly, it induces non-linearity in the encryption algorithm. Furthermore, to increase the chaotic range and keyspace of the algorithm, which is vital for security in distributed industrial networks, a hybrid chaotic map, i.e., logistic-sine map is utilized. Extensive security analysis has been carried out to validate the efficacy of the proposed algorithm. Results indicate that our algorithm achieves close-to-ideal values, with an entropy of 7.99 and a correlation of 0.002. This enhances the algorithm's resilience against potential cyber-attacks in the industrial domain.
Automated Stroke Lesion Segmentation in Rat Brain MR Images Using an Encoder-Decoder Framework

Stroke is a leading cause of serious long-term disability and the major cause of mortality worldwide. Experimental ischemic stroke models play an important role in realizing the mechanism of cerebral ischemia and evaluating the development of pathological extent. An accurate and reliable image segmentation tool to automatically identify the stroke lesion is important in the subsequent processes. However, the intensity distribution of the infarct region in the diffusion weighted imaging (DWI) images is usually nonuniform with blurred boundaries. A deep learning-based infarct region segmentation framework is developed in this paper to address the segmentation difficulties. The proposed solution is an encoder-decoder network that includes a hybrid block model for efficient multiscale feature extraction. An in-house DWI image dataset was created to evaluate this automated stroke lesion segmentation scheme. Through massive experiments, accurate segmentation results were obtained, which outperformed many competitive methods both qualitatively and quantitatively. Our stroke lesion segmentation system is potential in providing a decent tool to facilitate preclinical stroke investigation using DWI images.Clinical Relevance— This facilitates neuroscientists the investigation of a new scoring system with less examination time and better inter-rater reliability, which helps to understand the function of specific brain areas underlying neuroimaging signatures clinically
Single and multi-frame auto-calibration for 3D endoscopy with differential rendering

The use of 3D measurement in endoscopic images offers practicality in cancer diagnosis, computer-assisted interventions, and making annotations for machine learning training data. An effective approach is the implementation of an active stereo system, using a micro-sized pattern projector and an endoscope camera, which has been intensively developed. One open problem for such a system is the necessity of strict and complex calibration of the projector-camera system to precisely recover the shapes. Moreover, since the head of an endoscope should have enough elasticity to avoid harming target objects, the positions of the pattern projector cannot be tightly fixed to the head, resulting in limited accuracy. A straightforward approach to the problem is applying auto-calibration. However, it requires special markers in the pattern or a highly accurate initial position for stable calibration, which is impractical for real operation. In the paper, we propose a novel auto-calibration method based on differential rendering techniques, which are recently proposed and drawing wide attention. To apply the method to an endoscopic system, where a diffractive optical element (DOE) is used, we propose a technique to simultaneously estimate the focal length of the DOE as well as the extrinsic parameters between a projector and a camera. We also propose a multi-frame optimization algorithm to jointly optimize the intrinsic and extrinsic parameters, relative pose between frames, and the entire shape.Clinical relevance— One-shot endoscopic measurement of depth information is a practical solution for cancer diagnosis, computer-assisted interventions, and making annotations for machine learning training data.
Synergistic Label-Stability Learning for Semi-supervised Left Atrium Segmentation

Recent semi-supervised learning approaches appealingly advance medical image segmentation for their effectiveness in alleviating the need for a large amount of expert-demanding annotations. However, most of them have two limitations: (i) neglect of the intra-class variation caused by different patients and scanning protocols, which makes the pixel-level label propagation difficult; (ii) non-selective stability learning (a.k.a., consistency regularization), resulting in distraction by the redundant easy regions. To address these, in this work, we propose a novel synergistic label-stability learning (SLSL) framework for semi-supervised medical image segmentation. Specifically, our method is built upon the teacher-student framework. Then, the label learning process includes the typical pseudo label learning that reinforces confirmation of well-classified easy regions and the cyclic real label learning that takes advantage of real labels and class prototypes to regularize the distribution of intra-class features from unlabeled data to facilitate label propagation. In addition, the difficulty-selective stability learning aims to regularize the perturbed stability only at the high-entropy (can be regarded as difficult) regions, rather than being distracted by the less-informative easy regions. Extensive experiments on left atrium segmentation from MRI show that our method can effectively exploit the unlabeled data and outperform other semi-supervised medical image segmentation methods.Clinical relevance— The proposed method can help develop a high-performance automatic left atrium segmentation model for treating atrial fibrillation under limited expert-demanding annotation budgets.
Detection Of Fake Bank Currency Using Machine Learning

Abstract: The development of colour printing technology has accelerated the production of counterfeit currency notes and their mass duplication. Counterfeiting of banknotes poses a significant threat to the financial system and undermines the integrity of currency .Image analysis to distinguish genuine banknotes from counterfeit ones based on distinctive features. Various image processing algorithms are then employed to extract key features, such as texture, colour, and security elements embedded in the banknotes. These features are crucial for differentiating authentic currency from counterfeit replicas .Machine learning algorithms, particularly deep neural networks, are employed to train a robust classification model using a labeled dataset containing examples of both genuine and fake banknotes. The model is designed to generalize well to unseen data, allowing for accurate detection of counterfeit currency across various denominations and designs. The proposed fake banknote detection system demonstrates promising results in terms of accuracy, speed, and scalability. It offers a reliable and efficient solution to financial institutions and law enforcement agencies for identifying counterfeit currency in real-time, thereby contributing to the preservation of the integrity of financial transactions and safeguarding the economy against illicit activities. Keywords:- KNN, SVM, Image Processing
Synergy of Indonesian National Armed Forces, Indonesian national Police, Government and Society in Flood Mitigation in the Kodim 0301 Area, Fast Labuh Baru, Pekanbaru

The collaborative efforts involving the Indonesian National Armed Forces (TNI), Indonesian National Police (POLRI), government, and the local community in managing floods in Pekanbaru, Indonesia have been ongoing for an extensive period. These efforts require sustained attention, particularly during the prevailing rainy conditions. The military campaign approach comprises a series of joint operations executed to achieve strategic and operational objectives within specific spatial and temporal constraints. This military campaign involves Military Operations for War (MOW) and Military Operations Other Than War (MOOTW). The objective of this study was to describe and analyze the synergy among the Indonesian National Armed Forces (TNI), Indonesian National Police (POLRI), the government, and the local community in addressing floods in Pekanbaru through a military campaign strategy. This strategy involved TNI units with distinct branches and functions. The approach undertaken by the TNI (specifically those from KODIM 031 Pekanbaru) in flood disaster relief operations included collaboration with the Regional Disaster Management Agency of Pekanbaru to mobilize all available TNI and POLRI forces within the vicinity of Pekanbaru. This collaborative effort involved various stakeholders, such as TNI, POLRI, the government, NGOs, youth organizations (Karang Taruna), and other relevant entities. These activities took place in the East Labuh Baru, Payung Sekaki District, particularly along Arjuna Street. This effort is expected to establish an effective synergy among these groups, foster closer ties with the community, and manage floods by addressing issues related to drainage systems, canals, and overgrown vegetation.
Silicon Waveguide-based Single Photon Source with 12.4% High Off-chip Measured Heralding Efficiency

We designed and fabricated a silicon chip-based heralded single-photon source with 12.4% off-chip measured heralding efficiency. In addition, we achieve a coincidence-to-accidental ratio of 443.7 and heralded self-correlation function of 0.0037 measured at a pair rate of 313kHz. This demonstration of low noise and few multi-photon events at high pair rate is important for practical applications in quantum communication.
Power Reduction in Photonic Meshes by MZI Optimization

Here, we investigate power reduction techniques for meshes of Mach Zehnder Interferometers (MZI), used for photonic matrix multiplications. We also propose a novel bottom-up algorithm that optimally lowers the applied phase shift in each MZI.
Web- Based Service Search Information System (Programmer)

The development of information and technology today has become one of the sciences that is developing very rapidly and cannot be separated from the life of the world community for various needs, both for individuals, groups and large companies in the world. This is because of the many advantages derived from the use of information technology. Information technology is currently very important so that it can help and facilitate the work of everyday people who cannot be separated from the internet. There have been many information technologies developed in various fields as well as in the field of service ordering. It cannot be denied that there are already many applications that have been developed by developers and have been used by many people at this time
Web-Based Planning of Geographic Information System Mapping of Broken Roads in Kampar Regency

Road is a land transportation infrastructure and transportation route that is very vital at this time. Damage to the road is a big problem for some people who use the road every day. Kampar Regency during 2020 damaged roads including high, lightly damaged asphalt surface conditions 59.20 km, gravel heavily damaged along 4.00 km, and heavily damaged dirt road surface conditions along 2.80 km. Damage to roads in Kampar Regency can cause various kinds of losses, such as obstruction of road access to the destination, accidents, and damage to vehicles due to passing through bumpy and potholed roads, thereby increasing vehicle repair operational costs. Damage to roads can be overcome by making plans in a web of public complaints to the authorities in reporting the location of damaged roads. The purpose of this research is to help make it easier for the public when reporting damaged roads to PUPR by using the online damaged road reporting system by entering their name and email. The research method used in this research is the waterfall method. This waterfall method has a sequential flow of software starting from planning. The design of this system uses the Unified Modeling Language (UML) and Object-Oriented Analysis Design (OOAD).
Large-Scale Parallel Cognitive Diagnostic Test Assembly Using A Dual-Stage Differential Evolution-Based Approach

Parallel testing, which uses different test forms to assess examinees, is a necessary and important technique in both educational and psychometric assessments. A key but challenging problem for successful parallel testing lies in generating a high-quality parallel test set. Most existing parallel test assembly methods were developed for classic test theory and item response theory. In the context of cognitive diagnosis models, which is a new instrument featuring the ability to assess the examinee’s status on fine-grained attributes, the investigation of parallel test assembly is limited, particularly for large parallel scale. This study aims to provide an efficient dual-stage solution for the large-scale parallel cognitive diagnostic test (CDT) assembly problem. In the first stage, the assembly of individual CDTs is treated as a multimodal optimization problem and a niching differential evolution algorithm is developed to find an elite set of CDTs with near-optimal diagnostic performance. By redesigning evolutionary operators, the efficient search mechanism in differential evolution is transferred to the binary context and suits the purpose of optimizing item assignment to a CDT. In the second stage, a graph representation is defined to capture the set of elite CDTs and their overlapping relationships. A deterministic algorithm is applied to the graph to find specific nodal maximum cliques and provide two types of parallel test sets that satisfy different examiner preferences. Simulation studies under a variety of conditions and real-data demonstration show that the proposed method outperforms the existing approaches on large-scale instances while remaining competitive on small-scale cases.
On Estimating Time-Varying Pauli Noise

We consider the problem of estimating time-varying quantum noise. Specifically, we focus on Pauli qubit noise with time-variations and attempt to construct the most accurate instantaneous channel description. To this end, we propose an adaptive framework of simultaneous communication and parameter estimation (SCAPE) that efficiently and accurately estimates the time-varying Pauli channel while communicating reliably over the channel being estimated. This adaptive framework gives the informed control of communication rate–parameter estimation tradeoff to communicating parties. Interestingly, this adaptive SCAPE requires post-processing entirely on the receiver’s end and minimal feedback to the sender to increase, decrease, or continue with the same code rate of employed error correcting code. This procedure can be particularly useful in time-varying quantum channels with natural periodic deviations in channel conditions, e.g., in satellite communication channels.
Importance and implementation of virtual reality for organic photovoltaic educational purposes

This paper addresses the need for learning for students, which can be a motivational problem. Still, interactivity, i.e., "learning by doing," can tremendously decrease the psychological distance from learning a subject. Another challenge nowadays is the need for increasing interest in technical areas such as material engineering. With this, we developed a virtual reality game, "Organic Electronics," as an example for university students and secondary school children. A virtual environment is incredibly convenient for this generation of students. The game presents an attractive environment for teaching organic photovoltaic concepts and serves as a platform for upgrades and other discipline areas.