Reviewerly/exHarmony huggingface dataset:
Free-space optical link for quantum key distribution using unmanned vehicle

Here, we demonstrate a field-deployable free-space optical link for quantum key distribution (QKD) between a ground station and radio-controlled electric vehicle (RCEV). Compact and high-performance acquisition, pointing and tracking (APT) system is developed for small tracking error down to 3.8 μrad and average link loss of 16 dB. Such link is within the requirement of normal QKD system. With automatic acquisition and coarse tracking, the APT system can automatically establish the free-space optical link in 10 minutes. It is the first step towards mobile quantum secure communication network. Based on RCEV, the quantum optical link can be reconfigured at different link distances. This result greatly improves the flexibility of the link construction for the future QKD applications.
REVIEW ON DIGIT RECOGNITION USING CONVOLUTIONAL NEURAL NETWORK

Deep learning has recently taken a radical turn in the field of machine learning by making it more artificially intelligent, thanks to the advent of Convolutional Neural Networks (CNN). Neural networks are a group of algorithms that are identical to the human brain and are programmed to identify different patterns. A neural network learns from numerous levels of representation and reacts appropriately to different levels of abstraction where different patterns are learned by each layer. Handwritten Digit Recognition is an example of a computer's capacity to recognise human handwritten digits. Because handwritten numerals aren't flawless and might be generated with a variety of tastes, it's difficult work for the machine. In this paper, we use CNN to identify handwritten digits using different numbers of hidden layers and epochs to achieve highly accurate results. This research is carried out using the database of the Modified National Standards and Technology Institute (MNIST). This dataset was created using the convolutional neural network technique and Keras, a Python library for intensive computation of neural nodes that is supported by the Tensor Flow framework on the backend. We will be able to estimate the handwritten digits in an image using this model. This approach allows us to detect numerous digits.
A smart contract approach to access control based on distributed identities and roles

With the continuous development of digital communication in Internet of Things(IOT) and the demand of IOT for advanced interoperability and collaboration, organizations and entities directly need to interact with data. Access control technology is the key to current data protection and system management. In this paper, we details the method framework, contract design, and execution process of the DID-based blockchain access control, and analyze for data leakage, security issues, data storage and system complexity in the current solution. In order to solve the current problems, this paper combines blockchain technology with identity access control scheme based on the application of distributed identification; Secondly, a detailed of DID mark and smart contract interaction mechanism are design. Finally, an implementation scheme is proposed on this scheme combined with the IoT scenario and is demonstrated to run in a real scenario. The usability, security and efficiency of the framework are evaluated in simulation tests and performance.
A Radar classification system based on Gaussian NB

Radar recognition has gradually been widely used in daily life, especially in target recognition target classification has a good development prospect. With the rapid development of deep learning, a new material object classification system appears, and a radar classification system based on GaussianNB is proposed. We use radar to identify air,books, hands, knives and plastic boxes. By labeling the returned radar signals and conducting supervised training on them, classification models can be obtained and tested using these radar signals. The accuracy of GaussianNB algorithm is 96.36%. However, the accuracy of KNN algorithm is 92.14%, and the accuracy of gradient elevation decision tree is only 88.92%. This shows that GaussianNB based radar subsystem is more effective and accurate.
5G and Beyond: Channel Classification Enhancement Using VIF-Driven Preprocessing and Machine Learning

The classification of wireless communication channel scenarios is vital for modern wireless technologies. Efficient data preprocessing for identification, especially starting from 5G and beyond, where multiple scenario transitions occur, is crucial. Machine Learning (ML) is employed for scenario identification. Moreover, accurate ML classification is required to enhance the decision-making process in each communication layer. The proposed model in this study utilizes an enhanced preprocessing phase. The proposed model proves that adding the variance inflation factor (VIF) elimination layer has a significant effect in eliminating the residual noise after regularization. By evaluating the VIF, the high multi-collinear features are removed after adding a regularization penalty. Consequently, the total explained variance (TEV) was enhanced by 5% and reached 76%. Thus, the classification accuracy of the identification processes of different rural and urban scenarios was increased by 3%, on average, compared with previous work for each algorithm: Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Gaussian Mixture model (GMM).
Distributed Kalman filter

This chapter is dedicated to the distributed Kalman filter and its variants. It is shown that the Kalman filter recursion with its prediction and filtering for multiple sensors can be fully parallelized and distributed in the case of the standard Kalman assumptions, which are linear Gaussian models for the sensor likelihoods and transition model. The distributed computation is achieved based on the notion of global information, that is, all sensors have knowledge on the full amount of information in the system without any exchange of data between sensors. Since these assumptions are often not given, the closely related federated Kalman filter is introduced. Then, it is shown that optimal fusion can also be achieved based on the Accumulated State Densities which are derived in Chapter 4. This requires transmissions of cross-covariance matrices of multiple states in time, but allows to compute the exact fusion result without the assumptions of the distributed Kalman filter.
Formerge: Recover Spanning Cells in Complex Table Structure Using Transformer Network

Table structure recognition (TSR) task is indispensable in a robust document analysis system. Recently, the split-and-merge-based approach has attracted many researchers to develop the TSR problem. It is a two-stage method: firstly, split table region into row/column separation and obtain grid cells of the table; then recover spanning cells by merging some grid cells and complete the table structure. Most recent proposals focus on the first stage, with few solutions for the merge task. Therefore, this paper proposes a novel method to recover spanning cells using Transformer networks called Formerge. This model contains a Transformer encoder and two parallel left-right/top-down decoders. With grid structure output from a split branch, Formerge extracts cell features with RoIAlign and passes them into the encoder to enhance features before decoding to detect spanning cells. Our technique outperforms other methods on two benchmark datasets, including SciTSR and ICDAR19-cTDaR modern.
Information Redundancy and Biases in Public Document Information Extraction Benchmarks

Advances in the Visually-rich Document Understanding (VrDU) field and particularly the Key-Information Extraction (KIE) task are marked with the emergence of efficient Transformer-based approaches such as the LayoutLM models. Despite the good performance of KIE models when fine-tuned on public benchmarks, they still struggle to generalize on complex real-life use-cases lacking sufficient document annotations. Our research highlighted that KIE standard benchmarks such as SROIE and FUNSD contain significant similarity between training and testing documents and can be adjusted to better evaluate the generalization of models. In this work, we designed experiments to quantify the information redundancy in public benchmarks, revealing a 75% template replication in SROIE’s official test set and 16% in FUNSD’s. We also proposed re-sampling strategies to provide benchmarks more representative of the generalization ability of models. We showed that models not suited for document analysis struggle on the adjusted splits dropping on average 10,5% F1 score on SROIE and 3.5% on FUNSD compared to multi-modal models dropping only 7,5% F1 on SROIE and 0.5% F1 on FUNSD.
On Augmenting TCP/IP Stack via eBPF

As the data center networks' bandwidth-delay product is increasing and the applications are moving to nano services (with many small flows), managing flows in the network is becoming more challenging. Current TCP/IP stack faces fundamental limitations to meet these challenges. First, it lacks the ability to accurately estimate the network state under dynamic network settings. Second, the current stack is not flexible enough to be extended easily. In this work, we propose a framework, Augmenter, that augments (i.e., increases the network visibility of) the TCP/IP stack to address these challenges. Leveraging eBPF, Augmenter gathers the state of ongoing flows and uses this information to manage other flows that are currently active or arriving in the future. We present one specific use case of setting the initial congestion window of flows dynamically based on network conditions. Our initial tests, show that Augmenter can improve the application performance by up to 1.4x compared to the fixed initial window-based solutions. Implementing Augmenter in the TCP/IP stack itself is not trivial. Augmenter employs eBPF to implement its desired functionality as it enables introducing such changes relatively easy. We discuss potential challenges and solutions in designing and implementing Augmenter applications.
Implementation of the Cooperative Integrated Reading and Composition (CIRC) Strategy in Improving the Learning Ability of Elementary Students

This study aims to determine the application of Cooperative Integrated Reading and Composition (CIRC) learning strategies in improving the learning abilities of elementary age students. The type of research used is qualitative research with descriptive methods that aim to describe, explain and answer questions about phenomena and events that are happening at this time. Based on the research results from the implementation of the Cooperative Integrated Reading and Composition (CIRC) Strategy in improving the learning abilities of MI/SD students that: (1) Based on the results and discussion in this study, it can be concluded that the Cooperative Integrated Reading and Composition (CIRC) learning strategy applied for children of elementary school age it is quite good because it fulfills several indicators, including: (1) students can understand and complete guessing pictures easily, (2) students can present the results of guessing pictures in their own language, (3) students able to work well together.
Task Difficulty Aware Parameter Allocation &amp; Regularization for Lifelong Learning

Parameter regularization or allocation methods are effective in overcoming catastrophic forgetting in lifelong learning. However, they solve all tasks in a sequence uniformly and ignore the differences in the learning difficulty of different tasks. So parameter regularization methods face significant forgetting when learning a new task very different from learned tasks, and parameter allocation methods face unnecessary parameter overhead when learning simple tasks. In this paper, we propose the Parameter Allocation & Regularization (PAR), which adaptively select an appropriate strategy for each task from parameter allocation and regularization based on its learning difficulty. A task is easy for a model that has learned tasks related to it and vice versa. We propose a divergence estimation method based on the Nearest-Prototype distance to measure the task relatedness using only features of the new task. Moreover, we propose a time-efficient relatedness-aware sampling-based architecture search strategy to reduce the parameter overhead for allocation. Experimental results on multiple benchmarks demonstrate that, compared with SOTAs, our method is scalable and significantly reduces the model's redundancy while improving the model's performance. Further qualitative analysis indicates that PAR obtains reasonable task-relatedness.
An AUV Path Planning Method Based on Improved APF-RRT*

Path planning plays an important role when an Autonomous Underwater Vehicle (AUV) is performing a cruising task such as underwater Simultaneous Localization and Mapping (SLAM). A path planning method accomplish the goals of avoiding obstacles and finding shortest paths, but may meet the problems that the AUV trapped into local optima and low efficiency. A novel path planning method based on APF-RRT* is proposed, which introduces gravity and repulsion functions of the APF algorithm to the RRT* algorithm. The proposed method avoids the problem of local optima trapping and low efficiency. Considering the impact of water flow and the movement of dynamic obstacles on the AUV, these two factors are introduced to the gravity and repulsion function, which improves the effectiveness of the algorithm in the underwater environment. Furthermore, Bézier curve is used to smooth the planned path to make the path more consistent with the kinematics of the AUV. The simulation results show that this algorithm can quickly complete path searching and generate reachable paths in both static and dynamic underwater environments. After path optimization, it is more suitable for AUV driving and operation in underwater environments.
Global Synthesis of CNOT Circuits with Holes

A common approach to quantum circuit transformation is to use the properties of a specific gate set to create an efficient representation of a given circuit's unitary, such as a parity matrix or stabiliser tableau, and then resynthesise an improved circuit, e.g. with fewer gates or respecting connectivity constraints.Since these methods rely on a restricted gate set, generalisation to arbitrary circuits usually involves slicing the circuit into pieces that can be resynthesised and working with these separately.The choices made about what gates should go into each slice can have a major effect on the performance of the resynthesis.In this paper we propose an alternative approach to generalising these resynthesis algorithms to general quantum circuits.Instead of cutting the circuit into slices, we "cut out" the gates we can't resynthesise leaving holes in our quantum circuit.The result is a secondorder process called a quantum comb, which can be resynthesised directly.We apply this idea to the RowCol algorithm, which resynthesises CNOT circuits for topologically constrained hardware, explaining how we were able to extend it to work for quantum combs.We then compare the generalisation of RowCol using our method to the naïve "slice and build" method empirically on a variety of circuit sizes and hardware topologies.Finally, we outline how quantum combs could be used to help generalise other resynthesis algorithms.
Early warning model of illegal E-cigarette sales based on network public opinion analysis

The current online sales supervision of E-cigarette is in a blank stage. This paper proposes an early warning model for illegal E-cigarette sales based on network public opinion analysis, using web crawlers and third-party API interface calls to obtain online maps, relevant merchant names on social platforms, Text data such as business content and user comments. Then design a deep learning model with a dual-channel four-layer architecture to conduct early warning analysis on online E-cigarette sales. Through experimental comparison, it is shown that the model proposed in this paper has good performance and can provide strong support for the supervision of E-cigarette sales.
AGMG-Net: Leveraging multiscale and fine-grained features for improved cargo recognition

Security systems place great emphasis on the safety of stored cargo, as any loss or tampering can result in significant economic damage. The cargo identification module within the security system faces the challenge of achieving a 99.99% recognition accuracy. However, current identification methods are limited in accuracy due to the lack of cargo data, insufficient utilization of image features and minimal differences between actual cargo classes. First, we collected and created a cargo identification dataset named "Cargo" using industrial cameras. Subsequently, an Attention-guided Multi-granularity feature fusion model (AGMG-Net) was proposed for cargo identification. This model extracts both coarse-grained and fine-grained features of the cargo using two branch networks and fuses them to fully utilize the information contained in these features. Furthermore, the Attention-guided Multi-stage Attention Accumulation (AMAA) module is introduced for target localization, and the Multi-region Optimal Selection method Based on Confidence (MOSBC) module is used for target cropping. The features from the two branches are fused using a fusion branch in a Concat manner for multi-granularity feature fusion. The experimental results show that the proposed model achieves an average recognition rate of 99.58, 92.73 and 88.57% on the self-built dataset Cargo, and the publicly available datasets Flower and Butterfly20, respectively. This is better than the state-of-the-art model. Therefore, this research method accurately identifies cargo categories and provides valuable assistance to security systems.
Research Work Organization with Master’s Degree Students «Upbringing Activity in Educational Organization»

Constant progress and creative renewal are integral features of the educational process in higher education, actualizing the problem of developing future teachers’ competencies related to the theoretical justification of parenting strategies and upbringing work in educational organizations. In 2019, the Department of Pedagogy of Irkutsk State University started a master’s program «Upbringing activity in an educational organization», focused on solving the identified problems through the study of disciplines: «Design and organization of professional pedagogical activity», «Methods of educational work», «Design strategies and tactics of pedagogical interaction», «Diagnostics of the quality of the educational process», «Development of individual personal programs», etc. We believe that the experience of the Department of Pedagogy of the ISU in organizing the research work of undergraduates on the problems of upbringing deserves discussion and further dissemination.
Skillab - A Multimodal Augmented Reality Environment for Learning Manual Tasks

The paper investigates the usage of AR-based systems in teaching manual skills. We propose Skillab, a novel AR-based scaffolding system. It aids in the learning of manual work and functions as a multimodal immersive tool for feedback, including muscle actuation. As our initial investigation, we made a floor lamination tutorial. We evaluated our system’s performance and user experience in comparison to traditional paper instructions. With 20 participants, we conducted a between-group user study and obtained both qualitative and quantitative data. In terms of task performance, learnability, and overall user experience, Skillab significantly outperformed conventional paper instructions. In contrast to learning from paper instructions, Skillab training demonstrated a significant improvement in the systematic rating on the quality of the performed task. We believe that by demonstrating the potential of immersive multi-modal feedback technology for skill-building, researchers would be motivated to explore this area further.
Development of a Personnel and Payroll Information System at Madrasah Ibtidaiyah Misbahun Nasyiin School

MI. Misbahunnasyiin is an Islamic educational institution located on Jl Raden Saleh, Tangerang. MI Misbahunnasyiin has administrative and financial data that is still recorded manually, which is very dangerous and vulnerable or at risk of data loss and damage. so that this research aims to find a solution to solve this problem by making applications related to digitalized payroll and staffing so that it can facilitate user performance and solve problems that existed before. Then writing study This is expected to simplify and increase efficiency in data processing and help develop information systems at the MI Misbahunnasyiin school using the language of them program PHP and MySql as server database.
Adversarial Thumbnail-Preserving Transformation for Facial Images Based on GAN

People are used to capturing their daily lives with images and uploading them to the cloud for easy archiving. While cloud storage services bring convenience to users, malicious humans and artificial intelligence machines may pose privacy threats to these uploaded images. Thumbnail-preserving encryption (TPE) is a solution for handling privacy threats posed by the usage of cloud storage and it is able to balance the privacy against humans and usability for users. However, the present TPE schemes display certain vulnerabilities when subjected to machine-based attacks and when the size of the thumbnail block is small, the facial recognition (FR) models exhibit strong capability in recognizing the identity within the protected facial images. To this end, we propose an adversarial thumbnail-preserving transform scheme for facial images, which can not only balance privacy and usability but also resist recognition from FR models. The experiments demonstrate that the proposed scheme has a better ability to resist machine recognition compared with existing schemes.
Model Compression Based on SAR-Optical Image Mapping Task

In recent years, due to the scarcity of resources for pairing SAR images with optical images, image mapping tasks have become an important research direction in the field of Earth observation. Conditional Generative Adversarial Networks have demonstrated their superior performance and great potential on the task of SAR-Optical image translation. However, a more complex network means a larger amount of computing and more computing resource consumption, which makes task deployment and application landing become more challenging. In this paper, we propose a compression algorithm based on the image mapping model, which can minimize the number of parameters and the computational resource costs of the model, while preserving its performance of the model most. We analyze the structure and parameter distribution of the image generator, and design a lightweight mapping module based on Depthwise Separable Convolution. In view of the sensitivity of Conditional Generative Adversarial Networks to structure, we design an automatic channel pruning algorithm based on Neural Architecture Search. This algorithm further compresses the number of parameters on the lightweight generator to speed up inference. Finally, we test on the SAR-Optical image mapping task, and the model under the compression algorithm has a better mapping effect than the model of the same scale. The algorithm achieves a better mapping effect at a lower cost of computing resources, and provides more possibilities for the deployment and development of image mapping tasks.