Reviewerly/exHarmony huggingface dataset:
DNN-based Legibility Improvement for Air-writing in Millimeter-wave Band Radar System

In radar-based air-writing, the continuous measurements of the hand movements may result in the addition of unnecessary strokes for certain characters or digits (e.g., 4 and 5), making it difficult to accurately recognize the air-written results when observed by human eyes. Therefore, we propose a deep neural network (DNN)-based classifier designed to identify unnecessary strokes and clutter that arise during the radar-based air-writing. First, while air-writing the digits from 0 to 9, the range, angle, and signal amplitude of the hand movement are obtained through a radar system. Then, we represent the hand’s trajectory in the form of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</i> coordinates by using the information of range and angle. Next, we train the DNN-based classifier using the acquired <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</i> coordinates, signal amplitude, and frame index as input features. To ensure the classifier’s performance would not be impacted by changes in the position and size of the air-writing area, we apply the normalization to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</i> coordinates. Finally, the performance of the classifier is verified using the results of air-writing digits from 0 to 9. The proposed method identifies unnecessary strokes and clutter regardless of the position and size of the air-writing area, demonstrating an average classification accuracy of 94.57%. Furthermore, when the classifier was validated with different individuals conducting the air-writing, the classifier exhibited an average classification accuracy of 93.9%.
ForegroundNet: Domain Adaptive Transformer for Camouflaged Object Detection

Camouflaged object detection (COD) from optical images intends to detect the “perfectly” concealed objects from their complicated backgrounds, which has promising prospects for facilitating widespread valuable tasks. However, COD currently still faces the challenges of avoiding confusion detection, false detection, and correctly identifying local structured details from complex surroundings. In this paper, we strive to embrace challenges toward more accurate COD. To this end, we develop a novel paradigm called ForegroundNet for the COD tasks. Intuitively, we propose a domain adaptation module (DAM) to transfer the domain invariant object information from the generic object detection (GOD) domain to the COD domain, to help ForegroundNet has a better object interpretation ability, resulting in better object location performance. Furthermore, we propose a ConvTransformer mechanism-aided decoding module (CMADM) to enhance the decoding of the local structured details in complicated surroundings. Extensive experiments, in this study, implemented on three widely used benchmarks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., CHAMELEON, CAMO, and COD10K, clearly demonstrate that the proposed ForegroundNet not only achieves substantial improvements in reducing false and missed detection but also attains advantages in local details fine detection, as compared with numerous representative state-of-the-art COD algorithms.
Rigidity

Kripke (Naming and Necessity. Harvard University Press, Cambridge 1980) introduced the terminology of rigid designation into the modern literature on modal logic, though the notion was anticipated by Marcus (Modalities. Oxford University Press, New York, 1992). In Kripke’s terminology a rigid designator designates the same object in all possible worlds in which that object exists and never designates anything else.
A practical multi-tab website fingerprinting attack

The rapid development of Internet of Things technology has allowed a massive number of devices to be connected, resulting in a lot of private data being transmitted over the network. To protect the security and privacy, anonymous communication technologies such as Tor are widely deployed. They use complex mechanisms such as encryption and multi-hop forwarding to hide the communication relationship and content. However, much existing work on the website fingerprinting attack has proven that there is still a risk of privacy disclosure. Website fingerprinting attacks can extract side channel information from encrypted traffic to form a fingerprint that identifies the victim’s destination website. But most work is conducted in the ideal environments, assuming the absence of background traffic, which raises doubts about the effectiveness in the real world. In this paper, we relax the strong assumptions of the attack model and propose a practical multi-tab website fingerprinting attack. We first constructed a CNN model to distinguish whether the unknown traffic is generated by accessing a single web page or multiple web pages. Then we design a split point recognition method based on the BalanceCascade algorithm to separate the overlapping traffic. Furthermore, we build recognition models based on ResNet and multi-head self-attention mechanism to identify the tail-missing and head-overlapping traffic sequences respectively. Extensive experiments are carried out in the real-world scenario to evaluate the proposed method. When the time interval is randomly selected within 5–15s, we achieve an accuracy of 77.34% in split point recognition. And the accuracy of identifying the two pages is 88.19% and 63.02% respectively.
Positioning Knowledge Transfer within MNC Subsidiary through Episodes Development

With the increasing number of multinational corporations (MNCs), there has been an increased need to understand how parent companies manage the operations of their subsidiaries and transfer knowledge to them. Japanese companies have been pioneers in this field, employing methods such as the Toyota Production System (TPS) to impart Japanese manufacturing philosophy and maintain quality and control in overseas operations. The process of transferring Japanese manufacturing capabilities has gotten a lot of attention, but little is known about how the subsidiaries that are expected to use these procedures actually acquire and incorporate them into their operations. This article examines the transfer of manufacturing knowledge from the parent company to the subsidiary from their perspective. An in-depth qualitative investigation was thus undertaken in the subsidiary of a Japanese multinational, comprising three key manufacturing initiatives (or philosophies), namely 'TPS,' 'TPM,' and 'TS'. 52 in-depth interviews with project members, moderate-participant observations, and documentations were used to collect case data. This study increases our understanding of knowledge transfer by utilizing the methods used within the subsidiary, how the entire process is formed, and, most crucially, the use and development of episodes in snapshots in comprehending the process.
Boosting Few-Shot Learning in Remote Sensing Leveraging an Auxiliary Generator through Contrastive Learning

Remote sensing technology has revolutionized the way we perceive and interact with our planet, enablingus to acquire invaluable information about our environment, natural resources, and ecosystems. Frommonitoring land use changes to studying climate patterns and disaster management, remote sensing hasbecome an indispensable tool for scientific research and practical applications. However, the effective application of remote sensing data often relies on the ability to classify andinterpret these vast and complex datasets. This presents a substantial challenge, as remote sensing datatypically exhibit high dimensionality, encompassing diverse terrain types, weather conditions, andimaging modalities. Furthermore, acquiring labeled data for supervised machine learning approaches isoften arduous, time-consuming, and expensive in remote sensing contexts. Few-shot learning emerges as a promising paradigm to address these issues. It seeks to develop modelscapable of learning from a limited amount of labeled data, which is a common scenario in remote sensingapplications due to the cost and effort associated with ground truth data collection. Few-shot learning isespecially valuable in cases where we need to recognize previously unseen classes or adapt quickly tochanging conditions, such as tracking forest fires or monitoring urban development. Despite its potential, few-shot learning in remote sensing faces significant hurdles, such as inadequatemodel generalization, limited data diversity, and challenges in feature extraction from complex datasources. In recent years, several advancements have been made in remote sensing image analysis, including the integration of deep learning techniques, but there remains a critical need for improving few- shot learning capabilities in this domain. This paper introduces a novel approach to address the limitations of few-shot learning in remote sensing. We propose the incorporation of an auxiliary generator powered by contrastive learning, a technique thathas demonstrated success in diverse applications, from natural language processing to computer vision. This auxiliary generator, working in conjunction with our few-shot learning model, enhances the ability todiscriminate between different classes and adapt to new remote sensing scenarios with limited labeleddata
Peramalan Curah Hujan di Kota Bandung dengan Metode SARIMA (Seasonal Autoregressive Integrated Moving Average)

The need for future rainfall information, modeling and forecasting is important. The forecasting method is a method used to predict future conditions based on past data. Rainfall data is time-series data in the form of seasonality, a pattern that repeats at fixed time intervals, so the authors use the Seasonal Autoregressive Integrated Moving Average (SARIMA) method, which is appropriate for data with seasonal characteristics. The author takes monthly rainfall data in Bandung city for the period January 2016 to December 2021 to forecast rainfall in Bandung city for next year. After calculations using the SARIMA method, the best model for forecasting rainfall in the city of Bandung is then obtained, namely the SARIMA model (0,0,0)(0,1,1)12.
Analisis Sentimen Ulasan Pengguna Aplikasi E-Samsat Provinsi Jawa Barat Menggunakan Metode BiGRU

Organizing the facilitation of local revenue tasks and public services is one of the main tasks, functions, detailed unit tasks, and work procedures of the West Java Provincial Revenue Agency. One of the public services for the community in improving service to the West Java community is to launch an e-samsat innovation in providing annual Motor Vehicle Tax (PKB) payment services and updating ownership status through an Android-based smartphone application called Samsat Mobile Jawa Barat (SAMBARA) and can be downloaded for free on the Google Play Store. Service satisfaction is an important aspect in service development, therefore research was conducted. This study analyzes the sentiment of the Samsat Mobile Jawa Barat (SAMBARA) application on the Google Play Store by categorizing user reviews into three groups: Positive, Negative, and Neutral. The method chosen is the Bidirectional Gated Recurrent Unit (BiGRU). BiGRU is able to predict user reviews with an accuracy of up to 87.37%, which is considered good and can be used to help the development of service applications in West Java.
The Production of Immersive Journalism: Best Practices in the Age of the Dawning Metaverse

In the last decade, the development and expansion of different virtual reality technologies has contributed to the emergence and consolidation of immersive journalism, a new format that offers a different way of experiencing and bringing the information closer to the viewer. The irruption of the metaverse opens a door to the continuity of this type of journalism, which had begun to decline, especially since the crisis of COVID-19. To understand what are the best practices to make possible this transition, we conducted two focus groups with a total of 15 international leaders in the field, including both professional and academic points of view. Their advice includes reconsidering the traditional concept of framing and giving greater priority to the point of view of the user.
CodePrompt: Improving Source Code-Related Classification with Knowledge Features through Prompt Learning

Abstract Researchers have explored the potential of utilizing pre-trained language models, such as CodeBert, to improve source code-related tasks. Previous studies have mainly relied on CodeBert's text embedding capability and the '[CLS]' sentence embedding information as semantic representations for fine-tuning downstream source code-related tasks. However, these methods require additional neural network layers to extract effective features, resulting in higher computational costs. Furthermore, existing approaches have not leveraged the rich knowledge contained in both source code and related text, which can lead to lower accuracy. This paper presents a novel approach, CodePrompt, which utilizes rich knowledge recalled from a pre-trained model by prompt learning and an attention mechanism to improve source code-related classification tasks. Our approach initially motivates the language model with prompt information to retrieve abundant knowledge associated with the input as representative features, thus avoiding the need for additional neural network layers and reducing computational costs. Subsequently, we employ an attention mechanism to aggregate multiple layers of related knowledge for each task as final features to boost their accuracy. We conducted extensive experiments on four downstream source code-related tasks to evaluate our approach and our results demonstrate that CodePrompt achieves new state-of-the-art performance on the accuracy metric while also exhibiting computation cost-saving capabilities.
Research on image classification based on extreme learning machine and convolutional neural network

In image classification and recognition, in order to obtain higher classification accuracy, it is necessary to extract more accurate and more expressive features of image semantic information. This paper proposes an improved image classification model based on convolutional neural network. By improving and optimizing AlexNet from both the network architecture and the internal structure, we can further improve the expression ability of features. By introducing the extreme learning machine in the full connection layer, not only the classification ability and classification time of the network are improved, but also the structure has better data processing ability.
Real-time detection method for infrared pedestrian

We propose a real-time infrared pedestrian detection method, which achieves higher performance than the state-of-theart YOLO series in infrared pedestrian detection. Our method is based on the YOLOv6-n with some new technologies. In particular, Mixed Downsampling Module (MDM) and Cross-Stage Connection Structure (CSCS) are designed to reduce the inference latency of the network. In addition, Simplified Spatial Attention Module (SimSAM) is proposed to improve detection performance with negligible overheads. We validate our method through extensive experiments on FLIR detection datasets. Experimental results show that our method can achieve 84.86% AP with inference latency of 0.54 ms on an NVIDIA 1080Ti GPU.
Research on entity relation extraction based on head entity feature extraction

During the construction of knowledge graphs, entity relations serve as the basic unit whose extraction effect can greatly affect the precision of knowledge graphs. To avoid the insufficient information extraction of head entities in existing extraction models of entity relation triples, an entity relation extraction model based on head entity feature extraction (HFERE) was proposed in this paper. It first used the RoBERTa (robustly optimized BERT approach) model to perform feature coding to the input sentences and then extract the position of head entities through pointer tagging. After that, it performed the feature enhancement on the head entities through the Bi-LSTM (Bi-directional Long Short-Term Memory) bi-directional current neural network so as to fully utilize the deep information of head entities. The tail entity and the relation between entities were extracted after the head entities were tagged. At the relation extraction stage, the hierarchical pointer tagging was used to avoid the entity overlays. Baidu’s public entity relation extraction data set was used for tests in this paper. The experimental results show that the F1 value of the model on this data set was 3.04% higher than that of the baseline model, which can effectively improve the extraction precision of triples.
Motion recognition of two-stream based on multi-attention

To address the problems that the traditional two-stream network has limitations in effectively fusing spatial-temporal information and the extracted temporal information hard to capture complex motion patterns, which resulting in low recognition accuracy, an improved two-stream convolutional neural network method for human motion recognition is proposed based on multi-attention mechanism. Firstly, in the temporal network, the C3D network is used to replace the original two-dimensional network to solve the problem that the temporal information cannot be extracted effectively; secondly, in the spatial network, multi-scale convolutional Transformer encoder is coded based on the contextual relative position, features are integrated adaptively at different scales under the action of the adaptive scale attention mechanism. Experimental findings conducted on the UCF101 dataset indicate the approach described in this paper performs better in terms of the number of parameters and recognition accuracy.
Centering Humans in the Programming Languages Classroom: Building a Text for the Next Generation

This paper is a companion to the author's open-access textbook, "Human-Centered Programming Languages." Beyond the contributions of the textbook itself, this paper contributes a set of textbook design principles for overcoming those limitations and an analysis of students' stated needs and preferences drawn from anonymous course report data for three courses, the last of which was based on notes that became the basis of the textbook. The textbook is intended to be multi-purpose, with its primary audiences being undergraduate and master's-level elective courses on programming languages within computer science, but significant opportunity for cross-use in disciplines ranging from human-computer interaction and software engineering to gender studies and disability studies. The book is intended to be language-agnostic, but the course in which it will be used first is Rust-based.
Quantum simulation of Hamiltonian eigenstates

Abstract In this chapter, we present quantum algorithms to approximate Hamiltonian eigenstates. We start discussing the quantum phase estimation algorithms, an important subroutine that allows us to approximate eigenpairs of a Hamiltonian operator given an initial guess and a quantum circuit implementing the time evolution operator. We then present the adiabatic state preparation, a technique to transform the ground state of a Hamiltonian into the ground state of another Hamiltonian. We describe two important heuristic methods, the quantum approximate optimization algorithm, and the variational quantum eigensolver. The second part of the chapter discusses concrete applications in the field of quantum chemistry. After introducing the Born-Oppenheimer Hamiltonian in second quantization and the Hartree-Fock method, we illustrate how a chemical problem can be mapped onto a quantum computer. We conclude the chapter with the simulation of the hydrogen molecule in a minimal basis with the iterative phase estimation algorithm.
BLAC: A Blockchain-Based Lightweight Access Control Scheme in Vehicular Social Networks

Vehicular Social Networks (VSNs) rely on data shared by users to provide convenient services. Data is outsourced to the cloud server and the distributed roadside unit in VSNs. However, roadside unit has limited resources, so that data sharing process is inefficient and is vulnerable to security threats, such as illegal access, tampering attack and collusion attack. In this article, to overcome the shortcomings of security, we define a chain tolerance semi-trusted model to describe the credibility of distributed group based on the anti tampering feature of blockchain. We further propose a Blockchain-based Lightweight Access Control scheme in VSNs that resist tampering and collusion attacks, called BLAC. To overcome the shortcomings of efficiency, we design a ciphertext piece storage algorithm and a recovery one to achieve lightweight storage cost. In the decryption operation, we separate a pre-decryption algorithm based on outsourcing to achieve lightweight decryption computation cost on the user side. Finally, we present the formal security analyses and the simulation experiments for BLAC, and compare the results of experiments with existing relevant schemes. The security analyses show that our scheme is secure, and the results of experiments show that our scheme is lightweight and practical.
Blockchain Based Publicly Auditable Multi-party Computation with Cheater Detection

Secure Multi-Party Computation (MPC) allows parties to calculate a joint function using their respective secret inputs in a distributed environment without centralized server and has numerous applications across various fields. However, the presence of cheaters in the MPC protocol can lead to an unfair process. To address this issue, we propose a blockchain-based secure multi-party computation scheme in which the entire computing process is publicly auditable, and cheating parties can be detected. In our scheme, cheaters will be financially punished, while honest parties will be financially compensated, thereby deterring the cheating behaviors. The analysis demonstrates that our scheme ensures public auditability, preserves parties’ privacy, and maintains fairness throughout the MPC process.
Soft syndrome iterative decoding of quantum LDPC codes and hardware architectures

Abstract In practical quantum error correction implementations, the measurement of syndrome information is an unreliable step—typically modeled as a binary measurement outcome flipped with some probability. However, the measured syndrome is in fact a discretized value of the continuous voltage or current values obtained in the physical implementation of the syndrome extraction. In this paper, we use this “soft” or analog information to benefit iterative decoders for decoding quantum low-density parity-check (QLDPC) codes. Syndrome-based iterative belief propagation decoders are modified to utilize the soft syndrome to correct both data and syndrome errors simultaneously. We demonstrate the advantages of the proposed scheme not only in terms of comparison of thresholds and logical error rates for quasi-cyclic lifted-product QLDPC code families but also with faster convergence of iterative decoders. Additionally, we derive hardware (FPGA) architectures of these soft syndrome decoders and obtain similar performance in terms of error correction to the ideal models even with reduced precision in the soft information. The total latency of the hardware architectures is about 600 ns (for the QLDPC codes considered) in a 20 nm CMOS process FPGA device, and the area overhead is almost constant—less than 50% compared to min-sum decoders with noisy syndromes.