Chinese spelling correction (CSC) models detect and correct a text typo based on the misspelled character and its context. Recently, Bert-based models have dominated the research of Chinese spelling correction. However, these methods only focus on the semantic information of the text during the pretraining stage, neglecting the learning of correcting spelling errors. Moreover, when multiple incorrect characters are in the text, the context introduces noisy information, making it difficult for the model to accurately detect the positions of the incorrect characters, leading to false corrections. To address these limitations, we apply the multimodal pre-trained language model ChineseBert to the task of spelling correction. We propose a self-distillation learning-based pretraining strategy, where a confusion set is used to construct text containing erroneous characters, allowing the model to jointly learns how to understand language and correct spelling errors. Additionally, we introduce a single-channel masking mechanism to mitigate the noise caused by the incorrect characters. This mechanism masks the semantic encoding channel while preserving the phonetic and glyph encoding channels, reducing the noise introduced by incorrect characters during the prediction process. Finally, experiments are conducted on widely used benchmarks. Our model achieves superior performance against state-of-the-art methods by a remarkable gain.

E-learning, which can be used anywhere and at any time, is very convenient and has been introduced to improve learning efficiency. However, securing a completion rate has been a major challenge. Recently, the learning forms of e-learning require learners to be introspective, deliberate, and logical and have proven to be incompatible with many learners with low completion rates. Thus, we developed an e-learning system that incorporates a fill-in-the-blank question-type concept map to deepen learners' understanding of learning contents while watching learning videos. The developed system promotes active learning reflectively and logically by allowing learners to answer blank question labels on concept maps from video content and labels associated with the blank question labels. We confirmed in the laboratory experiment by comparing with a conventional video-based learning system that the developed system encouraged a learner to do more system operations for rechecking the learning content and to better understand the learning contents while watching the learning video. As the next step, a field experiment is needed to investigate the usefulness and effectiveness of the developed system in actual environments in order to boost the practicality of the developed system. In this study, we introduced the developed system into the two class of the uviversity course and investigated the level of understanding to the learning contents, the system operations, and the usefulness of the developed system by comparing with those in the laboratory experiment. The results showed that the developed system provided to support the understanding to learning content and the usefulness of each function in the field experiment, as in the laboratory experiment. On the other hand, the students in the field experiment gave lower usefulness of the developed system than those in the lab experiment, suggesting that the students who attempted to thoroughly understand the learning contents in the field experiment were fewer than those in the lab experiment from their system operations during the learning.

In Natural Language Understanding, intent detection and slot filling have been widely used to understand user queries. However, current methods tend to rely on single words and sentences to understand complex semantic concepts, and can only consider local information within the sentence. Therefore, they usually cannot capture long-distance dependencies well and are prone to problems where complex intentions in sentences are difficult to recognize. In order to solve the problem of long-distance dependency of the model, this paper uses ConceptNet as an external knowledge source and introduces its extensive semantic information into the multi-intent detection and slot filling model. Specifically, for a certain sentence, based on confidence scores and semantic relationships, the most relevant conceptual knowledge is selected to equip the sentence, and a concept context map with rich information is constructed. Then, the multi-head graph attention mechanism is used to strengthen context correlation and improve the semantic understanding ability of the model. The experimental results indicate that the model has significantly improved performance compared to other models on the MixATIS and MixSNIPS multi-intent datasets.

In recent years, location-based technologies for ubiquitous environments have aimed to realize services tailored to each purpose based on information about an individual's current location. To establish such advanced location-based services, an estimation technology that can accurately recognize and predict the movements of people and objects is necessary. Although global positioning system (GPS) has already been used as a standard for outdoor positioning technology and many services have been realized, several techniques using conventional wireless sensors such as Wi-Fi, RFID, and Bluetooth have been considered for indoor positioning technology. However, conventional wireless indoor positioning is prone to the effects of noise, and the large range of estimated indoor locations makes it difficult to identify human activities precisely. We propose a method to mine user activity patterns from time-series data of user's locationss in an indoor environment using ultra-wideband (UWB) sensors. An UWB sensor is useful for indoor positioning due to its high noise immunity and measurement accuracy, however, to our knowledge, estimation and prediction of human indoor activities using UWB sensors have not yet been addressed. The proposed method consists of three steps: 1) obtaining time-series data of the user's location using a UWB sensor attached to the user, and then estimating the areas where the user has stayed; 2) associating each area of the user's stay with a nearby landmark of activity and assigning indoor activities; and 3) mining the user's activity patterns based on the user's indoor activities and their transitions. We conducted experiments to evaluate the proposed method by investigating the accuracy of estimating the user's area of stay using a UWB sensor and observing the results of activity pattern mining applied to actual laboratory members over 30-days. The results showed that the proposed method is superior to a comparison method, Time-based clustering algorithm, in estimating the stay areas precisely, and that it is possible to reveal the user's activity patterns appropriately in the actual environment.

Despite recent advancements in utilizing meta-learning for addressing the generalization challenges of graph neural networks (GNN), their performance in argumentation mining tasks, such as argument classifications, remains relatively limited. This is primarily due to the under-utilization of potential pattern knowledge intrinsic to argumentation structures. To address this issue, our study proposes a two-stage, pattern-based meta-GNN method in contrast to conventional pattern-free meta-GNN approaches. Initially, our method focuses on learning a high-level pattern representation to effectively capture the pattern knowledge within an argumentation structure and then predicts edge types. It then utilizes a meta-learning framework in the second stage, designed to train a meta-learner based on the predicted edge types. This feature allows for rapid generalization to novel argumentation graphs. Through experiments on real English discussion datasets spanning diverse topics, our results demonstrate that our proposed method substantially outperforms conventional pattern-free GNN approaches, signifying a significant stride forward in this domain.

Both group process studies and collective intelligence studies are concerned with “which of the crowds and the best members perform better.” This can be seen as a matter of democracy versus dictatorship. Having evidence of the growth potential of crowds and experts can be useful in making correct predictions and can benefit humanity. In the collective intelligence experimental paradigm, experts' or best members ability is compared with the accuracy of the crowd average. In this research (n = 620), using repeated trials of simple tasks, we compare the correct answer of a class average (index of collective intelligence) and the best member (the one whose answer was closest to the correct answer). The results indicated that, for the cognition task, collective intelligence improved to the level of the best member through repeated trials without feedback; however, it depended on the ability of the best members for the prediction task. The present study suggested that best members' superiority over crowds for the prediction task on the premise of being free from social influence. However, machine learning results suggests that the best members among us cannot be easily found beforehand because they appear through repeated trials.

Despite the increasing use of conversational artificial intelligence (AI) in online discussion environments, few studies explore the application of AI as a facilitator in forming problem-solving debates and influencing opinions in cross-venue scenarios, particularly in diverse and war-ravaged countries. This study aims to investigate the impact of AI on enhancing participant engagement and collaborative problem-solving in online-mediated discussion environments, especially in diverse and heterogeneous discussion settings, such as the five cities in Afghanistan. We seek to assess the extent to which AI participation in online conversations succeeds by examining the depth of discussions and participants' contributions, comparing discussions facilitated by AI with those not facilitated by AI across different venues. The results are discussed with respect to forming and changing opinions with and without AI-mediated communication. The findings indicate that the number of opinions generated in AI-facilitated discussions significantly differs from discussions without AI support. Additionally, statistical analyses reveal quantitative disparities in online discourse sentiments when conversational AI is present compared to when it is absent. These findings contribute to a better understanding of the role of AI-mediated discussions and offer several practical and social implications, paving the way for future developments and improvements.

This paper presents the design and implementation of an automated multi-phase facilitation agent based on LLM to realize inclusive facilitation and efficient use of a large language model (LLM) to facilitate realistic discussions. Large-scale discussion support systems have been studied and implemented very widely since they enable a lot of people to discuss remotely and within 24 hours and 7 days. Furthermore, automated facilitation artificial intelligence (AI) agents have been realized since they can efficiently facilitate large-scale discussions. For example, D-Agree is a large-scale discussion support system where an automated facilitation AI agent facilitates discussion among people. Since the current automated facilitation agent was designed following the structure of the issue-based information system (IBIS) and the IBIS-based agent has been proven that it has superior performance. However, there are several problems that need to be addressed with the IBIS-based agent. In this paper, we focus on the following three problems: 1) The IBIS-based agent was designed to only promote other participants' posts by replying to existing posts accordingly, lacking the consideration of different behaviours taken by participants with diverse characteristics, leading to a result that sometimes the discussion is not sufficient. 2) The facilitation messages generated by the IBIS-based agent were not natural enough, leading to consequences that the participants were not sufficiently promoted and the participants did not follow the flow to discuss a topic. 3) Since the IBIS-based agent is not combined with LLM, designing the control of LLM is necessary. Thus, to solve the problems mentioned above, the design of a phase-based facilitation framework is proposed in this paper. Specifically, we propose two significant designs: One is the design for a multi-phase facilitation agent created based on the framework to address problem 1); The other one is the design for the combination with LLM to address problem 2) and 3). Particularly, the language model called “GPT-3.5” is used for the combination by using corresponding APIs from OPENAI. Furthermore, we demonstrate the improvement of our facilitation agent framework by presenting the evaluations and a case study. Besides, we present the difference between our framework and LangChain which has generic features to utilize LLMs.

We study the device length and/or band characteristics examined by two coupling analysis methods for our proposed fiber-type polarization splitter (FPS) composed of single mode fiber and polarization maintaining fiber. The first method is based on the power transition characteristics of the coupled-mode theory (CMT), and the second, a more accurate analysis method, is based on improved fundamental mode excitation (IFME). The CMT and IFME were evaluated and investigated with respect to the device length and bandwidth characteristics of the FPS. In addition, the influence of the excitation point shift of the fundamental mode, which has not been almost researched so far, is also analysed by using IFME.

A method for bandwidth enhancement of radar cross section (RCS) reduction by metasurfaces was studied. Scattering cancellation is one of common methods for reducing RCS of target scatterers. It occurs when the wave scattered by the target scatterer and the wave scattered by the canceling scatterer are the same amplitude and opposite phase. Since bandwidth of scattering cancellation is usually narrow, we proposed the bandwidth enhancement method using metasurfaces, which can control the frequency dependence of the scattering phase. We designed and fabricated a metasurface composed of a patch array on a grounded dielectric substrate. Numerical and experimental evaluations confirmed that the metasurface enhances the bandwidth of 10dB RCS reduction by 52% bandwidth ratio of the metasurface from 34% bandwidth ratio of metallic cancelling scatterers.

Wi-Fi-based person identification (PI) tasks are performed by analyzing the fluctuating characteristics of the Channel State Information (CSI) data to determine whether the person's identity is legitimate. This technology can be used for intrusion detection and keyless access to restricted areas. However, the related research rarely considers the restricted computing resources and the complexity of real-world environments, resulting in lacking practicality in some scenarios, such as intrusion detection tasks in remote substations without public network coverage. In this paper, we propose a novel neural network model named SimpleViTFi, a lightweight classification model based on Vision Transformer (ViT), which adds a downsampling mechanism, a distinctive patch embedding method and learnable positional embedding to the cropped ViT architecture. We employ the latest IEEE 802.11ac 80MHz CSI dataset provided by [1]. The CSI matrix is abstracted into a special “image” after pre-processing and fed into the trained SimpleViTFi for classification. The experimental results demonstrate that the proposed SimpleViTFi has lower computational resource overhead and better accuracy than traditional classification models, reflecting the robustness on LOS or NLOS CSI data generated by different Tx-Rx devices and acquired by different monitors.

The combination of multiple-input multiple-output (MIMO) technology and sparse code multiple access (SCMA) can significantly enhance the spectral efficiency of future wireless communication networks. However, the receiver design for downlink MIMO-SCMA systems faces challenges in developing multi-user detection (MUD) schemes that achieve both low latency and low bit error rate (BER). The separated detection scheme in the MIMO-SCMA system involves performing MIMO detection first to obtain estimated signals, followed by SCMA decoding. We propose an enhanced separated detection scheme based on lightweight graph neural networks (GNNs). In this scheme, we raise the concept of coordinate point relay and full-category training, which allow for the substitution of the conventional message passing algorithm (MPA) in SCMA decoding with image classification techniques based on deep learning (DL). The features of the images used for training encompass crucial information such as the amplitude and phase of estimated signals, as well as channel characteristics they have encountered. Furthermore, various types of images demonstrate distinct directional trends, contributing additional features that enhance the precision of classification by GNNs. Simulation results demonstrate that the enhanced separated detection scheme outperforms existing separated and joint detection schemes in terms of computational complexity, while having a better BER performance than the joint detection schemes at high Eb/N0 (energy per bit to noise power spectral density ratio) values.

In this paper, we investigated the overfitting characteristics of nonlinear equalizers based on an artificial neural network (ANN) and the Volterra series transfer function (VSTF), which were designed to compensate for optical nonlinear waveform distortion in optical fiber communication systems. Linear waveform distortion caused by, e.g., chromatic dispersion (CD) is commonly compensated by linear equalizers using digital signal processing (DSP) in digital coherent receivers. However, mitigation of nonlinear waveform distortion is considered to be one of the next important issues. An ANN-based nonlinear equalizer is one possible candidate for solving this problem. However, the risk of overfitting of ANNs is one obstacle in using the technology in practical applications. We evaluated and compared the overfitting of ANN- and conventional VSTF-based nonlinear equalizers used to compensate for optical nonlinear distortion. The equalizers were trained on repeated random bit sequences (RRBSs), while varying the length of the bit sequences. When the number of hidden-layer units of the ANN was as large as 100 or 1000, the overfitting characteristics were comparable to those of the VSTF. However, when the number of hidden-layer units was 10, which is usually enough to compensate for optical nonlinear distortion, the overfitting was weaker than that of the VSTF. Furthermore, we confirmed that even commonly used finite impulse response (FIR) filters showed overfitting to the RRBS when the length of the RRBS was equal to or shorter than the length of the tapped delay line of the filters. Conversely, when the RRBS used for the training was sufficiently longer than the tapped delay line, the overfitting could be suppressed, even when using an ANN-based nonlinear equalizer with 10 hidden-layer units.

This letter deals with the joint direction of arrival and direction of departure estimation problem for overloaded target in bistatic multiple-input multiple-output radar system. In order to achieve the purpose of effective estimation, the presented Khatri-Rao (KR) MUSIC estimator with the ability to handle overloaded targets mainly combines the subspace characteristics of the target reflected wave signal and the KR product based on the array response. This letter also presents a computationally efficient KR noise subspace projection matrix estimation technique to reduce the computational load due to perform high-dimensional singular value decomposition. Finally, the effectiveness of the proposed method is verified by computer simulation.

Given an odd prime q and an integer m ≤ q, a binary mq × q2 quasi-cyclic parity-check matrix H(m, q) can be constructed for an array low-density parity-check (LDPC) code C (m, q). In this letter, we investigate the first separating redundancy of C (m, q). We prove that H (m, q) is 1-separating for any pair of (m, q), from which we conclude that the first separating redundancy of C (m, q) is upper bounded by mq. Then we show that our upper bound on the first separating redundancy of C (m, q) is tighter than the general deterministic and constructive upper bounds in the literature. For m=2, we further prove that the first separating redundancy of C (2, q) is 2q for any odd prime q. For m ≥ 3, we conjecture that the first separating redundancy of C (m, q) is mq for any fixed m and sufficiently large q.

We considered the problem of forecasting the degradation recovery process of civil structures for prognosis and health management. In this process, structural health degrades over time but recovers when a maintenance intervention is performed. Maintenance interventions are typically recorded in terms of date and type. Such records can be represented as binary time series. Using binary maintenance intervention records, we forecast the process by using Long Short-Term Memory (LSTM). In this study, we experimentally examined how to feed binary time series data into LSTM. To this end, we compared the concatenation and reinitialization methods. The former is used to concatenate maintenance intervention records and health data and feed them into LSTM. The latter is used to reinitialize the LSTM internal memory when maintenance intervention is performed. The experimental results with the synthetic data revealed that the concatenation method outperformed the reinitialization method.

ARM chips with low energy consumption and low-cost investment have been rapidly applied to smart office and smart entertainment including cloud mobile phones and cloud games. This paper first summarizes key technologies and development status of the above scenarios including CPU, memory, IO hardware virtualization characteristics, ARM hypervisor and container, GPU virtualization, network virtualization, resource management and remote transmission technologies. Then, in view of the current lack of publicly referenced ARM cloud constructing solutions, this paper proposes and constructs an implementation framework for building an ARM cloud, and successively focuses on the formal definition of virtualization framework, Android container system and resource quota management methods, GPU virtualization based on API remoting and GPU pass-through, and the remote transmission technology. Finally, the experimental results show that the proposed model and corresponding component implementation methods are effective, especially, the pass-through mode for virtualizing GPU resources has higher performance and higher parallelism.

This paper proposes multi-gate reconfigurable (RECON) cells and a technology remapping approach using them as spare cells for post-mask functional engineering change orders (ECOs). With the rapid increase in circuit complexity, ECOs often occur in the post-mask stage of LSI designs. To deal with post-mask ECOs at a low cost, only the metal layers are redesigned by making functional changes using spare cells. For this purpose, 2T/4T/6T-RECON cells were proposed as reconfigurable spare cells. However, conventional RECON cells are used to implement single functions, which may result in unused transistors in the cells. In addition, the number of 2T/4T/6T-RECON spare cells used for post-mask ECOs varies greatly depending on the circuit to be implemented and the type of ECO that occurs. Therefore, functional ECOs may fail due to a lack of certain types of RECON cells, even if other types of RECON cells remain. To solve this problem, we propose multi-gate RECON cells that implement multiple functions in a single RECON cell while retaining the layouts of conventional 4T/6T-RECON base cells, and a technology remapping approach using them. The proposed approach not only reduces the number of used spare cells for modifications but also allows the flexible use of spare cells to fix them with less increase in wire length and delay. Experimental results have confirmed that the functional ECO success ratio is increased by 4.8pt on average and the total number of used spare cells is reduced by 5.6% on average. It has also been confirmed that the increase in wire length is reduced by 17.4% on average and the decrease in slack is suppressed by 21.6% on average.

In recent years, to meet strict time-to-market constraints, it has become difficult for only one semiconductor design company to design a VLSI. Thus, design companies purchase IP cores from third-party IP vendors and design only the necessary parts. On the other hand, since IP cores have the disadvantage that copyright infringement can be easily performed, logic locking has to be applied to them. Functional logic locking methods using TTLock are resilient to SAT attacks however vulnerable to FALL attacks. Additionally, it is difficult to design logic locking based on TTLock at the gate level. This paper proposes a logic locking method, CRLock, based on SAT attack and FALL attack resistance at the register transfer level. The CRLock is a logic locking method for controllers at RTL in which the designer selects a protected input pattern and modifies the controller based on the protection input pattern. In experimental results, we applied CRLock to MCNC'91 benchmark circuits and showed that all circuits are resistant to SAT and FALL attacks.

Logic circuits based on a photonic integrated circuit (PIC) have attracted significant interest due to their ultra-high-speed operation. However, they have a fundamental disadvantage that a large amount of the optical signal power is discarded in the path from the optical source to the optical output, which results in significant power consumption. This optical signal power loss is called a garbage output. To address this issue, this paper considers a circuit design without garbage outputs. Although a method for synthesizing an optical logic circuit without garbage outputs is proposed, this synthesis method can not obtain the optimal solution, such as a circuit with the minimum number of gates. This paper proposes a cross-bar gate logic (CBGL) as a new logic structure for optical logic circuits without garbage outputs, moreover enumerates the CBGLs with the minimum number of gates for all three input logic functions by an exhaustive search. Since the search space is vast, our enumeration algorithm incorporates a technique to prune it efficiently. Experimental results for all three-input logic functions demonstrate that the maximum number of gates required to implement the target function is five. In the best case, the number of gates in enumerated CBGLs is one-half compared to the existing method for optical logic circuits without garbage outputs.