The study proposes a personalised session-based recommender system that embeds items by using Word2Vec and sequentially updates the session and user embeddings with the hierarchicalization and aggregation of item embeddings. To process a recommendation request, the system constructs a real-time user embedding that considers users’ general preferences and sequential behaviour to handle short-term changes in user preferences with a low computational cost. The system performance was experimentally evaluated in terms of the accuracy, diversity, and novelty of the ranking of recommended items and the training and prediction times of the system for three different datasets. The results of these evaluations were then compared with those of the five baseline systems. According to the evaluation experiment, the proposed system achieved a relatively high recommendation accuracy compared with baseline systems and the diversity and novelty scores of the proposed system did not fall below 90% for any dataset. Furthermore, the training times of the Word2Vec-based systems, including the proposed system, were shorter than those of FPMC and GRU4Rec. The evaluation results suggest that the proposed recommender system succeeds in keeping the computational cost for training low while maintaining high-level recommendation accuracy, diversity, and novelty.

The concepts of programmable switches and software-defined networking (SDN) give developers flexible and deep control over the behavior of switches. We expect these concepts to dramatically improve the functionality of switches. In this paper, we focus on the concept of Deeply Programmable Networks (DPN), where data planes are programmable, and application switches based on DPN. We then propose a method to improve the performance of a key-value store (KVS) through an application switch. First, we explain the DPN and application switches. The DPN is a network that makes not only control planes but also data planes programmable. An application switch is a switch that implements some functions of network applications, such as database management system (DBMS). Second, we propose a method to improve the performance of Cassandra, one of the most popular key-value based DBMS, by implementing a caching function in a switch in a dedicated network such as a data center. The proposed method is expected to be effective even though it is a simple and traditional way because it is in the data path and the center of the network application. Third, we implement a switch with the caching function, which monitors the accessed data described in packets (Ethernet frames) and dynamically replaces the cached data in the switch, and then show that the proposed caching switch can significantly improve the KVS transaction performance with this implementation. In the case of our evaluation, our method improved the KVS transaction throughput by up to 47%.

Security-related issues have become more significant due to the proliferation of IT. Collating security-related information in a database improves security. For example, Common Vulnerabilities and Exposures (CVE) is a security knowledge repository containing descriptions of vulnerabilities about software or source code. Although the descriptions include various entities, there is not a uniform entity structure, making security analysis difficult using individual entities. Developing a consistent entity structure will enhance the security field. Herein we propose a method to automatically label select entities from CVE descriptions by applying the Named Entity Recognition (NER) technique. We manually labeled 3287 CVE descriptions and conducted experiments using a machine learning model called BERT to compare the proposed method to labeling with regular expressions. Machine learning using the proposed method significantly improves the labeling accuracy. It has an f1 score of about 0.93, precision of about 0.91, and recall of about 0.95, demonstrating that our method has potential to automatically label select entities from CVE descriptions.

Traditional intuitionistic fuzzy sets and hesitant fuzzy sets will lose some information while representing vague information, to avoid this problem, this paper constructs weighted generalized hesitant fuzzy sets by remaining multiple intuitionistic fuzzy values and giving them corresponding weights. For weighted generalized hesitant fuzzy elements in weighted generalized hesitant fuzzy sets, the paper defines some basic operations and proves their operation properties. On this basis, the paper gives the comparison rules of weighted generalized hesitant fuzzy elements and presents two kinds of aggregation operators. As for weighted generalized hesitant fuzzy preference relation, this paper proposes its definition and computing method of its corresponding consistency index. Furthermore, the paper designs an ensemble learning algorithm based on weighted generalized hesitant fuzzy sets, carries out experiments on 6 datasets in UCI database and compares with various classification algorithms. The experiments show that the ensemble learning algorithm based on weighted generalized hesitant fuzzy sets has better performance in all indicators.

This study investigates the efficacy of a partial decomposition approach in concept map recomposition tasks to reduce cognitive load while maintaining the benefits of traditional recomposition approaches. Prior research has demonstrated that concept map recomposition, involving the rearrangement of unconnected concepts and links, can enhance reading comprehension. However, this task often imposes a significant burden on learners’ working memory. To address this challenge, this study proposes a partial recomposition approach where learners are tasked with recomposing only a portion of the concept map, thereby reducing the problem space. The proposed approach aims at lowering the cognitive load while maintaining the benefits of traditional recomposition task, that is, learning effect and motivation. To investigate the differences in cognitive load, learning effect, and motivation between the full decomposition (the traditional approach) and partial decomposition (the proposed approach), we have conducted an experiment (N=78) where the participants were divided into two groups of “full decomposition” and “partial decomposition”. The full decomposition group was assigned the task of recomposing a concept map from a set of unconnected concept nodes and links, while the partial decomposition group worked with partially connected nodes and links. The experimental results show a significant reduction in the embedded cognitive load of concept map recomposition across different dimensions while learning effect and motivation remained similar between the conditions. On the basis of these findings, educators are recommended to incorporate partially disconnected concept maps in recomposition tasks to optimize time management and sustain learner motivation. By implementing this approach, instructors can conserve cognitive resources and allocate saved energy and time to other activities that enhance the overall learning process.

The segmentation performance of fresh tea sprouts is inadequate due to the uncontrollable posture. A novel method for Fresh Tea Sprouts Segmentation based on Capsule Network (FTS-SegCaps) is proposed in this paper. The spatial relationship between local parts and whole tea sprout is retained and effectively utilized by a deep encoder-decoder capsule network, which can reduce the effect of tea sprouts with uncontrollable posture. Meanwhile, a patch-based local dynamic routing algorithm is also proposed to solve the parameter explosion problem. The experimental results indicate that the segmented tea sprouts via FTS-SegCaps are almost coincident with the ground truth, and also show that the proposed method has a better performance than the state-of-the-art methods.

Streaming systems that can maintain Quality of Experience (QoE) for users have attracted much attention because they can be applied in various fields, such as emergency response training and medical surgery. Dynamic Adaptive Streaming over HTTP (DASH) is a typical protocol for streaming system. In order to improve QoE in DASH, a multi-server system has been presented by pseudo-increasing bandwidth through multiple servers. This multi-server system is designed to share streaming content efficiently in addition to having redundant server resources for each streaming content, which is excellent for fault tolerance. Assigning DASH server to users in these multi-servers environment is important to maintain QoE, thus a method of server assignment of users (user allocation method) for multi-servers is presented by using cooperative game theory. However, this conventional user allocation method does not take into account the size of the server bandwidth, thus users are concentrated on a particular server at the start of playback. Although the average required bit rate of video usually fluctuates, bit rate fluctuations are not taken into account. These phenomena may decrease QoE. In this paper, we propose a novel user allocation method using coalition structure generation in cooperative game theory to improve the QoE of all users in an immediate and stable manner in DASH environment. Our proposed method can avoid user concentration, since the bandwidth used by the overall system is taken into account. Moreover, our proposed method can be performed every time the average required bit rate changes. We demonstrate the effectiveness of our method through simulations using Network Simulator 3 (NS3).

In this paper, we propose a noise-robust scream detection method with the aim of expanding the scream detection system, a sound-based security system. The proposed method uses enhanced screams using Wave-U-Net, which was effective as a noise reduction method for noisy screams. However, the enhanced screams showed different frequency components from clean screams and erroneously emphasized frequency components similar to scream in noise. Therefore, Wave-U-Net was applied even in the process of training Gaussian mixture models, which are discriminators. We conducted detection experiments using the proposed method in various noise environments and determined that the false acceptance rate was reduced by an average of 2.1% or more compared with the conventional method.

We study a visible light communication (VLC) system that modulates data signals by changing the color components of image contents on a digital signage display, captures them with an image sensor, and demodulates them using image processing. This system requires that the modulated data signals should not be perceived by the human eye. Previous studies have proposed modulation methods with a chromaticity component that is difficult for the human eye to perceive, and we have also proposed a modulation method with perceptually uniform color space based on human perception characteristics. However, which chromaticity component performs better depends on the image contents, and the evaluation only for some specific image contents was not sufficient. In this paper, we evaluate the communication and visual quality of the modulation methods with chromaticity components for various standard images to clarify the superiority of the method with perceptually uniform color space. In addition, we propose a novel modulation and demodulation method using diversity combining to eliminate the dependency of performance on the image contents. Experimental results show that the proposed method can improve the communication and visual quality for almost all the standard images.

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.

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.

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.

Low capacity and reliability are the challenges in the development of ionosphere communication channel systems. To overcome this problem, one promising and state-of-the-art method is applying a multi-carrier modulation technique. Currently, the use of multi-carrier modulation technique is using a single transmission frequency with a bandwidth is no more than 24 kHz in real-world implementation. However, based on the range of the minimum and maximum ionospheric plasma frequency values, which could be in the MHz range, the use of these values as the main bandwidth in multi-carrier modulation techniques can optimize the use of available channel capacity. In this paper, we propose a multi-carrier modulation technique in combination with a model variation of Lowest Usable Frequency (LUF) and Maximum Usable Frequency (MUF) values as the main bandwidth to optimize the use of available channel capacity while also maintaining its reliability by following the variation of the ionosphere plasma frequency. To analyze its capacity and reliability, we performed a numeric simulation using a LUF-MUF model based on Long Short Term-Memory (LSTM) and Advanced Stand Alone Prediction System (ASAPS) in Near Vertical Incidence Skywave (NVIS) propagation mode with the assumption of perfect synchronization between transmitter and receiver with no Doppler and no time offsets. The results show the achievement of the ergodic channel capacity varies for every hour of the day, with values in the range of 10 Mbps and 100 Mbps with 0 to 20 dB SNR. Meanwhile, the reliability of the system is in the range of 8% to 100% for every hour of one day based on two different Mode Reliability calculation scenarios. The results also show that channel capacity and system reliability optimization are determined by the accuracy of the LUF-MUF model.

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.

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.

Displacement current is the last piece of the puzzle of electromagnetic theory. Its existence implies that electromagnetic disturbance can propagate at the speed of light and finally it led to the discovery of Hertzian waves. On the other hand, since magnetic fields can be calculated only with conduction currents using Biot-Savart's law, a popular belief that displacement current does not produce magnetic fields has started to circulate. But some people think if this is correct, what is the displacement current introduced for. The controversy over the meaning of displacement currents has been going on for more than hundred years. Such confusion is caused by forgetting the fact that in the case of non-stationary currents, neither magnetic fields created by conduction currents nor those created by displacement currents can be defined. It is also forgotten that the effect of displacement current is automatically incorporated in the magnetic field calculated by Biot-Savart's law. In this paper, mainly with the help of Helmholtz decomposition, we would like to clarify the confusion surrounding displacement currents and provide an opportunity to end the long standing controversy.

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.

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.

For 6G mobile communications, it is important to realize a 300 GHz band bandpass filter that fits the occupied bandwidth of wireless communication system to prevent inter-system interference. This paper presents the design of a 300-GHz-band dual-band bandstop filter composed of two types of different sized split ring resonator (SRR) unit cells. The SRR unit cells are formed by a 5-μm-thick gold pattern on a 200-μm-thick quartz substrate. When two different-sized SRR unit cells are placed alternately on the same quartz substrate and the SRR unit cell size is over 260 μm, the stopbands of the dual-band bandstop filter are almost the same as those of the bandstop filter, which is composed of a single SRR unit cell. The insertion loss of the dual-band bandstop filter at 297.4 GHz is 1.8 dB and the 3-dB passband becomes 16.0 GHz (290.4-306.4 GHz). The attenuation in the two stopbands is greater than 20 dB. Six types of dual-band bandstop filters with different arrangement and different distance between SRR unit cells are prototyped, and the effect of the distance and arrangement between different sized SRR unit cells on the transmission characteristics of dual-band bandstop filters were clarified.

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.